Dataset Information

A retrospective cohort study comparing differences in 30-day mortality among critically ill patients aged ≥ 70 years treated in European tax-based healthcare systems (THS) versus social health insurance systems.

ABSTRACT: In Europe, tax-based healthcare systems (THS) and social health insurance systems (SHI) coexist. We examined differences in 30-day mortality among critically ill patients aged ≥ 70 years treated in intensive care units in a THS or SHI. Retrospective cohort study. 2406 (THS n = 886; SHI n = 1520) critically ill ≥ 70 years patients in 129 ICUs. Generalized estimation equations with robust standard errors were chosen to create population average adjusted odds ratios (aOR). Data were adjusted for patient-specific variables, organ support and health economic data. The primary outcome was 30-day-mortality. Numerical differences between SHI and THS in SOFA scores (6 ± 3 vs. 5 ± 3; p = 0.002) were observed, but clinical frailty scores were similar (> 4; 17% vs. 14%; p = 0.09). Higher rates of renal replacement therapy (18% vs. 11%; p < 0.001) were found in SHI (aOR 0.61 95%CI 0.40-0.92; p = 0.02). No differences regarding intubation rates (68% vs. 70%; p = 0.33), vasopressor use (67% vs. 67%; p = 0.90) and 30-day-mortality rates (47% vs. 50%; p = 0.16) were found. Mortality remained similar between both systems after multivariable adjustment and sensitivity analyses. The retrospective character of this study. Baseline risk and mortality rates were similar between SHI and THS. The type of health care system does not appear to have played a role in the intensive care treatment of critically ill patients ≥ 70 years with COVID-19 in Europe.

SUBMITTER: Wernly B

PROVIDER: S-EPMC9580441 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A retrospective cohort study comparing differences in 30-day mortality among critically ill patients aged ≥ 70 years treated in European tax-based healthcare systems (THS) versus social health insurance systems.

Wernly Bernhard B Flaatten Hans H Beil Michael M Fjølner Jesper J Bruno Raphael Romano RR Artigas Antonio A Pinto Bernardo Bollen BB Schefold Joerg C JC Kelm Malte M Sigal Sviri S van Heerden Peter Vernon PV Szczeklik Wojciech W Elhadi Muhammed M Joannidis Michael M Rezar Richard R Oeyen Sandra S Wolff Georg G Marsh Brian B Andersen Finn H FH Moreno Rui R Wernly Sarah S Leaver Susannah S Boumendil Ariane A De Lange Dylan W DW Guidet Bertrand B Perings Stefan S Jung Christian C

Scientific reports 20221019 1

In Europe, tax-based healthcare systems (THS) and social health insurance systems (SHI) coexist. We examined differences in 30-day mortality among critically ill patients aged ≥ 70 years treated in intensive care units in a THS or SHI. Retrospective cohort study. 2406 (THS n = 886; SHI n = 1520) critically ill ≥ 70 years patients in 129 ICUs. Generalized estimation equations with robust standard errors were chosen to create population average adjusted odds ratios (aOR). Data were adjusted for pa ...[more]

PMID: 36261587

Similar Datasets

Project description:ObjectivesTo identify characteristics that predict 30-day mortality among patients critically ill with coronavirus disease 2019 in England, Wales, and Northern Ireland.DesignObservational cohort study.SettingA total of 258 adult critical care units.PatientsA total of 10,362 patients with confirmed coronavirus disease 2019 with a start of critical care between March 1, 2020, and June 22, 2020, of whom 9,990 were eligible (excluding patients with a duration of critical care less than 24 hr or missing core variables).Measurements and main resultsThe main outcome measure was time to death within 30 days of the start of critical care. Of 9,990 eligible patients (median age 60 yr, 70% male), 3,933 died within 30 days of the start of critical care. As of July 22, 2020, 189 patients were still receiving critical care and a further 446 were still in acute hospital. Data were missing for between 0.1% and 7.2% of patients across prognostic factors. We imputed missing data ten-fold, using fully conditional specification and continuous variables were modeled using restricted cubic splines. Associations between the candidate prognostic factors and time to death within 30 days of the start of critical care were determined after adjustment for multiple variables with Cox proportional hazards modeling. Significant associations were identified for age, ethnicity, deprivation, body mass index, prior dependency, immunocompromise, lowest systolic blood pressure, highest heart rate, highest respiratory rate, Pao2/Fio2 ratio (and interaction with mechanical ventilation), highest blood lactate concentration, highest serum urea, and lowest platelet count over the first 24 hours of critical care. Nonsignificant associations were found for sex, sedation, highest temperature, and lowest hemoglobin concentration.ConclusionsWe identified patient characteristics that predict an increased likelihood of death within 30 days of the start of critical care for patients with coronavirus disease 2019. These findings may support development of a prediction model for benchmarking critical care providers.

Project description:ObjectivesRapid and early severity-of-illness assessment appears to be important for critically ill patients with novel coronavirus disease (COVID-19). This study aimed to evaluate the performance of the rapid scoring system on admission of these patients.MethodsA total of 138 medical records of critically ill patients with COVID-19 were included in the study. Demographic and clinical characteristics on admission used for calculating Modified Early Warning Score (MEWS) and Rapid Emergency Medicine Score (REMS) and outcomes (survival or death) were collected for each case and extracted for analysis. All patients were divided into two age subgroups (<65 years and ≥65 years). The receiver operating characteristic (ROC) curve analyses were performed for overall patients and both subgroups.ResultsThe median [25th quartile, 75th quartile] of MEWS of survivors versus nonsurvivors were 1 [1, 2] and 2 [1, 3] and those of REMS were 5 [2, 6] and 7 [6, 10], respectively. In overall analysis, the area under the ROC curve for the REMS in predicting mortality was 0.833 (95% confidence interval [CI] = 0.737 to 0.928), higher than that of MEWS (0.677, 95% CI = 0.541 to 0.813). An optimal cutoff of REMS (≥6) had a sensitivity of 89.5%, a specificity of 69.8%, a positive predictive value of 39.5%, and a negative predictive value of 96.8%. In the analysis of subgroup of patients aged <65 years, the area under the ROC curve for the REMS in predicting mortality was 0.863 (95% CI = 0.743 to 0.941), higher than that of MEWS (0.603, 95% CI = 0.462 to 0.732).ConclusionTo our knowledge, this study was the first exploration on rapid scoring systems for critically ill patients with COVID-19. The REMS could provide emergency clinicians with an effective adjunct risk stratification tool for critically ill patients with COVID-19, especially for the patients aged <65 years. The effectiveness of REMS for screening these patients is attributed to its high negative predictive value.

Dataset Information

A retrospective cohort study comparing differences in 30-day mortality among critically ill patients aged ≥ 70 years treated in European tax-based healthcare systems (THS) versus social health insurance systems.

Publications

A retrospective cohort study comparing differences in 30-day mortality among critically ill patients aged ≥ 70 years treated in European tax-based healthcare systems (THS) versus social health insurance systems.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets