Variations in end-of-life care practices in older critically ill patients with COVID-19 in Europe.

BACKGROUND: Previous studies reported regional differences in end-of-life care (EoLC) for critically ill patients in Europe. OBJECTIVES: The purpose of this post-hoc analysis of the prospective multicentre COVIP study was to investigate variations in EoLC practices among older patients in intensive care units during the coronavirus disease 2019 pandemic. METHODS: A total of 3105 critically ill patients aged 70 years and older were enrolled in this study (Central Europe: n = 1573; Northern Europe: n = 821; Southern Europe: n = 711). Generalised estimation equations were used to calculate adjusted odds ratios (aORs) to population averages. Data were adjusted for patient-specific variables (demographic, disease-specific) and health economic data (gross domestic product, health expenditure per capita). The primary outcome was any treatment limitation, and 90-day mortality was a secondary outcome. RESULTS: The frequency of the primary endpoint (treatment limitation) was highest in Northern Europe (48%), intermediate in Central Europe (39%) and lowest in Southern Europe (24%). The likelihood for treatment limitations was lower in Southern than in Central Europe (aOR 0.39; 95% confidence interval [CI] 0.21-0.73; p = 0.004), even after multivariable adjustment, whereas no statistically significant differences were observed between Northern and Central Europe (aOR 0.57; 95%CI 0.27-1.22; p = 0.15). After multivariable adjustment, no statistically relevant mortality differences were found between Northern and Central Europe (aOR 1.29; 95%CI 0.80-2.09; p = 0.30) or between Southern and Central Europe (aOR 1.07; 95%CI 0.66-1.73; p = 0.78). CONCLUSION: This study shows a north-to-south gradient in rates of treatment limitation in Europe, highlighting the heterogeneity of EoLC practices across countries. However, mortality rates were not affected by these results.

Disease-Course Adapting Machine Learning Prognostication Models in Elderly Patients Critically Ill With COVID-19: Multicenter Cohort Study With External Validation.

BACKGROUND: The COVID-19 pandemic caused by SARS-CoV-2 is challenging health care systems globally. The disease disproportionately affects the elderly population, both in terms of disease severity and mortality risk. OBJECTIVE: The aim of this study was to evaluate machine learning-based prognostication models for critically ill elderly COVID-19 patients, which dynamically incorporated multifaceted clinical information on evolution of the disease. METHODS: This multicenter cohort study (COVIP study) obtained patient data from 151 intensive care units (ICUs) from 26 countries. Different models based on the Sequential Organ Failure Assessment (SOFA) score, logistic regression (LR), random forest (RF), and extreme gradient boosting (XGB) were derived as baseline models that included admission variables only. We subsequently included clinical events and time-to-event as additional variables to derive the final models using the same algorithms and compared their performance with that of the baseline group. Furthermore, we derived baseline and final models on a European patient cohort, which were externally validated on a non-European cohort that included Asian, African, and US patients. RESULTS: In total, 1432 elderly (≥70 years old) COVID-19-positive patients admitted to an ICU were included for analysis. Of these, 809 (56.49%) patients survived up to 30 days after admission. The average length of stay was 21.6 (SD 18.2) days. Final models that incorporated clinical events and time-to-event information provided superior performance (area under the receiver operating characteristic curve of 0.81; 95% CI 0.804-0.811), with respect to both the baseline models that used admission variables only and conventional ICU prediction models (SOFA score, P<.001). The average precision increased from 0.65 (95% CI 0.650-0.655) to 0.77 (95% CI 0.759-0.770). CONCLUSIONS: Integrating important clinical events and time-to-event information led to a superior accuracy of 30-day mortality prediction compared with models based on the admission information and conventional ICU prediction models. This study shows that machine-learning models provide additional information and may support complex decision-making in critically ill elderly COVID-19 patients. TRIAL REGISTRATION: ClinicalTrials.gov NCT04321265; https://clinicaltrials.gov/ct2/show/NCT04321265.

A new multi-national network studying Very old Intensive care Patients (VIPs).

In Europe there are increasing numbers of old (more than 65 years old) and very old (more than 80 years old) patients (very old intensive care patients - VIPs) (Figure 1). In addition to combinations of chronic conditions (multi-morbidity), there are geriatric disabilities and functional limitations, with a profound impact on management in the ICU and afterwards [1].