Evaluation of Comorbidity Burden on Disease Progression and Mortality in Patients with Interstitial Pneumonia with Autoimmune Features: a Retrospective Cohort Study

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Joerns, Michelle A. Ghebranious, Traci N. Adams, Una E. Makris This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3210870/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Interstitial pneumonia with autoimmune features (IPAF) is a subset of interstitial lung disease that manifests with interstitial pneumonia and features of autoimmunity while not meeting classification criteria for a defined rheumatic disease. Comorbidity burden is an important prognostic indicator in various rheumatic and interstitial lung diseases, but few studies have commented on comorbidities in this population. This study was conducted to evaluate the association of individual comorbidities, the Charlson Comorbidity Index (CCI), and the Rheumatic Disease Comorbidity Index (RDCI) with lung disease progression and transplant/mortality outcomes in patients with IPAF. Methods In a retrospective study, we evaluated the prevalence and severity of comorbidities in an institutional cohort of patients with IPAF. Using Cox regression, we correlated the association of individual comorbidities and comorbidity burden using CCI and RDCI with time to lung disease progression (defined as relative forced vital capacity (FVC) decline of 10% or more) and with time to lung transplant/all-cause mortality. We compared the performance of CCI and RDCI, while adjusting for the Interstitial Lung Disease Gender-Age-Physiology (ILD-GAP) index. Results In a sample of 201 individuals with IPAF, a history of cerebrovascular accident (CVA) or cardiovascular disease (CVD), moderate to severe chronic kidney disease, or fracture was associated with a faster onset of lung disease progression, while a history of gastroesophageal reflux was protective. History of CVA/CVD, diabetes mellitus, and lymphoma were associated with a faster onset of lung transplant/death. Both CCI and RDCI were significantly associated with shorter time to lung disease progression (hazard ratio [HR] 1.11, 95% confidence interval [CI] 1.04–1.19 and HR 1.12 with 95%CI 1.00-1.26, respectively) and lung transplant/mortality (HR 1.18 [1.07–1.30] and 1.31 [1.10–1.57], respectively). Conclusions CCI and RDCI may be useful tools in assessing prognosis in patients with IPAF in terms of both lung disease progression and mortality. Prospective studies are needed to further evaluate the performance of CCI and RDCI and the impact of optimizing comorbid conditions that may mitigate poor outcomes among patients with IPAF. interstitial lung disease comorbidity index autoimmune disease interstitial pneumonia BACKGROUND Interstitial pneumonia with autoimmune features (IPAF) is a subset of interstitial lung disease (ILD) that manifests with signs and symptoms of autoimmunity while not meeting classification criteria for a defined rheumatic disease (RD) ( 1 ). Multiple studies have examined the clinical characteristics and outcomes of IPAF patients ( 2 – 5 ), while few studies have commented on the presence and prevalence of comorbidities in this population ( 6 ). To our knowledge, no studies have evaluated the effect of comorbidities on lung function decline in IPAF. Comorbidity burden is an important prognostic indicator in various RDs ( 7 – 10 ) and ILD ( 11 , 12 ) and is associated with worse disease activity, physical function, quality of life, refractory treatment, and higher mortality risk ( 7 , 13 , 14 ), emphasizing the need for awareness, screening, and optimization of comorbid conditions. Composite scoring systems or indices can be used to quantify the total burden of comorbid illnesses to measure the overall burden and impact of comorbidities more accurately and to assess prognosis ( 15 ) Charlson Comorbidity Index (CCI), a summed score of 19 comorbidities weighted according to severity, was developed to predict one-year mortality in hospitalized patients. The CCI can predict various patient outcomes, including in-hospital mortality, length of hospital stays, readmission rates, functional decline, and healthcare utilization ( 14 , 16 ). CCI is also a strong prognostic predictor in ILD with the frequency of 3-year ILD-related events increasing with increasing CCI; however, IPAF patients were not included in this cohort ( 16 ). In addition, CCI does not account for common comorbidities seen in RDs such as hypertension, osteoporosis, obstructive sleep apnea, or depression which can significantly impact disease activity and quality of life ( 7 , 14 ). The rheumatic disease comorbidity index (RDCI) is comprised of 11 comorbid conditions and was developed based on self-report assessments from patients with RDs ( 15 , 17 ). It has performed well in predicting both physical disability and mortality in diseases such as rheumatoid arthritis and gout ( 15 , 18 ). However, its usefulness in presumably autoimmune ILD, such as IPAF, has not been evaluated. The Gender-Age-Physiology (GAP) index was developed as a multidimensional prognostic staging system for ILD using four variables including gender (G), age (A), and two lung physiology variables (P) - forced vital capacity (FVC) and diffusing capacity for carbon monoxide (DLCO) ( 19 ). Although initially validated in patients with idiopathic pulmonary fibrosis (IPF), the GAP model has also accurately predicted the risk of death in chronic non-IPF ILD ( 20 – 22 ). A modified GAP index, known as the ILD-GAP index, includes a disease subtype variable that accounts for better-adjusted survival in ILD subtypes, including RD-associated ILD, chronic hypersensitivity pneumonitis, and idiopathic nonspecific interstitial pneumonia. The ILD-GAP model has predicted mortality in major chronic ILD subtypes across all stages of disease ( 21 ). However, ILD-GAP does not consider the presence or severity of comorbidities, and a recent study demonstrated that the combination of ILD-GAP and CCI performs better in predicting outcomes in ILD than ILD-GAP or CCI alone ( 23 ). Few studies have examined comorbidities ( 24 – 26 ) and no studies have evaluated the association or effect of comorbidities, using indices such as the CCI or RDCI, with outcomes in IPAF. This study was conducted to determine the prevalence and type of comorbidities in an institutional cohort of patients with IPAF; to evaluate the effect of baseline comorbidities on lung disease progression and mortality; and to assess the performance of the CCI and RDCI, while adjusting for the ILD-GAP index, in predicting outcomes in IPAF patients. METHODS Cohort Assembly : This single-center retrospective medical records review study was performed at the University of Texas Southwestern (UTSW) Medical Center. Patients seen in the UTSW Interstitial Lung Disease Clinic between January 2005 and August 2019 who met the European Respiratory Society (ERS)/American Thoracic Society (ATS) classification criteria for IPAF were included ( 1 ). Date of entry into the cohort was considered as the first available pulmonary function test (PFT) date. The UTSW Medical Center Institutional Review Board approved the study prior to the initiation of data extraction (IRB #STU-2019-0913). Patient consent was not obtained for this retrospective medical records review study. Inclusion/Exclusion Criteria : All patients meeting 2015 ERS/ATS IPAF classification criteria were included for evaluation of prevalence of comorbidities and other baseline characteristics. Patients were excluded from primary outcome evaluation if they did not have at least two sets of PFT data and excluded from secondary outcome evaluation if they had less than two visits recorded in the medical record. Baseline characteristics : Demographic data included age at ILD diagnosis, sex, race, ethnicity, and smoking status. ILD diagnosis date was the time point at which ILD was first observed on imaging. Sex, race, and ethnicity data was recorded as documented in the electronic medical record (EMR). Smoking status was assigned as smoker if the patient ever smoked and otherwise as never smoker. Imaging features including lung lesion pattern, presence of honeycombing, and air trapping, were assigned by a pulmonologist (TAN) based on high-resolution computed tomography scans (HRCT) of the chest. The presence of a usual interstitial pneumonia (UIP) pattern on imaging was documented, given the correlation of UIP with increased mortality and faster lung function decline in prior studies ( 3 , 27 ). Comorbidities : Patient medical records were reviewed by an internal medicine physician (MAG) and rheumatologist (EKJ) using a standardized data extraction worksheet. Comorbidities were evaluated by searching for the key terms and reviewing pertinent documentation (including progress notes), laboratory values, and imaging at the time of entry into the cohort (defined as the date of the first PFTs recorded at the initial ILD clinic visit) (Supplementary Appendix A). Agreement between the reviewers was assessed after reviewing 50 charts to ensure accuracy. Calculation of Comorbidity Indices and ILD Severity : CCI and RDCI were calculated according to standardized procedures and a data extraction worksheet (Supplementary Tables 1 and 2) ( 15 , 28 ). The ILD-GAP index (Supplementary Table 3) was calculated using five predictor variables (gender, age, FVC, DLCO, and ILD category) with assigned points to obtain a total score from 0 to 8 ( 21 ). Since IPAF, by definition, is unclassified ILD due to lack of definable etiology such as RD ( 29 ), 0 points were assigned for the ILD category score for each patient in the cohort. Pulmonary Function Tests : PFT data included FVC, forced expiratory volume in one second (FEV1), FEV1/FVC ratio, and DLCO as percentages of predicted values. Baseline PFTs were the first available tests recorded at the first ILD clinic visit. All available additional PFT data were collected. If a patient underwent a lung transplantation, the last PFT data prior to transplant was included. Outcomes : The primary outcome was the time to lung disease progression, defined as relative %FVC decline of ≥ 10% after date of cohort entry. Secondary outcomes were the time to lung transplant and all-cause mortality (whichever occurred first) from the time point of entry into the cohort. All outcomes were obtained from chart review. Statistical Analysis Descriptive statistics were used to calculate the prevalence of baseline characteristics and comorbidities in our IPAF cohort. Continuous and categorical variables were expressed as means with standard deviation and counts with percentages, respectively. We used Cox proportional hazards to estimate the hazard ratios (HRs) and 95% confidence intervals (CIs) for each outcome, first in an unadjusted model then adjusted for ILD-GAP and presence of UIP. All analyses were completed in Stata (V.17, College Station, TX). RESULTS Baseline Characteristics: The cohort, comprised of 201 patients meeting the 2015 ERS/ATS IPAF criteria, was predominantly female (76.1%), with a mean age at the time of initial PFTs of 61.8 ± 12.4 years, and an average time of follow-up of 5.6 ± 3.7 years (Table 1 ). White patients represented 69.6% of the cohort and 84.6% of patients were non-Hispanic and 34.5% were former or current smokers. The baseline mean %FVC was 64.7% ± 19.1% of predicted. The mean %DLCO was 45.8% ± 20.3% of predicted. The mean baseline ILD-GAP index in our cohort was 3.2 ± 1.7 points. Forty-eight (23.9%) patients had a UIP pattern on imaging with 46.8% having unexplained air trapping and 21.4% having honeycombing on HRCT. Table 1 Baseline Features of the IPAF Cohort Baseline characteristic N = 201 Age at initial PFTs, years; Mean [SD] 61.8 [12.4] Male sex, n (%) 48 (23.9) Race, n (%) White Black Asian Other 140 (69.6) 34 (16.9) 10 (5) 17 (8.5) Ethnicity, n (%) Non-Hispanic Hispanic 170 (84.6) 31 (15.4) Never smoke, n (%) 114 (65.5) Baseline FVC, %; Mean [SD] 64.7 [19.1] Baseline DLCO (n = 200), %; Mean [SD] 45.8 [20.3] HRCT pattern, n (%) Unable to determine LIP NSIP NSIP/OP OP UIP 21 (10.5) 5 (2.5) 117 (58.2) 9 (4.5) 1 (0.5) 48 (23.9) Unexplained air trapping on HRCT, n (%) 94 (46.8) Honeycombing on HRCT, n (%) 43 (21.4) Follow-up time, years; Mean [SD] 5.6 [3.7] Mortality outcome, n (%) Loss to follow-up after one visit Alive at the end of follow-up Death due to all causes Lung transplant 1 (0.5) 137 (68.2) 48 (23.9) 15 (7.5) Time to FVC decline of 10% or more (n = 104), years; Mean [SD] 2.2 [2.0] Time to death or lung transplant (n = 63), years; Mean [SD] 4.2 [2.8] CCI; Mean [SD] 3.2 [2.4] RDCI; Mean [SD] 3.7 [1.3] ILD-GAP; Mean [SD] 3.2 [1.7] IPAF – interstitial pneumonia with autoimmune features; SD – standard deviation; PFTs – pulmonary function tests; FVC – forced vital capacity; DLCO – diffusing capacity of lung for carbon monoxide; HRCT – high resolution computed tomography; LIP – lymphocytic interstitial pneumonia; NSIP – non-specific interstitial pneumonia OP – organizing pneumonia; UIP – usual interstitial pneumonia. Table 1 included on the bottom of the file Prevalence of Comorbidities: The prevalence of individual comorbidities is shown in Table 2 . The most prevalent comorbidities in our cohort included hypertension (67.7%) and gastroesophageal reflux disease (GERD) (64.7%). More than 20% of the cohort had chronic obstructive pulmonary disease (COPD), depression, and diabetes mellitus (DM). The average baseline CCI and RDCI were 3.2 (± 2.4) and 3.7 (± 1.3), respectively (Table 1 ). Table 2 Baseline Comorbidities of the IPAF Cohort Baseline comorbidity N = 201 n (%) Myocardial infarction 21 (10.4) Congestive heart failure 32 (15.9) Peripheral vascular disease 12 (6) Cerebrovascular accident* 13 (6.5) Cardiovascular disease or cerebrovascular accident 41 (20.4) Chronic obstructive pulmonary disease 58 (28.9) Peptic ulcer disease 15 (7.5) Liver disease 7 (3.5) Diabetes mellitus Diet-controlled Uncomplicated requiring medications With end-organ damage 44 (21.9) 10 (5) 25 (12.4) 9 (4.5) Chronic kidney disease Any Mild Moderate-severe 38 (18.9) 34 (16.9) 4 (2) Malignancy** Solid tumor Localized Metastatic Leukemia Lymphoma 31 (15.4) 29 (14.4) 25 (12.4) 4 (2) 0 (0) 2 (1.0) Acquired immunodeficiency syndrome 0 (0) Hemiplegia 0 (0) Fracture (hip/leg/spine) 18 (9) Osteoporosis 25 (12.4) Depression 45 (22.4) Dementia 9 (4.5) Hypothyroidism 26 (12.9) Hypertension 136 (67.7) Obstructive sleep apnea 24 (11.9) Gastroesophageal reflux disease 130 (64.7) IPAF – interstitial pneumonia with autoimmune features *Including transient ischemic attack **Including skin cancer and hematologic malignancy Table 2 included on the bottom of the file Primary Outcome: One hundred and ninety-one (95.0%) patients had ≥ 2 PFTs and were included in lung function decline analysis. Of the 191 patients, 104 (54.5%) patients progressed over an average of 2.2 ± 2.0 years. In univariable analysis, older age, being of race other than Asian, history of cerebrovascular accident (CVA) and/or cardiovascular disease (CVD), moderate to severe chronic kidney disease (CKD), and fracture were significantly associated with faster onset of lung disease progression. The presence of honeycombing and GERD were associated with delayed onset of lung disease progression. These associations remained significant when adjusted for ILD-GAP and UIP (Table 3 ). Table 3 Association of Baseline Characteristics with Time to Lung Function Decline Baseline Characteristic Univariable Analysis Bivariable Analysis Model Adjusted for ILD-GAP Bivariable Analysis Model Adjusted for UIP Multivariable Analysis Model Adjusted for ILD-GAP and UIP N = 191 HR 95% CI P HR 95% CI P HR 95% CI P HR 95% CI P Age at initial PFTs (years) 1.01 1.00-1.03 0.04 1.01 0.999–1.03 0.069 1.01 0.998–1.03 0.099 1.01 0.996–1.02 0.15 Male sex 1.20 0.85–1.69 0.30 1.15 0.80–1.64 0.46 1.13 0.79–1.61 0.51 1.08 0.74–1.56 0.70 Race White (referent) Black Asian Other 1 0.71 0.36 0.66 0.48–1.04 0.18–0.75 0.39–1.10 0.08 0.01 0.11 1 0.71 0.37 0.66 0.49–1.05 0.18–0.78 0.39–1.10 0.08 0.01 0.11 1 0.71 0.36 0.68 0.49–1.05 0.17–0.75 0.41–1.14 0.08 0.01 0.14 1 0.72 0.37 0.68 0.49–1.05 0.18–0.77 0.41–1.14 0.09 0.01 0.14 Hispanic ethnicity 1.02 0.68–1.52 0.93 1.00 0.67–1.49 > 0.99 1.02 0.68–1.51 0.93 1.00 0.67–1.49 > 0.99 Smoking history 1.10 0.82–1.49 0.52 1.08 0.80–1.46 0.63 1.06 0.78–1.44 0.73 1.03 0.76–1.41 0.84 Baseline FVC (%) 1.01 0.998–1.01 0.15 1.01 1.00-1.02 0.01 1.00 0.997–1.01 0.26 1.01 1.00-1.02 0.03 Baseline DLCO (%) 1.00 0.99–1.01 0.92 1.01 0.996–1.02 0.24 0.999 0.99–1.01 0.88 1.00 0.99–1.02 0.41 UIP pattern on HRCT 1.28 0.92–1.79 0.14 1.27 0.91–1.78 0.16 - - - - - - Unexplained air trapping on HRCT 1.00 0.75–1.33 0.99 1.02 0.77–1.37 0.88 1.06 0.79–1.43 0.69 1.08 0.80–1.46 0.60 Honeycombing on HRCT 0.64 0.45–0.92 0.02 0.65 0.45–0.93 0.02 0.66 0.46–0.95 0.03 0.67 0.46–0.97 0.03 Hypertension 1.12 0.83–1.51 0.47 1.11 0.82–1.51 0.49 1.12 0.82–1.51 0.48 1.11 0.82–1.50 0.51 Myocardial infarction 1.56 0.99–2.47 0.06 1.57 0.99–2.48 0.05 1.45 0.90–2.35 0.13 1.47 0.91–2.37 0.12 Congestive heart failure 1.15 0.77–1.71 0.49 1.12 0.75–1.68 0.56 1.15 0.77–1.72 0.48 1.13 0.76–1.68 0.56 Peripheral vascular disease 1.52 0.84–2.74 