Development and validation of a predictive model to identify patients at risk of severe COPD exacerbations using administrative claims data

Developing and validating prediction models for severe exacerbations and readmissions in patients hospitalised for COPD exacerbation (SERCO) in China: a prospective observational study

BACKGROUND

There is a lack of individualised prediction models for patients hospitalised with chronic obstructive pulmonary disease (COPD) for clinical practice. We developed and validated prediction models of severe exacerbations and readmissions in patients hospitalised for COPD exacerbation (SERCO).

METHODS

Data were obtained from the Acute Exacerbations of Chronic Obstructive Pulmonary Disease Inpatient Registry study (NCT02657525) in China. Cause-specific hazard models were used to estimate coefficients. C-statistic was used to evaluate the discrimination. Slope and intercept were used to evaluate the calibration and used for model adjustment. Models were validated internally by 10-fold cross-validation and externally using data from different regions. Risk-stratified scoring scales and nomograms were provided. The discrimination ability of the SERCO model was compared with the exacerbation history in the previous year.

RESULTS

Two sets with 2196 and 1869 patients from different geographical regions were used for model development and external validation. The 12-month severe exacerbations cumulative incidence rates were 11.55% (95% CI 10.06% to 13.16%) in development cohorts and 12.30% (95% CI 10.67% to 14.05%) in validation cohorts. The COPD-specific readmission incidence rates were 11.31% (95% CI 9.83% to 12.91%) and 12.26% (95% CI 10.63% to 14.02%), respectively. Demographic characteristics, medical history, comorbidities, drug usage, Global Initiative for Chronic Obstructive Lung Disease stage and interactions were included as predictors. C-indexes for severe exacerbations were 77.3 (95% CI 70.7 to 83.9), 76.5 (95% CI 72.6 to 80.4) and 74.7 (95% CI 71.2 to 78.2) at 1, 6 and 12 months. The corresponding values for readmissions were 77.1 (95% CI 70.1 to 84.0), 76.3 (95% CI 72.3 to 80.4) and 74.5 (95% CI 71.0 to 78.0). The SERCO model was consistently discriminative and accurate with C-indexes in the derivation and internal validation groups. In external validation, the C-indexes were relatively lower at 60-70 levels. The SERCO model discriminated outcomes better than prior severe exacerbation history. The slope and intercept after adjustment showed close agreement between predicted and observed risks. However, in external validation, the models may overestimate the risk in higher-risk groups. The model-driven risk groups showed significant disparities in prognosis.

CONCLUSION

The SERCO model provides individual predictions for severe exacerbation and COPD-specific readmission risk, which enables identifying high-risk patients and implementing personalised preventive intervention for patients with COPD.

Risks of Zolpidem among Patients with Chronic Obstructive Pulmonary Disease

Rationale: Nonbenzodiazepine benzodiazepine receptor agonists (NBZRA, e.g., zolpidem) are frequently used to treat insomnia among patients with chronic obstructive pulmonary disease (COPD). However, multiple observational studies find that patients with COPD who are prescribed NBZRAs have greater risks for mortality and respiratory complications than patients without such prescriptions. Without an active comparator, these studies are susceptible to confounding by indication. Objectives: Compare the risk of death or inpatient COPD exacerbation among patients receiving zolpidem relative to patients receiving other hypnotics. Methods: Using nationwide Veterans Health Administration (VA) data, we identified patients with clinically diagnosed COPD and new receipt of zolpidem or another hypnotic available on VA formulary without prior authorization (melatonin, trazodone, doxepin). We excluded those receiving traditional benzodiazepines or multiple concurrent hypnotics. We propensity-matched patients receiving zolpidem to other hypnotics on 32 variables, including demographics, comorbidities, and markers of COPD severity. We compared risk of the primary composite outcome of death or inpatient COPD exacerbation over 1 year. In secondary analyses, we propensity-matched patients receiving zolpidem to those without hypnotic receipt. Results: Among 283,740 patients meeting inclusion criteria, 1,126 (0.4%) received zolpidem and 3,057 (1.1%) received other hypnotics. We propensity-matched patients receiving zolpidem 1:1 to peers receiving other hypnotics. We did not find a difference in the primary composite outcome of death or inpatient exacerbation (hazard ratio, 0.97; 95% confidence interval [CI], 0.77-1.23). In secondary analyses comparing patients receiving zolpidem to matched peers without hypnotic receipt, we observed greater risk of death or inpatient exacerbation with zolpidem (hazard ratio, 1.40; 95% CI, 1.09-1.81). Conclusions: Among patients with COPD, we did not observe greater risks after new receipt of zolpidem relative to other hypnotics. However, we did observe greater risks relative to those without hypnotic receipt. This latter finding may reflect: 1) residual, unmeasured confounding related to insomnia; or 2) true adverse effects of hypnotics across classes. Future work is needed to better understand the risks of hypnotics in COPD.

