Prediction of Long-Term Physical, Mental, and Cognitive Problems Following Critical Illness: Development and External Validation of the PROSPECT Prediction Model.

OBJECTIVES: ICU survivors often suffer from long-lasting physical, mental, and cognitive health problems after hospital discharge. As several interventions that treat or prevent these problems already start during ICU stay, patients at high risk should be identified early. This study aimed to develop a model for early prediction of post-ICU health problems within 48 hours after ICU admission. DESIGN: Prospective cohort study in seven Dutch ICUs. SETTING/PATIENTS: ICU patients older than 16 years and admitted for greater than or equal to 12 hours between July 2016 and March 2020. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Outcomes were physical problems (fatigue or ≥ 3 new physical symptoms), mental problems (anxiety, depression, or post-traumatic stress disorder), and cognitive impairment. Patient record data and questionnaire data were collected at ICU admission, and after 3 and 12 months, of 2,476 patients. Several models predicting physical, mental, or cognitive problems and a composite score at 3 and 12 months were developed using variables collected within 48 hours after ICU admission. Based on performance and clinical feasibility, a model, PROSPECT, predicting post-ICU health problems at 3 months was chosen, including the predictors of chronic obstructive pulmonary disease, admission type, expected length of ICU stay greater than or equal to 2 days, and preadmission anxiety and fatigue. Internal validation using bootstrapping on data of the largest hospital ( n = 1,244) yielded a C -statistic of 0.73 (95% CI, 0.70-0.76). External validation was performed on data ( n = 864) from the other six hospitals with a C -statistic of 0.77 (95% CI, 0.73-0.80). CONCLUSIONS: The developed and externally validated PROSPECT model can be used within 48 hours after ICU admission for identifying patients with an increased risk of post-ICU problems 3 months after ICU admission. Timely preventive interventions starting during ICU admission and follow-up care can prevent or mitigate post-ICU problems in these high-risk patients.

Development and External Validation of a Prediction Model for Quality of Life of ICU Survivors: A Subanalysis of the MONITOR-IC Prospective Cohort Study.

OBJECTIVES: 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. DESIGN: Data from a prospective multicenter cohort study (MONITOR-IC) were used. SETTING: Seven hospitals in the Netherlands. PATIENTS: ICU survivors greater than or equal to 16 years old. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: 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%). CONCLUSIONS: 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.

Efficacy of haloperidol to decrease the burden of delirium in adult critically ill patients: the EuRIDICE randomized clinical trial.

BACKGROUND: The role of haloperidol as treatment for ICU delirium and related symptoms remains controversial despite two recent large controlled trials evaluating its efficacy and safety. We sought to determine whether haloperidol when compared to placebo in critically ill adults with delirium reduces days with delirium and coma and improves delirium-related sequelae. METHODS: This multi-center double-blind, placebo-controlled randomized trial at eight mixed medical-surgical Dutch ICUs included critically ill adults with delirium (Intensive Care Delirium Screening Checklist ≥ 4 or a positive Confusion Assessment Method for the ICU) admitted between February 2018 and January 2020. Patients were randomized to intravenous haloperidol 2.5 mg or placebo every 8 h, titrated up to 5 mg every 8 h if delirium persisted until ICU discharge or up to 14 days. The primary outcome was ICU delirium- and coma-free days (DCFDs) within 14 days after randomization. Predefined secondary outcomes included the protocolized use of sedatives for agitation and related behaviors, patient-initiated extubation and invasive device removal, adverse drug associated events, mechanical ventilation, ICU length of stay, 28-day mortality, and long-term outcomes up to 1-year after randomization. RESULTS: The trial was terminated prematurely for primary endpoint futility on DSMB advice after enrolment of 132 (65 haloperidol; 67 placebo) patients [mean age 64 (15) years, APACHE IV score 73.1 (33.9), male 68%]. Haloperidol did not increase DCFDs (adjusted RR 0.98 [95% CI 0.73-1.31], p = 0.87). Patients treated with haloperidol (vs. placebo) were less likely to receive benzodiazepines (adjusted OR 0.41 [95% CI 0.18-0.89], p = 0.02). Effect measures of other secondary outcomes related to agitation (use of open label haloperidol [OR 0.43 (95% CI 0.12-1.56)] and other antipsychotics [OR 0.63 (95% CI 0.29-1.32)], self-extubation or invasive device removal [OR 0.70 (95% CI 0.22-2.18)]) appeared consistently more favorable with haloperidol, but the confidence interval also included harm. Adverse drug events were not different. Long-term secondary outcomes (e.g., ICU recall and quality of life) warrant further study. CONCLUSIONS: Haloperidol does not reduce delirium in critically ill delirious adults. However, it may reduce rescue medication requirements and agitation-related events in delirious ICU patients warranting further evaluation. TRIAL REGISTRATION: ClinicalTrials.gov (#NCT03628391), October 9, 2017.

