Pre-existing Chronic Kidney Disease and Mortality in Non-COVID Acute Respiratory Distress Syndrome: A Propensity-Matched Cohort Study

Pre-existing Chronic Kidney Disease and Mortality in Non-COVID Acute Respiratory Distress Syndrome: A Propensity-Matched Cohort Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Pre-existing Chronic Kidney Disease and Mortality in Non-COVID Acute Respiratory Distress Syndrome: A Propensity-Matched Cohort Study Mohammad Thaaer Alrajab, Syed Ibrahim, Hyla Alrajab, Saif Yousef, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9317583/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Background Acute respiratory distress syndrome (ARDS) and chronic kidney disease (CKD) are both associated with substantial morbidity and mortality in the intensive care unit (ICU). Previous research in ARDS has evaluated patients with acute kidney injury, a large portion of whom had coronavirus disease 2019 (COVID-19)-related ARDS. The association between pre-existing CKD and outcomes in non-COVID ARDS remains unclear. We hypothesized that pre-existing CKD is associated with increased mortality in non-COVID ARDS. Methods Using the TriNetX Research Network, we conducted a retrospective cohort study comparing mortality in non-COVID ARDS adults, with- versus without- pre-existing CKD. Patients < 18 years and those with current or prior COVID-19 were excluded. ARDS cases requiring mechanical ventilation within 3 days of the index encounter were included. The CKD and non-COVID cohorts were compared using 1:1 propensity score matching (PSM) for age, sex, race/ethnicity, comorbidities, and other risk factors. Mortality at 14, 30, and 120 days was evaluated using risk and hazard ratios with Kaplan-Meier curves and Cox proportional hazards models. Results In matched cohorts, mortality was higher in the CKD cohort at 14, 30, and 120 days (all p < 0.0001). Specifically, 14-day mortality was 35.01% for CKD patients versus 28.22% in the controls (HR 1.29, 95% CI: 1.17–1.43). At 30 days, the mortality risk was 44.22% and 37.10%, respectively (HR 1.26, 95% CI: 1.15–1.37). Likewise, 120-day mortality was 52.64% and 43.59%, respectively (HR 1.29, 95% CI: 1.19–1.40). Conclusion Pre-existing CKD was associated with a higher risk of mortality in non-COVID ARDS patients, supporting its consideration as a prognostic comorbidity for risk stratification. Acute respiratory distress syndrome Chronic kidney disease Mortality COVID-19 Mechanical ventilation Critical care Figures Figure 1 Figure 2 Introduction Acute respiratory distress syndrome (ARDS) is a heterogenous syndrome characterized by acute hypoxemic respiratory failure, arising from lung injury-induced inflammation and increased alveolar capillary permeability. Despite advances in supportive care, ARDS imposes substantial morbidity and high mortality worldwide [ 1 – 3 ]. This is despite implementation of evidence-based management strategies including lung-protective ventilation, prone positioning, and conservative fluid management [ 4 – 6 ]. International epidemiologic investigations demonstrate persistent variability in outcomes and clinical recognition, emphasizing the importance of identifying patient level risk factors that influence prognosis [ 2 ]. Increasing evidence further suggests that ARDS comprises biologically distinct sub-phenotypes with different inflammatory profiles, highlighting the need to better understand how underlying concurrent conditions modify disease severity and proliferation [ 7 , 8 ]. Chronic Kidney Disease (CKD) is highly prevalent upon hospitalized and critically ill patients, and it is characterized by chronic inflammation, immune dysregulation, endothelial dysfunction, and a prothrombotic state, all of which may influence adverse outcomes during acute respiratory failure [ 9 – 13 ]. Uremia-associated immune dysfunction impairs immune responses that provide host defenses to reduce lung inflammation [ 11 , 12 ]. Additionally, CKD may worsen microvascular injury and worsen capillary leak due to endothelial and vascular dysfunction [ 13 , 14 ]. Acute kidney injury development during ARDS is strongly associated with increased mortality [ 9 , 10 ]. However, most literature focuses on kidney injury as an ARDS complication rather than examining the impact of pre-existing CKD prior to ARDS. Observational studies across broader critical illness populations suggest that baseline CKD confers increased mortality risk, supporting the hypothesis that CKD may represent an important vulnerability influencing ARDS outcomes [ 15 ]. A critical limitation of contemporary ARDS literature is that a substantial proportion of recent outcome studies were from cohorts with co-existing COVID-19. Accumulating evidence indicates that COVID-19–associated ARDS differs in important physiologic aspects from traditional ARDS etiologies. Pathologic investigations have demonstrated prominent pulmonary endothelialitis, microvascular thrombosis, and vascular injury in COVID-19, as compared with other viral pneumonias [ 16 ]. Clinical and biomarker studies further suggest differences in inflammatory signaling, respiratory mechanics, and disease progression between COVID-19 ARDS and non-COVID ARDS populations [ 17 – 19 ]. These distinctions raise concerns regarding the generalizability of findings derived from pandemic-era cohorts to patients with ARDS arising from more traditional causes such as bacterial pneumonia, aspiration, trauma, or non-pulmonary sepsis. Because non-COVID ARDS accounts for a substantial proportion of intensive care admissions globally, it remains crucial to identify prognostic factors for this population [ 2 , 20 , 21 ]. Consequently, an important population and knowledge gap exists regarding whether pre-existing CKD is associated with mortality among patients with non-COVID ARDS. This clarification is particularly relevant for clinical risk stratification, as CKD represents a common chronic condition that may influence inflammatory responses, fluid management, and multiorgan dysfunction during critical illness. As such, we hypothesized that pre-existing CKD would increase the risk of mortality in non-COVID ARDS patients. To test this hypothesis, we conducted a retrospective multi-network cohort study using the TriNetX database to evaluate the association between pre-existing CKD and mortality among patients with non-COVID ARDS. This study aims to provide clinically relevant evidence regarding the prognostic significance of CKD and to improve understanding of comorbidity-associated risk in non-COVID ARDS, a population that remains highly relevant beyond the COVID-19 era. Methods Setting This retrospective cohort study was conducted using the TriNetX Research Network (TriNetX, LLC), a federated database of de-identified electronic health records (EHRs) from participating healthcare organizations. We used the United States Collaborative Network and included all available data from TriNetX inception (2007) through the query date of February 26, 2026. TriNetX provides only aggregated counts and statistical summaries and does not allow access to patient-identifiable information. This database is regularly updated and complies with the Health Insurance Portability & Accountability Act (HIPAA). TriNetX data are de-identified, and analyses are reported only in aggregate; therefore, informed consent and local institutional review board (IRB) approval was not required. This study is consistent with secondary analyses of de-identified data as reported in prior TriNetX-based studies. Cohort definition and exclusion criteria Adult patients (≥ 18 years) with ARDS were identified using the ICD-10 code J80. To increase specificity for clinically significant ARDS, we required evidence of mechanical ventilation occurring within 3 days on or after the ARDS diagnosis date. Mechanical ventilation was defined using ICD-10 respiratory ventilation procedure codes (5A1935Z, 5A1945Z, 5A1955Z) and ventilation/ventilator management CPT codes available in TriNetX (including 94002 and 94660). To restrict the analysis to non-COVID ARDS, we excluded patients with evidence of COVID-19/SARS-CoV-2 infection, including a TriNetX curated SARS-CoV-2 RNA positivity term, COVID-19 (U07.1), and personal history of COVID-19 (Z86.16). The exposure cohort consisted of patients with pre-existing chronic kidney disease (CKD) stages 3–5, defined using the codes N18.3, N18.4, and N18.5, with end-stage renal disease (ESRD) excluded (N18.6). ESRD was excluded to avoid confounding from dialysis-dependent renal failure, which carries a clinically distinct risk profile. Cohort logic was configured so that CKD was present on or before the qualifying ARDS index event to represent pre-existing disease. The control cohort included patients meeting identical non-COVID ventilated ARDS criteria with CKD and ESRD codes excluded. The index event was defined as the first qualifying ARDS event meeting the ventilation timing requirement. In TriNetX, baseline covariates were assessed in the 365-day period through 1 day before the index event. Outcomes were assessed through 14 days after the index event for the primary 14-day analysis. A flowchart of the cohort construction is shown in Fig. 1 . Outcomes The primary outcome was 14-day all-cause mortality, defined within TriNetX using mortality status (“Deceased”) and the mortality ICD-10 diagnosis term included in the outcome set (R99: ill-defined and unknown cause of mortality). Mortality was also evaluated 30 and 120 days after the index event. Propensity Score Matching (PSM) Propensity score matching (PSM) was performed within TriNetX to account for confounding variables. Patients with CKD were matched 1:1 to patients without CKD using demographics (age, sex, race/ethnicity) and clinically relevant comorbidities/clinical factors available in TriNetX, including but not limited to hypertension (I10), heart failure (I50), type 2 diabetes (E11), chronic ischemic heart disease (I25), sepsis (A41), septic shock (R65.21), overweight/obesity (E66), COPD (J44), pulmonary vascular disease (I27), acute myocardial infarction (I21), obstructive sleep apnea (G47.33), nicotine-related diagnoses/history (F17, Z87.891), alcohol-related disorders (F10), and available clinical measures/labs used in the matching workflow (including Body Mass Index [BMI] and hemoglobin A1c values). Only the ‘Hispanic or Latino’ and ‘Black or African American’ characteristics were matched for races/ethnicities, as others did not significantly differ amongst the cohorts. In addition, prevalence of asthma amongst the two cohorts did not differ significantly and was therefore excluded from the PSM. Covariate balance was assessed using standardized mean differences (SMDs), with SMD < 0.1 indicating acceptable balance. Statistical analysis For the primary outcome, we calculated risk ratios (RRs) with 95% confidence intervals (CIs) and p-values using the TriNetX Measures of Association tool. Survival was additionally evaluated using Kaplan-Meier curves with log–rank testing. A Cox proportional hazards model was used to estimate the association between pre-existing CKD and mortality over the different follow-up windows, reported as hazard ratios (HRs) with 95% CIs and p-values. Statistical significance was defined as p < 0.05 (two-sided), and all analyses were performed within the TriNetX platform. Table 1 Pre-existing CKD group (cohort 1) and Non-CKD Control (cohort 2) characteristics before and after PSM Before Matching After Matching Characteristic Name Category Cohort Patient Count % of Cohort (Mean ± SD) p-Value SMD Patient Count % of Cohort (Mean ± SD) p-Value SMD Age at Index (years) Cohort 1 2396 (67.13 ± 13.51) < 0.0001 0.886 2388 (67.08 ± 13.5) 0.261 0.033 Cohort 2 33225 (52.74 ± 18.56) 2388 (67.51 ± 13.0) Male Cohort 1 1391 58.06% 0.542 0.013 1389 58.17% 0.929 0.003 Cohort 2 19500 58.69% 1392 58.29% Female Cohort 1 1005 41.95% 0.521 0.014 999 41.83% 0.837 0.006 Cohort 2 13714 41.28% 992 41.54% Black or African American Cohort 1 447 18.66% < 0.0001 0.099 444 18.59% 0.709 0.011 Cohort 2 4965 14.94% 434 18.17% Hispanic or Latino Cohort 1 88 3.67% < 0.0001 0.163 88 3.69% 0.697 0.011 Cohort 2 2452 7.38% 83 3.48% Essential (primary) hypertension Cohort 1 1312 54.76% < 0.0001 0.394 1306 54.69% 0.029 0.063 Cohort 2 11807 35.54% 1231 51.55% Heart failure Cohort 1 1064 44.41% < 0.0001 0.637 1056 44.22% 0.641 0.014 Cohort 2 5474 16.48% 1040 43.55% Type 2 diabetes mellitus Cohort 1 1031 43.03% < 0.0001 0.588 