Early prediction of ventilator-associated pneumonia with machine learning models: A systematic review and meta-analysis of prediction model performance

Abstract

Artificial ventilation; Machine learning; Predictive analytics

Ventilación artificial; Aprendizaje automático; Análisis predictivo

Ventilació artificial; Aprenentatge automàtic; Anàlisi predictiu

Background Machine learning-based prediction models can catalog, classify, and correlate large amounts of multimodal data to aid clinicians at diagnostic, prognostic, and therapeutic levels. Early prediction of ventilator-associated pneumonia (VAP) may accelerate the diagnosis and guide preventive interventions. The performance of a variety of machine learning-based prediction models were analyzed among adults undergoing invasive mechanical ventilation. Methods This systematic review and meta-analysis was conducted in accordance with the Cochrane Collaboration. Machine learning-based prediction models were identified from a search of nine multi-disciplinary databases. Two authors independently selected and extracted data using predefined criteria and data extraction forms. The predictive performance, the interpretability, the technological readiness level, and the risk of bias of the included studies were evaluated. Results Final analysis included 10 static prediction models using supervised learning. The pooled area under the receiver operating characteristics curve, sensitivity, and specificity for VAP were 0.88 (95 % CI 0.82–0.94, I2 98.4 %), 0.72 (95 % CI 0.45–0.98, I2 97.4 %) and 0.90 (95 % CI 0.85–0.94, I2 97.9 %), respectively. All included studies had either a high or unclear risk of bias without significant improvements in applicability. The care-related risk factors for the best performing models were the duration of mechanical ventilation, the length of ICU stay, blood transfusion, nutrition strategy, and the presence of antibiotics. Conclusion A variety of the prediction models, prediction intervals, and prediction windows were identified to facilitate timely diagnosis. In addition, care-related risk factors susceptible for preventive interventions were identified. In future, there is a need for dynamic machine learning models using time-depended predictors in conjunction with feature importance of the models to predict real-time risk of VAP and related outcomes to optimize bundled care.