0.17 1.43 0.78–2.62 0.25 1.42 0.78–2.59 0.25 1.35 0.73–2.48 0.34 Cerebrovascular accident* 1.46 0.83–2.57 0.19 1.38 0.77–2.47 0.28 1.40 0.79–2.48 0.25 1.33 0.74–2.38 0.34 Cardiovascular disease or cerebrovascular accident 1.64 1.15–2.35 0.01 1.61 1.12–2.31 0.01 1.60 1.11–2.29 0.01 1.56 1.09–2.25 0.02 Chronic obstructive pulmonary disease 1.20 0.87–1.65 0.36 1.29 0.93–1.79 0.13 1.21 0.88–1.67 0.23 1.30 0.93–1.80 0.12 Obstructive sleep apnea 1.23 0.78–1.91 0.37 1.24 0.79–1.93 0.35 1.21 0.78–1.90 0.39 1.22 0.78–1.92 0.37 Gastroesophageal reflux disease 0.72 0.53–0.98 0.04 0.70 0.52–0.96 0.02 0.71 0.52–0.96 0.03 0.69 0.50–0.93 0.02 Peptic ulcer disease 0.89 0.52–1.55 0.69 0.91 0.52–1.57 0.73 0.89 0.52–1.55 0.69 0.90 0.52–1.56 0.72 Liver disease 1.03 0.45–2.32 0.95 1.07 0.47–2.43 0.87 0.97 0.43–2.20 0.94 1.00 0.44–2.28 > 0.99 Diabetes mellitus (any) None or diet controlled (referent) Uncomplicated requiring medications With end organ damage 1.10 1 0.84 1.67 0.78–1.55 0.55–1.29 0.85–3.28 0.58 0.42 0.14 1.09 1 0.83 1.59 0.77–1.54 0.54–1.28 0.80–3.15 0.62 0.41 0.18 1.11 1 0.86 1.58 0.79–1.57 0.56–1.34 0.80–3.12 0.54 0.51 0.19 1.11 1 0.86 1.51 0.78–1.56 0.56–1.33 0.76-3.00 0.57 0.50 0.24 Chronic kidney disease (any) None or mild (referent) Moderate-severe 1.43 1 3.19 0.98–2.07 1.18–8.68 0.06 0.02 1.40 1 3.23 0.96–2.04 1.19–8.78 0.07 0.02 1.40 1 3.14 0.97–2.04 1.15–8.54 0.075 0.03 1.38 1 3.19 0.95–2.01 1.17–8.67 0.09 0.02 Malignancy** (any) No solid tumor (referent) Local solid tumor Metastatic solid tumor 1.01 1 0.98 1.89 0.68–1.51 0.63–1.51 0.70–5.14 0.95 0.92 0.21 1.00 1 0.97 1.86 0.67–1.49 0.62–1.50 0.68–5.06 > 0.99 0.88 0.22 1.04 1 0.995 1.88 0.70–1.54 0.64–1.54 0.69–5.12 0.86 0.98 0.21 1.02 1 0.99 1.87 0.69–1.53 0.63–1.53 0.69–5.07 0.91 0.95 0.22 Lymphoma 2.66 0.65–10.83 0.17 2.69 0.66–10.97 0.17 2.36 0.57–9.72 0.23 2.40 0.58–9.87 0.22 Fracture (hip/leg/spine) 2.00 1.22–3.26 0.01 1.95 1.19–3.19 0.01 2.05 1.25–3.35 0.004 2.00 1.22–3.28 0.01 Osteoporosis 1.15 0.75–1.77 0.53 1.14 0.74–1.76 0.54 1.09 0.71–1.69 0.69 1.09 0.70–1.69 0.70 Depression 1.21 0.85–1.71 0.28 1.21 0.85–1.71 0.28 1.20 0.85–1.70 0.29 1.20 0.85–1.70 0.29 Dementia 1.44 0.71–2.93 0.31 1.50 0.74–3.07 0.26 1.49 0.73–3.04 0.27 1.55 0.76–3.17 0.23 Hypothyroidism 1.24 0.80–1.92 0.34 1.24 0.80–1.92 0.34 1.29 0.83–2.02 0.26 1.29 0.83–2.01 0.26 CCI 1.11 1.04–1.19 0.001 1.11 1.04–1.19 0.002 1.11 1.03–1.18 0.003 1.10 1.03–1.18 0.01 RDCI 1.12 1.00-1.26 0.04 1.12 0.999–1.25 0.05 1.12 1.00-1.26 0.048 1.12 0.996–1.25 0.06 ILD-GAP 1.05 0.96–1.15 0.28 - - - 1.05 0.96–1.15 0.31 - - - RDCI – rheumatic disease comorbidity index; CCI – Charlson Comorbidity Index, ILD-GAP – Gender, Age, Physiology Index for Interstitial Lung Disease; PFTs – pulmonary function tests; FVC – forced vital capacity; DLCO – diffusing capacity of lung for carbon monoxide; UIP – usual interstitial pneumonia; HRCT – high resolution computed tomography; HR – hazard ratio; CI – confidence interval. *Including transient ischemic attack **Including skin cancer and hematologic malignancy Table 3 included on the bottom of the file Secondary Outcome: Two hundred patients (99.5%) in our cohort had ≥1 medical visit recorded in the EMR. One patient was lost to follow-up after one visit. Forty-eight (23.9%) patients died and 15 (7.5%) patients had lung transplant (Table 1 ). Older age, male sex, smoking history, lower FVC or DLCO, presence of UIP pattern, and history of CVA and/or CVD, DM (in particular with end-organ damage), and lymphoma were associated with significantly shorter time to lung transplant/death. After adjusting for ILD-GAP, age, UIP pattern on HRCT, DM, and lymphoma remained significantly associated with shorter time to lung transplant/death. After adjusting for the presence of UIP pattern, older age, male sex, smoking history, lower PFT parameters, congestive heart failure (CHF), DM, and lymphoma were significant factors associated with the outcome. Interestingly, only DM with end organ damage and lymphoma remained significantly associated with lung transplant/death after adjusting for both ILD-GAP and UIP (Table 4 ). Table 4 Association of Baseline Characteristics with Time to Lung Transplant/Death Baseline Characteristic Univariable Analysis Bivariable Analysis Model Adjusted for ILD-GAP Bivariable Analysis Model Adjusted for UIP Multivariable Analysis Model Adjusted for ILD-GAP and UIP N = 200 HR 95% CI P HR 95% CI P HR 95% CI P HR 95% CI P Age (years) 1.04 1.01–1.06 0.002 1.02 1.00-1.05 0.04 1.03 1.01–1.06 0.007 1.02 0.99–1.04 0.19 Male sex 2.49 1.49–4.15 < 0.001 1.55 0.89–2.72 0.12 2.32 1.38–3.91 0.002 1.42 0.81–2.48 0.22 Race White (referent) Black Asian Other 1 0.55 0.17 0.35 0.27–1.12 0.026–1.35 0.11–1.13 0.10 0.10 0.08 1 0.63 0.26 0.34 0.30–1.29 0.04–1.92 0.11–1.09 0.20 0.19 0.07 1 0.59 0.19 0.38 0.29–1.21 0.03–1.38 0.12–1.21 0.15 0.10 0.10 1 0.68 0.28 0.37 0.33–1.41 0.04–2.08 0.11–1.21 0.30 0.21 0.10 Hispanic ethnicity 1.43 0.79–2.60 0.24 1.18 0.64–2.17 0.60 1.40 0.77–2.53 0.27 1.19 0.65–2.19 0.57 Smoking history 1.97 1.19–3.24 0.01 1.42 0.84–2.42 0.19 1.84 1.11–3.06 0.02 1.32 0.77–2.24 0.31 Baseline FVC (%) 0.98 0.97 − 0.96 0.01 1.00 0.99–1.02 0.71 0.98 0.96–0.99 0.001 0.997 0.98–1.01 0.70 Baseline DLCO (%) 0.98 0.97–0.99 0.002 1.00 0.99–1.02 0.72 0.97 0.96–0.99 < 0.001 0.997 0.98–1.01 0.70 UIP pattern on HRCT 1.70 1.01–2.47 0.047 1.86 1.09–3.17 0.02 - - - - - - Unexplained air trapping on HRCT 1.19 0.73–1.93 0.50 1.42 0.86–2.35 0.17 1.41 0.84–2.37 0.20 1.62 0.97–2.72 0.07 Honeycombing on HRCT 1.12 0.65–1.93 0.69 1.14 0.66–1.97 0.64 1.18 0.68–2.04 0.56 1.25 0.72–2.18 0.43 Hypertension 1.33 0.77–2.29 0.31 1.18 0.68–2.05 0.56 1.39 0.80–2.42 0.24 1.19 0.68–2.07 0.54 Myocardial infarction 1.10 0.50–2.42 0.81 1.06 0.48–2.34 0.88 0.91 0.40–2.04 0.82 0.93 0.42–2.07 0.87 Congestive heart failure 1.83 0.97–3.45 0.06 1.34 0.70–2.55 0.37 2.00 1.05–3.81 0.03 1.47 0.77–2.82 0.24 Peripheral vascular disease 2.23 0.96–5.19 0.06 1.29 0.54–3.08 0.57 1.98 0.84–4.66 0.12 1.20 0.50–2.85 0.68 Cerebrovascular accident* 1.60 0.64–4.01 0.31 0.91 0.35–2.34 0.85 1.40 0.55–3.54 0.48 0.85 0.33–2.16 0.73 Cardiovascular disease of cerebrovascular accident* 1.87 1.07–3.27 0.03 1.36 0.76–2.43 0.29 1.72 0.97–3.04 0.06 1.27 0.71–2.25 0.42 Chronic obstructive pulmonary disease 0.86 0.48–1.51 0.59 1.08 0.61–1.92 0.79 0.87 0.49–1.53 0.62 1.06 0.60–1.88 0.83 Obstructive sleep apnea 1.24 0.59–2.61 0.57 1.59 0.75–3.38 0.23 1.23 0.59–2.59 0.58 1.61 0.75–3.44 0.22 Gastroesophageal reflux disease 0.78 0.46–1.31 0.34 0.76 0.45–1.28 0.30 0.73 0.43–1.24 0.24 0.69 0.40–1.17 0.17 Peptic ulcer disease 1.46 0.63–3.39 0.38 1.50 0.64–3.50 0.35 1.28 0.54-3.00 0.58 1.14 0.47–2.76 0.77 Liver disease 0.48 0.066–3.45 0.46 0.58 0.08–4.18 0.58 0.40 0.06–2.90 0.36 0.41 0.06–3.06 0.39 Diabetes mellitus (any) None or diet controlled (referent) Uncomplicated requiring medications With end organ damage 1.84 1 0.94 5.07 1.08–3.17 0.43–2.08 2.25–11.39 0.03 0.88 < 0.001 1.74 1 0.94 3.01 1.01–2.99 0.42–2.08 1.31–6.94 0.045 0.88 0.01 1.79 1 0.95 4.61 1.04–3.08 0.43–2.10 2.04–10.41 0.04 0.90 < 0.001 1.71 1 0.98 2.81 0.99–2.94 0.44–2.18 1.23–6.41 0.05 0.96 0.01 Chronic kidney disease (any) None or mild (referent) Moderate-severe 1.37 1 0.82 0.76–2.49 0.11–5.91 0.30 0.84 0.96 1 0.75 0.52–1.78 0.10–5.44 0.89 0.78 1.25 1 0.69 0.68–2.28 0.10-5.00 0.47 0.71 0.88 1 0.71 0.48–1.63 0.10–5.14 0.68 0.74 Malignancy** (any) No solid tumor (referent) Local solid tumor Metastatic solid tumor 1.44 1 1.43 0.82 0.77–2.71 0.72–2.81 0.11–5.93 0.26 0.31 0.84 1.18 1 1.11 1.22 0.63–2.24 0.56–2.22 0.17–8.90 0.60 0.76 0.85 1.42 1 1.46 0.62 0.75–2.66 0.74–2.88 0.08–4.60 0.28 0.27 0.64 1.20 1 1.14 1.07 0.64–2.27 0.57–2.26 0.15–7.91 0.57 0.71 0.95 Lymphoma 20.50 4.66–90.09 < 0.001 27.77 6.23-123.66 < 0.001 15.42 3.40-70.02 < 0.001 20.30 4.44–92.8 < 0.001 Fracture 1.57 0.67–3.65 0.30 1.47 0.63–3.42 0.37 1.44 0.62–3.36 0.4 1.44 0.62–3.36 0.4 Osteoporosis 1.02 0.47–2.25 0.95 0.92 0.42–2.03 0.84 0.92 0.42–2.04 0.84 0.79 0.35–1.76 0.56 Depression 1.42 0.78–2.58 0.25 1.41 0.78–2.57 0.26 1.41 0.78–2.57 0.26 1.38 0.76–2.51 0.29 Dementia 1.48 0.46–4.74 0.51 1.97 0.61–6.34 0.26 1.71 0.53–5.52 0.37 2.33 0.71–7.59 0.16 Hypothyroidism 1.53 0.75–3.12 0.25 1.91 0.92–2.96 0.08 1.67 0.81–3.43 0.16 2.02 0.97–4.17 0.06 CCI 1.18 1.07–1.30 0.001 1.13 1.02–1.26 0.02 1.16 1.04–1.29 0.01 1.10 0.99–1.23 0.08 RDCI 1.31 1.10–1.57 0.003 1.24 1.03–1.50 0.02 1.29 1.07–1.54 0.01 1.20 0.99–1.46 0.06 ILD-GAP 1.53 1.31–1.80 < 0.001 - - - 1.57 1.33–1.85 < 0.001 - - - PFTs – pulmonary function tests; FVC – forced vital capacity; DLCO – diffusing capacity of lung for carbon monoxide; UIP – usual interstitial pneumonia; HRCT – high resolution computed tomography; HR – hazard ratio; CI – confidence interval; CCI – Charlson Comorbidity Index; RDCI – rheumatic disease comorbidity index; ILD-GAP – Gender, Age, Physiology Index for Interstitial Lung Disease. *Including transient ischemic attack **Including skin cancer and hematologic malignancy Table 4 included on the bottom of the file Effect of Baseline Comorbidity Indices on Outcomes: In univariable analysis, both CCI and RDCI were significantly associated with lung disease progression (Table 3 ) and lung transplant/mortality outcomes (Table 4 ). ILD-GAP was significantly associated with lung transplant/mortality but not with lung disease progression (Tables 3 and 4 ). The association of CCI with lung disease progression, but not that of RDCI, remained significant when adjusted for ILD-GAP and ILD-GAP and UIP. When adjusted for the presence or absence of UIP pattern alone, both CCI and RDCI remained significant predictors of the outcome (Table 3 ). CCI and RDCI were significantly associated with shorter time to lung transplant/death even when adjusted for ILD-GAP or UIP. However, neither CCI nor RDCI were significantly associated with the lung transplant/mortality outcome after adjusting for both ILD-GAP and UIP pattern in multivariable analysis. Higher ILD-GAP was associated with significantly shorter time to lung transplant/death outcomes in univariable analysis and when adjusted for UIP (Table 4 ). DISCUSSION In this study we explored the prevalence and the effect of baseline comorbidities on lung disease progression and lung transplant/mortality in a cohort of patients with IPAF. We also assessed the performance of two well-established comorbidity indices, CCI and RDCI, in predicting outcomes in this population. We found that comorbidity indices provide comprehensive information regarding a patient’s prognosis, including lung disease progression. Hypertension and GERD were the most prevalent comorbidities in our cohort. This was consistent with a study by Oldham, et al., where GERD was the most prevalent (52.8%) comorbidity in an IPAF cohort raising suspicion for a potential contribution of GERD to disease pathogenesis in IPAF ( 3 ). In addition, COPD, depression, and DM were found to be common comorbid conditions in our cohort, a finding which was not discussed in prior IPAF literature but has been demonstrated in other forms of ILD ( 12 , 30 , 31 ). When evaluating the effect of comorbidities on lung disease progression, this study revealed that multiple comorbidities such as a history of CVA/CVD, moderate to severe CKD and fracture, specifically that of lower extremity or vertebrae, were associated with a faster onset of relative FVC decline of ≥ 10% or more from the time of cohort entry in patients with IPAF. Conversely, a history of GERD was associated with a longer time to lung disease progression. Although it is difficult to ascertain the association between the various comorbidities, prior studies have commented on the effects of these comorbidities on lung disease progression in patients with other forms of ILD. For example, CVD is a treatable comorbidity frequently observed in IPF and has been linked to worse outcomes, with proposed mechanisms including systemic inflammation, hypercoagulability, platelet activation, and oxidative stress ( 32 – 34 ). A complex relationship between CKD and lung disease progression, particularly through alterations of fluid homeostasis and acid-base balance, has also been demonstrated ( 35 , 36 ). A potential explanation for the observed association of fracture and lung disease progression, in this study, is the increased prevalence of steroid use in patients with more severe disease which can thereby increase fracture risk. However, fracture was a baseline comorbidity in the cohort and thus steroid use would not be expected to contribute significantly to its prevalence at the time of baseline data collection. To evaluate this possibility, data regarding osteoporosis was also collected, but did not demonstrate a significant association with the studied outcomes. In other autoimmune diseases, such as rheumatoid arthritis, osteopenia and osteoporosis have correlated with higher mortality and fracture risk, when compared to the general population ( 18 ). Interestingly, GERD was found to be a protective factor for lung disease progression in our cohort, a finding which varies from prior studies that considered GERD to be a risk factor for ILD development ( 37 ). One consideration is the high prevalence of proton pump inhibitor use, an intervention hypothesized to slow lung disease progression in patients with IPF ( 38 , 39 ). Based on our results, the association of CVD, CKD, fracture risk, and GERD with lung disease progression in patients with IPAF warrants further investigation. Importantly, few prior studies have explored the prognostic impact of comorbid conditions in IPAF. Two studies suggested that hypothyroidism was associated with greater mortality ( 7 , 8 ), but a diagnosis of obstructive sleep apnea (OSA) was correlated with better survival when compared to IPAF patients without these conditions ( 7 ). Malignancy was noted to be a serious comorbidity associated with faster progression of fibrotic lung disease in RD-ILD and IPAF ( 9 ). In our study, OSA and hypothyroidism were not significantly associated with outcomes, but lymphoma, as well as CKD and DM, were associated with shorter time to lung transplant/mortality. Similar to other studies, increasing age, male sex, and smoking history were also associated with shorter time to lung transplant/mortality in our cohort ( 40 – 44 ). We evaluated the performance of the CCI and RDCI in patients with IPAF and found that both CCI and RDCI were helpful in predicting lung function and transplant/mortality outcomes, even after adjusting for UIP pattern. However, the performance of indices was variable for both outcomes when adjusted for both ILD-GAP and UIP pattern. Notably, CVA/CVD and fracture, which are components of RDCI, but not CCI, were important comorbidities associated with poor outcomes in our study. Additionally, depression was highly prevalent in our cohort but is only reflected within the RDCI score. These findings suggest that both CCI and RDCI may be useful tools for prognosticating outcomes in IPAF patients. Our study has several strengths. We had a large, diverse IPAF cohort and the ability to explore numerous comorbidities. Sixteen percent of our cohort included Black patients, increasing the external validity of our findings. Comorbidities have been variably explored in cohorts of patients with IPAF, resulting in highly heterogeneous data of the prevalence of multiple comorbidities. In our study, we systematically and rigorously assessed the comorbidities used to calculate two commonly used comorbidity indices, particularly in autoimmune diseases, making our findings relevant to presumably autoimmune-related ILD such as IPAF. Our study was the first to use comorbidity indices CCI and RDCI to assess the comorbidity burden in this specific population and demonstrate their usefulness in assessing functional and survival outcomes. Specifically, this study supports the use of RDCI in this population, a possible advantage for rheumatologists seeing these patients. Our results also emphasize the need for comprehensive evaluation of IPAF patients and multiple tools for optimal management and monitoring, including HRCT imaging, PFT monitoring, and ILD-GAP and comorbidity assessments. We acknowledge several limitations of our study. Given the retrospective nature of the study, the variables were collected by medical record review which can lead to misclassification bias, attrition bias, and the presence of missing data. We used precise and consistent definitions of the key comorbidities and variables to guide the data collection by two clinical researchers (with familiarity with both the medical record and management of these conditions and comorbidities) to mitigate the effects of misclassification bias and imaging data by an experienced ILD pulmonologist. However, loss to follow-up could have introduced selection bias. Patients who did not have repeat PFT data were excluded from the study, which could have been due to unknown death or a different cause that was not recorded in the medical record. Finally, while our study emphasizes the need for comorbidity screening for patients with IPAF, we were unable to evaluate whether controlling comorbidities would mitigate the often detrimental association with disease outcomes. CONCLUSION In