Risk Calculator to Predict 30-Day Readmission After Coronary Artery Bypass: A Strategic Decision Support Tool

Prediction models for hospital readmissions in patients with heart disease: a systematic review and meta-analysis

OBJECTIVE

To describe the discrimination and calibration of clinical prediction models, identify characteristics that contribute to better predictions and investigate predictors that are associated with unplanned hospital readmissions.

DESIGN

Systematic review and meta-analysis.

DATA SOURCE

Medline, EMBASE, ICTPR (for study protocols) and Web of Science (for conference proceedings) were searched up to 25 August 2020.

ELIGIBILITY CRITERIA FOR SELECTING STUDIES

Studies were eligible if they reported on (1) hospitalised adult patients with acute heart disease; (2) a clinical presentation of prediction models with c-statistic; (3) unplanned hospital readmission within 6 months.

PRIMARY AND SECONDARY OUTCOME MEASURES

Model discrimination for unplanned hospital readmission within 6 months measured using concordance (c) statistics and model calibration. Meta-regression and subgroup analyses were performed to investigate predefined sources of heterogeneity. Outcome measures from models reported in multiple independent cohorts and similarly defined risk predictors were pooled.

RESULTS

Sixty studies describing 81 models were included: 43 models were newly developed, and 38 were externally validated. Included populations were mainly patients with heart failure (HF) (n=29). The average age ranged between 56.5 and 84 years. The incidence of readmission ranged from 3% to 43%. Risk of bias (RoB) was high in almost all studies. The c-statistic was <0.7 in 72 models, between 0.7 and 0.8 in 16 models and >0.8 in 5 models. The study population, data source and number of predictors were significant moderators for the discrimination. Calibration was reported for 27 models. Only the GRACE (Global Registration of Acute Coronary Events) score had adequate discrimination in independent cohorts (0.78, 95% CI 0.63 to 0.86). Eighteen predictors were pooled.

CONCLUSION

Some promising models require updating and validation before use in clinical practice. The lack of independent validation studies, high RoB and low consistency in measured predictors limit their applicability.

PROSPERO REGISTRATION NUMBER

CRD42020159839.

Current Trends in Readmission Prediction: An Overview of Approaches

Hospital readmission shortly after discharge threatens the quality of patient care and leads to increased medical care costs. In the United States, hospitals with high readmission rates are subject to federal financial penalties. This concern calls for incentives for healthcare facilities to reduce their readmission rates by predicting patients who are at high risk of readmission. Conventional practices involve the use of rule-based assessment scores and traditional statistical methods, such as logistic regression, in developing risk prediction models. The recent advancements in machine learning driven by improved computing power and sophisticated algorithms have the potential to produce highly accurate predictions. However, the value of such models could be overrated. Meanwhile, the use of other flexible models that leverage simple algorithms offer great transparency in terms of feature interpretation, which is beneficial in clinical settings. This work presents an overview of the current trends in risk prediction models developed in the field of readmission. The various techniques adopted by researchers in recent years are described, and the topic of whether complex models outperform simple ones in readmission risk stratification is investigated.

Patient and hospital factors associated with 30-day readmissions after coronary artery bypass graft (CABG) surgery: a systematic review and meta-analysis

BACKGROUND

Readmission after coronary artery bypass graft (CABG) surgery is associated with adverse outcomes and significant healthcare costs, and 30-day readmission rate is considered as a key indicator of the quality of care. This study aims to: quantify rates of readmission within 30 days of CABG surgery; explore the causes of readmissions; and investigate how patient- and hospital-level factors influence readmission.

METHODS

We conducted systematic searches (until June 2020) of PubMed and Embase databases to retrieve observational studies that investigated readmission after CABG. Random effect meta-analysis was used to estimate rates and predictors of 30-day post-CABG readmission.

