Health status instruments predict exacerbations of COPD: findings from the prospective TIE cohort study

Health status instruments predict exacerbations of COPD: findings from the prospective TIE 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 Article Health status instruments predict exacerbations of COPD: findings from the prospective TIE cohort study Andreas Palm, Jens Ellingsen, Kristina Bröms, Amir Farkhooy, Marieann Högman, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6334543/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Aim Identifying patients at risk for acute exacerbations of COPD (AECOPDs) is crucial to improve outcomes. We aimed to evaluate the ability of three health status instruments to predict AECOPDs. Methods A prospective cohort study of COPD patients. AECOPDs were retrieved from medical records one year before inclusion until three years after. Instruments evaluated were the modified Medical Research Council Dyspnoea scale (mMRC), the COPD Assessment Test (CAT) and the Clinical COPD Questionnaire (CCQ). Thresholds for the prediction of AECOPDs were estimated using receiver operator characteristic curves. The predictive value of each instrument and combinations of instruments were assessed by crude and multivariable Cox regression models. Results In total, 572 patients (59% women, age 69 ± 8 years, FEV 1 57 ± 18% of predicted) were included in 2014–2016. Optimal thresholds for predicting AECOPDs were estimated to be ≥ 2 for mMRC, ≥ 13 for CAT and ≥ 1.55 for CCQ. The adjusted HR (aHR) for a future AECOPD was 1.5 (95% confidence interval 1.2-2.0) for mMRC, 1.8 (1.3–2.3) for CAT, and 1.6 (1.2–2.1) for CCQ if scores were above the thresholds. When combining instruments, the aHR for a future AECOPD was 1.5 (1.0-2.3), 1.7 (1.1–2.5) and 2.1 (1.5-3.0) for one, two and three instruments above the thresholds, respectively. For ≥ 1 AECOPD during the year before inclusion, the aHR for a future AECOPD was 2.7 (2.1–3.5). Conclusion mMRC, CAT, and CCQ independently predicted AECOPDs. Combining the instruments improved the predictive value. Health sciences/Diseases Health sciences/Health care COPD exacerbations health status instruments prospective observational study mMRC CAT CCQ Figures Figure 1 Figure 2 Figure 3 INTRODUCTION An acute exacerbation of chronic obstructive pulmonary disease (AECOPD) is an acute worsening of respiratory symptoms 1 , 2 . AECOPDs are associated with unfavourable individual health outcomes but are also an economic burden to the healthcare system. Loss of lung function, reduced quality of life, increased need for hospitalisation, and premature death are all consequences of frequent exacerbations 3 . Traditionally, lung function measurements served as the foundation for assessing the severity of chronic obstructive pulmonary disease (COPD). Over the last decade, exacerbation frequency and symptom evaluation have proved crucial for evaluating COPD severity and aiding treatment decisions 2 . In Sweden, a majority of patients with chronic obstructive pulmonary disease (COPD) are cared for within primary healthcare. The modified Medical Research Council (mMRC) dyspnoea scale was the first questionnaire developed to assess dyspnoea in patients with COPD 4 . In clinical practice, two short comprehensive health status instruments, the COPD Assessment Test (CAT) for the evaluation of symptom burden in daily life 5 and the Clinical COPD Questionnaire (CCQ) 6 , 7 in the assessment of disease control, are used. In the Global Initiative of Obstructive Lung Disease (GOLD) 2024 guidelines, mMRC and CAT guide treatment decisions 2 . Identifying groups of patients at high risk of imminent AECOPDs would benefit the efficacy of healthcare management and save resources. Having inexpensive and easy “close-to-patient” measurements to identify patients at risk of future AECOPDs would be highly beneficial. Based on a three-year follow-up of 572 patients with COPD, this study aimed to evaluate the ability of three commonly used health status instruments (mMRC, CAT and CCQ), individually and in combination, to predict future AECOPDs. MATERIAL AND METHODS Study design and population The Tools Identifying Exacerbations in COPD (TIE) study is a prospective cohort study of patients with COPD in three regions in Sweden (Dalarna, Uppsala and Gävleborg) 8 , 9 . Participants were included between September 2014 and September 2016. Patients diagnosed with COPD in primary care and pulmonary outpatient clinics were identified by the International Classification of Disease version 10 (ICD-10) diagnosis codes J44.9, J44.1, J44.0 and J44.8 and evaluated for inclusion. Patients who had visited emergency departments due to COPD were invited by mail. Inclusion criteria were COPD diagnosis, post-bronchodilator forced expiratory volume in 1 second (FEV 1 )/(the highest of slow vital capacity (SVC) and forced vital capacity (FVC)) ratio 40 years, ability to perform questionnaires and physical tests. Exclusion criteria were inability to conduct the study or pronounced comorbidities, such as metastatic cancer, severe congestive heart failure or angina pectoris, or severe stroke sequels. Patients were scheduled for a physical study visit at inclusion. At the time of the study visit, all patients had a stable COPD phase at least four weeks since a previous AECOPD. Data assessment Baseline data was retrieved from questionnaires including age, sex, smoking habits (ex-smoker and current smoker), ischaemic heart disease (IHD) (reported as a history of myocardial infarction or angina pectoris) and heart failure. At the inclusion visit, weight and height were assessed, and a subsequent calculation of body mass index (BMI) and spirometry was performed with and without bronchodilatation. Lung function was graded based on spirometry according to the GOLD standards 2 . The patients completed the mMRC, CAT, and CCQ health status instruments upon inclusion. The mMRC scale is a self-rating tool to measure dyspnoea, where the degree of disability that breathlessness poses on day-to-day activities is measured on a scale from 0 to 4, where more dyspnoea yields a higher score 4 . The CAT score indicates disease control, that is, to what extent COPD symptoms affect the patients' daily life 10 . The score is 0–40 based on eight questions, where a higher score reflects more COPD-related symptoms and a lower well-being. The CCQ score assesses the level of disease control in patients with COPD based on ten questions, including symptoms from the airways (four questions), limitation of physical activity (four questions) and emotional dysfunction (two questions) 7 , 10 . Each question is scored between 0 and 6 points, and the CCQ score is the average score of all ten questions. Data on AECOPDs The primary outcome variable was AECOPD, defined as an unscheduled or scheduled healthcare visit with increased respiratory symptoms leading to inhalation of bronchodilators (at the healthcare facility), treatment with oral corticosteroids, treatment with antibiotics, referral to the emergency department, and/or hospitalisation due to COPD. Experienced healthcare personnel retrieved information about AECOPDs from medical records one year before inclusion and three years after inclusion. Statistical analyses Baseline characteristics were stratified based on the occurrence of ≥ 1 AECOPD during the year before inclusion. Categorical data were presented as frequencies and percentages. Normally distributed continuous data were expressed as mean ± SD, and non-normally distributed data were expressed as median with interquartile range (IQR). Differences between groups were analysed using the Chi-square test for categorical variables, the Student´s t-test for normally distributed, and the Wilcoxon rank-sum test for non-normally distributed continuous variables. The ability of each instrument (mMRC, CAT and CCQ) to identify future occurrence of ≥ 1 AECOPD within three years was assessed by the area under the curve (AUC) of receiver operator characteristic (ROC) curves. The optimal threshold values for mMRC, CAT total score and CCQ total mean score, respectively, were estimated based on sensitivity and specificity trade-offs using the Youden index. A combination score, denoted TIE-score, with a range of 0–3, was calculated by adding 1 point for each instrument with a score above the threshold value. Linear regression analysis estimated the correlation between CAT total score and CCQ total mean scores, and the regression equation was used to find the corresponding score. Associations between future AECOPDs and mMRC, CAT and CCQ and their combinations were analysed using crude and adjusted Cox proportional hazards regression models. Based on clinical experience and the literature (13), significant risk factors for developing AECOPD were identified and evaluated by directed acyclic graphs (DAG) (Supplemental Fig. 1; https://www.dagitty.net ). The analyses were adjusted for age, sex, BMI, and lung function expressed as FEV 1 % of predicted, IHD, HF, current smoking, and ≥ 1 AECOPD the year before inclusion 11 . The estimates of the regression analysis were visualised in a forest plot. The ability of each instrument and the TIE score to predict AECOPDs was visualised using Kaplan-Meier mortality curves, and differences between groups were analysed with the log-rank test. Estimates were presented with 95% confidence intervals (CIs). Statistical significance was defined as a two-sided p < 0.05. Statistical analyses were conducted using Stata, version 18.0 (StataCorp LP; College Station, TX 77845 USA). Ethical considerations The study was approved by the Regional Ethics Review Board in Uppsala, Sweden (Dnr 2013/358) on 28 April 2014. All participants provided written informed consent. RESULTS In total, 572 patients (59% women, 69 ± 8 years, FEV 1 56 ± 18% of predicted) were included between September 2014 and September 2016. Of these, 85% were recruited from primary care, 14% from hospital-based outpatient care and 1% from outside health care, i.e., recruited at patient association events. Baseline characteristics, stratified by the presence of AECOPD one year before inclusion, are presented in Table 1 . During the study period, 54 patients were deceased after 684 ± 282 days. The remaining patients were followed for three years after inclusion. Table 1 Patient characteristics stratified by the presence of AECOPDs the year before inclusion. No exacerbations ≥ 1 exacerbation N = 405 N = 167 Age (years) 68.6 (7.8) 68.6 (7.3) Females (%) 226 (56%) 109 (65%) Current smoker* 115 (29%) 51 (31%) WHO BMI categories** Underweight, 25–30 149 (37%) 55 (33%) Obese, > 30 96 (24%) 39 (23%) COPD severity FEV1, % predicted 59.1 (17.1) 50.2 (18.1) GOLD grade 1 48 (12%) 9 (5%) 2 234 (58%) 80 (48%) 3 103 (25%) 52 (31%) 4 20 (5%) 26 (16%) Comorbidities Heart failure 14 (3%) 15 (9%) Hypertension 191 (47%) 87 (52%) IHD 43 (11%) 19 (11%) Diabetes* 41 (10%) 13 (8%) Health status instrument scores mMRC 1.0 (1.0–2.0) 2.0 (1.0–3.0) CAT 11.0 (6.0–16.0) 15.0 (8.0–22.0) CCQ 1.4 (0.8–2.2) 2.1 (1.0-3.2) Data are presented as mean (SD) and median (IQR) for continuous measures and n (%) for categorical measures.*missing values, n = 2; **missing values, n = 3 AECOPD: acute exacerbation of COPD; BMI: body mass index; CAT: COPD assessment test; CCQ: Clinical COPD questionnaire; COPD: Chronic Obstructive Lung Disease; FEV1: forced expiratory volume in 1 second; GOLD: Global Initiative for Chronic Obstructive Lung Disease; IHD: ischaemic heart disease, mMRC: modified Medical Research Council dyspnoea scale, In total, 257 patients (45%) experienced ≥ 1 AECOPD during the study period. Based on ROC analysis, the health status instruments identified the outcome “≥1 AECOPD” over the three-year follow-up with an AUC of 0.64 (0.60–0.69; mMRC), 0.65 (0.61–0.70; CAT total score) and 0.66 (0.61–0.70; CCQ total mean score), respectively (Fig. 1 A-C). The optimal threshold value for identifying future AECOPD was estimated to be ≥ 2 for mMRC, ≥ 13 for CAT and ≥ 1.6 for CCQ. According to the CAT and CCQ instruments, individual scoring at inclusion was highly correlated (r = 0.861, p < 0.001). A CAT score of 10 corresponded to a CCQ score of 1.4 and a CCQ score of 1.5 to a CAT score of 11 based on the linear regression equation (data not shown). All health status instruments independently predicted the occurrence of ≥ 1 AECOPD within three years (Table 2 , Fig. 2 a-d). One instrument above the threshold (TIE-score 1; n = 88) resulted in an aHR of 1.5 (1.0-2.3), two instruments above the threshold (TIE-score 2; n = 137) in an aHR of 1.7 (1.1–2.5) and all three instruments above the threshold (TIE-score 3; n = 215) in an aHR of 2.2 (1.5–3.1) (Fig. 3 ). Having ≥ 1 AECOPD the year before inclusion was the strongest predictor of future AECOPDs in all models. Lower FEV 1 , current smoking, and heart failure were also identified as predictors of AECOPDs, whereas no significant associations were found between age, sex, IHD, and increased risk of future AECOPDs (Table 2 and Fig. 3 ). Table 2 Hazard ratios for independent variables for predicting AECOPDs at 3-year follow-up, crude and adjusted models. Adjusted for all variables in the table. Crude model Adjusted models HR (95% CI) aHR (95% CI) aHR (95% CI) aHR (95% CI) Age, per 10 years 1.1 (0.9–1.3) 1.0 (0.9–1.2 1.1 (0.9–1.3) 1.1 (0.9–1.3) Female sex 1.2 (0.9–1.5) 1.0 (0.8–1.3) 1.1(0.8–1.4) 1.1(0.8–1.4) Current smoking 1.0 (0.8–1.4) 1.2 (0.9–1.6) 1.1 (0.9–1.5) 1.1 (0.9–1.5) BMI categories (kg/m2) Normal weight, 18.5–25 (ref) 1 1 1 1 Underweight, 25–30 1.0 (0.7–1.3) 1.2 (0.9–1.7) 1.2 (0.9–1.6) 1.2 (0.9–1.6) Obesity, > 30 0.9 (0.7–1.3) 0.9 (0.6–1.3) 0.9 (0.7–1.3) 0.9 (0.6–1.3) FEV1, per 10%-units decrease 1.4 (1.3–1.5) 1.3 (1.2–1.4) 1.2 (1.1–1.3) 1.2 (1.1–1.3) Comorbidities Heart failure 2.1 (1.3–3.2) 1.6 (1.0-2.6) 1.5 (0.9–1.5) 1.6 (1.0-2.6) Ischaemic heart disease 1.0 (0.7–1.5) 1.6 (1.0-2.6) 0.9 (0.6–1.4) 0.9 (0.6–1.4) AECOPD the year before inclusion 4.5 (2.7–4.5) 2.7 (2.1–3.5) 2.7 (2.1–3.5) 2.8 (2.1–3.6) Health status instruments mMRC ≥ 2 2.2 (1.7–2.8) 1.6 (1.2–2.1) CAT ≥ 13 2.5 (1.9–3.2) 1.8 (1.3–2.3) CCQ ≥ 1.55 2.4 (1.8–3.1) 1.6 (1.2_2.1) AECOPD: acute exacerbation of chronic obstructive pulmonary disease; aHR: Adjusted Hazard ratio; BMI: Body Mass Index; CI: Confidence Interval; FEV1: Forced Vital Capacity in 1 Second; mMRC: modified Medical Research Council dyspnoea scale; CAT: COPD Assessment Test; CCQ: Clinical COPD Questionnaire; HR: Hazard Ratio DISCUSSION The main finding of this study was that the three health status instruments commonly used in clinical practice (mMRC, CAT, and CCQ), individually and combined, predicted AECOPDs over a three-year follow-up. We also estimated optimal threshold values for predicting AECOPDs. Combining the three health status instruments increased the predictive value. The health status instruments mMRC, CAT, and CCQ have proved valuable in assessing and monitoring deterioration during exacerbations, treatment effects, and rehabilitation in COPD 12 – 15 . According to the GOLD report, mMRC score ≥ 2 or CAT score ≥ 10 are considered thresholds for high symptom burden in COPD 2 . The mMRC dyspnoea scale only refers to dyspnoea, while both CAT and CCQ evaluate broader aspects of health status. Since some of the symptoms assessed are similar, CAT and CCQ correlate 10 . In our study population, a CAT score of 10 corresponded to a CCQ score of 1.4, similar to what has been found by others 10 , 12 . In the CAT questionnaire, one question out of eight evaluates dyspnoea (grading how out of breath you feel after walking uphill or climbing stairs). At the same time, there are two dyspnoea questions in CCQ (dyspnoea at rest or physical activity). In addition, questions regarding cough, increased sputum and limitation due to symptoms are similar in CAT and CCQ. When combining the three instruments in our current prediction model of AECOPDs, these symptoms will be counted twice and contribute to the higher predictive value. In previous studies, both mMRC 16 – 18 and CAT 19 – 22 are strong predictors of future AECOPDs, whereas the ability of CCQ to predict AECOPDs has been more ambiguous 20 , 23 . Our ROC analysis showed the optimal threshold values for predicting AECOPD up to three years was ≥ 2 for mMRC and ≥ 13 for CAT. Previous studies with shorter follow-ups of up to one year have identified slightly higher threshold values for predicting AECOPDs. Lee et al. thoroughly discuss in a retrospective study of 428 COPD patients how a CAT score of ≥ 15 corresponds to an mMRC ≥ 2 and also predicts AECOPD better than CAT ≥ 10 24 . In a small study of 121 COPD patients, Jo et al . found that a CAT score of ≥ 15 indicates an increased risk of exacerbation, while no such evidence was observed for CCQ score 20 , 21 , 25 . One possible explanation for this could be the two questions in the domain of “emotional dysfunction”, which could add variability to the data not associated with disease severity. In this study and previous studies, a history