1023 42.84% 0.159 0.041 Cohort 2 5696 17.14% 975 40.83% Chronic ischemic heart disease Cohort 1 961 40.11% < 0.0001 0.563 954 39.95% 0.496 0.019 Cohort 2 5241 15.77% 931 38.99% Other sepsis Cohort 1 932 38.90% < 0.0001 0.243 928 38.86% 0.189 0.038 Cohort 2 9146 27.53% 884 37.02% Severe sepsis with septic shock Cohort 1 692 28.88% < 0.0001 0.225 689 28.85% 0.335 0.028 Cohort 2 6422 19.33% 659 27.60% Overweight and obesity Cohort 1 655 27.34% < 0.0001 0.317 649 27.18% 0.279 0.031 Cohort 2 4849 14.59% 616 25.80% Personal history of nicotine dependence Cohort 1 624 26.04% < 0.0001 0.312 617 25.84% 0.026 0.064 Cohort 2 4563 13.73% 551 23.07% Other chronic obstructive pulmonary disease Cohort 1 595 24.83% < 0.0001 0.323 589 24.67% 0.478 0.021 Cohort 2 4130 12.43% 568 23.79% Other pulmonary heart diseases Cohort 1 455 18.99% < 0.0001 0.364 447 18.72% 0.525 0.018 Cohort 2 2311 6.96% 430 18.01% Acute myocardial infarction Cohort 1 437 18.24% < 0.0001 0.324 433 18.13% 0.287 0.031 Cohort 2 2496 7.51% 405 16.96% Obstructive sleep apnea (adult) (pediatric) Cohort 1 424 17.70% < 0.0001 0.306 419 17.55% 0.909 0.003 Cohort 2 2540 7.65% 416 17.42% Nicotine dependence Cohort 1 390 16.28% 0.056 0.041 390 16.33% 0.284 0.031 Cohort 2 5921 17.82% 363 15.20% Alcohol related disorders Cohort 1 159 6.64% < 0.0001 0.133 159 6.66% 0.954 0.002 Cohort 2 3438 10.35% 160 6.70% BMI (kg/m2) Cohort 1 1577 (31.03 ± 8.83) < 0.0001 0.203 1569 (31.02 ± 8.84) 0.192 0.047 Cohort 2 17283 (29.26 ± 8.54) 1541 (30.6 ± 8.66) BMI 15–28 kg/m2 Cohort 1 812 33.89% < 0.0001 0.079 809 33.88% 0.311 0.029 15–28 kg/m2 Cohort 2 10027 30.18% 776 32.50% BMI 29–35 kg/m2 Cohort 1 667 27.84% < 0.0001 0.227 662 27.72% 0.160 0.041 29–35 kg/m2 Cohort 2 6094 18.34% 619 25.92% BMI ≥ 36 kg/m2 Cohort 1 493 20.58% < 0.0001 0.231 487 20.39% 0.613 0.015 ≥ 36 kg/m2 Cohort 2 4017 12.09% 473 19.81% Hemoglobin A1c/Hemoglobin.total in Blood (mg/dL) Cohort 1 959 (6.71 ± 1.78) < 0.0001 0.156 951 (6.705 ± 1.79) 0.909 0.005 Cohort 2 6928 (6.43 ± 1.81) 918 (6.695 ± 1.66) Hemoglobin A1c/Hemoglobin.total in Blood 3-5.9% Cohort 1 399 16.65% < 0.0001 0.153 397 16.63% 0.815 0.008 3-5.9% Cohort 2 3774 11.36% 391 16.37% Hemoglobin A1c/Hemoglobin.total in Blood 6-6.9% Cohort 1 343 14.32% < 0.0001 0.281 341 14.28% 0.335 0.028 6-6.9% Cohort 2 1970 5.93% 318 13.32% Hemoglobin A1c/Hemoglobin.total in Blood 7–13% Cohort 1 345 14.40% < 0.0001 0.321 340 14.24% 0.531 0.018 7–13% Cohort 2 1665 5.01% 325 13.61% Note: 983 patients in the Non-CKD group were excluded because they met the index event more than 20 years ago. (SD = standard deviation, SMD = standardized mean difference) Please include this table at the end of the “methods” or after the beginning of the “results” section. Results After applying inclusion and exclusion criteria, our database identified 2396 patients for the CKD cohort and 34208 patients for the control. The mean age in the CKD cohort was significantly higher than the control (67.1 years versus 52.7 years). Both cohorts were mostly male (58.05% and 58.69% for the CKD cohort and control respectively). The CKD cohort was also made up of more patients who identify as African American compared to the control (18.66% vs. 14.94%). In the control, there were more patients who identified as Hispanic or Latino relative to the CKD cohort (7.38% versus 3.67%). Baseline characteristics varied between patients with and without pre-existing CKD. Those with pre-existing CKD had a higher prevalence of comorbidities, including type 2 diabetes, heart failure, obesity, personal history of nicotine dependence, alcohol related disorders, sepsis, and respiratory conditions like chronic obstructive pulmonary disease. Average BMI was higher in the pre-existing CKD cohort relative to the control (31 vs 29.3). Table 1 summarizes the baseline characteristics in both cohorts before and after propensity score matching. Propensity score matching and adjusted baseline characteristics We applied propensity score matching to our pre-existing CKD cohort and control, successfully balancing the previously observed differences in baseline covariates between the groups. This resulted in each group consisting of 2388 patients (4776 total). After matching, none of the covariates, including demographic factors and comorbidities, were statistically significant, with all standardized mean differences < 0.1. This adjustment ensures that comparison of risk ratios for mortality is based on cohorts with similar baseline characteristics, allowing for a more accurate comparison while minimizing confounding. Mortality outcomes Mortality was consistently higher in the pre-existing CKD cohort compared with matched non-existing CKD controls across all evaluated timepoints. At 14 days, mortality was 35.01% in the pre-existing CKD cohort versus 28.22% in the controls (p < 0.0001). This corresponded to an absolute risk difference of 6.78% (95% CI 4.15%–9.41%), a risk ratio of 1.24 (95% CI 1.14–1.35), and an odds ratio of 1.37 (95% CI 1.21–1.55). Time to event analysis similarly demonstrated an increased hazard ratio through 14 days (HR 1.29, 95% CI 1.17–1.43; p < 0.0001). Table 2 summarizes our findings for 14-day mortality. Table 2 14-Day mortality risk difference in non-COVID ARDS patients with and without pre-existing CKD Cohort Number of Patients Number of Mortalities Risk (%) CKD Cohort 2388 836 35.01% Non-CKD Cohort 2388 674 28.22% Statistic Value Risk Difference (%) Risk Ratio Odds Ratio 6.78% 1.24 1.37 95% Confidence Intervals (CI) (4.15%–9.41%) (1.14–1.35) (1.21–1.55) Note: The risk difference amongst the cohorts differs significantly (p < 0.0001) At 30 days, mortality was 44.22% in the pre-existing CKD cohort compared with 37.10% in the controls (HR 1.26, 95% CI 1.15–1.37; p < 0.0001). By 120 days, mortality was 52.64% in the pre-existing CKD cohort versus 43.59% in controls (HR 1.29, 95% CI 1.19–1.40; p 0.05). Mortality across all 14-, 30-, and 120-day intervals are shown in Table 3 . Table 3 Mortality risk differences and hazard ratios (HR) at 14-, 30-, and 120-day intervals Outcome CKD Cohort (%) Non-CKD Cohort (%) Hazard Ratio (95% CI) p-value 14-day mortality 35.01% 28.22% 1.29 (1.17–1.43) < 0.0001 30-day mortality 44.22% 37.10% 1.26 (1.15–1.37) < 0.0001 120-day mortality 52.64% 43.59% 1.29 (1.19–1.40) < 0.0001 Our Kaplan Meier analysis demonstrated lower survival in the pre-existing cohort over 14-, 30-, and 120-day follow-ups, consistent with our Cox proportional hazards model. By 120 days, the survival probability was 42.81% for the pre-existing CKD cohort and 52.17% for the non-CKD controls. The Log-Rank test was p < 0.0001 and the chi-square was 37.42. Figure 2 shows the Kaplan Meier curve of survival probability at 120 days. Discussion Our retrospective, multi-center EHR study found evidence that pre-existing moderate-to-severe CKD (stages 3–5, excluding ESRD) was associated with higher short term mortality rates compared to propensity-matched controls without CKD. This study is amongst the first to investigate pre-existing CKD in the context of non-COVID ARDS. Our results indicated an elevated risk of mortality in the CKD cohort across all time intervals, even after adjusting for confounders. In particular, the higher mean BMI in the pre-existing CKD cohort (pre-match) may partially reflect age-related differences in body composition and survivor/selection effects, rather than a direct CKD relationship with BMI. Still, our findings suggest that pre-existing CKD is an important prognostic comorbidity in ventilated, non-COVID ARDS. ARDS remains a major cause of ICU mortality and prolonged mechanical ventilation, and despite advances in supportive care, outcomes remain poor in real-world practice [ 24 ]. Prognostication in ARDS is important for early risk stratification, counseling patients’ families, and anticipating complications that arise in ICU settings. The Berlin Definition further underscores that ARDS severity categories correlate with mortality risk, highlighting why baseline comorbidities may meaningfully influence outcomes [ 1 ]. CKD is common in hospitalized and critically ill populations and is frequently accompanied by cardiometabolic comorbidities and reduced physiologic reserve [ 11 , 16 ]. For clinicians, clarifying whether baseline CKD meaningfully shifts outcomes in non-COVID ARDS can inform bedside expectations and support closer monitoring and more proactive care planning. Prior literature has associated CKD with worse outcomes in critical illness, and much of the kidney-lung discussion in ARDS has historically emphasized acute kidney injury (AKI) developing during the ICU course rather than baseline CKD that precedes ARDS onset. Across observational cohorts of severe respiratory failure and critical illness, CKD has repeatedly been linked to higher mortality and complication rates, although the magnitude of risk varies depending on case mix and analytic approach [ 25 ]. In ARDS-specific analyses, CKD appears to be an adverse prognostic comorbidity. An administrative database study found that CKD was associated with worse in-hospital outcomes among patients with ARDS, including higher mortality, and the authors highlighted an unmet need for additional epidemiologic evidence specifically addressing CKD in ARDS [ 26 ]. However, direct comparisons across studies are limited by heterogeneity in ARDS ascertainment (clinical criteria versus diagnostic coding), cohort restriction (e.g., ventilated-only versus mixed severity), CKD definition (any CKD versus stage-based), outcome time horizons (in-hospital versus fixed follow-up), and confounding control (unadjusted comparisons versus matching and/or survival modeling). Collectively, the existing evidence supports CKD as a clinically relevant comorbidity in ARDS. These results also underscore the value of analyses that apply consistent ARDS definitions and robust methods to address measured confounding. A central contribution of this study is its emphasis on non-COVID ARDS. Since 2020, ARDS research and clinical experience have been heavily shaped by the COVID-19 pandemic, and COVID-associated ARDS has been reported to differ from non-COVID ARDS in physiologic characteristics, inflammatory biomarkers, and clinical course in several cohorts. By excluding SARS-CoV-2 positivity and COVID-19 diagnosis/history codes and by restricting the cohort to patients receiving mechanical ventilation near the ARDS diagnosis, our approach aims to improve cohort specificity and better represent clinically significant non-COVID ARDS. In that context, the persistence of an association between pre-existing CKD and mortality after matching suggests that baseline kidney disease may remain relevant for prognosis even outside COVID-driven ARDS cohorts [ 18 ]. Several biologically plausible mechanisms may explain why pre-existing CKD is associated with worse outcomes in ARDS. First, CKD is accompanied by a chronic inflammatory environment and clinically relevant immune dysfunction, which may increase susceptibility to secondary infections and sepsis and may amplify dysregulated inflammatory injury once ARDS develops [ 25 ]. Second, CKD is closely linked with endothelial dysfunction and accelerated vascular disease, which may worsen microvascular injury and contribute to maladaptive organ crosstalk during critical illness, potentially increasing the risk of multi-organ failure [ 26 ]. Third, fluid balance is central to ARDS management, and reduced renal reserve may make it harder to navigate the tight window between adequate resuscitation and avoiding volume overload. Excess fluid burden can plausibly worsen pulmonary edema and prolong ventilator dependence. Evidence from the fluid and catheter treatment trial (FACTT) supports the clinical importance of fluid strategy in acute lung injury/ARDS, where a conservative approach improved lung function and shortened ventilation/ICU duration without increasing non-pulmonary organ failures [ 6 ]. CKD-related metabolic derangements (uremic toxin accumulation, acid-base disturbances) may reduce physiologic reserve during severe hypoxemia and shock, potentially lowering tolerance to ARDS-related stressors [ 12 – 14 ]. While our observational data cannot determine which pathway predominates, these mechanisms provide clinical plausibility for the consistent mortality and survival differences observed in the CKD cohort [ 24 ]. Clinically, pre-existing CKD may serve as a readily available marker of potentially increased early mortality risk in