Dataset Information

Prediction of Chronic Obstructive Pulmonary Disease Exacerbation Events by Using Patient Self-reported Data in a Digital Health App: Statistical Evaluation and Machine Learning Approach.

ABSTRACT:

Background

Self-reporting digital apps provide a way of remotely monitoring and managing patients with chronic conditions in the community. Leveraging the data collected by these apps in prognostic models could provide increased personalization of care and reduce the burden of care for people who live with chronic conditions. This study evaluated the predictive ability of prognostic models for the prediction of acute exacerbation events in people with chronic obstructive pulmonary disease by using data self-reported to a digital health app.

Objective

The aim of this study was to evaluate if data self-reported to a digital health app can be used to predict acute exacerbation events in the near future.

Methods

This is a retrospective study evaluating the use of symptom and chronic obstructive pulmonary disease assessment test data self-reported to a digital health app (myCOPD) in predicting acute exacerbation events. We include data from 2374 patients who made 68,139 self-reports. We evaluated the degree to which the different variables self-reported to the app are predictive of exacerbation events and developed both heuristic and machine learning models to predict whether the patient will report an exacerbation event within 3 days of self-reporting to the app. The model's predictive ability was evaluated based on self-reports from an independent set of patients.

Results

Users self-reported symptoms, and standard chronic obstructive pulmonary disease assessment tests displayed correlation with future exacerbation events. Both a baseline model (area under the receiver operating characteristic curve [AUROC] 0.655, 95% CI 0.689-0.676) and a machine learning model (AUROC 0.727, 95% CI 0.720-0.735) showed moderate ability in predicting exacerbation events, occurring within 3 days of a given self-report. Although the baseline model obtained a fixed sensitivity and specificity of 0.551 (95% CI 0.508-0.596) and 0.759 (95% CI 0.752-0.767) respectively, the sensitivity and specificity of the machine learning model can be tuned by dichotomizing the continuous predictions it provides with different thresholds.

Conclusions

Data self-reported to health care apps designed to remotely monitor patients with chronic obstructive pulmonary disease can be used to predict acute exacerbation events with moderate performance. This could increase personalization of care by allowing preemptive action to be taken to mitigate the risk of future exacerbation events.

SUBMITTER: Chmiel FP

PROVIDER: S-EPMC8981014 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature

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Publications

Prediction of Chronic Obstructive Pulmonary Disease Exacerbation Events by Using Patient Self-reported Data in a Digital Health App: Statistical Evaluation and Machine Learning Approach.

Chmiel Francis P FP Burns Dan K DK Pickering John Brian JB Blythin Alison A Wilkinson Thomas Ma TM Boniface Michael J MJ

JMIR medical informatics 20220321 3

<h4>Background</h4>Self-reporting digital apps provide a way of remotely monitoring and managing patients with chronic conditions in the community. Leveraging the data collected by these apps in prognostic models could provide increased personalization of care and reduce the burden of care for people who live with chronic conditions. This study evaluated the predictive ability of prognostic models for the prediction of acute exacerbation events in people with chronic obstructive pulmonary diseas ...[more]

PMID: 35311685

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Project description:The study developed accurate explainable machine learning (ML) models for predicting first-time acute exacerbation of chronic obstructive pulmonary disease (COPD, AECOPD) at an individual level. We conducted a retrospective case-control study. A total of 606 patients with COPD were screened for eligibility using registry data from the COPD Pay-for-Performance Program (COPD P4P program) database at Changhua Christian Hospital between January 2017 and December 2019. Recursive feature elimination technology was used to select the optimal subset of features for predicting the occurrence of AECOPD. We developed four ML models to predict first-time AECOPD, and the highest-performing model was applied. Finally, an explainable approach based on ML and the SHapley Additive exPlanations (SHAP) and a local explanation method were used to evaluate the risk of AECOPD and to generate individual explanations of the model's decisions. The gradient boosting machine (GBM) and support vector machine (SVM) models exhibited superior discrimination ability (area under curve [AUC] = 0.833 [95% confidence interval (CI) 0.745-0.921] and AUC = 0.836 [95% CI 0.757-0.915], respectively). The decision curve analysis indicated that the GBM model exhibited a higher net benefit in distinguishing patients at high risk for AECOPD when the threshold probability was <0.55. The COPD Assessment Test (CAT) and the symptom of wheezing were the two most important features and exhibited the highest SHAP values, followed by monocyte count and white blood cell (WBC) count, coughing, red blood cell (RBC) count, breathing rate, oral long-acting bronchodilator use, chronic pulmonary disease (CPD), systolic blood pressure (SBP), and others. Higher CAT score; monocyte, WBC, and RBC counts; BMI; diastolic blood pressure (DBP); neutrophil-to-lymphocyte ratio; and eosinophil and lymphocyte counts were associated with AECOPD. The presence of symptoms (wheezing, dyspnea, coughing), chronic disease (CPD, congestive heart failure [CHF], sleep disorders, and pneumonia), and use of COPD medications (triple-therapy long-acting bronchodilators, short-acting bronchodilators, oral long-acting bronchodilators, and antibiotics) were also positively associated with AECOPD. A high breathing rate, heart rate, or systolic blood pressure and methylxanthine use were negatively correlated with AECOPD. The ML model was able to accurately assess the risk of AECOPD. The ML model combined with SHAP and the local explanation method were able to provide interpretable and visual explanations of individualized risk predictions, which may assist clinical physicians in understanding the effects of key features in the model and the model's decision-making process.