of AECOPDs was the most potent risk factor for future AECOPDs 19 , 26 , 27 . In addition, this study confirms the significance of already known risk factors for AECOPDs, worse airflow limitation measured as FEV 1 19,26,28 , and comorbid heart failure 29 , 30 . Recent studies have shown an association between being underweight and having an increased risk of AECOPDs 31 , 32 . In the present study, underweight individuals tended to have more AECOPD; however, this association did not reach statistical significance. A plausible explanation is that the study lacked sufficient power to detect such associations with only 25 patients classified as underweight with BMI < 18 kg/m². Strengths and limitations The proportion of missing data was low, which strengthened the regression analyses. Experienced healthcare personnel identified AECOPDs through primary and secondary care medical records, limiting recall bias, and there was no loss to follow-up. The majority of patients in this study, 85%, were included from primary care settings, making the results generalisable to this patient group. Conversely, the generalisability is more limited for patients with advanced disease who frequently attend hospital outpatient clinics or are admitted to inpatient wards. This study has some limitations. The assessment of AECOPDs based on medical records excludes self-managed episodes or those treated by healthcare providers not linked to the Regions´ electronic medical system, such as providers in other regions or abroad. Additionally, data relied solely on self-reports, increasing the risk of recall bias and potential misunderstandings. CONCLUSION The health status instruments commonly used to evaluate COPD patients—mMRC, CAT, and CCQ—independently predicted AECOPDs. Combining the instruments improved their predictive value. Abbreviations aHR Adjusted Hazard Ratio AECOPD Acute Exacerbations of Chronic Obstructive Pulmonary Disease AUC Area Under Curve BMI Body Mass Index CAT COPD Assessment Test CCQ Clinical COPD Questionnaire COPD Chronic Obstructive Pulmonary Disease DAG Directed Acyclic Graphs FEV 1 Forced Expiratory Volume in 1 Second FVC Forced Vital Capacity GOLD Global Initiative for Obstructive Lung Disease HR Hazard Ratio IHD Ischemic Heart Disease ICS Inhaled Corticosteroids IQR Interquartile Range mMRC Modified Medical Research Council Dyspnoea Scale ROC Receiver Operator Characteristic SD Standard Deviation TIE Tools for Identifying Exacerbations Declarations ACKNOWLEDGEMENTS The authors want to thank all participants for their contributions and all study personnel for their invaluable efforts. Author contributions AP: conceptualisation, methodology, formal analysis, writing—original draft, writing—review and editing, visualisation; JE: formal analysis, writing—review and editing; AF: writing—review and editing; K.B: writing—review and editing; M.Hö.: writing—review and editing; K.L.: writing—review and editing; B.S.: data curation, writing—review and editing; C.J: conceptualisation, methodology, writing—review and editing; A.M: conceptualisation, methodology, writing—review and editing; MHå: conceptualisation, methodology, formal analysis, writing—original draft, writing—review and editing, visualisation. All authors contributed to the interpretation of data and critically revised the manuscript for important intellectual content. All authors have read and agreed to the published version of the manuscript. Informed Consent Statement Informed consent was obtained from all subjects involved in the study. FUNDING Funding: This work was supported by the Swedish Heart and Lung Association (20230392), the Uppsala County Association against Heart and Lung Diseases, the Bror Hjerpstedt Foundation, the Regional Research Council Mid Sweden, the Centre for Research and Development, Uppsala University/Region Gävleborg, and the Centre for Clinical Research Dalarna, Uppsala University, Region Dalarna. COMPETING INTERESTS No conflicts of interest exist for the authors about the submitted manuscript. Outside the topic of the current study, AP reports lecturing activities for ResMed. JE has received personal fees for lectures from and/or served on advisory boards arranged by AstraZeneca, Chiesi, GlaxoSmithKline, and Pierre Fabre outside the submitted work. BS has received personal fees for educational activities and lectures from AstraZeneca, Boehringer Ingelheim, Novartis and GlaxoSmithKline and served on advisory boards arranged by AstraZeneca, Novartis, GlaxoSmithKline, and Boehringer Ingelheim outside the submitted work. KL has received personal fees for educational activities and lectures from AstraZeneca and Novartis. DATA SHARING STATEMENT De-identified data underlying the analyses are available upon reasonable request and approval by the National Ethical Review Authority by contacting [email protected] . The TIE steering committee encourage collaborations, and for proposals, contact the study PI at [email protected] PATIENT AND PUBLIC INVOLVEMENT Neither patients nor the public were involved in the research's design, conduct, or reporting. References Celli, B. R. et al. An Updated Definition and Severity Classification of Chronic Obstructive Pulmonary Disease Exacerbations: The Rome Proposal. 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Early and Long-term Outcomes of Older Adults after Acute Care Encounters for Chronic Obstructive Pulmonary Disease Exacerbation. Annals of the American Thoracic Society 12, 1805–1812 (2015). https://doi.org/10.1513/AnnalsATS.201504-250OC Yuan, F. Z. et al. The Relationship Between Prognostic Nutritional Indexes and the Clinical Outcomes of Patients with Acute Exacerbation of Chronic Obstructive Pulmonary Disease. International journal of chronic obstructive pulmonary disease 18, 1155–1167 (2023). https://doi.org/10.2147/COPD.S402717 Kim, T. et al. Longitudinal BMI change and outcomes in Chronic Obstructive Pulmonary Disease: a nationwide population-based cohort study. Respiratory research 25, 150 (2024). https://doi.org/10.1186/s12931-024-02788-0 Additional Declarations No competing interests reported. Supplementary Files Onlinefloatimage4.png Supplemental Figure 1: Directed acyclic graph for identification of risk factors of AECOPD to use for adjustment of Cox proportional hazards regression models AECOPD: Acute exacerbation of Chronic obstructive pulmonary disease; BMI: Body Mass Index; IHD: Ischemic heart disease; FEV1: Forced Expiratory Volume in 1 second Cite Share Download PDF Status: Posted Version 1 posted 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-6334543","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":444243412,"identity":"4f0205df-fc75-4a4d-bf18-12b0097c6405","order_by":0,"name":"Andreas Palm","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABA0lEQVRIiWNgGAWjYHACAyjN3MDwgYGBsQGEiNTC2MA4g2QtzDwMRKjnn9287cOHmjoGc/aDrZttc2xk57cfbmDmbWOw58ehReLOseKZM44dZrDsSWy7nbstzXjDmUSwlsSZuOy7kWPMzMN2gMHgAFjL4cQNEoxgLQkGB7DrkAdp+fOvjsHg/MO225bb/ifOnwHRYm+PQ4sBSAtjGzOQAbSFcduBxIYbEC2MG3C4y/BGWjFjb99hHssZD9tu9m5LBvvl4JxzEokzcNgidyN5M8OPb3Vy5vzJx2783GYHDLHjDx+8KbOx58flfSjgMUDmAc2XwK8e7CnCSkbBKBgFo2CkAgCx6V/55th8AwAAAABJRU5ErkJggg==","orcid":"","institution":"Uppsala University","correspondingAuthor":true,"prefix":"","firstName":"Andreas","middleName":"","lastName":"Palm","suffix":""},{"id":444243413,"identity":"793676a1-4bd2-4915-80c3-39d350509a19","order_by":1,"name":"Jens Ellingsen","email":"","orcid":"","institution":"Uppsala University","correspondingAuthor":false,"prefix":"","firstName":"Jens","middleName":"","lastName":"Ellingsen","suffix":""},{"id":444243414,"identity":"4a97272f-9d8f-46d5-9b2f-7993a884745d","order_by":2,"name":"Kristina Bröms","email":"","orcid":"","institution":"Uppsala University","correspondingAuthor":false,"prefix":"","firstName":"Kristina","middleName":"","lastName":"Bröms","suffix":""},{"id":444243415,"identity":"35aaa1f7-e8a3-4fdd-94fd-0c282878fe23","order_by":3,"name":"Amir Farkhooy","email":"","orcid":"","institution":"Uppsala University","correspondingAuthor":false,"prefix":"","firstName":"Amir","middleName":"","lastName":"Farkhooy","suffix":""},{"id":444243416,"identity":"3defe3c9-54bf-452a-ad40-21093d3ad052","order_by":4,"name":"Marieann Högman","email":"","orcid":"","institution":"Uppsala University","correspondingAuthor":false,"prefix":"","firstName":"Marieann","middleName":"","lastName":"Högman","suffix":""},{"id":444243417,"identity":"0ee5db13-4396-426b-b022-006cdbfba895","order_by":5,"name":"Karin