summary, our study addresses a substantial healthcare gap for patients with IPAF, as few studies have examined comorbidities in this population. Multiple comorbidities are highly prevalent in IPAF and their burden, as assessed by comorbidity indices, influences functional and survival outcomes. Importantly, our study highlights the utility of two common comorbidity indices in assessing the outcomes of patients with IPAF. Particularly, the comorbidity burden in IPAF may be slightly better assessed by RDCI than CCI due to the inclusion of fracture and depression. Importantly, several of the comorbidities frequently seen in our cohort are modifiable and can be optimized, including DM, depression, COPD, GERD and others. Furthermore, potential mechanisms underlying CVA/CVD, CKD, and fracture with lung disease progression in IPAF need to be explored. Further prospective studies are needed to investigate whether aggressive treatment of comorbidities will mitigate poor outcomes. Clinicians taking care of patients with IPAF should be aware of common comorbid conditions, the potential prognostic implications of comorbidity indices, and the increased morbidity effect of specific comorbidities in this patient population. List Of Abbreviations IPAF: interstitial pneumonia with autoimmune features ILD: interstitial lung disease RD: rheumatic disease CCI: Charlson Comorbidity Index RDCI: rheumatic disease comorbidity index GAP index: Gender-Age-Physiology index FVC: forced vital capacity DLCO: diffusing capacity for carbon monoxide IPF: idiopathic pulmonary fibrosis UTSW: University of Texas Southwestern ERS: European Respiratory Society ATS: American Thoracic Society PFT: pulmonary function test EMR: electronic medical record HRCT: high-resolution computed tomography UIP: usual interstitial pneumonia FEV1: forced expiratory volume in one second HR: hazard ratio CI: confidence interval GERD: gastroesophageal reflux disease COPD: chronic obstructive pulmonary disease DM: diabetes mellitus CVA: cerebrovascular accident CVD: cardiovascular disease CKD: chronic kidney disease CHF: congestive heart failure Declarations Ethics Approval and Consent to Participate : The University of Texas Southwestern Medical Center Institutional Review Board approved the study prior to the initiation of data extraction (IRB #STU-2019-0913). Waiver of informed consent from University of Texas Southwestern Medical Center Institutional Review Board (IRB #STU-2019-0913) was obtained for each subject for this medical record review retrospective study. All aspects of the study were performed in accordance with all relevant guidelines and regulations including Declaration of Helsinki. Consent for Publication: Not applicable. Availability of Data and Materials: The data sets generated during the current study are not publicly available due to protected health information but are available from the corresponding author on reasonable request. Competing Interests : EJ reports salary support from Ruth L. Kirschstein Institutional National Research Service Award (T32). MG reports no conflict of interest, financial or otherwise. TA reports no conflict of interest, financial or otherwise. UM reports no conflict of interest, financial or otherwise. Funding : The conduction of this study was supported by Ruth L. Kirschstein Institutional National Research Service Award (T32). Authors’ Contributions: EJ, MG, TA, and UM were involved in study design. EJ, MG and TA collected the data and EJ and MG analyzed the patient data. EJ, MG, TA, and UM have contributed to the writing of the manuscript and have read and approved the final manuscript. Acknowledgments: Not applicable References Fischer A, Antoniou KM, Brown KK, Cadranel J, Corte TJ, du Bois RM, et al. An official European Respiratory Society/American Thoracic Society research statement: interstitial pneumonia with autoimmune features. Eur Respir J. 2015;46(4):976-87. Jee AP, MJS; Bleasel, JF; et al. Baseline Characteristics and Survival of an Australian Interstitial Pneumonia with Autoimmune Features Cohort. Respiration. 2021. Oldham JM, Adegunsoye A, Valenzi E, Lee C, Witt L, Chen L, et al. Characterisation of patients with interstitial pneumonia with autoimmune features. Eur Respir J. 2016;47(6):1767-75. Ito Y, Arita M, Kumagai S, Takei R, Noyama M, Tokioka F, et al. Serological and morphological prognostic factors in patients with interstitial pneumonia with autoimmune features. BMC Pulm Med. 2017;17(1):111. Joerns EK, Adams TN, Newton CA, Bermas B, Karp D, Batra K, et al. Variables Associated With Response to Therapy in Patients With Interstitial Pneumonia With Autoimmune Features. J Clin Rheumatol. 2022;28(2):84-8. Karampeli M, Thomas K, Flouda S, Chavatza A, Nikolopoulos D, Pieta A, et al. Interstitial Pneumonia with Autoimmune Features (IPAF): A Single-Centre, Prospective Study. Mediterr J Rheumatol. 2020;31(3):330-6. Radner H. Multimorbidity in rheumatic conditions. Wien Klin Wochenschr. 2016;128(21-22):786-90. Roubille C, Richer V, Starnino T, McCourt C, McFarlane A, Fleming P, et al. Evidence-based Recommendations for the Management of Comorbidities in Rheumatoid Arthritis, Psoriasis, and Psoriatic Arthritis: Expert Opinion of the Canadian Dermatology-Rheumatology Comorbidity Initiative. J Rheumatol. 2015;42(10):1767-80. Crowson CS, Gunderson TM, Dykhoff HJ, Myasoedova E, Atkinson EJ, Kronzer VL, et al. Comprehensive assessment of multimorbidity burden in a population-based cohort of patients with rheumatoid arthritis. RMD Open. 2022;8(1). Gunderson TM, Myasoedova E, Davis JM, 3rd, Crowson CS. Multimorbidity Burden in Rheumatoid Arthritis: A Population-based Cohort Study. J Rheumatol. 2021;48(11):1648-54. Margaritopoulos GA, Antoniou KM, Wells AU. Comorbidities in interstitial lung diseases. Eur Respir Rev. 2017;26(143). Schwarzkopf L, Witt S, Waelscher J, Polke M, Kreuter M. Associations between comorbidities, their treatment and survival in patients with interstitial lung diseases - a claims data analysis. Respir Res. 2018;19(1):73. Radner H, Yoshida K, Hmamouchi I, Dougados M, Smolen JS, Solomon DH. Treatment Patterns of Multimorbid Patients with Rheumatoid Arthritis: Results from an International Cross-sectional Study. J Rheumatol. 2015;42(7):1099-104. Aslam F, Khan NA. Tools for the Assessment of Comorbidity Burden in Rheumatoid Arthritis. Front Med (Lausanne). 2018;5:39. England BR, Sayles H, Mikuls TR, Johnson DS, Michaud K. Validation of the rheumatic disease comorbidity index. Arthritis Care Res (Hoboken). 2015;67(6):865-72. Yagyu H, Murohashi K, Hara Y, Saigusa Y, Aoki A, Kobayashi N, et al. Clinical utility of a composite scoring system including Charlson Comorbidity Index score in patients with interstitial lung disease. J Thorac Dis. 2020;12(10):5774-82. Michaud K, Wolfe F. Comorbidities in rheumatoid arthritis. Best Pract Res Clin Rheumatol. 2007;21(5):885-906. Spaetgens B, Wijnands JM, van Durme C, Boonen A. Content and construct validity of the Rheumatic Diseases Comorbidity Index in patients with gout. Rheumatology (Oxford). 2015;54(9):1659-63. Ley B, Ryerson CJ, Vittinghoff E, Ryu JH, Tomassetti S, Lee JS, et al. A multidimensional index and staging system for idiopathic pulmonary fibrosis. Ann Intern Med. 2012;156(10):684-91. Lee SH, Kim SY, Kim DS, Kim YW, Chung MP, Uh ST, et al. Predicting survival of patients with idiopathic pulmonary fibrosis using GAP score: a nationwide cohort study. Respir Res. 2016;17(1):131. Ryerson CJ, Vittinghoff E, Ley B, Lee JS, Mooney JJ, Jones KD, et al. Predicting survival across chronic interstitial lung disease: the ILD-GAP model. Chest. 2014;145(4):723-8. Nurmi HM, Purokivi MK, Kärkkäinen MS, Kettunen HP, Selander TA, Kaarteenaho RL. Are risk predicting models useful for estimating survival of patients with rheumatoid arthritis-associated interstitial lung disease? BMC Pulm Med. 2017;17(1):16. Fujii H, Hara Y, Saigusa Y, Tagami Y, Murohashi K, Nagasawa R, et al. ILD-GAP Combined with the Charlson Comorbidity Index Score (ILD-GAPC) as a Prognostic Prediction Model in Patients with Interstitial Lung Disease. Can Respir J. 2023;2023:5088207. Mari P-V, Kay S, Belloli E, Salisbury M, Sheth J, Holtze C, et al. Impact of comorbidities in interstitial pneumonia with autoimmune features (IPAF)2019. PA1303 p. O'Dwyer DN, Wang BR, Nagaraja V, Flaherty KR, Khanna D, Murray S, et al. Hypothyroidism Is Associated with Increased Mortality in Interstitial Pneumonia with Autoimmune Features. Ann Am Thorac Soc. 2022;19(10):1772-6. Nagy A, Nagy T, Kolonics-Farkas AM, Eszes N, Vincze K, Barczi E, et al. Autoimmune Progressive Fibrosing Interstitial Lung Disease: Predictors of Fast Decline. Front Pharmacol. 2021;12:778649. Nieto MA, Sanchez-Pernaute O, Vadillo C, Rodriguez-Nieto MJ, Romero-Bueno F, López-Muñiz B, et al. Functional respiratory impairment and related factors in patients with interstitial pneumonia with autoimmune features (IPAF): Multicenter study from NEREA registry. Respir Res. 2023;24(1):19. Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40(5):373-83. Maher TM, Corte TJ, Fischer A, Kreuter M, Lederer DJ, Molina-Molina M, et al. Pirfenidone in patients with unclassifiable progressive fibrosing interstitial lung disease: a double-blind, randomised, placebo-controlled, phase 2 trial. The Lancet Respiratory Medicine. 2020;8(2):147-57. Prior TS, Hyldgaard C, Torrisi SE, Kronborg-White S, Ganter C, Bendstrup E, et al. Comorbidities in unclassifiable interstitial lung disease. Respir Res. 2022;23(1):59. Butler SJ, Li LSK, Ellerton L, Gershon AS, Goldstein RS, Brooks D. Prevalence of comorbidities and impact on pulmonary rehabilitation outcomes. ERJ Open Res. 2019;5(4). Reed RM, Eberlein M, Girgis RE, Hashmi S, Iacono A, Jones S, et al. Coronary artery disease is under-diagnosed and under-treated in advanced lung disease. Am J Med. 2012;125(12):1228.e13-.e22. Kizer JR, Zisman DA, Blumenthal NP, Kotloff RM, Kimmel SE, Strieter RM, et al. Association between pulmonary fibrosis and coronary artery disease. Arch Intern Med. 2004;164(5):551-6. Nathan SD, Basavaraj A, Reichner C, Shlobin OA, Ahmad S, Kiernan J, et al. Prevalence and impact of coronary artery disease in idiopathic pulmonary fibrosis. Respir Med. 2010;104(7):1035-41. Gembillo G, Calimeri S, Tranchida V, Silipigni S, Vella D, Ferrara D, et al. Lung Dysfunction and Chronic Kidney Disease: A Complex Network of Multiple Interactions. J Pers Med. 2023;13(2). Mukai H, Ming P, Lindholm B, Heimbürger O, Barany P, Stenvinkel P, et al. Lung Dysfunction and Mortality in Patients with Chronic Kidney Disease. Kidney Blood Press Res. 2018;43(2):522-35. McCoy SS, Mukadam Z, Meyer KC, Kanne JP, Meyer CA, Martin MD, et al. Mycophenolate therapy in interstitial pneumonia with autoimmune features: a cohort study. Ther Clin Risk Manag. 2018;14:2171-81. Raghu G, Yang ST, Spada C, Hayes J, Pellegrini CA. Sole treatment of acid gastroesophageal reflux in idiopathic pulmonary fibrosis: a case series. Chest. 2006;129(3):794-800. Lee JS, Collard HR, Anstrom KJ, Martinez FJ, Noth I, Roberts RS, et al. Anti-acid treatment and disease progression in idiopathic pulmonary fibrosis: an analysis of data from three randomised controlled trials. Lancet Respir Med. 2013;1(5):369-76. Huapaya JA, Boulougoura A, Fried J, Mesdaghinia S, Culotta BJ, Carson S, et al. Long-term evaluation of pulmonary function and survival of patients with interstitial pneumonia with autoimmune features. Clin Exp Rheumatol. 2023;41(1):15-23. Jiwrajka N, Loizidis G, Patterson KC, Kreider ME, Johnson CR, Miller WT, Jr., et al. Identification and Prognosis of Patients With Interstitial Pneumonia With Autoimmune Features. J Clin Rheumatol. 2022;28(5):257-64. Dai J, Wang L, Yan X, Li H, Zhou K, He J, et al. Clinical features, risk factors, and outcomes of patients with interstitial pneumonia with autoimmune features: a population-based study. Clin Rheumatol. 2018;37(8):2125-32. Kawano-Dourado L, Glassberg MK, Assayag D, Borie R, Johannson KA. Sex and gender in interstitial lung diseases. Eur Respir Rev. 2021;30(162). Hoyer N, Wille MMW, Thomsen LH, Wilcke T, Dirksen A, Pedersen JH, et al. Interstitial lung abnormalities are associated with increased mortality in smokers. Respir Med. 2018;136:77-82. Additional Declarations Competing interest reported. EJ reports salary support from Ruth L. Kirschstein Institutional National Research Service Award (T32). MG reports no conflict of interest, financial or otherwise. TA reports no conflict of interest, financial or otherwise. 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Joerns","email":"data:image/png;base64,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","orcid":"","institution":"University of Texas Southwestern Medical Center","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Elena","middleName":"K.","lastName":"Joerns","suffix":""},{"id":225713266,"identity":"6f08bfff-358b-41ed-9927-b3a34455761c","order_by":1,"name":"Michelle A. Ghebranious","email":"","orcid":"","institution":"University of Texas Southwestern Medical Center","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Michelle","middleName":"A.","lastName":"Ghebranious","suffix":""},{"id":225713267,"identity":"c8d56e19-3d71-45d6-a627-f7b6b60f57f5","order_by":2,"name":"Traci N. Adams","email":"","orcid":"","institution":"University of Texas Southwestern Medical Center","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Traci","middleName":"N.","lastName":"Adams","suffix":""},{"id":225713268,"identity":"e7a917cc-d75f-46d5-8f91-1735bfc3b61f","order_by":3,"name":"Una E. Makris","email":"","orcid":"","institution":"University of Texas Southwestern Medical Center","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Una","middleName":"E.","lastName":"Makris","suffix":""}],"badges":[],"createdAt":"2023-07-27 19:29:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3210870/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3210870/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":46340773,"identity":"2ae20e92-b627-4c81-8dbf-872bf004a555","added_by":"auto","created_at":"2023-11-13 12:52:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":978411,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3210870/v1/11095f61-0dd9-4d80-a1e5-507bfa63ed06.pdf"},{"id":41675716,"identity":"0a7bb76a-7ba4-4c06-a332-ba2dc3fa2e5a","added_by":"auto","created_at":"2023-08-17 05:17:45","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":75692,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementary.docx","url":"https://assets-eu.researchsquare.com/files/rs-3210870/v1/13e853ef5a3d43aab1b77bcd.docx"}],"financialInterests":"Competing interest reported. EJ reports salary support from Ruth L. Kirschstein Institutional National Research Service Award (T32).\nMG reports no conflict of interest, financial or otherwise.\nTA reports no conflict of interest, financial or otherwise.\nUM reports no conflict of interest, financial or otherwise.","formattedTitle":"Evaluation of Comorbidity Burden on Disease Progression and Mortality in Patients with Interstitial Pneumonia with Autoimmune Features: a Retrospective Cohort Study","fulltext":[{"header":"BACKGROUND","content":"\u003cp\u003eInterstitial pneumonia with autoimmune features (IPAF) is a subset of interstitial lung disease (ILD) that manifests with signs and symptoms of autoimmunity while not meeting classification criteria for a defined rheumatic disease (RD) (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Multiple studies have examined the clinical characteristics and outcomes of IPAF patients (\u003cspan additionalcitationids=\"CR3 CR4\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e), while few studies have commented on the presence and prevalence of comorbidities in this population (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). To our knowledge, no studies have evaluated the effect of comorbidities on lung function decline in IPAF. Comorbidity burden is an important prognostic indicator in various RDs (\u003cspan additionalcitationids=\"CR8 CR9\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e) and ILD (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e) and is associated with worse disease activity, physical function, quality of life, refractory treatment, and higher mortality risk (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e), emphasizing the need for awareness, screening, and optimization of comorbid conditions.\u003c/p\u003e \u003cp\u003eComposite scoring systems or indices can be used to quantify the total burden of comorbid illnesses to measure the overall burden and impact of comorbidities more accurately and to assess prognosis (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e) Charlson Comorbidity Index (CCI), a summed score of 19 comorbidities weighted according to severity, was developed to predict one-year mortality in hospitalized patients. The CCI can predict various patient outcomes, including in-hospital mortality, length of hospital stays, readmission rates, functional decline, and healthcare utilization (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). CCI is also a strong prognostic predictor in ILD with the frequency of 3-year ILD-related events increasing with increasing CCI; however, IPAF patients were not included in this cohort (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). In addition, CCI does not account for common comorbidities seen in RDs such as hypertension, osteoporosis, obstructive sleep apnea, or depression which can significantly impact disease activity and quality of life (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe rheumatic disease comorbidity index (RDCI) is comprised of 11 comorbid conditions and was developed based on self-report assessments from patients with RDs (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). It has performed well in predicting both physical disability and mortality in diseases such as rheumatoid arthritis and gout (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). However, its usefulness in presumably autoimmune ILD, such as IPAF, has not been evaluated.