Developing a Machine Learning Model to Predict Severe Chronic Obstructive Pulmonary Disease Exacerbations: Retrospective Cohort Study

Introduction

Background

In the United States, chronic obstructive pulmonary disease (COPD) affects 6.5% of adults [1] and is the fourth leading cause of death, excluding COVID-19 [2]. Each year, COPD causes 1.5 million emergency department (ED) visits, 0.7 million inpatient stays, and US $32.1 billion in total medical cost [1]. Severe COPD exacerbations are those that require ED visits or inpatient stays [3], account for 90.3% of the total medical cost related to COPD [4], and often cause irreversible decline in lung function and health status [5-10]. Many severe COPD exacerbations (eg, 47% of the inpatient stays for COPD) are deemed preventable with appropriate outpatient care [3,11] because COPD is an ambulatory care–sensitive condition [12]. A commonly used method to reduce severe COPD exacerbations is to place patients at high risk in a care management program for preventive care [13-15]. Patients at high risk can be identified prospectively using a predictive model [16]. Once a patient enters the care management program, a care manager will periodically contact the patient for health status assessment and to help coordinate health and related services. This method is adopted by many health plans, such as those in 9 of 12 metropolitan communities [13], and many health care systems. Successful care management can reduce up to 27% of the ED visits [14] and 40% of the inpatient stays [15] in patients with COPD.

However, because of limitations of resources and service capacity, only ≤3% of patients could enter a care management program [17]. Its effectiveness is upper bounded by these patients’ risk levels, which are determined by how accurate the used predictive model is. Neither the stage of COPD nor having prior severe COPD exacerbations alone can predict a patient’s risk level for future severe COPD exacerbations well [18,19]. Previously, researchers had built several models to predict severe COPD exacerbations in patients with COPD [20-53]. These models are inaccurate and suboptimal for use in care management because they missed more than 50% of the patients who will experience severe COPD exacerbations in the future, incorrectly projected many other patients to experience severe COPD exacerbations [20-22,53], used data unavailable in routine clinical practice [23-31,33,34,36,42-50,52], or were designed for patients who have different characteristics from typical patients with COPD [25-34]. In addition, most of these models predicted only inpatient stays for COPD. To better guide the use of care management, we need to predict both ED visits and inpatient stays for COPD, which only 2 of these models [34,36] do. In practice, once a model is deployed for care management, the prediction errors produced by the model would lead to degraded patient outcomes and unnecessary health care costs. Because of the large number of patients with COPD, even a small improvement in model accuracy coupled with appropriate preventive interventions could help improve outcomes and avoid many ED visits and inpatient stays for COPD every year.

Objective

This study aims to develop a more accurate model to predict severe COPD exacerbations in the next year in patients with COPD. To be suitable for use in care management, the model should use data available in routine clinical practice and target all patients with COPD.

Methods

Ethics Approval and Study Design

The institutional review board of the University of Washington Medicine (UWM) approved this secondary analysis study on administrative and clinical data.

Patient Population

In Washington state, the UWM is the largest academic health care system. The UWM enterprise data warehouse includes administrative and clinical data from 3 hospitals and 12 clinics. The patient cohort consisted of the patients with COPD who visited any of these facilities between 2011 and 2019. Using our prior method for identifying patients with COPD [54] that was adapted from the literature [55-58], we regarded a patient to have COPD if the patient was aged ≥40 years and met ≥1 of the 4 criteria listed in Textbox 1. When computing the data instances in any year, we excluded the patients who had no encounter at the UWM or died during that year. No other exclusion criterion was used.

Prediction Target (Also Known as the Outcome or the Dependent Variable)

Given a patient with COPD who had ≥1 encounter at the UWM in a specific year (the index year), we used the patient’s data up to the last day of the year to predict the outcome of whether the patient would experience any severe COPD exacerbation, that is, any ED visit or inpatient stay with a principal diagnosis of COPD (International Classification of Diseases, Ninth Revision: 491.22, 491.21, 491.9, 491.8, 493.2x, 492.8, 496; International Classification of Diseases, Tenth Revision: J42, J41.8, J44.*, J43.*), in the next year (Figure 1).

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Data Set

We obtained a structured data set from the UWM enterprise data warehouse. This data set included administrative and clinical data relating to the patient cohort’s encounters at the 3 hospitals and 12 clinics of the UWM from 2011 to 2020.

