Development and validation of risk prediction nomograms for acute respiratory failure in elderly patients with hip fracture

Abstract

Background: Hip fractures in the elderly often lead to acute respiratory failure, but there is currently no tool to assess the prognosis of such patients. This study aims to develop a risk prediction model for respiratory failure in these patients.

Methods: A retrospective cross-sectional study was conducted using the Medical Information Mart for Intensive Care (MIMIC)-IV database, incorporating data from 3,266 patients with hip fractures aged over 55 years from 2008 to 2019. Data included demographic information, laboratory indicators, comorbidities, and treatment methods. Patients were divided into a training group (70%) and a validation group (30%). Least Absolute Shrinkage and Selection Operator (LASSO) regression was applied to select prognostic predictors, and a visualized nomogram model was constructed using multivariate logistic regression analysis. Model performance and clinical applicability were assessed. Statistical analyses were done using R4.2.2, with P < 0.05 deemed significant.

Results: Seven key factors, including age, height, albumin, chloride, pneumonia, acute kidney injury (AKI), and heparin use, were associated with respiratory failure risk. The model demonstrated good performance with area under the curve (AUC) values of 0.77 and 0.73 in the training and validation sets, respectively. The calibration curve showed good agreement, and decision curve analysis (DCA) indicated the model's clinical benefit.

Conclusions: This risk prediction model can effectively predict respiratory failure in hip fracture patients, assisting clinicians in identifying high-risk individuals and providing evidence-based references for treatment strategies.

Keywords: Hip fracture; MIMIC database; Nomogram; Respiratory failure.

Fig. 1

Flow diagram of study design

Fig. 2

Clinical variables were selected using the LASSO logistic regression model tuning parameter (λ) selection using LASSO penalized logistic regression with tenfold cross-validation. LASSO coefficient profiles of the radiomic features

Fig. 3

A line chart of the in-hospital mortality rate of patients with femoral fracture complicated by respiratory failure

Fig. 4

ROC curves. A training set; B validation set

Fig. 5

Calibration curves. A training set; B validation set

Fig. 6

Decision curve analysis. A training set; B validation set