
==== Front
eClinicalMedicine
EClinicalMedicine
eClinicalMedicine
2589-5370
Elsevier

S2589-5370(24)00365-1
10.1016/j.eclinm.2024.102786
102786
Articles
Development and validation of a mortality risk prediction model for chronic obstructive pulmonary disease: a cross-sectional study using probabilistic graphical modelling
Lovelace Tyler C. ab
Ryu Min Hyung c
Jia Minxue ab
Castaldi Peter c
Sciurba Frank C. d
Hersh Craig P. c
Benos Panayiotis V. pbenos@ufl.edu
abe∗
a Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
b Joint CMU-Pitt PhD Program in Computational Biology, Pittsburgh, PA, USA
c Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA
d Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
e Department of Epidemiology, University of Florida, Gainesville, FL, USA
∗ Corresponding author. University of Florida, 2004 Mowry Rd, Gainesville, FL, 32610, USA. pbenos@ufl.edu
22 8 2024
9 2024
22 8 2024
75 10278627 2 2024
22 7 2024
26 7 2024
© 2024 The Authors
2024
https://creativecommons.org/licenses/by-nc/4.0/ This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Summary

Background

Chronic Obstructive Pulmonary Disease (COPD) is a leading cause of mortality. Predicting mortality risk in patients with COPD can be important for disease management strategies. Although all-cause mortality predictors have been developed previously, limited research exists on factors directly affecting COPD-specific mortality.

Methods

In a retrospective study, we used probabilistic graphs to analyse clinical cross-sectional data (COPDGene cohort), including demographics, spirometry, quantitative chest imaging, and symptom features, as well as gene expression data. COPDGene recruited current and former smokers, aged 45–80 years with >10 pack-years smoking history, from across the USA (Phase 1, 11/2007-4/2011) and invited them for a follow-up visit (Phase 2, 7/2013-7/2017). ECLIPSE cohort recruited current and former smokers (COPD patients and controls from USA and Europe), aged 45–80 with smoking history >10 pack-years (12/2005-11/2007). We applied graphical models on multi-modal data COPDGene Phase 1 participants to identify factors directly affecting all-cause and COPD-specific mortality (primary outcomes); and on Phase 2 follow-up cohort to identify additional molecular and social factors affecting mortality. We used penalized Cox regression with features selected by the causal graph to build VAPORED, a mortality risk prediction model. VAPORED was compared to existing scores (BODE: BMI, airflow obstruction, dyspnoea, exercise capacity; ADO: age, dyspnoea, airflow obstruction) on the ability to rank individuals by mortality risk, using four evaluation metrics (concordance, concordance probability estimate (CPE), cumulative/dynamic (C/D) area under the receiver operating characteristic curve (AUC), and integrated C/D AUC). The results were validated in ECLIPSE.

Findings

Graphical models, applied on the COPDGene Phase 1 samples (n = 8610), identified 11 and 7 variables directly linked to all-cause and COPD-specific mortality, respectively. Although many appear in both models, non-lung comorbidities appear only in the all-cause model, while forced vital capacity (FVC %predicted) appears in COPD-specific mortality model only. Additionally, the graph model of Phase 2 data (n = 3182) identified internet access, CD4 T cells and platelets to be linked to lower mortality risk. Furthermore, using the 7 variables linked to COPD-specific mortality (forced expiratory volume in 1 s/forced vital capacity (FEV1/FVC) ration, FVC %predicted, age, history of pneumonia, oxygen saturation, 6-min walk distance, dyspnoea) we developed VAPORED mortality risk score, which we validated on the ECLIPSE cohort (3-yr all-cause mortality data, n = 2312). VAPORED performed significantly better than ADO, BODE, and updated BODE indices in predicting all-cause mortality in ECLIPSE in terms of concordance (VAPORED [0.719] vs ADO [0.693; FDR p-value 0.014], BODE [0.695; FDR p-value 0.020], and updated BODE [0.694; FDR p-value 0.021]); CPE (VAPORED [0.714] vs ADO [0.673; FDR p-value <0.0001], BODE [0.662; FDR p-value <0.0001], and updated BODE [0.646; FDR p-value <0.0001]); 3-year C/D AUC (VAPORED [0.728] vs ADO [0.702; FDR p-value 0.017], BODE [0.704; FDR p-value 0.021], and updated BODE [0.703; FDR p-value 0.024]); integrated C/D AUC (VAPORED [0.723] vs ADO [0.698; FDR p-value 0.047], BODE [0.695; FDR p-value 0.024], and updated BODE [0.690; FDR p-value 0.021]). Finally, we developed a web tool to help clinicians calculate VAPORED mortality risk and compare it to ADO and BODE predictions.

Interpretation

Our work is an important step towards improving our identification of high-risk patients and generating hypotheses of potential biological mechanisms and social factors driving mortality in patients with COPD at the population level. The main limitation of our study is the fact that the analysed datasets consist of older people with extensive smoking history and limited racial diversity. Thus, the results are relevant to high-risk individuals or those diagnosed with COPD and the VAPORED score is validated for them.

Funding

This research was supported by 10.13039/100000002 NIH [NHLBI, NLM]. The COPDGene study is supported by the 10.13039/100008184 COPD Foundation , through grants from 10.13039/100004325 AstraZeneca , Bayer Pharmaceuticals, 10.13039/100001003 Boehringer Ingelheim , 10.13039/100004328 Genentech , 10.13039/100004330 GlaxoSmithKline , 10.13039/100004336 Novartis , 10.13039/100004319 Pfizer and 10.13039/100009655 Sunovion .