Features (Also Known as Independent Variables)

To improve model accuracy, we examined an extensive set of candidate features computed on the structured attributes in the data set. Table S1 of Multimedia Appendix 1 [3,18,28,30,50,59-83] shows these 278 candidate features coming from four sources: the known risk factors for COPD exacerbations [3,18,28,30,50,59-72], the features used in prior models to predict severe COPD exacerbations [20-53], the features that the clinician ZCL in our team suggested, and the features used in our prior models to predict asthma hospital encounters [84,85]. Asthma shares many similarities with COPD. Throughout this paper, whenever we mention the number of a given type of item (eg, medication) without using the word distinct, we count multiplicity.

Each input data instance to the predictive model contained 278 features, corresponded to a distinct patient and index year pair, and was used to predict the outcome of the patient in the next year. For this pair, the patient’s age was computed based on the age at the end of the index year. The patient’s primary care provider (PCP) was computed as the last recorded PCP of the patient by the end of the index year. The percentage of the PCP’s patients with COPD in the preindex year having severe COPD exacerbations in the index year was computed on the data in the preindex and index years. Using the data from 2011 to the index year, we computed 26 features: the number of years from the first encounter related to COPD in the data set, the type of the first encounter related to COPD in the data set, 7 allergy features, and 17 features related to the problem list. The other 251 features were computed on the data in the index year.

Data Analysis

Data Preparation

Using the data preparation approach used in our papers [84,85], we identified the biologically implausible values, replaced them with null values, and normalized the data. As outcomes came from the next year, the data set had 9 years of effective data (2011-2019) over a time span of 10 years (2011-2020). To reflect future model use in clinical practice and to evaluate the impact of the COVID-19 pandemic on patient outcomes and model performance, we conducted two analyses:

1. Main analysis: we used the 2011-2018 data instances as the training set to train models and the 2019 data instances as the test set to assess model performance.
2. Performance stability analysis: we used the 2011-2017 data instances as the training set to train models and the 2018 data instances as the test set to assess model performance.

Classification Algorithms

We created machine learning classification models using Waikato Environment for Knowledge Analysis (WEKA; version 3.9) [86]. WEKA is a major open source software package for machine learning and data mining. It integrates many commonly used machine learning algorithms and feature selection techniques. We examined the 39 classification algorithms supported by WEKA and listed in the web-based multimedia appendix of our paper [84], as well as Extreme Gradient Boosting (XGBoost) [87] implemented in the XGBoost4J package [88]. XGBoost is a classification algorithm using an ensemble of decision trees. As XGBoost only takes numerical features, we converted categorical features to binary features through one-hot encoding. In the main analysis, we used the training set and our formerly published automatic machine learning model selection method [89] to automate the selection of the classification algorithm, feature selection technique, data balancing method to deal with imbalanced data, and hyperparameter values among all applicable ones. Compared with the Auto-WEKA automatic machine learning model selection method [90], our method achieved an average of 11% (SD 15%) reduction in model error rate and a 28-fold reduction in search time. In the performance stability analysis, we used the same classification algorithm, feature selection technique, and hyperparameter values as those used in the final model of the main analysis.

Performance Metrics

As shown in the formulas, the performance of the models was evaluated with respect to the following metrics: accuracy (Table 1); sensitivity, also known as recall; specificity; positive predictive value (PPV), also known as precision; negative predictive value (NPV); and area under the receiver operating characteristic curve (AUC):

Accuracy = (TP + TN) / (TP + TN + FP + FN) (1)

Sensitivity = TP / (TP + FN) (2)

Specificity = TN / (TN + FP) (3)

PPV = TP / (TP + FP) (4)

NPV = TN / (TN + FN) (5)

where TP stands for true positive, TN stands for true negative, FP stands for false positive, and FN stands for false negative.

We computed the 95% CIs of the performance measures using the bootstrapping method [91]. We obtained 1000 bootstrap samples from the test set and computed the model’s performance measures based on each bootstrap sample. This produced 1000 values for each performance metric. Their 2.5th and 97.5th percentiles provided the 95% CI of the corresponding performance measures. To depict the trade-off between sensitivity and specificity, we drew the receiver operating characteristic curve.

