Impact of Hospital Caseload and Elective Admission on Outcomes After Extracranial-Intracranial Bypass Surgery

Weekend effect on 30-day mortality for ischemic and hemorrhagic stroke analyzed using severity index and staffing level

BACKGROUND AND PURPOSE

Previous studies on the weekend effect-a phenomenon where stroke outcomes differ depending on whether the stroke occurred on a weekend-mostly targeted ischemic stroke and showed inconsistent results. Thus, we investigated the weekend effect on 30-day mortality in patients with ischemic or hemorrhagic stroke considering the confounding effect of stroke severity and staffing level.

METHODS

We retrospectively analyzed data of patients hospitalized for ischemic or hemorrhagic stroke between January 1, 2015, and December 31, 2018, which were extracted from the claims database of the National Health Insurance System and the Medical Resource Report by the Health Insurance Review & Assessment Service. The primary outcome measure was 30-day all-cause mortality.

RESULTS

In total, 278,632 patients were included, among whom 84,240 and 194,392 had a hemorrhagic and ischemic stroke, respectively, with 25.8% and 25.1% of patients, respectively, being hospitalized during the weekend. Patients admitted on weekends had significantly higher 30-day mortality rates (hemorrhagic stroke 16.84%>15.55%, p<0.0001; ischemic stroke 5.06%>4.92%, p<0.0001). However, in the multi-level logistic regression analysis adjusted for case-mix, pre-hospital, and hospital level factors, the weekend effect remained consistent in patients with hemorrhagic stroke (odds ratio [OR] 1.05, 95% confidence interval [CI] 1.00-1.10), while the association was no longer evident in patients with ischemic stroke (OR 1.01, 95% CI 0.96-1.06).

CONCLUSIONS

Weekend admission for hemorrhagic stroke was significantly associated with a higher mortality rate after adjusting for confounding factors. Further studies are required to understand factors contributing to mortality during weekend admission.

Readmission following extracranial-intracranial bypass surgery in the United States: nationwide rates, causes, risk factors, and volume-driven outcomes

OBJECTIVE

Extracranial-intracranial (EC-IC) bypass surgery remains an important treatment option for patients with moyamoya disease (MMD), intracranial arteriosclerotic disease (ICAD) with symptomatic stenosis despite the best medical management, and complex aneurysms. The therapeutic benefit of cerebral bypass surgery depends on optimal patient selection and the minimization of periprocedural complications. The nationwide burden of readmissions and associated complications following EC-IC bypass surgery has not been previously described. Therefore, the authors sought to analyze a nationwide database to describe the national rates, causes, risk factors, complications, and morbidity associated with readmission following EC-IC bypass surgery for MMD, ICAD, and aneurysms.

METHODS

The Nationwide Readmissions Database (NRD) was queried for the years 2010-2014 to identify patients who had undergone EC-IC bypass for MMD, medically failed symptomatic ICAD, or unruptured aneurysms. Predictor variables included demographics, preexisting comorbidities, indication for surgery, and hospital bypass case volume. A high-volume center (HVC) was defined as one that performed 10 or more cases/year. Outcome variables included perioperative stroke, discharge disposition, length of stay, total hospital costs, and readmission (30 days, 90 days). Multivariable analysis was used to identify predictors of readmission and to study the effect of treatment at HVCs on quality outcomes.

RESULTS

In total, 2500 patients with a mean age of 41 years were treated with EC-IC bypass surgery for MMD (63.1%), ICAD (24.5%), or unruptured aneurysms (12.4%). The 30- and 90-day readmission rates were 7.5% and 14.0%, respectively. Causes of readmission included new stroke (2.5%), wound complications (2.5%), graft failure (1.5%), and other infection (1.3%). In the multivariable analysis, risk factors for readmission included Medicaid/self-pay (OR 1.6, 95% CI 1.1-2.4, vs private insurance), comorbidity score (OR 1.2, 95% CI 1.1-1.4, per additional comorbidity), and treatment at a non-HVC (OR 1.9, 95% CI 1.1-3.0). Treatment at an HVC (17% of patients) was associated with significantly lower rates of nonroutine discharge dispositions (13.4% vs 26.7%, p = 0.004), ischemic stroke within 90 days (0.8% vs 2.9%, p = 0.03), 30-day readmission (3.9% vs 8.2%, p = 0.03), and 90-day readmission (8.6% vs 15.2%, p = 0.01). These findings were confirmed in a multivariable analysis. The authors estimate that centralization to HVCs may result in 333 fewer nonroutine discharges (50% reduction), 12,000 fewer hospital days (44% reduction), 165 fewer readmissions (43%), and a cost savings of $15.3 million (11% reduction).

CONCLUSIONS

Readmission rates for patients after EC-IC bypass are comparable with those after other common cranial procedures and are primarily driven by preexisting comorbidities, socioeconomic status, and treatment at low-volume centers. Periprocedural complications, including stroke, graft failure, and wound complications, occurred at the expected rates, consistent with those in prior clinical series. The centralization of care may significantly reduce perioperative complications, readmissions, and hospital resource utilization.

Composite Score for Outcome Prediction in Gynecologic Surgery Patients

BACKGROUND

The LACE+ index is a well-studied metric that compacts patient data in an effort to assess readmission risk.

PURPOSE

Assess the capacity of LACE+ scores for predicting short-term undesirable outcomes in an entire single-center population of patients undergoing gynecologic surgery.

IMPORTANCE AND RELEVANCE TO HEALTHCARE QUALITY

Proactive identification of high-risk patients, with tools such as the LACE+ index, may serve as the first step toward appropriately engaging resources for reducing readmissions.

METHODS

This study was a retrospective analysis that used coarsened exact matching. All gynecologic surgery cases over 2 years within a single health system (n = 12,225) were included for analysis. Outcomes of interest were unplanned readmission, emergency room (ER) evaluation, and return to surgery. Composite LACE+ scores were separated into quartiles and matched. For outcome comparison, matched patients were assessed by LACE+ quartile, using Q4 as the reference group.

RESULTS

Increasing LACE+ score reflected a higher rate of readmission (p = .003, p = .001) and visits to the ER at 30 postoperative days (p < .001).

CONCLUSION

The data presented here suggest that LACE+ index is a viable metric for patient outcome prediction following gynecologic surgery.

Assessment of Short-Term Patient Outcomes Following Overlapping Orthopaedic Surgery at a Large Academic Medical Center

BACKGROUND

Overlapping surgery is a long-standing practice that has not been well studied. The aim of this study was to assess whether overlapping surgery is associated with untoward outcomes for orthopaedic patients.

METHODS

Coarsened exact matching was used to assess the impact of overlap on outcomes among elective orthopaedic surgical interventions (n = 18,316) over 2 years (2014 and 2015) at 1 health-care system. Overlap was categorized as any overlap, and subcategories of exclusively beginning overlap and exclusively end overlap. Study subjects were matched on the Charlson comorbidity index score, duration of surgery, surgical costs, body mass index, length of stay, payer, and race, among others. Serious unanticipated events were studied.

RESULTS

A total of 3,395 patients had any overlap and were matched (a match rate of 90.8% of 3,738). For beginning and end overlap, matched groups were created, with a match rate of 95.2% of 1043 and 94.7% of 863, respectively. Among matched patients, any overlap did not predict an unanticipated return to surgery at 30 days (8.2% for any overlap and 8.3% for no overlap; p = 0.922) or 90 days (14.1% and 14.1%, respectively; p = 1.000). Patients who had surgery with any overlap demonstrated no difference compared with controls with respect to reoperation, readmission, or emergency room (ER) visits at 30 or 90 days (a reoperation rate of 3.1% and 3.2%, respectively [p = 0.884] at 30 days and 4.2% and 3.5% [p = 0.173] at 90 days; a readmission rate of 10.3% and 11.0% [p = 0.352] at 30 days and 5.5% and 5.2% [p = 0.570] at 90 days; and an ER visit rate of 5.2% and 4.6% [p = 0.276] at 30 days and 4.8% and 4.3% [p = 0.304] at 90 days). Patients with surgical overlap showed reduced mortality compared with controls during follow-up (1.8% and 2.6%, respectively; p = 0.029). Patients with beginning and/or end overlap had a similar lack of association with serious unanticipated events; however, patients with end overlap showed an increased unexpected rate of return to the operating room after reoperation at 90 days (13.3% versus 9.7%; p = 0.015).

