Mortality prediction in chronic obstructive pulmonary disease comparing the GOLD 2015 and GOLD 2019 staging: a pooled analysis of individual patient data

Mortality prediction in chronic obstructive pulmonary disease comparing the GOLD 2015 and GOLD 2019 staging: a pooled analysis of individual patient data Elena García Castillo 1,41 , Tamara Alonso Pérez 1,41 , Julio Ancochea 1,17 , Maria Teresa Pastor Sanz 1 , Pere Almagro 2 , Pablo Martínez-Camblor 3 , Marc Miravitlles 4 , Mónica Rodríguez-Carballeira 2 , Annie Navarro 5 , Bernd Lamprecht 6 , Ana S. Ramírez-García Luna 7 , Bernhard Kaiser 8 , Inmaculada Alfageme 9 , Ciro Casanova 11 , Cristóbal Esteban 11 , Juan J. Soler-Cataluña 12 ,JuanP.de-Torres 13,14 , Bartolomé R. Celli 15 , Jose M. Marín 16,17 , Gerben ter Riet 18,19 ,PatriciaSobradillo 20 , Peter Lange 21 , Judith Garcia-Aymerich 22,23,24 , Josep M. Anto 22,23,24 ,AliceM.Turner 25 , MeiLan K. Han 26 , Arnulf Langhammer 27,26,28 ,SigridAnnaAalbergVikjord 27 , Alice Sternberg 29 ,LindaLeivseth 30 ,PerBakke 31 , Ane Johannessen 32 ,ToruOga 33 , Borja G. Cosío 34 , Andrés Echazarreta 35 , Nicolás Roche 36 ,Pierre-RégisBurgel 36 , Don D. Sin 37,38 , Milo A. Puhan 39 , Jose Luis López-Campos 17,40 , Laura Carrasco 40 , Joan B. Soriano 1,17 , for the 3CIA collaboration ABSTRACT In 2019, The Global Initiative for Chronic Obstructive Lung Disease (GOLD) modified the grading system for patients with COPD, creating 16 subgroups (1A–4D). As part of the COPD Cohorts Collaborative International Assessment (3CIA) initiative, we aim to compare the mortality prediction of the 2015 and 2019 COPD GOLD staging systems. We studied 17139 COPD patients from the 3CIA study, selecting those with complete data. Patients were classified by the 2015 and 2019 GOLD ABCD systems, and we compared the predictive ability for 5year mortality of both classifications. In total, 17139 patients with COPD were enrolled in 22 cohorts from 11 countries between 2003 and 2017; 8823 of them had complete data and were analysed. Mean±SD age was 63.9±9.8 years and 62.9% were male. GOLD 2019 classified the patients in milder degrees of COPD. For both classifications, group D had higher mortality. 5-year mortality did not differ between groups B and C in GOLD 2015; in GOLD 2019, mortality was greater for group B than C. Patients classified as group A and B had better sensitivity and positive predictive value with the GOLD 2019 classification than GOLD 2015. GOLD 2015 had better sensitivity for group C and D than GOLD 2019. The area under the curve values for 5-year mortality were only 0.67 (95% CI 0.66–0.68) for GOLD 2015 and 0.65 (95% CI 0.63–0.66) for GOLD 2019. The new GOLD 2019 classification does not predict mortality better than the previous GOLD 2015 system. @ERSpublications GOLD 2019 staging system created 16 subgroups. GOLD 2015 and GOLD 2019 are not strong predictors of mortality, and do not have sufficient discriminatory power to be used as a tool for risk classification of mortality in patients with COPD. https://bit.ly/3idBuaN Cite this article as: García Castillo E, Alonso Pérez T, Ancochea J, et al. Mortality prediction in chronic obstructive pulmonary disease comparing the GOLD 2015 and GOLD 2019 staging: a pooled analysis of individual patient data. ERJ Open Res 2020; 6: 00253-2020 [https://doi.org/ 10.1183/23120541.00253-2020]. Copyright ©ERS 2020. This article is open access and distributed under the terms of the Creative Commons Attribution Non-Commercial Licence 4.0. Received: 7 May 2020 | Accepted after revision: 31 July 2020 https://doi.org/10.1183/23120541.00253-2020 ERJ Open Res 2020; 6: 00253-2020 ORIGINAL ARTICLE COPD Introduction COPD is a common cause of morbidity and mortality in the world. COPD affects ∼328 million people worldwide, and COPD-related deaths amount to 4 million every year [1]. Assessment of