A Clinical Risk Scoring Tool to Predict Readmission After Cardiac Surgery: An Ontario Administrative and Clinical Population Database Study

Outcomes following coronary artery bypass grafting with multiple arterial grafting by pump status in men and women

BACKGROUND

Multiple arterial grafting (MAG) and off-pump surgery are strategies proposed to improve outcomes with coronary artery bypass grafting (CABG). This study was conducted to determine the impact of off-pump surgery on outcomes after CABG with MAG in men and women.

METHODS

This cohort study used population-based data to identify all Ontarians undergoing isolated CABG with MAG between October 2008 and September 2019. The primary outcome was all-cause mortality. Secondary outcomes included major adverse cardiac and cerebrovascular events (MACCE; hospitalization for stroke, myocardial infarction hospitalization or heart failure, or repeat revascularization). Analysis used propensity-score overlap-weighted cause-specific Cox proportional hazard regression.

RESULTS

A total of 2989 women (1188 off-pump, 1801 on-pump) and 16,209 men (6065 off-pump, 10,144 on-pump) underwent MAG with a median follow-up of 5.0 years (interquartile range, 2.7-8.0) years. Compared to the on-pump approach, all-cause mortality was not changed with off-pump status (hazard ratio [HR] in women: 1.25 [95% CI, 0.83-1.88]; in men: 1.08 [95% CI, 0.85-1.37]). In women, the risk of MACCE was significantly higher off-pump (HR, 1.45; 95% CI, 1.04-2.03), with nonsignificantly increased risk observed for all component outcomes.

CONCLUSIONS

In patients undergoing CABG with MAG, this population-based analysis found no association between pump status and survival in either men or women. However, it did suggest that off-pump MAG in women may be associated with an increased risk of MACCE.

Derivation and validation of predictive indices for cardiac readmission after coronary and valvular surgery - A multicenter study

Objective

To derive and validate models to predict the risk of a cardiac readmission within one year after specific cardiac surgeries using information that is commonly available from hospital electronic medical records.

Methods

In this retrospective cohort study, we derived and externally validated clinical models to predict the likelihood of cardiac readmissions within one-year of isolated CABG, AVR, and combined CABG+AVR in Ontario, Canada, using multiple clinical registries and routinely collected administrative databases. For all adult patients who underwent these procedures, multiple Fine and Gray subdistribution hazard models were derived within a competing-risk framework using the cohort from April 2015 to March 2018 and validated in an independent cohort (April 2018 to March 2020).

Results

For the model that predicted post-CABG cardiac readmission, the c-statistic was 0.73 in the derivation cohort and 0.70 in the validation cohort at one-year. For the model that predicted post-AVR cardiac readmission, the c-statistic was 0.74 in the derivation and 0.73 in the validation cohort at one-year. For the model that predicted cardiac readmission following CABG+AVR, the c-statistic was 0.70 in the derivation and 0.66 in the validation cohort at one-year.

Conclusions

Prediction of one-year cardiac readmission for isolated CABG, AVR, and combined CABG+AVR can be achieved parsimoniously using multidimensional data sources. Model discrimination was better than existing models derived from single and multicenter registries.

Population study of sex-based outcomes after surgical aortic valve replacement

Background

Surgical aortic valve replacement (SAVR) is a key strategy for the treatment of aortic valve disease. However, studies have involved primarily male patients, and whether the benefits of this approach can be extrapolated to female patients is unclear.

Methods

Clinical and administrative datasets for 12,207 patients undergoing isolated SAVR in Ontario from 2008 to 2019 were linked. Male and female patients were balanced using inverse probability treatment weighting. Mortality, endocarditis, and major hemorrhagic and thrombotic events, as well as 2 composite outcomes-major adverse cerebral and cardiovascular events (MACCE) and patient-derived adverse cardiovascular and noncardiovascular events (PACE)-and their component events, were compared in the weighted groups with a stratified log-rank test.

Results

A total of 7485 male patients and 4722 female patients were included in the study. Median follow-up was 5.2 years in both sexes. All-cause mortality did not differ between sexes (hazard ratio [HR] 0.949 [95% confidence interval {CI} 0.851-1.059]). Male sex was associated with an increased risk of new-onset dialysis (HR 0.689 [95% CI 0.488-0.974]). Female sex was associated with a significantly increased risk of both new-onset heart failure (HR 1.211 [95% CI 1.051-1.394], P = 0.0081) and heart failure hospitalization (HR 1.200 [95% CI 1.036-1.390], P = 0.015). No statistically significant differences were seen in any of the other secondary outcomes between sexes.

