AUTHOR=Guo Jinyan , Huang Zhen , Huang Maoxin , He Yujie , Han Bing , Ma Ning , Yu Zujiang , Liu Shengyun , Ren Zhigang 

TITLE=Development of a Novel Simple Model to Predict Mortality in Patients With Systemic Lupus Erythematosus Admitted to the Intensive Care Unit

JOURNAL=Frontiers in Medicine

VOLUME=Volume 8 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2021.689871

DOI=10.3389/fmed.2021.689871

ISSN=2296-858X

ABSTRACT=Background: To identify risk factors, develop and evaluate a risk model to predict survival of patients with systemic lupus erythematosus (SLE) in intensive care unit (ICU).
Patients and Methods: This was a retrospective cohort study. Patients (n=391) with SLE admitted in ICU were consecutively enrolled from 2010 to 2019. The clinical feature and outcomes of patients were analyzed. Patients were further randomly divided into two mutually exclusively groups named training (n=293) and test (n=98). Risk factors were identified by a Cox model with Markov Chain Monte Carlo simulation and evaluated by latent analysis. The risk score was developed based on the training group and evaluated using the test group. 
Results: The median age of patients was 34 years, and 348 (89.0%) patients were females. Infections (320 patients [81.8%]) and lupus nephritis (246 patients [62.9%]) were the two most prevalent manifestations, as well as the main causes of ICU admission. The in-ICU mortality rate was 55.9% (95% CI, 50.3 - 61.7%) and 45.9% (95% CI, 36.1% - 55.8%) for the training and test groups, respectively. Nine risk factors including age, white blood cell count, alanine transaminase, uric acid, intracranial infection, shock, intracranial hemorrhage, respiratory failure, and calcineurin inhibitors were identified. The risk model had C statistic of 0.912 (95% CI, 0.889 - 0.948) and 0.807 (95% CI 0.703 - 0.889), with predictive range of 5.2% to 98.3% and 6.3% to 94.7% for the training and test groups, respectively. Notably, the risk model stratified 25.3%, 49.5%, and 25.2% of patients in the training group into the high, average, and low-risk groups, with corresponding probabilities of 0.937, 0.593, and 0.118 for in-ICU death events, respectively.
Conclusion: Nine risk factors were identified, and a risk model was developed and evaluated to predict in-ICU death events of patients with SLE. These findings may help clinicians in early identifying high-risk patients, hence reducing the in-ICU mortality rate.