RESULTS

In total, 53 studies meeting inclusion criteria were identified, including 8,937,457 CABG patients. The pooled 30-day readmission rate was 12.9% (95% CI: 11.3-14.4%). The most frequently reported underlying causes of 30-day readmissions were infection and sepsis (range: 6.9-28.6%), cardiac arrythmia (4.5-26.7%), congestive heart failure (5.8-15.7%), respiratory complications (1-20%) and pleural effusion (0.4-22.5%). Individual factors including age (OR per 10-year increase 1.12 [95% CI: 1.04-1.20]), female sex (OR 1.29 [1.25-1.34]), non-White race (OR 1.15 [1.10-1.21]), not having private insurance (OR 1.39 [1.27-1.51]) and various comorbidities were strongly associated with 30-day readmission rates, whereas associations with hospital factors including hospital CABG volume, surgeon CABG volume, hospital size, hospital quality and teaching status were inconsistent.

CONCLUSIONS

Nearly 1 in 8 CABG patients are readmitted within 30 days and the majority of these are readmitted for noncardiac causes. Readmission rates are strongly influenced by patients' demographic and clinical characteristics, but not by broadly defined hospital characteristics.

Predictors of unplanned 30-day readmissions after coronary artery bypass graft: a systematic review and meta-analysis of cohort studies

AIMS

Coronary artery bypass graft (CABG) is one of the most performed cardiac surgery globally. CABG is known to have a high rate of short-term readmissions. The 30-day unplanned readmission rate as a quality measure is associated with adverse health outcomes. This study aimed to identify and synthesize the perioperative risk factors for 30-day unplanned readmission after CABG.

METHODS AND RESULTS

We systematically searched seven databases and reviewed studies to identify all eligible English articles published from 1 October 1999 to 30 September 2019. Random-effect models were employed to perform pooled analyses. Odds ratio and 95% confidence interval were used to estimate the risk factors for 30-day unplanned readmission. The 30-day hospital readmission rates after CABG ranged from 9.2% to 18.9% in 14 cohort studies. Among preoperative characteristics, older adults, female, weight loss, high serum creatinine, anticoagulant use or dialysis, and comorbidities were found to be statistically significant. Postoperative complications, prolonged length of hospital stay, and mechanical ventilation were revealed as the postoperative risk factors for 30-day unplanned readmission. However, intraoperative risk factors were not found to be significant in this review.

CONCLUSION

Our findings emphasize the importance of a comprehensive assessment during the perioperative period of CABG. Healthcare professionals can perform a readmission risk stratification and develop strategies to reduce readmission rates after CABG using the risk factors identified in this review. Future studies with prospective cohort samples are needed to identify the personal or psychosocial factors influencing readmission after CABG, including perioperative risk factors.

Thirty-day Hospital Readmissions Following Cardiac Surgery are Associated With Mortality and Subsequent Readmission

The aim of the current study was to assess the impact of hospital readmissions within 30-days of discharge, on long-term postoperative outcomes. All patients who underwent cardiac surgery from 2011 - 2018 were included. Patients who had transcatheter procedures, VAD, and transplant were excluded. Inverse probability of treatment weighting (IPTW) propensity scoring was used for population risk adjustment. Multivariable analysis was performed to identify association with long-term mortality and readmission. The total risk adjusted (propensity scoring with IPTW) patient population consisted of 14,538 patients divided into those who were not readmitted in 30-days (nonreadmitted) (n = 12,627) and patients who were readmitted within 30-days (30-day readmitted) (n = 1911). Following IPTW, all baseline characteristics and postoperative complications were equivalent between cohorts (SMD <0.10). Patients who required intraoperative [OR 1.178 (1.05, 1.32); P = 0.006] and postoperative [1.32 (1.18, 1.48); P < 0.001] blood transfusions were at greater risk for 30-day readmission. Median follow-up period was 4.19 years (2.45 - 6.10). The 30-day readmission cohort had a significantly higher mortality risk during early (6 months) follow-up [HR 2.49 (2.01-3.10); P < 0.001] and late (60 months) follow-up [HR 1.30 (1.16-1.47); P < 0.001]. After risk adjustment, the 30-day readmission cohort was significantly associated with increased mortality over the study follow-up period [HR 1.62 (1.48, 1.78); P < 0.001]. 30-day readmissions were an independent predictor of subsequent long-term hospital readmission [HR 1.61 (1.50, 1.73); P < 0.001]. Patients who require 30-day readmissions following cardiac surgery are at increased risk of long-term mortality and repeat readmissions. Early postoperative hospital readmission may be a marker for worse long-term outcomes in cardiac surgery.