Keywords

COPD mortality
Graphical models
Machine learning
==== Body
pmc Research in context

Evidence before this study

Identification of risk factors of and factors causally associated with COPD mortality is very important and has the potential of improving people's health. A PubMed search with terms like (“COPD and “cause-specific mortality”) and (“score” or “index” or “develop” or “validation”) published between January 1, 1950 and December 31, 2023 resulted in 44 papers, most of which identified a handful of factors correlated with mortality in people with COPD (all causes); or studied the effect of comorbidities in COPD mortality. Finally, a limited number of available predictors of all-cause mortality (GOLD, BODE, COTE, DOSE) have also been validated in COPD-specific mortality data.

Added value of this study

Based on the literature search, this is the first study to utilize a data-driven approach to distinguish factors directly linked to all-cause and COPD-specific mortality. This was made possible with the use of novel causal graph methods we developed. We discovered that forced vital capacity (FVC) %predicted measurement is connected only to COPD-specific mortality; and that high platelet and resting memory CD4 cells and low plasma cells in the blood are linked to lower mortality risk. Finally, we developed a new improved score, VAPORED, trained on COPD-specific mortality data, to assess COPD mortality risk up to 10-yrs. VAPORED performs significantly better than existing methods like BODE and ADO.

Implications of all the available evidence

Our findings generate new hypotheses and open the way for further research. The observation that FVC %predicted may be directly linked to the COPD-specific but not the all-cause mortality might indicate that hyperinflation (i.e., residual volume) affects mortality in patients with COPD and offers a potential intervention point for future disease management strategies. Furthermore, the new VAPORED scoring system, which consists of seven easily measured variables, can help clinicians assess more accurately the mortality risk of COPD patients. However, the study cohort limits the application of VAPORED risk prediction to older individuals with substantial smoking history; so further validation is needed to younger individuals with less smoking history. Also, further validation is needed on more racially diverse cohorts.

Introduction

Chronic Obstructive Pulmonary Disease (COPD) is a leading cause of mortality worldwide.1 Predictive models of mortality in COPD can be used to identify high-risk individuals who may benefit from earlier or targeted interventions. Current models that predict mortality risk in individuals with COPD include the BODE (BMI, airflow obstruction, dyspnoea, exercise capacity) index2 and its updated3 or expanded variants,4 the ADO (age, dyspnoea, airflow obstruction) index,3 and the DOSE (dyspnoea, airflow obstruction, smoking status, exacerbation frequency) index.5 Of these, the ADO and updated BODE indices were found to perform best in a large-scale meta-analysis in external validation cohorts.6 In addition to these simple clinical predictors, more complex machine-learning approaches that incorporate clinical, demographic, and imaging features have demonstrated improved prediction of all-cause mortality.7,8

These approaches have two key limitations which we seek to address. First, traditional machine learning methods, such as regression models9,10 and random survival forests (RFs),11 identify purely associative predictors and cannot provide insights into the possible causality of the observed interactions. In contrast, probabilistic graphs (referred to in the literature as “causal graphs”)12 seek to learn potential population level cause–effect relationships from observational datasets,13 by considering and factoring out any confounders. In essence they constitute multiple causal mediation analyses of all variable pairs, conditioned on all factors and all combinations of factors. In biomedical settings, such approaches have been applied to identify direct effectors of an outcome and to develop efficient predictors14,15 or predict effects of interventions.16, 17, 18 Here, we use a recently developed algorithm (Supplementary Methods) to construct probabilistic graph models from multi-modal data (i.e., demographic, clinical, spirometry, chest CT scan, and biological features) to identify predictors that provide independent information for COPD-specific mortality.

Second, existing mortality predictors are trained, calibrated, and validated on all-cause rather than COPD-specific mortality of patients with COPD. This introduces the possibility of incorrectly estimating the degree to which general risk factors contribute to COPD-specific mortality; or may introduce spurious associations due to the presence of comorbidities that act independently of the COPD-specific risk. Graph models, by construction, consider all confounder combinations and factor out the indirect effects and simple correlations. Here, we construct and compare separate graph models of all-cause and COPD-specific mortality. This allows us to disentangle features that are strongly independently informative to COPD-specific mortality from features informative of all-cause mortality. Furthermore, direct effectors identified by the graph models can be used to construct robust predictors of COPD-specific mortality risk. To further investigate blood-derived molecular signatures affecting mortality in patients with COPD, we additionally construct graph models of all-cause mortality utilizing features derived from whole blood samples.

Methods

This study adhered to the STROBE guidelines for cohort studies.

The COPDGene Study was approved by the Institutional Review Boards at all study centers. Participants provided written informed consent.

Study population and features

The discovery cohorts were derived from the COPD Genetic Epidemiology (COPDGene) Study (NCT00608764), which recruited 10,198 current and former smokers, aged 45–80, with >10 pack-years smoking history from across US19 from November 2007 to April 2011. Various demographic, clinical, spirometry, and chest CT scan features were collected. Additionally, all-cause and COPD-specific mortality was recorded. A death was attributed to COPD if its cause was adjudicated to be COPD-related by the COPDGene criteria20 adapted from the TORCH UCD.21 Under these criteria, deaths from correlated comorbidities, such as cardiovascular disease or cancer, are excluded. The features included in our mortality graph model analyses are key demographics (e.g., age, sex, race), clinical measures (e.g., BMI, resting oxygen saturation (SaO2)), measures of COPD-related physiology, and symptoms), and quantitative chest CT scan features (e.g., %emphysema of the lungs,22 segmental airway wall thickness (AWT)22). Relevant medical history and comorbidities (smoking status, pneumonia, diabetes) were also included (Supplementary Table S4 for full list). Our final COPDGene Phase 1 study cohort consisted of the 8610 participants who had no missing values in the set of selected baseline features and longitudinal follow up data for all-cause and COPD-specific mortality (Fig. 1A).Fig. 1 Study flowchart showing the number of patients considered and excluded for the COPDGene Phase 1 study cohort (A), COPDGene Phase 2 study cohort (B), and ECLIPSE study cohort (C).