Trends and Rural-Urban Differences in the Initial Prescription of Low-Value Inhaled Corticosteroids among U.S. Veterans with Chronic Obstructive Pulmonary Disease

Rationale: Guidelines recommend inhaled corticosteroids (ICS) for patients with chronic obstructive pulmonary disease (COPD) and select indications, including asthma history, high exacerbation risk, or high serum eosinophils. ICS are commonly prescribed outside of these indications, despite evidence of harm. We defined a "low-value" ICS prescription as the receipt of an ICS without evidence of a guideline-recommended indication. ICS prescription patterns are not well characterized and could inform health system interventions to reduce low-value practices. Objectives: To evaluate the national trends in initial low-value ICS prescriptions in the U.S. Department of Veterans Affairs and to determine whether rural-urban differences in low-value ICS prescribing exist. Methods: We performed a cross-sectional study between January 4, 2010, and December 31, 2018, identifying veterans with COPD who were new users of inhaler therapy. We defined low-value ICS as prescriptions in patients with 1) no asthma, 2) low risk of future exacerbation (Global Initiative for Chronic Obstructive Lung Disease group A or B), and 3) serum eosinophils <300 cells/μl. We performed multivariable logistic regression to evaluate trends in low-value ICS prescription over time, adjusting for potential confounders. We performed fixed effects logistic regression to assess rural-urban prescribing patterns. Results: We identified a total of 131,009 veterans with COPD starting inhaler therapy, 57,472 (44%) of whom were prescribed low-value ICS as initial therapy. From 2010 to 2018, the probability of receiving low-value ICS as initial therapy increased by 0.42 percentage points per year (95% confidence interval, 0.31-0.53). Compared with urban residence, rural residence was associated with a 2.5-percentage-point (95% confidence interval, 1.9-3.1) higher probability of receiving low-value ICS as initial therapy. Conclusions: The prescription of low-value ICS as initial therapy is common and increasing slightly over time for both rural and urban veterans. Given the widespread and persistent nature of low-value ICS prescribing, health system leaders should consider system-wide approaches to address this low-value prescribing practice.

Can markers of disease severity improve the predictive power of claims-based multimorbidity indices?

BACKGROUND

Claims-based measures of multimorbidity, which evaluate the presence of a defined list of diseases, are limited in their ability to predict future outcomes. We evaluated whether claims-based markers of disease severity could improve assessments of multimorbid burden.

METHODS

We developed 7 dichotomous markers of disease severity which could be applied to a range of diseases using claims data. These markers were based on the number of disease-associated outpatient visits, emergency department visits, and hospitalizations made by an individual over a defined interval; whether an individual with a given disease had outpatient visits to a specialist who typically treats that disease; and ICD-9 codes which connote more versus less advanced or symptomatic manifestations of a disease. Using Medicare claims linked with Health and Retirement Study data, we tested whether including these markers improved ability to predict ADL decline, IADL decline, hospitalization, and death compared to equivalent models which only included the presence or absence of diseases.

RESULTS

Of 5012 subjects, median age was 76 years and 58% were female. For a majority of diseases tested individually, adding each of the 7 severity markers yielded minimal increase in c-statistic (≤0.002) for outcomes of ADL decline and mortality compared to models considering only the presence versus absence of disease. Gains in predictive power were more substantial for a small number of individual diseases. Inclusion of the most promising marker in multi-disease multimorbidity indices yielded minimal gains in c-statistics (<0.001-0.007) for predicting ADL decline, IADL decline, hospitalization, and death compared to indices without these markers.

