What Is the Applicability of a Novel Surveillance Concept of Ventilator-Associated Events?

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

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.

Diagnostic and prognostic prediction models in ventilator-associated pneumonia: Systematic review and meta-analysis of prediction modelling studies

PURPOSE

Existing expert systems have not improved the diagnostic accuracy of ventilator-associated pneumonia (VAP). The aim of this systematic literature review was to review and summarize state-of-the-art prediction models detecting or predicting VAP from exhaled breath, patient reports and demographic and clinical characteristics.

METHODS

Both diagnostic and prognostic prediction models were searched from a representative list of multidisciplinary databases. An extensive list of validated search terms was added to the search to cover papers failing to mention predictive research in their title or abstract. Two authors independently selected studies, while three authors extracted data using predefined criteria and data extraction forms. The Prediction Model Risk of Bias Assessment Tool was used to assess both the risk of bias and the applicability of the prediction modelling studies. Technology readiness was also assessed.

RESULTS

Out of 2052 identified studies, 20 were included. Fourteen (70%) studies reported the predictive performance of diagnostic models to detect VAP from exhaled human breath with a high degree of sensitivity and a moderate specificity. In addition, the majority of them were validated on a realistic dataset. The rest of the studies reported the predictive performance of diagnostic and prognostic prediction models to detect VAP from unstructured narratives [2 (10%)] as well as baseline demographics and clinical characteristics [4 (20%)]. All studies, however, had either a high or unclear risk of bias without significant improvements in applicability.

CONCLUSIONS

The development and deployment of prediction modelling studies are limited in VAP and related outcomes. More computational, translational, and clinical research is needed to bring these tools from the bench to the bedside.

REGISTRATION

PROSPERO CRD42020180218, registered on 05-07-2020.

Limiting ventilator-associated complications in ICU intubated subjects: strategies to prevent ventilator-associated events and improve outcomes

Introduction: Intubation is required to maintain the airways in comatose patients and enhance oxygenation in hypoxemic or ventilation in hypercapnic subjects. Recently, the Centers of Disease Control (CDC) created new surveillance definitions designed to identify complications associated with poor outcomes. Areas covered: The new framework proposed by CDC, Ventilator-Associated Events (VAE), has a range of definitions encompassing Ventilator-Associated Conditions (VAC), Infection-related Ventilator-Associated Complications (IVAC), or Possible Ventilator-Associated Pneumonia - suggesting replacing the traditional definitions of Ventilator-Associated Tracheobronchitis (VAT) and Ventilator-Associated Pneumonia (VAP). They focused more on oxygenation variations than on Chest-X rays or inflammatory biomarkers. This article will review the spectrum of infectious (VAP & VAT) complications, as well as the main non-infectious complications, namely pulmonary edema, acute respiratory distress syndrome (ARDS) and atelectasis. Strategies to limit these complications and improve outcomes will be presented. Expert commentary: Improving outcomes should be the objective of implementing bundles of prevention, based on risk factors amenable of intervention. Promotion of measures that reduce the exposition or duration of intubation should be a priority.