Thirty-Day Nonindex Readmissions and Clinical Outcomes After Cardiac Surgery

Incidence, timing and variation in unplanned readmissions within 30-days following isolated coronary artery bypass grafting

Background

Coronary Artery Bypass Grafting (CABG) is the most common cardiac surgery, yet little is known about unplanned readmissions after CABG despite increasing clinical and policy focus on reducing readmissions. We assessed the incidence, timing, and reasons for unplanned readmission within 30 days of CABG and evaluated for variation in readmission rates across hospitals in Australia and New Zealand (ANZ).

Method

We identified isolated CABG procedures from 2013 to 2017 across all public and most private hospitals in ANZ. The primary outcome was unplanned (acute) readmissions within 30-days of discharge. Hospital specific risk standardised readmission rates (RSRRs) and 95% CI were estimated using a hierarchical generalized linear model accounting for differences in patient characteristics.

Results

52,104 patients (mean age 66.1 ± 9.9 years, 17.6 % female, 30.7 % acute) were included. The 30-day unplanned readmission rate was 12.7 % (n = 6,613) and was higher following urgent surgery (16.2 %, n = 2,595). Readmission rates peaked on days 2-4 with a median time to readmission of 9 (IQR: 4-17) days. Procedural complications and chest pain were the most common diagnoses on readmission. Risk adjustment model demonstrated satisfactory performance (C-statistic = 0.62). The median RSRR was 12.8 % (range: 6.1-20.3 %) across 37 hospitals. Only one hospital had its RSRR estimate lower than average and no hospitals had higher than average RSRR.

Conclusion

One-in-8 patients undergoing CABG experienced an unplanned readmission within 30-day, rising to one-in-6 following urgent CABG. There was little statistically significant institutional variation in RSRR. Nevertheless, many readmissions are likely related to care quality and potentially preventable, highlighting scope for clinical and policy interventions to reduce readmissions.

Prehabilitation before cardiac surgery

Prehabilitation aims to reduce the impact of major surgery by improving the physical and psychological resilience of patients. Although exercise represents one component of prehabilitation, nutritional and psychological support are also critical to its effectiveness, and any benefits are only likely to be realised if the different components are implemented together, ideally in a behaviour change framework. Implementation of prehabilitation in cardiac surgery has not been as widespread as in other types of surgery, despite many randomised controlled trials (RCTs) of single interventions in this setting. The late adoption of a prehabilitation programme in cardiac surgery represents an opportunity to ensure that it is both clinically effective and cost-effective before widespread roll-out. This was mostly not done for prehabilitation in noncardiac surgery, where programmes were implemented largely without trials of these combined interventions. The most likely chance of an effective prehabilitation programme for cardiac surgery is to combine all the efficacious and implementable single interventions together in one comprehensive evidence-based programme. This should then be tested in an adequately powered multicentre RCT in a representative cardiac surgery population.

Readmission destination following cardiac surgery and its association with mortality outcomes: a population-based retrospective study

Background

It is unclear how pre-surgery transfer relates to readmission destination among patients undergoing cardiac surgery and whether readmission to a hospital other than the operating hospital is associated with increased mortality.

Methods

We analysed linked hospital and death records for residents of New South Wales, Australia, aged ≥18 years who had an emergency readmission within 30 days following coronary artery bypass graft (CABG) or surgical aortic valve replacement (SAVR) in 2003-2022. Mixed-effect multi-level modelling was used to evaluate associations of readmission destination with 30-day mortality, overall and stratified by pre-surgery transfer.

Findings

Of 102,540 patients undergoing cardiac surgery (isolated CABG = 63,000, SAVR = 27,482, combined = 12,058), 28.7% (n = 29,398) had pre-surgery transfer, while the 30-day readmission rate was 14.7% (n = 14,708). During readmission, 35.7% (3499/9795) of those without pre-surgery transfer and 12.0% (590/4913) of those with pre-surgery transfer returned to the operating hospital. Among readmitted patients, 30-day mortality did not differ significantly for those who were readmitted to a non-index hospital, both overall (adjusted odds ratio [aOR] = 1.03 95% CI 0.75-1.41), and in analyses stratified by pre-surgery transfer (no transfer: aOR = 1.07, 95% CI 0.75-1.52; transfer: aOR = 0.88, 95% CI 0.45-1.72). Among patients who had pre-surgery transfer, 30-day mortality was similar among patients who were readmitted to the index operating hospital (reference), the initial admitting hospital (aOR = 1.00, 95% CI 0.50-2.00) or a third, different, hospital (aOR = 0.70, 95% CI 0.33-1.48).

