IMPORTANCE One-year outcomes in patients who have had COVID-19 and who received treatment in the intensive care unit (ICU) are unknown.OBJECTIVE To assess the occurrence of physical, mental, and cognitive symptoms among patients with COVID-19 at 1 year after ICU treatment. DESIGN, SETTING, AND PARTICIPANTS An exploratory prospective multicenter cohort study conducted in ICUs of 11 Dutch hospitals. Patients (N = 452) with COVID-19, aged 16 years and older, and alive after hospital discharge following admission to 1 of the 11 ICUs during the first COVID-19 surge (March 1, 2020, until July 1, 2020) were eligible for inclusion. Patients were followed up for 1 year, and the date of final follow-up was June 16, 2021. EXPOSURES Patients with COVID-19 who received ICU treatment and survived 1 year after ICU admission. MAIN OUTCOMES AND MEASURES The main outcomes were self-reported occurrence of physical symptoms (frailty [Clinical Frailty Scale score Ն5], fatigue [Checklist Individual Strength-fatigue subscale score Ն27], physical problems), mental symptoms (anxiety [Hospital Anxiety and Depression {HADS} subscale score Ն8], depression [HADS subscale score Ն8], posttraumatic stress disorder [mean Impact of Event Scale score Ն1.75]), and cognitive symptoms (Cognitive Failure Questionnaire-14 score Ն43) 1 year after ICU treatment and measured with validated questionnaires. RESULTS Of the 452 eligible patients, 301 (66.8%) patients could be included, and 246 (81.5%) patients (mean [SD] age, 61.2 [9.3] years; 176 men [71.5%]; median ICU stay, 18 days [IQR, 11 to 32]) completed the 1-year follow-up questionnaires. At 1 year after ICU treatment for COVID-19, physical symptoms were reported by 182 of 245 patients (74.3% [95% CI, 68.3% to 79.6%]), mental symptoms were reported by 64 of 244 patients (26.2% [95% CI, 20.8% to 32.2%]), and cognitive symptoms were reported by 39 of 241 patients (16.2% [95% CI, 11.8% to 21.5%]). The most frequently reported new physical problems were weakened condition (95/244 patients [38.9%]), joint stiffness (64/243 patients [26.3%]) joint pain (62/243 patients [25.5%]), muscle weakness (60/242 patients [24.8%]) and myalgia (52/244 patients [21.3%]). CONCLUSIONS AND RELEVANCEIn this exploratory study of patients in 11 Dutch hospitals who survived 1 year following ICU treatment for COVID-19, physical, mental, or cognitive symptoms were frequently reported.

RationaleDelirium incidence in intensive care unit (ICU) patients is high and associated with poor outcome. Identification of high-risk patients may facilitate its prevention.PurposeTo develop and validate a model based on data available at ICU admission to predict delirium development during a patient’s complete ICU stay and to determine the predictive value of this model in relation to the time of delirium development.MethodsProspective cohort study in 13 ICUs from seven countries. Multiple logistic regression analysis was used to develop the early prediction (E-PRE-DELIRIC) model on data of the first two-thirds and validated on data of the last one-third of the patients from every participating ICU.ResultsIn total, 2914 patients were included. Delirium incidence was 23.6 %. The E-PRE-DELIRIC model consists of nine predictors assessed at ICU admission: age, history of cognitive impairment, history of alcohol abuse, blood urea nitrogen, admission category, urgent admission, mean arterial blood pressure, use of corticosteroids, and respiratory failure. The area under the receiver operating characteristic curve (AUROC) was 0.76 [95 % confidence interval (CI) 0.73–0.77] in the development dataset and 0.75 (95 % CI 0.71–0.79) in the validation dataset. The model was well calibrated. AUROC increased from 0.70 (95 % CI 0.67–0.74), for delirium that developed <2 days, to 0.81 (95 % CI 0.78–0.84), for delirium that developed >6 days.ConclusionPatients’ delirium risk for the complete ICU length of stay can be predicted at admission using the E-PRE-DELIRIC model, allowing early preventive interventions aimed to reduce incidence and severity of ICU delirium.Electronic supplementary materialThe online version of this article (doi:10.1007/s00134-015-3777-2) contains supplementary material, which is available to authorized users.

clinicaltrials.gov Identifier: NCT01785290.

