Risk factors for adverse outcomes during mechanical ventilation of 1152 COVID-19 patients: a multicenter machine learning study with highly granular data from the Dutch Data Warehouse

Background: The identification of risk factors for adverse outcomes and prolonged intensive care unit (ICU) stay in COVID-19 patients is essential for prognostication, determining treatment intensity, and resource allocation. Previous studies have determined risk factors on admission only, and included a limited number of predictors. Therefore, using data from the highly granular and multicenter Dutch Data Warehouse, we developed machine learning models to identify risk factors for ICU mortality, ventilator-free days and ICU-free days during the course of invasive mechanical ventilation (IMV) in COVID-19 patients. Methods: The DDW is a growing electronic health record database of critically ill COVID-19 patients in the Netherlands. All adult ICU patients on IMV were eligible for inclusion. Transfers, patients admitted for less than 24 h, and patients still admitted at time of data extraction were excluded. Predictors were selected based on the literature, and included medication dosage and fluid balance. Multiple algorithms were trained and validated on up to three sets of observations per patient on day 1, 7, and 14 using fivefold nested cross-validation, keeping observations from an individual patient in the same split. Results: A total of 1152 patients were included in the model. XGBoost models performed best for all outcomes and were used to calculate predictor importance. Using Shapley additive explanations (SHAP), age was the most important demographic risk factor for the outcomes upon start of IMV and throughout its course. The relative probability of death across age values is visualized in Partial Dependence Plots (PDPs), with an increase starting at 54 years. Besides age, acidaemia, low P/F-ratios and high driving pressures demonstrated a higher probability of death. The PDP for driving pressure showed a relative probability increase starting at 12 cmH2O. Conclusion: Age is the most important demographic risk factor of ICU mortality, ICU-free days and ventilator-free days throughout the course of invasive mechanical ventilation in critically ill COVID-19 patients. pH, P/F ratio, and driving pressure should be monitored closely over the course of mechanical ventilation as risk factors predictive of these outcomes

Importance of Baseline Prognostic Factors With Increasing Time Since Initiation of Highly Active Antiretroviral Therapy: Collaborative Analysis of Cohorts of HIV-1-Infected Patients

Background: The extent to which the prognosis for AIDS and death of patients initiating highly active antiretroviral therapy (HAART) continues to be affected by their characteristics at the time of initiation (baseline) is unclear. Methods: We analyzed data on 20,379 treatment-naive HIV-1- infected adults who started HAART in 1 of 12 cohort studies in Europe and North America (61,798 person-years of follow-up, 1844 AIDS events, and 1005 deaths). Results: Although baseline CD4 cell count became less prognostic with time, individuals with a baseline CD4 count 350 cells/μL (hazard ratio for AIDS = 2.3, 95% confidence interval [CI]: 1.0 to 2.3; mortality hazard ratio = 2.5, 95% CI: 1.2 to 5.5, 4 to 6 years after starting HAART). Rates of AIDS were persistently higher in individuals who had experienced an AIDS event before starting HAART. Individuals with presumed transmission by means of injection drug use experienced substantially higher rates of AIDS and death than other individuals throughout follow-up (AIDS hazard ratio = 1.6, 95% CI: 0.8 to 3.0; mortality hazard ratio = 3.5, 95% CI: 2.2 to 5.5, 4 to 6 years after starting HAART). Conclusions: Compared with other patient groups, injection drug users and patients with advanced immunodeficiency at baseline experience substantially increased rates of AIDS and death up to 6 years after starting HAART