Interpretation

Although many Australian patients who are readmitted following cardiac surgery are readmitted to hospitals different to the operating or initial admitting hospital, such readmissions are not associated with increased mortality.

Funding

This study was funded by a National Health and Medical Research Foundation of Australia (NHMRC) Project Grant (#1162833).

Assessing the impact of socioeconomic distress on hospital readmissions after cardiac surgery

Background

The impact of socioeconomic distress on readmission rates following cardiac surgery has not been studied. We hypothesized that patients living in distressed communities would have a higher 30-day readmission rate after cardiac surgery compared to those living in less distressed communities.

Methods

Patients undergoing isolated coronary artery bypass grafting (CABG) between 2016 and 2023 within a regional collaborative were identified. The Distressed Communities Index (DCI) and Area Deprivation Index (ADI) were used to measure socioeconomic distress. Two logistic regression models were performed to evaluate 30-day readmission rates: one incorporating ADI and the other including DCI. Models were adjusted for the Society of Thoracic Surgeons (STS) Predicted Risk of Mortality (PROM) score, postoperative complications, length of stay (LOS), year of surgery, and discharge disposition.

Results

A total of 16,369 patients were included, of whom 10% were readmitted within 30 days of discharge. Readmitted patients were more likely to be female (32% vs 23.3%) and to develop postoperative complications (47% vs 35%) and less likely to be discharged to home (70.6% vs 83.5%; P < .001 for all). On multivariable analysis, STS PROM score, postoperative complications, prolonged LOS, and discharge to a facility or leaving against medical advice were predictive of higher readmission rates. Socioeconomic distress was not an independent predictor of readmission in the model that used DCI (odds ratio [OR], 0.93; 95% confidence interval [CI], 0.76-1.15) or in the model that used ADI (OR, 1.17; 95% CI, 0.83-1.64).

Conclusions

In patients undergoing CABG, increasing socioeconomic distress does not predict higher 30-day readmission rate. Other factors, such as discharge location, have a greater impact on readmission rate.

The effect of early follow-up after open cardiac surgery in a student clinic

Objectives. Readmission rates following open cardiac surgery are high, affecting patients and the cost of care. This study aimed to investigate the effect of early additional follow-up after open cardiac surgery when 5th-year medical students conducted follow-ups under the supervision of physicians. The primary endpoint was unplanned cardiac-related readmissions within one year. The secondary outcomes were the detection of impending complications and health-related quality of life (HRQOL). Methods. Patients undergoing open cardiac surgery were prospectively included. For intervention, additional follow-up visits, including point-of-care ultrasound, were conducted by supervised 5th-year medical students on postoperative days 3, 14 and 25. Unplanned cardiac-related readmissions, including emergency department visits, were registered within the first year of surgery. Danish National Health Survey 2010 questionnaire was used for HRQOL. In standard follow-up, all patients were seen 4-6 weeks postoperative. Results. For data analysis, 100 of 124 patients in the intervention group and 319 of 335 patients in the control group were included. The 1-year unplanned readmission rates did not differ; 32% and 30% in the intervention and control groups, respectively (p = 0.71). After discharge, 1% of patients underwent pericardiocentesis. The additional follow-up initiated scheduled drainage, contrary to more unscheduled/acute drainages in the control group. Pleurocentesis was more common in the intervention group (17% (n = 17) vs 8% (n = 25), p = 0.01) and performed earlier. There was no difference between groups on HRQOL. Conclusion. Supervised student-led follow-up of newly cardiac-operated patients did not alter readmission rates or HRQOL but may detect complications earlier and initiate non-emergent treatment of complications.

Risk Factors for Heart Failure Readmission After Cardiac Surgery

Background

Heart failure (HF) is a leading cause of readmission after cardiac surgery, yet risk factors for HF readmission after cardiac surgery remain poorly characterized.

Objectives

This study aimed to identify risk factors associated with 30-day HF-specific readmissions after cardiac surgery using a national database.