ventilated, non-COVID ARDS. It may help clinicians identify a subgroup that warrants monitoring with earlier prognostic discussions. In practice, this could lead to more emphasis towards kidney protection in critical care medicine; avoiding nephrotoxins when feasible, optimizing hemodynamics to preserve renal perfusion, and involving nephrology early. Fluid strategy is a particularly relevant domain, given evidence that conservative fluid management in acute lung injury/ARDS improves lung function and reduces duration of mechanical ventilation and ICU stay [ 20 ]. These considerations align with established ARDS management frameworks emphasizing lung-protective ventilation and supportive ICU care, while recognizing that comorbid organ dysfunction can influence trajectories [ 27 ]. Importantly, our findings should be interpreted as associations; prospective studies with granular physiologic data are needed to determine whether CKD-tailored management strategies can modify outcomes in non-COVID ARDS. Limitations This study has several limitations. As this was a retrospective observational study using a federated EHR network, our findings should be interpreted as associations rather than causation. Although propensity score matching helped balance many measured baseline differences between the cohorts, residual confounding is still possible, particularly from factors not captured in the matching process such as ARDS severity, ventilator settings, fluid balance, prone positioning, vasopressor use, nephrotoxin exposure, renal replacement therapy, and other ICU level management variables. Second, ARDS, CKD, and mechanical ventilation were identified using diagnostic and procedure codes within TriNetX rather than direct chart review, which introduces the possibility of coding error or misclassification. Third, our cohort definition was intentionally restrictive, requiring mechanical ventilation within 3 days of ARDS diagnosis and excluding patients with COVID-19 and ESRD to improve specificity for clinically significant non-COVID ARDS. However, this may limit generalizability to patients with less severe ARDS, those intubated later in their course, patients with dialysis-dependent renal failure, or those with COVID associated ARDS. In addition, CKD was evaluated as a broad stage 3–5 exposure, which does not allow assessment of whether mortality risk differs across individual CKD stages or by more granular measures of kidney function. As with other TriNetX based studies, use of de-identified aggregate data also limits access to granular physiologic variables and may reflect variation in coding practices and care patterns across contributing healthcare organizations. Conclusions This retrospective propensity score-matched cohort study provides evidence that pre-existing CKD is associated with higher mortality in non-COVID ARDS patients across both short- and intermediate-term follow-up, extending insights beyond predominantly COVID-era ARDS cohorts. These results suggest that pre-existing CKD functions as an important prognostic comorbidity that may aid early risk stratification in non-COVID ARDS. Identifying patients with CKD as a high-risk population for targeted management may improve prognoses and guide the intensity of supportive care. Given the retrospective nature of data analysis and potential for residual confounding and bias, future research is warranted to confirm these associations and to determine whether CKD-specific management strategies can improve outcomes in this high-risk population. Abbreviations AKI acute kidney injury ARDS acute respiratory distress syndrome BMI body mass index CI confidence interval CKD chronic kidney disease COVID 19–coronavirus disease 2019 EHR electronic health records ESRD end–stage renal disease FACTT fluid and catheter treatment trial HCOs healthcare organizations HIPAA Health Insurance Portability & Accountability Act HR hazard ratio ICU intensive care unit IRB institutional review board PSM propensity score match SD standard deviation SMD standardized mean difference Declarations Clinical Trial Number: Not Applicable. Ethics Approval and Consent to Participate This study is a secondary analysis of de-identified, aggregated data from the TriNetX Research Network and did not involve intervention or interaction with human participants. Because the TriNetX dataset is de-identified in accordance with the HIPAA de-identification standard (45 CFR 164.514(a)) with expert determination (45 CFR 164.514(b)(1)), ethics committee/IRB approval and informed consent to participate were not required under applicable U.S. regulations. All analyses were performed in accordance with relevant guidelines and regulations, including the principles of the Declaration of Helsinki. Consent for Publication Not applicable. Competing Interests All authors declare no competing interests. Funding The research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Author Contribution MTA and SI designed the study, performed data analysis, and wrote parts of the manuscript; SY, HA, and NS contributed to the study design and wrote the manuscript. PS supervised the study and revised the manuscript. All authors read and approved the submitted version. Acknowledgement We would like to acknowledge support from Dr. Daniel Novak with the University of California Riverside School of Medicine, who contributed in important ways to the early conception of this study and has mentored us in the TriNetX database usage. Data Availability This study used population-level aggregate and HIPAA de-identified data collected by the TriNetX platform (“US Collaborative Network”) and available from TriNetX, LLC ( [https://trinetx.com/](https:/trinetx.com) ), but third-party restrictions apply to the availability of these data. The data were used under license for this study with restrictions that do not allow for the data to be redistributed or made publicly available. To gain access to the data, a request can be made to TriNetX ( [ [email protected] ](mailto: [email protected] ) ), but costs may be incurred, and a data-sharing agreement may be necessary. Data specific to this study, including diagnosis codes and cohort characteristics in aggregated format, are included in the manuscript as tables and figures. Data through the TriNetX platform is queried in real time. Data from the underlying electronic health records of participating healthcare organizations are refreshed in the TriNetX platform from daily to every couple of months depending on the healthcare organization. References The ARDS Definition Task Force. Acute Respiratory Distress Syndrome: The Berlin Definition. JAMA. 2012. https://doi.org/10.1001/jama.2012.5669 . Bellani G, Laffey JG, Pham T, et al. Epidemiology, Patterns of Care, and Mortality for Patients With Acute Respiratory Distress Syndrome in Intensive Care Units in 50 Countries. JAMA. 2016;315:788–800. Thompson BT, Chambers RC, Liu KD. Acute Respiratory Distress Syndrome. N Engl J Med. 2017;377:562–72. Brower RG, Matthay MA, Morris A, Schoenfeld D, Thompson BT, Wheeler A. Ventilation with Lower Tidal Volumes as Compared with Traditional Tidal Volumes for Acute Lung Injury and the Acute Respiratory Distress Syndrome. N Engl J Med. 2000;342:1301–8. Guérin C, Reignier J, Richard J-C, et al. Prone Positioning in Severe Acute Respiratory Distress Syndrome. N Engl J Med. 2013;368:2159–68. The National Heart, Lung, and Blood Institute Acute Respiratory Distress Syndrome (ARDS) Clinical Trials Network. Comparison of Two Fluid-Management Strategies in Acute Lung Injury. N Engl J Med. 2006;354:2564–75. Calfee CS, Delucchi K, Parsons PE, Thompson BT, Ware LB, Matthay MA. Subphenotypes in acute respiratory distress syndrome: latent class analysis of data from two randomised controlled trials. Lancet Respiratory Med. 2014;2:611–20. Sinha P, Calfee CS. Phenotypes in acute respiratory distress syndrome. Curr Opin Crit Care. 2019;25:12–20. Panitchote A, Mehkri O, Hastings A, Hanane T, Demirjian S, Torbic H, Mireles-Cabodevila E, Krishnan S, Duggal A. Factors associated with acute kidney injury in acute respiratory distress syndrome. Ann Intensiv Care. 2019. https://doi.org/10.1186/s13613-019-0552-5 . Charkviani M, Truong HH, Nasrin Nikravangolsefid, Ninan J, Prokop LJ, Reddy S, Kashani KB, Pablo J. Temporal Relationship and Clinical Outcomes of Acute Kidney Injury Following Acute Respiratory Distress Syndrome: A Systematic Review and Meta-Analysis. Crit Care Explorations. 2024;6:e1054–1054. Abdalrahim MS, Khalil AA, Alramly M, Alshlool KN, Abed MA, Moser DK. Pre-existing chronic kidney disease and acute kidney injury among critically ill patients. Heart Lung. 2020. https://doi.org/10.1016/j.hrtlng.2020.04.013 . Cohen G. Immune Dysfunction in Uremia 2020. Toxins. 2020;12:439. Espi M, Koppe L, Fouque D, Thaunat O. Chronic Kidney Disease-Associated Immune Dysfunctions: Impact of Protein-Bound Uremic Retention Solutes on Immune Cells. Toxins. 2020;12:300. Stenvinkel P, Chertow GM, Devarajan P, Levin A, Andreoli SP, Bangalore S, Warady BA. (2021) Chronic Inflammation in Chronic Kidney Disease Progression: Role of Nrf2. Kidney International Reports. https://doi.org/10.1016/j.ekir.2021.04.023 Diaz-Ricart M, Sergi Torramade-Moix, Pascual G, Palomo M, Moreno-Castaño AB, Martinez-Sanchez J, Vera M, Cases A, Gines E. Endothelial Damage, Inflammation and Immunity in Chronic Kidney Disease. Toxins. 2020;12:361–361. Constance, Vondenhoff S, Noels H. Endothelial Cell Dysfunction and Increased Cardiovascular Risk in Patients With Chronic Kidney Disease. Circul Res. 2023;132:970–92. Ackermann M, Verleden SE, Kuehnel M, et al. Pulmonary Vascular Endothelialitis, Thrombosis, and Angiogenesis in Covid-19. N Engl J Med. 2020. https://doi.org/10.1056/nejmoa2015432 . Bain W, Yang H, Shah FA, et al. COVID-19 versus Non-COVID-19 Acute Respiratory Distress Syndrome: Comparison of Demographics, Physiologic Parameters, Inflammatory Biomarkers, and Clinical Outcomes. Annals Am Thorac Soc. 2021;18:1202–10. Beloncle FM. Is COVID-19 different from other causes of acute respiratory distress syndrome? J Intensive Med. 2023. https://doi.org/10.1016/j.jointm.2023.02.003 . Gattinoni L, Chiumello D, Caironi P, Busana M, Romitti F, Brazzi L, Camporota L. (2020) COVID-19 pneumonia: different respiratory treatments for different phenotypes? Intensive Care Medicine. https://doi.org/10.1007/s00134-020-06033-2 Bernauer E, Alebrand F, Heurich M. Same but Different? Comparing the Epidemiology, Treatments and Outcomes of COVID-19 and Non-COVID-19 ARDS Cases in Germany Using a Sample of Claims Data from 2021 and 2019. Viruses. 2023;15:1324–1324. Palchuk MB, London JW, Perez-Rey D, Drebert, Zuzanna J, Winer-Jones, Jessamine P, Thompson CN, Esposito J, Claerhout B. A global federated real-world data and analytics platform for research. JAMIA Open. 2023. https://doi.org/10.1093/jamiaopen/ooad035 . TriNetX LLC. (2025) Publication Guidelines - TriNetX. In: TriNetX. https://trinetx.com/real-world-resources/case-studies-publications/trinetx-publication-guidelines/ Basu RK, Wheeler DS. Kidney–lung cross-talk and acute kidney injury. Pediatr Nephrol. 2013;28:2239–48. Rao A, Anwar A, Agrawal A, Kichloo A, Singh J, Karki A. The Impact of Chronic Kidney Disease on In-Hospital Outcomes in Patients With Acute Respiratory Distress Syndrome. Can Respir J. 2026;2026:9063636. Husain-Syed F, Slutsky AS, Ronco C. Lung–Kidney Cross-Talk in the Critically Ill Patient. Am J Respir Crit Care Med. 2016;194:402–14. Fan E, Del Sorbo L, Goligher EC, Hodgson CL, Munshi L, Walkey AJ et al. An official american thoracic society/european society of intensive care medicine/society of critical care medicine clinical practice guideline: Mechanical ventilation in adult patients with acute respiratory distress syndrome. American Journal of Respiratory and Critical Care Medicine [Internet]. 