\u003c/p\u003e \u003cp\u003eThe Gender-Age-Physiology (GAP) index was developed as a multidimensional prognostic staging system for ILD using four variables including gender (G), age (A), and two lung physiology variables (P) - forced vital capacity (FVC) and diffusing capacity for carbon monoxide (DLCO) (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Although initially validated in patients with idiopathic pulmonary fibrosis (IPF), the GAP model has also accurately predicted the risk of death in chronic non-IPF ILD (\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). A modified GAP index, known as the ILD-GAP index, includes a disease subtype variable that accounts for better-adjusted survival in ILD subtypes, including RD-associated ILD, chronic hypersensitivity pneumonitis, and idiopathic nonspecific interstitial pneumonia. The ILD-GAP model has predicted mortality in major chronic ILD subtypes across all stages of disease (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). However, ILD-GAP does not consider the presence or severity of comorbidities, and a recent study demonstrated that the combination of ILD-GAP and CCI performs better in predicting outcomes in ILD than ILD-GAP or CCI alone (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFew studies have examined comorbidities (\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e) and no studies have evaluated the association or effect of comorbidities, using indices such as the CCI or RDCI, with outcomes in IPAF. This study was conducted to determine the prevalence and type of comorbidities in an institutional cohort of patients with IPAF; to evaluate the effect of baseline comorbidities on lung disease progression and mortality; and to assess the performance of the CCI and RDCI, while adjusting for the ILD-GAP index, in predicting outcomes in IPAF patients.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eCohort Assembly\u003c/span\u003e:\u003c/h2\u003e \u003cp\u003e This single-center retrospective medical records review study was performed at the University of Texas Southwestern (UTSW) Medical Center. Patients seen in the UTSW Interstitial Lung Disease Clinic between January 2005 and August 2019 who met the European Respiratory Society (ERS)/American Thoracic Society (ATS) classification criteria for IPAF were included (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Date of entry into the cohort was considered as the first available pulmonary function test (PFT) date. The UTSW Medical Center Institutional Review Board approved the study prior to the initiation of data extraction (IRB #STU-2019-0913). Patient consent was not obtained for this retrospective medical records review study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eInclusion/Exclusion Criteria\u003c/span\u003e:\u003c/h2\u003e \u003cp\u003eAll patients meeting 2015 ERS/ATS IPAF classification criteria were included for evaluation of prevalence of comorbidities and other baseline characteristics. Patients were excluded from primary outcome evaluation if they did not have at least two sets of PFT data and excluded from secondary outcome evaluation if they had less than two visits recorded in the medical record.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eBaseline characteristics\u003c/span\u003e:\u003c/h2\u003e \u003cp\u003eDemographic data included age at ILD diagnosis, sex, race, ethnicity, and smoking status. ILD diagnosis date was the time point at which ILD was first observed on imaging. Sex, race, and ethnicity data was recorded as documented in the electronic medical record (EMR). Smoking status was assigned as smoker if the patient ever smoked and otherwise as never smoker.\u003c/p\u003e \u003cp\u003eImaging features including lung lesion pattern, presence of honeycombing, and air trapping, were assigned by a pulmonologist (TAN) based on high-resolution computed tomography scans (HRCT) of the chest. The presence of a usual interstitial pneumonia (UIP) pattern on imaging was documented, given the correlation of UIP with increased mortality and faster lung function decline in prior studies (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eComorbidities\u003c/span\u003e:\u003c/h2\u003e \u003cp\u003ePatient medical records were reviewed by an internal medicine physician (MAG) and rheumatologist (EKJ) using a standardized data extraction worksheet. Comorbidities were evaluated by searching for the key terms and reviewing pertinent documentation (including progress notes), laboratory values, and imaging at the time of entry into the cohort (defined as the date of the first PFTs recorded at the initial ILD clinic visit) (Supplementary Appendix A). Agreement between the reviewers was assessed after reviewing 50 charts to ensure accuracy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eCalculation of Comorbidity Indices and ILD Severity\u003c/span\u003e:\u003c/h2\u003e \u003cp\u003eCCI and RDCI were calculated according to standardized procedures and a data extraction worksheet (Supplementary Tables\u0026nbsp;1 and 2) (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe ILD-GAP index (Supplementary Table\u0026nbsp;3) was calculated using five predictor variables (gender, age, FVC, DLCO, and ILD category) with assigned points to obtain a total score from 0 to 8 (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Since IPAF, by definition, is unclassified ILD due to lack of definable etiology such as RD (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e), 0 points were assigned for the ILD category score for each patient in the cohort.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ePulmonary Function Tests\u003c/span\u003e:\u003c/h2\u003e \u003cp\u003ePFT data included FVC, forced expiratory volume in one second (FEV1), FEV1/FVC ratio, and DLCO as percentages of predicted values. Baseline PFTs were the first available tests recorded at the first ILD clinic visit. All available additional PFT data were collected. If a patient underwent a lung transplantation, the last PFT data prior to transplant was included.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eOutcomes\u003c/span\u003e:\u003c/h2\u003e \u003cp\u003eThe primary outcome was the time to lung disease progression, defined as relative %FVC decline of \u0026ge;\u0026thinsp;10% after date of cohort entry. Secondary outcomes were the time to lung transplant and all-cause mortality (whichever occurred first) from the time point of entry into the cohort. All outcomes were obtained from chart review.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eDescriptive statistics were used to calculate the prevalence of baseline characteristics and comorbidities in our IPAF cohort. Continuous and categorical variables were expressed as means with standard deviation and counts with percentages, respectively. We used Cox proportional hazards to estimate the hazard ratios (HRs) and 95% confidence intervals (CIs) for each outcome, first in an unadjusted model then adjusted for ILD-GAP and presence of UIP. All analyses were completed in Stata (V.17, College Station, TX).\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eBaseline Characteristics:\u003c/h2\u003e \u003cp\u003eThe cohort, comprised of 201 patients meeting the 2015 ERS/ATS IPAF criteria, was predominantly female (76.1%), with a mean age at the time of initial PFTs of 61.8 \u0026plusmn; 12.4 years, and an average time of follow-up of 5.6 \u0026plusmn; 3.7 years (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). White patients represented 69.6% of the cohort and 84.6% of patients were non-Hispanic and 34.5% were former or current smokers. The baseline mean %FVC was 64.7% \u0026plusmn; 19.1% of predicted. The mean %DLCO was 45.8% \u0026plusmn; 20.3% of predicted. The mean baseline ILD-GAP index in our cohort was 3.2 \u0026plusmn; 1.7 points. Forty-eight (23.9%) patients had a UIP pattern on imaging with 46.8% having unexplained air trapping and 21.4% having honeycombing on HRCT.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline Features of the IPAF Cohort\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline characteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;201\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge at initial PFTs, years; Mean [SD]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e61.8 [12.4]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale sex, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e48 (23.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace, n (%)\u003c/p\u003e \u003cp\u003eWhite\u003c/p\u003e \u003cp\u003eBlack\u003c/p\u003e \u003cp\u003eAsian\u003c/p\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e140 (69.6)\u003c/p\u003e \u003cp\u003e34 (16.9)\u003c/p\u003e \u003cp\u003e10 (5)\u003c/p\u003e \u003cp\u003e17 (8.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEthnicity, n (%)\u003c/p\u003e \u003cp\u003eNon-Hispanic\u003c/p\u003e \u003cp\u003eHispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e170 (84.6)\u003c/p\u003e \u003cp\u003e31 (15.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever smoke, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e114 (65.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline FVC, %; Mean [SD]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e64.7 [19.1]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline DLCO (n\u0026thinsp;=\u0026thinsp;200), %; Mean [SD]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e45.8 [20.3]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHRCT pattern, n (%)\u003c/p\u003e \u003cp\u003eUnable to determine\u003c/p\u003e \u003cp\u003eLIP\u003c/p\u003e \u003cp\u003eNSIP\u003c/p\u003e \u003cp\u003eNSIP/OP\u003c/p\u003e \u003cp\u003eOP\u003c/p\u003e \u003cp\u003eUIP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21 (10.5)\u003c/p\u003e \u003cp\u003e5 (2.5)\u003c/p\u003e \u003cp\u003e117 (58.2)\u003c/p\u003e \u003cp\u003e9 (4.5)\u003c/p\u003e \u003cp\u003e1 (0.5)\u003c/p\u003e \u003cp\u003e48 (23.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnexplained air trapping on HRCT, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e94 (46.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHoneycombing on HRCT, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e43 (21.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFollow-up time, years; Mean [SD]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.6 [3.7]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMortality outcome, n (%)\u003c/p\u003e \u003cp\u003eLoss to follow-up after one visit\u003c/p\u003e \u003cp\u003eAlive at the end of follow-up\u003c/p\u003e \u003cp\u003eDeath due to all causes\u003c/p\u003e \u003cp\u003eLung transplant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (0.5)\u003c/p\u003e \u003cp\u003e137 (68.2)\u003c/p\u003e \u003cp\u003e48 (23.9)\u003c/p\u003e \u003cp\u003e15 (7.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime to FVC decline of 10% or more (n\u0026thinsp;=\u0026thinsp;104), years;\u003c/p\u003e \u003cp\u003eMean [SD]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.2 [2.0]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime to death or lung transplant (n\u0026thinsp;=\u0026thinsp;63), years;\u003c/p\u003e \u003cp\u003eMean [SD]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.2 [2.8]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCI; Mean [SD]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.2 [2.4]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRDCI; Mean [SD]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.7 [1.3]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eILD-GAP; Mean [SD]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.2 [1.7]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003eIPAF \u0026ndash; interstitial pneumonia with autoimmune features; SD \u0026ndash; standard deviation; PFTs \u0026ndash; pulmonary function tests; FVC \u0026ndash; forced vital capacity; DLCO \u0026ndash; diffusing capacity of lung for carbon monoxide; HRCT \u0026ndash; high resolution computed tomography; LIP \u0026ndash; lymphocytic interstitial pneumonia; NSIP \u0026ndash; non-specific interstitial pneumonia OP \u0026ndash; organizing pneumonia; UIP \u0026ndash; usual interstitial pneumonia.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003eincluded on the bottom of the file\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003ePrevalence of Comorbidities:\u003c/h2\u003e \u003cp\u003eThe prevalence of individual comorbidities is shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The most prevalent comorbidities in our cohort included hypertension (67.7%) and gastroesophageal reflux disease (GERD) (64.7%). More than 20% of the cohort had chronic obstructive pulmonary disease (COPD), depression, and diabetes mellitus (DM). The average baseline CCI and RDCI were 3.2 (\u0026plusmn; 2.4) and 3.7 (\u0026plusmn; 1.3), respectively (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline Comorbidities of the IPAF Cohort\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline comorbidity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;201\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMyocardial infarction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21 (10.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCongestive heart failure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32 (15.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeripheral vascular disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCerebrovascular accident*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (6.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiovascular disease or cerebrovascular accident\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41 (20.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic obstructive pulmonary disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58 (28.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeptic ulcer disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 (7.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiver disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (3.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes mellitus\u003c/p\u003e \u003cp\u003eDiet-controlled\u003c/p\u003e \u003cp\u003eUncomplicated requiring medications\u003c/p\u003e \u003cp\u003eWith end-organ damage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44 (21.9)\u003c/p\u003e \u003cp\u003e10 (5)\u003c/p\u003e \u003cp\u003e25 (12.4)\u003c/p\u003e \u003cp\u003e9 (4.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic kidney disease\u003c/p\u003e \u003cp\u003eAny\u003c/p\u003e \u003cp\u003eMild\u003c/p\u003e \u003cp\u003eModerate-severe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38 (18.9)\u003c/p\u003e \u003cp\u003e34 (16.9)\u003c/p\u003e \u003cp\u003e4 (2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMalignancy**\u003c/p\u003e \u003cp\u003eSolid tumor\u003c/p\u003e \u003cp\u003eLocalized\u003c/p\u003e \u003cp\u003eMetastatic\u003c/p\u003e \u003cp\u003eLeukemia\u003c/p\u003e \u003cp\u003eLymphoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31 (15.4)\u003c/p\u003e \u003cp\u003e29 (14.4)\u003c/p\u003e \u003cp\u003e25 (12.4)\u003c/p\u003e \u003cp\u003e4 (2)\u003c/p\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003cp\u003e2 (1.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcquired immunodeficiency syndrome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemiplegia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFracture (hip/leg/spine)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18 (9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOsteoporosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25 (12.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45 (22.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDementia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (4.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypothyroidism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26 (12.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e136 (67.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObstructive sleep apnea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24 (11.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGastroesophageal reflux disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e130 (64.