Early Detection of Septic Shock Onset Using Interpretable Machine Learners.

Area of Science:

• Healthcare innovation
• Translational research
• Machine learning applications

Background:

• Strategic innovation in healthcare involves developing decision support systems using machine learning.
• Predicting patient progression to septic shock is a key area of translational research.
• This study aimed to create a clinical decision support system for early septic shock prediction.

Purpose of the Study:

• To develop a predictive model for septic shock progression in acute care settings.
• To predict septic shock up to 6 hours from patient admission.
• To integrate machine learning models into Electronic Health Records (EHR) for real-time decision support.

Main Methods:

• Utilized Electronic Health Record (EHR) data for model development.
• Compared eight machine learning algorithms and two sampling strategies for class imbalance.
• Defined septic shock using Centers for Medicare & Medicaid Services (CMS) criteria from clinical and billing data.
• Assessed model performance using Area Under Receiving Operator Characteristics (AUROC), sensitivity, and specificity.

Main Results:

• Developed 96 prediction models using data from 45,425 in-patient visits.
• Four models achieved an AUROC greater than 0.9; all models had an AUROC of at least 0.8820.
• The best performing model, a Random Forest model, achieved an AUROC of 0.9483 with 83.9% sensitivity and 88.1% specificity.
• The 6-hour prediction window and models using clinical data showed superior performance.

Conclusions:

• Machine learning models can reliably predict septic shock using combined clinical and administrative data.
• Clinical information significantly improved model performance compared to administrative data alone.
• Intelligent decision support tools integrated into EHR can enhance clinical outcomes and resource optimization.