Generalizability of a prediction model for walking independence at discharge in older adults after hip fracture surgery: a multicenter study

Abstract This study aimed to evaluate the generalizability of a clinical prediction model for walking independence at discharge from acute-care hospitals after hip surgery in older patients. This retrospective multicenter study was conducted at acute-care hospitals. A total of 2,548 older patients registered in a hip fracture database involving 19 hospitals who were able to walk independently before injury and underwent surgery were included. To evaluate generalizability, we conducted internal–external cross-validation (IECV)across the participating hospitals. The performance of the prediction model was evaluated using the area under the curve (AUC), calibration intercepts, and calibration slopes. The generalizability of the model was assessed using 95% prediction intervals (PIs). Among the 2,014 patients included in the final analysis, 38.2% regained independent walking ability at discharge. Across IECV, the AUC was 0.81 (95% PI: 0.62–0.92). Calibration slopes were 0.96 (95% PI: 0.34–1.58) and calibration intercepts were 0.11 (95% PI: −1.56 to 1.77). Given the marked heterogeneity observed across clusters, our findings indicate that the generalizability of the prediction model for walking independence at discharge after hip fracture surgery in older adults is limited, highlighting the impact of between-hospital heterogeneity on model performance.

Authors

Institutions

Publication Details

Journal
Scientific Reports
Published
2026-10-06
DOI
https://doi.org/10.1038/s41598-026-73542-3
Primary Topic
Hip and Femur Fractures
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Generalizability of a prediction model for walking independence at discharge in older adults after hip fracture surgery: a multicenter study

Yasushi Kurobe, Keisuke Nakamura, Naoko Ushiyama, Kimito Momose
Scientific Reports
Hip and Femur Fractures
article

Generalizability of a prediction model for walking independence at discharge in older adults after hip fracture surgery: a multicenter study

Yasushi Kurobe, Keisuke Nakamura, Naoko Ushiyama, Kimito Momose
article en

Abstract

Abstract This study aimed to evaluate the generalizability of a clinical prediction model for walking independence at discharge from acute-care hospitals after hip surgery in older patients. This retrospective multicenter study was conducted at acute-care hospitals. A total of 2,548 older patients registered in a hip fracture database involving 19 hospitals who were able to walk independently before injury and underwent surgery were included. To evaluate generalizability, we conducted internal–external cross-validation (IECV)across the participating hospitals. The performance of the prediction model was evaluated using the area under the curve (AUC), calibration intercepts, and calibration slopes. The generalizability of the model was assessed using 95% prediction intervals (PIs). Among the 2,014 patients included in the final analysis, 38.2% regained independent walking ability at discharge. Across IECV, the AUC was 0.81 (95% PI: 0.62–0.92). Calibration slopes were 0.96 (95% PI: 0.34–1.58) and calibration intercepts were 0.11 (95% PI: −1.56 to 1.77). Given the marked heterogeneity observed across clusters, our findings indicate that the generalizability of the prediction model for walking independence at discharge after hip fracture surgery in older adults is limited, highlighting the impact of between-hospital heterogeneity on model performance.

Scientific Reports
Shinshu University (JP)
Openalex Percentile: Top 9%
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.