Is It Time to Move on from Identifying Risk Factors for 30-Day Chronic Obstructive Pulmonary Disease Readmission? A Call for Risk Prediction Tools

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.

Effectiveness of a Bundled Payments for Care Improvement Program for Chronic Obstructive Pulmonary Disease

BACKGROUND

The Medicare Bundled Payments for Care Improvement (BPCI) program reimburses 90-day care episodes post-hospitalization. COPD is a leading cause of early readmissions making it a target for value-based payment reform.

OBJECTIVE

Evaluate the financial impact of a COPD BPCI program.

DESIGN, PARTICIPANTS, INTERVENTIONS

A single-site retrospective observational study evaluated the impact of an evidence-based transitions of care program on episode costs and readmission rates, comparing patients hospitalized for COPD exacerbations who received versus those who did not receive the intervention.

MAIN MEASURES

Mean episode costs and readmissions.

KEY RESULTS

Between October 2015 and September 2018, 132 received and 161 did not receive the program, respectively. Mean episode costs were below target for six out of eleven quarters for the intervention group, as opposed to only one out of twelve quarters for the control group. Overall, there were non-significant mean savings of $2551 (95% CI: - $811 to $5795) in episode costs relative to target costs for the intervention group, though results varied by index admission diagnosis-related group (DRG); there were additional costs of $4184 per episode for the least-complicated cohort (DRG 192), but savings of $1897 and $1753 for the most complicated index admissions (DRGs 191 and 190, respectively). A significant mean decrease of 0.24 readmissions per episode was observed in 90-day readmission rates for intervention relative to control. Readmissions and hospital discharges to skilled nursing facilities were factors of higher costs (mean increases of $9098 and $17,095 per episode respectively).

CONCLUSIONS

Our COPD BPCI program had a non-significant cost-saving effect, although sample size limited study power. The differential impact of the intervention by DRG suggests that targeting interventions to more clinically complex patients could increase the financial impact of the program. Further evaluations are needed to determine if our BPCI program decreased care variation and improved quality of care.

PRIMARY SOURCE OF FUNDING

This research was supported by NIH NIA grant #5T35AG029795-12.

Using Machine Learning to Predict Likelihood and Cause of Readmission After Hospitalization for Chronic Obstructive Pulmonary Disease Exacerbation

Background

Chronic obstructive pulmonary disease (COPD) is a leading cause of hospital readmissions. Few existing tools use electronic health record (EHR) data to forecast patients’ readmission risk during index hospitalizations.

Objective

We used machine learning and in-hospital data to model 90-day risk for and cause of readmission among inpatients with acute exacerbations of COPD (AE-COPD).

Design

Retrospective cohort study.

Participants

Adult patients admitted for AE-COPD at the University of Chicago Medicine between November 7, 2008 and December 31, 2018 meeting International Classification of Diseases (ICD)-9 or −10 criteria consistent with AE-COPD were included.

Methods

Random forest models were fit to predict readmission risk and respiratory-related readmission cause. Predictor variables included demographics, comorbidities, and EHR data from patients’ index hospital stays. Models were derived on 70% of observations and validated on a 30% holdout set. Performance of the readmission risk model was compared to that of the HOSPITAL score.

Results

Among 3238 patients admitted for AE-COPD, 1103 patients were readmitted within 90 days. Of the readmission causes, 61% (n = 672) were respiratory-related and COPD (n = 452) was the most common. Our readmission risk model had a significantly higher area under the receiver operating characteristic curve (AUROC) (0.69 [0.66, 0.73]) compared to the HOSPITAL score (0.63 [0.59, 0.67]; p = 0.002). The respiratory-related readmission cause model had an AUROC of 0.73 [0.68, 0.79].

Conclusion

Our models improve on current tools by predicting 90-day readmission risk and cause at the time of discharge from index admissions for AE-COPD. These models could be used to identify patients at higher risk of readmission and direct tailored post-discharge transition of care interventions that lower readmission risk.

Health System Approach to Improve COPD Care After Hospital Discharge: Stepped Wedge Clinical Trial

RATIONALE

Patients discharged from hospital for COPD exacerbation have impaired quality of life and frequent readmission and death. Clinical trials to reduce readmission demonstrate inconsistent results, including some demonstrating potential harms.

OBJECTIVE

We tested whether a pragmatic proactive interdisciplinary and virtual review of patients discharged after hospitalization for COPD exacerbation would improve quality of life, using the Clinical COPD Questionnaire (CCQ), and reduce all-cause 180-day readmission/mortality.