2017;195(9):1253–63. Available from: http://www.thoracic.org/statements/resources/cc/ards-guidelines.pdf Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 04 May, 2026 Reviewers invited by journal 28 Apr, 2026 Editor assigned by journal 26 Apr, 2026 Editor invited by journal 09 Apr, 2026 Submission checks completed at journal 07 Apr, 2026 First submitted to journal 07 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9317583","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":620489831,"identity":"dfb25d38-82eb-4695-bc8b-0e077b6c1d3a","order_by":0,"name":"Mohammad Thaaer Alrajab","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuUlEQVRIiWNgGAWjYFAC5gYGBgMJOX4ojxgtjEAtFTbGkg2kaTmTlrjhALFadNsPtn342XbY2PhG8rMPDBXWiQ2EtJidSWye2dt2WM7sRprxDIYz6URoOZDYzMALtMXsRg4zA2PbYSK0nH/YzPgXqHLzDJCWf8RouZHYzMwD8r4ESEsDUVoeNjPLAANZ4swzY4aEY+nGRDgs+TDjG1BUtic/ZvhQYy1LUAsqSCBN+SgYBaNgFIwCXAAARg5AoVu6/GYAAAAASUVORK5CYII=","orcid":"","institution":"University of California, Riverside","correspondingAuthor":true,"prefix":"","firstName":"Mohammad","middleName":"Thaaer","lastName":"Alrajab","suffix":""},{"id":620489832,"identity":"92105a4d-79f2-4582-9e86-7969df8e808a","order_by":1,"name":"Syed Ibrahim","email":"","orcid":"","institution":"University of California, Riverside","correspondingAuthor":false,"prefix":"","firstName":"Syed","middleName":"","lastName":"Ibrahim","suffix":""},{"id":620489833,"identity":"04786485-c3be-4f45-b267-48cf842b4457","order_by":2,"name":"Hyla Alrajab","email":"","orcid":"","institution":"University of California, Riverside","correspondingAuthor":false,"prefix":"","firstName":"Hyla","middleName":"","lastName":"Alrajab","suffix":""},{"id":620489834,"identity":"66822831-de3f-445b-aee3-dce691c355c5","order_by":3,"name":"Saif Yousef","email":"","orcid":"","institution":"University of California, Riverside","correspondingAuthor":false,"prefix":"","firstName":"Saif","middleName":"","lastName":"Yousef","suffix":""},{"id":620489835,"identity":"8d96f05a-f18f-439d-9cb4-3ed991e1e1b1","order_by":4,"name":"Nazmi Salaymeh","email":"","orcid":"","institution":"University of California, Riverside","correspondingAuthor":false,"prefix":"","firstName":"Nazmi","middleName":"","lastName":"Salaymeh","suffix":""},{"id":620489836,"identity":"20ff41cc-57a3-449c-a826-78f7395d6255","order_by":5,"name":"Paul J. Simpson","email":"","orcid":"","institution":"Orange County Lung Center","correspondingAuthor":false,"prefix":"","firstName":"Paul","middleName":"J.","lastName":"Simpson","suffix":""}],"badges":[],"createdAt":"2026-04-04 05:08:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9317583/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9317583/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106596644,"identity":"0bbad1f9-779d-4296-830c-1b91a6fa7b4b","added_by":"auto","created_at":"2026-04-10 09:38:04","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":65815,"visible":true,"origin":"","legend":"\u003cp\u003eA flowchart of the cohort selection process and analysis\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9317583/v1/970fa7392e21ca412773a263.png"},{"id":106725423,"identity":"f9d98694-867a-4b9f-ba03-577c1bc8e8cc","added_by":"auto","created_at":"2026-04-12 18:32:50","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":196781,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan Meier Survival Curve for 120-day mortality\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9317583/v1/f95911f56366031e53bfa916.png"},{"id":107868725,"identity":"bb1a3eb5-5919-40e3-aea8-eb113fa35182","added_by":"auto","created_at":"2026-04-27 07:32:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":922155,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9317583/v1/fd2f4628-a0c2-473b-8669-e81511b88c66.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Pre-existing Chronic Kidney Disease and Mortality in Non-COVID Acute Respiratory Distress Syndrome: A Propensity-Matched Cohort Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAcute respiratory distress syndrome (ARDS) is a heterogenous syndrome characterized by acute hypoxemic respiratory failure, arising from lung injury-induced inflammation and increased alveolar capillary permeability. Despite advances in supportive care, ARDS imposes substantial morbidity and high mortality worldwide [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. This is despite implementation of evidence-based management strategies including lung-protective ventilation, prone positioning, and conservative fluid management [\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. International epidemiologic investigations demonstrate persistent variability in outcomes and clinical recognition, emphasizing the importance of identifying patient level risk factors that influence prognosis [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Increasing evidence further suggests that ARDS comprises biologically distinct sub-phenotypes with different inflammatory profiles, highlighting the need to better understand how underlying concurrent conditions modify disease severity and proliferation [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eChronic Kidney Disease (CKD) is highly prevalent upon hospitalized and critically ill patients, and it is characterized by chronic inflammation, immune dysregulation, endothelial dysfunction, and a prothrombotic state, all of which may influence adverse outcomes during acute respiratory failure [\u003cspan additionalcitationids=\"CR10 CR11 CR12\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Uremia-associated immune dysfunction impairs immune responses that provide host defenses to reduce lung inflammation [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Additionally, CKD may worsen microvascular injury and worsen capillary leak due to endothelial and vascular dysfunction [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Acute kidney injury development during ARDS is strongly associated with increased mortality [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. However, most literature focuses on kidney injury as an ARDS complication rather than examining the impact of pre-existing CKD prior to ARDS. Observational studies across broader critical illness populations suggest that baseline CKD confers increased mortality risk, supporting the hypothesis that CKD may represent an important vulnerability influencing ARDS outcomes [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA critical limitation of contemporary ARDS literature is that a substantial proportion of recent outcome studies were from cohorts with co-existing COVID-19. Accumulating evidence indicates that COVID-19\u0026ndash;associated ARDS differs in important physiologic aspects from traditional ARDS etiologies. Pathologic investigations have demonstrated prominent pulmonary endothelialitis, microvascular thrombosis, and vascular injury in COVID-19, as compared with other viral pneumonias [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Clinical and biomarker studies further suggest differences in inflammatory signaling, respiratory mechanics, and disease progression between COVID-19 ARDS and non-COVID ARDS populations [\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. These distinctions raise concerns regarding the generalizability of findings derived from pandemic-era cohorts to patients with ARDS arising from more traditional causes such as bacterial pneumonia, aspiration, trauma, or non-pulmonary sepsis. Because non-COVID ARDS accounts for a substantial proportion of intensive care admissions globally, it remains crucial to identify prognostic factors for this population [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eConsequently, an important population and knowledge gap exists regarding whether pre-existing CKD is associated with mortality among patients with non-COVID ARDS. This clarification is particularly relevant for clinical risk stratification, as CKD represents a common chronic condition that may influence inflammatory responses, fluid management, and multiorgan dysfunction during critical illness. As such, we hypothesized that pre-existing CKD would increase the risk of mortality in non-COVID ARDS patients.\u003c/p\u003e \u003cp\u003eTo test this hypothesis, we conducted a retrospective multi-network cohort study using the TriNetX database to evaluate the association between pre-existing CKD and mortality among patients with non-COVID ARDS. This study aims to provide clinically relevant evidence regarding the prognostic significance of CKD and to improve understanding of comorbidity-associated risk in non-COVID ARDS, a population that remains highly relevant beyond the COVID-19 era.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSetting\u003c/h2\u003e \u003cp\u003e This retrospective cohort study was conducted using the TriNetX Research Network (TriNetX, LLC), a federated database of de-identified electronic health records (EHRs) from participating healthcare organizations. We used the United States Collaborative Network and included all available data from TriNetX inception (2007) through the query date of February 26, 2026. TriNetX provides only aggregated counts and statistical summaries and does not allow access to patient-identifiable information. This database is regularly updated and complies with the Health Insurance Portability \u0026amp; Accountability Act (HIPAA).\u003c/p\u003e \u003cp\u003eTriNetX data are de-identified, and analyses are reported only in aggregate; therefore, informed consent and local institutional review board (IRB) approval was not required. This study is consistent with secondary analyses of de-identified data as reported in prior TriNetX-based studies.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCohort definition and exclusion criteria\u003c/h3\u003e\n\u003cp\u003eAdult patients (\u0026ge;\u0026thinsp;18 years) with ARDS were identified using the ICD-10 code J80. To increase specificity for clinically significant ARDS, we required evidence of mechanical ventilation occurring within 3 days on or after the ARDS diagnosis date. Mechanical ventilation was defined using ICD-10 respiratory ventilation procedure codes (5A1935Z, 5A1945Z, 5A1955Z) and ventilation/ventilator management CPT codes available in TriNetX (including 94002 and 94660).\u003c/p\u003e \u003cp\u003eTo restrict the analysis to non-COVID ARDS, we excluded patients with evidence of COVID-19/SARS-CoV-2 infection, including a TriNetX curated SARS-CoV-2 RNA positivity term, COVID-19 (U07.1), and personal history of COVID-19 (Z86.16).\u003c/p\u003e \u003cp\u003eThe exposure cohort consisted of patients with pre-existing chronic kidney disease (CKD) stages 3\u0026ndash;5, defined using the codes N18.3, N18.4, and N18.5, with end-stage renal disease (ESRD) excluded (N18.6). ESRD was excluded to avoid confounding from dialysis-dependent renal failure, which carries a clinically distinct risk profile. Cohort logic was configured so that CKD was present on or before the qualifying ARDS index event to represent pre-existing disease. The control cohort included patients meeting identical non-COVID ventilated ARDS criteria with CKD and ESRD codes excluded. The index event was defined as the first qualifying ARDS event meeting the ventilation timing requirement. In TriNetX, baseline covariates were assessed in the 365-day period through 1 day before the index event. Outcomes were assessed through 14 days after the index event for the primary 14-day analysis. A flowchart of the cohort construction is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eOutcomes\u003c/h3\u003e\n\u003cp\u003eThe primary outcome was 14-day all-cause mortality, defined within TriNetX using mortality status (\u0026ldquo;Deceased\u0026rdquo;) and the mortality ICD-10 diagnosis term included in the outcome set (R99: ill-defined and unknown cause of mortality). Mortality was also evaluated 30 and 120 days after the index event.\u003c/p\u003e\n\u003ch3\u003ePropensity Score Matching (PSM)\u003c/h3\u003e\n\u003cp\u003ePropensity score matching (PSM) was performed within TriNetX to account for confounding variables. Patients with CKD were matched 1:1 to patients without CKD using demographics (age, sex, race/ethnicity) and clinically relevant comorbidities/clinical factors available in TriNetX, including but not limited to hypertension (I10), heart failure (I50), type 2 diabetes (E11), chronic ischemic heart disease (I25), sepsis (A41), septic shock (R65.21), overweight/obesity (E66), COPD (J44), pulmonary vascular disease (I27), acute myocardial infarction (I21), obstructive sleep apnea (G47.33), nicotine-related diagnoses/history (F17, Z87.891), alcohol-related disorders (F10), and available clinical measures/labs used in the matching workflow (including Body Mass Index [BMI] and hemoglobin A1c values).\u003c/p\u003e \u003cp\u003eOnly the \u0026lsquo;Hispanic or Latino\u0026rsquo; and \u0026lsquo;Black or African American\u0026rsquo; characteristics were matched for races/ethnicities, as others did not significantly differ amongst the cohorts. In addition, prevalence of asthma amongst the two cohorts did not differ significantly and was therefore excluded from the PSM. Covariate balance was assessed using standardized mean differences (SMDs), with SMD\u0026thinsp;\u0026lt;\u0026thinsp;0.1 indicating acceptable balance.