disease severity is essential to predict prognosis and to standardise treatment regimes. The Global Initiative for Chronic Obstructive Lung Disease (GOLD) document is the most widely used treatment guide for the staging and management of COPD. The GOLD grading system for COPD has significantly evolved since first publication in 2001 to the current version in 2019. Initially, in the GOLD 2007 classification, only post-bronchodilator airflow limitation based on spirometry forced expiratory volume in 1 s (FEV 1 )was used to grade the severity of COPD [2]. Later on, some criticism arose on this grading score because it relied only upon FEV 1 , which is not a good predictor of dyspnoea, quality of life or exercise tolerance. Further, other important variables to evaluate prognosis in COPD, such as sub-phenotypes, exacerbations, dyspnoea severity or comorbidities have been proposed, among others. Therefore, GOLD 2011 proposed a classification system of four groups, ABCD, combining FEV 1 and two clinical parameters: history of exacerbations and respiratory symptoms measured by the modified Medical Research Council (mMRC) dyspnoea score, or the COPD Assessment Test score (CAT) [3]. The 2011 ABCD classification was considered an improvement in the management of patients with COPD, providing an opportunity to further guide the individualised care of these patients. GOLD 2011 predicted future exacerbations better than GOLD 2007, but there was no difference in mortality predictions or respiratory outcomes [4–7]. In 2015 an updated report was published with the same measurement parameters (FEV 1 , dyspnoea and exacerbations) than the 2011 classification [8]. The latest GOLD update, published in 2019, uses a composite of spirometry, symptoms and exacerbations, but importantly separating the spirometric 1–4 staging from the ABCD groups [9]. This separation is relevant because it is known there are differences in the rate of exacerbations for the most severe COPD patients, depending on whether the risk is based on pulmonary function tests, on the history of exacerbations or both [10]. All these classifications were initially designed not to assess prognosis, but to aid Affiliations: 1 Pneumology Dept, Hospital Universitario de la Princesa, Instituto de Investigación Hospital Universitario de la Princesa (IISP), Universidad Autónoma de Madrid, Madrid, Spain. 2 Internal Medicine Department, Mútua Terrassa University Hospital, Barcelona, Spain. 3 Geisel School of Medicine at Dartmouth, Hanover, NH, USA. 4 Pneumology Dept, Hospital Universitary Vall d’Hebron, CIBER de Enfermedades Respiratorias (CIBERES), Barcelona, Spain. 5 Pneumology Service, Hospital Universitari Mútua Terrassa, Barcelona, Spain. 6 Dept of Pulmonary Medicine, Kepler-University-Hospital, Faculty of Medicine, JohannesKepler-University Linz, Linz, Austria. 7 Facultad de Medicina, Universidad Autónoma de San Luis Potosí, San Luis Potosí, Mexico. 8 Dept of Pulmonary Medicine, Paracelsus Medical University Hospital, Salzburg, Austria. 9 Departamento de Medicina, Universidad de Sevilla, HU Virgen de Valme, Seville, Spain. 10 Pulmonology Department, Hospital Galdakao-Usansolo, Galdakao, Spain. 11 Pulmonary Department, Research Unit, Hospital Universitario Nuestra Señora de La Candelaria, Universidad de La Laguna, Tenerife, Spain. 12 Servicio de Neumología, Hospital Arnau de Vilanova, Valencia, Spain. 13 Clinica Universidad de Navarra, Pamplona, Spain. 14 Respirology and Sleep Medicine Division, Queen’s University, Kingston, Canada. 15 Pulmonary and Critical Care Medicine, Harvard University, Brigham and Women’s Hospital, Boston, MA, USA. 16 Hospital Universitario Miguel Servet, Zaragoza, Spain. 17 Centro de Investigación Biomédica en Red de Enfermedades Respiratorias (CIBERES), Instituto de Salud Carlos III, Madrid, Spain. 