One-year outcomes in patients who have had COVID-19 and who received treatment in the intensive care unit (ICU) are unknown.
To assess the occurrence of physical, mental, and cognitive symptoms among ...patients with COVID-19 at 1 year after ICU treatment.
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.
Patients with COVID-19 who received ICU treatment and survived 1 year after ICU admission.
The main outcomes were self-reported occurrence of physical symptoms (frailty Clinical Frailty Scale score ≥5, fatigue Checklist Individual Strength-fatigue subscale score ≥27, physical problems), mental symptoms (anxiety Hospital Anxiety and Depression {HADS} subscale score ≥8, depression HADS subscale score ≥8, posttraumatic stress disorder mean Impact of Event Scale score ≥1.75), and cognitive symptoms (Cognitive Failure Questionnaire-14 score ≥43) 1 year after ICU treatment and measured with validated questionnaires.
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%).
In 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.
Early diagnosis and treatment has proven to be of utmost importance in the outcome of sepsis patients. We compared the accuracy of the neutrophil-lymphocyte count ratio (NLCR) to conventional ...inflammatory markers in patients admitted to the Intensive Care Unit (ICU).
We performed a retrospective cohort study consisting of 276 ICU patients with sepsis and 388 ICU patients without sepsis. We compared the NLCR as well as C-reactive protein (CRP) level, procalcitonin (PCT) level, white blood cell (WBC) count, neutrophil count and lymphocyte count on ICU admission between sepsis and non-sepsis ICU patients. To evaluate the sensitivity and specificity, we constructed receiver operating characteristics (ROC) curves.
Significant differences in NLCR values were observed between sepsis and non-sepsis patients (15.3 10.8-38.2 (median interquartile range vs. 9.3 6.2-14.5; P<0.001), as well as for CRP level, PCT level and lymphocyte count. The area under the ROC curve (AUROC) of the NLCR was 0.66 (95%CI = 0.62-0.71). AUROC was significantly higher for CRP and PCT level with AUROC's of 0.89 (95%CI 0.87-0.92) and 0.88 (95%CI 0.86-0.91) respectively.
The NLCR is less suitable than conventional inflammatory markers CRP and PCT to detect the presence of sepsis in ICU patients.
ClinicalTrials.gov NCT01274819.
High 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.
This 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.
In 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 (L
) 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.
Noise levels are negatively associated and restorative periods and female gender are positively associated with subjective sleep quality in ICU patients.
www.ClinicalTrials.gov, NCT01826799 . Registered on 9 April 2013.
Results of studies on use of prophylactic haloperidol in critically ill adults are inconclusive, especially in patients at high risk of delirium.
To determine whether prophylactic use of haloperidol ...improves survival among critically ill adults at high risk of delirium, which was defined as an anticipated intensive care unit (ICU) stay of at least 2 days.
Randomized, double-blind, placebo-controlled investigator-driven study involving 1789 critically ill adults treated at 21 ICUs, at which nonpharmacological interventions for delirium prevention are routinely used in the Netherlands. Patients without delirium whose expected ICU stay was at least a day were included. Recruitment was from July 2013 to December 2016 and follow-up was conducted at 90 days with the final follow-up on March 1, 2017.
Patients received prophylactic treatment 3 times daily intravenously either 1 mg (n = 350) or 2 mg (n = 732) of haloperidol or placebo (n = 707), consisting of 0.9% sodium chloride.
The primary outcome was the number of days that patients survived in 28 days. There were 15 secondary outcomes, including delirium incidence, 28-day delirium-free and coma-free days, duration of mechanical ventilation, and ICU and hospital length of stay.
All 1789 randomized patients (mean, age 66.6 years SD, 12.6; 1099 men 61.4%) completed the study. The 1-mg haloperidol group was prematurely stopped because of futility. There was no difference in the median days patients survived in 28 days, 28 days in the 2-mg haloperidol group vs 28 days in the placebo group, for a difference of 0 days (95% CI, 0-0; P = .93) and a hazard ratio of 1.003 (95% CI, 0.78-1.30, P=.82). All of the 15 secondary outcomes were not statistically different. These included delirium incidence (mean difference, 1.5%, 95% CI, -3.6% to 6.7%), delirium-free and coma-free days (mean difference, 0 days, 95% CI, 0-0 days), and duration of mechanical ventilation, ICU, and hospital length of stay (mean difference, 0 days, 95% CI, 0-0 days for all 3 measures). The number of reported adverse effects did not differ between groups (2 0.3% for the 2-mg haloperidol group vs 1 0.1% for the placebo group).
