Kota Sahara1, Diamantis I Tsilimigras2, Anghela Z Paredes2, Syeda A Farooq2, J Madison Hyer2, Amika Moro2, Rittal Mehta2, Lu Wu2, Itaru Endo3, Aslam Ejaz2, Jordan Cloyd2, Timothy M Pawlik4. 1. Department of Surgery, Division of Surgical Oncology, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH, USA; Department of Gastroenterological Surgery, Yokohama City University School of Medicine, Yokohama, Japan. 2. Department of Surgery, Division of Surgical Oncology, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH, USA. 3. Department of Gastroenterological Surgery, Yokohama City University School of Medicine, Yokohama, Japan. 4. Department of Surgery, Division of Surgical Oncology, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH, USA. Electronic address: tim.pawlik@osumc.edu.
Abstract
BACKGROUND: Although most conventional risk prediction models have been based on preoperative information, intra- and post-operative events may be more relevant to mortality after surgery. We sought to develop a mortality risk calculator based on real time characteristics associated with hepatectomy. METHODS: Patients who underwent hepatectomy between 2014 and 2017 were identified in the ACS-NSQIP dataset. Three prediction models (pre-, intra-, post-operative) were developed and validated using perioperative data. RESULTS: Among 14,720 patients, 197 (1.3%) experienced 30-day mortality. The predictive ability of the real-time mortality risk calculator was very good based on only preoperative factors (AUC; training cohort: 0.813, validation cohort: 0.731). Incorporating intra-operative variables into the model increased the AUC (training: 0.838, validation: 0.777), while the post-operative model achieved an AUC of 0.922 in the training and 0.885 in the validation cohorts, respectively. While patients with low preoperative risk had only very small fluctuations in the estimated 30-day mortality risk during the intraoperative (Δ0.4%) and postoperative (Δ0.6%) phases, patients who were already deemed high risk preoperatively had additional increased mortality risk based on factors that occurred in the intraoperative (Δ5.4%) and postoperative (Δ9.3%) periods. CONCLUSION: A real-time mortality risk calculator may better help clinicians identify patients at risk of death at the different stages of the surgical episode.
BACKGROUND: Although most conventional risk prediction models have been based on preoperative information, intra- and post-operative events may be more relevant to mortality after surgery. We sought to develop a mortality risk calculator based on real time characteristics associated with hepatectomy. METHODS:Patients who underwent hepatectomy between 2014 and 2017 were identified in the ACS-NSQIP dataset. Three prediction models (pre-, intra-, post-operative) were developed and validated using perioperative data. RESULTS: Among 14,720 patients, 197 (1.3%) experienced 30-day mortality. The predictive ability of the real-time mortality risk calculator was very good based on only preoperative factors (AUC; training cohort: 0.813, validation cohort: 0.731). Incorporating intra-operative variables into the model increased the AUC (training: 0.838, validation: 0.777), while the post-operative model achieved an AUC of 0.922 in the training and 0.885 in the validation cohorts, respectively. While patients with low preoperative risk had only very small fluctuations in the estimated 30-day mortality risk during the intraoperative (Δ0.4%) and postoperative (Δ0.6%) phases, patients who were already deemed high risk preoperatively had additional increased mortality risk based on factors that occurred in the intraoperative (Δ5.4%) and postoperative (Δ9.3%) periods. CONCLUSION: A real-time mortality risk calculator may better help clinicians identify patients at risk of death at the different stages of the surgical episode.