Development and external validation of a nomogram for predicting in‐hospital mortality in status epilepticus patients in the intensive care unit: A MIMIC ‐ IV and eICU ‐ CRD study

OBJECTIVE: To develop and externally validate a nomogram for predicting in-hospital mortality in ICU patients with SE, using readily available clinical data collected within the first 24 h of ICU admission. METHODS: We identified adult SE patients from MIMIC-IV (training cohort, 2008-2019) using ICD-9/ICD-10 codes. Twenty-seven candidate predictors were screened by LASSO logistic regression with 10-fold cross-validation. The final model was constructed with multivariable logistic regression and presented as a nomogram. Internal validation used 1000 bootstrap resamples with optimism correction. External validation was performed in an independent SE cohort from the eICU-CRD database (2014-2015). RESULTS: , white blood cell count, glucose, bicarbonate, and the Elixhauser comorbidity index. The optimism-corrected AUC was .807 (95% CI, .754-.860) with a Brier score of .124. External validation in 597 eICU patients yielded an AUC of .750 (95% CI, .685-.815). In sensitivity analyses, a reduced model without the comorbidity score achieved an optimism-corrected AUC of .786 and an external AUC of .730, and a model without the ICU care-unit type achieved .817 and .764, respectively. SIGNIFICANCE: This nine-variable nomogram provides a validated, bedside-applicable tool for mortality risk stratification in ICU SE patients, enabling manual risk calculation without electronic computation.

Authors

Institutions

Publication Details

Journal
Epileptic Disorders
Published
2026-10-13
DOI
https://doi.org/10.1002/epd2.70437
Primary Topic
Epilepsy research and treatment
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Development and external validation of a nomogram for predicting in‐hospital mortality in status epilepticus patients in the intensive care unit: A MIMIC ‐ IV and eICU ‐ CRD study

Fangzhao Yin, Yudan Wang, Cui Deqiu
Epileptic Disorders
Epilepsy research and treatment
article

Development and external validation of a nomogram for predicting in‐hospital mortality in status epilepticus patients in the intensive care unit: A MIMIC ‐ IV and eICU ‐ CRD study

Fangzhao Yin, Yudan Wang, Cui Deqiu
article en

Abstract

OBJECTIVE: To develop and externally validate a nomogram for predicting in-hospital mortality in ICU patients with SE, using readily available clinical data collected within the first 24 h of ICU admission. METHODS: We identified adult SE patients from MIMIC-IV (training cohort, 2008-2019) using ICD-9/ICD-10 codes. Twenty-seven candidate predictors were screened by LASSO logistic regression with 10-fold cross-validation. The final model was constructed with multivariable logistic regression and presented as a nomogram. Internal validation used 1000 bootstrap resamples with optimism correction. External validation was performed in an independent SE cohort from the eICU-CRD database (2014-2015). RESULTS: , white blood cell count, glucose, bicarbonate, and the Elixhauser comorbidity index. The optimism-corrected AUC was .807 (95% CI, .754-.860) with a Brier score of .124. External validation in 597 eICU patients yielded an AUC of .750 (95% CI, .685-.815). In sensitivity analyses, a reduced model without the comorbidity score achieved an optimism-corrected AUC of .786 and an external AUC of .730, and a model without the ICU care-unit type achieved .817 and .764, respectively. SIGNIFICANCE: This nine-variable nomogram provides a validated, bedside-applicable tool for mortality risk stratification in ICU SE patients, enabling manual risk calculation without electronic computation.

Epileptic Disorders
Tianjin Huanhu Hospital (CN), Tianjin Chest Hospital (CN), Tianjin Medical University (CN)
Openalex Percentile: Top 13%
Epilepsy research 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.