Adverse Effect of Post-Discharge Care Fragmentation on Outcomes after Readmissions after Liver Transplantation.

BACKGROUND: Post-discharge surgical care fragmentation is defined as readmission to any hospital other than the hospital at which surgery was performed. The objective of this study was to assess the impact of fragmented readmissions within the first year after orthotopic liver transplantation (OLT). STUDY DESIGN: The Healthcare Cost and Utilization Project State Inpatient Databases for Florida and California from 2006 to 2011 were used to identify OLT patients. Post-discharge fragmentation was defined as any readmission to a non-index hospital, including readmitted patients transferred to the index hospital after 24 hours. Outcomes included adverse events, defined as 30-day mortality and 30-day readmission after a fragmented readmission. All statistical analyses considered a hierarchical data structure and were performed with multilevel, mixed-effects models. RESULTS: We analyzed 2,996 patients with 7,485 readmission encounters at 299 hospitals; 1,236 (16.5%) readmissions were fragmented. After adjustment for age, sex, readmission reason, index liver transplantation cost, readmission length of stay, number of previous readmissions, and time from transplantation, post-discharge fragmentation increased the odds of both 30-day mortality (odds ratio [OR] = 1.75; 95% CI 1.16 to 2.65) and 30-day readmission (OR = 2.14; 95% CI 1.83 to 2.49). Predictors of adverse events after a fragmented readmission included increased number of previous readmissions (OR = 1.07; 95% CI 1.01 to 1.14) and readmission within 90 days of OLT (OR = 2.19; 95% CI 1.61 to 2.98). CONCLUSIONS: Post-discharge fragmentation significantly increases the risk of both 30-day mortality and subsequent readmission after a readmission in the first year after OLT. More inpatient visits before a readmission and less time elapsed from index surgery increase the odds of an adverse event after discharge from a fragmented readmission. These parameters could guide transfer decisions for patients with post-discharge fragmentation.

"Take the Volume Pledge" may result in disparity in access to care.

BACKGROUND: "Take the Volume Pledge" proposes restricting pancreatectomies to hospitals that perform ≥20 per year. Our purpose was to identify those factors that characterize patients at risk for loss of access to pancreatic cancer care with enforcement of volume standards. METHODS: Using the Healthcare Cost and Utilization Project State Inpatient Database from Florida, we identified patients who underwent pancreatectomy for pancreatic malignancy from 2007-2011. American Hospital Association and United States Census Bureau data were linked to patient-level data. High-volume hospitals were defined as performing ≥20 pancreatic resections per year. Univariable and multivariable statistics compared patient characteristics and utilization of high-volume hospitals. Classification and Regression Tree modeling was used to predict patients at risk for losing access to care. RESULTS: Our study included 1,663 patients. Five high-volume hospitals were identified, and they treated 1,056 (63.5%) patients. Patients residing far from high-volume hospitals, in areas with the highest population density, non-Caucasian ethnicity, and greater income had decreased odds of obtaining care at high-volume hospitals. Using these factors, we developed a Classification and Regression Tree-based predictive tool to identify these patients. CONCLUSION: Implementation of "Take the Volume Pledge" is an important step toward improving pancreatectomy outcomes; however, policymakers must consider the potential impact on limiting access and possible health disparities that may arise.

Perioperative support, not volume, is necessary to optimize outcomes in surgical management of necrotizing enterocolitis.

BACKGROUND: This study examines the relationship between hospital volume of surgical cases for necrotizing enterocolitis (NEC) and patient outcomes. METHODS: A retrospective cross-sectional review was performed using the HCUP SID for California from 2007 to 2011. Patients with NEC who underwent surgery were identified using ICD-9CM codes. Risk-adjusted models were constructed with mixed-effects logistic regression using patient and demographic covariates. RESULTS: 23 hospitals with 618 patients undergoing NEC-related surgical intervention were included. Overall mortality rate was 22.5%. There were no significant differences in the number of NICU beds (p = 0.135) or NICU intensivists (p = 0.469) between high and low volume hospitals. Following risk adjustment, no difference in mortality rate was observed between high and low volume hospitals respectively (24.0% vs. 20.3%, p = 0.555). CONCLUSIONS: Our observation that neonates with NEC treated at low-volume centers have no increased risk of mortality may be explained by similar availability of NICU and intensivists resources across hospitals.