METHODS

We performed a stepped-wedge clinical trial. We enrolled primary care providers and their patients after hospital discharge for COPD at two VA Medical Centers and ten outpatient clinics. A multidisciplinary team reviewed health records and developed treatment recommendations delivered to primary care providers via E-consult. We facilitated uptake by entering recommendations as unsigned orders that could be accepted, modified, or canceled. Providers and patients made all final treatment decisions.

MEASUREMENTS AND MAIN RESULTS

We enrolled 365 primary care providers. Over a 30-month period, 352 patients met eligibility criteria, with 191 (54.3%) patients participating in the control and 161 (45.7%) in the intervention. The intervention led to clinically significant better CCQ scores (-0.47 (95% CI, -0.85 to -0.09), 52.6% missing), but did not reduce 180-day readmission/mortality (aOR 0.83 (95% CI, 0.49 to 1.38)), in part because of wide confidence intervals. Among the 161 intervention patients, we entered 519 recommendations as unsigned orders, of which 401 (77.3%) were endorsed.

CONCLUSION

A pragmatic health system-level intervention that delivered proactive specialty supported care improved quality of life but did not reduce 180-day readmission or death. Clinical trial registration available at www.

CLINICALTRIALS

gov, ID: NCT02021955.

Preventing COPD Readmissions Under the Hospital Readmissions Reduction Program: How Far Have We Come?

The Hospital Readmissions Reduction Program (HRRP) was developed and implemented by the Centers for Medicare & Medicaid Services to curb the rate of 30-day hospital readmissions for certain common, high-impact conditions. In October 2014, COPD became a target condition for which hospitals were penalized for excess readmissions. The appropriateness, utility, and potential unintended consequences of the metric have been a topic of debate since it was first enacted. Nevertheless, there is evidence that hospital policies broadly implemented in response to the HRRP may have been responsible for reducing the rate of readmissions following COPD hospitalizations even before it was added as a target condition. Since the addition of the COPD condition to the HRRP, several predictive models have been developed to predict COPD survival and readmissions, with the intention of identifying modifiable risk factors. A number of interventions have also been studied, with mixed results. Bundled care interventions using the electronic health record and patient education interventions for inhaler education have been shown to reduce readmissions, whereas pulmonary rehabilitation, follow-up visits, and self-management programs have not been consistently shown to do the same. Through this program, COPD has become recognized as a public health priority. However, 5 years after COPD became a target condition for HRRP, there continues to be no single intervention that reliably prevents readmissions in this patient population. Further research is needed to understand the long-term effects of the policy, the role of competing risks in measuring quality, the optimal postdischarge care for patients with COPD, and the integrated use of predictive modeling and advanced technologies to prevent COPD readmissions.

Contribution of social factors to readmissions within 30 days after hospitalization for COPD exacerbation

BACKGROUND

To investigate whether, in patients hospitalized for COPD, the addition of social factors improves the predictive ability for the risk of overall 30-day readmissions, early readmissions (within 7 days after discharge), and late readmissions (8-30 days after discharge).

METHODS

Patients (aged ≥40 years) hospitalized for COPD were identified in the Medicare Current Beneficiary Survey from 2006 through 2012. With the use of 1000 bootstrap resampling from the original cohort (training-set), two prediction models were derived: 1) the reference model including age, comorbidities, and mechanical ventilation use, and 2) the optimized model including social factors (e.g., educational level, marital status) in addition to the covariates in the reference model. Prediction performance was examined separately for 30-day, early, and late readmissions.

RESULTS

Following 905 index hospitalizations for COPD, 18.5% were readmitted within 30 days. In the test-set, for overall 30-day readmissions, the discrimination ability between reference and optimized models did not change materially (C-statistic, 0.57 vs. 0.58). By contrast, for early readmissions, the optimized model had significantly improved discrimination (C-statistic, 0.57 vs. 0.63; integrated discrimination improvement [IDI], 0.018 [95%CI, 0.003-0.032]) and reclassification (continuous net reclassification index [NRI], 0.298 [95%CI 0.060-0.537]). Likewise, for late readmissions, the optimized model also had significantly improved discrimination (C-statistic, 0.65 vs. 0.68; IDI, 0.026 [95%CI 0.009-0.042]) and reclassification (continuous NRI, 0.243 [95%CI 0.028-0.459]).

CONCLUSIONS

In a nationally-representative sample of Medicare beneficiaries hospitalized for COPD, we found that the addition of social factors improved the predictive ability for readmissions when early and late readmissions were examined separately.