New Physical, Mental, and Cognitive Problems 1 Year after ICU Admission: A Prospective Multicenter Study.

Rationale: Comprehensive studies addressing the incidence of physical, mental, and cognitive problems after ICU admission are lacking. With an increasing number of ICU survivors, an improved understanding of post-ICU problems is necessary. Objectives: To determine the occurrence and cooccurrence of new physical, mental, and cognitive problems among ICU survivors 1 year after ICU admission, their impact on daily functioning, and risk factors associated with 1-year outcomes. Methods: Prospective multicenter cohort study, including ICU patients ⩾16 years of age, admitted for ⩾12 hours between July 2016 and June 2019. Patients, or proxies, rated their health status before and 1 year after ICU admission using questionnaires. Measurements and Main Results: Validated questionnaires were used to measure frailty, fatigue, new physical symptoms, anxiety and depression, post-traumatic stress disorder, cognitive impairment, and quality of life. Of the 4,793 patients included, 2,345 completed the questionnaires both before and 1 year after ICU admission. New physical, mental, and/or cognitive problems 1 year after ICU admission were experienced by 58% of the medical patients, 64% of the urgent surgical patients, and 43% of the elective surgical patients. Urgent surgical patients experienced a significant deterioration in their physical and mental functioning, whereas elective surgical patients experienced a significant improvement. Medical patients experienced an increase in symptoms of depression. A significant decline in cognitive functioning was experienced by all types of patients. Pre-ICU health status was strongly associated with post-ICU health problems. Conclusions: Overall, 50% of ICU survivors suffer from new physical, mental, and/or cognitive problems. An improved insight into the specific health problems of ICU survivors would enable more personalized post-ICU care.

Clinical Outcomes Among Patients With 1-Year Survival Following Intensive Care Unit Treatment for COVID-19.

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 ≥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. 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 RELEVANCE: 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.

Determining the impact of timing and of clinical factors during end-of-life decision-making in potential controlled donation after circulatory death donors.

Controlled donation after circulatory death (cDCD) occurs after a decision to withdraw life-sustaining treatment and subsequent family approach and approval for donation. We currently lack data on factors that impact the decision-making process on withdraw life-sustaining treatment and whether time from admission to family approach, influences family consent rates. Such insights could be important in improving the clinical practice of potential cDCD donors. In a prospective multicenter observational study, we evaluated the impact of timing and of the clinical factors during the end-of-life decision-making process in potential cDCD donors. Characteristics and medication use of 409 potential cDCD donors admitted to the intensive care units (ICUs) were assessed. End-of-life decision-making was made after a mean time of 97 hours after ICU admission and mostly during the day. Intracranial hemorrhage or ischemic stroke and a high APACHE IV score were associated with a short decision-making process. Preserved brainstem reflexes, high Glasgow Coma Scale scores, or cerebral infections were associated with longer time to decision-making. Our data also suggest that the organ donation request could be made shortly after the decision to stop active treatment and consent rates were not influenced by daytime or nighttime or by the duration of the ICU stay.

Physical, Mental, and Cognitive Health Status of ICU Survivors Before ICU Admission: A Cohort Study.

OBJECTIVES: Although patient's health status before ICU admission is the most important predictor for long-term outcomes, it is often not taken into account, potentially overestimating the attributable effects of critical illness. Studies that did assess the pre-ICU health status often included specific patient groups or assessed one specific health domain. Our aim was to explore patient's physical, mental, and cognitive functioning, as well as their quality of life before ICU admission. DESIGN: Baseline data were used from the longitudinal prospective MONITOR-IC cohort study. SETTING: ICUs of four Dutch hospitals. PATIENTS: Adult ICU survivors (n = 2,467) admitted between July 2016 and December 2018. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Patients, or their proxy, rated their level of frailty (Clinical Frailty Scale), fatigue (Checklist Individual Strength-8), anxiety and depression (Hospital Anxiety and Depression Scale), cognitive functioning (Cognitive Failure Questionnaire-14), and quality of life (Short Form-36) before ICU admission. Unplanned patients rated their pre-ICU health status retrospectively after ICU admission. Before ICU admission, 13% of all patients was frail, 65% suffered from fatigue, 28% and 26% from symptoms of anxiety and depression, respectively, and 6% from cognitive problems. Unplanned patients were significantly more frail and depressed. Patients with a poor pre-ICU health status were more often likely to be female, older, lower educated, divorced or widowed, living in a healthcare facility, and suffering from a chronic condition. CONCLUSIONS: In an era with increasing attention for health problems after ICU admission, the results of this study indicate that a part of the ICU survivors already experience serious impairments in their physical, mental, and cognitive functioning before ICU admission. Substantial differences were seen between patient subgroups. These findings underline the importance of accounting for pre-ICU health status when studying long-term outcomes.