18 Urban Vitality –Centre of Expertise, Faculty of Health, Amsterdam University of Applied Sciences, Amsterdam, The Netherlands. 19 Dept of Cardiology, Amsterdam UMC, location Academic Medical Center, Amsterdam, The Netherlands. 20 University Hospital of Cruces in Barakaldo, Barakaldo, Spain. 21 Section of Social Medicine, Dept of Public Health, Copenhagen University, Copenhagen City Heart Study, Frederiksberg Hospital, Copenhagen, Denmark. 22 ISGlobal, Barcelona, Spain. 23 Universitat Pompeu Fabra (UPF), Barcelona, Spain. 24 CIBER Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain. 25 Institute of Applied Health Research, University of Birmingham, Edgbaston, UK. 26 Internal Medicine, Division of Pulmonary and Critical Care Medicine, University of Michigan, Ann Arbor, MI, USA. 27 Dept of Public Health and Nursing, NTNU (Norwegian University of Science and Technology), Trondheim, Norway. 28 Levanger Hospital, Nord-Trøndelag Hospital Trust, Levanger, Norway. 29 Dept of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA. 30 Centre for Clinical Documentation and Evaluation, Northern Norway Regional Health Authority, Tromso, Norway. 31 Dept of Clinical Science, University of Bergen, Bergen, Norway. 32 Dept of Global Public Health and Primary Care, University of Bergen, Bergen, Norway. 33 Dept of Respiratory Care and Sleep Control Medicine, Kyoto University, Kyoto, Japan. 34 Hospital Universitario Son Espases-IdISPa, Mallorca, Spain. 35 Servicio de Neumonología, Hospital San Juan de Dios de La Plata, Buenos Aires, Argentina. 36 Respiratory Medicine, Cochin Hospital, APHP Centre–University of Paris, Cochin Institute (INSERM UMR1016), Paris, France. 37 UBC Centre for Heart Lung Innovation, Vancouver, BC, Canada. 38 Division of Respiratory Medicine, Dept of Medicine, University of British Columbia, Vancouver, BC, Canada. 39 Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland. 40 Unidad Médico Quirúrgica de Enfermedades Respiratorias, Instituto de Biomedicina de Sevilla (IBiS), Hospital Universitario Virgen del Rocío/Universidad de Sevilla, Seville, Spain. 41 These authors contributed equally. Correspondence: Joan B. Soriano, Hospital Universitario de la Princesa, Diego de León 62, Neumología 6ª planta, 28006-Madrid, Spain. E-mail: [email protected] https://doi.org/10.1183/23120541.00253-2020 2 COPD | E. GARCÍA CASTILLO ET AL. clinicians in creating optimal treatment regimens for patients. Thus, the prognostic ability of GOLD 2019 compared to previous classifications is largely unknown, with only a few published studies [11, 12]. To address this issue, we used pooled data from 17139 patients of 22 COPD cohorts and 11 countries and compared the prognostic capacity of the 2019 versus 2015 GOLD staging classifications to predict mortality. Methods Study population In this international study, we assessed 17139 patients from the COPD Cohorts Collaborative International Assessment (3CIA) initiative. All were prospective cohorts that recruited patients within the period 1999 to 2017, except for one which was a population-based cohort. All patients had a definition of COPD characterised by spirometry, that is post-bronchodilator FEV 1 to forced vital capacity (FVC) ratio <0.7 and a clinical diagnosis of COPD. Spirometry was performed using the standards provided by the American Thoracic Society and European Respiratory Society [13]. The primary investigators of each of the participating 3CIA cohorts provided individual patient’s data for pooled analysis. We obtained a minimum individual dataset including the vital status (up to death, right truncation or 2017), age, sex, smoking status, pre-bronchodilator and post-bronchodilator FEV 1 and FVC, and dyspnoea measured with the modified mMRC, among others [14]. Only in a number of 3CIA cohorts were data of number of exacerbations in the previous year available. For the current study, we