Reducing Chronic Obstructive Pulmonary Disease Hospital Readmissions. An Official American Thoracic Society Workshop Report

Chronic obstructive pulmonary disease (COPD) is the third leading cause of hospital readmissions in the United States. The quality of care delivered to patients with COPD is known to be lacking across the care continuum, and may contribute to high rates of readmission. As part of the response to these issues, the Centers for Medicare and Medicaid instituted a penalty for 30-day readmissions as part of their Hospital Readmission Reduction Program in October 2014. At the time the penalty was instated, there was little published evidence on effective hospital-based programs to reduce readmissions after acute exacerbations of COPD. Even now, several years later, few published programs exist, and we continue to lack consistent approaches that lead to improved readmission rates. In addition, there was concern that the penalty would widen health disparities. Despite the dearth of published evidence to reduce readmissions beyond available COPD guidelines, many hospitals across the United States began to develop and implement programs, based on little evidence, due to the financial penalty. We, therefore, assembled a diverse group of clinicians, researchers, payers, and program leaders from across the country to present and discuss approaches that had the greatest potential for success. We drew on expertise from ongoing readmission reduction programs, implementation methodologies, and stakeholder perspectives to develop this Workshop Report on current best practices and models for addressing COPD hospital readmissions.

Factors Associated with Differential Readmission Diagnoses Following Acute Exacerbations of Chronic Obstructive Pulmonary Disease

BACKGROUND

Readmissions after exacerbations of chronic obstructive pulmonary disease (COPD) are penalized under the Hospital Readmissions Reduction Program (HRRP). Understanding attributable diagnoses at readmission would improve readmission reduction strategies.

OBJECTIVES

Determine factors that portend 30-day readmissions attributable to COPD versus non-COPD diagnoses among patients discharged following COPD exacerbations.

DESIGN, SETTING, AND PARTICIPANTS

We analyzed COPD discharges in the Nationwide Readmissions Database from 2010 to 2016 using inclusion and readmission definitions in HRRP.

MAIN OUTCOMES AND MEASURES

We evaluated readmission odds for COPD versus non-COPD returns using a multilevel, multinomial logistic regression model. Patient-level covariates included age, sex, community characteristics, payer, discharge disposition, and Elixhauser Comorbidity Index. Hospital-level covariates included hospital ownership, teaching status, volume of annual discharges, and proportion of Medicaid patients.

RESULTS

Of 1,622,983 (a weighted effective sample of 3,743,164) eligible COPD hospitalizations, 17.25% were readmitted within 30 days (7.69% for COPD and 9.56% for other diagnoses). Sepsis, heart failure, and respiratory infections were the most common non-COPD return diagnoses. Patients readmitted for COPD were younger with fewer comorbidities than patients readmitted for non-COPD. COPD returns were more prevalent the first two days after discharge than non-COPD returns. Comorbidity was a stronger driver for non-COPD (odds ratio [OR] 1.19) than COPD (OR 1.04) readmissions.

CONCLUSION

Thirty-day readmissions following COPD exacerbations are common, and 55% of them are attributable to non-COPD diagnoses at the time of return. Higher burden of comorbidity is observed among non-COPD than COPD rehospitalizations. Readmission reduction efforts should focus intensively on factors beyond COPD disease management to reduce readmissions considerably by aggressively attempting to mitigate comorbid conditions.

Social and clinical predictors of short- and long-term readmission after a severe exacerbation of copd

INTRODUCTION AND OBJECTIVES

The aim of this study was to evaluate the predictive ability of multiple social, and clinical factors for readmission after a severe acute exacerbation of COPD (AECOPD) during various time periods.

METHODS

We performed a prospective cohort study in which recruited patients with AECOPD. We systematically collected numerous clinical (symptoms, pulmonary function, comorbidities, and treatment) and social (financial situation, housing situation, family support, caregiver overload, ability to perform activities, and risk of social exclusion) variables using several questionnaires and indices. The patients were followed closely for one year and readmissions at 30, 60, and 365 days were analysed.

RESULTS

253 patients were included, aged 68.9±9.8years, FEV1 = 42.1%±14.2%, and a Charlson's index = 1.8±0.9. Of these patients, 20.2%, 39.6%, and 63.7% were readmitted within the first 30, 90, and 365 days after discharge, respectively. In the multivariate model applied, the variables that were independently associated with readmission over all three periods of the analysis were dependence to perform basic activities of daily living (BADLs) (odds ratio [OR] = 2.10-4.10) and a history of two or more admissions within the previous year (OR = 2.78-3.78). At 90 days, a history of bacterial isolates in a previous sputum culture (OR = 2.39) and at 365 days, a high grade of dyspnoea (OR = 2.51) and obesity (OR = 2.38) were also identified as predictors of hospital readmission.

CONCLUSIONS

The patients' limitation to perform BADLs and their history of admissions for AECOPD were the best predictive variables for the likelihood of readmission when adjusted for many other social and clinical variables, regardless of the time period considered for such prediction.