Among critically ill adults at high risk of delirium, the use of prophylactic haloperidol compared with placebo did not improve survival at 28 days. These findings do not support the use of prophylactic haloperidol for reducing mortality in critically ill adults.
clinicaltrials.gov Identifier: NCT01785290.
Accurate 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.
This 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.
In 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.
While 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.
ClinicalTrials.gov, NCT02518646 . Registered on 21 July 2015.
Neuroinflammation is thought to play an important role in the pathogenesis of ICU-acquired delirium, but the association between inflammatory and brain-specific proteins and ICU delirium is poor. We ...investigated whether or not serial determinations of markers may improve this association.
Critically ill patients with a high risk of ICU delirium and with an ICU length of stay of at least 6 days were included in the study. Blood was drawn on days 1, 2, 4 and 6 after ICU admission and analyzed for different markers of inflammation and several brain proteins. Differences in courses over time prior to and following the onset of delirium and absolute differences over time were analyzed in patients with and without delirium using repeated measurement analysis of variance. In addition, a cross-sectional analysis of levels of these markers before the first onset of delirium was performed.
Fifty patients were included in this study. In the longitudinal analysis, there were no differences in the levels of any of the markers immediately prior to and following the onset of delirium, but overall, median levels of adiponectin (9019 (IQR 5776-15,442) vs. 6148 (IQR 4447-8742) ng/ml, p = 0.05) were significantly higher in patients with delirium compared to patients without delirium. In the cross-sectional analysis, median levels of the brain protein Tau (90 (IQR 46-224) vs. 31 (IQR 31-52) pg/ml, p = 0.009) and the ratio Tau/amyloid β
(1.42 ((IQR 0.9-2.57) vs. 0.68 (IQR 0.54-0.96), p = 0.003) were significantly higher in patients with hypoactive delirium compared to patients without. Levels of neopterin (111 (IQR 37-111) vs. 29 (IQR 16-64) mmol/l, p = 0.004) and IL-10 (28 (IQR 12-39) vs. 9 (IQR 4-12) pg/ml, p = 0.001) were significantly higher in patients with hypoactive delirium compared to patients with mixed-type delirium.
While there are differences in markers (adiponectin and several brain proteins) between patients with and without delirium, the development of delirium is not preceded by a change in the biomarker profile of inflammatory markers or brain proteins. Patients with hypoactive delirium account for the observed differences in biomarkers.
ClinicalTrials.gov, NCT 01274819 . Registered on 12 January 2011.
To develop and externally validate a prediction model for ICU survivors' change in quality of life 1 year after ICU admission that can support ICU physicians in preparing patients for life after ICU ...and managing their expectations.
Data from a prospective multicenter cohort study (MONITOR-IC) were used.
Seven hospitals in the Netherlands.
ICU survivors greater than or equal to 16 years old.
None.
Outcome was defined as change in quality of life, measured using the EuroQol 5D questionnaire. The developed model was based on data from an academic hospital, using multivariable linear regression analysis. To assist usability, variables were selected using the least absolute shrinkage and selection operator method. External validation was executed using data of six nonacademic hospitals. Of 1,804 patients included in analysis, 1,057 patients (58.6%) were admitted to the academic hospital, and 747 patients (41.4%) were admitted to a nonacademic hospital. Forty-nine variables were entered into a linear regression model, resulting in an explained variance ( R2 ) of 56.6%. Only three variables, baseline quality of life, admission type, and Glasgow Coma Scale, were selected for the final model ( R2 = 52.5%). External validation showed good predictive power ( R2 = 53.2%).
This study developed and externally validated a prediction model for change in quality of life 1 year after ICU admission. Due to the small number of predictors, the model is appealing for use in clinical practice, where it can be implemented to prepare patients for life after ICU. The next step is to evaluate the impact of this prediction model on outcomes and experiences of patients.
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