CONCLUSIONS

Claims-based markers of disease severity did not contribute meaningfully to the ability of multimorbidity indices to predict ADL decline, mortality, and other important outcomes.

IMplementing Predictive Analytics towards efficient COPD Treatments (IMPACT): protocol for a stepped-wedge cluster randomized impact study

INTRODUCTION

Personalized disease management informed by quantitative risk prediction has the potential to improve patient care and outcomes. The integration of risk prediction into clinical workflow should be informed by the experiences and preferences of stakeholders, and the impact of such integration should be evaluated in prospective comparative studies. The objectives of the IMplementing Predictive Analytics towards efficient chronic obstructive pulmonary disease (COPD) treatments (IMPACT) study are to integrate an exacerbation risk prediction tool into routine care and to determine its impact on prescription appropriateness (primary outcome), medication adherence, quality of life, exacerbation rates, and sex and gender disparities in COPD care (secondary outcomes).

METHODS

IMPACT will be conducted in two phases. Phase 1 will include the systematic and user-centered development of two decision support tools: (1) a decision tool for pulmonologists called the ACCEPT decision intervention (ADI), which combines risk prediction from the previously developed Acute COPD Exacerbation Prediction Tool with treatment algorithms recommended by the Canadian Thoracic Society's COPD pharmacotherapy guidelines, and (2) an information pamphlet for COPD patients (patient tool), tailored to their prescribed medication, clinical needs, and lung function. In phase 2, we will conduct a stepped-wedge cluster randomized controlled trial in two outpatient respiratory clinics to evaluate the impact of the decision support tools on quality of care and patient outcomes. Clusters will be practicing pulmonologists (n ≥ 24), who will progressively switch to the intervention over 18 months. At the end of the study, a qualitative process evaluation will be carried out to determine the barriers and enablers of uptake of the tools.

DISCUSSION

The IMPACT study coincides with a planned harmonization of electronic health record systems across tertiary care centers in British Columbia, Canada. The harmonization of these systems combined with IMPACT's implementation-oriented design and partnership with stakeholders will facilitate integration of the tools into routine care, if the results of the proposed study reveal positive association with improvement in the process and outcomes of clinical care. The process evaluation at the end of the trial will inform subsequent design iterations before largescale implementation.

TRIAL REGISTRATION

NCT05309356.

Prognostic risk factors for moderate-to-severe exacerbations in patients with chronic obstructive pulmonary disease: a systematic literature review

Background

Chronic obstructive pulmonary disease (COPD) is a leading cause of morbidity and mortality worldwide. COPD exacerbations are associated with a worsening of lung function, increased disease burden, and mortality, and, therefore, preventing their occurrence is an important goal of COPD management. This review was conducted to identify the evidence base regarding risk factors and predictors of moderate-to-severe exacerbations in patients with COPD.

Methods

A literature review was performed in Embase, MEDLINE, MEDLINE In-Process, and the Cochrane Central Register of Controlled Trials (CENTRAL). Searches were conducted from January 2015 to July 2019. Eligible publications were peer-reviewed journal articles, published in English, that reported risk factors or predictors for the occurrence of moderate-to-severe exacerbations in adults age ≥ 40 years with a diagnosis of COPD.

Results

The literature review identified 5112 references, of which 113 publications (reporting results for 76 studies) met the eligibility criteria and were included in the review. Among the 76 studies included, 61 were observational and 15 were randomized controlled clinical trials. Exacerbation history was the strongest predictor of future exacerbations, with 34 studies reporting a significant association between history of exacerbations and risk of future moderate or severe exacerbations. Other significant risk factors identified in multiple studies included disease severity or bronchodilator reversibility (39 studies), comorbidities (34 studies), higher symptom burden (17 studies), and higher blood eosinophil count (16 studies).

Conclusions

This systematic literature review identified several demographic and clinical characteristics that predict the future risk of COPD exacerbations. Prior exacerbation history was confirmed as the most important predictor of future exacerbations. These prognostic factors may help clinicians identify patients at high risk of exacerbations, which are a major driver of the global burden of COPD, including morbidity and mortality.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12931-022-02123-5.