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003eIPAF \u0026ndash; interstitial pneumonia with autoimmune features\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003e*Including transient ischemic attack\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003e**Including skin cancer and hematologic malignancy\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003eincluded on the bottom of the file\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003ePrimary Outcome:\u003c/h2\u003e \u003cp\u003eOne hundred and ninety-one (95.0%) patients had\u0026thinsp;\u0026ge;\u0026thinsp;2 PFTs and were included in lung function decline analysis. Of the 191 patients, 104 (54.5%) patients progressed over an average of 2.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0 years. In univariable analysis, older age, being of race other than Asian, history of cerebrovascular accident (CVA) and/or cardiovascular disease (CVD), moderate to severe chronic kidney disease (CKD), and fracture were significantly associated with faster onset of lung disease progression. The presence of honeycombing and GERD were associated with delayed onset of lung disease progression. These associations remained significant when adjusted for ILD-GAP and UIP (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation of Baseline Characteristics with Time to Lung Function Decline\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline Characteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eUnivariable Analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eBivariable Analysis Model Adjusted for ILD-GAP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003eBivariable Analysis Model Adjusted for UIP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e \u003cp\u003eMultivariable Analysis Model Adjusted for ILD-GAP and UIP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge at initial PFTs (years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.00-1.03\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.04\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.999\u0026ndash;1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.998\u0026ndash;1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.996\u0026ndash;1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale sex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.85\u0026ndash;1.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.80\u0026ndash;1.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.79\u0026ndash;1.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.74\u0026ndash;1.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace\u003c/p\u003e \u003cp\u003eWhite (referent)\u003c/p\u003e \u003cp\u003eBlack\u003c/p\u003e \u003cp\u003e\u003cb\u003eAsian\u003c/b\u003e\u003c/p\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e0.71\u003c/p\u003e \u003cp\u003e\u003cb\u003e0.36\u003c/b\u003e\u003c/p\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.48\u0026ndash;1.04\u003c/p\u003e \u003cp\u003e\u003cb\u003e0.18\u0026ndash;0.75\u003c/b\u003e\u003c/p\u003e \u003cp\u003e0.39\u0026ndash;1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003cp\u003e\u003cb\u003e0.01\u003c/b\u003e\u003c/p\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e0.71\u003c/p\u003e \u003cp\u003e\u003cb\u003e0.37\u003c/b\u003e\u003c/p\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.49\u0026ndash;1.05\u003c/p\u003e \u003cp\u003e\u003cb\u003e0.18\u0026ndash;0.78\u003c/b\u003e\u003c/p\u003e \u003cp\u003e0.39\u0026ndash;1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003cp\u003e\u003cb\u003e0.01\u003c/b\u003e\u003c/p\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e0.71\u003c/p\u003e \u003cp\u003e\u003cb\u003e0.36\u003c/b\u003e\u003c/p\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.49\u0026ndash;1.05\u003c/p\u003e \u003cp\u003e\u003cb\u003e0.17\u0026ndash;0.75\u003c/b\u003e\u003c/p\u003e \u003cp\u003e0.41\u0026ndash;1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003cp\u003e\u003cb\u003e0.01\u003c/b\u003e\u003c/p\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e0.72\u003c/p\u003e \u003cp\u003e\u003cb\u003e0.37\u003c/b\u003e\u003c/p\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.49\u0026ndash;1.05\u003c/p\u003e \u003cp\u003e\u003cb\u003e0.18\u0026ndash;0.77\u003c/b\u003e\u003c/p\u003e \u003cp\u003e0.41\u0026ndash;1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003cp\u003e\u003cb\u003e0.01\u003c/b\u003e\u003c/p\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHispanic ethnicity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.68\u0026ndash;1.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.67\u0026ndash;1.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.68\u0026ndash;1.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.67\u0026ndash;1.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking history\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.82\u0026ndash;1.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.80\u0026ndash;1.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.78\u0026ndash;1.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.76\u0026ndash;1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline FVC (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.998\u0026ndash;1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e1.00-1.02\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.997\u0026ndash;1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e1.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e1.00-1.02\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e0.03\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline DLCO (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.99\u0026ndash;1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.996\u0026ndash;1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.99\u0026ndash;1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.99\u0026ndash;1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUIP pattern on HRCT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.92\u0026ndash;1.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.91\u0026ndash;1.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnexplained air trapping on HRCT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.75\u0026ndash;1.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.77\u0026ndash;1.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.79\u0026ndash;1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.80\u0026ndash;1.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHoneycombing on HRCT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.64\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.45\u0026ndash;0.92\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.02\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.65\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.45\u0026ndash;0.93\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.02\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.66\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.46\u0026ndash;0.95\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.03\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e0.67\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e0.46\u0026ndash;0.97\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e0.03\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.83\u0026ndash;1.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.82\u0026ndash;1.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.82\u0026ndash;1.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.82\u0026ndash;1.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMyocardial infarction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.99\u0026ndash;2.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.99\u0026ndash;2.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.90\u0026ndash;2.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.91\u0026ndash;2.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCongestive heart failure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.77\u0026ndash;1.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.75\u0026ndash;1.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.77\u0026ndash;1.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.76\u0026ndash;1.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeripheral vascular disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.84\u0026ndash;2.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.78\u0026ndash;2.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.78\u0026ndash;2.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.73\u0026ndash;2.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCerebrovascular accident*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.83\u0026ndash;2.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.77\u0026ndash;2.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.79\u0026ndash;2.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.74\u0026ndash;2.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCardiovascular disease or cerebrovascular accident\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.64\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.15\u0026ndash;2.35\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.61\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e1.12\u0026ndash;2.31\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e1.60\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e1.11\u0026ndash;2.29\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e1.56\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e1.09\u0026ndash;2.25\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e0.02\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic obstructive pulmonary disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.87\u0026ndash;1.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.93\u0026ndash;1.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.88\u0026ndash;1.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.93\u0026ndash;1.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObstructive sleep apnea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.78\u0026ndash;1.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.79\u0026ndash;1.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.78\u0026ndash;1.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.78\u0026ndash;1.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGastroesophageal reflux disease\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.72\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.53\u0026ndash;0.98\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.04\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.70\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.52\u0026ndash;0.96\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.02\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.71\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.52\u0026ndash;0.96\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.03\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e0.69\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e0.50\u0026ndash;0.93\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e0.02\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeptic ulcer disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.52\u0026ndash;1.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.52\u0026ndash;1.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.52\u0026ndash;1.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.52\u0026ndash;1.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiver disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.45\u0026ndash;2.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.47\u0026ndash;2.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.43\u0026ndash;2.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.44\u0026ndash;2.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes mellitus (any)\u003c/p\u003e \u003cp\u003eNone or diet controlled (referent)\u003c/p\u003e \u003cp\u003eUncomplicated requiring medications\u003c/p\u003e \u003cp\u003eWith end organ damage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e0.84\u003c/p\u003e \u003cp\u003e1.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.78\u0026ndash;1.55\u003c/p\u003e \u003cp\u003e0.55\u0026ndash;1.29\u003c/p\u003e \u003cp\u003e0.85\u0026ndash;3.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003cp\u003e0.42\u003c/p\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.09\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e0.83\u003c/p\u003e \u003cp\u003e1.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.77\u0026ndash;1.54\u003c/p\u003e \u003cp\u003e0.54\u0026ndash;1.28\u003c/p\u003e \u003cp\u003e0.80\u0026ndash;3.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003cp\u003e0.41\u003c/p\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e0.86\u003c/p\u003e \u003cp\u003e1.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.79\u0026ndash;1.57\u003c/p\u003e \u003cp\u003e0.56\u0026ndash;1.34\u003c/p\u003e \u003cp\u003e0.80\u0026ndash;3.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003cp\u003e0.51\u003c/p\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e0.86\u003c/p\u003e \u003cp\u003e1.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.78\u0026ndash;1.56\u003c/p\u003e \u003cp\u003e0.56\u0026ndash;1.33\u003c/p\u003e \u003cp\u003e0.76-3.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003cp\u003e0.50\u003c/p\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic kidney disease (any)\u003c/p\u003e \u003cp\u003eNone or mild (referent)\u003c/p\u003e \u003cp\u003e\u003cb\u003eModerate-severe\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.43\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e\u003cb\u003e3.19\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.98\u0026ndash;2.07\u003c/p\u003e \u003cp\u003e\u003cb\u003e1.18\u0026ndash;8.68\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003cp\u003e\u003cb\u003e0.02\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.40\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e\u003cb\u003e3.23\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.96\u0026ndash;2.04\u003c/p\u003e \u003cp\u003e\u003cb\u003e1.19\u0026ndash;8.78\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003cp\u003e\u003cb\u003e0.02\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.40\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e\u003cb\u003e3.14\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.97\u0026ndash;2.04\u003c/p\u003e \u003cp\u003e\u003cb\u003e1.15\u0026ndash;8.54\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003cp\u003e\u003cb\u003e0.03\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.38\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e\u003cb\u003e3.19\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.95\u0026ndash;2.01\u003c/p\u003e \u003cp\u003e\u003cb\u003e1.17\u0026ndash;8.67\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003cp\u003e\u003cb\u003e0.02\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMalignancy** (any)\u003c/p\u003e \u003cp\u003eNo solid tumor (referent)\u003c/p\u003e \u003cp\u003eLocal solid tumor\u003c/p\u003e \u003cp\u003eMetastatic solid tumor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e0.98\u003c/p\u003e \u003cp\u003e1.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.68\u0026ndash;1.51\u003c/p\u003e \u003cp\u003e0.63\u0026ndash;1.51\u003c/p\u003e \u003cp\u003e0.70\u0026ndash;5.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003cp\u003e0.92\u003c/p\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e0.97\u003c/p\u003e \u003cp\u003e1.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.67\u0026ndash;1.49\u003c/p\u003e \u003cp\u003e0.62\u0026ndash;1.50\u003c/p\u003e \u003cp\u003e0.68\u0026ndash;5.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.99\u003c/p\u003e \u003cp\u003e0.88\u003c/p\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e0.995\u003c/p\u003e \u003cp\u003e1.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.70\u0026ndash;1.54\u003c/p\u003e \u003cp\u003e0.64\u0026ndash;1.54\u003c/p\u003e \u003cp\u003e0.69\u0026ndash;5.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003cp\u003e0.98\u003c/p\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e0.99\u003c/p\u003e \u003cp\u003e1.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.69\u0026ndash;1.53\u003c/p\u003e \u003cp\u003e0.63\u0026ndash;1.53\u003c/p\u003e \u003cp\u003e0.69\u0026ndash;5.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003cp\u003e0.95\u003c/p\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymphoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.65\u0026ndash;10.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.66\u0026ndash;10.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.57\u0026ndash;9.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.58\u0026ndash;9.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFracture (hip/leg/spine)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e2.00\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.22\u0026ndash;3.26\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.95\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e1.19\u0026ndash;3.19\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e2.