Early prognosis prediction in mechanically ventilated patients using machine learning for tertiary care hospital...

Intensive care units (ICUs) handle mechanically ventilated patients with life-threatening conditions, who require intensive monitoring and treatment. In a low physician-patient ratio setting, providing consistent care to all patients is challenging. A survival prediction model using machine-learning can potentially improve prognosis evaluation and resource allocation. This study aims to develop a machine-learning model to predict survival/mortality in mechanically ventilated patients using clinical features recorded at the time of ICU admission and compare its performance with the Sequential Organ Failure Assessment (SOFA) score as a standalone predictor.

A dataset consisting of 660 mechanically ventilated patients and 98 clinical parameters (n = 660, Male: Female = 365:295, Age = 44.45 ± 19.36 years) from three ICUs at AIIMS, Delhi, was retrospectively evaluated after institutional ethical approval. Binary classification models were trained using 10-fold cross-validation with 70% data and 30% reserved for testing. The outcome was based on the survival/death of the patient during their ICU stay.

A total of 39 features were selected using Shapley-Additive-Explanations (SHAP) and Random Forest model. The top three features were SOFA score, International normalized ratio (INR) and respiratory rate with feature importance values of 7.3%, 4.5% and 3.4% respectively. The K-nearest-neighbour (KNN) model using SHAP-selected features achieved the best test performance with an accuracy = 0.80, area-under-receiver-operating-characteristics-curve (AUROC) = 0.84, sensitivity = 0.82, specificity = 0.77, positive-predictive-value (PPV) = 0.78 and negative-predictive-value (NPV) = 0.82, compared to the SOFA-only model showing accuracy = 0.73, AUROC = 0.73, sensitivity = 0.82, specificity = 0.63, PPV = 0.69 and NPV = 0.78.

The automated machine-learning method for prognosis prediction may assist clinicians in the early triage of patients. These models may offer valuable support to ICU physicians for timely alerts and informed clinical judgment. The study also highlights the continued utility of the SOFA score used by clinicians as the first assessment tool in ICUs, while suggesting that carefully developed machine-learning models may offer complementary support in high-risk ICU settings.