Shifting trends in modes of death in the Intensive Care Unit.

PURPOSE: To describe the way patients die in a Spanish ICU, and how the modes of death have changed in the last 10 years. MATERIALS AND METHODS: Retrospective observational study evaluating all patients who died in a Spanish tertiary ICU over a 10-year period. Modes of death were classified as death despite maximal support (D-MS), brain death (BD), and death following life-sustaining treatment limitation (D-LSTL). RESULTS: Amongst 9264 ICU admissions, 1553 (16.8%) deaths were recorded. The ICU mortality rate declined (1.7%/year, 95% CI 1.4-2.0; p = 0.021) while ICU admissions increased (3.5%/year, 95% CI 3.3-3.7; p < 0.001). More than half of the patients (888, 57.2%) died D-MS, 389 (25.0%) died after a shared decision of D-LSTL and 276 (17.8%) died due to BD. Modes of death have changed significantly over the past decade. D-LSTL increased by 15.1%/year (95% CI 14.4-15.8; p < 0.001) and D-MS at the end-of-life decreased by 7.1%/year (95% CI 6.6-7.6; p < 0.001). The proportion of patients diagnosed with BD remained stable over time. CONCLUSIONS: End-of-life practices and modes of death in our ICU have steadily changed. The proportion of patients who died in ICU following limitation of life-prolonging therapies substantially increased, whereas death after maximal support occurred significantly less frequently.

Development and validation of a clinical score to estimate progression to severe or critical state in COVID-19 pneumonia hospitalized patients.

The prognosis of a patient with COVID-19 pneumonia is uncertain. Our objective was to establish a predictive model of disease progression to facilitate early decision-making. A retrospective study was performed of patients admitted with COVID-19 pneumonia, classified as severe (admission to the intensive care unit, mechanic invasive ventilation, or death) or non-severe. A predictive model based on clinical, laboratory, and radiological parameters was built. The probability of progression to severe disease was estimated by logistic regression analysis. Calibration and discrimination (receiver operating characteristics curves and AUC) were assessed to determine model performance. During the study period 1152 patients presented with SARS-CoV-2 infection, of whom 229 (19.9%) were admitted for pneumonia. During hospitalization, 51 (22.3%) progressed to severe disease, of whom 26 required ICU care (11.4); 17 (7.4%) underwent invasive mechanical ventilation, and 32 (14%) died of any cause. Five predictors determined within 24 h of admission were identified: Diabetes, Age, Lymphocyte count, SaO2, and pH (DALSH score). The prediction model showed a good clinical performance, including discrimination (AUC 0.87 CI 0.81, 0.92) and calibration (Brier score = 0.11). In total, 0%, 12%, and 50% of patients with severity risk scores ≤ 5%, 6-25%, and > 25% exhibited disease progression, respectively. A risk score based on five factors predicts disease progression and facilitates early decision-making according to prognosis.