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Objective: To describe the 12-month mortality of Dutch COVID-19 intensive care unit patients, the total COVID-19 population and various subgroups on the basis of the number of comorbidities, age, sex, mechanical ventilation, and vasoactive medication use., Methods: We included all patients admitted with COVID-19 between March 1, 2020, and March 29, 2022, from the Dutch National Intensive Care (NICE) database. The crude 12-month mortality rate is presented via Kaplan-Meier survival curves for each patient subgroup. We used Cox regression models to analyze the effects of patient characteristics on 12-month mortality after hospital discharge., Results: We included 16,605 COVID-19 patients. The in-hospital mortality rate was 28.1%, and the 12-month mortality rate after intensive care unit admission was 29.8%. Among hospital survivors, 12-month mortality after hospital discharge was 2.5% (300/11,931). The hazard of death at 12 months after hospital discharge was greater in patients between 60 and 79 years (HR 4.74; 95%CI 2.23 - 10.06) and ≥ 80 years (HR 22.77; 95%CI 9.91 - 52.28) than in patients < 40 years of age; in male patients than in female patients (HR 1.38; 95%CI 1.07 - 1.78); and in patients with one (adjusted HR 1.95; 95%CI 1.5 - 2.53), two (adjusted HR 4.49; 95%CI 3.27 - 6.16) or more than two comorbidities (adjusted HR 4.99; 95%CI 2.62 - 9.5) than in patients with no comorbidities. Neither vasoactive medication use nor mechanical ventilation resulted in statistically significant results., Conclusion: For Dutch COVID-19 intensive care unit patients, most deaths occurred during their hospital stay. For hospital survivors, the crude 12-month mortality rate was low. Patient age (older than 60), sex and the number of comorbidities were associated with a greater hazard of death at 12 months after hospital discharge, whereas mechanical ventilation and vasoactive medication were not.

Background: The Coronavirus disease 2019 (COVID-19) pandemic has underlined the urgent need for reliable, multicenter, and full-admission intensive care data to advance our understanding of the course of the disease and investigate potential treatment strategies. In this study, we present the Dutch Data Warehouse (DDW), the first multicenter electronic health record (EHR) database with full-admission data from critically ill COVID-19 patients., Methods: A nation-wide data sharing collaboration was launched at the beginning of the pandemic in March 2020. All hospitals in the Netherlands were asked to participate and share pseudonymized EHR data from adult critically ill COVID-19 patients. Data included patient demographics, clinical observations, administered medication, laboratory determinations, and data from vital sign monitors and life support devices. Data sharing agreements were signed with participating hospitals before any data transfers took place. Data were extracted from the local EHRs with prespecified queries and combined into a staging dataset through an extract-transform-load (ETL) pipeline. In the consecutive processing pipeline, data were mapped to a common concept vocabulary and enriched with derived concepts. Data validation was a continuous process throughout the project. All participating hospitals have access to the DDW. Within legal and ethical boundaries, data are available to clinicians and researchers., Results: Out of the 81 intensive care units in the Netherlands, 66 participated in the collaboration, 47 have signed the data sharing agreement, and 35 have shared their data. Data from 25 hospitals have passed through the ETL and processing pipeline. Currently, 3464 patients are included in the DDW, both from wave 1 and wave 2 in the Netherlands. More than 200 million clinical data points are available. Overall ICU mortality was 24.4%. Respiratory and hemodynamic parameters were most frequently measured throughout a patient's stay. For each patient, all administered medication and their daily fluid balance were available. Missing data are reported for each descriptive., Conclusions: In this study, we show that EHR data from critically ill COVID-19 patients may be lawfully collected and can be combined into a data warehouse. These initiatives are indispensable to advance medical data science in the field of intensive care medicine., (© 2021. The Author(s).)

Purpose: The aim of this study is to assess the incidence of sublingual microcirculatory flow alterations, according to a predefined arbitrary cutoff value, in patients with 'clinical signs of impaired organ perfusion'. Secondary endpoints were the changes in microvascular flow index (MFI), 'clinical signs of impaired organ perfusion', and stroke volume (SV) after fluid administration, and the differences between groups. Methods: Prospective, single-center, observational study in a 22-bed mixed intensive care unit (ICU). Patients ≥18 years with invasive hemodynamic monitoring and 'clinical signs of impaired organ perfusion' as the principal reason for fluid administration were included. Before and after fluid challenge, systemic hemodynamics and direct in vivo observation of the sublingual microcirculation with sidestream dark-field imaging were obtained. Microvascular flow index (MFI) <2.6 was predefined as abnormal. Results: N = 50. At baseline, MFI <2.6 was present in 66 % of the patients. In these patients, MFI increased from 2.3 (2-2.5) at baseline to 2.5 (2.1-2.8) after fluid challenge ( p = 0.003). This was accompanied by a reduction in the number of 'clinical signs of impaired organ perfusion' from 2 (1-2) to 1 (0-2) ( p < 0.001). However, in patients with MFI >2.6 at baseline, MFI and clinical signs changed insignificantly [2.8 (2.8-2.9) versus 2.8 (2.7-3), p = 0.45, respectively, 1 (1-2) versus 1 (1-2), p < 0.32]. These changes were not restricted to patients with a rise in SV ≥10 %. Conclusions: These data add to the understanding that noninvasive assessment of microvascular blood flow may help to identify patients eligible for fluid therapy, and to evaluate its effect. [ABSTRACT FROM AUTHOR]