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eFor the primary outcome, we calculated risk ratios (RRs) with 95% confidence intervals (CIs) and p-values using the TriNetX Measures of Association tool. Survival was additionally evaluated using Kaplan-Meier curves with log\u0026ndash;rank testing. A Cox proportional hazards model was used to estimate the association between pre-existing CKD and mortality over the different follow-up windows, reported as hazard ratios (HRs) with 95% CIs and p-values. Statistical significance was defined as p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (two-sided), and all analyses were performed within the TriNetX platform.\u003c/p\u003e \u003c/div\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\u003ePre-existing CKD group (cohort 1) and Non-CKD Control (cohort 2) characteristics before and after PSM\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\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=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c7\" namest=\"c4\"\u003e \u003cp\u003eBefore Matching\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c11\" namest=\"c8\"\u003e \u003cp\u003eAfter Matching\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic Name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCohort\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePatient Count\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e% of Cohort (Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSMD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePatient Count\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e% of Cohort (Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003ep-Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eSMD\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 Index (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCohort 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2396\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(67.13\u0026thinsp;\u0026plusmn;\u0026thinsp;13.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.886\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2388\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(67.08\u0026thinsp;\u0026plusmn;\u0026thinsp;13.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCohort 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(52.74\u0026thinsp;\u0026plusmn;\u0026thinsp;18.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2388\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(67.51\u0026thinsp;\u0026plusmn;\u0026thinsp;13.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCohort 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1391\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e58.06%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.542\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e58.17%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.929\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCohort 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e58.69%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1392\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e58.29%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCohort 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e41.95%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.521\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e41.83%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.837\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCohort 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13714\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e41.28%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e41.54%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack or African American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCohort 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e447\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18.66%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e444\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e18.59%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.709\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCohort 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4965\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.94%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e434\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e18.17%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHispanic or Latino\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCohort 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.67%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.69%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.697\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCohort 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2452\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.38%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.48%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEssential (primary) hypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCohort 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e54.76%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.394\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e54.69%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCohort 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11807\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35.54%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e51.55%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeart failure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCohort 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e44.41%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.637\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e44.22%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.641\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCohort 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.48%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e43.55%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType 2 diabetes mellitus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCohort 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e43.03%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.588\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e42.84%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCohort 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5696\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.14%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e975\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e40.83%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic ischemic heart disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCohort 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e961\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40.11%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.563\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e954\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e39.95%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.496\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCohort 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.77%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e931\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e38.99%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther sepsis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCohort 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e932\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38.90%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e928\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e38.86%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCohort 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27.53%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e884\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e37.02%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSevere sepsis with septic shock\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCohort 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e692\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28.88%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e689\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e28.85%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.335\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCohort 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19.33%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e659\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e27.60%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverweight and obesity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCohort 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e655\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27.34%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e649\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e27.18%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCohort 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4849\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.59%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e616\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e25.80%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePersonal history of nicotine dependence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCohort 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e624\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26.04%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e617\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e25.84%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.064\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCohort 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4563\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.73%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e551\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e23.07%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther chronic obstructive pulmonary disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCohort 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e595\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.83%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e589\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e24.67%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.478\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCohort 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.43%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e568\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e23.79%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther pulmonary heart diseases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCohort 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18.99%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e447\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e18.72%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.525\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCohort 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.96%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e430\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e18.01%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcute myocardial infarction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCohort 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18.24%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e433\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e18.13%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.287\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCohort 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2496\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.51%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e405\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e16.96%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObstructive sleep apnea (adult) (pediatric)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCohort 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e424\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.70%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e419\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e17.55%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCohort 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.65%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e416\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e17.42%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNicotine dependence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCohort 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e390\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.28%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e390\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e16.33%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.284\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCohort 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5921\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.82%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e15.20%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol related disorders\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCohort 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.64%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6.66%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.954\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCohort 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3438\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.35%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6.70%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCohort 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1577\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(31.03\u0026thinsp;\u0026plusmn;\u0026thinsp;8.