A risk scoring system integrating postoperative factors for predicting early mortality after major non‐cardiac surgery

Abstract

We aimed to develop a risk scoring system for 1-week and 1-month mortality after major non-cardiac surgery, and assess the impact of postoperative factors on 1-week and 1-month mortality using machine learning algorithms. We retrospectively reviewed the medical records of 21,510 patients who were transfused with red blood cells during non-cardiac surgery and collected pre-, intra-, and postoperative features. We derived two patient cohorts to predict 1-week and 1-month mortality and randomly split each of them into training and test cohorts at a ratio of 8:2. All the modeling steps were carried out solely based on the training cohorts, whereas the test cohorts were reserved for the evaluation of predictive performance. Incorporation of postoperative information demonstrated no significant benefit in predicting 1-week mortality but led to substantial improvement in predicting 1-month mortality. Risk scores predicting 1-week and 1-month mortality were associated with area under receiver operating characteristic curves of 84.58% and 90.66%, respectively. Brain surgery, amount of intraoperative red blood cell transfusion, preoperative platelet count, preoperative serum albumin, and American Society of Anesthesiologists physical status were included in the risk score predicting 1-week mortality. Postoperative day (POD) 5 (neutrophil count × mean platelet volume) to (lymphocyte count × platelet count) ratio, preoperative and POD 5 serum albumin, and occurrence of acute kidney injury were included in the risk score predicting 1-month mortality. Our scoring system advocates the importance of postoperative complete blood count differential and serum albumin to better predict mortality beyond the first week post-surgery. Study Highlights WHAT IS THE CURRENT KNOWLEDGE ON THE TOPIC?Postoperative mortality accounts for 7.7% of all-cause mortality and is the third greatest contributor to death. Hence, risk stratification and prediction of | 2231

Prediction of Complications and Prognostication in Perioperative Medicine: A Systematic Review and PROBAST Assessment of Machine Learning Tools

“…Development of postoperative complications: Most studies focused on the prediction of postoperative acute complications 26–28,42,44–49,59,99,110,112,113,118,123 such as pain and opioid use, 53–57 postoperative atrial fibrillation (new-onset atrial fibrillation), 82 postoperative risk of stroke or myocardial infarction, 50,71,77 and delirium or cognitive decline. 65–70 Other models focused on the risk of developing pneumonia or respiratory failure, 83,85,125 acute kidney injury, 43,52,58,60–63,120–122 liver failure 117 or development of sepsis or surgical site infection.…”

“…28,29,32,36,100 One study predicted 30-day mortality risk related to myocardial injury in noncardiac surgery patients, 37 while another developed a natural language processing model using deep learning to analyze medical records and obtain diagnoses directly from notes written by a physician. 27–35,38,100…”

“…Elective noncardiac surgery Mortality 30 days and/or 1 yr [26][27][28][29][30][31][32][33][34][35][36][37][38] In surgical patients with perioperative SarS-CoV-2 39 Morbidity Multiple postoperative complications 26,27,29,[40][41][42][43][44][45][46][47][48][49][50][51] acute and chronic pain 52-57 acute kidney failure 52,58-63 aSa score prediction 64 Delirium and cognitive decline [65][66][67][68][69][70] Cerebral/myocardial infarction 71 Difficult intubation prediciton 72 Ileus 73 Infection risk [74][75][76] Myocardial injury 77 Nausea and vomiting 78 Obstructive apnoea screening 79 Perioperative transfusion 80,81 Postoperative atrial fibrillation 82 respiratory failure and depression Liver failure 117 Major bleeding 118,119 Kidney failure…”

“…• Cardiac surgery 30,115,116,130 • Major elective surgery: Mortality was assessed postoperatively [27][28][29][30][31][32][33][34][35][36]100 in the surgical intensive care unit 35 or in the hospital. 28,29,32,36,100 One study predicted 30-day mortality risk related to myocardial injury in noncardiac surgery patients, 37 while another developed a natural language processing model using deep learning to analyze medical records and obtain diagnoses directly from notes written by a physician.…”

“…• Cardiac surgery 30,115,116,130 • Major elective surgery: Mortality was assessed postoperatively [27][28][29][30][31][32][33][34][35][36]100 in the surgical intensive care unit 35 or in the hospital. 28,29,32,36,100 One study predicted 30-day mortality risk related to myocardial injury in noncardiac surgery patients, 37 while another developed a natural language processing model using deep learning to analyze medical records and obtain diagnoses directly from notes written by a physician. [27][28][29][30][31][32][33][34][35]38,100 • Emergency surgery 26,37,38,[110][111][112] • Mortality in surgical COVID-19 patients: The COVIDSurg collaborative international panel conducted an international prospective study to develop and validate models that predict postoperative mortality risk in patients with perioperative SARS-CoV-2 infection.…”