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e1.25\u0026ndash;3.35\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e2.00\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e1.22\u0026ndash;3.28\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOsteoporosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.75\u0026ndash;1.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.74\u0026ndash;1.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.71\u0026ndash;1.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.70\u0026ndash;1.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.85\u0026ndash;1.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.85\u0026ndash;1.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.85\u0026ndash;1.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.85\u0026ndash;1.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDementia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.71\u0026ndash;2.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.74\u0026ndash;3.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.73\u0026ndash;3.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.76\u0026ndash;3.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypothyroidism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.80\u0026ndash;1.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.80\u0026ndash;1.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.83\u0026ndash;2.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.83\u0026ndash;2.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCCI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.11\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.04\u0026ndash;1.19\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.11\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.04\u0026ndash;1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e1.11\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e1.03\u0026ndash;1.18\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e1.10\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.03\u0026ndash;1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRDCI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.12\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.00-1.26\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.04\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.999\u0026ndash;1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e1.12\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e1.00-1.26\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.048\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.996\u0026ndash;1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eILD-GAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.96\u0026ndash;1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.96\u0026ndash;1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"13\"\u003eRDCI \u0026ndash; rheumatic disease comorbidity index; CCI \u0026ndash; Charlson Comorbidity Index, ILD-GAP \u0026ndash; Gender, Age, Physiology Index for Interstitial Lung Disease; PFTs \u0026ndash; pulmonary function tests; FVC \u0026ndash; forced vital capacity; DLCO \u0026ndash; diffusing capacity of lung for carbon monoxide; UIP \u0026ndash; usual interstitial pneumonia; HRCT \u0026ndash; high resolution computed tomography; HR \u0026ndash; hazard ratio; CI \u0026ndash; confidence interval.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"13\"\u003e*Including transient ischemic attack\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"13\"\u003e**Including skin cancer and hematologic malignancy\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003eincluded on the bottom of the file\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eSecondary Outcome:\u003c/h2\u003e \u003cp\u003eTwo hundred patients (99.5%) in our cohort had \u0026ge;1 medical visit recorded in the EMR. One patient was lost to follow-up after one visit. Forty-eight (23.9%) patients died and 15 (7.5%) patients had lung transplant (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Older age, male sex, smoking history, lower FVC or DLCO, presence of UIP pattern, and history of CVA and/or CVD, DM (in particular with end-organ damage), and lymphoma were associated with significantly shorter time to lung transplant/death. After adjusting for ILD-GAP, age, UIP pattern on HRCT, DM, and lymphoma remained significantly associated with shorter time to lung transplant/death. After adjusting for the presence of UIP pattern, older age, male sex, smoking history, lower PFT parameters, congestive heart failure (CHF), DM, and lymphoma were significant factors associated with the outcome. Interestingly, only DM with end organ damage and lymphoma remained significantly associated with lung transplant/death after adjusting for both ILD-GAP and UIP (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation of Baseline Characteristics with Time to Lung Transplant/Death\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline Characteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eUnivariable Analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eBivariable Analysis Model Adjusted for ILD-GAP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003eBivariable Analysis Model Adjusted for UIP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e \u003cp\u003eMultivariable Analysis Model Adjusted for ILD-GAP and UIP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.04\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.01\u0026ndash;1.06\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.02\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e1.00-1.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.04\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e1.03\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e1.01\u0026ndash;1.06\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.007\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.99\u0026ndash;1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMale sex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e2.49\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.49\u0026ndash;4.15\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.89\u0026ndash;2.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e2.32\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e1.38\u0026ndash;3.91\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.81\u0026ndash;2.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace\u003c/p\u003e \u003cp\u003eWhite (referent)\u003c/p\u003e \u003cp\u003eBlack\u003c/p\u003e \u003cp\u003eAsian\u003c/p\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e0.55\u003c/p\u003e \u003cp\u003e0.17\u003c/p\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.27\u0026ndash;1.12\u003c/p\u003e \u003cp\u003e0.026\u0026ndash;1.35\u003c/p\u003e \u003cp\u003e0.11\u0026ndash;1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003cp\u003e0.10\u003c/p\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e0.63\u003c/p\u003e \u003cp\u003e0.26\u003c/p\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.30\u0026ndash;1.29\u003c/p\u003e \u003cp\u003e0.04\u0026ndash;1.92\u003c/p\u003e \u003cp\u003e0.11\u0026ndash;1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003cp\u003e0.19\u003c/p\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e0.59\u003c/p\u003e \u003cp\u003e0.19\u003c/p\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.29\u0026ndash;1.21\u003c/p\u003e \u003cp\u003e0.03\u0026ndash;1.38\u003c/p\u003e \u003cp\u003e0.12\u0026ndash;1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003cp\u003e0.10\u003c/p\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e0.68\u003c/p\u003e \u003cp\u003e0.28\u003c/p\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.33\u0026ndash;1.41\u003c/p\u003e \u003cp\u003e0.04\u0026ndash;2.08\u003c/p\u003e \u003cp\u003e0.11\u0026ndash;1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003cp\u003e0.21\u003c/p\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHispanic ethnicity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.79\u0026ndash;2.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.64\u0026ndash;2.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.77\u0026ndash;2.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.65\u0026ndash;2.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking history\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.97\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.19\u0026ndash;3.24\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.84\u0026ndash;2.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e1.84\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e1.11\u0026ndash;3.06\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.02\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.77\u0026ndash;2.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBaseline FVC (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.98\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.97\u0026thinsp;\u0026minus;\u0026thinsp;0.96\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.99\u0026ndash;1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.98\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.96\u0026ndash;0.99\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.98\u0026ndash;1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBaseline DLCO (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.98\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.97\u0026ndash;0.99\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.99\u0026ndash;1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.97\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.96\u0026ndash;0.99\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.98\u0026ndash;1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUIP pattern on HRCT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.70\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.01\u0026ndash;2.47\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.047\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.86\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e1.09\u0026ndash;3.17\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.02\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnexplained air trapping on HRCT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.73\u0026ndash;1.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.86\u0026ndash;2.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.84\u0026ndash;2.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.97\u0026ndash;2.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHoneycombing on HRCT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.65\u0026ndash;1.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.66\u0026ndash;1.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.68\u0026ndash;2.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.72\u0026ndash;2.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.77\u0026ndash;2.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.68\u0026ndash;2.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.80\u0026ndash;2.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.68\u0026ndash;2.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMyocardial infarction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.50\u0026ndash;2.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.48\u0026ndash;2.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.40\u0026ndash;2.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.42\u0026ndash;2.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCongestive heart failure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.97\u0026ndash;3.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.70\u0026ndash;2.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e2.00\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e1.05\u0026ndash;3.81\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.03\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.77\u0026ndash;2.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeripheral vascular disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.96\u0026ndash;5.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.54\u0026ndash;3.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.84\u0026ndash;4.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.50\u0026ndash;2.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCerebrovascular accident*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.64\u0026ndash;4.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.35\u0026ndash;2.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.55\u0026ndash;3.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.33\u0026ndash;2.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCardiovascular disease of cerebrovascular accident*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.87\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.07\u0026ndash;3.27\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.03\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.76\u0026ndash;2.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.97\u0026ndash;3.04\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.71\u0026ndash;2.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic obstructive pulmonary disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.48\u0026ndash;1.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.61\u0026ndash;1.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.49\u0026ndash;1.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.60\u0026ndash;1.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObstructive sleep apnea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.59\u0026ndash;2.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.75\u0026ndash;3.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.59\u0026ndash;2.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.75\u0026ndash;3.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGastroesophageal reflux disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.46\u0026ndash;1.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.45\u0026ndash;1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.43\u0026ndash;1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.40\u0026ndash;1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeptic ulcer disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.63\u0026ndash;3.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.64\u0026ndash;3.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.54-3.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.47\u0026ndash;2.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiver disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.066\u0026ndash;3.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.08\u0026ndash;4.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.06\u0026ndash;2.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.06\u0026ndash;3.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiabetes mellitus (any)\u003c/b\u003e\u003c/p\u003e \u003cp\u003eNone or diet controlled (referent)\u003c/p\u003e \u003cp\u003eUncomplicated requiring medications\u003c/p\u003e \u003cp\u003e\u003cb\u003eWith end organ damage\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.84\u003c/b\u003e\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e0.94\u003c/p\u003e \u003cp\u003e\u003cb\u003e5.07\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.08\u0026ndash;3.17\u003c/b\u003e\u003c/p\u003e \u003cp\u003e0.43\u0026ndash;2.08\u003c/p\u003e \u003cp\u003e\u003cb\u003e2.25\u0026ndash;11.39\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.03\u003c/b\u003e\u003c/p\u003e \u003cp\u003e0.88\u003c/p\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.74\u003c/b\u003e\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e0.94\u003c/p\u003e \u003cp\u003e\u003cb\u003e3.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e1.01\u0026ndash;2.99\u003c/b\u003e\u003c/p\u003e \u003cp\u003e0.42\u0026ndash;2.08\u003c/p\u003e \u003cp\u003e\u003cb\u003e1.31\u0026ndash;6.94\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.045\u003c/b\u003e\u003c/p\u003e \u003cp\u003e0.88\u003c/p\u003e \u003cp\u003e\u003cb\u003e0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e1.79\u003c/b\u003e\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e0.95\u003c/p\u003e \u003cp\u003e\u003cb\u003e4.61\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e1.04\u0026ndash;3.08\u003c/b\u003e\u003c/p\u003e \u003cp\u003e0.43\u0026ndash;2.10\u003c/p\u003e \u003cp\u003e\u003cb\u003e2.04\u0026ndash;10.41\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.04\u003c/b\u003e\u003c/p\u003e \u003cp\u003e0.90\u003c/p\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.71\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e0.98\u003c/p\u003e \u003cp\u003e\u003cb\u003e2.81\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.99\u0026ndash;2.94\u003c/p\u003e \u003cp\u003e0.44\u0026ndash;2.18\u003c/p\u003e \u003cp\u003e\u003cb\u003e1.23\u0026ndash;6.41\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003cp\u003e0.96\u003c/p\u003e \u003cp\u003e\u003cb\u003e0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic kidney disease (any)\u003c/p\u003e \u003cp\u003eNone or mild (referent)\u003c/p\u003e \u003cp\u003eModerate-severe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.37\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.76\u0026ndash;2.49\u003c/p\u003e \u003cp\u003e0.11\u0026ndash;5.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.52\u0026ndash;1.78\u003c/p\u003e \u003cp\u003e0.10\u0026ndash;5.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.68\u0026ndash;2.28\u003c/p\u003e \u003cp\u003e0.10-5.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.48\u0026ndash;1.63\u003c/p\u003e \u003cp\u003e0.10\u0026ndash;5.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMalignancy** (any)\u003c/p\u003e \u003cp\u003eNo solid tumor (referent)\u003c/p\u003e \u003cp\u003eLocal solid tumor\u003c/p\u003e \u003cp\u003eMetastatic solid tumor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.44\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e1.43\u003c/p\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.77\u0026ndash;2.71\u003c/p\u003e \u003cp\u003e0.72\u0026ndash;2.81\u003c/p\u003e \u003cp\u003e0.11\u0026ndash;5.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003cp\u003e0.31\u003c/p\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.18\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e1.11\u003c/p\u003e \u003cp\u003e1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.63\u0026ndash;2.24\u003c/p\u003e \u003cp\u003e0.56\u0026ndash;2.22\u003c/p\u003e \u003cp\u003e0.17\u0026ndash;8.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003cp\u003e0.76\u003c/p\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.42\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e1.46\u003c/p\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.75\u0026ndash;2.66\u003c/p\u003e \u003cp\u003e0.74\u0026ndash;2.88\u003c/p\u003e \u003cp\u003e0.08\u0026ndash;4.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003cp\u003e0.27\u003c/p\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e1.14\u003c/p\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.64\u0026ndash;2.27\u003c/p\u003e \u003cp\u003e0.57\u0026ndash;2.26\u003c/p\u003e \u003cp\u003e0.15\u0026ndash;7.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003cp\u003e0.71\u003c/p\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLymphoma\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e20.50\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e4.66\u0026ndash;90.09\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e27.77\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e6.23-123.66\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e15.42\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e3.40-70.02\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e20.30\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e4.44\u0026ndash;92.8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFracture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.67\u0026ndash;3.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.63\u0026ndash;3.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.62\u0026ndash;3.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.62\u0026ndash;3.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOsteoporosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.47\u0026ndash;2.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.42\u0026ndash;2.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.42\u0026ndash;2.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.35\u0026ndash;1.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.78\u0026ndash;2.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.78\u0026ndash;2.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.78\u0026ndash;2.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.76\u0026ndash;2.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDementia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.46\u0026ndash;4.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.61\u0026ndash;6.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.53\u0026ndash;5.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.71\u0026ndash;7.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypothyroidism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.75\u0026ndash;3.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.92\u0026ndash;2.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.81\u0026ndash;3.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.97\u0026ndash;4.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCCI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.18\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.07\u0026ndash;1.30\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.13\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e1.02\u0026ndash;1.26\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.02\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e1.16\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e1.04\u0026ndash;1.29\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.99\u0026ndash;1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRDCI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.31\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.10\u0026ndash;1.57\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.24\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e1.03\u0026ndash;1.50\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.02\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e1.29\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e1.07\u0026ndash;1.54\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.99\u0026ndash;1.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eILD-GAP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.53\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.31\u0026ndash;1.80\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e1.57\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e1.33\u0026ndash;1.85\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"13\"\u003ePFTs \u0026ndash; pulmonary function tests; FVC \u0026ndash; forced vital capacity; DLCO \u0026ndash; diffusing capacity of lung for carbon monoxide; UIP \u0026ndash; usual interstitial pneumonia; HRCT \u0026ndash; high resolution computed tomography; HR \u0026ndash; hazard ratio; CI \u0026ndash; confidence interval; CCI \u0026ndash; Charlson Comorbidity Index; RDCI \u0026ndash; rheumatic disease comorbidity index; ILD-GAP \u0026ndash; Gender, Age, Physiology Index for Interstitial Lung Disease.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"13\"\u003e*Including transient ischemic attack\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"13\"\u003e**Including skin cancer and hematologic malignancy\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003cb\u003eincluded on the bottom of the file\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eEffect of Baseline Comorbidity Indices on Outcomes:\u003c/h2\u003e \u003cp\u003eIn univariable analysis, both CCI and RDCI were significantly associated with lung disease progression (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) and lung transplant/mortality outcomes (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). ILD-GAP was significantly associated with lung transplant/mortality but not with lung disease progression (Tables\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe association of CCI with lung disease progression, but not that of RDCI, remained significant when adjusted for ILD-GAP and ILD-GAP and UIP. When adjusted for the presence or absence of UIP pattern alone, both CCI and RDCI remained significant predictors of the outcome (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCCI and RDCI were significantly associated with shorter time to lung transplant/death even when adjusted for ILD-GAP or UIP. However, neither CCI nor RDCI were significantly associated with the lung transplant/mortality outcome after adjusting for both ILD-GAP and UIP pattern in multivariable analysis. Higher ILD-GAP was associated with significantly shorter time to lung transplant/death outcomes in univariable analysis and when adjusted for UIP (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eIn this study we explored the prevalence and the effect of baseline comorbidities on lung disease progression and lung transplant/mortality in a cohort of patients with IPAF. We also assessed the performance of two well-established comorbidity indices, CCI and RDCI, in predicting outcomes in this population. We found that comorbidity indices provide comprehensive information regarding a patient\u0026rsquo;s prognosis, including lung disease progression.\u003c/p\u003e \u003cp\u003eHypertension and GERD were the most prevalent comorbidities in our cohort. This was consistent with a study by Oldham, et al., where GERD was the most prevalent (52.8%) comorbidity in an IPAF cohort raising suspicion for a potential contribution of GERD to disease pathogenesis in IPAF (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). In addition, COPD, depression, and DM were found to be common comorbid conditions in our cohort, a finding which was not discussed in prior IPAF literature but has been demonstrated in other forms of ILD (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhen evaluating the effect of comorbidities on lung disease progression, this study revealed that multiple comorbidities such as a history of CVA/CVD, moderate to severe CKD and fracture, specifically that of lower extremity or vertebrae, were associated with a faster onset of relative FVC decline of \u0026ge;\u0026thinsp;10% or more from the time of cohort entry in patients with IPAF. Conversely, a history of GERD was associated with a longer time to lung disease progression.\u003c/p\u003e \u003cp\u003eAlthough it is difficult to ascertain the association between the various comorbidities, prior studies have commented on the effects of these comorbidities on lung disease progression in patients with other forms of ILD. For example, CVD is a treatable comorbidity frequently observed in IPF and has been linked to worse outcomes, with proposed mechanisms including systemic inflammation, hypercoagulability, platelet activation, and oxidative stress (\u003cspan additionalcitationids=\"CR33\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). A complex relationship between CKD and lung disease progression, particularly through alterations of fluid homeostasis and acid-base balance, has also been demonstrated (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). A potential explanation for the observed association of fracture and lung disease progression, in this study, is the increased prevalence of steroid use in patients with more severe disease which can thereby increase fracture risk. However, fracture was a baseline comorbidity in the cohort and thus steroid use would not be expected to contribute significantly to its prevalence at the time of baseline data collection. To evaluate this possibility, data regarding osteoporosis was also collected, but did not demonstrate a significant association with the studied outcomes. In other autoimmune diseases, such as rheumatoid arthritis, osteopenia and osteoporosis have correlated with higher mortality and fracture risk, when compared to the general population (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Interestingly, GERD was found to be a protective factor for lung disease progression in our cohort, a finding which varies from prior studies that considered GERD to be a risk factor for ILD development (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). One consideration is the high prevalence of proton pump inhibitor use, an intervention hypothesized to slow lung disease progression in patients with IPF (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). Based on our results, the association of CVD, CKD, fracture risk, and GERD with lung disease progression in patients with IPAF warrants further investigation.\u003c/p\u003e \u003cp\u003eImportantly, few prior studies have explored the prognostic impact of comorbid conditions in IPAF. Two studies suggested that hypothyroidism was associated with greater mortality (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e), but a diagnosis of obstructive sleep apnea (OSA) was correlated with better survival when compared to IPAF patients without these conditions (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Malignancy was noted to be a serious comorbidity associated with faster progression of fibrotic lung disease in RD-ILD and IPAF (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). In our study, OSA and hypothyroidism were not significantly associated with outcomes, but lymphoma, as well as CKD and DM, were associated with shorter time to lung transplant/mortality. Similar to other studies, increasing age, male sex, and smoking history were also associated with shorter time to lung transplant/mortality in our cohort (\u003cspan additionalcitationids=\"CR41 CR42 CR43\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe evaluated the performance of the CCI and RDCI in patients with IPAF and found that both CCI and RDCI were helpful in predicting lung function and transplant/mortality outcomes, even after adjusting for UIP pattern. However, the performance of indices was variable for both outcomes when adjusted for both ILD-GAP and UIP pattern. Notably, CVA/CVD and fracture, which are components of RDCI, but not CCI, were important comorbidities associated with poor outcomes in our study. Additionally, depression was highly prevalent in our cohort but is only reflected within the RDCI score. These findings suggest that both CCI and RDCI may be useful tools for prognosticating outcomes in IPAF patients.\u003c/p\u003e \u003cp\u003eOur study has several strengths. We had a large, diverse IPAF cohort and the ability to explore numerous comorbidities. Sixteen percent of our cohort included Black patients, increasing the external validity of our findings. Comorbidities have been variably explored in cohorts of patients with IPAF, resulting in highly heterogeneous data of the prevalence of multiple comorbidities. In our study, we systematically and rigorously assessed the comorbidities used to calculate two commonly used comorbidity indices, particularly in autoimmune diseases, making our findings relevant to presumably autoimmune-related ILD such as IPAF. Our study was the first to use comorbidity indices CCI and RDCI to assess the comorbidity burden in this specific population and demonstrate their usefulness in assessing functional and survival outcomes. Specifically, this study supports the use of RDCI in this population, a possible advantage for rheumatologists seeing these patients. Our results also emphasize the need for comprehensive evaluation of IPAF patients and multiple tools for optimal management and monitoring, including HRCT imaging, PFT monitoring, and ILD-GAP and comorbidity assessments.\u003c/p\u003e \u003cp\u003eWe acknowledge several limitations of our study. Given the retrospective nature of the study, the variables were collected by medical record review which can lead to misclassification bias, attrition bias, and the presence of missing data. We used precise and consistent definitions of the key comorbidities and variables to guide the data collection by two clinical researchers (with familiarity with both the medical record and management of these conditions and comorbidities) to mitigate the effects of misclassification bias and imaging data by an experienced ILD pulmonologist. However, loss to follow-up could have introduced selection bias. Patients who did not have repeat PFT data were excluded from the study, which could have been due to unknown death or a different cause that was not recorded in the medical record. Finally, while our study emphasizes the need for comorbidity screening for patients with IPAF, we were unable to evaluate whether controlling comorbidities would mitigate the often detrimental association with disease outcomes.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eIn summary, our study addresses a substantial healthcare gap for patients with IPAF, as few studies have examined comorbidities in this population. Multiple comorbidities are highly prevalent in IPAF and their burden, as assessed by comorbidity indices, influences functional and survival outcomes. Importantly, our study highlights the utility of two common comorbidity indices in assessing the outcomes of patients with IPAF. Particularly, the comorbidity burden in IPAF may be slightly better assessed by RDCI than CCI due to the inclusion of fracture and depression. Importantly, several of the comorbidities frequently seen in our cohort are modifiable and can be optimized, including DM, depression, COPD, GERD and others. Furthermore, potential mechanisms underlying CVA/CVD, CKD, and fracture with lung disease progression in IPAF need to be explored. Further prospective studies are needed to investigate whether aggressive treatment of comorbidities will mitigate poor outcomes. Clinicians taking care of patients with IPAF should be aware of common comorbid conditions, the potential prognostic implications of comorbidity indices, and the increased morbidity effect of specific comorbidities in this patient population.