Pilot Project for a Web-Based Dynamic Nomogram to Predict Survival 1 Year After Hip Fracture Surgery: Retrospective Observational Study

Abstract

Background Hip fracture is associated with high mortality. Identification of individual risk informs anesthetic and surgical decision-making and can reduce the risk of death. However, interpreting mathematical models and applying them in clinical practice can be difficult. There is a need to simplify risk indices for clinicians and laypeople alike. Objective Our primary objective was to develop a web-based nomogram for prediction of survival up to 365 days after hip fracture surgery. Methods We collected data from 329 patients. Our variables included sex; age; BMI; white cell count; levels of lactate, creatinine, hemoglobin, and C-reactive protein; physical status according to the American Society of Anesthesiologists Physical Status Classification System; socioeconomic status; duration of surgery; total time in the operating room; side of surgery; and procedure urgency. Thereafter, we internally calibrated and validated a Cox proportional hazards model of survival 365 days after hip fracture surgery; logistic regression models of survival 30, 120, and 365 days after surgery; and a binomial model. To present the models on a laptop, tablet, or mobile phone in a user-friendly way, we built an app using Shiny (RStudio). The app showed a drop-down box for model selection and horizontal sliders for data entry, model summaries, and prediction and survival plots. A slider represented patient follow-up over 365 days. Results Of the 329 patients, 24 (7.3%) died within 30 days of surgery, 65 (19.8%) within 120 days, and 94 (28.6%) within 365 days. In all models, the independent predictors of mortality were age, BMI, creatinine level, and lactate level. The logistic model also incorporated white cell count as a predictor. The Cox proportional hazards model showed that mortality differed as follows: age 80 vs 60 years had a hazard ratio (HR) of 0.6 (95% CI 0.3-1.1), a plasma lactate level of 2 vs 1 mmol/L had an HR of 2.4 (95% CI 1.5-3.9), and a plasma creatinine level of 60 vs 90 mol/L had an HR of 2.3 (95% CI 1.3-3.9). Conclusions In conclusion, we provide an easy-to-read web-based nomogram that predicts survival up to 365 days after hip fracture. The Cox proportional hazards model and logistic models showed good discrimination, with concordance index values of 0.732 and 0.781, respectively.

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

“…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…”

“…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. 39 Mortality in perioperative medicine is defined as a rare event (probability less than 5%).…”

“…(1) Multivariate logistic regression 30,31,36,43,48,54,55,60,62,65,67,69,75,76,78,80,95,106,107,110 (2) Previously validated scores such as perioperative medicine-related scores (e.g., ASA status, POSSUM, Charlson Comorbidity Index, or National Surgical Quality Improvement Program calculator scores) 30,32,49,61,100,104 or other scores 58,62 (e.g., Bariclot tool, STOP-BANG score, Mallampati test, various frailty indexes, and the acute kidney injury score) 34,49,58,72,79,82,86,114,121 (3) Clinical assessment 42,52,88 Overall, the machine learning models described in these articles outperformed their technical or clinical comparator, with an average increase in AUC and accuracy between 0.2-0.3, except for that of Chen et al 38 where the ASA score alone, despite a lower AUC, had higher accuracy compared to neural network and logistic regression models.…”

“…• 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.…”