Development and validation of a nomogram for predicting in-hospital mortality in patients with nonhip femoral fractures

Abstract

Background: The incidence of nonhip femoral fractures is gradually increasing, but few studies have explored the risk factors for in-hospital death in patients with nonhip femoral fractures in the ICU or developed mortality prediction models. Therefore, we chose to study this specific patient group, hoping to help clinicians improve the prognosis of patients.

Methods: This is a retrospective study based on the data from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. Least absolute shrinkage and selection operator (LASSO) regression was used to screen risk factors. The receiver operating characteristic (ROC) curve was drawn, and the areas under the curve (AUC), net reclassification index (NRI) and integrated discrimination improvement (IDI) were calculated to evaluate the discrimination of the model. The consistency between the actual probability and the predicted probability was assessed by the calibration curve and Hosmer-Lemeshow goodness of fit test (HL test). Decision curve analysis (DCA) was performed, and the nomogram was compared with the scoring system commonly used in clinical practice to evaluate the clinical net benefit.

Results: The LASSO regression analysis showed that heart rate, temperature, red blood cell distribution width, blood urea nitrogen, Glasgow Coma Scale (GCS), Simplified Acute Physiology Score II (SAPSII), Charlson comorbidity index and cerebrovascular disease were independent risk factors for in-hospital death in patients with nonhip femoral fractures. The AUC, IDI and NRI of our model in the training set and validation set were better than those of the GCS and SAPSII scoring systems. The calibration curve and HL test results showed that our model prediction results were in good agreement with the actual results (P = 0.833 for the HL test of the training set and P = 0.767 for the HL test of the validation set). DCA showed that our model had a better clinical net benefit than the GCS and SAPSII scoring systems.

Conclusion: In this study, the independent risk factors for in-hospital death in patients with nonhip femoral fractures were determined, and a prediction model was constructed. The results of this study may help to improve the clinical prognosis of patients with nonhip femoral fractures.

Keywords: In-hospital mortality; Intensive care unit; Nomogram; Nonhip femoral fracture.

Fig. 1

Workflow of the study. ICU, Intensive care unit; MIMIC-IV, Medical Information Mart for Intensive Care IV; LASSO, Least absolute shrinkage and selection operator; ROC, Receiver operating characteristic; AUC, Area under the receiver operating characteristic curve; NRI, Net reclassification improvement; IDI, Integrated discrimination improvement; HL test, Hosmer‒Lemeshow test; DCA, Decision curve analysis

Fig. 2

Clinical variables were selected using the lasso logistic regression model. a Tuning parameter (λ) selection using LASSO penalized logistic regression with fivefold cross-validation. b LASSO coefficient profiles of the radiomic features

Fig. 3

Nomogram for predicting the risk of in-hospital mortality in patients with nonhip femoral fractures in the ICU. GCS, Glasgow Coma Scale; SAPSII, Simplified Acute Physiology Score II; BUN, Blood urea nitrogen; RDW, Red blood cell distribution width; HR, Heart rate. *means p < 0.05,**means p < 0.01,***means p < 0.001

Fig. 4

Receiver operating characteristic curve of the established nomogram, GCS and SAPSII. a Training cohort, b Verification cohort. GCS, Glasgow Coma Scale; SAPSII, Simplified Acute Physiology Score II; AUC, Area under the receiver operating characteristic curve

Fig. 5

Calibration curve of the established nomogram. a Training cohort, b Verification cohort

Fig. 6

Decision curve analysis of the established nomogram, GCS, and SAPS II. a Training cohort, b Verification cohort. GCS, Glasgow Coma Scale; SAPSII, Simplified Acute Physiology Score II