\u003c/p\u003e"},{"header":"List Of Abbreviations","content":"\u003cp\u003eIPAF: interstitial pneumonia with autoimmune features\u003c/p\u003e\n\u003cp\u003eILD: interstitial lung disease\u003c/p\u003e\n\u003cp\u003eRD: rheumatic disease\u003c/p\u003e\n\u003cp\u003eCCI: Charlson Comorbidity Index\u003c/p\u003e\n\u003cp\u003eRDCI: rheumatic disease comorbidity index\u003c/p\u003e\n\u003cp\u003eGAP index: Gender-Age-Physiology index\u003c/p\u003e\n\u003cp\u003eFVC: forced vital capacity\u003c/p\u003e\n\u003cp\u003eDLCO: diffusing capacity for carbon monoxide\u003c/p\u003e\n\u003cp\u003eIPF: idiopathic pulmonary fibrosis\u003c/p\u003e\n\u003cp\u003eUTSW: University of Texas Southwestern\u003c/p\u003e\n\u003cp\u003eERS: European Respiratory Society\u003c/p\u003e\n\u003cp\u003eATS: American Thoracic Society\u003c/p\u003e\n\u003cp\u003ePFT: pulmonary function test\u003c/p\u003e\n\u003cp\u003eEMR: electronic medical record\u003c/p\u003e\n\u003cp\u003eHRCT: high-resolution computed tomography\u003c/p\u003e\n\u003cp\u003eUIP: usual interstitial pneumonia\u003c/p\u003e\n\u003cp\u003eFEV1: forced expiratory volume in one second\u003c/p\u003e\n\u003cp\u003eHR: hazard ratio\u003c/p\u003e\n\u003cp\u003eCI: confidence interval\u003c/p\u003e\n\u003cp\u003eGERD: gastroesophageal reflux disease\u003c/p\u003e\n\u003cp\u003eCOPD: chronic obstructive pulmonary disease\u003c/p\u003e\n\u003cp\u003eDM: diabetes mellitus\u003c/p\u003e\n\u003cp\u003eCVA: cerebrovascular accident\u003c/p\u003e\n\u003cp\u003eCVD: cardiovascular disease\u003c/p\u003e\n\u003cp\u003eCKD: chronic kidney disease\u003c/p\u003e\n\u003cp\u003eCHF: congestive heart failure\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics Approval and Consent to Participate\u003c/strong\u003e:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe University of Texas Southwestern Medical Center Institutional Review Board approved the study prior to the initiation of data extraction (IRB #STU-2019-0913). Waiver of informed consent from University of Texas Southwestern Medical Center Institutional Review Board (IRB #STU-2019-0913) was obtained for each subject for this medical record review retrospective study. All aspects of the study were performed in accordance with all relevant guidelines and regulations including Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of Data and Materials:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data sets generated during the current study are not publicly available due to protected\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ehealth information but are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEJ reports salary support from Ruth L. Kirschstein Institutional National Research Service Award (T32).\u003c/p\u003e\n\u003cp\u003eMG reports no conflict of interest, financial or otherwise.\u003c/p\u003e\n\u003cp\u003eTA reports no conflict of interest, financial or otherwise.\u003c/p\u003e\n\u003cp\u003eUM reports no conflict of interest, financial or otherwise.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe conduction of this study was supported by Ruth L. Kirschstein Institutional National Research Service Award (T32).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; Contributions:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEJ, MG, TA, and UM were involved in study design. EJ, MG and TA collected the data and EJ and MG analyzed the patient data. EJ, MG, TA, and UM have contributed to the writing of the manuscript and have read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eFischer A, Antoniou KM, Brown KK, Cadranel J, Corte TJ, du Bois RM, et al. An official European Respiratory Society/American Thoracic Society research statement: interstitial pneumonia with autoimmune features. Eur Respir J. 2015;46(4):976-87.\u003c/li\u003e\n\u003cli\u003eJee AP, MJS; Bleasel, JF; et al. Baseline Characteristics and Survival of an Australian Interstitial Pneumonia with Autoimmune Features Cohort. Respiration. 2021.\u003c/li\u003e\n\u003cli\u003eOldham JM, Adegunsoye A, Valenzi E, Lee C, Witt L, Chen L, et al. Characterisation of patients with interstitial pneumonia with autoimmune features. Eur Respir J. 2016;47(6):1767-75.\u003c/li\u003e\n\u003cli\u003eIto Y, Arita M, Kumagai S, Takei R, Noyama M, Tokioka F, et al. Serological and morphological prognostic factors in patients with interstitial pneumonia with autoimmune features. BMC Pulm Med. 2017;17(1):111.\u003c/li\u003e\n\u003cli\u003eJoerns EK, Adams TN, Newton CA, Bermas B, Karp D, Batra K, et al. Variables Associated With Response to Therapy in Patients With Interstitial Pneumonia With Autoimmune Features. J Clin Rheumatol. 2022;28(2):84-8.\u003c/li\u003e\n\u003cli\u003eKarampeli M, Thomas K, Flouda S, Chavatza A, Nikolopoulos D, Pieta A, et al. Interstitial Pneumonia with Autoimmune Features (IPAF): A Single-Centre, Prospective Study. Mediterr J Rheumatol. 2020;31(3):330-6.\u003c/li\u003e\n\u003cli\u003eRadner H. Multimorbidity in rheumatic conditions. Wien Klin Wochenschr. 2016;128(21-22):786-90.\u003c/li\u003e\n\u003cli\u003eRoubille C, Richer V, Starnino T, McCourt C, McFarlane A, Fleming P, et al. Evidence-based Recommendations for the Management of Comorbidities in Rheumatoid Arthritis, Psoriasis, and Psoriatic Arthritis: Expert Opinion of the Canadian Dermatology-Rheumatology Comorbidity Initiative. J Rheumatol. 2015;42(10):1767-80.\u003c/li\u003e\n\u003cli\u003eCrowson CS, Gunderson TM, Dykhoff HJ, Myasoedova E, Atkinson EJ, Kronzer VL, et al. Comprehensive assessment of multimorbidity burden in a population-based cohort of patients with rheumatoid arthritis. RMD Open. 2022;8(1).\u003c/li\u003e\n\u003cli\u003eGunderson TM, Myasoedova E, Davis JM, 3rd, Crowson CS. Multimorbidity Burden in Rheumatoid Arthritis: A Population-based Cohort Study. J Rheumatol. 2021;48(11):1648-54.\u003c/li\u003e\n\u003cli\u003eMargaritopoulos GA, Antoniou KM, Wells AU. Comorbidities in interstitial lung diseases. Eur Respir Rev. 2017;26(143).\u003c/li\u003e\n\u003cli\u003eSchwarzkopf L, Witt S, Waelscher J, Polke M, Kreuter M. Associations between comorbidities, their treatment and survival in patients with interstitial lung diseases - a claims data analysis. Respir Res. 2018;19(1):73.\u003c/li\u003e\n\u003cli\u003eRadner H, Yoshida K, Hmamouchi I, Dougados M, Smolen JS, Solomon DH. Treatment Patterns of Multimorbid Patients with Rheumatoid Arthritis: Results from an International Cross-sectional Study. J Rheumatol. 2015;42(7):1099-104.\u003c/li\u003e\n\u003cli\u003eAslam F, Khan NA. Tools for the Assessment of Comorbidity Burden in Rheumatoid Arthritis. Front Med (Lausanne). 2018;5:39.\u003c/li\u003e\n\u003cli\u003eEngland BR, Sayles H, Mikuls TR, Johnson DS, Michaud K. Validation of the rheumatic disease comorbidity index. Arthritis Care Res (Hoboken). 2015;67(6):865-72.\u003c/li\u003e\n\u003cli\u003eYagyu H, Murohashi K, Hara Y, Saigusa Y, Aoki A, Kobayashi N, et al. Clinical utility of a composite scoring system including Charlson Comorbidity Index score in patients with interstitial lung disease. J Thorac Dis. 2020;12(10):5774-82.\u003c/li\u003e\n\u003cli\u003eMichaud K, Wolfe F. Comorbidities in rheumatoid arthritis. Best Pract Res Clin Rheumatol. 2007;21(5):885-906.\u003c/li\u003e\n\u003cli\u003eSpaetgens B, Wijnands JM, van Durme C, Boonen A. Content and construct validity of the Rheumatic Diseases Comorbidity Index in patients with gout. Rheumatology (Oxford). 2015;54(9):1659-63.\u003c/li\u003e\n\u003cli\u003eLey B, Ryerson CJ, Vittinghoff E, Ryu JH, Tomassetti S, Lee JS, et al. A multidimensional index and staging system for idiopathic pulmonary fibrosis. Ann Intern Med. 2012;156(10):684-91.\u003c/li\u003e\n\u003cli\u003eLee SH, Kim SY, Kim DS, Kim YW, Chung MP, Uh ST, et al. Predicting survival of patients with idiopathic pulmonary fibrosis using GAP score: a nationwide cohort study. Respir Res. 2016;17(1):131.\u003c/li\u003e\n\u003cli\u003eRyerson CJ, Vittinghoff E, Ley B, Lee JS, Mooney JJ, Jones KD, et al. Predicting survival across chronic interstitial lung disease: the ILD-GAP model. Chest. 2014;145(4):723-8.\u003c/li\u003e\n\u003cli\u003eNurmi HM, Purokivi MK, K\u0026auml;rkk\u0026auml;inen MS, Kettunen HP, Selander TA, Kaarteenaho RL. Are risk predicting models useful for estimating survival of patients with rheumatoid arthritis-associated interstitial lung disease? BMC Pulm Med. 2017;17(1):16.\u003c/li\u003e\n\u003cli\u003eFujii H, Hara Y, Saigusa Y, Tagami Y, Murohashi K, Nagasawa R, et al. ILD-GAP Combined with the Charlson Comorbidity Index Score (ILD-GAPC) as a Prognostic Prediction Model in Patients with Interstitial Lung Disease. Can Respir J. 2023;2023:5088207.\u003c/li\u003e\n\u003cli\u003eMari P-V, Kay S, Belloli E, Salisbury M, Sheth J, Holtze C, et al. Impact of comorbidities in interstitial pneumonia with autoimmune features (IPAF)2019. PA1303 p.\u003c/li\u003e\n\u003cli\u003eO\u0026apos;Dwyer DN, Wang BR, Nagaraja V, Flaherty KR, Khanna D, Murray S, et al. Hypothyroidism Is Associated with Increased Mortality in Interstitial Pneumonia with Autoimmune Features. Ann Am Thorac Soc. 2022;19(10):1772-6.\u003c/li\u003e\n\u003cli\u003eNagy A, Nagy T, Kolonics-Farkas AM, Eszes N, Vincze K, Barczi E, et al. Autoimmune Progressive Fibrosing Interstitial Lung Disease: Predictors of Fast Decline. Front Pharmacol. 2021;12:778649.\u003c/li\u003e\n\u003cli\u003eNieto MA, Sanchez-Pernaute O, Vadillo C, Rodriguez-Nieto MJ, Romero-Bueno F, L\u0026oacute;pez-Mu\u0026ntilde;iz B, et al. Functional respiratory impairment and related factors in patients with interstitial pneumonia with autoimmune features (IPAF): Multicenter study from NEREA registry. Respir Res. 2023;24(1):19.\u003c/li\u003e\n\u003cli\u003eCharlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40(5):373-83.\u003c/li\u003e\n\u003cli\u003eMaher TM, Corte TJ, Fischer A, Kreuter M, Lederer DJ, Molina-Molina M, et al. Pirfenidone in patients with unclassifiable progressive fibrosing interstitial lung disease: a double-blind, randomised, placebo-controlled, phase 2 trial. The Lancet Respiratory Medicine. 2020;8(2):147-57.\u003c/li\u003e\n\u003cli\u003ePrior TS, Hyldgaard C, Torrisi SE, Kronborg-White S, Ganter C, Bendstrup E, et al. Comorbidities in unclassifiable interstitial lung disease. Respir Res. 2022;23(1):59.\u003c/li\u003e\n\u003cli\u003eButler SJ, Li LSK, Ellerton L, Gershon AS, Goldstein RS, Brooks D. Prevalence of comorbidities and impact on pulmonary rehabilitation outcomes. ERJ Open Res. 2019;5(4).\u003c/li\u003e\n\u003cli\u003eReed RM, Eberlein M, Girgis RE, Hashmi S, Iacono A, Jones S, et al. Coronary artery disease is under-diagnosed and under-treated in advanced lung disease. Am J Med. 2012;125(12):1228.e13-.e22.\u003c/li\u003e\n\u003cli\u003eKizer JR, Zisman DA, Blumenthal NP, Kotloff RM, Kimmel SE, Strieter RM, et al. Association between pulmonary fibrosis and coronary artery disease. Arch Intern Med. 2004;164(5):551-6.\u003c/li\u003e\n\u003cli\u003eNathan SD, Basavaraj A, Reichner C, Shlobin OA, Ahmad S, Kiernan J, et al. Prevalence and impact of coronary artery disease in idiopathic pulmonary fibrosis. Respir Med. 2010;104(7):1035-41.\u003c/li\u003e\n\u003cli\u003eGembillo G, Calimeri S, Tranchida V, Silipigni S, Vella D, Ferrara D, et al. Lung Dysfunction and Chronic Kidney Disease: A Complex Network of Multiple Interactions. J Pers Med. 2023;13(2).\u003c/li\u003e\n\u003cli\u003eMukai H, Ming P, Lindholm B, Heimb\u0026uuml;rger O, Barany P, Stenvinkel P, et al. Lung Dysfunction and Mortality in Patients with Chronic Kidney Disease. Kidney Blood Press Res. 2018;43(2):522-35.\u003c/li\u003e\n\u003cli\u003eMcCoy SS, Mukadam Z, Meyer KC, Kanne JP, Meyer CA, Martin MD, et al. Mycophenolate therapy in interstitial pneumonia with autoimmune features: a cohort study. Ther Clin Risk Manag. 2018;14:2171-81.\u003c/li\u003e\n\u003cli\u003eRaghu G, Yang ST, Spada C, Hayes J, Pellegrini CA. Sole treatment of acid gastroesophageal reflux in idiopathic pulmonary fibrosis: a case series. Chest. 2006;129(3):794-800.\u003c/li\u003e\n\u003cli\u003eLee JS, Collard HR, Anstrom KJ, Martinez FJ, Noth I, Roberts RS, et al. Anti-acid treatment and disease progression in idiopathic pulmonary fibrosis: an analysis of data from three randomised controlled trials. Lancet Respir Med. 2013;1(5):369-76.\u003c/li\u003e\n\u003cli\u003eHuapaya JA, Boulougoura A, Fried J, Mesdaghinia S, Culotta BJ, Carson S, et al. Long-term evaluation of pulmonary function and survival of patients with interstitial pneumonia with autoimmune features. Clin Exp Rheumatol. 2023;41(1):15-23.\u003c/li\u003e\n\u003cli\u003eJiwrajka N, Loizidis G, Patterson KC, Kreider ME, Johnson CR, Miller WT, Jr., et al. Identification and Prognosis of Patients With Interstitial Pneumonia With Autoimmune Features. J Clin Rheumatol. 2022;28(5):257-64.\u003c/li\u003e\n\u003cli\u003eDai J, Wang L, Yan X, Li H, Zhou K, He J, et al. Clinical features, risk factors, and outcomes of patients with interstitial pneumonia with autoimmune features: a population-based study. Clin Rheumatol. 2018;37(8):2125-32.\u003c/li\u003e\n\u003cli\u003eKawano-Dourado L, Glassberg MK, Assayag D, Borie R, Johannson KA. Sex and gender in interstitial lung diseases. Eur Respir Rev. 2021;30(162).\u003c/li\u003e\n\u003cli\u003eHoyer N, Wille MMW, Thomsen LH, Wilcke T, Dirksen A, Pedersen JH, et al. Interstitial lung abnormalities are associated with increased mortality in smokers. Respir Med. 2018;136:77-82.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"interstitial lung disease, comorbidity index, autoimmune disease, interstitial pneumonia","lastPublishedDoi":"10.21203/rs.3.rs-3210870/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3210870/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eInterstitial pneumonia with autoimmune features (IPAF) is a subset of interstitial lung disease that manifests with interstitial pneumonia and features of autoimmunity while not meeting classification criteria for a defined rheumatic disease. Comorbidity burden is an important prognostic indicator in various rheumatic and interstitial lung diseases, but few studies have commented on comorbidities in this population. This study was conducted to evaluate the association of individual comorbidities, the Charlson Comorbidity Index (CCI), and the Rheumatic Disease Comorbidity Index (RDCI) with lung disease progression and transplant/mortality outcomes in patients with IPAF.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eIn a retrospective study, we evaluated the prevalence and severity of comorbidities in an institutional cohort of patients with IPAF. Using Cox regression, we correlated the association of individual comorbidities and comorbidity burden using CCI and RDCI with time to lung disease progression (defined as relative forced vital capacity (FVC) decline of 10% or more) and with time to lung transplant/all-cause mortality. We compared the performance of CCI and RDCI, while adjusting for the Interstitial Lung Disease Gender-Age-Physiology (ILD-GAP) index.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eIn a sample of 201 individuals with IPAF, a history of cerebrovascular accident (CVA) or cardiovascular disease (CVD), moderate to severe chronic kidney disease, or fracture was associated with a faster onset of lung disease progression, while a history of gastroesophageal reflux was protective. History of CVA/CVD, diabetes mellitus, and lymphoma were associated with a faster onset of lung transplant/death. Both CCI and RDCI were significantly associated with shorter time to lung disease progression (hazard ratio [HR] 1.11, 95% confidence interval [CI] 1.04\u0026ndash;1.19 and HR 1.12 with 95%CI 1.00-1.26, respectively) and lung transplant/mortality (HR 1.18 [1.07\u0026ndash;1.30] and 1.31 [1.10\u0026ndash;1.57], respectively).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eCCI and RDCI may be useful tools in assessing prognosis in patients with IPAF in terms of both lung disease progression and mortality. Prospective studies are needed to further evaluate the performance of CCI and RDCI and the impact of optimizing comorbid conditions that may mitigate poor outcomes among patients with IPAF.\u003c/p\u003e","manuscriptTitle":"Evaluation of Comorbidity Burden on Disease Progression and Mortality in Patients with Interstitial Pneumonia with Autoimmune Features: a Retrospective Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-08-17 05:17:41","doi":"10.21203/rs.3.rs-3210870/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5765a8b8-a1f5-4343-a5db-92388b20339c","owner":[],"postedDate":"August 17th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-11-13T12:44:32+00:00","versionOfRecord":[],"versionCreatedAt":"2023-08-17 05:17:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3210870","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3210870","identity":"rs-3210870","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}