A clinical nomogram predicting unplanned intensive care unit admission after hip fracture surgery

Abstract

BackgroundDespite the improvement of surgical procedures and perioperative management, a portion of patients were still at high risk for intensive care unit admission owing to severe morbidity after hip fracture surgeries. The purpose of this study was to analyze influencing factors and to construct a clinical nomogram to predict unscheduled intensive care unit admission among inpatients after hip fracture surgeries. MethodsWe enrolled a total of 1,234 hip fracture patients, with 40 unplanned intensive care unit admissions, from January 2011 to December 2018. Demographics, chronic coexisting conditions at admission, laboratory tests, and surgical variables were collected and compared between intensive care unit admission and nonadmission groups using univariate analysis. The optimal lasso model was refined to the whole data set, and multivariate logistic regression was used to assign relative weights. A nomogram incorporating these predictors was constructed to visualize these predictors and their corresponding points of the risk for unplanned intensive care unit admission. The model was validated temporally using an independent data set from January 2019 to December 2019 by receiver operating characteristic area under the curve analysis. ResultsIn the development group, we identified age, chronic heart failure, coronary heart disease, chronic obstructive pulmonary disease, Parkinson disease, and serum albumin and creatinine concentration were associated with unscheduled intensive care unit admission using multivariate analysis. The final model had an area under the curve of 0.854 (95% confidence interval, 0.742–0.966). The median calculated odds ratio of intensive care unit admission based on the nomogram was significantly higher for patients in the intensive care unit admission group than in the non-intensive care unit admission group (65.93% vs 0.02%, P < .01). The validation group proved its high predictive power with an area under the curve of 0.96 (95% confidence interval, 0.91–0.99). ConclusionIn this study, we identified several independent factors that may increase the risk for unexpected intensive care unit admission after hip fracture surgery and developed a clinical nomogram based on these variables. Preoperative evaluation using this nomogram might facilitate advanced intensive care unit resource management for high-risk patients whose conditions might easily deteriorate if not closely monitored in general wards after surgeries.