Influence of sedation on delirium recognition in critically ill patients: A multinational cohort study.

BACKGROUND: Guidelines advocate intensive care unit (ICU) patients be regularly assessed for delirium using either the Confusion Assessment Method for the ICU (CAM-ICU) or the Intensive Care Delirium Screening Checklist (ICDSC). Single-centre studies, primarily with the CAM-ICU, suggest level of sedation may influence delirium screening results. OBJECTIVE: The objective of this study was to determine the association between level of sedation and delirium occurrence in critically ill patients assessed with either the CAM-ICU or the ICDSC. METHODS: This was a secondary analysis of a multinational, prospective cohort study performed in nine ICUs from seven countries. Consecutive ICU patients with a Richmond Agitation-Sedation Scale (RASS) of -3 to 0 at the time of delirium assessment where a RASS ≤ 0 was secondary to a sedating medication. Patients were assessed with either the CAM-ICU or the ICDSC. Logistic regression analysis was used to account for factors with the potential to influence level of sedation or delirium occurrence. RESULTS: Among 1660 patients, 1203 patients underwent 5741 CAM-ICU assessments [9.6% were delirium positive; at RASS = 0 (3.3% were delirium positive), RASS = -1 (19.3%), RASS = -2 (35.1%); RASS = -3 (39.0%)]. The other 457 patients underwent 3210 ICDSC assessments [11.6% delirium positive; at RASS = 0 (4.9% were delirium positive), RASS = -1 (15.8%), RASS = -2 (26.6%); RASS = -3 (20.6%)]. A RASS of -3 was associated with more positive delirium evaluations (odds ratio: 2.31; 95% confidence interval: 1.34-3.98) in the CAM-ICU-assessed patients (vs. the ICDSC-assessed patients). At a RASS of 0, assessment with the CAM-ICU (vs. the ICDSC) was associated with fewer positive delirium evaluations (odds ratio: 0.58; 95% confidence interval: 0.43-0.78). At a RASS of -1 or -2, no association was found between the delirium assessment method used (i.e., CAM-ICU or ICDSC) and a positive delirium evaluation. CONCLUSIONS: The influence of level of sedation on a delirium assessment result depends on whether the CAM-ICU or ICDSC is used. Bedside ICU nurses should consider these results when evaluating their sedated patients for delirium. Future research is necessary to compare the CAM-ICU and the ICDSC simultaneously in sedated and nonsedated ICU patients. TRIAL REGISTRATION: ClinicalTrials.gov; NCT02518646.

External Validation of Two Models to Predict Delirium in Critically Ill Adults Using Either the Confusion Assessment Method-ICU or the Intensive Care Delirium Screening Checklist for Delirium Assessment.