COPDGene participants returned for a 5-yr follow-up visit (from July 2013 to July 2017), during which blood-derived biological features were measured (haemoglobin levels, platelet counts, white blood cell differential percentages, whole blood gene expression).23 To identify biological signatures linked to all-cause mortality in COPD, we constructed the COPDGene Phase 2 study cohort, that contains these blood-derived features in addition to the Phase 1 COPDGene measurements (Supplementary Table S5 for full list). The RNA-seq data was processed using CIBERSORTx24 and the leukocyte signature matrix (LM22)25 to infer more detailed cell type proportions. VIPER26 was used to infer transcription factor activity. This resulted in 3182 participants with longitudinal follow up data for all-cause mortality, RNA-seq gene expression profiles, and no missing values in the set of selected features (Fig. 1B).

The ECLIPSE study27 (NCT00292552) was used for external validation of our findings. ECLIPSE recruited 2501 current and former smokers (COPD patients and controls), aged 45–80 with smoking history >10 pack-years in US and Europe (December 2005 to November 2007). We selected individuals with 3-year mortality data and no missing values among our selected features (n = 2312; Fig. 1C). Table 1 presents the main characteristics of the cohorts, while Supplementary Table S1 presents the differences in characteristics between the two COPDGene cohorts.Table 1 Clinical characteristics of the COPDGene Phase1 and Phase 2 study cohorts, used for the construction of graphical models of all-cause and COPD-specific mortality.

Characteristic	COPDGene Phase 1	COPDGene Phase 2	ECLIPSE	p-value	
Number of samples	8610	3182	2312		
Number of events					
 All-cause mortality	2318	510	189		
 COPD-specific mortality	694				
Age, mean (SD), years	59.99 (9.01)	65.45 (8.61)	62.22 (7.93)	<0.0001	
Sex, Male, number (%)	4509 (52.4)	1624 (51.04)	1493 (64.6)	<0.0001	
Sex, Female, number (%)	4101 (47.6)	1558 (48.96)	819 (35.4)		
Race, African American, number (%)	2624 (30.5)	826 (25.96)	40 (1.7)	<0.0001	
Smoking Status, Current Smoker, number (%)	4347 (50.5)	1167 (36.68)	686 (29.7)	<0.0001	
BMI, mean (SD), kg/mˆ2	28.83 (6.19)	28.98 (6.27)	26.57 (5.44)	<0.0001	
ATS Pack Years, mean (SD), years	44.41 (24.91)	43.82 (23.99)	46.22 (26.89)	0.013	
FEV1% Predicted, mean (SD)	76.39 (25.29)	79.04 (24.09)	56.71 (25.95)	<0.0001	
FVC% Predicted, mean (SD)	87.11 (17.99)	87.48 (17.58)	84.12 (22.21)	<0.0001	
FEV1/FVC Ratio, mean (SD)	0.67 (0.16)	0.68 (0.15)	49.57 (16.18)	<0.0001	
GOLD Stage, number (%)				<0.0001	
 0	3695 (42.9)	1407 (44.22)	325 (14.1)		
 1	691 (8)	330 (10.37)	0 (0)		
 2	1691 (19.6)	612 (19.23)	868 (37.6)		
 3	1008 (11.7)	306 (9.62)	851 (36.8)		
 4	483 (5.6)	119 (3.74)	267 (11.6)		
PRISm	1042 (12.1)	408 (12.82)	0 (0)		
mMRC Dyspnoea Score, mean (SD)	1.34 (1.44)	1.11 (1.36)	1.47 (1.11)	<0.0001	
6-min Walk Distance, mean (SD), m	414.06 (120.74)	402.11 (131.92)	416.13 (161.25)	<0.0001	
Resting SaO2, mean (SD), %	96.16 (2.79)	96.17 (2.67)	94.95 (2.92)	<0.0001	
Cardiovascular Disease, number (%)	1419 (16.5)	665 (20.9)	641 (27.7)	<0.0001	
Diabetes, number (%)	1110 (12.9)	517 (16.25)	224 (9.7)	<0.0001	
BODE, mean (SD)	1.81 (2.25)	1.54 (2.06)	2.74 (2.24)	<0.0001	
Updated BODE, mean (SD)	2.68 (3.54)	2.64 (3.62)	3.63 (3.65)	<0.0001	
ADO, mean (SD)	2.66 (1.85)	3 (1.65)	3.38 (1.69)	<0.0001	
All-Cause Mortality				<0.0001	
Days Followed Up, mean (SD)	3397.4 (1409.33)	2230.34 (684.68)	1017.49 (159.86)	<0.0001	
Dead at 3 years, number (%)	462 (5.4)	175 (5.5)	189 (8.2)	<0.0001	
Dead at 5 years, number (%)	873 (10.1)	348 (10.94)		0.22	
Dead at 8 years, number (%)	1471 (17.1)	510 (16.03)		0.18	
Total Dead, number (%)	2318 (26.9)	511 (16.06)	189 (8.2)	<0.0001	
COPD Mortality					
Days Followed Up, mean (SD)	2629.78 (804.39)				
Dead at 3 years, number (%)	251 (2.9)				
Dead at 5 years, number (%)	466 (5.4)				
Dead at 8 years, number (%)	678 (7.9)				
Total Dead, number (%)	694 (8.1)				
Phase 1 and Phase 2 visits are about 5 years apart and their mortality records extend to at least 10 years. ECLIPSE, used for external validation, has 3-yr mortality follow up data. COPDGene recruited non-Hispanic white and African American individuals from across USA. ECLIPSE recruited mostly non-Hispanic white individuals from USA and Europe.