Results

Distributions of Data Instances and Bad Outcomes

The number of data instances increased over time. The proportion of data instances linked to bad outcomes remained relatively stable over time. The only exception was the sudden drop from 5.21% (369/7089) in 2018 to 2.42% (182/7529) in 2019 (Table 2), which resulted from the large drop in ED visits and inpatient stays for COPD in 2020 caused by the COVID-19 pandemic [92]. In the main analysis, 5.66% (2040/36,047) of the data instances in the training set and 2.42% (182/7529) of the data instances in the test set were linked to severe COPD exacerbations in the next year. In the performance stability analysis, 5.77% (1671/28,958) of the data instances in the training set and 5.21% (369/7089) of the data instances in the test set were linked to severe COPD exacerbations in the next year.

Patient Characteristics

Each patient and index year pair matched a data instance. For both the training set and the test set of the main analysis, when comparing the patient characteristic distributions between the data instances linked to severe COPD exacerbations in the next year and those linked to no severe COPD exacerbation in the next year, P values were computed using the chi-square 2-sample test and the Cochran–Armitage trend test [93] for categorical and numerical characteristics, respectively (Tables 3 and 4).

In the training set of the main analysis, most patient characteristics exhibited statistically significantly different distributions between the data instances linked to severe COPD exacerbations in the next year and those linked to no severe COPD exacerbation in the next year. Exceptions occurred on the patient characteristics of having prescriptions of inhaled corticosteroid, long-acting beta-2 agonist (LABA), and long-acting muscarinic antagonist (LAMA) combinations (P=.66); having prescriptions of phosphodiesterase-4 inhibitor (P=.06); presence of diabetes (P=.43); presence of eczema (P=.30); presence of lung cancer (P=.31); and presence of sinusitis (P=.61). In the test set of the main analysis, most patient characteristics exhibited statistically significantly different distributions between the data instances linked to severe COPD exacerbations in the next year and those linked to no severe COPD exacerbation in the next year. Exceptions occurred on the patient characteristics of having private insurance (P=.79); having prescriptions of LABA and LAMA combinations (P=.54); having prescriptions of inhaled corticosteroid, LABA, and LAMA combinations (P=.90); having prescriptions of phosphodiesterase-4 inhibitor (P=.27); presence of allergic rhinitis (P=.24); presence of anxiety or depression (P=.08); presence of congestive heart failure (P=.11); presence of diabetes (P=.95); presence of eczema (P=.08); presence of hypertension (P=.05); presence of lung cancer (P=.51); presence of obesity (P=.25); presence of sinusitis (P=.99); and presence of sleep apnea (P=.22).

Classification Algorithm and Features Used in the Final Model

The XGBoost algorithm was chosen by our automatic machine learning model selection method [89]. As a tree-based algorithm, XGBoost handles missing values in the features naturally. As detailed in Hastie et al [94], XGBoost automatically calculates an importance value for each feature based on the feature’s apportioned contribution to the model. In the main analysis, the final model was created using XGBoost and the 229 features shown in descending order of their importance values in Table S2 of Multimedia Appendix 1. The other features contributed no extra predictive power and were automatically dropped by XGBoost.

Model Performance in the Main Analysis

In the main analysis with the test set, the final model had an AUC of 0.866 (95% CI 0.838-0.892), as computed from the model’s receiver operating characteristic curve (Figure 2). The model’s performance measures varied with the cutoff threshold for binary classification (Table 5). When using the top 9.99% (752/7529) of the patients with the largest predicted risk to set the cutoff threshold for binary classification, the model had an accuracy of 90.33% (6801/7529; 95% CI 89.61%-91.01%), a sensitivity of 56.6% (103/182; 95% CI 49.2%-64.2%), a specificity of 91.17% (6698/7347; 95% CI 90.51%-91.83%), a PPV of 13.7% (103/752; 95% CI 11.2%-16.2%), and an NPV of 98.83% (6698/6777; 95% CI 98.55%-99.08%), as computed from the corresponding confusion matrix of the model (Table 6).

