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Conventional gabaminergic sedatives such as benzodiazepines and propofol are commonly used in mechanically ventilated patients in the intensive care unit (ICU). Dexmedetomidine is an alternative sedative that may achieve lighter sedation, reduce delirium, and provide analgesia. Our objective was to perform a comprehensive systematic review summarizing the large body of evidence, determining if dexmedetomidine reduces delirium compared to conventional sedatives. We searched MEDLINE, EMBASE, CENTRAL, ClinicalTrials.gov and the WHO ICTRP from inception to October 2021. Independent pairs of reviewers identified randomized clinical trials comparing dexmedetomidine to other sedatives for mechanically ventilated adults in the ICU. We conducted meta-analyses using random-effects models. The results were reported as relative risks (RRs) for binary outcomes and mean differences (MDs) for continuous outcomes, with corresponding 95% confidence intervals (CIs). In total, 77 randomized trials (n = 11,997) were included. Compared to other sedatives, dexmedetomidine reduced the risk of delirium (RR 0.67, 95% CI 0.55 to 0.81; moderate certainty), the duration of mechanical ventilation (MD − 1.8 h, 95% CI  – 2.89 to  – 0.71; low certainty), and ICU length of stay (MD  – 0.32 days, 95% CI  – 0.42 to  – 0.22; low certainty). Dexmedetomidine use increased the risk of bradycardia (RR 2.39, 95% CI 1.82 to 3.13; moderate certainty) and hypotension (RR 1.32, 95% CI 1.07 to 1.63; low certainty). In mechanically ventilated adults, the use of dexmedetomidine compared to other sedatives, resulted in a lower risk of delirium, and a modest reduction in duration of mechanical ventilation and ICU stay, but increased the risks of bradycardia and hypotension.

While medication regimen complexity, as measured by a novel medication regimen complexity-intensive care unit (MRC-ICU) score, correlates with baseline severity of illness and mortality, whether the MRC-ICU improves hospital mortality prediction is not known. After characterizing the association between MRC-ICU, severity of illness and hospital mortality we sought to evaluate the incremental benefit of adding MRC-ICU to illness severity-based hospital mortality prediction models. This was a single-center, observational cohort study of adult intensive care units (ICUs). A random sample of 991 adults admitted ≥ 24 h to the ICU from 10/2015 to 10/2020 were included. The logistic regression models for the primary outcome of mortality were assessed via area under the receiver operating characteristic (AUROC). Medication regimen complexity was evaluated daily using the MRC-ICU. This previously validated index is a weighted summation of medications prescribed in the first 24 h of ICU stay [e.g., a patient prescribed insulin (1 point) and vancomycin (3 points) has a MRC-ICU = 4 points]. Baseline demographic features (e.g., age, sex, ICU type) were collected and severity of illness (based on worst values within the first 24 h of ICU admission) was characterized using both the Acute Physiology and Chronic Health Evaluation (APACHE II) and the Sequential Organ Failure Assessment (SOFA) score. Univariate analysis of 991 patients revealed every one-point increase in the average 24-h MRC-ICU score was associated with a 5% increase in hospital mortality [Odds Ratio (OR) 1.05, 95% confidence interval 1.02–1.08, p  = 0.002]. The model including MRC-ICU, APACHE II and SOFA had a AUROC for mortality of 0.81 whereas the model including only APACHE-II and SOFA had a AUROC for mortality of 0.76. Medication regimen complexity is associated with increased hospital mortality. A prediction model including medication regimen complexity only modestly improves hospital mortality prediction.

Fluid overload, while common in the ICU and associated with serious sequelae, is hard to predict and may be influenced by ICU medication use. Machine learning (ML) approaches may offer advantages over traditional regression techniques to predict it. We compared the ability of traditional regression techniques and different ML-based modeling approaches to identify clinically meaningful fluid overload predictors. This was a retrospective, observational cohort study of adult patients admitted to an ICU ≥ 72 h between 10/1/2015 and 10/31/2020 with available fluid balance data. Models to predict fluid overload (a positive fluid balance ≥ 10% of the admission body weight) in the 48–72 h after ICU admission were created. Potential patient and medication fluid overload predictor variables (n = 28) were collected at either baseline or 24 h after ICU admission. The optimal traditional logistic regression model was created using backward selection. Supervised, classification-based ML models were trained and optimized, including a meta-modeling approach. Area under the receiver operating characteristic (AUROC), positive predictive value (PPV), and negative predictive value (NPV) were compared between the traditional and ML fluid prediction models. A total of 49 of the 391 (12.5%) patients developed fluid overload. Among the ML models, the XGBoost model had the highest performance (AUROC 0.78, PPV 0.27, NPV 0.94) for fluid overload prediction. The XGBoost model performed similarly to the final traditional logistic regression model (AUROC 0.70; PPV 0.20, NPV 0.94). Feature importance analysis revealed severity of illness scores and medication-related data were the most important predictors of fluid overload. In the context of our study, ML and traditional models appear to perform similarly to predict fluid overload in the ICU. Baseline severity of illness and ICU medication regimen complexity are important predictors of fluid overload.