Prediction of 30-day risk of acute exacerbation of readmission in elderly patients with COPD based on support vector machine model

Background

Acute exacerbation of chronic obstructive pulmonary disease (COPD) is an important event in the process of disease management. Early identification of high-risk groups for readmission and appropriate measures can avoid readmission in some groups, but there is still a lack of specific prediction tools. The predictive performance of the model built by support vector machine (SVM) has been gradually recognized by the medical field. This study intends to predict the risk of acute exacerbation of readmission in elderly COPD patients within 30 days by SVM, in order to provide scientific basis for screening and prevention of high-risk patients with readmission.

Methods

A total of 1058 elderly COPD patients from the respiratory department of 13 general hospitals in Ningxia region of China from April 2019 to August 2020 were selected as the study subjects by convenience sampling method, and were followed up to 30 days after discharge. Discuss the influencing factors of patient readmission, and built four kernel function models of Linear-SVM, Polynomial-SVM, Sigmoid-SVM and RBF-SVM based on the influencing factors. According to the ratio of training set and test set 7:3, they are divided into training set samples and test set samples, Analyze and Compare the prediction efficiency of the four kernel functions by the precision, recall, accuracy, F1 index and area under the ROC curve (AUC).

Results

Education level, smoking status, coronary heart disease, hospitalization times of acute exacerbation of COPD in the past 1 year, whether long-term home oxygen therapy, whether regular medication, nutritional status and seasonal factors were the influencing factors for readmission. The training set shows that Linear-SVM, Polynomial-SVM, Sigmoid-SVM and RBF-SVM precision respectively were 69.89, 78.07, 79.37 and 84.21; Recall respectively were 50.78, 69.53, 78.74 and 88.19; Accuracy respectively were 83.92, 88.69, 90.81 and 93.82; F1 index respectively were 0.59, 0.74, 0.79 and 0.86; AUC were 0.722, 0.819, 0.866 and 0.918. Test set precision respectively were86.36, 87.50, 80.77 and 88.24; Recall respectively were51.35, 75.68, 56.76 and 81.08; Accuracy respectively were 85.11, 90.78, 85.11 and 92.20; F1 index respectively were 0.64, 0.81, 0.67 and 0.85; AUC respectively were 0.742, 0.858, 0.759 and 0.885.

Conclusions

This study found the factors that may affect readmission, and the SVM model constructed based on the above factors achieved a certain predictive effect on the risk of readmission, which has certain reference value.

Automatically Explaining Machine Learning Predictions on Severe Chronic Obstructive Pulmonary Disease Exacerbations: Retrospective Cohort Study

Background

Chronic obstructive pulmonary disease (COPD) is a major cause of death and places a heavy burden on health care. To optimize the allocation of precious preventive care management resources and improve the outcomes for high-risk patients with COPD, we recently built the most accurate model to date to predict severe COPD exacerbations, which need inpatient stays or emergency department visits, in the following 12 months. Our model is a machine learning model. As is the case with most machine learning models, our model does not explain its predictions, forming a barrier for clinical use. Previously, we designed a method to automatically provide rule-type explanations for machine learning predictions and suggest tailored interventions with no loss of model performance. This method has been tested before for asthma outcome prediction but not for COPD outcome prediction.

Objective

This study aims to assess the generalizability of our automatic explanation method for predicting severe COPD exacerbations.

Methods

The patient cohort included all patients with COPD who visited the University of Washington Medicine facilities between 2011 and 2019. In a secondary analysis of 43,576 data instances, we used our formerly developed automatic explanation method to automatically explain our model’s predictions and suggest tailored interventions.

Results

Our method explained the predictions for 97.1% (100/103) of the patients with COPD whom our model correctly predicted to have severe COPD exacerbations in the following 12 months and the predictions for 73.6% (134/182) of the patients with COPD who had ≥1 severe COPD exacerbation in the following 12 months.

Conclusions

Our automatic explanation method worked well for predicting severe COPD exacerbations. After further improving our method, we hope to use it to facilitate future clinical use of our model.