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1569\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(31.02\u0026thinsp;\u0026plusmn;\u0026thinsp;8.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCohort 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(29.26\u0026thinsp;\u0026plusmn;\u0026thinsp;8.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1541\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(30.6\u0026thinsp;\u0026plusmn;\u0026thinsp;8.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u0026ndash;28 kg/m2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCohort 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e812\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33.89%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e809\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e33.88%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u0026ndash;28 kg/m2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCohort 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30.18%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e776\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e32.50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29\u0026ndash;35 kg/m2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCohort 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27.84%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e662\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e27.72%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29\u0026ndash;35 kg/m2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCohort 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18.34%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e619\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e25.92%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;36 kg/m2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCohort 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e493\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20.58%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e487\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e20.39%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.613\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;36 kg/m2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCohort 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.09%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e473\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e19.81%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin A1c/Hemoglobin.total in Blood (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCohort 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e959\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(6.71\u0026thinsp;\u0026plusmn;\u0026thinsp;1.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e951\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(6.705\u0026thinsp;\u0026plusmn;\u0026thinsp;1.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCohort 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6928\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(6.43\u0026thinsp;\u0026plusmn;\u0026thinsp;1.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e918\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(6.695\u0026thinsp;\u0026plusmn;\u0026thinsp;1.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin A1c/Hemoglobin.total in Blood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3-5.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCohort 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e399\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.65%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e397\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e16.63%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.815\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3-5.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCohort 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3774\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.36%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e391\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e16.37%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin A1c/Hemoglobin.total in Blood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6-6.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCohort 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.32%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e341\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e14.28%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.335\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6-6.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCohort 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1970\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.93%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e13.32%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin A1c/Hemoglobin.total in Blood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u0026ndash;13%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCohort 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.40%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.321\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e14.24%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.531\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u0026ndash;13%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCohort 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1665\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.01%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e13.61%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003eNote: 983 patients in the Non-CKD group were excluded because they met the index event more than 20 years ago. (SD\u0026thinsp;=\u0026thinsp;standard deviation, SMD\u0026thinsp;=\u0026thinsp;standardized mean difference)\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003e\u003cb\u003ePlease include this table at the end of the \u0026ldquo;methods\u0026rdquo; or after the beginning of the \u0026ldquo;results\u0026rdquo; section.\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eAfter applying inclusion and exclusion criteria, our database identified 2396 patients for the CKD cohort and 34208 patients for the control. The mean age in the CKD cohort was significantly higher than the control (67.1 years versus 52.7 years). Both cohorts were mostly male (58.05% and 58.69% for the CKD cohort and control respectively). The CKD cohort was also made up of more patients who identify as African American compared to the control (18.66% vs. 14.94%). In the control, there were more patients who identified as Hispanic or Latino relative to the CKD cohort (7.38% versus 3.67%).\u003c/p\u003e \u003cp\u003eBaseline characteristics varied between patients with and without pre-existing CKD. Those with pre-existing CKD had a higher prevalence of comorbidities, including type 2 diabetes, heart failure, obesity, personal history of nicotine dependence, alcohol related disorders, sepsis, and respiratory conditions like chronic obstructive pulmonary disease. Average BMI was higher in the pre-existing CKD cohort relative to the control (31 vs 29.3). Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the baseline characteristics in both cohorts before and after propensity score matching.\u003c/p\u003e \n\u003ch3\u003ePropensity score matching and adjusted baseline characteristics\u003c/h3\u003e\n\u003cp\u003eWe applied propensity score matching to our pre-existing CKD cohort and control, successfully balancing the previously observed differences in baseline covariates between the groups. This resulted in each group consisting of 2388 patients (4776 total). After matching, none of the covariates, including demographic factors and comorbidities, were statistically significant, with all standardized mean differences\u0026thinsp;\u0026lt;\u0026thinsp;0.1. This adjustment ensures that comparison of risk ratios for mortality is based on cohorts with similar baseline characteristics, allowing for a more accurate comparison while minimizing confounding.\u003c/p\u003e\n\u003ch3\u003eMortality outcomes\u003c/h3\u003e\n\u003cp\u003eMortality was consistently higher in the pre-existing CKD cohort compared with matched non-existing CKD controls across all evaluated timepoints.\u003c/p\u003e \u003cp\u003eAt 14 days, mortality was 35.01% in the pre-existing CKD cohort versus 28.22% in the controls (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). This corresponded to an absolute risk difference of 6.78% (95% CI 4.15%\u0026ndash;9.41%), a risk ratio of 1.24 (95% CI 1.14\u0026ndash;1.35), and an odds ratio of 1.37 (95% CI 1.21\u0026ndash;1.55). Time to event analysis similarly demonstrated an increased hazard ratio through 14 days (HR 1.29, 95% CI 1.17\u0026ndash;1.43; p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e summarizes our findings for 14-day mortality.\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\u003e14-Day mortality risk difference in non-COVID ARDS patients with and without pre-existing CKD\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCohort\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of Patients\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber of Mortalities\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRisk (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCKD Cohort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2388\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e836\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35.01%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-CKD Cohort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2388\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e674\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28.22%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"4\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eStatistic Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRisk Difference (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRisk Ratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOdds Ratio\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.78%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.37\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e95% Confidence Intervals (CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e(4.15%\u0026ndash;9.41%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(1.14\u0026ndash;1.