Suchergebnisse

Objectives In Europe, there is a distinction between two different healthcare organisation systems, the tax-based healthcare system (THS) and the social health insurance system (SHI). Our aim was to investigate whether the characteristics, treatment and mortality of older, critically ill patients in the intensive care unit (ICU) differed between THS and SHI. Setting ICUs in 16 European countries. Participants In total, 7817 critically ill older (≥80 years) patients were included in this study, 4941 in THS and 2876 in the SHI systems. Primary and secondary outcomes measures We chose generalised estimation equations with robust standard errors to produce population average adjusted OR (aOR). We adjusted for patient-specific variables, health economic data, including gross domestic product (GDP) and human development index (HDI), and treatment strategies. Results In SHI systems, there were higher rates of frail patients (Clinical Frailty Scale >4; 46% vs 41%; p<0.001), longer length of ICU stays (90±162 vs 72±134 hours; p<0.001) and increased levels of organ support. The ICU mortality (aOR 1.50, 95% CI 1.09 to 2.06; p=0.01) was consistently higher in the SHI; however, the 30-day mortality (aOR 0.89, 95% CI 0.66 to 1.21; p=0.47) was similar between THS and SHI. In a sensitivity analysis stratifying for the health economic data, the 30-day mortality was higher in SHI, in low GDP per capita (aOR 2.17, 95% CI 1.42 to 3.58) and low HDI (aOR 1.22, 95% CI 1.64 to 2.20) settings. Conclusions The 30-day mortality was similar in both systems. Patients in SHI were older, sicker and frailer at baseline, which could be interpreted as a sign for a more liberal admission policy in SHI. We believe that the observed trend towards ICU excess mortality in SHI results mainly from a more liberal admission policy and an increase in treatment limitations. Trial registration numbers NCT03134807 and NCT03370692.

Funding: he support of the study in France by a grant from Fondation Assistance Publique-Hôpitaux de Paris Pour la Recherche is greatly appreciated. In Norway, the study was supported by a grant from Health Region West. In addition, EOSCsecretariat.eu provided support and has received funding from the European Union's Horizon Programme call H2020-INFRAEOSC-05-2018-2019, grant agreement number 831644. This work was supported by the Forschungskommission of the Medical Faculty of Heinrich-Heine-University Düsseldorf (grant 2018-32 to GW and grant 2020-21 to RB for a Clinician Scientist Track). The complete list of COVIP collaborators is provided in Multimedia Appendix 10. ; BACKGROUND: The SARS-CoV-2 coronavirus disease (COVID-19) pandemic is challenging health care systems globally. The disease disproportionately affects the elderly population, both in terms of disease severity and mortality risk. OBJECTIVE: This study aimed to evaluate machine-learning based prognostication models for critically ill elderly COVID-19 patients, which dynamically incorporated multifaceted clinical information on the evolution of the disease. METHODS: This multi-centre cohort study obtained patient data from 151 ICUs from 26 countries (COVIP study). Different models based on the Sequential Organ Failure Assessment (SOFA), Logistic Regression (LR), Random Forest (RF) and Extreme Gradient Boosting (XGBoost) 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 the baseline group. Furthermore, we derived baseline and final models on a European patient cohort and externally validated them on a non-European cohort that included Asian, African and American patients. RESULTS: In total, 1,432 elderly (≥70 years) COVID-19 positive patients were admitted to an intensive care unit. Of these 809 (56.5%) patients survived up to 30 days after admission. The average length of stay was 21.6 (±18.2) days. Final models that incorporated clinical events and time-to-event provided superior performance with AUC 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. The present study shows that machine-learning models provide additional information and may support complex decision-making in critically ill elderly COVID-19 patients. CLINICALTRIAL: Nct04321265. ; publishersversion ; published

Funding: In addition, the study was supported by a grant from the European Open Science Cloud (EOSC). EOSCsecretariat.eu has received funding from the European Union's Horizon Programme call H2020-INFRAEOSC-05-2018-2019, grant agreement number 831644. This work was supported by the Forschungskommission of the Medical Faculty of the Heinrich-Heine-University Düsseldorf, No. 2018-32 to G.W. and No. 2020-21 to R.R.B. for a Clinician Scientist Track. ; BACKGROUND: health-related quality of life (HRQoL) is an important patient-centred outcome in patients surviving ICU admission for COVID-19. It is currently not clear which domains of the HRQoL are most affected. OBJECTIVE: to quantify HRQoL in order to identify areas of interventions. DESIGN: prospective observation study. SETTING: admissions to European ICUs between March 2020 and February 2021. SUBJECTS: patients aged 70 years or older admitted with COVID-19 disease. METHODS: collected determinants include SOFA-score, Clinical Frailty Scale (CFS), number and timing of ICU procedures and limitation of care, Katz Activities of Daily Living (ADL) dependence score. HRQoL was assessed at 3 months after ICU admission with the Euro-QoL-5D-5L questionnaire. An outcome of ≥4 on any of Euro-QoL-5D-5L domains was considered unfavourable. RESULTS: in total 3,140 patients from 14 European countries were included in this study. Three months after inclusion, 1,224 patients (39.0%) were alive and the EQ-5D-5L from was obtained. The CFS was associated with an increased odds ratio for an unfavourable HRQoL outcome after 3 months; OR 1.15 (95% confidence interval (CI): 0.71-1.87) for CFS 2 to OR 4.33 (95% CI: 1.57-11.9) for CFS ≧ 7. The Katz ADL was not statistically significantly associated with HRQoL after 3 months. CONCLUSIONS: in critically ill old intensive care patients suffering from COVID-19, the CFS is associated with the subjectively perceived quality of life. The CFS on admission can be used to inform patients and relatives on the risk of an unfavourable qualitative outcome if such patients survive. ; publishersversion ; published