International Registered Report Identifier (IRRID)

RR2-10.2196/13783

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

Background

Chronic obstructive pulmonary disease (COPD) poses a large burden on health care. Severe COPD exacerbations require emergency department visits or inpatient stays, often cause an irreversible decline in lung function and health status, and account for 90.3% of the total medical cost related to COPD. Many severe COPD exacerbations are deemed preventable with appropriate outpatient care. Current models for predicting severe COPD exacerbations lack accuracy, making it difficult to effectively target patients at high risk for preventive care management to reduce severe COPD exacerbations and improve outcomes.

Objective

The aim of this study is to develop a more accurate model to predict severe COPD exacerbations.

Methods

We examined all patients with COPD who visited the University of Washington Medicine facilities between 2011 and 2019 and identified 278 candidate features. By performing secondary analysis on 43,576 University of Washington Medicine data instances from 2011 to 2019, we created a machine learning model to predict severe COPD exacerbations in the next year for patients with COPD.

Results

The final model had an area under the receiver operating characteristic curve of 0.866. 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 gained an accuracy of 90.33% (6801/7529), a sensitivity of 56.6% (103/182), and a specificity of 91.17% (6698/7347).

Conclusions

Our model provided a more accurate prediction of severe COPD exacerbations in the next year compared with prior published models. After further improvement of its performance measures (eg, by adding features extracted from clinical notes), our model could be used in a decision support tool to guide the identification of patients with COPD and at high risk for care management to improve outcomes.

International Registered Report Identifier (IRRID)

RR2-10.2196/13783

Predictive modeling of COPD exacerbation rates using baseline risk factors

Background:

Demographic and disease characteristics have been associated with the risk of chronic obstructive pulmonary disease (COPD) exacerbations. Using previously collected multinational clinical trial data, we developed models that use baseline risk factors to predict an individual’s rate of moderate/severe exacerbations in the next year on various pharmacological treatments for COPD.

Methods:

Exacerbation data from 20,054 patients in the ETHOS, KRONOS, TELOS, SOPHOS, and PINNACLE-1, PINNACLE-2, and PINNACLE-4 studies were pooled. Machine learning was used to identify predictors of moderate/severe exacerbation rates. Important factors were selected for generalized linear modeling, further informed by backward variable selection. An independent test set was held back for validation.

Results:

Prior exacerbations, eosinophil count, forced expiratory volume in 1 s percent predicted, prior maintenance treatments, reliever medication use, sex, COPD Assessment Test score, smoking status, and region were significant predictors of exacerbation risk, with response to inhaled corticosteroids (ICSs) increasing with higher eosinophil counts, more prior exacerbations, or additional prior treatments. Model fit was similar in the training and test set. Prediction metrics were ~10% better in the full model than in a simplified model based only on eosinophil count, prior exacerbations, and ICS use.

Conclusion:

These models predicting rates of moderate/severe exacerbations can be applied to a broad range of patients with COPD in terms of airway obstruction, eosinophil counts, exacerbation history, symptoms, and treatment history. Understanding the relative and absolute risks related to these factors may be useful for clinicians in evaluating the benefit: risk ratio of various treatment decisions for individual patients.

Clinical trials registered with www.clinicaltrials.gov (NCT02465567, NCT02497001, NCT02766608, NCT02727660, NCT01854645, NCT01854658, NCT02343458, NCT03262012, NCT02536508, and NCT01970878)

Predicting Severe Chronic Obstructive Pulmonary Disease Exacerbations. Developing a Population Surveillance Approach with Administrative Data