Construction of graph-based predictive models

Directed probabilistic graph models for the COPDGene cohorts were constructed using the recently developed CausalCoxMGM method (Supplementary Methods). This method uses a two-step procedure to learn directed graphs from observational data in the presence of unmeasured confounders. The resulting graphs can provide insights into potential mechanisms, generate hypotheses, and select minimal sets of the most relevant predictors (the direct neighbours only or the Markov blanket-MB).28 By construction, the MB variables of an outcome provide independent information to it.

We used both the direct neighbours and the full MB in the graphs to develop predictive Cox regression models9 of all-cause and COPD-specific mortality. These models were compared to the ADO and Updated BODE indices,3 and to models learned with standard machine learning approaches, like LASSO Cox Regression29 and RF.11 Performance of these models was assessed through 5-fold cross-validation, with each model (except ADO and the updated BODE indices) trained on 80% of the data to predict the held-out 20% in each fold. Model performance was scored using the Harrell's concordance index,30 which represents the probability of a higher risk individual dying before a lower risk one. For models that perform feature selection (direct neighbours, MB, and LASSO Cox regression) we also compared the number of features each predictive model selected. For further details on causal graph discovery, model selection, and graph-based feature selection, see Supplementary Methods.

Construction of the VAPORED risk score

To create an accessible predictor of COPD mortality risk, we constructed the VAPORED risk score from the seven features directly linked to COPD-specific mortality (Table 2). To this end, we trained a regularized Cox regression model predicting COPD-specific mortality in the COPDGene Phase 1 study cohort. This model was then used to construct a discrete clinical risk score in a manner similar to the ADO and Updated BODE indices3 (Supplementary Methods). The resulting model enables us to rank patients by their COPD-specific mortality risk but is unable to predict time-varying survival probabilities directly. Thus, we trained a parametric Cox regression model of all-cause mortality with a Weibull baseline hazard function using the VAPORED risk score as a predictor.10 The calibration of these survival probabilities was assessed in the ECLIPSE study cohort at one, two, and three years using the Hosmer–Lemeshow test (Supplementary Methods).Table 2 Scoring criteria for VAPORED mortality score, constructed from the direct neighbours of COPD-specific mortality in the graphical model.

VAPORED Risk Score	
Feature	Category	Score	
Age (years)	<50	0	
	[50, 60)	1	
	[60, 70)	3	
	[70, 80)	5	
	≥80	6	
6-min Walk Distance (m)	≥550	0	
	[350, 550)	2	
	[250, 350)	4	
	[150, 250)	5	
	<150	7	
FEV1/FVC Ratio (%)	≥80	0	
	[65, 80)	3	
	[50, 65)	5	
	[35, 50)	6	
	<35	8	
FVC% Predicted (%)	≥100	0	
	[80, 100)	1	
	[60, 80)	3	
	[45, 60)	4	
	<45	5	
mMRC Dyspnoea score	[0, 1]	0	
	2	1	
	[3, 4]	2	
Pneumonia	No/Unknown	0	
	Yes	1	
Resting SaO2 (%)	≥93	0	
	[85, 93)	2	
	<85	3	

Statistical analysis

Comparisons of clinical characteristics across study cohorts were conducted as follows: for continuous variables, a Kruskal–Wallis test was applied to ascertain whether each variable was sampled from the same distribution across cohorts. For categorical variables, a chi-squared test was applied to determine whether the frequency of each category differed significantly across cohorts. Finally, a log-rank test was applied to determine whether the all-cause mortality differed significantly among cohorts. Comparisons of all model performance metrics (Supplementary Methods) and feature selection sparsity were performed with paired t-tests. Comparisons conducted during cross-validation were performed using two-sided paired t-tests with Nadeau and Bengio's corrected estimate of the standard error.31 In the ECLIPSE Study cohort, a one-sided paired t-test was performed to assess whether VAPORED performs significantly better than the BODE, Updated BODE, and ADO indices. The standard error of difference in model performance was estimated using 2000 bootstrapped samples. The Benjamini-Hochberg procedure was applied to control the false discovery rate for all comparisons of model performance.32 Additional analysis of the ability of each model to stratify patients into distinct risk groups compared to the BODE index was performed for our graph derived risk scores in COPDGene and VAPORED in ECLIPSE (Supplementary Methods). The significance of differences in survival probability was assessed with log-rank tests.

Role of the funding source

The funder of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report. The authors had full access to the data in the study and had final responsibility for the decision to submit for publication.

Results

Characteristics of discovery and validation cohorts

As the Phase 2 study comprised from a subset of the Phase 1 study participants, there are some expected significant changes to clinical covariates (Supplementary Table S1), such as an approximately five-year increase in age (p < 0.0001) and a statistically significant reduction of patients in more severe GOLD categories (p < 0.0001). We also observe significant increases in comorbidities’ incidence, such as cardiovascular disease (CVD; defined in8) and diabetes (p < 0.0001). Among the standard mortality risk indices, there is a significant decrease in the BODE index2 and a significant increase in the ADO index3 of participants of Phase 2 compared to Phase 1 (p < 0.0001). Of particular interest is the distribution of all-cause mortality in the two phases. We see that there is not a statistically significant difference in the survival functions of the two phases, nor in the proportion of deaths at time points shared by both groups (3, 5, and 8 years). This is important as it suggests that, despite the older participants in Phase 2 and the exclusion of individuals who died before the five-year follow up, the all-cause mortality does not differ significantly between the two phases.

The ECLIPSE study27 differs significantly in its patient population compared to COPDGene (Table 1). It has significantly more male participants (p < 0.0001) and is less racially diverse (98.3% white). Additionally, ECLIPSE is enriched for more severe cases of COPD (GOLD Stages 2–4; p < 0.0001). This is also reflected in significant increases in the ADO, BODE, and updated BODE indices compared to COPDGene. Finally, likely due to the difference in COPD severity in the patient population, we see a significant difference in all-cause mortality between the two studies (p < 0.0001). This is also reflected in the number of deaths observed in the first three years, which is significantly higher in the ECLIPSE study (p < 0.0001).

Comparison of all-cause and COPD-specific mortality models identifies features directly linked to COPD mortality

The MB variables (see Supplementary Methods) of all-cause mortality and COPD-specific mortality overlap substantially in COPDGene Phase 1. Overlapping features include classical predictors of mortality in COPD, such as age, 6-min walking distance (6MWD), mMRC Dyspnoea score, BMI, and forced expiratory volume in 1s (FEV1)/forced vital capacity (FVC) ratio3,33 (Fig. 2, Supplementary Table S2). Additionally, prior diagnosis of pneumonia, participant's resting SaO2, AWT, history of severe exacerbations, and high blood pressure were linked to both all-cause and COPD-specific mortality. However, there were differences between the two models. FVC %predicted is strongly associated with COPD-specific mortality only, while FEV1 %predicted independently informs all-cause mortality only. Also, as expected, comorbidities such as CVD and diabetes were associated with all-cause mortality only; same applies to risk factors such as smoking status (at the visit), pack years, and sex. Finally, heart rate and the coughing up phlegm symptom are also linked to all-cause mortality only.Fig. 2 Graph models of all-cause (A, left) and COPD-specific (A, right) mortality in the COPDGene Phase 1 study. Only the Markov blanket (MB) mortality features are shown. Node boundaries are coloured to represent whether a feature is present in the MB of all-cause mortality (yellow), COPD-specific mortality (red), or both (orange). Adjacencies in the graphical model represent a direct interaction between two variables, while edge orientations represent the type of interaction inferred by CausalCoxMGM. Undirected edges (X --- Y) represent a direct interaction between X and Y, while bidirected edges (X <-> Y) mean that some unobserved confounder affects X and Y. Edge colour designates whether higher values of this MB variable represent lower (blue) or (red) higher mortality risk. The standardized hazard ratios of the direct neighbours all-cause (B, left) and COPD-specific mortality (B, right) display the direction, relative effect size, and significance of each covariates' association with mortality. Hazard ratios for numeric features represent the change in hazard for a 1 SD increase. Abbreviations: 6MWD: 6-min walk distance; BMI: body mass index; CVD: cardiovascular disease; FEV1: forced expiratory volume in 1 s; FVC: forced vital capacity.

Looking only at the direct neighbours of COPD-specific mortality provides some additional insights. BMI, although it provides some independent information for COPD-specific mortality, is not directly linked to it, suggesting that its association with mortality in COPD is through its interactions with FEV1/FVC, FVC %predicted, 6MWD, mMRC Dyspnoea score, and resting SaO2 (Fig. 2A, right). However, BMI is directly linked to all-cause mortality. Additionally, we observe a direct effect of FVC %predicted to COPD-specific but not to all-cause mortality. Fig. 2B shows the relative importance (hazard ratios) of each direct neighbor to mortality in the two graphs, which are all independently significant.

Predictive models of COPD mortality

In addition to providing insights on direct interactions in observational datasets, graph models are useful tools for constructing robust, parsimonious, and powerful predictive models. We found that the graph-based predictive models (Neighbours, MB) significantly outperformed the ADO and updated BODE indices for all-cause and COPD-specific mortality (Fig. 3A). Also, the difference in performance between the graph-based models and Lasso Cox regression or RF was not statistically significant for the COPD-specific mortality. For the all-cause mortality, the only significant difference was between the models based on direct neighbours only (i.e., excluding “spouses” from the MB) and the full Markov Blanket, Lasso Cox regression, and RF models (Fig. 3A). In these cases, the graph-based models used significantly fewer variables than Lasso Cox regression (Fig. 3A) and, obviously, RF.Fig. 3 Performance of predictive models of all-cause and COPD-specific models assessed through 5-fold cross-validation (A). Models constructed using the direct neighbours of mortality (Neighbours) and all Markov blanket variables (full MB) were compared against the ADO and updated BODE indices, as well as LASSO Cox regression and random survival forests (RF) models trained on the same cross-validation splits. Model performance was assessed via Harrell's concordance. The number of features for models where feature selection was performed (Neighbours, full MB, LASSO Cox) was also assessed. Error bars denote the 95% confidence intervals of the cross-validation estimates and p-values are FDR corrected. To demonstrate the ability of the full MB models to stratify patients by risk of all-cause (B) and COPD-specific (C) all-cause mortality compared to the BODE index, we stratify individuals into four risk groups using the BODE index (left) as well as four risk groups of equal size predicted by the full MB models (right). The global log-rank test p value and test statistic are reported in each Kaplan–Meier plot. Confidence bands represent the 95% confidence interval. Abbreviations: ADO: age, dyspnoea, airflow obstruction (score parameters); BODE: body mass index, airflow obstruction, dyspnoea, exercise capacity (score parameters); MB: Markov blanket.

