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BACKGROUND: Although early discharge after colectomy has garnered significant interest, contemporary, large-scale analyses are lacking. OBJECTIVE: The present study utilized a national cohort of patients undergoing colectomy to examine costs and readmissions following early discharge. METHODS: All adults undergoing elective colectomy for primary colon cancer were identified in the 2016-2019 Nationwide Readmissions Database. Patients with perioperative complications or prolonged length of stay (>8 days) were excluded to enhance cohort homogeneity. Patients discharged by postoperative day 3 were classified as Early, and others as Routine. Entropy balancing and multivariable regression were used to assess the risk-adjusted association of early discharge with costs and non-elective readmissions. Importantly, we compared 90-day stroke rates to examine whether our results were influenced by preferential early discharge of healthier patients. RESULTS: Of an estimated 153,996 patients, 45.5% comprised the Early cohort. Compared to Routine, the Early cohort was younger and more commonly male. Patients in the Early group more commonly underwent left-sided colectomy and laparoscopic operations. Following multivariable adjustment, expedited discharge was associated with a $4,500 reduction in costs as well as lower 30-day (adjusted odds ratio [AOR] 0.74, p

BACKGROUND: Expedited discharge following esophagectomy is controversial due to concerns for higher readmissions and financial burden. The present study aimed to evaluate the association of expedited discharge with hospitalization costs and unplanned readmissions following esophagectomy for malignant lesions. METHODS: Adults undergoing elective esophagectomy for cancer were identified in the 2014-2019 Nationwide Readmissions Database. Patients discharged by postoperative day 7 were considered Expedited and others as Routine. Patients who did not survive to discharge or had major perioperative complications were excluded. Multivariable regression models were constructed to assess association of expedited discharge with index hospitalization costs as well as 30- and 90-day non-elective readmissions. RESULTS: Of 9,886 patients who met study criteria, 34.6% comprised the Expedited cohort. After adjustment, female sex (adjusted odds ratio [AOR] 0.71, p = 0.001) and increasing Elixhauser Comorbidity Index (AOR 0.88/point, p

BackgroundDespite the known advantages of minimally invasive surgery (MIS) for diverticular disease, the impact of conversions to open (CtO) colectomy remains understudied. The present study used a nationally representative database to characterize risk factors and outcomes associated with CtO in patients with diverticular disease.MethodsAll elective adult hospitalizations entailing colectomy for diverticulitis were identified in the 2017-2019 Nationwide Readmissions Database. Annual institutional caseloads of MIS and open colectomy were independently tabulated. Restricted cubic splines were utilized to non-linearly estimate the risk-adjusted association between hospital volumes and CtO. Additional regression models were developed to evaluate the association of CtO with outcomes of interest.ResultsOf an estimated 110,281 patients with diverticulitis who met study criteria, 39.3% underwent planned open colectomy, 53.3% completed MIS, and 7.4% had a CtO. Following adjustment, an inverse relationship between hospital MIS volume and risk of CtO was observed. In contrast, increasing hospital open volume was positively associated with greater risk of CtO. On multivariable analysis, CtO was associated with lower odds of mortality (AOR 0.3, p = 0.001) when compared to open approach, and similar risk of mortality when compared to completed MIS (AOR 0.7, p = 0.436).ConclusionIn the present study, institutional MIS volume exhibited inverse correlation with adjusted rates of CtO, independent of open colectomy volume. CtO was associated with decreased rates of mortality compared to planned open approach but equivalence risk relative to completed MIS. Our findings highlight the importance of MIS experience and suggest that MIS may be safely pursued as the initial surgical approach among diverticulitis patients.

BackgroundEmergency general surgery (EGS) operations are associated with substantial risk of morbidity including postoperative respiratory failure (PRF). While existing risk models are not widely utilized and rely on traditional statistical methods, application of machine learning (ML) in prediction of PRF following EGS remains unexplored.ObjectiveThe present study aimed to develop ML-based prediction models for respiratory failure following EGS and compare their performance to traditional regression models using a nationally-representative cohort.MethodsNon-elective hospitalizations for EGS (appendectomy, cholecystectomy, repair of perforated ulcer, large or small bowel resection, lysis of adhesions) were identified in the 2016-18 Nationwide Readmissions Database. Factors associated with PRF were identified using ML techniques and logistic regression. The performance of XGBoost and logistic regression was evaluated using the receiver operating characteristic curve and coefficient of determination (R2). The impact of PRF on mortality, length of stay (LOS) and hospitalization costs was secondarily assessed using generalized linear models.ResultsOf 1,003,703 hospitalizations, 8.8% developed PRF. The XGBoost model exhibited slightly superior discrimination compared to logistic regression (0.900, 95% CI 0.899-0.901 vs 0.894, 95% CI 0.862-0.896). Compared to logistic regression, XGBoost demonstrated excellent calibration across all risk levels (R2: 0.998 vs 0.962). Congestive heart failure, neurologic disorders, and coagulopathy were significantly associated with increased risk of PRF. After risk-adjustment, PRF was associated with 10-fold greater odds (95% confidence interval (CI) 9.8-11.1) of mortality and incremental increases in LOS by 3.1 days (95% CI 3.0-3.2) and $11,900 (95% CI 11,600-12,300) in costs.ConclusionsLogistic regression and XGBoost perform similarly in overall classification of PRF risk. However, due to superior calibration at extremes of risk, ML-based models may prove more useful in the clinical setting, where probabilities rather than classifications are desired.