Development of a Risk Score to Predict 90-Day Readmission After Coronary Artery Bypass Graft

BACKGROUND

Readmission after coronary artery bypass grafting (CABG) is used for quality metrics and may negatively affect hospital reimbursement. Our objective was to develop a risk score system from a national cohort that can predict 90-day readmission risk for CABG patients.

METHODS

Using the National Readmission Database between 2013 and 2014, we identified 104,930 patients discharged after CABG, for a total of 234,483 patients after weighted analysis. Using structured random sampling, patients were divided into a training set (60%) and test data set (40%). In the training data set, we used multivariable analysis to identify risk factors. A point system risk score was developed based on the odds ratios. Variables with odds ratio less than 1.3 were excluded from the final model to reduce noise. Performance was assessed in the test data set using receiver operator characteristics and accuracy.

RESULTS

In the United States, overall 90-day readmission rate after CABG was 19% (n = 44,559 of 234,483). Nine demographic and clinical variables were identified as important in the training data set. The final risk score ranged from 0 to 52; the 2 largest risks were associated with length of stay greater than 10 days (score = +10) and Medicaid insurance (score = +7). The final model's C-statistic was 0.67. Using an optimal cutoff of 18 points, the accuracy of the risk score was 77%.

CONCLUSIONS

Ninety-day readmission after CABG surgery is frequent. A readmission risk score higher than 18 points predicts readmission in 77% of patients. Based on 9 demographic and clinical factors, this risk score can be used to target high-risk patients for additional postdischarge resources to reduce readmission.

Unplanned 30-Day Readmission after Coronary Artery Bypass in Patients with Acute Myocardial Infarction

BACKGROUND/PURPOSE

The Centers for Medicare and Medicaid services penalizes hospitals with higher than expected readmissions for coronary artery bypass graft surgery (CABG). Little information exists regarding outcomes in patients who sustain an acute myocardial infarction (MI) and undergo CABG as the primary revascularization strategy. Our goal was to determine the unplanned 30-day readmission rate in this high-risk population and predictors of readmission.

MATERIALS/METHODS

An institutional database was queried to identify patients from 2011 to 2017 who were admitted with an acute MI and underwent CABG within 30 days. Chart review was performed to collect demographics, medical comorbidities and clinical information related to hospital course and readmission status.

RESULTS

A total of 150 patients were included. The 30-day unplanned readmission rate was 23%, and the majority (80%) were non-cardiac related. Predictors of unplanned readmission included female sex (OR 2.61, 95% CI 1.042-6.549, p = 0.041), CABG performed <7 days following MI (OR 2.82, 95% CI 1.21-6.59, p = 0.017), and post-operative atrial fibrillation (OR 3.25, 95% CI 1.07-9.87, p = 0.038). Complications were identified in 32% of clinic visits in patients who did not require readmission.

CONCLUSIONS

Patients who undergo CABG following MI are a high-risk population with nearly one-quarter readmitted within 30 days. Female sex, <7 days between the index MI and CABG, and post-operative atrial fibrillation are strong predictors for readmission. Early outpatient follow-up may be an effective intervention to reduce hospital readmissions by reassuring patients that non-cardiac symptoms are in line with anticipated post-operative pain and healing.