Conclusions

This population health study demonstrated that survival did not differ between male and female patients undergoing SAVR. Significant sex-related differences were found in the risk of heart failure and new-onset dialysis, but these findings should be considered exploratory and require further study.

Developing a random forest algorithm to identify patent foramen ovale and atrial septal defects in Ontario administrative databases

Purpose

Routinely collected administrative data is widely used for population-based research. However, although clinically very different, atrial septal defects (ASD) and patent foramen ovale (PFO) share a single diagnostic code (ICD-9: 745.5, ICD-10: Q21.1). Using machine-learning based approaches, we developed and validated an algorithm to differentiate between PFO and ASD patient populations within healthcare administrative data.

Methods

Using data housed at ICES, we identified patients who underwent transcatheter closure in Ontario between October 2002 and December 2017 using a Canadian Classification of Interventions code (1HN80GPFL, N = 4680). A novel random forest model was developed using demographic and clinical information to differentiate those who underwent transcatheter closure for PFO or ASD. Those patients who had undergone transcatheter closure and had records in the CorHealth Ontario cardiac procedure registry (N = 1482) were used as the reference standard. Several algorithms were tested and evaluated for accuracy, sensitivity, and specificity. Variable importance was examined via mean decrease in Gini index.

Results

We tested 7 models in total. The final model included 24 variables, including demographic, comorbidity, and procedural information. After hyperparameter tuning, the final model achieved 0.76 accuracy, 0.76 sensitivity, and 0.75 specificity. Patient age group had the greatest influence on node impurity, and thus ranked highest in variable importance.

Conclusions

Our random forest classification method achieved reasonable accuracy in identifying PFO and ASD closure in administrative data. The algorithm can now be applied to evaluate long term PFO and ASD closure outcomes in Ontario, pending future external validation studies to further test the algorithm.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12911-022-01837-2.

Leveraging Machine Learning to Predict 30-Day Hospital Readmission after Cardiac Surgery

BACKGROUND

Hospital readmission within 30 days of discharge is a well-studied outcome. Predicting readmission after cardiac surgery, however, is notoriously challenging; the best-performing models in the literature have AUCs around .65. A reliable predictive model would enable clinicians to identify patients at-risk for readmission and develop prevention strategies.

METHODS

We analyzed our institution's STS Adult Cardiac Surgery Database (STS), augmented with electronic medical record (EMR) data. Predictors included demographics, pre-operative comorbidities, proxies for intra-operative risk, indicators of post-operative complications, and timeseries-derived variables. We trained several machine learning models, evaluating each on a held-out test set.

RESULTS

Our analysis cohort consisted of 4,924 cases from 2011-2016. 723 (14.7%) were readmitted within 30 days of discharge. Our models included 141 STS-derived and 24 EMR-derived variables. A random forest model performed best, with test AUC .76 (95% CI: (.73, .79)). Using exclusively pre-operative variables, as in STS calculated risk scores, degraded the AUC: .64 (95% CI: .60, .68). Key predictors included length of stay (12.5x more important than the average variable) and whether the patient was discharged to a rehab facility (11.2x).

CONCLUSIONS

Our approach, augmenting STS variables with EMR data and employing flexible machine learning modeling, yielded state-of-the-art performance for predicting 30-day readmission. Separately, the importance of variables not directly related to in-patient care, such as discharge location, amplifies questions about the efficacy of assessing care quality via readmissions.

Derivation and validation of predictive indices for 30-day mortality after coronary and valvular surgery in Ontario, Canada

Background:

Coronary artery bypass grafting (CABG) and surgical aortic valve replacement (AVR) are the 2 most common cardiac surgery procedures in North America. We derived and externally validated clinical models to estimate the likelihood of death within 30 days of CABG, AVR or combined CABG + AVR.

Methods:

We obtained data from the CorHealth Ontario Cardiac Registry and several linked population health administrative databases from Ontario, Canada. We derived multiple logistic regression models from all adult patients who underwent CABG, AVR or combined CABG + AVR from April 2017 to March 2019, and validated them in 2 temporally distinct cohorts (April 2015 to March 2017 and April 2019 to March 2020).

Results:

The derivation cohorts included 13 435 patients who underwent CABG (30-d mortality 1.73%), 1970 patients who underwent AVR (30-d mortality 1.68%) and 1510 patients who underwent combined CABG + AVR (30-d mortality 3.05%). The final models for predicting 30-day mortality included 15 variables for patients undergoing CABG, 5 variables for patients undergoing AVR and 5 variables for patients undergoing combined CABG + AVR. Model discrimination was excellent for the CABG (c-statistic 0.888, optimism-corrected 0.866) AVR (c-statistic 0.850, optimism-corrected 0.762) and CABG + AVR (c-statistic 0.844, optimism-corrected 0.776) models, with similar results in the validation cohorts.

