Development of Multivariable Prediction Models for the Identification of Patients Admitted to Hospital with an Exacerbation of COPD and the Prediction of Risk of Readmission: A Retrospective Cohort Study using Electronic Medical Record Data.

BACKGROUND: Approximately 20% of patients who are discharged from hospital for an acute exacerbation of COPD (AECOPD) are readmitted within 30 days. To reduce this, it is important both to identify all individuals admitted with AECOPD and to predict those who are at higher risk for readmission. OBJECTIVES: To develop two clinical prediction models using data available in electronic medical records: 1) identifying patients admitted with AECOPD and 2) predicting 30-day readmission in patients discharged after AECOPD. METHODS: Two datasets were created using all admissions to General Internal Medicine from 2012 to 2018 at two hospitals: one cohort to identify AECOPD and a second cohort to predict 30-day readmissions. We fit and internally validated models with four algorithms. RESULTS: Of the 64,609 admissions, 3,620 (5.6%) were diagnosed with an AECOPD. Of those discharged, 518 (15.4%) had a readmission to hospital within 30 days. For identification of patients with a diagnosis of an AECOPD, the top-performing models were LASSO and a four-variable regression model that consisted of specific medications ordered within the first 72 hours of admission. For 30-day readmission prediction, a two-variable regression model was the top performing model consisting of number of COPD admissions in the previous year and the number of non-COPD admissions in the previous year. CONCLUSION: We generated clinical prediction models to identify AECOPDs during hospitalization and to predict 30-day readmissions after an acute exacerbation from a dataset derived from available EMR data. Further work is needed to improve and externally validate these models.

Research Quality and Impact of Cardiac Rehabilitation in Cancer Survivors: A Systematic Review and Meta-Analysis.

Background: Cardiac rehabilitation (CR) is endorsed to improve cardiovascular outcomes in cancer survivors. The quality of CR-based research in oncology has not been assessed. Objectives: The aim of this study was to evaluate the quality of reporting and evidence from CR-based intervention studies in oncology and to explore associations between intervention participation and outcomes. Methods: Systematic searches of 5 databases were conducted (January 2020) and updated (September 2021). Randomized and nonrandomized studies evaluating CR-based interventions in adult cancer survivors during and after treatment were eligible. Independent reviewers extracted data using 2 reporting guidelines (Template for Intervention Description and Replication and Consolidated Standards for Reporting Trials Harms extension), risk of bias (ROB) assessment tools (Cochrane ROB 2.0 and Cochrane Risk of Bias in Non-Randomized Studies of Interventions), and a combined inventory (Tool for the Assessment of Study Quality and reporting in Exercise). A meta-analysis was used to explore pre-intervention/post-intervention differences for commonly assessed outcomes. Results: Ten studies involving data from 685 survivors were included. The mean quality scores for intervention reporting (Template for Intervention Description and Replication) and harms (Consolidated Standards for Reporting Trials Harms extension) were 62% and 17%, respectively. There was moderate-to-high ROB across nonrandomized (Cochrane Risk of Bias in Non-Randomized Studies of Interventions score: 25%) and randomized (ROB 2.0 score: 50%) studies. The mean standardized cardiorespiratory fitness was higher (0.42; 95% CI: 0.27-0.57), fatigue was lower (-0.45; 95% CI: -0.55 to -0.34), and percent body fat (0.07; 95% CI: -0.23 to 0.38) was not different in survivors completing CR compared with those not completing CR. Conclusions: CR-based studies in oncology have low-to-moderate reporting quality and moderate-to-high ROB limiting interpretation, reproducibility, and translation of this evidence into practice.