selected exclusively those cohorts in which the number of exacerbations in the previous year were available in the database, since this variable is required to calculate the GOLD 2015 and 2019 grading systems. Fifteen out of a total of 22 cohorts contained data on history of exacerbations, so that the final number of patients available to be classified according to GOLD 2015 and GOLD 2019 was 8823. Symptoms were assessed using the mMRC dyspnoea scale. To determine the risk descriptor in the 2015 grouping system, we used exacerbations history and GOLD spirometry stages. The combination of symptoms (mMRC) and the worse risk descriptor (spirometry or exacerbation history) were used to classify patients by the GOLD 2015 system. Participants were classified using the GOLD 2019 system into four grades (1–4) based on post-bronchodilator FEV 1 percentage of prediction as stage 1 (FEV 1 ⩾80), stage 2 (FEV 1 79–50), stage 3 (FEV 1 30–49) and stage 4 (FEV 1 <30). Groups ABCD were defined by self-reported severity of dyspnoea (mMRC) and number of exacerbations in the previous year. All participants provided informed written consent, and each study was conducted with the formal approval of the local ethics institutional committees following the principles of the Declaration of Helsinki. Outcomes The primary outcome was the prediction ability of all-cause mortality in the individuals by the two GOLD systems. Statistical analysis The 3CIA database manager quality-controlled all data centrally and created a clean database with a data dictionary. All implausible or missing variables were queried with the original study investigators, and data were removed from the central database if errors could not be corrected. Because the cohorts had different follow-up times, patients were right-censored at 5 years of follow-up. Descriptive statistics used mean and standard deviation for continuous variables and the number of cases and percentages for categorical variables. Comparisons between groups were performed with the Chi-squared test for categorical variables and the t-test for continuous variables. We estimated 5-year all-cause mortality, according to GOLD 2015 and 2019 staging systems, using Kaplan–Meier survival statistics. Statistical comparisons were performed using the log-rank test receiver operation characteristic (ROC) curve analyses and area under the curve (AUC) and the 95% confidence intervals of the AUC were calculated to measure the predictive accuracy for mortality. Also, we compared the prediction ability of mortality on both classifications using sensitivity, positive predictive value and the Youden’s index with Epidat 3.1 programme. Results We pooled data from 22 COPD cohorts with a total of 17139 patients, finally including 8823 patients from 15 cohorts that had all complete variables to be classified as GOLD 2015 and GOLD 2019. A comparison of baseline characteristics of included and not included patients is presented in table 1. There were statistically significant differences in many variables given the large size, but most should be considered not clinically relevant (table 1). The 8823 included patients were 62.9% male, with a mean±SD age of 63.9 ±9.8 years, body mass index 27.0±5.8 kg·m −2 and mMRC dyspnoea score of 1.8±1.4. Post-bronchodilator FEV 1 was 54.8%±22.3 of the https://doi.org/10.1183/23120541.00253-2020 3 COPD | E. GARCÍA CASTILLO ET AL. predictive value, and 6-min walk distance was 376.9±129.1 m. Based on spirometry staging, 1153 (13%) had mild (stage 1), 3711 (42.1%) had moderate (stage 2), 2654 (30.1%) had severe (stage 3) and 1301 (14.8%) had very severe (stage 4) disease. TABLE 1 Comparison of demographic and clinical characteristics in COPD Cohorts Collaborative International Assessment (3CIA) COPD patients included/excluded in this analysis Excluded Included p-value Subjects n 