Rationale: Automatic prediction algorithms based on routinely collected health data may be able to identify patients at high risk for hospitalizations related to acute exacerbations of chronic obstructive pulmonary disease (COPD).Objectives: To conduct a proof-of-concept study of a population surveillance approach for identifying individuals at high risk of severe COPD exacerbations.Methods: We used British Columbia's administrative health databases (1997-2016) to identify patients with diagnosed COPD. We used data from the previous 6 months to predict the risk of severe exacerbation in the next 2 months after a randomly selected index date. We applied statistical and machine-learning algorithms for risk prediction (logistic regression, random forest, neural network, and gradient boosting). We used calibration plots and receiver operating characteristic curves to evaluate model performance based on a randomly chosen future date at least 1 year later (temporal validation).Results: There were 108,433 patients in the development dataset and 113,786 in the validation dataset; of these, 1,126 and 1,136, respectively, were hospitalized for COPD within their outcome windows. The best prediction algorithm (gradient boosting) had an area under the receiver operating characteristic curve of 0.82 (95% confidence interval, 0.80-0.83), which was significantly higher than the corresponding value for the model with exacerbation history as the only predictor (current standard of care: 0.68). The predicted risk scores were well calibrated in the validation dataset.Conclusions: Imminent COPD-related hospitalizations can be predicted with good accuracy using administrative health data. This model may be used as a means to target high-risk patients for preventive exacerbation therapies.

[Incidence of severe exacerbation in patients diagnosed with diabetes and chronic obstructive pulmonary disease: Cohort study]

Objetivo

Estimar en una cohorte de pacientes diagnosticados de EPOC y diabetes la incidencia de hospitalizaciones por exacerbación grave de la EPOC y sus factores asociados.

Diseño

Estudio prospectivo de cohorte.

Emplazamiento

Centros de Atención Primaria de Lleida ciudad (en total 7 centros).

Participantes

Se estudiaron 761 pacientes codiagnosticados de EPOC y diabetes. Los criterios de inclusión fueron pacientes de ambos sexos, igual o mayores de 40 años, residentes en el área geográfica de Lleida ciudad, con el diagnóstico de EPOC según los criterios de la guía GOLD, con espirometría reciente y una fracción FEV₁/FVC < 0,7; diagnosticados de DM2 según la guía de la International Diabetes Federation. Los criterios de exclusión fueron padecer alguna enfermedad física o psíquica grave.

Mediciones principales

Las variables del estudio fueron: el sexo, la edad, su área básica de salud en Lleida, índice de masa corporal, perímetro de cintura, hábito tabáquico y enólico, hipertensión arterial, insuficiencia cardiaca, insuficiencia renal crónica, FEV₁, FEV₁/FVC, categorización GOLD, HbA1c. Se registró la vacuna antigripal y antineumocócica. La variable dependiente fue la exacerbación grave. En el análisis estadístico la asociación de la variable dependiente con las variables independientes se determinó mediante el cálculo de la hazard ratio (HR) con el intervalo de confianza del 95%. La HR se estimó de forma ajustada mediante modelos de regresión de Cox no condicional.

Resultados

La incidencia de hospitalización por exacerbación grave de la EPOC fue del 9,98%; se objetivó un aumento del riesgo de exacerbación grave en pacientes diagnosticados de insuficiencia cardiaca (HR = 2,27; p = 0,002), y con una menor fracción de FEV₁/FVC. La vacuna antigripal y antineumocócica presentaron un papel protector débil sin ser estadísticamente significativa.

Conclusión

Se documenta una incidencia de exacerbaciones elevada en los pacientes codiagnosticados de EPOC y DM2. La insuficiencia cardiaca y una menor fracción FEV₁/FVC podrían aumentar el riesgo de exacerbación.

Using Computational Methods to Improve Integrated Disease Management for Asthma and Chronic Obstructive Pulmonary Disease: Protocol for a Secondary Analysis

Background

Asthma and chronic obstructive pulmonary disease (COPD) impose a heavy burden on health care. Approximately one-fourth of patients with asthma and patients with COPD are prone to exacerbations, which can be greatly reduced by preventive care via integrated disease management that has a limited service capacity. To do this well, a predictive model for proneness to exacerbation is required, but no such model exists. It would be suboptimal to build such models using the current model building approach for asthma and COPD, which has 2 gaps due to rarely factoring in temporal features showing early health changes and general directions. First, existing models for other asthma and COPD outcomes rarely use more advanced temporal features, such as the slope of the number of days to albuterol refill, and are inaccurate. Second, existing models seldom show the reason a patient is deemed high risk and the potential interventions to reduce the risk, making already occupied clinicians expend more time on chart review and overlook suitable interventions. Regular automatic explanation methods cannot deal with temporal data and address this issue well.

