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. 2023 Jul 8;13(1):11091.
doi: 10.1038/s41598-023-38297-1.

Utilizing a nomogram to predict the one-year postoperative mortality risk for geriatric patients with a hip fracture

Cheng-Yi Wu  1   2 , Ching-Fang Tsai  3 , Hsin-Yi Yang  4
Affiliations

Utilizing a nomogram to predict the one-year postoperative mortality risk for geriatric patients with a hip fracture

Cheng-Yi Wu et al. Sci Rep. .

Abstract

Despite the abundance of research on the risk factors for mortality following hip fracture surgery, there has been a dearth of studies on prediction models in this population. The objective of this research was to explore the influencing factors and construct a clinical nomogram to predict one-year postoperative mortality in patients with hip fracture surgeries. Using the Ditmanson Research Database (DRD), we included 2333 subjects, aged ≥ 50 years who underwent hip fracture surgery between October, 2008 and August, 2021. The endpoint was all-cause mortality. A least absolute shrinkage and selection operator (LASSO) derived Cox regression was performed to select the independent predictors of one-year postoperative mortality. A nomogram was built for predicting one-year postoperative mortality. The prognostic performance of nomogram was evaluated. On the basis of tertiary points in a nomogram, the patients were divided into low, middle and high risk groups, and compared by the Kaplan-Meier analysis. Within 1 year after hip fracture surgery, 274 patients (11.74%) died. Variables retained in the final model comprised age, sex, length of stay, RBC transfusions, hemoglobin, platelet, and eGFR. The AUC for one-year mortality predictions were 0.717 (95% CI = 0.685-0.749). The Kaplan-Meier curves were significantly different among the three risk groups (p < 0.001). The nomogram showed good calibration. In summary, we explored the one-year postoperative mortality risk in geriatric patients with a hip fracture and developed a prediction model that could help clinicians identify patients at high risk of postoperative mortality.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
(a) Predictor selection using the LASSO Cox regression analysis; (b) seven risk factors selected using LASSO Cox regression analysis. The two dotted vertical lines were drawn at the optimal scores by minimum criteria and 1-s.e. criteria (at minimum criteria including age, sex, length of stay, RBC transfusions, hip fracture type, type of anaesthesia, operation time, diabetes mellitus, congestive heart failure, chronic obstructive pulmonary disease, cancer, dementia, hemoglobin, platelet, eGFR and WBC count; at 1-s.e. criteria including age, sex, length of stay, RBC transfusions, hemoglobin, platelet, and eGFR).
Figure 2
Figure 2
A nomogram for prediction of one-year mortality after surgery in patients with hip fractures.
Figure 3
Figure 3
ROC curve for the one-year mortality prediction.
Figure 4
Figure 4
The calibration curves of the nomogram for predicting the overall survival rate after hip fracture surgery at 1-year.
Figure 5
Figure 5
Decision curve analysis (DCA) curves.
Figure 6
Figure 6
Kaplan–Meier survival curve of nomogram.
See this image and copyright information in PMC

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