To demonstrate the ability of our models to stratify patients better than the BODE index, we created four risk groups based on the BODE score as well as groups of equal size from our full MB model. The Kaplan–Meier survival probability estimates34 were plotted for these four risk groups for both all-cause (Fig. 3B) and COPD-specific mortality (Fig. 3C). While both the BODE index and new full MB risk groups stratify patients into groups with significantly different survival curves for both all-cause and COPD-specific mortality, our MB risk groups differ more from the null. Additionally, COPD-specific mortality was considerably easier to predict compared to all-cause mortality (Fig. 3).

External validation: the ECLIPSE study

In our model, COPD-specific mortality has seven direct neighbours (Supplementary Table S2, bold), which are easily obtainable clinical measurements (no need for chest CT scans), and they are frequently measured across COPD studies. We trained a predictor model of COPD-specific mortality with these seven variables from COPDGene Phase 1 and constructed an index of COPD mortality risk3 with the categories presented in Table 2. Our model, VAPORED (Vital capacity-FVC %predicted, Age, history of Pneumonia, Oxygen saturation, the FEV1/FVC Ratio, 6-min walk Exercise capacity, Dyspnoea), was validated on the 3-year mortality data in the ECLIPSE study and compared to the standard ADO, BODE, and updated BODE indices. We used four measures to assess predictive power: concordance, the concordance probability estimate (CPE), the cumulative/dynamic (C/D) AUC at 3 years, and the integrated C/D AUC (Supplementary Methods). While the ADO, BODE, and Updated BODE indices have similar predictive power across all metrics, VAPORED had significantly higher predictive power in all metrics (Fig. 4A), particularly in CPE (false discovery rate–FDR p < 0.0001). We additionally estimated all-cause mortality survival functions for the VAPORED score on COPDGene Phase 1 data (Supplementary Figure S1A; Supplementary Table S3). In Supplementary Figure S1B we see that the model's survival probability predictions are well calibrated in the ECLIPSE study at one, two, and three years. Patients in the ECLIPSE dataset were also stratified into four risk groups according to the BODE score and four risk groups of equivalent size based on the VAPORED score (Fig. 4B). While both the BODE and VAPORED score categories significantly stratify patients by survival probability in the ECLIPSE study, only VAPORED shows significant separation amongst all adjacent risk groups (Fig. 4B).Fig. 4 Performance of the VAPORED risk score in the ECLIPSE 3-year all-cause mortality external validation cohort. The concordance, concordance probability estimate (CPE), 3-year C/D AUC, and integrated C/D AUC for the VAPORED, ADO, BODE, and updated BODE scores (A). Error bars denote the standard errors of the bootstrapped estimates and p-values are FDR corrected. To demonstrate the ability of the VAPORED risk score to stratify patients by risk of all-cause mortality in the ECLIPSE study compared to the updated BODE index (B), we stratify individuals into four risk groups using the BODE index (left) as well as four risk groups of equal size by VAPORED risk score (right). Post-hoc comparisons of adjacent risk groups were performed with log-rank tests. Confidence bands represent the 95% confidence interval and post-hoc p-values are adjusted using Holm's method. Abbreviations: ADO: age, dyspnoea, airflow obstruction (score parameters); BODE: body mass index, airflow obstruction, dyspnoea, exercise capacity (score parameters); VAPORED: forced vital capacity-FVC %predicted, age, history of pneumonia, oxygen saturation, the FEV1/FVC ratio, 6-min walk exercise capacity, dyspnoea (score parameters).

Graph-based model of all-cause mortality in COPD reveals biological signatures of mortality risk

In addition to the demographic, clinical, spirometry, and chest CT scan features, COPDGene Phase 2 Study also collected biological data, such as white blood cell differential percentages, haemoglobin levels, and whole blood gene expression. This provides a unique opportunity to investigate how informative the biological features are in the context of the clinical features and comorbidities. We applied our graph-modelling approach to identify features potentially affecting all-cause mortality in this dataset. We found eight of the 18 previously identified clinical features in the MB (age, sex, BMI, 6MWD, mMRC Dyspnoea score, FEV1/FVC, heart rate, CVD; Supplementary Table S2). Additional variables linked to all-cause mortality include: a socioeconomic feature, Internet Access, which was not collected during Phase 1, and five biological features (platelets, haemoglobin, SPIB transcription factor activity, and CIBERSORTx inferred proportions of plasma cells and resting memory CD4+ T cells) (Fig. 5AB). We used 5-fold cross-validation (internally) to ensure that our graph modelling approach was learning robust sets of features of all-cause mortality. As above, the model based on the identified predictors of all-cause mortality significantly outperformed the ADO and updated BODE indices in terms of concordance, while being significantly more parsimonious than the other machine learning methods without a significant loss in predictive accuracy (Fig. 5C). Finally, while both the BODE index and COPDGene Phase 2 full MB model can stratify individuals into significantly different mortality risk groups, the full MB model differs more from the null (Fig. 5D). These results strengthen the claim that our method derives relevant direct interactions between features from this multi-modal and multi-scale dataset and all-cause mortality.Fig. 5 Graph models of all-cause mortality in the COPDGene Phase 2 study (A). Only the Markov blanket (MB) of all-cause mortality is shown. Adjacencies in the graphical model have the same notation as in Fig. 1. The standardized hazard ratios of the direct neighbours all-cause mortality (B) display the direction, relative effect size, and significance of each covariates' association with mortality. Hazard ratios for numeric features represent the change in hazard for a 1 SD increase. The performance of predictive models of all-cause and COPD-specific models assessed through 5-fold cross-validation (C). Models constructed using the direct neighbours of mortality (Neighbours) and Markov blanket of mortality (full MB) were compared against the ADO and updated BODE indices, as well as LASSO Cox regression and random survival forests (RF) models trained on the same cross-validation splits. Model performance was assessed via Harrell's concordance and the number of features for models where feature selection was performed (Neighbours, full MB, LASSO Cox). Error bars denote the 95% confidence intervals of the cross-validation estimates and p-values are FDR corrected. To demonstrate the ability of the full MB model to stratify patients by risk of all-cause mortality compared to the BODE index (D), we stratify individuals into four risk groups using the BODE index (left) as well as four risk groups of equal size using full MB model predictions (right). The global log-rank test p value and test statistic are reported in each Kaplan–Meier plot. Confidence bands represent the 95% confidence interval. Abbreviations: 6MWD: 6-min walk distance; BMI: body mass index; CVD: cardiovascular disease; FEV1: forced expiratory volume in 1 s; FVC: forced vital capacity.