OBJECTIVES: To externally validate two delirium prediction models (early prediction model for ICU delirium and recalibrated prediction model for ICU delirium) using either the Confusion Assessment Method-ICU or the Intensive Care Delirium Screening Checklist for delirium assessment. DESIGN: Prospective, multinational cohort study. SETTING: Eleven ICUs from seven countries in three continents. PATIENTS: Consecutive, delirium-free adults admitted to the ICU for greater than or equal to 6 hours in whom delirium could be reliably assessed. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The predictors included in each model were collected at the time of ICU admission (early prediction model for ICU delirium) or within 24 hours of ICU admission (recalibrated prediction model for ICU delirium). Delirium was assessed using the Confusion Assessment Method-ICU or the Intensive Care Delirium Screening Checklist. Discrimination was determined using the area under the receiver operating characteristic curve. The predictive performance was determined for the Confusion Assessment Method-ICU and Intensive Care Delirium Screening Checklist cohort, and compared with both prediction models' original reported performance. A total of 1,286 Confusion Assessment Method-ICU-assessed patients and 892 Intensive Care Delirium Screening Checklist-assessed patients were included. Compared with the area under the receiver operating characteristic curve of 0.75 (95% CI, 0.71-0.79) in the original study, the area under the receiver operating characteristic curve of the early prediction model for ICU delirium was 0.67 (95% CI, 0.64-0.71) for delirium as assessed using the Confusion Assessment Method-ICU and 0.70 (95% CI, 0.66-0.74) using the Intensive Care Delirium Screening Checklist. Compared with the original area under the receiver operating characteristic curve of 0.77 (95% CI, 0.74-0.79), the area under the receiver operating characteristic curve of the recalibrated prediction model for ICU delirium was 0.75 (95% CI, 0.72-0.78) for assessing delirium using the Confusion Assessment Method-ICU and 0.71 (95% CI, 0.67-0.75) using the Intensive Care Delirium Screening Checklist. CONCLUSIONS: Both the early prediction model for ICU delirium and recalibrated prediction model for ICU delirium are externally validated using either the Confusion Assessment Method-ICU or the Intensive Care Delirium Screening Checklist for delirium assessment. Per delirium prediction model, both assessment tools showed a similar moderate-to-good statistical performance. These results support the use of either the early prediction model for ICU delirium or recalibrated prediction model for ICU delirium in ICUs around the world regardless of whether delirium is evaluated with the Confusion Assessment Method-ICU or Intensive Care Delirium Screening Checklist.

Prophylactic Haloperidol Effects on Long-term Quality of Life in Critically Ill Patients at High Risk for Delirium: Results of the REDUCE Study.

BACKGROUND: Delirium incidence in intensive care unit patients is high and associated with impaired long-term outcomes. The use of prophylactic haloperidol did not improve short-term outcome among critically ill adults at high risk of delirium. This study evaluated the effects of prophylactic haloperidol use on long-term quality of life in this group of patients and explored which factors are associated with change in quality of life. METHODS: A preplanned secondary analysis of long-term outcomes of the pRophylactic haloperidol usE for DeliriUm in iCu patients at high risk for dElirium (REDUCE) study was conducted. In this multicenter randomized clinical trial, nondelirious intensive care unit patients were assigned to prophylactic haloperidol (1 or 2 mg) or placebo (0.9% sodium chloride). In all groups, patients finally received study medication for median duration of 3 days [interquartile range, 2 to 6] until onset of delirium or until intensive care unit discharge. Long-term outcomes were assessed using the Short Form-12 questionnaire at intensive care unit admission (baseline) and after 1 and 6 months. Quality of life was summarized in the physical component summary and mental component summary scores. Differences between the haloperidol and placebo group and factors associated with changes in quality of life were analyzed. RESULTS: Of 1,789 study patients, 1,245 intensive care unit patients were approached, of which 887 (71%) responded. Long-term quality of life did not differ between the haloperidol and placebo group (physical component summary mean score of 39 ± 11 and 39 ± 11, respectively, and P = 0.350; and mental component summary score of 50 ± 10 and 51 ± 10, respectively, and P = 0.678). Age, medical and trauma admission, quality of life score at baseline, risk for delirium (PRE-DELIRIC) score, and the number of sedation-induced coma days were significantly associated with a decline in long-term quality of life. CONCLUSIONS: Prophylactic haloperidol use does not affect long-term quality of life in critically ill patients at high risk for delirium. Several factors, including the modifiable factor number of sedation-induced coma days, are associated with decline in long-term outcomes.

Temporal biomarker profiles and their association with ICU acquired delirium: a cohort study.

BACKGROUND: 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. METHODS: 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. RESULTS: 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 ß1-42 (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. CONCLUSIONS: 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. TRIAL REGISTRATION: ClinicalTrials.gov, NCT 01274819 . Registered on 12 January 2011.

Noise in the intensive care unit and its influence on sleep quality: a multicenter observational study in Dutch intensive care units.

BACKGROUND: 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. METHODS: 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. RESULTS: 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 (LAeq, 24h) was 54.0 dBA (± 2.4). Mixed-effects regression analyses showed that background noise (ß = - 0.51, p < 0.05) had a negative impact on sleep quality, whereas number of restorative periods (ß = 0.53, p < 0.01) and female sex (ß = 1.25, p < 0.01) were weakly but significantly correlated with sleep. CONCLUSIONS: Noise levels are negatively associated and restorative periods and female gender are positively associated with subjective sleep quality in ICU patients. TRIAL REGISTRATION: www.ClinicalTrials.gov, NCT01826799 . Registered on 9 April 2013.