Project description:BackgroundPatients with chronic obstructive pulmonary disease (COPD) can experience 'exacerbations' of their conditions. An exacerbation is an event defined in terms of subjective descriptors or symptoms, namely dyspnoea, cough and sputum that worsen sufficiently to warrant a change in medical management. There is a need for reliable markers that reflect the pathological mechanisms that underlie exacerbation severity and that can be used as a surrogate to assess treatment effects in clinical studies. Little is known as to how existing study variables and suggested markers change in both the stable and exacerbation phases of COPD. In an attempt to find the best surrogates for exacerbations, we have reviewed the literature to identify which of these markers change in a consistent manner with the severity of the exacerbation event.MethodsWe have searched standard databases between 1966 to July 2004 using major keywords and terms. Studies that provided demographics, spirometry, potential markers, and clear eligibility criteria were included in this study. Central tendencies and dispersions for all the variables and markers reported and collected by us were first tabulated according to sample size and ATS/ERS 2004 Exacerbation Severity Levels I to III criteria. Due to the possible similarity of patients in Levels II and III, the data was also redefined into categories of exacerbations, namely out-patient (Level I) and in-patient (Levels II & III combined). For both approaches, we performed a fixed effect meta-analysis on each of the reported variables.ResultsWe included a total of 268 studies reported between 1979 to July 2004. These studies investigated 142,407 patients with COPD. Arterial carbon dioxide tension and breathing rate were statistically different between all levels of exacerbation severity and between in out- and in-patient settings. Most other measures showed weak relationships with either level or setting, or they had insufficient data to permit meta-analysis.ConclusionArterial carbon dioxide and breathing rate varied in a consistent manner with exacerbation severity and patient setting. Many other measures showed weak correlations that should be further explored in future longitudinal studies or assessed using suggested mathematical modelling techniques.

Project description:ObjectiveThe prognosis for acute exacerbation of chronic obstructive pulmonary disease (AECOPD) is not optimistic, and severe AECOPD leads to an increased risk of mortality. Prediction models help distinguish between high- and low-risk groups. At present, many prediction models have been established and validated, which need to be systematically reviewed to screen out more suitable models that can be used in the clinic and provide evidence for future research.MethodsWe searched PubMed, EMBASE, Cochrane Library and Web of Science databases for studies on risk models for AECOPD mortality from their inception to 10 April 2022. The risk of bias was assessed using the prediction model risk of bias assessment tool (PROBAST). Stata software (version 16) was used to synthesize the C-statistics for each model.ResultsA total of 37 studies were included. The development of risk prediction models for mortality in patients with AECOPD was described in 26 articles, in which the most common predictors were age (n = 17), dyspnea grade (n = 11), altered mental status (n = 8), pneumonia (n = 6) and blood urea nitrogen (BUN, n = 6). The remaining 11 articles only externally validated existing models. All 37 studies were evaluated at a high risk of bias using PROBAST. We performed a meta-analysis of five models included in 15 studies. DECAF (dyspnoea, eosinopenia, consolidation, acidemia and atrial fibrillation) performed well in predicting in-hospital death [C-statistic = 0.91, 95% confidence interval (CI): 0.83, 0.98] and 90-day death [C-statistic = 0.76, 95% CI: 0.69, 0.82] and CURB-65 (confusion, urea, respiratory rate, blood pressure and age) performed well in predicting 30-day death [C-statistic = 0.74, 95% CI: 0.70, 0.77].ConclusionsThis study provides information on the characteristics, performance and risk of bias of a risk model for AECOPD mortality. This pooled analysis of the present study suggests that the DECAF performs well in predicting in-hospital and 90-day deaths. Yet, external validation in different populations is still needed to prove this performance.

Dataset Information

Prediction of Chronic Obstructive Pulmonary Disease Exacerbation Events by Using Patient Self-reported Data in a Digital Health App: Statistical Evaluation and Machine Learning Approach.

Background

Objective

Methods

Results

Conclusions

Publications

Prediction of Chronic Obstructive Pulmonary Disease Exacerbation Events by Using Patient Self-reported Data in a Digital Health App: Statistical Evaluation and Machine Learning Approach.

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