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Background: This study compared the clinical and economic outcomes of laparoscopic (LLR) and open liver resection (OLR) for all hepatectomies, including minor and major hepatectomies., Study Design: This retrospective study included 920 consecutive elective patients undergoing liver resection from 2017 to 2023. Patient demographics, postoperative surgical outcomes, postoperative length of stay (LOS), and costs were compared between LLR and OLR before and after propensity score matching (PSM). A decision model was developed to assess the cost-effectiveness of LLR versus OLR., Results: After PSM, LLR was associated with significantly fewer postoperative transfusions for all hepatectomies (P < 0.001) and major hepatectomy (P = 0.001). LLR was associated with a shorter postoperative median LOS (P < 0.001), lower 30-day readmission (P = 0.022) and reoperation (P = 0.044) rates, and significantly reduced postoperative pneumonia (P = 0.038), unplanned intubation (P = 0.020), sepsis (P = 0.041), and major complications (P < 0.001) for all hepatectomies. This clinical superiority was complemented by a significant reduction in total costs for all (P < 0.001), minor (P = 0.001), and major (P < 0.001) hepatectomies. Cost-effectiveness analysis revealed that LLR was dominant over OLR, with a negative incremental cost-effectiveness ratio (-$2120.72) and an increased net monetary benefit ($75,015.92) at the willingness-to-pay (WTP) threshold of $25,000. The probability of LLR being cost-effective was 99.8% across various WTP thresholds., Conclusions: LLR is a safe and cost-effective alternative to OLR. While LLR has higher initial procedural costs, these are offset by significant reductions in postoperative major complications, LOS, and total costs., (Copyright © 2024 by the American College of Surgeons. Published by Wolters Kluwer Health, Inc. All rights reserved.)

This systematic review and meta-analysis aimed to evaluate the impact of prospective payment systems (PPSs) on cholecystectomy. A comprehensive literature review was conducted, examining studies published until December 2023. The review process focused on identifying research across major databases that reported critical outcomes such as length of stay (LOS), mortality, complications, admissions, readmissions, and costs following PPS for cholecystectomy. The studies were specifically selected for their relevance to the impact of PPS or the transition from fee-for-service (FFS) to PPS. The study analyzed six papers, with three eligible for meta-analysis, to assess the impact of the shift from FFS to PPS in laparoscopic and open cholecystectomy procedures. Our findings indicated no significant changes in LOS and mortality rates following the transition from FFS to PPS. Complication rates varied and were influenced by the diagnosis-related group categorization and surgeon cost profiles under episode-based payment. There was a slight increase in admissions and readmissions, and mixed effects on hospital costs and financial margins, suggesting varied responses to PPS for cholecystectomy procedures. The impact of PPS on cholecystectomy is nuanced and varies across different aspects of healthcare delivery. Our findings indicate a need for adaptable, patient-centered PPS models that balance economic efficiency with high-quality patient care. The study emphasizes the importance of considering specific surgical procedures and patient demographics in healthcare payment reforms.

This study aims to assess the quality and performance of predictive models for colorectal cancer liver metastasis (CRCLM). A systematic review was performed to identify relevant studies from various databases. Studies that described or validated predictive models for CRCLM were included. The methodological quality of the predictive models was assessed. Model performance was evaluated by the reported area under the receiver operating characteristic curve (AUC). Of the 117 articles screened, seven studies comprising 14 predictive models were included. The distribution of included predictive models was as follows: radiomics (n = 3), logistic regression (n = 3), Cox regression (n = 2), nomogram (n = 3), support vector machine (SVM, n = 2), random forest (n = 2), and convolutional neural network (CNN, n = 2). Age, sex, carcinoembryonic antigen, and tumor staging (T and N stage) were the most frequently used clinicopathological predictors for CRCLM. The mean AUCs ranged from 0.697 to 0.870, with 86% of the models demonstrating clear discriminative ability (AUC > 0.70). A hybrid approach combining clinical and radiomic features with SVM provided the best performance, achieving an AUC of 0.870. The overall risk of bias was identified as high in 71% of the included studies. This review highlights the potential of predictive modeling to accurately predict the occurrence of CRCLM. Integrating clinicopathological and radiomic features with machine learning algorithms demonstrates superior predictive capabilities.

