Faiz Gani1, Marcelo Cerullo1, Neda Amini1, Stefan Buettner1, Georgios A Margonis1, Kazunari Sasaki1, Yuhree Kim1, Timothy M Pawlik2,3. 1. Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, MD, USA. 2. Department of Surgery, Wexner Medical Center at The Ohio State University, Columbus, OH, USA. tim.pawlik@osumc.edu. 3. Department of Surgery, The Urban Meyer III and Shelley Meyer Chair for Cancer Research, Wexner Medical Center, The Ohio State University, 395 W. 12th Avenue, Suite 670, Columbus, OH, 43210, USA. tim.pawlik@osumc.edu.
Abstract
BACKGROUND: Given the increasing number of elderly and comorbid patients undergoing surgery, there is increased interest in preoperatively identifying patients at high risk of morbidity and mortality following liver resection. We sought to develop and validate the use of a frailty index (FI) to predict poor postoperative outcomes following liver surgery. METHODS: Patients undergoing a liver resection were identified using the National Surgical Quality Improvement Program Hepatectomy-targeted database for 2014 and randomized into a training or validation cohort. Multivariable logistic regression analysis was performed to develop a revised frailty index (rFI) to predict adverse postoperative clinical outcomes. Leave one out cross-validation was performed to validate the proposed rFI. RESULTS: A total of 2714 patients were identified who met the inclusion criteria. Postoperatively, 826 patients (30.4%) developed a postoperative complication, while 39 patients died within 30 days of surgery. Five preoperative variables (ASA class, BMI, serum albumin, hematocrit, underlying pathology, and type of liver resection) were used to develop the rFI. The rFI demonstrated good discrimination (AUROC = 0.68) and outperformed the previously proposed modified frailty index (mFI; AUROC = 0.53, p < 0.001) when evaluated among patients included in the training cohort. On validation, the rFI demonstrated good model discrimination (AUROC = 0.68) and was accurately able to risk-stratify patients within the validation cohort at risk for developing a postoperative complication, prolonged length-of-stay, and postoperative mortality (all p < 0.05). CONCLUSION: Frailty, as measured by the rFI, was predictive of increased risk of morbidity and mortality following liver surgery and can be used to guide patient decision-making.
BACKGROUND: Given the increasing number of elderly and comorbid patients undergoing surgery, there is increased interest in preoperatively identifying patients at high risk of morbidity and mortality following liver resection. We sought to develop and validate the use of a frailty index (FI) to predict poor postoperative outcomes following liver surgery. METHODS:Patients undergoing a liver resection were identified using the National Surgical Quality Improvement Program Hepatectomy-targeted database for 2014 and randomized into a training or validation cohort. Multivariable logistic regression analysis was performed to develop a revised frailty index (rFI) to predict adverse postoperative clinical outcomes. Leave one out cross-validation was performed to validate the proposed rFI. RESULTS: A total of 2714 patients were identified who met the inclusion criteria. Postoperatively, 826 patients (30.4%) developed a postoperative complication, while 39 patients died within 30 days of surgery. Five preoperative variables (ASA class, BMI, serum albumin, hematocrit, underlying pathology, and type of liver resection) were used to develop the rFI. The rFI demonstrated good discrimination (AUROC = 0.68) and outperformed the previously proposed modified frailty index (mFI; AUROC = 0.53, p < 0.001) when evaluated among patients included in the training cohort. On validation, the rFI demonstrated good model discrimination (AUROC = 0.68) and was accurately able to risk-stratify patients within the validation cohort at risk for developing a postoperative complication, prolonged length-of-stay, and postoperative mortality (all p < 0.05). CONCLUSION: Frailty, as measured by the rFI, was predictive of increased risk of morbidity and mortality following liver surgery and can be used to guide patient decision-making.
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