Background Identifying patterns within ICU medication regimens may help artificial intelligence algorithms to better predict patient outcomes; however, machine learning methods incorporating medications require further development, including standardized terminology. The Common Data Model for Intensive Care Unit (ICU) Medications (CDM-ICURx) may provide important infrastructure to clinicians and researchers to support artificial intelligence analysis of medication-related outcomes and healthcare costs. Using an unsupervised cluster analysis approach in combination with this common data model, the objective of this evaluation was to identify novel patterns of medication clusters (termed ‘pharmacophenotypes’) correlated with ICU adverse events (e.g., fluid overload) and patient-centered outcomes (e.g., mortality). Methods This was a retrospective, observational cohort study of 991 critically ill adults. To identify pharmacophenotypes, unsupervised machine learning analysis with automated feature learning using restricted Boltzmann machine and hierarchical clustering was performed on the medication administration records of each patient during the first 24 h of their ICU stay. Hierarchical agglomerative clustering was applied to identify unique patient clusters. Distributions of medications across pharmacophenotypes were described, and differences among patient clusters were compared using signed rank tests and Fisher's exact tests, as appropriate. Results A total of 30,550 medication orders for the 991 patients were analyzed; five unique patient clusters and six unique pharmacophenotypes were identified. For patient outcomes, compared to patients in Clusters 1 and 3, patients in Cluster 5 had a significantly shorter duration of mechanical ventilation and ICU length of stay ( p  < 0.05); for medications, Cluster 5 had a higher distribution of Pharmacophenotype 1 and a smaller distribution of Pharmacophenotype 2, compared to Clusters 1 and 3. For outcomes, patients in Cluster 2, despite having the highest severity of illness and greatest medication regimen complexity, had the lowest overall mortality; for medications, Cluster 2 also had a comparably higher distribution of Pharmacophenotype 6. Conclusion The results of this evaluation suggest that patterns among patient clusters and medication regimens may be observed using empiric methods of unsupervised machine learning in combination with a common data model. These results have potential because while phenotyping approaches have been used to classify heterogenous syndromes in critical illness to better define treatment response, the entire medication administration record has not been incorporated in those analyses. Applying knowledge of these patterns at the bedside requires further algorithm development and clinical application but may have the future potential to be leveraged in guiding medication-related decision making to improve treatment outcomes.

Background While delirium prevalence and duration are each associated with increased 30-day, 6-month, and 1-year mortality, the association between incident ICU delirium and mortality remains unclear. We evaluated the association between both incident ICU delirium and days spent with delirium in the 28 days after ICU admission and mortality within 28 and 90 days. Methods Secondary cohort analysis of a randomized, double-blind, placebo-controlled trial conducted among 1495 delirium-free, critically ill adults in 14 Dutch ICUs with an expected ICU stay ≥2 days where all delirium assessments were completed. In the 28 days after ICU admission, patients were evaluated for delirium and coma 3x daily; each day was coded as a delirium day [≥1 positive Confusion Assessment Method for the ICU (CAM-ICU)], a coma day [no delirium and ≥ 1 Richmond Agitation Sedation Scale (RASS) score ≤ − 4], or neither. Four Cox-regression models were constructed for 28-day mortality and 90-day mortality; each accounted for potential confounders (i.e., age, APACHE-II score, sepsis, use of mechanical ventilation, ICU length of stay, and haloperidol dose) and: 1) delirium occurrence, 2) days spent with delirium, 3) days spent in coma, and 4) days spent with delirium and/or coma. Results Among the 1495 patients, 28 day mortality was 17% and 90 day mortality was 21%. Neither incident delirium (28 day mortality hazard ratio [HR] = 1.02, 95%CI = 0.75–1.39; 90 day mortality HR = 1.05, 95%CI = 0.79–1.38) nor days spent with delirium (28 day mortality HR = 1.00, 95%CI = 0.95–1.05; 90 day mortality HR = 1.02, 95%CI = 0.98–1.07) were significantly associated with mortality. However, both days spent with coma (28 day mortality HR = 1.05, 95%CI = 1.02–1.08; 90 day mortality HR = 1.05, 95%CI = 1.02–1.08) and days spent with delirium or coma (28 day mortality HR = 1.03, 95%CI = 1.00–1.05; 90 day mortality HR = 1.03, 95%CI = 1.01–1.06) were significantly associated with mortality. Conclusions This analysis suggests neither incident delirium nor days spent with delirium are associated with short-term mortality after ICU admission. Trial registration ClinicalTrials.gov , Identifier NCT01785290 Registered 7 February 2013.