Delirium prediction in the intensive care unit: comparison of two delirium prediction models.

BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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. TRIAL REGISTRATION: ClinicalTrials.gov, NCT02518646 . Registered on 21 July 2015.

Effect of Haloperidol on Survival Among Critically Ill Adults With a High Risk of Delirium: The REDUCE Randomized Clinical Trial.

Importance: Results of studies on use of prophylactic haloperidol in critically ill adults are inconclusive, especially in patients at high risk of delirium. Objective: 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. Design, Setting, and Participants: 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. Interventions: 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. Main Outcome and Measures: 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. Results: 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). Conclusions and Relevance: 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. Trial Registration: clinicaltrials.gov Identifier: NCT01785290.

[Mycoplasma pneumoniaeoutbreak in the Netherlands 2023-2024]. / Mycoplasma pneumoniae-uitbraak in Nederland 2023-2024.

Currently, there is a nationwide outbreak of Mycoplasma pneumoniae infections. M. pneumoniae is a bacterium that can cause atypical pneumonia, especially in children and young adults, and does not respond to the standard antibiotics prescribed for pneumonia. In addition, the bacterium regularly causes extra-pulmonary symptoms. In our hospitals, we have admitted 100 patients (including 20 children) with M. pneumoniae since the fall of 2023, many of which were young and had severe clinical symptoms. It is important to recognize the clinical picture to start effective antibiotic treatment. In this clinical lesson, we will provide two examples of recently admitted patients and discuss the characteristics of all inpatients who have presented to our hospitals during this epidemic. Finally, we pay attention to antibiotic policy and antibiotic resistance.

One-year outcomes in COVID-19 and non-COVID-19 intensive care unit survivors.

PURPOSE: To determine differences in one-year multi-domain health outcomes in COVID-19 and non-COVID-19 intensive care unit (ICU) survivors. MATERIALS AND METHODS: Adult ICU survivors treated for COVID-19 were compared to a control group consisting of survivors admitted for respiratory distress due to other causes, i.e. non-COVID-19 ARDS or pneumonia. Occurrence of physical (frailty, fatigue, physical symptoms), mental (anxiety, depression, post-traumatic stress) and cognitive symptoms, and quality of life (QoL) scores were measured, using validated questionnaires, before and one year after ICU treatment. RESULTS: In total, 506 COVID-19 survivors could be compared to 228 non-COVID-19 survivors. At one-year follow-up, COVID-19 ICU survivors had less physical (76.2% vs. 86.9%, p = 0.001) and mental symptoms (32.0% vs. 47.1%, p < 0.001) than the control group. Cognitive symptoms were comparable (22.5% vs. 17.2%, p = 0.12). However, compared to pre-ICU health symptoms and scores, COVID-19 survivors experienced an increase in symptom occurrence rates in all domains and a decrease in QoL, whereas the control group only experienced an increase in mental and cognitive symptoms, with a similar QoL at one-year follow-up. CONCLUSIONS: COVID-19 ICU survivors experience equal or less health problems but a greater decline in QoL one year after ICU admission compared to non-COVID-19 ARDS or pneumonia survivors.

Differences in long-term outcomes between ICU patients with persistent delirium, non-persistent delirium and no delirium: A longitudinal cohort study.

PURPOSE: Determine differences in physical, mental and cognitive outcomes 1-year post-ICU between patients with persistent delirium (PD), non-persistent delirium (NPD) and no delirium (ND). MATERIALS AND METHODS: A longitudinal cohort study was performed in adult ICU patients of two hospitals admitted between July 2016-February 2020. Questionnaires on physical, mental and cognitive health, frailty and QoL were completed regarding patients' pre-ICU health status and 1-year post-ICU. Delirium data were from patients' total hospital stay. Patients were divided in PD (≥14 days delirium), NPD (<14 days delirium) or ND patients. RESULTS: 2400 patients completed both questionnaires, of whom 529 (22.0%) patients developed delirium; 35 (6.6%) patients had PD and 494 (93.4%) had NPD. Patients with delirium (PD or NPD) had worse outcomes in all domains compared to ND patients. Compared to NPD, more PD patients were frail (34.3% vs. 14.6%, p = 0.006) and fatigued (85.7% vs. 61.1%, p = 0.012). After adjustment, PD was significantly associated with long-term cognitive impairment only (aOR 3.90; 95%CI 1.31-11.63). CONCLUSIONS: Patients with PD had a higher likelihood to develop cognitive impairment 1-year post-ICU compared to NPD or ND. Patients with PD and NPD were more likely to experience impairment on all health domains (i.e. physical, mental and cognitive), compared to ND patients.