Recall that 27 candidate features were computed on ≥2 years of data. When we ignored these features and considered only those computed with the data in the index year, the model’s AUC dropped from 0.866 to 0.859 (95% CI 0.834-0.884). The top 19 features shown in Table S2 of Multimedia Appendix 1 have importance values ≥1%. When using only these features, the model’s AUC dropped from 0.866 to 0.862 (95% CI 0.837-0.887). In this case, when using the top 9.99% (752/7529) of the patients with the largest predicted risk to set the cutoff threshold for binary classification, the model had an accuracy of 90.25% (6795/7529; 95% CI 89.56%-90.9%), a sensitivity of 54.9% (100/182; 95% CI 47.8%-61.9%), a specificity of 91.13% (6695/7347; 95% CI 90.43%-91.78%), a PPV of 13.3% (100/752; 95% CI 10.9%-15.7%), and an NPV of 98.79 (6695/6777; 95% CI 98.52%-99.06%).

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Performance Stability Analysis

The final model in the main analysis and the model in the performance stability analysis had relatively similar performance (Table 7).

Discussion

Principal Findings

We created a machine learning model to predict severe COPD exacerbations in the next year in patients with COPD. The model had a higher AUC than the formerly published AUC of every prior model for predicting severe COPD exacerbations in the next year [20,25,27,28,30,33,35-43,46-49,51] (Tables 8 and 9). After improving our model’s performance measures further (eg, by adding features extracted from clinical notes) and using our recently published automatic explanation method [95] to automatically explain the model’s predictions, our model could be used as a decision support tool to advise the use of care management for patients with COPD and at high risk to improve outcomes.

In Table S2 of Multimedia Appendix 1, many of the top 19 features match the published (risk) factors that were highly correlated with COPD exacerbations, such as prior COPD exacerbations [18,60], prior health care encounters related to COPD [28,50], COPD medication use [50], BMI [70], peripheral capillary oxygen saturation [28], and heart rate [71].

We examined 278 candidate features, 82.4% (229/278) of which were used in the final model. Many omitted features are correlated with the outcome, but they provided no extra predictive power on the UWM data set beyond the 229 features used in the final model.

The prevalence rate of severe COPD exacerbations had a sudden drop in 2019. Despite this drop, our model still showed reasonably robust performance over time. This is desired for clinical decision support.

Comparison With Prior Work

Researchers formerly created several models to predict severe COPD exacerbations in patients with COPD [20-53]. Tables 8 and 9 present comparisons between our final model and these models, which include all related models listed in the systematic reviews by Guerra et al [96] and Bellou et al [97] as well as several recent models that were published after the reviews. Our final model predicted severe COPD exacerbations in the next year. Every prior model for predicting severe COPD exacerbations in the next year had an AUC ≤0.809, that is, at least 0.057 lower than that of our final model. Compared with the prior models for predicting severe COPD exacerbations other than the model developed by Ställberg et al [53], our final model used more extensive features with predictive power, which helped improve model performance.

Our final model’s prediction target covered both future ED visits and future inpatient stays for COPD, which we want to use care management to prevent. Among all prior models, only 2 [34,36] had prediction targets covering both future ED visits and future inpatient stays for COPD. Most of the prior models predicted either only future ED visits [48,52] or only future inpatient stays for COPD [20-22,24,25,28,30,32,35,37-45, 47,50-52]. This would be insufficient for preventing both future ED visits and future inpatient stays for COPD. The other prior models [23,26,27,29,31,33,46,49] had prediction targets covering both moderate and severe COPD exacerbations, with moderate COPD exacerbations typically referring to COPD medication change such as the use of systemic corticosteroids. These prediction targets were not specific enough for identifying patients at the highest risk for care management because a care management program can host only a small portion of patients [17].

To make it suitable for use in daily clinical practice, our final model was built on routinely available administrative and clinical data. In comparison, the models developed by several other research groups [23-31,33,34,36,42-50,52] used research data, some of which are unavailable in usual clinical practice. Thus, these models would be unsuitable for daily clinical use.