Lisspers","email":"","orcid":"","institution":"Center for Clinical Research Dalarna-Uppsala University","correspondingAuthor":false,"prefix":"","firstName":"Karin","middleName":"","lastName":"Lisspers","suffix":""},{"id":444243418,"identity":"fda6c2ae-23d9-4cde-bc7a-3bc32d6684b9","order_by":6,"name":"Björn Ställberg","email":"","orcid":"","institution":"Center for Clinical Research Dalarna-Uppsala University","correspondingAuthor":false,"prefix":"","firstName":"Björn","middleName":"","lastName":"Ställberg","suffix":""},{"id":444243419,"identity":"e0bb5c3e-af8d-4af8-8406-a61ccdebd708","order_by":7,"name":"Christer Janson","email":"","orcid":"","institution":"Uppsala University","correspondingAuthor":false,"prefix":"","firstName":"Christer","middleName":"","lastName":"Janson","suffix":""},{"id":444243420,"identity":"55399d0a-fea5-42f8-a973-553214cf4800","order_by":8,"name":"Andrei Malinovschi","email":"","orcid":"","institution":"Uppsala University","correspondingAuthor":false,"prefix":"","firstName":"Andrei","middleName":"","lastName":"Malinovschi","suffix":""},{"id":444243421,"identity":"b212cc1b-03da-4d1f-a976-d1d9dd1d23da","order_by":9,"name":"Maria Hårdstedt","email":"","orcid":"","institution":"Uppsala University","correspondingAuthor":false,"prefix":"","firstName":"Maria","middleName":"","lastName":"Hårdstedt","suffix":""}],"badges":[],"createdAt":"2025-03-29 14:08:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6334543/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6334543/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82130942,"identity":"bc2d27c3-72ac-42e5-95fc-3ce403974374","added_by":"auto","created_at":"2025-05-07 05:32:57","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":288725,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea-c.\u003c/strong\u003e Receiver operating characteristic curves assessing the optimal thresholds, based on Youden's index, for the ability of mMRC (a), CAT (b), and CCQ (c) to predict future AECOPDs.\u003c/p\u003e\n\u003cp\u003eAECOPD: Acute Exacerbation of COPD; mMRC: modified Medical Research Council dyspnoea scale; CAT: COPD assessment test; CCQ: Clinical COPD questionnaire.\u003c/p\u003e\n\u003cp\u003eAUC\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6334543/v1/37760a92b39116f52eef6309.jpeg"},{"id":82130945,"identity":"6829133a-087b-4c3e-877d-ef3e97531fd4","added_by":"auto","created_at":"2025-05-07 05:32:57","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":288707,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea-d. \u003c/strong\u003eKaplan-Meier curves for the probability of AECOPD by instrument threshold values for mMRC ≥2 (a), CAT ≥13 (b), CCQ ≥1.6 (c) and for TIE-score values (d) (each instrument with a score above the threshold value adds 1 point to the TIE-score).\u003c/p\u003e\n\u003cp\u003eAECOPD: Acute exacerbation of COPD; CAT: COPD assessment test; CCQ: Clinical COPD questionnaire; FEV1: forced expiratory volume in 1 second; mMRC: modified Medical Research Council dyspnoea scale; TIE: Tools identifying Exacerbations\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6334543/v1/6c0202ef333e1802eebeaf91.jpeg"},{"id":82130948,"identity":"f3988296-2f11-4df1-8bed-8ec9e56c8c5f","added_by":"auto","created_at":"2025-05-07 05:32:57","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":232271,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot illustrating aHRs (95% CI) for AECOPDs during 3-year follow-up by different TIE scores (range 0-3) in a Cox proportional hazards regression model adjusted for age, sex, BMI, FEV\u003csub\u003e, \u003c/sub\u003eheart failure, ischemic heart disease, current smoking and AECOPD the year before study start. Each instrument with a score above the threshold value (CAT ≥13, CCQ ≥1.55, and mMRC ≥2) adds 1 point to the TIE score.\u003c/p\u003e\n\u003cp\u003eAECOPD: Acute exacerbation of COPD; BMI: body mass index; CAT: COPD assessment test; CCQ: Clinical COPD questionnaire; FEV\u003csub\u003e1\u003c/sub\u003e: forced expiratory volume in 1 second; mMRC: modified Medical Research Council dyspnoea scale; TIE: Tools identifying exacerbations\u003c/p\u003e\n\u003cp\u003eaHR\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6334543/v1/d4cbefc1182d2fb67c3df10a.jpeg"},{"id":83859117,"identity":"930cca23-1263-46dc-beb6-8b50187982b6","added_by":"auto","created_at":"2025-06-03 18:38:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1507562,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6334543/v1/77d0b7ed-904b-4700-9b51-4ad76b66c256.pdf"},{"id":82130943,"identity":"03b77f8a-9975-43ee-bbe3-7a92f1bd8528","added_by":"auto","created_at":"2025-05-07 05:32:57","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":35891,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplemental Figure 1:\u003c/strong\u003e Directed acyclic graph for identification of risk factors of AECOPD to use for adjustment of Cox proportional hazards regression models\u003c/p\u003e\n\u003cp\u003eAECOPD: Acute exacerbation of Chronic obstructive pulmonary disease; BMI: Body Mass Index; IHD: Ischemic heart disease; FEV1: Forced Expiratory Volume in 1 second\u003c/p\u003e","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6334543/v1/fde643f6c98f813c1a928c8e.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"Health status instruments predict exacerbations of COPD: findings from the prospective TIE cohort study","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eAn acute exacerbation of chronic obstructive pulmonary disease (AECOPD) is an acute worsening of respiratory symptoms \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. AECOPDs are associated with unfavourable individual health outcomes but are also an economic burden to the healthcare system. Loss of lung function, reduced quality of life, increased need for hospitalisation, and premature death are all consequences of frequent exacerbations \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTraditionally, lung function measurements served as the foundation for assessing the severity of chronic obstructive pulmonary disease (COPD). Over the last decade, exacerbation frequency and symptom evaluation have proved crucial for evaluating COPD severity and aiding treatment decisions \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. In Sweden, a majority of patients with chronic obstructive pulmonary disease (COPD) are cared for within primary healthcare.\u003c/p\u003e \u003cp\u003eThe modified Medical Research Council (mMRC) dyspnoea scale was the first questionnaire developed to assess dyspnoea in patients with COPD \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. In clinical practice, two short comprehensive health status instruments, the COPD Assessment Test (CAT) for the evaluation of symptom burden in daily life \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e and the Clinical COPD Questionnaire (CCQ) \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003ein the assessment of disease control, are used. In the Global Initiative of Obstructive Lung Disease (GOLD) 2024 guidelines, mMRC and CAT guide treatment decisions \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIdentifying groups of patients at high risk of imminent AECOPDs would benefit the efficacy of healthcare management and save resources. Having inexpensive and easy \u0026ldquo;close-to-patient\u0026rdquo; measurements to identify patients at risk of future AECOPDs would be highly beneficial. Based on a three-year follow-up of 572 patients with COPD, this study aimed to evaluate the ability of three commonly used health status instruments (mMRC, CAT and CCQ), individually and in combination, to predict future AECOPDs.\u003c/p\u003e"},{"header":"MATERIAL AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and population\u003c/h2\u003e \u003cp\u003eThe Tools Identifying Exacerbations in COPD (TIE) study is a prospective cohort study of patients with COPD in three regions in Sweden (Dalarna, Uppsala and G\u0026auml;vleborg) \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Participants were included between September 2014 and September 2016. Patients diagnosed with COPD in primary care and pulmonary outpatient clinics were identified by the International Classification of Disease version 10 (ICD-10) diagnosis codes J44.9, J44.1, J44.0 and J44.8 and evaluated for inclusion. Patients who had visited emergency departments due to COPD were invited by mail.