[Communication about in hospital resuscitation orders]. / Communicatie over reanimatiebeleid in het ziekenhuis.

BACKGROUND: The decision to attempt or refrain from resuscitation is preferably based on prognostic factors for outcome and subsequently communicated with patients. Both patients and physicians consider good communication important, however little is known about patient involvement in and understanding of cardiopulmonary resuscitation (CPR) directives. AIM: To determine the prevalence of Do Not Resuscitate (DNR)-orders, to describe recollection of CPR-directive conversations and factors associated with patient recollection and understanding. METHODS: This was a two-week nationwide multicentre cross-sectional observational study using a study-specific survey. The study population consisted of patients admitted to non-monitored wards in 13 hospitals. Data were collected from the electronic medical record (EMR) concerning CPR-directive, comorbidity and at-home medication. Patients reported their perception and expectations about CPR-counselling through a questionnaire. RESULTS: A total of 1136 patients completed the questionnaire. Patients' CPR-directives were documented in the EMR as follows: 63.7% full code, 27.5% DNR and in 8.8% no directive was documented. DNR was most often documented for patients >80 years (66.4%) and in patients using >10 medications (45.3%). Overall, 55.8% of patients recalled having had a conversation about their CPR-directive and 48.1% patients reported the same CPR-directive as the EMR. Most patients had a good experience with the CPR-directive conversation in general (66.1%), as well as its timing (84%) and location (94%) specifically. CONCLUSIONS: The average DNR-prevalence is 27.5%. Correct understanding of their CPR-directive is lowest in patients aged ≥80 years and multimorbid patients. CPR-directive counselling should focus more on patient involvement and their correct understanding.

Preserved metabolic coupling and cerebrovascular reactivity during mild hypothermia after cardiac arrest.

OBJECTIVE: Although mild hypothermia improves outcome in patients after out-of-hospital cardiac arrest, the cardiodepressive effects of hypothermia may lead to secondary brain damage. This study was performed to assess the cerebral blood flow, cerebral oxygen extraction, and cerebrovascular reactivity to changes in partial pressure of carbon dioxide in the arterial blood in comatose patients after out-of-hospital cardiac arrest treated with mild hypothermia. DESIGN: Observational study. SETTING: Tertiary care university hospital. PATIENTS: Ten comatose patients after out-of-hospital cardiac arrest. INTERVENTIONS: All patients were cooled to 32-34 degrees C for 24 hrs. Cerebrovascular reactivity to changes in carbon dioxide in the arterial blood was measured after increasing or decreasing the minute ventilation by 20%. MEASUREMENTS AND MAIN RESULTS: Mean flow velocity in the middle cerebral artery and pulsatility index were measured by transcranial Doppler at 0, 3, 6, 9, 12, 18, 24, and 48 hrs after admission. Jugular bulb oxygenation was measured at the same intervals. Cerebrovascular reactivity to changes in carbon dioxide in the arterial blood was studied on admission to the intensive care unit and at 6, 12, 18, and 24 hrs by measurement of mean flow velocity in the middle cerebral artery and jugular bulb oxygenation. Mean flow velocity in the middle cerebral artery was low (30.3+/-9.5 cm/sec) on admission and remained relatively stable for the first 24 hrs. After rewarming, it increased to 67.5+/-33.0 cm/sec at 48 hrs after admission from 30.3+/-9.5 at admission (p=.009). Jugular bulb oxygenation at the start of the study was 66.2+/-8.5% and gradually increased to 82.9+/-4.9% at 48 hrs (p<.001). Regression analysis showed a significant correlation between changes in carbon dioxide in the arterial blood, mean flow velocity in the middle cerebral artery (p<.001) and jugular bulb oxygenation (p<.001). The mean percentage change in mean flow velocity in the middle cerebral artery was 3.6+/-2.9% per 1-mm Hg change of carbon dioxide in the arterial blood. CONCLUSIONS: The mean flow velocity in the middle cerebral artery, as a parameter of cerebral blood flow, was low during mild hypothermia, whereas cerebral oxygen extraction remained normal, suggesting decreased cerebral metabolic activity. We demonstrated that CO2 reactivity is preserved during hypothermia in these patients.