Methods

We queried the 2016 to 2018 National Readmissions Database to identify U.S. patients who underwent coronary artery bypass grafting (CABG), mitral valve repair/replacement, and/or aortic valve repair/replacement. Exclusion criteria included history of ventricular assist device or heart transplant, dialysis-dependent renal insufficiency, and death during index admission. Clinical variables were defined using International Classification of Diseases-10th Revision codes. The primary outcome was a 30-day readmission for HF following discharge. Multivariable logistic regression was used to account for relevant clinical and demographic covariates and identify independent risk factors for HF readmissions following cardiac surgery.

Results

Our study included 394,050 patients who underwent cardiac surgery (mean age 66 ± 12 years, 63% isolated CABG, 27% isolated valve, 11% CABG + valve). Of these patients, 7,318 were readmitted within 30 days of discharge for a principal diagnosis of HF. Independent risk factors of HF-specific readmission included older age, female sex, prolonged length of stay, comorbid congestive HF, nondialysis dependent chronic kidney disease, chronic obstructive pulmonary disease, chronic liver disease, obesity, atrial fibrillation, and acute kidney injury. Prior CABG was marginally protective for HF-specific readmission.

Conclusions

Using a national registry, we identified risk factors associated with HF readmission after cardiac surgery. Further analysis of these risk factors and their association with HF readmission is warranted.

Elective ascending aortic aneurysm repair outcomes in a nationwide US cohort

OBJECTIVE

To quantify contemporary outcomes following elective ascending aortic aneurysm repair, to determine risk factors for adverse events and to evaluate difference by institutional surgical volume.

METHODS

We included all elective hospitalisations of adult patients with an ascending aortic aneurysm who underwent aneurysm repair in the Nationwide Readmissions Database between 2016 and 2019. The primary outcome was a composite of in-hospital mortality, stroke (ischaemic and non-ischaemic) and myocardial infarction (MI). We identified independent predictor of adverse events and investigated outcomes by institutional volume.

RESULTS

Among 12 043 patients (mean 62.8 years of age, 28.0% female), MI, stroke or in-hospital death occurred in 598 (4.9%) patients during the index admission (acute stroke: 2.7%, MI: 0.7%, in-hospital death: 2.0%). The strongest predictors of in-hospital death, stroke or MI were chronic weight loss, pulmonary circulation disorder and concomitant descending aortic surgery. Higher procedural volume was associated with a lower incidence of in-hospital death, stroke or MI (OR comparing the highest with the lowest tertile 0.71, 95% CI 0.57 to 0.87; p=0.001) and in-hospital death (OR 0.51, 95% CI 0.37 to 0.72; p<0.001), but no difference in 30-day readmissions.

CONCLUSIONS

The overall rate of in-hospital death, stroke and MI is nearly 5% in patients undergoing elective ascending aortic aneurysm repair. Among several predictors, chronic weight loss is associated with the largest increase in the risk of poor outcomes. Higher hospital volume is associated with a lower in-hospital mortality, highlighting the importance to refer patients to high-volume centres while discussing the risks and benefits of proceeding with repair.

Failure to Rescue in Emergency General Surgery: Impact of Fragmentation of Care

OBJECTIVE

Compare EGS patient outcomes after index and nonindex hospital readmissions, and explore predictive factors for nonindex readmission.

BACKGROUND

Readmission to a different hospital leads to fragmentation of care. The impact of nonindex readmission on patient outcomes after EGS is not well established.

METHODS

The Nationwide Readmissions Database (2017) was queried for adult patients readmitted after an EGS procedure. Patients were stratified and propensity-matched according to readmission destination: index versus nonindex hospital. Outcomes were failure to rescue (FTR), mortality, number of subsequent readmissions, overall hospital length of stay, and total costs. Hierarchical logistic regression was performed to account for clustering effect within hospitals and adjusting for patient- and hospital-level potential confounding factors.