\u003c/p\u003e \u003cp\u003eInclusion criteria were COPD diagnosis, post-bronchodilator forced expiratory volume in 1 second (FEV\u003csub\u003e1\u003c/sub\u003e)/(the highest of slow vital capacity (SVC) and forced vital capacity (FVC)) ratio\u0026thinsp;\u0026lt;\u0026thinsp;0.70, age\u0026thinsp;\u0026gt;\u0026thinsp;40 years, ability to perform questionnaires and physical tests. Exclusion criteria were inability to conduct the study or pronounced comorbidities, such as metastatic cancer, severe congestive heart failure or angina pectoris, or severe stroke sequels. Patients were scheduled for a physical study visit at inclusion. At the time of the study visit, all patients had a stable COPD phase at least four weeks since a previous AECOPD.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData assessment\u003c/h3\u003e\n\u003cp\u003eBaseline data was retrieved from questionnaires including age, sex, smoking habits (ex-smoker and current smoker), ischaemic heart disease (IHD) (reported as a history of myocardial infarction or angina pectoris) and heart failure. At the inclusion visit, weight and height were assessed, and a subsequent calculation of body mass index (BMI) and spirometry was performed with and without bronchodilatation. Lung function was graded based on spirometry according to the GOLD standards \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe patients completed the mMRC, CAT, and CCQ health status instruments upon inclusion. The mMRC scale is a self-rating tool to measure dyspnoea, where the degree of disability that breathlessness poses on day-to-day activities is measured on a scale from 0 to 4, where more dyspnoea yields a higher score \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. The CAT score indicates disease control, that is, to what extent COPD symptoms affect the patients' daily life \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. The score is 0\u0026ndash;40 based on eight questions, where a higher score reflects more COPD-related symptoms and a lower well-being. The CCQ score assesses the level of disease control in patients with COPD based on ten questions, including symptoms from the airways (four questions), limitation of physical activity (four questions) and emotional dysfunction (two questions) \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Each question is scored between 0 and 6 points, and the CCQ score is the average score of all ten questions.\u003c/p\u003e\n\u003ch3\u003eData on AECOPDs\u003c/h3\u003e\n\u003cp\u003eThe primary outcome variable was AECOPD, defined as an unscheduled or scheduled healthcare visit with increased respiratory symptoms leading to inhalation of bronchodilators (at the healthcare facility), treatment with oral corticosteroids, treatment with antibiotics, referral to the emergency department, and/or hospitalisation due to COPD. Experienced healthcare personnel retrieved information about AECOPDs from medical records one year before inclusion and three years after inclusion.\u003c/p\u003e\n\u003ch3\u003eStatistical analyses\u003c/h3\u003e\n\u003cp\u003eBaseline characteristics were stratified based on the occurrence of \u0026ge;\u0026thinsp;1 AECOPD during the year before inclusion. Categorical data were presented as frequencies and percentages. Normally distributed continuous data were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, and non-normally distributed data were expressed as median with interquartile range (IQR). Differences between groups were analysed using the Chi-square test for categorical variables, the Student\u0026acute;s t-test for normally distributed, and the Wilcoxon rank-sum test for non-normally distributed continuous variables.\u003c/p\u003e \u003cp\u003eThe ability of each instrument (mMRC, CAT and CCQ) to identify future occurrence of \u0026ge;\u0026thinsp;1 AECOPD within three years was assessed by the area under the curve (AUC) of receiver operator characteristic (ROC) curves. The optimal threshold values for mMRC, CAT total score and CCQ total mean score, respectively, were estimated based on sensitivity and specificity trade-offs using the Youden index. A combination score, denoted TIE-score, with a range of 0\u0026ndash;3, was calculated by adding 1 point for each instrument with a score above the threshold value. Linear regression analysis estimated the correlation between CAT total score and CCQ total mean scores, and the regression equation was used to find the corresponding score.\u003c/p\u003e \u003cp\u003eAssociations between future AECOPDs and mMRC, CAT and CCQ and their combinations were analysed using crude and adjusted Cox proportional hazards regression models. Based on clinical experience and the literature (13), significant risk factors for developing AECOPD were identified and evaluated by directed acyclic graphs (DAG) (Supplemental Fig.\u0026nbsp;1; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.dagitty.net\u003c/span\u003e\u003cspan address=\"https://www.dagitty.net\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The analyses were adjusted for age, sex, BMI, and lung function expressed as FEV\u003csub\u003e1\u003c/sub\u003e% of predicted, IHD, HF, current smoking, and \u0026ge;\u0026thinsp;1 AECOPD the year before inclusion \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. The estimates of the regression analysis were visualised in a forest plot. The ability of each instrument and the TIE score to predict AECOPDs was visualised using Kaplan-Meier mortality curves, and differences between groups were analysed with the log-rank test. Estimates were presented with 95% confidence intervals (CIs). Statistical significance was defined as a two-sided p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Statistical analyses were conducted using Stata, version 18.0 (StataCorp LP; College Station, TX 77845 USA).\u003c/p\u003e\n\u003ch3\u003eEthical considerations\u003c/h3\u003e\n\u003cp\u003eThe study was approved by the Regional Ethics Review Board in Uppsala, Sweden (Dnr 2013/358) on 28 April 2014. All participants provided written informed consent.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eIn total, 572 patients (59% women, 69\u0026thinsp;\u0026plusmn;\u0026thinsp;8 years, FEV\u003csub\u003e1\u003c/sub\u003e 56\u0026thinsp;\u0026plusmn;\u0026thinsp;18% of predicted) were included between September 2014 and September 2016. Of these, 85% were recruited from primary care, 14% from hospital-based outpatient care and 1% from outside health care, i.e., recruited at patient association events. Baseline characteristics, stratified by the presence of AECOPD one year before inclusion, are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. During the study period, 54 patients were deceased after 684\u0026thinsp;\u0026plusmn;\u0026thinsp;282 days. The remaining patients were followed for three years after inclusion.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePatient characteristics stratified by the presence of AECOPDs the year before inclusion.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo exacerbations\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;1 exacerbation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;405\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;167\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68.6 (7.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68.6 (7.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemales (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e226 (56%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e109 (65%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent smoker*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e115 (29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51 (31%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWHO BMI categories**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnderweight, \u0026lt;\u0026thinsp;18.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal weight, 18.5\u0026ndash;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e143 (36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62 (37%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverweight, \u0026gt;\u0026thinsp;25\u0026ndash;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e149 (37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55 (33%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObese, \u0026gt;\u0026thinsp;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e96 (24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39 (23%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCOPD severity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFEV1, % predicted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59.1 (17.