Laboratory-based Intermountain Validated Exacerbation (LIVE) Score stability in patients with chronic obstructive pulmonary disease

BACKGROUND

The Laboratory-based Intermountain Validated Exacerbation (LIVE) Score is associated with mortality and chronic obstructive pulmonary disease (COPD) exacerbation risk across multiple health systems. However, whether the LIVE Score and its associated risk is a stable patient characteristic is unknown.

METHODS

We validated the LIVE Score in a fourth health system. Then we determined the LIVE Score stability in a retrospective cohort of 98 766 patients with COPD in four health systems where it was previously validated. We assessed whether LIVE Scores changed or remained the same over time. Stability was defined as a majority of surviving patients having the same LIVE Score 4 years later.

RESULTS

The LIVE Score separated patients into three LIVE Score risk groups of low, medium, and high mortality and LIVE Score stability. Mortality ranged from 6.2% for low-risk LIVE to 45.8% for high-risk LIVE (p<0.001). We found that low-risk LIVE groups were stable and high-risk LIVE groups were unstable. Low-risk LIVE group patients remained low risk, but few high-risk LIVE group patients remained high risk (79.0% high vs 48.1% medium vs 8.8% low, p<0.001 for all pairwise comparisons).

CONCLUSION

The LIVE Score identifies three major clinically actionable cohorts: a stable low-risk LIVE group, an unstable high-risk LIVE group with high mortality rates, and a medium-risk LIVE group. These observations further our understanding of how existing data used to calculate the LIVE Score may target interventions across risk cohorts of patients with COPD in a health system.

Per cent low attenuation volume and fractal dimension of low attenuation clusters on CT predict different long-term outcomes in COPD

BACKGROUND

Fractal dimension (D) characterises the size distribution of low attenuation clusters on CT and assesses the spatial heterogeneity of emphysema that per cent low attenuation volume (%LAV) cannot detect. This study tested the hypothesis that %LAV and D have different roles in predicting decline in FEV₁, exacerbation and mortality in patients with COPD.

METHODS

Chest inspiratory CT scans in the baseline and longitudinal follow-up records for FEV₁, exacerbation and mortality prospectively collected over 10 years in the Hokkaido COPD Cohort Study were examined (n=96). The associations between CT measures and long-term outcomes were replicated in the Kyoto University cohort (n=130).

RESULTS

In the Hokkaido COPD cohort, higher %LAV, but not D, was associated with a greater decline in FEV₁ and 10-year mortality, whereas lower D, but not %LAV, was associated with shorter time to first exacerbation. Multivariable analysis for the Kyoto University cohort confirmed that lower D at baseline was independently associated with shorter time to first exacerbation and that higher LAV% was independently associated with increased mortality after adjusting for age, height, weight, FEV₁ and smoking status.

CONCLUSION

These well-established cohorts clarify the different prognostic roles of %LAV and D, whereby lower D is associated with a higher risk of exacerbation and higher %LAV is associated with a rapid decline in lung function and long-term mortality. Combination of %LAV and fractal D may identify COPD subgroups at high risk of a poor clinical outcome more sensitively.

Socioeconomic status (SES) and 30-day hospital readmissions for chronic obstructive pulmonary (COPD) disease: A population-based cohort study

BACKGROUND

Patients with chronic obstructive pulmonary disease (COPD) are more likely to be readmitted than patients with other chronic medical conditions, yet knowledge regarding such readmissions is limited. We aimed to determine factors associated with readmission within 30 days of a COPD hospitalization or death with an emphasis on examining aspects of socioeconomic status and specific comorbidities.

METHODS

A population-based cohort study was conducted using health administrative data from Ontario, Canada. All hospitalizations for COPD between 2004 and 2014 were considered. The primary exposures were socioeconomic status as measured by residential instability (an ecologic variable), and comorbidities such as cardiovascular disease and cancer. Other domains of socioeconomic status were considered as secondary exposures. Logistic regression with generalized estimating equations was used to examine the effect of exposures, adjusting for other patient factors, on 30-day readmission or death.

RESULTS

There were 126,013 patients contributing to 252,756 index COPD hospitalizations from 168 Ontario hospitals. Of these hospitalizations, 19.4% resulted in a readmission and 2.8% resulted in death within 30 days. After adjusting for other factors, readmissions or death were modestly more likely among people with the highest residential instability compared to the lowest (OR 1.05, 95% CI 1.01-1.09). Comorbidities such as cardiovascular disease and cancer, as well as other aspects of low socioeconomic status also increased readmission or death risk.

INTERPRETATION

Socioeconomic status, measured in various ways, and many comorbidities predict 30-day readmission or death in patients hospitalized for COPD. Strategies that address these factors may help reduce readmissions and death.