Objective

To enable more patients with asthma and patients with COPD to obtain suitable and timely care to avoid exacerbations, we aim to implement comprehensible computational methods to accurately predict proneness to exacerbation and recommend customized interventions.

Methods

We will use temporal features to accurately predict proneness to exacerbation, automatically find modifiable temporal risk factors for every high-risk patient, and assess the impact of actionable warnings on clinicians’ decisions to use integrated disease management to prevent proneness to exacerbation.

Results

We have obtained most of the clinical and administrative data of patients with asthma from 3 prominent American health care systems. We are retrieving other clinical and administrative data, mostly of patients with COPD, needed for the study. We intend to complete the study in 6 years.

Conclusions

Our results will help make asthma and COPD care more proactive, effective, and efficient, improving outcomes and saving resources.

International Registered Report Identifier (IRRID)

PRR1-10.2196/27065

Predicting Hospitalization Due to COPD Exacerbations in Swedish Primary Care Patients Using Machine Learning - Based on the ARCTIC Study

Purpose

Chronic obstructive pulmonary disease (COPD) exacerbations can negatively impact disease severity, progression, mortality and lead to hospitalizations. We aimed to develop a model that predicts a patient's risk of hospitalization due to severe exacerbations (defined as COPD-related hospitalizations) of COPD, using Swedish patient level data.

Patients and Methods

Patient level data for 7823 Swedish patients with COPD was collected from electronic medical records (EMRs) and national registries covering healthcare contacts, diagnoses, prescriptions, lab tests, hospitalizations and socioeconomic factors between 2000 and 2013. Models were created using machine-learning methods to predict risk of imminent exacerbation causing patient hospitalization due to COPD within the next 10 days. Exacerbations occurring within this period were considered as one event. Model performance was assessed using the Area under the Precision-Recall Curve (AUPRC). To compare performance with previous similar studies, the Area Under Receiver Operating Curve (AUROC) was also reported. The model with the highest mean cross validation AUPRC was selected as the final model and was in a final step trained on the entire training dataset.

Results

The most important factors for predicting severe exacerbations were exacerbations in the previous six months and in whole history, number of COPD-related healthcare contacts and comorbidity burden. Validation on test data yielded an AUROC of 0.86 and AUPRC of 0.08, which was high in comparison to previously published attempts to predict COPD exacerbation.

Conclusion

Our work suggests that clinically available information on patient history collected via automated retrieval from EMRs and national registries or directly during patient consultation can form the basis for future clinical tools to predict risk of severe COPD exacerbations.

Predicting Re-Exacerbation Timing and Understanding Prolonged Exacerbations: An Analysis of Patients with COPD in the ECLIPSE Cohort

PURPOSE

Understanding risk factors for an acute exacerbation of chronic obstructive pulmonary disease (AECOPD) is important for optimizing patient care. We re-analyzed data from the Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints (ECLIPSE) study (NCT00292552) to identify factors predictive of re-exacerbations and associated with prolonged AECOPDs.

METHODS

Patients with COPD from ECLIPSE with moderate/severe AECOPDs were included. The end of the first exacerbation was the index date. Timing of re-exacerbation risk was assessed in patients with 180 days' post-index-date follow-up data. Factors predictive of early (1-90 days) vs late (91-180 days) vs no re-exacerbation were identified using a multivariable partial-proportional-odds-predictive model. Explanatory logistic-regression modeling identified factors associated with prolonged AECOPDs.

RESULTS

Of the 1,554 eligible patients from ECLIPSE, 1,420 had 180 days' follow-up data: more patients experienced early (30.9%) than late (18.7%) re-exacerbations; 50.4% had no re-exacerbation within 180 days. Lower post-bronchodilator FEV1 (P=0.0019), a higher number of moderate/severe exacerbations on/before index date (P<0.0001), higher St. George's Respiratory Questionnaire total score (P=0.0036), and season of index exacerbation (autumn vs winter, P=0.00164) were identified as predictors of early (vs late/none) re-exacerbation risk within 180 days. Similarly, these were all predictors of any (vs none) re-exacerbation risk within 180 days. Median moderate/severe AECOPD duration was 12 days; 22.7% of patients experienced a prolonged AECOPD. The odds of experiencing a prolonged AECOPD were greater for severe vs moderate AECOPDs (adjusted odds ratio=1.917, P=0.002) and lower for spring vs winter AECOPDs (adjusted odds ratio=0.578, P=0.017).