Our predictive model was developed to guide COPD care management’s enrollment decisions and to prevent severe COPD exacerbations. To give enough lead time for preventive interventions to be effective and to use precious care management resources well, we chose severe COPD exacerbation in the next year as the prediction target. In comparison, the model developed by Orchard et al [24] predicted inpatient stays for COPD on the next day. If a patient will incur an inpatient stay for COPD tomorrow, intervening starting from today could be too late to avoid the inpatient stay. At present, we are aware of no published conclusion on how long it will take for any intervention to be effective at preventing severe COPD exacerbations. In the studies by Longman et al [98] and Johnston et al [99], several clinicians had expressed the opinion that it could take as long as 3 months for any intervention to be effective at preventing inpatient stays for a chronic, ambulatory care–sensitive condition. Our final model will have a different clinical use from the models that make short-term predictions. Foreseeing a severe COPD exacerbation in the next 12 months would be useful for identifying and personalizing medium-term interventions and maintenance therapies to change the course of the disease. In comparison, foreseeing a severe COPD exacerbation in the next 1 or few days can be useful for deciding acute management approaches to improve outcomes, such as preemptive hospitalization of the patient to avoid more severe adverse outcomes, but would be inadequate for trying to improve the course of the disease in a short amount of time. In fact, treatment approaches proven to be effective at reducing severe COPD exacerbations are usually not indicated for acute management.

Marin et al [52] built a model to predict inpatient stays for COPD in up to the next 8 years with an AUC of 0.88 and a separate model to predict ED visits for COPD in up to the next 8 years with an AUC of 0.78. An inpatient stay or an ED visit that will happen several years later is too remote to be worth using precious care management resources now to prevent.

For the patients with COPD who will have severe COPD exacerbations in the future, sensitivity is the proportion of patients whom the model identifies. The difference in sensitivity could greatly affect hospital use. Our final model’s sensitivity is higher than the sensitivities achieved by the models developed by several other research groups [20-22,25,26,53]. Compared with our final model, the models developed by Orchard et al [24], Faganello et al [27], and Alcázar et al [28] each reached a higher sensitivity at the price of a much lower specificity. For each of these 3 models, if we adjust the cutoff threshold for binary classification and make our final model have the same specificity as that model, our final model would achieve a higher sensitivity than that model. More specifically, at a specificity of 60.02% (4410/7347), our final model achieved a sensitivity of 90.1% (164/182), whereas the model developed by Orchard et al [24] achieved a sensitivity of 80%. At a specificity of 73.3% (5385/7347), our final model achieved a sensitivity of 84.1% (153/182), whereas the model developed by Faganello et al [27] achieved a sensitivity of 58.3%. At a specificity of 77.34% (5682/7347), our final model achieved a sensitivity of 81.9% (149/182), whereas the model developed by Alcázar et al [28] achieved a sensitivity of 76.2%.

The prevalence rate of poor outcomes has a large impact on any model’s PPV [100]. On our data set, where this prevalence rate is approximately 5%, our final model reached a PPV of <14%. In comparison, on a data set where this prevalence rate is 11.63%, the model developed by Annavarapu et al [20] reached a PPV of 48.1%. On a data set where this prevalence rate is 6.4%, the model developed by Thomsen et al [23] reached a PPV of 18%. On a data set where this prevalence rate is 39.4%, the model developed by Alcázar et al [28] reached a PPV of 61.5%. In all 3 cases, the higher prevalence rates of poor outcomes permitted the PPV to be larger.

Our data set is imbalanced, with only a small portion of patients to have severe COPD exacerbations in the next year. For imbalanced data sets, the area under the precision–recall curve (AUPRC) is a better measure of overall model performance than the AUC [101]. The AUPRC was reported for only the model developed by Ställberg et al [53] among all the prior models. Although the model developed by Ställberg et al [53] had an AUC of 0.86, which is only slightly lower than that of our final model, our final model had an AUPRC of 0.24 (95% CI 0.18-0.31) that is 3 times as large as the 0.08 AUPRC of that model. In addition, that model predicted COPD-related inpatient stays, for which COPD can be any of the diagnoses, in the next 10 days. If a patient will incur an inpatient stay in the next 10 days, intervening starting from today could be too late to avoid the inpatient stay. In comparison, our final model predicted ED visits or inpatient stays with a principal diagnosis of COPD in the next year, allowing more lead time for preventive interventions to be effective.

Considerations for Future Clinical Use

Our final model reached an AUC that is larger than every AUC formerly reported in the literature for predicting severe COPD exacerbations in the next year. Despite having a relatively low PPV, our final model could still benefit health care for 3 reasons.

First, health care systems such as the UWM and Intermountain Healthcare use proprietary models, which have similar performance to the formerly published models, to allocate COPD care management resources. Our final model had a higher AUC than all formerly reported AUCs for predicting severe COPD exacerbations in the next year. Hence, although we plan to investigate using various techniques to further improve model performance in the future, we think it is already worth considering using our final model to replace the proprietary models currently being used at health care systems such as the UWM for COPD care management.