8316 8823 Age years 64.2±10.7 63.9±9.8 0.08 Male sex 6232 (74.9%) 5552 (62.9%) <0.001 BMI kg·m -2 26.5±4.9 27.0±5.8 <0.001 Modified MRC dyspnoea scale 1.5±1.3 1.8±1.4 <0.001 0 1647 (23.4%) 1957 (22.2%) <0.001 1 2261(32.1%) 1886 (21.4%) 2 1641 (23.3%) 1772 (20.1%) 3 688 (9.8%) 1951 (22.1%) 4 805 (11.4%) 1257 (14.3%) Six-min walk distance m 415.4±108.8 376.9±129.1 <0.001 FEV 1 post BD mL 1.7±0.7 1.6±0.8 <0.001 FEV 1 post BD % 60.8±22.1 54.8±22.3 <0.001 Smoker <0.001 Former 3989 (49.1%) 5392 (61.4%) Current 3589 (44.2%) 3174 (36.1%) Never 542 (6.7%) 222 (2.5%) Pack-years 46.4±28.8 42.1±28.3 <0.001 Cough 1103 (54.2%) 342 (43.9%) <0.001 Sputum 1159 (42.1%) 341 (43.9%) 0.353 Diabetes 354 (6.7%) 303(16.6%) <0.001 Cardiac disease 1072 (30.8%) 467 (25.9%) <0.001 Chronic bronchitis 166 (38.7%) 787 (69.5%) <0.001 Hypertension 454 (40.4%) 826 (44.8%) 0.028 Asthma 1243 (26.1%) 209 (10.7%) <0.001 Spirometry staging <0.001 1 1567 (19%) 1153 (13.1%) 2 3892 (47.1%) 3711 (42.1%) 3 2126 (25.8%) 2654 (30.1%) 4 671 (8.1%) 1301 (14.8%) Long-term oxygen therapy 119 (1.4%) 430 (4.8%) 0.259 Data are presented as n (%), mean±SD or median (interquartile range), unless otherwise stated. BMI: Body mass index; MRC: Medical Research Council; FEV 1 : forced expiratory volume in 1 s; BD: bronchodilator. 45 40 35 30 25 20 15 10 5 0ABCD 5.8% 13.6% 17.8% 40% 38.6% 37.7% 29.9% 16.4% 2015 2019 FIGURE 1 Distribution of participants by classification in Global Initiative for Chronic Obstructive Lung Disease (GOLD) 2015 and GOLD 2019. https://doi.org/10.1183/23120541.00253-2020 4 COPD | E. GARCÍA CASTILLO ET AL. The distribution of these 8823 patients according to GOLD 2015 and GOLD 2019 is presented in figure 1. With GOLD 2019 there is a shift towards the less severe staging of disease (absolute increase in stage A and B of 7.8% and 22.2% respectively; and absolute decrease in stage C and D of 7.8% and 22.2% respectively). The overall 5-year mortality rate was 18.3%. The all-cause 5-year mortality rates according to both classifications are shown in table 2. Figure 2 shows Kaplan–Meier curves for 5-year mortality according to GOLD 2015 (figure 2a) and GOLD 2019 (figure 2b). All-cause mortality at 5 years in the 2015 GOLD classification was higher in grade D, followed by grades B, C (with similar mortality), and finally grade A, log-rank test p<0.001 (table 2 and figure 2a). Grade D diverged from the beginning of follow-up, while grades B and C diverged after 1 year of follow-up. In GOLD 2019, the four Kaplan–Meier curves diverge during the first year, but interestingly, grade B had higher mortality than grade C, so mortality was higher in groups B and D (more symptoms) than in groups A and C (fewer symptoms; figure 2b). The degree of obstruction measured by FEV 1 % further subclassified patients into 16 subgroups with different mortality rates, increasing from 1A to 4D in the GOLD 2019 grading system (table 3). Figure 3 shows Kaplan–Meier curves for each of the spirometry strata. The higher mortality of group B over group C persisted in each of the strata with the exception of spirometry strata 1, with a higher mortality in group C. Similarly, to GOLD 2015, in GOLD 2019, grades A and D had the lowest and highest mortality, respectively, with very similar absolute numbers (table 2, figure 2). The primary outcome, the prediction capacity as measured by the AUC of ROC curve for mortality up to 5 years, was intermediate (<0.70) for both systems (figure 4). GOLD 2015 exhibited slightly better discrimination in predicting mortality (AUC 0.67, 95% CI 0.66–0.68) than the GOLD 2019 classification (AUC 0.64, 95% CI 0.63–0.66). TABLE 2 Mortality risk among COPD patients according to Global Initiative for Chronic Obstructive Lung Disease (GOLD) 2019 and GOLD 2015 classifications GOLD 2015 5-year mortality % GOLD 2019 5-year mortality % Group A 5.8 