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BackgroundAccurate prediction of delirium in the intensive care unit (ICU) may facilitate efficient use of early preventive strategies and stratification of ICU patients by delirium risk in clinical research, but the optimal delirium prediction model to use is unclear. We compared the predictive performance and user convenience of the prediction  model for delirium (PRE-DELIRIC) and early prediction model for delirium (E-PRE-DELIRIC) in ICU patients and determined the value of a two-stage calculation.MethodsThis 7-country, 11-hospital, prospective cohort study evaluated consecutive adults admitted to the ICU who could be reliably assessed for delirium using the Confusion Assessment Method-ICU or the Intensive Care Delirium Screening Checklist. The predictive performance of the models was measured using the area under the receiver operating characteristic curve. Calibration was assessed graphically. A physician questionnaire evaluated user convenience. For the two-stage calculation we used E-PRE-DELIRIC immediately after ICU admission and updated the prediction using PRE-DELIRIC after 24 h.ResultsIn total 2178 patients were included. The area under the receiver operating characteristic curve was significantly greater for PRE-DELIRIC (0.74 (95% confidence interval 0.71–0.76)) compared to E-PRE-DELIRIC (0.68 (95% confidence interval 0.66–0.71)) (z score of − 2.73 (p < 0.01)). Both models were well-calibrated. The sensitivity improved when using the two-stage calculation in low-risk patients. Compared to PRE-DELIRIC, ICU physicians (n = 68) rated the E-PRE-DELIRIC model more feasible.ConclusionsWhile both ICU delirium prediction models have moderate-to-good performance, the PRE-DELIRIC model predicts delirium better. However, ICU physicians rated the user convenience of E-PRE-DELIRIC superior to PRE-DELIRIC. In low-risk patients the delirium prediction further improves after an update with the PRE-DELIRIC model after 24 h.Trial registrationClinicalTrials.gov, NCT02518646. Registered on 21 July 2015.Electronic supplementary materialThe online version of this article (10.1186/s13054-018-2037-6) contains supplementary material, which is available to authorized users.

BackgroundHigh noise levels in the intensive care unit (ICU) are a well-known problem. Little is known about the effect of noise on sleep quality in ICU patients. The study aim is to determine the effect of noise on subjective sleep quality.MethodsThis was a multicenter observational study in six Dutch ICUs. Noise recording equipment was installed in 2–4 rooms per ICU. Adult patients were eligible for the study 48 h after ICU admission and were followed up to maximum of five nights in the ICU. Exclusion criteria were presence of delirium and/or inability to be assessed for sleep quality. Sleep was evaluated using the Richards Campbell Sleep Questionnaire (range 0–100 mm). Noise recordings were used for analysis of various auditory parameters, including the number and duration of restorative periods. Hierarchical mixed model regression analysis was used to determine associations between noise and sleep.ResultsIn total, 64 patients (68% male), mean age 63.9 (± 11.7) years and mean Acute Physiology And Chronic Health Evaluation (APACHE) II score 21.1 (± 7.1) were included. Average sleep quality score was 56 ± 24 mm. The mean of the 24-h average sound pressure levels (LAeq, 24h) was 54.0 dBA (± 2.4). Mixed-effects regression analyses showed that background noise (β = − 0.51, p < 0.05) had a negative impact on sleep quality, whereas number of restorative periods (β = 0.53, p < 0.01) and female sex (β = 1.25, p < 0.01) were weakly but significantly correlated with sleep.ConclusionsNoise levels are negatively associated and restorative periods and female gender are positively associated with subjective sleep quality in ICU patients.Trial registrationwww.ClinicalTrials.gov, NCT01826799. Registered on 9 April 2013.Electronic supplementary materialThe online version of this article (10.1186/s13054-018-2182-y) contains supplementary material, which is available to authorized users.

To study the efficacy of lopinavir-ritonavir and hydroxychloroquine in critically ill patients with coronavirus disease 2019 .Methods: Critically ill adults with COVID-19 were randomized to receive lopinavir-ritonavir, hydroxychloroquine, combination therapy of lopinavir-ritonavir and hydroxychloroquine or no antiviral therapy (control). The primary endpoint was an ordinal scale of organ support-free days. Analyses used a Bayesian cumulative logistic model and expressed treatment effects as an adjusted odds ratio (OR) where an OR > 1 is favorable. Results:We randomized 694 patients to receive lopinavir-ritonavir (n = 255), hydroxychloroquine (n = 50), combination therapy (n = 27) or control (n = 362). The median organ support-free days among patients in lopinavir-ritonavir, hydroxychloroquine, and combination therapy groups was 4 (-1 to 15), 0 (-1 to 9) and-1 (-1 to 7), respectively,