CONCLUSION

Prior exacerbation history, reduced lung function, poorer respiratory-related quality-of-life (greater disease burden), and season may help identify patients who will re-exacerbate within 90 days of an AECOPD. Severe AECOPDs and winter AECOPDs are likely to be prolonged and may require close monitoring.

Identification of potentially undiagnosed patients with nontuberculous mycobacterial lung disease using machine learning applied to primary care data in the UK

Nontuberculous mycobacterial lung disease (NTMLD) is a rare lung disease often missed due to a low index of suspicion and unspecific clinical presentation. This retrospective study was designed to characterise the prediagnosis features of NTMLD patients in primary care and to assess the feasibility of using machine learning to identify undiagnosed NTMLD patients.IQVIA Medical Research Data (incorporating THIN, a Cegedim Database), a UK electronic medical records primary care database was used. NTMLD patients were identified between 2003 and 2017 by diagnosis in primary or secondary care or record of NTMLD treatment regimen. Risk factors and treatments were extracted in the prediagnosis period, guided by literature and expert clinical opinion. The control population was enriched to have at least one of these features.741 NTMLD and 112 784 control patients were selected. Annual prevalence rates of NTMLD from 2006 to 2016 increased from 2.7 to 5.1 per 100 000. The most common pre-existing diagnoses and treatments for NTMLD patients were COPD and asthma and penicillin, macrolides and inhaled corticosteroids. Compared to random testing, machine learning improved detection of patients with NTMLD by almost a thousand-fold with AUC of 0.94. The total prevalence of diagnosed and undiagnosed cases of NTMLD in 2016 was estimated to range between 9 and 16 per 100 000.This study supports the feasibility of machine learning applied to primary care data to screen for undiagnosed NTMLD patients, with results indicating that there may be a substantial number of undiagnosed cases of NTMLD in the UK.

The Acute COPD Exacerbation Prediction Tool (ACCEPT): a modelling study

BACKGROUND

Accurate prediction of exacerbation risk enables personalised care for patients with chronic obstructive pulmonary disease (COPD). We developed and validated a generalisable model to predict individualised rate and severity of COPD exacerbations.

METHODS

In this risk modelling study, we pooled data from three COPD trials on patients with a history of exacerbations. We developed a mixed-effect model to predict exacerbations over 1 year. Severe exacerbations were those requiring inpatient care. Predictors were history of exacerbations, age, sex, body-mass index, smoking status, domiciliary oxygen therapy, lung function, symptom burden, and current medication use. Evaluation of COPD Longitudinally to Identify Predictive Surrogate End-points (ECLIPSE), a multicentre cohort study, was used for external validation.

RESULTS

The development dataset included 2380 patients, 1373 (58%) of whom were men. Mean age was 64·7 years (SD 8·8). Mean exacerbation rate was 1·42 events per year and 0·29 events per year were severe. When validated against all patients with COPD in ECLIPSE (mean exacerbation rate was 1·20 events per year, 0·27 events per year were severe), the area-under-curve (AUC) was 0·81 (95% CI 0·79-0·83) for at least two exacerbations and 0·77 (95% CI 0·74-0·80) for at least one severe exacerbation. Predicted exacerbation and observed exacerbation rates were similar (1·31 events per year for all exacerbations and 0·25 events per year for severe exacerbations vs 1·20 events per year and 0·27 events per year). In ECLIPSE, in patients with previous exacerbation history (mean exacerbation rate was 1·82 events per year, 0·40 events per year were severe), AUC was 0·73 (95% CI 0·70-0·76) for two or more exacerbations and 0·74 (95% CI 0·70-0·78) for at least one severe exacerbation. Calibration was accurate for severe exacerbations (predicted 0·37 events per year vs observed 0·40 events per year) and all exacerbations (predicted 1·80 events per year vs observed 1·82 events per year).

INTERPRETATION

This model can be used as a decision tool to personalise COPD treatment and prevent exacerbations.

FUNDING

Canadian Institutes of Health Research.