Web-based tool for all-cause and COPD-specific mortality

To help people further evaluate our VAPORED score predictor, we developed a web-based tool. The web tool allows the user to input the seven VAPORED key variables (FVC %predicted, age, history of pneumonia, SaO2, FEV1/FVC ratio, 6MWD, mMRC Dyspnoea score) for an individual and outputs two mortality risk curves (all-cause, COPD-specific) for the next 10 years. In addition, if the user provides values for BMI and FEV1 %predicted, the web tool outputs similar curves for BODE and ADO risk scores (for comparison purposes). The tool is available as a Shiny app from: https://vapored.shinyapps.io/VAPORED/. Some example values have been pre-loaded.

Discussion

In this study, we applied a probabilistic graph modelling approach to distinguish features directly linked to COPD-specific and to all-cause mortality from simple correlates. We analysed demographic, clinical, spirometry, and chest CT scan features from baseline COPDGene measurements, and biological and social factors from the 5-yr follow-up visit. The graphical models, by construction, consider all possible combinations of covariates and filter out the indirect effects at the population level in a manner similar to causal mediation analysis.

We found previously known risk factors for all-cause mortality in COPD to inform both models. These include age, mMRC Dyspnoea score, 6MWD, and BMI, which are used in BODE and ADO indices. Interestingly, these features remained informative of all-cause mortality even after biological data were added to the model (from COPDGene Phase 2). Other common features of the all-cause and COPD-specific mortality baseline models (history of pneumonia, and resting SaO2) did not appear in the Phase 2 model, probably because their information is superseded by the biological variables of this model (Platelets, Resting Memory CD4+ T Cells, SPIB Activity, and Plasma Cells). Further investigation is needed to determine potential long-term biological effects of pneumonia to patients with COPD.

The all-cause and COPD-specific mortality models have some unique characteristics, despite their substantial overlap. FVC %predicted appeared only in the COPD-specific model. This might indicate the effect of hyperinflation (i.e., residual volume) in COPD mortality, which was recently shown to be better represented by FVC %predicted and FEV1/FVC rather than FEV1 %predicted.35 Further, hyperinflation is more strongly linked to mortality than FEV1.36,37 FEV1/FVC ratio, which does not require race-specific reference equations, is a better discriminator of mortality than FEV1 %predicted.38 Not surprisingly, certain comorbidities are informative for the all-cause mortality only, including diabetes, CVD, and heart rate. Similarly, pack years, which affects multiple systems, is informative for the all-cause mortality model only. This is probably because direct measurement of impacted lung variables has incorporated the smoking information. Finally, the important contribution of pneumonia to our COPD-specific model is unique, although not unexpected given the established relationship between pneumonia and mortality in individuals with COPD and the potential that pneumonia may cause impairment in lung immunity not reflected in the other metrics.39,40

The graph models enabled us to develop a new, parsimonious but informative, 7-feature risk score for COPD-specific mortality (VAPORED) consisting of easily obtainable characteristics (FVC %predicted, age, history of pneumonia, resting SaO2, FEV1/FVC, 6MWD, mMRC Dyspnoea score). We validated this new model in the ECLIPSE 3-year all-cause mortality data, as ECLIPSE has not recorded COPD-specific mortality. VAPORED consistently significantly outperformed the ADO, BODE, and updated BODE indices across multiple metrics.

We also took advantage of COPDGene 5-year follow-up data, which additionally included socioeconomic factors and biological measurements. Our graph approach identified potentially important clinical and biological factors of all-cause mortality in current and former smokers with or at high risk of developing COPD. Age, BMI, FEV1/FVC, mMRC, 6MWD were still connected to all-cause mortality, as well as comorbidities CVD and heart rate. In addition, Internet Access was directly linked to all-cause mortality. Further investigation is needed to determine whether this is a surrogate for income or rural vs urban population characteristics.

Five biological features were linked to all-cause mortality in the Phase 2 model: haemoglobin levels, platelet counts, SPIB transcription factor activity, plasma cell proportions, and resting memory CD4+ T cell proportions. Low haemoglobin levels were associated with increased risk of all-cause mortality in individuals with COPD, as has been previously reported41 and in patients who were admitted to hospital for COPD.42 Platelets have also been previously associated with all-cause mortality in COPD, and antiplatelet therapies have been shown to reduce all-cause mortality in individuals with COPD.43,44 However, this contradicts the hazard ratio learned by our model, which suggests that low platelet levels are associated with increased all-cause mortality. This is probably because previous analyses have found a U-shaped association of platelet counts with all-cause mortality in COPD,45 which indicates that both high and low platelet counts are associated with a higher risk of all-cause mortality. Thus, the hazard ratio observed in our model is likely due to limitations of our modelling assumptions (i.e., monotonic associations). Even so, our graph-based discovery algorithm correctly identified a biological signature that has a likely causal effect on all-cause mortality in patients with COPD,43,44 and thus provides a possible intervention point.