7.5 Group B 13.8 23.2 Group C 14.1 14.8 Group D 30.8 32.8 A GOLD 2015 GOLD 2019 B C D A B C D 1.0a) b) 0.8 0.6 0.4 0.2 0.0 Cumulative survival 1.0 0.8 0.6 0.4 0.2 0.0 Cumulative survival Patients at risk A B C D 2388 1259 1129 3229 2285 1184 1070 2962 2129 1045 968 2609 1914 876 875 2128 1420 608 693 1525 937 365 563 977 0 1224364860 Follow-up months Patients at risk A B C D 3039 3053 478 1435 2909 2854 446 1292 2687 2539 410 1115 2419 2100 370 904 1806 1453 307 680 1238 873 262 469 0 1224364860 Follow-up months Log rank test p<0.01 Log rank test p<0.01 FIGURE 2 Kaplan–Meier survival curves by a) Global Initiative for Chronic Obstructive Lung Disease (GOLD) 2015 and b) GOLD 2019. https://doi.org/10.1183/23120541.00253-2020 5 COPD | E. GARCÍA CASTILLO ET AL. TABLE 3 Five-year all-cause mortality (%) among spirometry strata within Global Initiative for Chronic Obstructive Lung Disease (GOLD) 2019 ABCD classification GOLD 2019 A B C D Spirometry I 3.4 9.8 16.2 5.5 Spirometry II 7 14.9 8.3 22.1 Spirometry III 13 23.7 17.7 32.6 Spirometry IV 16.7 39.1 29.5 46 1.0a) b) c) d) 0.8 0.6 0.4 0.2 0.0 Patients at risk A B C D A B C D A B C D A B C D A B C D 756 188 47 40 721 176 40 38 674 158 38 37 604 131 35 34 424 77 25 27 219 35 20 15 0 1224364860 Follow-up months Patients at risk A B C D 1659 1094 236 398 1591 1029 223 360 1481 907 205 317 1335 760 184 247 1020 542 152 180 738 338 132 129 0 1224364860 Follow-up months Patients at risk A B C D 539 1166 139 594 518 1090 130 535 459 985 121 469 413 814 111 400 309 567 98 308 233 351 80 217 01224364860 Follow-up months Patients at risk A B C D 85 605 55 399 79 559 52 358 73 489 45 291 67 395 40 223 53 267 32 165 48 149 30 108 01224364860 Follow-up months Log rank test p<0.01 Log rank test p<0.01 Log rank test p<0.01 Log rank test p<0.01 Cumulative survival 1.0 0.8 0.6 0.4 0.2 0.0 Cumulative survival 1.0 0.8 0.6 0.4 0.2 0.0 Cumulative survival 1.0 0.8 0.6 0.4 0.2 0.0 Cumulative survival FIGURE 3 Kaplan–Meier survival curves by Global Initiative for Chronic Obstructive Lung Disease (GOLD) 2019 and spirometry subgroups. a) Spirometry subgroup I; b) spirometry subgroup II; c) spirometry subgroup III; d) spirometry subgroup IV. https://doi.org/10.1183/23120541.00253-2020 6 COPD | E. GARCÍA CASTILLO ET AL. Regarding sensitivity parameters, both classifications had quite shallow values. GOLD 2019 had a higher sensitivity for predicting mortality on A and B groups versus the 2015 classification (19.1 versus 11.1 and 43.6 versus 11.6). On the other hand, GOLD 2015 had a higher sensitivity on groups C and D versus the 2019 classification (14.6 versus 6.6 and 62.5 versus 30.6) based on overlapping 95% confidence intervals (table 4). The positive predictive values were also higher in GOLD 2019 group A and B versus the same groups in GOLD 2015 (9.5 versus 6.9 and 21.2 versus 13.3); but not different (overlapping confidence intervals) in groups C and D. The Youden indices were quite low for both classifications, even with negative values, showing that it is not an optimal classification system to assess mortality. Discussion Our study evaluates mortality according to the last two GOLD classifications, and it is one of the most extensive to date. The most important finding is that the new GOLD 2019 classification (based on symptoms or exacerbations along with stages spirometry analysed altogether) did not predict 5-year mortality better than the GOLD 2015 classification (based on spirometry, history of exacerbation and symptoms). How to define and stage COPD exacerbations is a matter of intense, long debate [15]. Most 3CIA individual cohorts used subsequent iterations of GOLD-accepted definitions in their protocols, mostly based on RODRIGUEZ-ROISIN et al.’s [16] seminal paper, including mild (symptom-only) exacerbations. However, when pooling for 3CIA, we only focused on those