Background: Surgery remains the primary treatment for localized colorectal cancer (CRC). Improving surgical decision-making for elderly CRC patients necessitates an accurate predictive tool., Aim: To build a nomogram to predict the overall survival of elderly patients over 80 years undergoing CRC resection., Methods: Two hundred and ninety-five elderly CRC patients over 80 years undergoing surgery at Singapore General Hospital between 2018 and 2021 were identified from the American College of Surgeons - National Surgical Quality Improvement Program (ACS-NSQIP) database. Prognostic variables were selected using univariate Cox regression, and clinical feature selection was performed by the least absolute shrinkage and selection operator regression. A nomogram for 1- and 3-year overall survival was constructed based on 60% of the study cohort and tested on the remaining 40%. The performance of the nomogram was evaluated using the concordance index (C-index), area under the receiver operating characteristic curve (AUC), and calibration plots. Risk groups were stratified using the total risk points derived from the nomogram and the optimal cut-off point. Survival curves were compared between the high- and low-risk groups., Results: Eight predictors: Age, Charlson comorbidity index, body mass index, serum albumin level, distant metastasis, emergency surgery, postoperative pneumonia, and postoperative myocardial infarction, were included in the nomogram. The AUC values for the 1-year survival were 0.843 and 0.826 for the training and validation cohorts, respectively. The AUC values for the 3-year survival were 0.788 and 0.750 for the training and validation cohorts, respectively. C-index values of the training cohort (0.845) and validation cohort (0.793) suggested the excellent discriminative ability of the nomogram. Calibration curves demonstrated a good consistency between the predictions and actual observations of overall survival in both training and validation cohorts. A significant difference in overall survival was seen between elderly patients stratified into low- and high-risk groups ( P < 0.001)., Conclusion: We constructed and validated a nomogram predicting 1- and 3-year survival probability in elderly patients over 80 years undergoing CRC resection, thereby facilitating holistic and informed decision-making among these patients., Competing Interests: Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article., (©The Author(s) 2023. Published by Baishideng Publishing Group Inc. All rights reserved.)

Background: While Singapore attains good health outcomes, Singapore's healthcare system is confronted with bed shortages and prolonged stays for elderly people recovering from surgery in acute hospitals. An Acute Hospital-Community Hospital (AH-CH) care bundle has been developed to assist patients in postoperative rehabilitation. The core concept is to transfer patients out of AHs when clinically recommended and into CHs, where they can receive more beneficial dedicated care to aid in their recovery, while freeing up bed capacities in AHs., Aim: To analyze the AH length of stay (LOS), costs, and savings associated with the AH-CH care bundle intervention initiated and implemented in elderly patients aged 75 years and above undergoing elective orthopedic surgery., Methods: A total of 862 1:1 propensity score-matched patients aged 75 years and above who underwent elective orthopedic surgery in Singapore General Hospital (SGH) before (2017-2018) and after (2019-2021) the care bundle intervention period was analyzed. Outcome measures were AH LOS, CH LOS, hospitalization metrics, postoperative 30-d mortality, and modified Barthel Index (MBI) scores. The costs of AH inpatient hospital stay in the matched cohorts were compared using cost data in Singapore dollars., Results: Of the 862 matched elderly patients undergoing elective orthopedic surgery before and after the care bundle intervention, the age distribution, sex, American Society of Anesthesiologists classification, Charlson Comorbidity Index, and surgical approach were comparable between both groups. Patients transferred to CHs after the surgery had a shorter median AH LOS (7 d vs 9 d, P < 0.001). The mean total AH inpatient cost per patient was 14.9% less for the elderly group transferred to CHs (S$24497.3 vs S$28772.8, P < 0.001). The overall AH U-turn rates for elderly patients within the care bundle were low, with a 0% mortality rate following orthopedic surgery. When elderly patients were discharged from CHs, their MBI scores increased significantly (50.9 vs 71.9, P < 0.001)., Conclusion: The AH-CH care bundle initiated and implemented in the Department of Orthopedic Surgery appears to be effective and cost-saving for SGH. Our results indicate that transitioning care between acute and community hospitals using this care bundle effectively reduces AH LOS in elderly patients receiving orthopedic surgery. Collaboration between acute and community care providers can assist in closing the care delivery gap and enhancing service quality., Competing Interests: Conflict-of-interest statement: All authors declare that they have no relevant or material financial interests related to the research described in this paper., (©The Author(s) 2023. Published by Baishideng Publishing Group Inc. All rights reserved.)