Regarding potential mechanistic insights, our model suggests that the plasma cell proportion and SPIB transcription factor activity may be affecting mortality. The two likely interact as the SPIB transcription factor is a negative regulator of plasma cell differentiation and immunoglobulin production.46,47 Previous studies have linked B cell activity and the humoral immune response with COPD progression.48 The formation of lymphoid follicles in the lung48,49 and larger numbers of infiltrating B cells, memory B cells, and plasma cells are associated with COPD severity.48 Our model provides support for this mechanism of COPD progression, as elevated plasma cells and reduced SPIB transcription factor activity are found to be associated with an increased risk of all-cause mortality.

Our graph-based model also suggests that lower resting memory CD4+ T cells may directly increase the all-cause mortality risk, independently of age or sex. Interestingly, circulating memory CD4+ T cells are known to increase with age,50 and smoking,51,52 and have been linked to an increase in IL-22 secretion, which has been previously implicated in COPD pathogenesis.53 However, to our knowledge, this is the first time that it is shown that memory CD4+ T cells may play a protective role in individuals with COPD independently of age. A previous study in individuals with COPD found that CD4+ T cell cytokine production in response to stimulus is restricted almost entirely to memory CD4+ T cells.54 In healthy populations, memory CD4+ T cell populations can respond faster and more efficiently to previously experienced infections.50,55 In both healthy individuals and those with COPD, circulating and tissue resident memory CD4+ T cells that respond to common viral respiratory pathogens were found, without any significant defects between the COPD and control individuals.56 The association of low levels of resting memory CD4+ T cells with an elevated risk of all-cause mortality observed in our analysis may reflect the importance of memory CD4+ T cell populations in the response to respiratory infections in individuals with COPD.

There are several limitations of our study to note. The observational nature of both cohorts (i.e., lack of interventional data, medication, etc) makes it difficult to prove causal relationships. Additionally, COPDGene and ECLIPSE contain older individuals with extensive smoking histories, so the VAPORED score reflects this. Thus, its application should be limited to individuals who are diagnosed or at high risk of developing COPD. We are also limited by the population of the COPDGene Study, which contains only African American and non-Hispanic white individuals; and the exclusion of some patients with missing values in some measured variables may introduce selection bias. Additional selection likely occurred in the COPDGene Phase 2 cohort due to deaths of individuals with more severe COPD, though we note that the all-cause mortality distribution was not significantly different. Although we show VAPORED generalizes well to the ECLIPSE study cohort, further validation on additional external cohorts is necessary to establish its generalizability. Finally, the modelling assumptions of our probabilistic graph models (additive monotonic relationships, Markov faithfulness) place limitations on the interactions our models can recover. Despite these limitations, our approach had superior predictive power compared to standard indices, like ADO and BODE, which are used for mortality prediction.

In conclusion, our graph-based models can go beyond simple correlates and identify direct effectors of outcomes. It is also important to distinguish effectors of COPD-specific from all-cause mortality, a subject that has been understudied in the past. We developed a new COPD-specific mortality risk score (VAPORED), which is significantly better than established risk scores, and we validated our findings in an external cohort. Furthermore, we identified socioeconomic and biological factors that may contribute to all-cause mortality in patients with COPD. In the future, we plan to extend this study to additional modalities, such as genetic information, blood proteomics or methylomics, and develop truly comprehensive mortality risk scores. Accurate risk stratification of patients with COPD can aid in the identification of high-risk individuals who may benefit most from targeted interventions.

Contributors

PVB and TCL conceived and planned the study, interpreted the results wrote and edited the paper. TCL also performed all the experiments and organized the results. MHR and MJ provided materials and edited the paper. PC, FCS, and CPH helped with the interpretation of the results and the writing of the paper. TCL, PVB, FCS, PC, and CPH have access to and verify the underlying study data.

Data sharing statement

COPDGene clinical, imaging, and genetic data are available through dbGAP accession number phs000179.v6.p2. ECLIPSE data are available through dbGAP accession number phs001252.v1.p1.

Declaration of interests

PC received grant support from Bayer and Sanofi; and consulting fees from Verona Pharma. FCS has received institutional grant support and consulting fees from Sanofi/Regeneron, AstraZeneca, Verona Pharma, Nuvaira, Gala Therapeutics, GlaxoSmithKline, Boehringer Ingelheim. CPH has received institutional grant support and consulting fees from Alpha-1 Foundation, Bayer, Boehringer Ingelheim, Vertex, Chiesi, Ono, Takeda, Sanofi. TCL, MHR, MJ, PVB declare no competing interests.

Appendix A Supplementary data

Supplementary Methods, Figure, and Tables

Acknowledgements

This work was supported by 10.13039/100000050 NHLBI grants R01HL159805 and R01HL157879 (to PVB) and NLM fellowship F31LM013966 (to TCL). This work was also supported by 10.13039/100000050 NHLBI U01HL089897 and U01HL089856 . The COPDGene study (NCT00608764) is also supported by the 10.13039/100008184 COPD Foundation through contributions made to an Industry Advisory Committee comprised of 10.13039/100004325 AstraZeneca , Bayer Pharmaceuticals, 10.13039/100001003 Boehringer Ingelheim , 10.13039/100004328 Genentech , 10.13039/100004330 GlaxoSmithKline , 10.13039/100004336 Novartis , 10.13039/100004319 Pfizer and 10.13039/100009655 Sunovion .

Appendix A Supplementary data related to this article can be found at https://doi.org/10.1016/j.eclinm.2024.102786.
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