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50.2 (18.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOLD grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48 (12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e234 (58%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80 (48%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e103 (25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52 (31%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 (5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26 (16%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eComorbidities\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\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 \u003cp\u003e14 (3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e191 (47%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e87 (52%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIHD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43 (11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (11%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41 (10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHealth status instrument scores\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emMRC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.0 (1.0\u0026ndash;2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.0 (1.0\u0026ndash;3.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.0 (6.0\u0026ndash;16.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.0 (8.0\u0026ndash;22.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.4 (0.8\u0026ndash;2.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.1 (1.0-3.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eData are presented as mean (SD) and median (IQR) for continuous measures and n (%) for categorical measures.*missing values, n\u0026thinsp;=\u0026thinsp;2; **missing values, n\u0026thinsp;=\u0026thinsp;3\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eAECOPD: acute exacerbation of COPD; BMI: body mass index; CAT: COPD assessment test; CCQ: Clinical COPD questionnaire; COPD: Chronic Obstructive Lung Disease; FEV1: forced expiratory volume in 1 second; GOLD: Global Initiative for Chronic Obstructive Lung Disease; IHD: ischaemic heart disease, mMRC: modified Medical Research Council dyspnoea scale,\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn total, 257 patients (45%) experienced\u0026thinsp;\u0026ge;\u0026thinsp;1 AECOPD during the study period. Based on ROC analysis, the health status instruments identified the outcome \u0026ldquo;\u0026ge;1 AECOPD\u0026rdquo; over the three-year follow-up with an AUC of 0.64 (0.60\u0026ndash;0.69; mMRC), 0.65 (0.61\u0026ndash;0.70; CAT total score) and 0.66 (0.61\u0026ndash;0.70; CCQ total mean score), respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA-C). The optimal threshold value for identifying future AECOPD was estimated to be \u0026ge;\u0026thinsp;2 for mMRC, \u0026ge;\u0026thinsp;13 for CAT and \u0026ge;\u0026thinsp;1.6 for CCQ. According to the CAT and CCQ instruments, individual scoring at inclusion was highly correlated (r\u0026thinsp;=\u0026thinsp;0.861, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). A CAT score of 10 corresponded to a CCQ score of 1.4 and a CCQ score of 1.5 to a CAT score of 11 based on the linear regression equation (data not shown).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAll health status instruments independently predicted the occurrence of \u0026ge;\u0026thinsp;1 AECOPD within three years (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea-d). One instrument above the threshold (TIE-score 1; n\u0026thinsp;=\u0026thinsp;88) resulted in an aHR of 1.5 (1.0-2.3), two instruments above the threshold (TIE-score 2; n\u0026thinsp;=\u0026thinsp;137) in an aHR of 1.7 (1.1\u0026ndash;2.5) and all three instruments above the threshold (TIE-score 3; n\u0026thinsp;=\u0026thinsp;215) in an aHR of 2.2 (1.5\u0026ndash;3.1) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Having\u0026thinsp;\u0026ge;\u0026thinsp;1 AECOPD the year before inclusion was the strongest predictor of future AECOPDs in all models. Lower FEV\u003csub\u003e1\u003c/sub\u003e, current smoking, and heart failure were also identified as predictors of AECOPDs, whereas no significant associations were found between age, sex, IHD, and increased risk of future AECOPDs (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHazard ratios for independent variables for predicting AECOPDs at 3-year follow-up, crude and adjusted models. Adjusted for all variables in the table.\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=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCrude model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eAdjusted models\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eaHR (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eaHR (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eaHR (95% CI)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, per 10 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.1 (0.9\u0026ndash;1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.0 (0.9\u0026ndash;1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.1 (0.9\u0026ndash;1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.1 (0.9\u0026ndash;1.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale sex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.2 (0.9\u0026ndash;1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.0 (0.8\u0026ndash;1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.1(0.8\u0026ndash;1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.1(0.8\u0026ndash;1.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent smoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.0 (0.8\u0026ndash;1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.2 (0.9\u0026ndash;1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.1 (0.9\u0026ndash;1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.1 (0.9\u0026ndash;1.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI categories (kg/m2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal weight, 18.5\u0026ndash;25 (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnderweight, \u0026lt;\u0026thinsp;18.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.7 (1.6\u0026ndash;4.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.5 (0.9\u0026ndash;2.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.6 (0.9\u0026ndash;2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.5 (0.9\u0026ndash;2.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverweight, \u0026gt;\u0026thinsp;25\u0026ndash;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.0 (0.7\u0026ndash;1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.2 (0.9\u0026ndash;1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.2 (0.9\u0026ndash;1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.2 (0.9\u0026ndash;1.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObesity, \u0026gt;\u0026thinsp;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.9 (0.7\u0026ndash;1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.9 (0.6\u0026ndash;1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.9 (0.7\u0026ndash;1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.9 (0.6\u0026ndash;1.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFEV1, per 10%-units decrease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.4 (1.3\u0026ndash;1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.3 (1.2\u0026ndash;1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.2 (1.1\u0026ndash;1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.2 (1.1\u0026ndash;1.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComorbidities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\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 \u003cp\u003e2.1 (1.3\u0026ndash;3.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.6 (1.0-2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.5 (0.9\u0026ndash;1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.6 (1.0-2.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIschaemic heart disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.0 (0.7\u0026ndash;1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.6 (1.0-2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.9 (0.6\u0026ndash;1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.9 (0.6\u0026ndash;1.