Development and Validation of a Prediction Model for Intensive Care Unit-Acquired Weakness in Adult Patients with Sepsis: A Prospective, Observational, Single-Center Study

Purpose: This study aimed to develop and validate a clinical prediction model for intensive care unit-acquired weakness (ICU-AW) in sepsis patients, in order aid the early identification of high-risk patients and enable targeted intervention measures. Patients and Methods: This prospective observational study was a single-center study conducted in a tertiary hospital in Shenzhen, China. Eligible inpatients diagnosed with sepsis between January 2023 and June 2024 were enrolled. The least absolute shrinkage and selection operator (LASSO) regression model was used to optimize the feature selection for the risk prediction model for ICU-AW in sepsis patients. Multivariable logistic regression analysis was applied to build a predicting model that incorporated the features selected in the LASSO regression model. Receiver operating characteristic (ROC) and calibration curves, and decision curve analysis (DCA) were applied to assess the model. Results: A total of 344 patients were included in the present study. Among these patients, 257 and 87 patients were assigned to the modeling and validation groups, respectively. Six independent predictors were identified: age, multiple organ dysfunction syndrome (MODS), use of neuromuscular blocking agents (NMBAs), duration of mechanical ventilation, duration of sedation, and Acute Physiology and Chronic Health Evaluation II (APACHE II) score. The nomogram revealed good performance, with an area under the ROC curve (AUC) of 0.905 (95% CI: 0.871-0.940) for the modeling group and 0.861 (95% CI: 0.784-0.939) for the validation group. The calibration curves indicated a good agreement between the predicted and observed outcomes. The DCA demonstrated a broad benefit threshold and good clinical effectiveness. Conclusion: The risk prediction model constructed in the present study demonstrated good predictive performance, providing a valuable reference for clinical practitioners to identify the risk of ICU-AW in patients with sepsis and implement prompt intervention.

Authors

Institutions

Publication Details

Journal
International Journal of General Medicine
Published
2026-04-01
DOI
https://doi.org/10.2147/ijgm.s569937
Primary Topic
Sepsis Diagnosis and Treatment
Type
article
Field-Weighted Citation Impact
0.00

Funders

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

Development and Validation of a Prediction Model for Intensive Care Unit-Acquired Weakness in Adult Patients with Sepsis: A Prospective, Observational, Single-Center Study

Siya Meng, Min Zhang, Linna He, Binghan Wang et al.
International Journal of General Medicine
Sepsis Diagnosis and Treatment
article

Development and Validation of a Prediction Model for Intensive Care Unit-Acquired Weakness in Adult Patients with Sepsis: A Prospective, Observational, Single-Center Study

Siya Meng, Min Zhang, Linna He, Binghan Wang, Guilan He, Sha Sun, Yuting Liu
article en

Abstract

Purpose: This study aimed to develop and validate a clinical prediction model for intensive care unit-acquired weakness (ICU-AW) in sepsis patients, in order aid the early identification of high-risk patients and enable targeted intervention measures. Patients and Methods: This prospective observational study was a single-center study conducted in a tertiary hospital in Shenzhen, China. Eligible inpatients diagnosed with sepsis between January 2023 and June 2024 were enrolled. The least absolute shrinkage and selection operator (LASSO) regression model was used to optimize the feature selection for the risk prediction model for ICU-AW in sepsis patients. Multivariable logistic regression analysis was applied to build a predicting model that incorporated the features selected in the LASSO regression model. Receiver operating characteristic (ROC) and calibration curves, and decision curve analysis (DCA) were applied to assess the model. Results: A total of 344 patients were included in the present study. Among these patients, 257 and 87 patients were assigned to the modeling and validation groups, respectively. Six independent predictors were identified: age, multiple organ dysfunction syndrome (MODS), use of neuromuscular blocking agents (NMBAs), duration of mechanical ventilation, duration of sedation, and Acute Physiology and Chronic Health Evaluation II (APACHE II) score. The nomogram revealed good performance, with an area under the ROC curve (AUC) of 0.905 (95% CI: 0.871-0.940) for the modeling group and 0.861 (95% CI: 0.784-0.939) for the validation group. The calibration curves indicated a good agreement between the predicted and observed outcomes. The DCA demonstrated a broad benefit threshold and good clinical effectiveness. Conclusion: The risk prediction model constructed in the present study demonstrated good predictive performance, providing a valuable reference for clinical practitioners to identify the risk of ICU-AW in patients with sepsis and implement prompt intervention.

International Journal of General MedicineVol. Volume 19
Shenzhen Sixth People's Hospital (CN), ShenZhen People’s Hospital (CN), Shenzhen Municipal People's Government (CN)
Shenzhen Fundamental Research Program, Sanming Project of Medicine in Shenzhen
Openalex Percentile: Top 19%
Sepsis Diagnosis and Treatment
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.