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(1.21\u0026ndash;1.55)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote: The risk difference amongst the cohorts differs significantly (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAt 30 days, mortality was 44.22% in the pre-existing CKD cohort compared with 37.10% in the controls (HR 1.26, 95% CI 1.15\u0026ndash;1.37; p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). By 120 days, mortality was 52.64% in the pre-existing CKD cohort versus 43.59% in controls (HR 1.29, 95% CI 1.19\u0026ndash;1.40; p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). The proportional hazards assumption was assessed for each interval and was not violated (all tests p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Mortality across all 14-, 30-, and 120-day intervals are shown in 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\u003eMortality risk differences and hazard ratios (HR) at 14-, 30-, and 120-day intervals\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCKD Cohort (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-CKD Cohort (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHazard Ratio (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14-day mortality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e35.01%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28.22%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.29 (1.17\u0026ndash;1.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30-day mortality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e44.22%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37.10%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.26 (1.15\u0026ndash;1.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e120-day mortality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e52.64%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43.59%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.29 (1.19\u0026ndash;1.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eOur Kaplan Meier analysis demonstrated lower survival in the pre-existing cohort over 14-, 30-, and 120-day follow-ups, consistent with our Cox proportional hazards model. By 120 days, the survival probability was 42.81% for the pre-existing CKD cohort and 52.17% for the non-CKD controls. The Log-Rank test was p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001 and the chi-square was 37.42. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the Kaplan Meier curve of survival probability at 120 days.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur retrospective, multi-center EHR study found evidence that pre-existing moderate-to-severe CKD (stages 3\u0026ndash;5, excluding ESRD) was associated with higher short term mortality rates compared to propensity-matched controls without CKD. This study is amongst the first to investigate pre-existing CKD in the context of non-COVID ARDS. Our results indicated an elevated risk of mortality in the CKD cohort across all time intervals, even after adjusting for confounders. In particular, the higher mean BMI in the pre-existing CKD cohort (pre-match) may partially reflect age-related differences in body composition and survivor/selection effects, rather than a direct CKD relationship with BMI. Still, our findings suggest that pre-existing CKD is an important prognostic comorbidity in ventilated, non-COVID ARDS.\u003c/p\u003e \u003cp\u003eARDS remains a major cause of ICU mortality and prolonged mechanical ventilation, and despite advances in supportive care, outcomes remain poor in real-world practice [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Prognostication in ARDS is important for early risk stratification, counseling patients\u0026rsquo; families, and anticipating complications that arise in ICU settings. The Berlin Definition further underscores that ARDS severity categories correlate with mortality risk, highlighting why baseline comorbidities may meaningfully influence outcomes [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. CKD is common in hospitalized and critically ill populations and is frequently accompanied by cardiometabolic comorbidities and reduced physiologic reserve [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. For clinicians, clarifying whether baseline CKD meaningfully shifts outcomes in non-COVID ARDS can inform bedside expectations and support closer monitoring and more proactive care planning.\u003c/p\u003e \u003cp\u003ePrior literature has associated CKD with worse outcomes in critical illness, and much of the kidney-lung discussion in ARDS has historically emphasized acute kidney injury (AKI) developing during the ICU course rather than baseline CKD that precedes ARDS onset. Across observational cohorts of severe respiratory failure and critical illness, CKD has repeatedly been linked to higher mortality and complication rates, although the magnitude of risk varies depending on case mix and analytic approach [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. In ARDS-specific analyses, CKD appears to be an adverse prognostic comorbidity. An administrative database study found that CKD was associated with worse in-hospital outcomes among patients with ARDS, including higher mortality, and the authors highlighted an unmet need for additional epidemiologic evidence specifically addressing CKD in ARDS [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. However, direct comparisons across studies are limited by heterogeneity in ARDS ascertainment (clinical criteria versus diagnostic coding), cohort restriction (e.g., ventilated-only versus mixed severity), CKD definition (any CKD versus stage-based), outcome time horizons (in-hospital versus fixed follow-up), and confounding control (unadjusted comparisons versus matching and/or survival modeling). Collectively, the existing evidence supports CKD as a clinically relevant comorbidity in ARDS. These results also underscore the value of analyses that apply consistent ARDS definitions and robust methods to address measured confounding.\u003c/p\u003e \u003cp\u003eA central contribution of this study is its emphasis on non-COVID ARDS. Since 2020, ARDS research and clinical experience have been heavily shaped by the COVID-19 pandemic, and COVID-associated ARDS has been reported to differ from non-COVID ARDS in physiologic characteristics, inflammatory biomarkers, and clinical course in several cohorts. By excluding SARS-CoV-2 positivity and COVID-19 diagnosis/history codes and by restricting the cohort to patients receiving mechanical ventilation near the ARDS diagnosis, our approach aims to improve cohort specificity and better represent clinically significant non-COVID ARDS. In that context, the persistence of an association between pre-existing CKD and mortality after matching suggests that baseline kidney disease may remain relevant for prognosis even outside COVID-driven ARDS cohorts [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSeveral biologically plausible mechanisms may explain why pre-existing CKD is associated with worse outcomes in ARDS. First, CKD is accompanied by a chronic inflammatory environment and clinically relevant immune dysfunction, which may increase susceptibility to secondary infections and sepsis and may amplify dysregulated inflammatory injury once ARDS develops [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Second, CKD is closely linked with endothelial dysfunction and accelerated vascular disease, which may worsen microvascular injury and contribute to maladaptive organ crosstalk during critical illness, potentially increasing the risk of multi-organ failure [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Third, fluid balance is central to ARDS management, and reduced renal reserve may make it harder to navigate the tight window between adequate resuscitation and avoiding volume overload. Excess fluid burden can plausibly worsen pulmonary edema and prolong ventilator dependence. Evidence from the fluid and catheter treatment trial (FACTT) supports the clinical importance of fluid strategy in acute lung injury/ARDS, where a conservative approach improved lung function and shortened ventilation/ICU duration without increasing non-pulmonary organ failures [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. CKD-related metabolic derangements (uremic toxin accumulation, acid-base disturbances) may reduce physiologic reserve during severe hypoxemia and shock, potentially lowering tolerance to ARDS-related stressors [\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. While our observational data cannot determine which pathway predominates, these mechanisms provide clinical plausibility for the consistent mortality and survival differences observed in the CKD cohort [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eClinically, pre-existing CKD may serve as a readily available marker of potentially increased early mortality risk in ventilated, non-COVID ARDS. It may help clinicians identify a subgroup that warrants monitoring with earlier prognostic discussions. In practice, this could lead to more emphasis towards kidney protection in critical care medicine; avoiding nephrotoxins when feasible, optimizing hemodynamics to preserve renal perfusion, and involving nephrology early. Fluid strategy is a particularly relevant domain, given evidence that conservative fluid management in acute lung injury/ARDS improves lung function and reduces duration of mechanical ventilation and ICU stay [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. These considerations align with established ARDS management frameworks emphasizing lung-protective ventilation and supportive ICU care, while recognizing that comorbid organ dysfunction can influence trajectories [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Importantly, our findings should be interpreted as associations; prospective studies with granular physiologic data are needed to determine whether CKD-tailored management strategies can modify outcomes in non-COVID ARDS.\u003c/p\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThis study has several limitations. As this was a retrospective observational study using a federated EHR network, our findings should be interpreted as associations rather than causation. Although propensity score matching helped balance many measured baseline differences between the cohorts, residual confounding is still possible, particularly from factors not captured in the matching process such as ARDS severity, ventilator settings, fluid balance, prone positioning, vasopressor use, nephrotoxin exposure, renal replacement therapy, and other ICU level management variables. Second, ARDS, CKD, and mechanical ventilation were identified using diagnostic and procedure codes within TriNetX rather than direct chart review, which introduces the possibility of coding error or misclassification. Third, our cohort definition was intentionally restrictive, requiring mechanical ventilation within 3 days of ARDS diagnosis and excluding patients with COVID-19 and ESRD to improve specificity for clinically significant non-COVID ARDS. However, this may limit generalizability to patients with less severe ARDS, those intubated later in their course, patients with dialysis-dependent renal failure, or those with COVID associated ARDS. In addition, CKD was evaluated as a broad stage 3\u0026ndash;5 exposure, which does not allow assessment of whether mortality risk differs across individual CKD stages or by more granular measures of kidney function. As with other TriNetX based studies, use of de-identified aggregate data also limits access to granular physiologic variables and may reflect variation in coding practices and care patterns across contributing healthcare organizations.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis retrospective propensity score-matched cohort study provides evidence that pre-existing CKD is associated with higher mortality in non-COVID ARDS patients across both short- and intermediate-term follow-up, extending insights beyond predominantly COVID-era ARDS cohorts. These results suggest that pre-existing CKD functions as an important prognostic comorbidity that may aid early risk stratification in non-COVID ARDS. Identifying patients with CKD as a high-risk population for targeted management may improve prognoses and guide the intensity of supportive care. Given the retrospective nature of data analysis and potential for residual confounding and bias, future research is warranted to confirm these associations and to determine whether CKD-specific management strategies can improve outcomes in this high-risk population.