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAECOPD the year before inclusion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.5 (2.7\u0026ndash;4.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.7 (2.1\u0026ndash;3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.7 (2.1\u0026ndash;3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.8 (2.1\u0026ndash;3.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth status instruments\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emMRC\u0026thinsp;\u0026ge;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.2 (1.7\u0026ndash;2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.6 (1.2\u0026ndash;2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAT\u0026thinsp;\u0026ge;\u0026thinsp;13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.5 (1.9\u0026ndash;3.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.8 (1.3\u0026ndash;2.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCQ\u0026thinsp;\u0026ge;\u0026thinsp;1.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.4 (1.8\u0026ndash;3.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.6 (1.2_2.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eAECOPD: acute exacerbation of chronic obstructive pulmonary disease; aHR: Adjusted Hazard ratio; BMI: Body Mass Index; CI: Confidence Interval; FEV1: Forced Vital Capacity in 1 Second; mMRC: modified Medical Research Council dyspnoea scale; CAT: COPD Assessment Test; CCQ: Clinical COPD Questionnaire; HR: Hazard Ratio\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e "},{"header":"DISCUSSION","content":"\u003cp\u003eThe main finding of this study was that the three health status instruments commonly used in clinical practice (mMRC, CAT, and CCQ), individually and combined, predicted AECOPDs over a three-year follow-up. We also estimated optimal threshold values for predicting AECOPDs. Combining the three health status instruments increased the predictive value.\u003c/p\u003e \u003cp\u003eThe health status instruments mMRC, CAT, and CCQ have proved valuable in assessing and monitoring deterioration during exacerbations, treatment effects, and rehabilitation in COPD \u003csup\u003e\u003cspan additionalcitationids=\"CR13 CR14\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. According to the GOLD report, mMRC score\u0026thinsp;\u0026ge;\u0026thinsp;2 or CAT score\u0026thinsp;\u0026ge;\u0026thinsp;10 are considered thresholds for high symptom burden in COPD \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. The mMRC dyspnoea scale only refers to dyspnoea, while both CAT and CCQ evaluate broader aspects of health status. Since some of the symptoms assessed are similar, CAT and CCQ correlate \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. In our study population, a CAT score of 10 corresponded to a CCQ score of 1.4, similar to what has been found by others \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. In the CAT questionnaire, one question out of eight evaluates dyspnoea (grading how out of breath you feel after walking uphill or climbing stairs). At the same time, there are two dyspnoea questions in CCQ (dyspnoea at rest or physical activity). In addition, questions regarding cough, increased sputum and limitation due to symptoms are similar in CAT and CCQ. When combining the three instruments in our current prediction model of AECOPDs, these symptoms will be counted twice and contribute to the higher predictive value.\u003c/p\u003e \u003cp\u003eIn previous studies, both mMRC \u003csup\u003e\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e and CAT \u003csup\u003e\u003cspan additionalcitationids=\"CR20 CR21\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e are strong predictors of future AECOPDs, whereas the ability of CCQ to predict AECOPDs has been more ambiguous \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Our ROC analysis showed the optimal threshold values for predicting AECOPD up to three years was \u0026ge;\u0026thinsp;2 for mMRC and \u0026ge;\u0026thinsp;13 for CAT. Previous studies with shorter follow-ups of up to one year have identified slightly higher threshold values for predicting AECOPDs. Lee et al. thoroughly discuss in a retrospective study of 428 COPD patients how a CAT score of \u0026ge;\u0026thinsp;15 corresponds to an mMRC\u0026thinsp;\u0026ge;\u0026thinsp;2 and also predicts AECOPD better than CAT\u0026thinsp;\u0026ge;\u0026thinsp;10 \u003csup\u003e24\u003c/sup\u003e. In a small study of 121 COPD patients, Jo \u003cem\u003eet al\u003c/em\u003e. found that a CAT score of \u0026ge;\u0026thinsp;15 indicates an increased risk of exacerbation, while no such evidence was observed for CCQ score \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. One possible explanation for this could be the two questions in the domain of \u0026ldquo;emotional dysfunction\u0026rdquo;, which could add variability to the data not associated with disease severity.\u003c/p\u003e \u003cp\u003eIn this study and previous studies, a history of AECOPDs was the most potent risk factor for future AECOPDs \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. In addition, this study confirms the significance of already known risk factors for AECOPDs, worse airflow limitation measured as FEV\u003csub\u003e1\u003c/sub\u003e \u003csup\u003e19,26,28\u003c/sup\u003e, and comorbid heart failure \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Recent studies have shown an association between being underweight and having an increased risk of AECOPDs \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. In the present study, underweight individuals tended to have more AECOPD; however, this association did not reach statistical significance. A plausible explanation is that the study lacked sufficient power to detect such associations with only 25 patients classified as underweight with BMI\u0026thinsp;\u0026lt;\u0026thinsp;18 kg/m\u0026sup2;.\u003c/p\u003e \u003cp\u003eStrengths and limitations\u003c/p\u003e \u003cp\u003eThe proportion of missing data was low, which strengthened the regression analyses. Experienced healthcare personnel identified AECOPDs through primary and secondary care medical records, limiting recall bias, and there was no loss to follow-up. The majority of patients in this study, 85%, were included from primary care settings, making the results generalisable to this patient group. Conversely, the generalisability is more limited for patients with advanced disease who frequently attend hospital outpatient clinics or are admitted to inpatient wards. This study has some limitations. The assessment of AECOPDs based on medical records excludes self-managed episodes or those treated by healthcare providers not linked to the Regions\u0026acute; electronic medical system, such as providers in other regions or abroad. Additionally, data relied solely on self-reports, increasing the risk of recall bias and potential misunderstandings.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThe health status instruments commonly used to evaluate COPD patients\u0026mdash;mMRC, CAT, and CCQ\u0026mdash;independently predicted AECOPDs. Combining the instruments improved their predictive value.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eaHR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAdjusted Hazard Ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAECOPD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAcute Exacerbations of Chronic Obstructive Pulmonary Disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAUC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eArea Under Curve\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\"\u003eCAT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCOPD Assessment Test\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCCQ\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eClinical COPD Questionnaire\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCOPD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eChronic Obstructive Pulmonary Disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDAG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDirected Acyclic Graphs\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFEV\u003csub\u003e1\u003c/sub\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eForced Expiratory Volume in 1 Second\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFVC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eForced Vital Capacity\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGOLD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGlobal Initiative for Obstructive Lung Disease\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\"\u003eIHD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIschemic Heart Disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eICS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInhaled Corticosteroids\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIQR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInterquartile Range\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003emMRC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eModified Medical Research Council Dyspnoea Scale\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eROC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eReceiver Operator Characteristic\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\"\u003eTIE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTools for Identifying Exacerbations\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eACKNOWLEDGEMENTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors want to thank all participants for their contributions and all study personnel for their invaluable efforts.