Construction of a Nomogram for Predicting ICU Mortality Risk in Patients with Spinal Fractures Based on the APACHE IV

This study aimed to develop and validate a nomogram for predicting ICU mortality risk in patients with spinal fractures to improve prognostic accuracy. Using data from 1,146 patients in the eICU Collaborative Research Database, independent risk factors—including age, BMI, APACHE IV score, admission source, mechanical ventilation, spinal cord injury, sepsis, oxygen saturation, white blood cell count, hemoglobin, and glucose—were identified via forward stepwise logistic regression and incorporated into the nomogram. The model demonstrated excellent performance, with AUCs of 0.902 (0.857–0.938) in the training cohort and 0.903 (0.825–0.953) in the validation cohort, significantly outperforming APACHE IV according to the DeLong test. Further validation via Hosmer-Lemeshow test, calibration curves, NRI, IDI, and DCA confirmed the nomogram’s superior calibration and clinical utility. As the first comprehensive predictive tool of its kind for spinal fracture patients, this nomogram offers improved mortality risk estimation and supports clinical decision-making.

Authors

Institutions

Publication Details

Journal
Clinical Medicine Research
Published
2025-09-23
DOI
https://doi.org/10.11648/j.cmr.20251405.12
Primary Topic
Hip and Femur Fractures
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Construction of a Nomogram for Predicting ICU Mortality Risk in Patients with Spinal Fractures Based on the APACHE IV

Shaojin Li, Li Wenxu
Clinical Medicine Research
Hip and Femur Fractures
article

Construction of a Nomogram for Predicting ICU Mortality Risk in Patients with Spinal Fractures Based on the APACHE IV

Shaojin Li, Li Wenxu
article en

Abstract

This study aimed to develop and validate a nomogram for predicting ICU mortality risk in patients with spinal fractures to improve prognostic accuracy. Using data from 1,146 patients in the eICU Collaborative Research Database, independent risk factors—including age, BMI, APACHE IV score, admission source, mechanical ventilation, spinal cord injury, sepsis, oxygen saturation, white blood cell count, hemoglobin, and glucose—were identified via forward stepwise logistic regression and incorporated into the nomogram. The model demonstrated excellent performance, with AUCs of 0.902 (0.857–0.938) in the training cohort and 0.903 (0.825–0.953) in the validation cohort, significantly outperforming APACHE IV according to the DeLong test. Further validation via Hosmer-Lemeshow test, calibration curves, NRI, IDI, and DCA confirmed the nomogram’s superior calibration and clinical utility. As the first comprehensive predictive tool of its kind for spinal fracture patients, this nomogram offers improved mortality risk estimation and supports clinical decision-making.

Clinical Medicine ResearchVol. 14(5)
Jinan University (CN), Red Cross Hospital (GR)
Guangzhou Municipal Science and Technology Bureau
Openalex Percentile: Top 58%
Hip and Femur Fractures
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.