COPD exacerbations that required health services use, emergency room admission or death. In the GOLD 2019 classification, subgroups B and D had the worst mortality, highlighting that the higher burden of symptoms conveys a higher mortality independently of spirometry (table 2 and figure 2b). Finally, we show an important shift in the proportions of patients between the 2015 and 2019 ABCD grades, with milder severity in GOLD 2019. FIGURE 4 Receiver operating curves for all-cause mortality at 5 years follow-up. 1.00 GOLD 2015 AUC 0.67 (95% CI 0.66–0.68) GOLD 2019 AUC 0.65 (95% CI 0.63–0.66) Reference line 0.75 0.50 0.25 0.00 0.00 0.25 0.50 0.75 1.00 1-specificity Sensitivity TABLE 4 Accuracy for predicting mortality of the ABCD groups classifications by Global Initiative for Chronic Obstructive Lung Disease (GOLD) 2015 and GOLD 2019 schemes Classification Sensitivity (95% CI) PPV (95% CI) Youden index (95% CI) GOLD 2015 group A 11.1 (9.6–12.7) 6.9 (5.9–7.9) −0.23 (−0.25–−0.22) GOLD 2015 group B 11.6 (10–13.2) 13.3 (11.5–15.1) −0.06 (−0.08–−0.04) GOLD 2015 group C 14.6 (12.8–16.4) 20.2 (17.9–22.6) +0.01 (−0.01–+0.03) GOLD 2015 group D 62.5 (60.1–64.9) 29.5 (28–31.1) +0.28 (+0.25–+0.31) GOLD 2019 group A 19.1 (17.2–21.1) 9.5 (8.4–10.5) −0.23 (−0.25–−0.21) GOLD 2019 group B 43.6 (41.1–46.1) 21.2 (19.8–22.6) +0.06 (+0.03–+0.09) GOLD 2019 group C 6.6 (5.3–7.8) 21.7 (18–25.5) +0.01 (0.0–+0.02) GOLD 2019 group D 30.6 (28.3–32.8) 32.8 (30.4–35.2) +0.16 (+0.14–+0.18) PPV: positive predictive value. https://doi.org/10.1183/23120541.00253-2020 7 COPD | E. GARCÍA CASTILLO ET AL. GOLD 2015 and GOLD 2019 classifications had a low discriminatory power as per the AUCs, ranging from 0.67 to 0.65, and similar to other studies (by consensus, AUCs below 0.70 are considered low or weak) [11]. Sensitivity and positive predictive values indicate that the general performance of the two models is similar and very low. Also, the Youden’s indices are very low with negative values that have no meaningful interpretation in practice. These findings support the results of other studies, suggesting that GOLD classification is not a good predictor for mortality [11, 12]. There may be various reasons for these poor results. The main reason is that these classifications were conceived to guide treatment, so it is not surprising that their capacity for predicting mortality is low. There are clinical phenotypes such as the asthma-COPD, the frequent exacerbator with emphysema or chronic bronchitis comorbidities and different indexes, that are significant predictors of mortality and are not entirely included in the GOLD stages [17–21]. In our study, sensitivity and positive predictive value are higher in GOLD 2019 groups A and B. On the contrary, sensitivity is higher in GOLD 2015 in groups C and D. These results suggest that GOLD 2019 predicts slightly better mortality in low-risk groups (A and B) and GOLD 2015 in high-risk groups (C and D), requiring more studies to corroborate it. The discriminatory power of GOLD 2019 was lower than GOLD 2015 as shown in AUC values. The partition of FEV 1 as a direct classifier in GOLD 2019 reduced ability to discriminate survival, highlighting the need to consider the severity of airflow obstruction in assessing mortality risk. When using GOLD 2019 with a composite of spirometry, exacerbations and symptoms (16 subgroups 1A to 4D classification), we found an increase in all-cause mortality between GOLD 2019 stage 1 and GOLD 2019 stage 4 across grades A, B and D, highlighting the persisting importance of FEV 1 as a predictor of mortality (table 3 and figure 3). In group C, mortality was higher in spirometry stage 1 than in stage 2, probably due to a significant difference of the proportion in patients between the two stages. Our study confirms that patients classified as GOLD A had the best survival, and