Interpretation:

Our models, leveraging readily available, multidimensional data sources, computed accurate risk-adjusted 30-day mortality rates for CABG, AVR and combined CABG + AVR, with discrimination comparable to more complex American and European models. The ability to accurately predict perioperative mortality rates for these procedures will be valuable for quality improvement initiatives across institutions.

Elements of the care environment influence coronary artery bypass surgery readmission

Background

Coronary artery bypass grafting 30-day unplanned readmission is a focus for the CMS Hospital Readmissions Reduction Program. Awareness of the critical elements of the care delivery environment, including hospital infrastructure and patient clinical profiles that predispose toward readmission, is essential to proactively decrease readmissions.

Methods

The Healthcare Cost and Utilization Project-State Inpatient Database, American Hospital Association Annual Health Survey Database, and Healthcare Information Management Systems Society data sets were merged to create a single data set of patient- and hospital-level data from 8 states. Isolated coronary artery bypass grafting procedures were queried for all-cause 30-day readmission, and backwards stepwise logistic regression was performed. Readmission rate was then used to categorize hospitals into quartiles, and analysis focused on the hospitals with the lowest (Q1) and highest (Q4) readmission rates. Univariate analysis was performed comparing Q1 and Q4 hospitals.

Results

A total of 150,215 patients underwent isolated coronary artery bypass grafting with 23,244 (15.5%) readmitted patients among 903 hospitals. Model area under the curve was 0.709 (95% confidence interval, 0.702–0.716), with the top 3 readmission determinants related to discharge disposition. Compared to Q1, Q4 patients more often were female, were > 70 years of age, and had Medicare as a primary payor (P < .001). Low readmission rate hospitals were characterized by higher costs; not-for-profit status; having Joint Commission accreditation; and higher total admissions, operative volume, hospital/ICU beds, full-time physicians, nurses, and ancillary personnel (P < .001).

Conclusion

Readmission after coronary artery bypass grafting is strongly influenced by discharge disposition. However, hospital factors such as scale, personnel, and ownership structure are significant contributors to readmission. Focus beyond patient factors to include the entire continuum of care is required to enhance outcomes, of which readmission is one surrogate measure.

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.

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.

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.

Postoperative complications associated with coronary artery bypass graft surgery and their therapeutic interventions

Coronary artery disease is one of the commonest surgery demanding cardiovascular diseases. Coronary artery bypass graft surgery is practiced all over the world for the treatment of coronary artery disease. Systemic trauma during the surgery is associated with a wide range of complications, some of which are fatal. Preoperative risk factors such as age, previous illness and obesity are common predictors of these adverse events. Advances in therapeutic medicine have allowed timely treatment of these adverse events and co-morbidities. This review summarizes some of the most occurring complications associated with coronary artery bypass graft and corresponding treatment options.

The Association between Cytokines and 365-Day Readmission or Mortality in Adult Cardiac Surgery

Cardiac surgery results in a multifactorial systemic inflammatory response with inflammatory cytokines, such as interleukin-10 and 6 (IL-10 and IL-6), shown to have potential in the prediction of adverse outcomes including readmission or mortality. This study sought to measure the association between IL-6 and IL-10 levels and 1-year hospital readmission or mortality following cardiac surgery. Plasma biomarkers IL-6 and IL-10 were measured in 1,047 patients discharged alive after isolated coronary artery bypass graft surgery from eight medical centers participating in the Northern New England Cardiovascular Disease Study Group between 2004 and 2007. Readmission status and mortality were ascertained using Medicare, state all-payer claims, and the National Death Index. We evaluated the association between preoperative and postoperative cytokines and 1-year readmission or mortality using Kaplan-Meier estimates and Cox's proportional hazards modeling, adjusting for covariates used in the Society of Thoracic Surgeons 30-day readmission model. The median follow-up time was 1 year. After adjustment, patients in the highest tertile of postoperative IL-6 values had a significantly increased risk of readmission or death within 1 year (HR: 1.38; 95% CI: 1.03-1.85), and an increased risk of death within 1 year of discharge (HR: 4.88; 95% CI: 1.26-18.85) compared with patients in the lowest tertile. However, postoperative IL-10 levels, although increasing through tertiles, were not found to be significantly associated independently with 1-year readmission or mortality (HR: 1.25; 95% CI: .93-1.69). Pro-inflammatory cytokine IL-6 and anti-inflammatory cytokine IL-10 may be postoperative markers of cardiac injury, and IL-6, specifically, shows promise in predicting readmission and mortality following cardiac surgery.