Leveraging AI and Transfer Learning to Enhance Outcome Prediction for Out-of-Hospital Cardiac Arrest in Diverse Settings: Insights from the Pan-Asian Resuscitation Outcomes Study

BackgroundAccess to trustworthy artificial intelligence (AI) models for clinical applications like emergency care is unevenly distributed globally due to healthcare inequities. Low-resource settings face challenges in AI model development due to limited data, small sample sizes, and inconsistent data quality. Moreover, models developed from high-resource settings are often not readily applicable in low-resource contexts. Transfer learning (TL) is an AI technology that adapts established models to new settings and offers a potential solution. This study explores the feasibility of TL in clinical contexts, using neurological outcome prediction for out-of-hospital cardiac arrest (OHCA) as a proof of concept. MethodsThe Pan-Asian Resuscitation Outcomes Study (PAROS) network provides a multicenter registry for OHCA across the Asia-Pacific region. We applied TL to adapt a neurological outcome prediction model for OHCA, originally developed using a large Japanese cohort (i.e., the external model), to two PAROS registry countries: Vietnam (243 patients) and Singapore (15,916 patients). Separate TL models, calibrated with local data from Vietnam or Singapore, were developed and compared with the external model. Their predictive performance was then compared with that of the external model. FindingsThe external model performed poorly on the Vietnam cohort, with an area under the receiver operating characteristic curve (AUROC) of 0{middle dot}467 (95% CI: 0{middle dot}141-0{middle dot}785). The TL-Vietnam model significantly improved performance (AUROC = 0{middle dot}807, 95% CI: 0{middle dot}626-0{middle dot}948). In Singapore, the TL-Singapore model demonstrated modest improvements (AUROC = 0{middle dot}955, 95% CI: 0{middle dot}940-0{middle dot}967), up from 0{middle dot}945 (95% CI: 0{middle dot}929-0{middle dot}958) in the external model. InterpretationThis study highlights the potential of TL to improve prediction accuracy in low-resource settings worldwide, promoting global healthcare equity. FundingThis study was supported by SingHealth Duke-NUS ACP Programme Funding, National Medical Research Council, Clinician Scientist Awards, Ministry of Health, Health Services Research Grant, Singapore, and Laerdal Foundation.