Second, we set the cutoff threshold for binary classification at the top 9.99% (752/7529) of the patients with the largest predicted risk. In this case, a perfect model would achieve the theoretically maximum possible PPV of 24.2% (182/752). Our final model’s PPV is 56.6% (103/182) of the theoretically maximum possible PPV. In other words, our final model captured 56.6% (103/182) of the patients with COPD who would have severe COPD exacerbations in the next year. If we change the cutoff threshold to the top 25% of the patients with the largest predicted risk, the final model would capture 83% (151/182) of the patients with COPD who would have severe COPD exacerbations in the next year.

Third, a PPV at the level of our final model’s PPV is suitable for identifying patients with COPD and at high risk for low-cost preventive interventions such as arranging a nurse to further follow up with the patient through phone calls, teaching the patient to correctly use a COPD inhaler, teaching the patient the correct use of a peak flow meter to self-monitor symptoms at home, and enrolling the patient in a home-based pulmonary rehabilitation program [102].

Our final model used 229 features. To ease clinical deployment, we could reduce features, for example, to the top 19 with importance values ≥1%. A feature’s importance value differs across health care systems. If conditions permit, we should use a data set from the target health care system to compute the features’ importance values and decide which features to retain.

Our final model was based on XGBoost [87], which leverages the hyperparameter scale_pos_weight to balance the weights of the 2 outcome classes in our data set [103]. The scale_pos_weight hyperparameter was set by our automatic model selection method [89] to a nondefault value to maximize our final model’s AUC [104]. This caused the side effect of greatly increasing our model’s predicted probabilities of having future severe COPD exacerbations to values much larger than the true probabilities [103]. However, it does not affect our ability to identify the top portion of the patients with the largest predicted risk for preventive interventions. If preferred, we could forgo the balancing by keeping scale_pos_weight at its default value 1. In this case, our model’s AUC would drop by 0.003 to 0.863 (95% CI 0.835-0.888), which is still larger than every formerly published AUC for predicting severe COPD exacerbations in the next year.

Limitations

This study includes several limitations that are worth future work.

First, this study used solely structured data. It is worth considering performing natural language processing to extract features from unstructured clinical notes to improve model performance. A model with higher performance can be used to better facilitate COPD care management.

Second, this study used age, diagnosis codes, and medication data to identify patients with COPD and used diagnosis codes and encounter information to define the prediction target. One can use age, diagnosis codes, and medication data to identify patients with COPD reasonably well [56]; yet, diagnosis codes were shown to have a low sensitivity in capturing inpatient stays for COPD [105]. Our predictive model is likely to perform poorly at finding those patients who would experience only future inpatient stays for COPD that are not captured by our current definition of the prediction target. We expect that this will not greatly affect our predictive model’s usefulness for facilitating COPD care management. On the basis of our current definition of the prediction target, >5% of the patients in our data set had severe COPD exacerbations in the following year. If fully captured by the predictive model, these patients would have already exceeded the service capacity of a typical care management program, which can take ≤3% of the patients [17]. In the future, one could consider adding both medication data and information extracted from clinical notes through natural language processing to better capture inpatient stays for COPD.

Third, this study used non–deep learning classification algorithms. Deep learning has improved model performance for many clinical predictive modeling tasks [106-111]. It is worth investigating whether using deep learning can improve model performance for predicting severe COPD exacerbations.

Fourth, this study used data from a single health care system: the UWM. It is worth evaluating our model’s generalizability to other health care systems. We are working on obtaining a data set of patients with COPD from Intermountain Healthcare for this purpose [112].

Fifth, our data set contained no information on UWM patients’ health care use at other health care systems. It is worth evaluating how our model’s performance would change if data on UWM patients’ health care use at other health care systems are available.

Conclusions

This work improved the state of the art of predicting severe COPD exacerbations in patients with COPD. In particular, our final model had a higher AUC than every formerly published model AUC on predicting severe COPD exacerbations in the next year. After improving our model’s performance measures further and using our recently published automatic explanation method [95] to automatically explain the model’s predictions, our model could be used in a decision support tool to guide the use of care management for patients with COPD and at high risk to improve outcomes.