Abstract

Objectives This study aimed to develop and validate machine learning models to predict in-hospital mortality among systemic lupus erythematosus (SLE) patients using administrative claims data in a tertiary referral center in Indonesia.

Methods We conducted a retrospective cohort study of 327 SLE hospital admissions between January 2019 and June 2025. Predictor variables included demographics, hospitalisation characteristics, and the ten most frequent comorbidities. We developed Logistic Regression, Random Forest, and Extreme Gradient Boosting (XGBoost) models. Class imbalance was addressed using the Synthetic Minority Over-sampling Technique.

Results The overall in-hospital mortality rate was 7.7%. While models achieved comparable discrimination (Area Under the Curve ~0.71), XGBoost was selected for its superior sensitivity (0.93) compared to Logistic Regression (0.80) and Random Forest (0.97). Feature importance analysis revealed pneumonia as the most significant predictor, followed by acute kidney failure and length of stay. Hypoalbuminemia and hyponatremia were also identified as key prognostic markers.

Conclusions Machine learning models utilising registry-based administrative data effectively stratify mortality risk in hospitalised SLE patients with high sensitivity. The dominance of pneumonia and renal failure as predictors underscores the critical need for aggressive infection control and renal monitoring in this population.

Competing Interest Statement

The authors have declared no competing interest.

Funding Statement

The author(s) received no specific funding for this work.

Author Declarations

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

Yes

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

This study was approved by the Medical and Health Research Ethics Committee of the Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Yogyakarta, Indonesia (Approval Number: KE-FK-1629-EC-2025). The study was conducted in accordance with the Declaration of Helsinki. As this study involved a retrospective analysis of routinely collected administrative and clinical registry data from the ISLET registry, individual patient consent was waived by the ethics committee. All data were analysed in de-identified form.

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

Yes

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

Yes

I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

Yes

Data Availability

The minimal dataset underlying the findings of this study, consisting of de-identified patient-level administrative variables used in the machine learning analyses, as well as The R analysis code is available from the corresponding author upon reasonable request. Requests will be reviewed to ensure compliance with the ethical approval granted by the Medical and Health Research Ethics Committee, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada (Ref: KE-FK-1629-EC-2025).

Abbreviation listACRAmerican College of RheumatologyAUCArea Under the CurveGLMGeneralized Linear ModelICD-10International Classification of Diseases, 10th RevisionIQRInterquartile RangeISLETIndonesian Systemic Lupus Erythematosus RegionalLNLupus NephritisMLMachine LearningREDCapResearch Electronic Data CaptureROCReceiver Operating CharacteristicSIADHSyndrome of Inappropriate Antidiuretic Hormone SecretionSLESystemic Lupus ErythematosusSLEDAISystemic Lupus Erythematosus Disease Activity IndexSLICCSystemic Lupus International Collaborating ClinicsSMOTESynthetic Minority Over-sampling TechniqueVIPVariable Importance PlotsXGBoostExtreme Gradient Boosting