RESULTS

A total of 471,570 EGS patients were identified, of which 79,127 (16.8%) were readmitted within 30 days: index hospital (61,472; 77.7%) versus nonindex hospital (17,655; 22.3%). After 1:1 propensity matching, patients with nonindex readmission had higher rates of FTR (5.6% vs 4.3%; P < 0.001), mortality (2.7% vs 2.1%; P < 0.001), and overall hospital costs [in $1000; 37 (27-64) vs 28 (21-48); P < 0.001]. Nonindex readmission was independently associated with higher odds of FTR [adjusted odds ratio 1.18 (1.03-1.36); P < 0.001]. Predictors of nonindex readmission included top quartile for zip code median household income [1.35 (1.08-1.69); P < 0.001], fringe county residence [1.08 (1.01-1.16); P = 0.049], discharge to a skilled nursing facility [1.28 (1.20-1.36); P < 0.001], and leaving against medical advice [2.32 (1.81-2.98); P < 0.001].

CONCLUSION

One in 5 readmissions after EGS occur at a different hospital. Nonindex readmission carries a heightened risk of FTR.

LEVEL OF EVIDENCE

Level III Prognostic.

STUDY TYPE

Prognostic.

Re-Admission in Patients with Necrotizing Soft Tissue Infections: Continuity of Care Matters

Background: Necrotizing soft tissue infections (NSTIs) are rapidly progressive infections with high mortality and complication rates. The incidence of NSTIs has been increasing steadily whereas mortality has decreased; survivors have a high risk of re-hospitalization. We hypothesized that re-admission to the index hospital where the first admission occurred would be associated with better clinical outcomes compared with re-admission to a non-index hospital. Patients and Methods: We identified patients from the 2017 Nationwide Readmissions Database with an index admission for NSTIs and examined all-cause re-admissions within 90 days of discharge. We noted whether re-admission occurred at the index or a non-index hospital. Survey-weighted logistic regression identified factors associated with death at the first re-admission and re-admission to index hospital. We also compared patient outcomes between patients admitted to index versus non-index hospitals. Results: We identified 27,051 NSTI survivors, of whom 6,954 (25.7%) had an unplanned re-admission within 90 days. A large proportion of re-admission occurred at non-index hospitals (28.3%; n = 1,966). Factors associated with non-index re-admission included prolonged index length of stay, discharge to short-term hospital, and leaving against medical advice. Patients re-admitted to index hospitals had a lower mortality rate (4.7% vs. 6.7%; p = 0.003), lower admission costs (in $1000; 45 [23-88] vs. 50 [24-104]; p = 0.004) and higher discharge rate to home (55.7% vs. 48.6%; p < 0.001). Conclusions: More than one-quarter of re-admissions among NSTI survivors were to non-index hospitals. Continuity of care is important because re-admission to the index hospital was associated with better patient outcomes.

Comorbidity Trends in Patients Requiring Sternectomy and Reconstruction: Updated Data Analysis From 2005 to 2020

INTRODUCTION

Comorbidity trends after median sternectomy were studied at our institution by Vasconze et al (Comorbidity trends in patients requiring sternectomy and reconstruction. Ann Plast Surg. 2005;54:5). Although techniques for sternal reconstruction have remained unchanged, the patient population has become more complex in recent years. This study offers insight into changing trends in this patient population.

METHODS

A retrospective review was performed of patients who underwent median sternectomy followed by flap reconstruction at out institution between 2005 and 2020. Comorbidities, reconstruction method, average laboratory values, and complications were analyzed.

RESULTS

A total of 105 patients were identified. Comorbidities noted were diabetes (27%), immunosuppression (16%), hypertension (58%), renal insufficiency (23%), chronic obstructive pulmonary disease (16%), and tobacco utilization (24%). The most common reconstruction methods were omentum (45%) or pectoralis major flaps (34%). Thirty-day mortality rates were 10%, and presence of at least 1 complication was 34% (hematoma, seroma, osteomyelitis, dehiscence, wound infection, flap failure, and graft exposure). Univariate analysis demonstrated that sex (P = 0.048), renal insufficiency, surgical site complication, wound dehiscence, and flap failure (P < 0.05) had statistically significant associations with mortality. In addition, body mass index, creatinine, and albumin had a significant univariate association with mortality (P < 0.05).

CONCLUSIONS

Similar to the original study, there is an association between renal insufficiency and mortality. However, the mortality rate is decreased to 10%, likely because of improved medical management of patients with increasing comorbidities (80% with greater than one comorbidity). This has led to the increased use of omentum as a first-line option. Subsequent wound dehiscence and flap failure demonstrate an association with mortality, suggesting that increasingly complex patients are requiring a method of reconstruction once used a last resort as a first-line option.

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.