patients with GOLD D had the higher mortality in both classifications [22–24]. Mortality of groups B and C in GOLD 2015 lay in between A and D groups and often overlapped. In GOLD 2019 mortality was significantly higher in group B patients than in group C. This finding is similar to other previous reports published [6, 23, 25], showing that group B is an intermediateto high-risk group associated with more exacerbations and likely with other comorbidities that may cause dyspnoea (such as heart failure). Furthermore, we show that the burden of symptoms (represented by groups B and D) have a prognostic value additive but independent to spirometry. The current study demonstrates that the use of the GOLD 2019 classification scheme shifted patients with COPD to groups of milder severity compared with GOLD 2015. This happened in keeping with previous reports, but in a smaller proportion of patients (30% of patients reassigned to group A or B compared to 53% in LEE et al. [25] study or 66% in TAN et al. [26] study). The further distribution of spirometric parameters from two categories in GOLD 2015 (FEV 1 less or higher than 50%) to four categories in GOLD 2019 was one of the possible reasons for the patients’shift from C and D in GOLD 2015 to A and B groups in GOLD 2019. This phenomenon is opposite to the one observed with the use of the revised GOLD 2011 classification, which shifted the patients from the GOLD 2007 classification towards more advanced stages of the disease (D group increased almost three times) [7]. Remarkably, in both classifications, group C was consistently the smallest group (figure 1), as seen in other reports [23]. The implication of progressively milder disease classifications for the treatment choices clinicians make in real life practice guided by GOLD is not yet clear given the recent nature of the latest GOLD iteration but will be important to monitor. The strengths of our study include a large sample size, the study design (a pooled analysis of individual patient data from several cohorts) and the different degrees of severity of patients from different cohorts, maximising its high external validity. Prospective data collection of spirometry with a post-bronchodilator test, dyspnoea by mMRC scale, history of exacerbation and mortality enabled direct classification of patients by the 2015 and 2019 GOLD staging schemes. We also have a significant representation of women, which other COPD studies might not have achieved [28]. Our study has several limitations. First, although we started with 17139 patients with COPD, a considerable number were excluded because of missing information on key variables, mainly regarding the history of exacerbations. These missing data are unlikely to affect the validity of our results, as we can see in our analysis comparing included and non-included patients. Second, the mortality analysis used all-cause death, and we have no data regarding specific causes of death (this data was not collected consistently in all cohorts). Third, symptoms were only evaluated using the mMRC dyspnoea score, but not with the COPD Assessment Test, or other instruments [29]; however, that is in line with other reported cohorts [30, 31]. Fourth, most patients came from hospital-based cohorts, so we likely have more patients with moderate to severe disease and less patients with mild and moderate disease compared to an https://doi.org/10.1183/23120541.00253-2020 8 COPD | E. GARCÍA CASTILLO ET AL. outpatient setting, or a primary care population. Indeed, the 22 cohorts from 11 countries in 3CIA within our initiative are only a sample representing the estimated 300 million COPD patients worldwide [32]. Finally, the recruitment timeframe was long (10 years), so evolving treatment recommendations might influence results. 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