Machine Learning Analysis of Age-Related Factors in Ventilator Weaning: Implication for Patient Care in Medical Intensive Care Unit

Background: Mechanical ventilation is a critical intervention for patients with respiratory failure, but prolonged ventilation is associated with adverse outcomes. Weaning from the ventilator is a complex process influenced by various factors, including patient age. Older patients may have different physiological responses and comorbidities that affect weaning outcomes compared to younger patients. Machine learning algorithms can analyze vast amounts of patient data and perhaps reveal age-related factors associated with ventilator weaning outcomes. Methods: We retrospectively evaluated data from patients(10/2023-11/2023). who underwent intubation for respiratory failure and received mechanical ventilation in a medical intensive care unit. Information on 15 features, including patient demographics, vital signs, and ventilator data were extracted. We analyzed patients final extubation readiness trial before 4 h. Machine learning methods are the trained on dataset to analyze the relationship between age and ventilator parameter(feature engineering): Ppeak, Pmean, PEEP, FIO2, VT and frequency. Extubation failure was defined as reintubation within 48 h. Learning algorithms (Random Forest) to predict successful extubation. We also analyzed important features and evaluated the area under curve (AUC) (0.7) and prediction metrics. Results: Fifty-three consecutive patients (48% female) who were on mechanical ventilation and had undergone intubation . The rate of weaning failure was 37% in patient age under 76, 22% below 60 y old. Elderly patients presented higher f/V (>169) and areas under the receiver operating characteristic curves for ventilator parameter were smaller (0.4) than those published previously. Machine learning analysis reveals age-related factors associated with ventilator weaning outcomes, including increased comorbidities, decreased respiratory reserve, and altered response to weaning protocols in older patients Conclusions: Machine learning analysis highlights the importance of age in ventilator weaning outcomes and offers insights into personalized management approaches for ventilated patients. By incorporating age-related factors into predictive models can better identify patients at risk of weaning failure and optimize strategies accordingly. Future study should focus on prospective validation and integrated of these models into clinical practice to improve patient care and outcomes in older adult is warranted.