CONCLUSIONS

Nonconcurrent overlapping surgery was not associated with adverse outcomes in a large, matched orthopaedic surgery population across 1 academic health system.

LEVEL OF EVIDENCE

Therapeutic Level III. See Instructions for Authors for a complete description of levels of evidence.

Association of overlapping cardiac surgery with short-term patient outcomes

OBJECTIVE

This study seeks to assess the safety of overlap in cardiac surgery.

METHODS

Coarsened exact matching was used to assess the impact of overlap on outcomes among cardiac surgical interventions (n = 4463) over 2 years (2014-2016). Overlap was categorized as any, beginning, or end overlap. Study subjects were matched 1:1 on 11 variables, including Charlson comorbidity score, surgical costs, body mass index, length of postoperative hospitalization, and race, among others. Serious unanticipated events were studied, including readmission, unplanned return to the operating room, and mortality.

RESULTS

A total of 984 patients had any overlap and were matched to similar patients without overlap (n = 1501). For beginning/end overlap, separate matched groups were created (n = 462, n = 329 patients, respectively). Among matched patients, any overlap did not predict unanticipated return to surgery at 30 or 90 days. Any overlap did not predict increased readmission, reoperation, or emergency department visits at 30 or 90 days. Overlap did not predict higher rates of death over follow-up. Beginning/end overlap had results similar to any overlap.

CONCLUSIONS

Nonconcurrent, overlapping surgery is not associated with an increase in adverse outcomes in a large, matched cardiac surgery population.

Proposing a validated clinical app predicting hospitalization cost for extracranial-intracranial bypass surgery

OBJECT

United States healthcare reforms are focused on curtailing rising expenditures. In neurosurgical domain, limited or no data exists identifying potential modifiable targets associated with high-hospitalization cost for cerebrovascular procedures such as extracranial-intracranial (ECIC) bypass. Our study objective was to develop a predictive model of initial cost for patients undergoing bypass surgery.

METHODS

In an observational cohort study, we analyzed patients registered in the Nationwide Inpatient Sample (2002-2011) that underwent ECIC bypass. Split-sample 1:1 randomization of the study cohort was performed. Hospital cost data was modelled using ordinary least square to identity potential drivers impacting initial hospitalization cost. Subsequently, a validated clinical app for estimated hospitalization cost is proposed (https://www.neurosurgerycost.com/calc/ec-ic-by-pass).

RESULTS

Overall, 1533 patients [mean age: 45.18 ± 19.51 years; 58% female] underwent ECIC bypass for moyamoya disease [45.1%], cerebro-occlusive disease (COD) [23% without infarction; 12% with infarction], unruptured [12%] and ruptured [4%] aneurysms. Median hospitalization cost was $37,525 (IQR: $16,225-$58,825). Common drivers impacting cost include Asian race, private payer, elective admission, hyponatremia, neurological and respiratory complications, acute renal failure, bypass for moyamoya disease, COD without infarction, medium and high volume centers, hospitals located in Midwest, Northeast, and West region, total number of diagnosis and procedures, days to bypass and post-procedural LOS. Our model was validated in an independent cohort and using 1000-bootstrapped replacement samples.

CONCLUSIONS

Identified drivers of hospital cost after ECIC bypass could potentially be used as an adjunct for creation of data driven policies, impact reimbursement criteria, aid in-hospital auditing, and in the cost containment debate.