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAP: conceptualisation, methodology, formal analysis, writing\u0026mdash;original draft, writing\u0026mdash;review and editing, visualisation; JE: formal analysis, \u0026nbsp;writing\u0026mdash;review and editing; \u0026nbsp;AF: writing\u0026mdash;review and editing; K.B: writing\u0026mdash;review and editing; M.H\u0026ouml;.: writing\u0026mdash;review and editing; K.L.: writing\u0026mdash;review and editing; B.S.: data curation, writing\u0026mdash;review and editing; C.J: conceptualisation, methodology, writing\u0026mdash;review and editing; A.M: \u0026nbsp;conceptualisation, methodology, writing\u0026mdash;review and editing; MH\u0026aring;: conceptualisation, methodology, formal analysis, writing\u0026mdash;original draft, writing\u0026mdash;review and editing, visualisation. All authors contributed to the interpretation of data and critically revised the manuscript for important intellectual content. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent Statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all subjects involved in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFUNDING\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFunding: This work was supported by the Swedish Heart and Lung Association (20230392), the Uppsala County Association against Heart and Lung Diseases, the Bror Hjerpstedt Foundation, the Regional Research Council Mid Sweden, the Centre for Research and Development, Uppsala University/Region G\u0026auml;vleborg, and the Centre for Clinical Research Dalarna, Uppsala University, Region Dalarna.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCOMPETING INTERESTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo conflicts of interest exist for the authors about the submitted manuscript. Outside the topic of the current study, AP reports lecturing activities for ResMed. JE has received personal fees for lectures from and/or served on advisory boards arranged by AstraZeneca, Chiesi, GlaxoSmithKline, and Pierre Fabre outside the submitted work. BS has received personal fees for educational activities and lectures from AstraZeneca, Boehringer Ingelheim, Novartis and GlaxoSmithKline and served on advisory boards arranged by AstraZeneca, Novartis, GlaxoSmithKline, and Boehringer Ingelheim outside the submitted work. KL has received personal fees for educational activities and lectures from AstraZeneca and Novartis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDATA SHARING STATEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDe-identified data underlying the analyses are available upon reasonable request and approval by the National Ethical Review Authority by contacting [email protected]. The TIE steering committee encourage collaborations, and for proposals, contact the study PI at [email protected]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePATIENT AND PUBLIC INVOLVEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNeither patients nor the public were involved in the research\u0026apos;s design, conduct, or reporting.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCelli, B. R. \u003cem\u003eet al.\u003c/em\u003e An Updated Definition and Severity Classification of Chronic Obstructive Pulmonary Disease Exacerbations: The Rome Proposal. 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Z. \u003cem\u003eet al.\u003c/em\u003e The Relationship Between Prognostic Nutritional Indexes and the Clinical Outcomes of Patients with Acute Exacerbation of Chronic Obstructive Pulmonary Disease. International journal of chronic obstructive pulmonary disease 18, 1155\u0026ndash;1167 (2023). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2147/COPD.S402717\u003c/span\u003e\u003cspan address=\"10.2147/COPD.S402717\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim, T. \u003cem\u003eet al.\u003c/em\u003e Longitudinal BMI change and outcomes in Chronic Obstructive Pulmonary Disease: a nationwide population-based cohort study. Respiratory research 25, 150 (2024). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12931-024-02788-0\u003c/span\u003e\u003cspan address=\"10.1186/s12931-024-02788-0\" targettype=\"DOI\" 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":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"COPD exacerbations, health status instruments, prospective observational study, mMRC, CAT, CCQ","lastPublishedDoi":"10.21203/rs.3.rs-6334543/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6334543/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eAim\u003c/h2\u003e \u003cp\u003eIdentifying patients at risk for acute exacerbations of COPD (AECOPDs) is crucial to improve outcomes. We aimed to evaluate the ability of three health status instruments to predict AECOPDs.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA prospective cohort study of COPD patients. AECOPDs were retrieved from medical records one year before inclusion until three years after. Instruments evaluated were the modified Medical Research Council Dyspnoea scale (mMRC), the COPD Assessment Test (CAT) and the Clinical COPD Questionnaire (CCQ). Thresholds for the prediction of AECOPDs were estimated using receiver operator characteristic curves. The predictive value of each instrument and combinations of instruments were assessed by crude and multivariable Cox regression models.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eIn total, 572 patients (59% women, age 69\u0026thinsp;\u0026plusmn;\u0026thinsp;8 years, FEV\u003csub\u003e1\u003c/sub\u003e 57\u0026thinsp;\u0026plusmn;\u0026thinsp;18% of predicted) were included in 2014\u0026ndash;2016. Optimal thresholds for predicting AECOPDs were estimated to be \u0026ge;\u0026thinsp;2 for mMRC, \u0026ge;\u0026thinsp;13 for CAT and \u0026ge;\u0026thinsp;1.55 for CCQ. The adjusted HR (aHR) for a future AECOPD was 1.5 (95% confidence interval 1.2-2.0) for mMRC, 1.8 (1.3\u0026ndash;2.3) for CAT, and 1.6 (1.2\u0026ndash;2.1) for CCQ if scores were above the thresholds. When combining instruments, the aHR for a future AECOPD was 1.5 (1.0-2.3), 1.7 (1.1\u0026ndash;2.5) and 2.1 (1.5-3.0) for one, two and three instruments above the thresholds, respectively. For \u0026ge;\u0026thinsp;1 AECOPD during the year before inclusion, the aHR for a future AECOPD was 2.7 (2.1\u0026ndash;3.5).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003emMRC, CAT, and CCQ independently predicted AECOPDs. Combining the instruments improved the predictive value.\u003c/p\u003e","manuscriptTitle":"Health status instruments predict exacerbations of COPD: findings from the prospective TIE cohort study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-07 05:32:52","doi":"10.21203/rs.3.rs-6334543/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c7d43f73-a730-424e-b584-2b7166c80abd","owner":[],"postedDate":"May 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":47296277,"name":"Health sciences/Diseases"},{"id":47296278,"name":"Health sciences/Health care"}],"tags":[],"updatedAt":"2025-06-03T18:38:19+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-07 05:32:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6334543","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6334543","identity":"rs-6334543","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}