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAKI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eacute kidney injury\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eARDS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eacute respiratory distress syndrome\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ebody mass index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003econfidence interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCKD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003echronic kidney disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCOVID\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e19\u0026ndash;coronavirus disease 2019\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEHR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eelectronic health records\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eESRD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eend\u0026ndash;stage renal disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFACTT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003efluid and catheter treatment trial\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHCOs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehealthcare organizations\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHIPAA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHealth Insurance Portability \u0026amp; Accountability Act\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehazard ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eICU\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eintensive care unit\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIRB\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003einstitutional review board\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePSM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003epropensity score match\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003estandard deviation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSMD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003estandardized mean difference\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003eClinical Trial Number: Not Applicable.\u003c/p\u003e\n \u003cp\u003e \u003cstrong\u003eEthics Approval and Consent to Participate\u003c/strong\u003e \u003cp\u003eThis study is a secondary analysis of de-identified, aggregated data from the TriNetX Research Network and did not involve intervention or interaction with human participants. Because the TriNetX dataset is de-identified in accordance with the HIPAA de-identification standard (45 CFR 164.514(a)) with expert determination (45 CFR 164.514(b)(1)), ethics committee/IRB approval and informed consent to participate were not required under applicable U.S. regulations. All analyses were performed in accordance with relevant guidelines and regulations, including the principles of the Declaration of Helsinki.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eConsent for Publication\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCompeting Interests\u003c/strong\u003e \u003cp\u003eAll authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThe research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eMTA and SI designed the study, performed data analysis, and wrote parts of the manuscript; SY, HA, and NS contributed to the study design and wrote the manuscript. PS supervised the study and revised the manuscript. All authors read and approved the submitted version.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe would like to acknowledge support from Dr. Daniel Novak with the University of California Riverside School of Medicine, who contributed in important ways to the early conception of this study and has mentored us in the TriNetX database usage.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThis study used population-level aggregate and HIPAA de-identified data collected by the TriNetX platform (\u0026ldquo;US Collaborative Network\u0026rdquo;) and available from TriNetX, LLC ( [https://trinetx.com/](https:/trinetx.com) ), but third-party restrictions apply to the availability of these data. The data were used under license for this study with restrictions that do not allow for the data to be redistributed or made publicly available. To gain access to the data, a request can be made to TriNetX ( [[email protected]](mailto:[email protected]) ), but costs may be incurred, and a data-sharing agreement may be necessary. Data specific to this study, including diagnosis codes and cohort characteristics in aggregated format, are included in the manuscript as tables and figures. Data through the TriNetX platform is queried in real time. Data from the underlying electronic health records of participating healthcare organizations are refreshed in the TriNetX platform from daily to every couple of months depending on the healthcare organization.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eThe ARDS Definition Task Force. Acute Respiratory Distress Syndrome: The Berlin Definition. 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Endothelial Cell Dysfunction and Increased Cardiovascular Risk in Patients With Chronic Kidney Disease. Circul Res. 2023;132:970\u0026ndash;92.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAckermann M, Verleden SE, Kuehnel M, et al. Pulmonary Vascular Endothelialitis, Thrombosis, and Angiogenesis in Covid-19. N Engl J Med. 2020. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1056/nejmoa2015432\u003c/span\u003e\u003cspan address=\"10.1056/nejmoa2015432\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBain W, Yang H, Shah FA, et al. COVID-19 versus Non-COVID-19 Acute Respiratory Distress Syndrome: Comparison of Demographics, Physiologic Parameters, Inflammatory Biomarkers, and Clinical Outcomes. Annals Am Thorac Soc. 2021;18:1202\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBeloncle FM. 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Same but Different? Comparing the Epidemiology, Treatments and Outcomes of COVID-19 and Non-COVID-19 ARDS Cases in Germany Using a Sample of Claims Data from 2021 and 2019. Viruses. 2023;15:1324\u0026ndash;1324.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePalchuk MB, London JW, Perez-Rey D, Drebert, Zuzanna J, Winer-Jones, Jessamine P, Thompson CN, Esposito J, Claerhout B. A global federated real-world data and analytics platform for research. JAMIA Open. 2023. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/jamiaopen/ooad035\u003c/span\u003e\u003cspan address=\"10.1093/jamiaopen/ooad035\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTriNetX LLC. (2025) Publication Guidelines - TriNetX. In: TriNetX. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://trinetx.com/real-world-resources/case-studies-publications/trinetx-publication-guidelines/\u003c/span\u003e\u003cspan address=\"https://trinetx.com/real-world-resources/case-studies-publications/trinetx-publication-guidelines/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBasu RK, Wheeler DS. Kidney\u0026ndash;lung cross-talk and acute kidney injury. Pediatr Nephrol. 2013;28:2239\u0026ndash;48.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRao A, Anwar A, Agrawal A, Kichloo A, Singh J, Karki A. The Impact of Chronic Kidney Disease on In-Hospital Outcomes in Patients With Acute Respiratory Distress Syndrome. Can Respir J. 2026;2026:9063636.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHusain-Syed F, Slutsky AS, Ronco C. Lung\u0026ndash;Kidney Cross-Talk in the Critically Ill Patient. Am J Respir Crit Care Med. 2016;194:402\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFan E, Del Sorbo L, Goligher EC, Hodgson CL, Munshi L, Walkey AJ et al. An official american thoracic society/european society of intensive care medicine/society of critical care medicine clinical practice guideline: Mechanical ventilation in adult patients with acute respiratory distress syndrome. American Journal of Respiratory and Critical Care Medicine [Internet]. 2017;195(9):1253\u0026ndash;63. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.thoracic.org/statements/resources/cc/ards-guidelines.pdf\u003c/span\u003e\u003cspan address=\"http://www.thoracic.org/statements/resources/cc/ards-guidelines.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"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":"bmc-pulmonary-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pulm","sideBox":"Learn more about [BMC Pulmonary Medicine](http://bmcpulmmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pulm/default.aspx","title":"BMC Pulmonary Medicine","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Acute respiratory distress syndrome, Chronic kidney disease, Mortality, COVID-19, Mechanical ventilation, Critical care","lastPublishedDoi":"10.21203/rs.3.rs-9317583/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9317583/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAcute respiratory distress syndrome (ARDS) and chronic kidney disease (CKD) are both associated with substantial morbidity and mortality in the intensive care unit (ICU). Previous research in ARDS has evaluated patients with acute kidney injury, a large portion of whom had coronavirus disease 2019 (COVID-19)-related ARDS. The association between pre-existing CKD and outcomes in non-COVID ARDS remains unclear. We hypothesized that pre-existing CKD is associated with increased mortality in non-COVID ARDS.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eUsing the TriNetX Research Network, we conducted a retrospective cohort study comparing mortality in non-COVID ARDS adults, with- versus without- pre-existing CKD. Patients\u0026thinsp;\u0026lt;\u0026thinsp;18 years and those with current or prior COVID-19 were excluded. ARDS cases requiring mechanical ventilation within 3 days of the index encounter were included. The CKD and non-COVID cohorts were compared using 1:1 propensity score matching (PSM) for age, sex, race/ethnicity, comorbidities, and other risk factors. Mortality at 14, 30, and 120 days was evaluated using risk and hazard ratios with Kaplan-Meier curves and Cox proportional hazards models.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eIn matched cohorts, mortality was higher in the CKD cohort at 14, 30, and 120 days (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Specifically, 14-day mortality was 35.01% for CKD patients versus 28.22% in the controls (HR 1.29, 95% CI: 1.17\u0026ndash;1.43). At 30 days, the mortality risk was 44.22% and 37.10%, respectively (HR 1.26, 95% CI: 1.15\u0026ndash;1.37). Likewise, 120-day mortality was 52.64% and 43.59%, respectively (HR 1.29, 95% CI: 1.19\u0026ndash;1.40).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003ePre-existing CKD was associated with a higher risk of mortality in non-COVID ARDS patients, supporting its consideration as a prognostic comorbidity for risk stratification.\u003c/p\u003e","manuscriptTitle":"Pre-existing Chronic Kidney Disease and Mortality in Non-COVID Acute Respiratory Distress Syndrome: A Propensity-Matched Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-10 09:38:00","doi":"10.21203/rs.3.rs-9317583/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"325497584613186061960846305675685959844","date":"2026-05-04T15:45:13+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-28T16:59:26+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-26T22:57:12+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-09T10:18:34+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-07T22:16:07+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pulmonary Medicine","date":"2026-04-07T20:53:49+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-pulmonary-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pulm","sideBox":"Learn more about [BMC Pulmonary Medicine](http://bmcpulmmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pulm/default.aspx","title":"BMC Pulmonary Medicine","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1fa66b11-d3c5-4d46-9b42-0caae68b2e83","owner":[],"postedDate":"April 10th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"325497584613186061960846305675685959844","date":"2026-05-04T15:45:13+00:00","index":48,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-28T17:08:29+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-10 09:38:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9317583","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9317583","identity":"rs-9317583","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}