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.

IntroductionSedation in mechanically ventilated adults in the intensive care unit (ICU) is commonly achieved with intravenous infusions of propofol, dexmedetomidine or benzodiazepines. Significant limitations associated with each can impact their usage. Inhaled isoflurane has potential benefit for ICU sedation due to its safety record, sedation profile, lack of metabolism and accumulation, and fast wake-up time. Administration in the ICU has historically been restricted by the lack of a safe and effective delivery system for the ICU. The Sedaconda Anaesthetic Conserving Device-S (Sedaconda ACD-S) has enabled the delivery of inhaled volatile anaesthetics for sedation with standard ICU ventilators, but it has not yet been rigorously evaluated in the USA. We aim to evaluate the efficacy and safety of inhaled isoflurane delivered via the Sedaconda ACD-S compared with intravenous propofol for sedation of mechanically ventilated ICU adults in USA hospitals.Methods and analysisINhaled Sedation versus Propofol in REspiratory failure in the ICU (INSPiRE-ICU1) is a phase 3, multicentre, randomised, controlled, open-label, assessor-blinded trial that aims to enrol 235 critically ill adults in 14 hospitals across the USA. Eligible patients are randomised in a 1.5:1 ratio for a treatment duration of up to 48 (±6) hours or extubation, whichever occurs first, with primary follow-up period of 30 days and additional follow-up to 6 months. Primary outcome is percentage of time at target sedation range. Key secondary outcomes include use of opioids during treatment, spontaneous breathing efforts during treatment, wake-up time at end of treatment and cognitive recovery after treatment.Ethics and disseminationTrial protocol has been approved by US Food and Drug Administration (FDA) and central (Advarra SSU00208265) or local institutional review boards ((IRB), Cleveland Clinic IRB FWA 00005367, Tufts HS IRB 20221969, Houston Methodist IRB PRO00035247, Mayo Clinic IRB Mod22-001084-08, University of Chicago IRB21-1917-AM011 and Intermountain IRB 033175). Results will be presented at scientific conferences, submitted for publication, and provided to the FDA.Trial registration numberNCT05312385.

Background Trials of interventions to prevent or treat delirium in adults in an acute hospital setting report heterogeneous outcomes. Our objective was to develop international consensus among key stakeholders for a core outcome set (COS) for future trials of interventions to prevent and/or treat delirium in adults with an acute care hospital admission and not admitted to an intensive care unit. Methods A rigorous COS development process was used including a systematic review, qualitative interviews, modified Delphi consensus process, and in-person consensus using nominal group technique (registration http://www.comet - initiative.org/studies/details/796 ). Participants in qualitative interviews were delirium survivors or family members. Participants in consensus methods comprised international representatives from three stakeholder groups: researchers, clinicians, and delirium survivors and family members. Results Item generation identified 8 delirium-specific outcomes and 71 other outcomes from 183 studies, and 30 outcomes from 18 qualitative interviews, including 2 that were not extracted from the systematic review. De-duplication of outcomes and formal consensus processes involving 110 experts including researchers (N = 32), clinicians (N = 63), and delirium survivors and family members (N = 15) resulted in a COS comprising 6 outcomes: delirium occurrence and reoccurrence, delirium severity, delirium duration, cognition, emotional distress, and health-related quality of life. Study limitations included exclusion of non-English studies and stakeholders and small representation of delirium survivors/family at the in-person consensus meeting. Conclusions This COS, endorsed by the American and Australian Delirium Societies and European Delirium Association, is recommended for future clinical trials evaluating delirium prevention or treatment interventions in adults presenting to an acute care hospital and not admitted to an intensive care unit.

Dexmedetomidine is associated with less delirium than benzodiazepines and better sleep architecture than either benzodiazepines or propofol; its effect on delirium and sleep when administered at night to patients requiring sedation remains unclear. To determine if nocturnal dexmedetomidine prevents delirium and improves sleep in critically ill adults. This two-center, double-blind, placebo-controlled trial randomized 100 delirium-free critically ill adults receiving sedatives to receive nocturnal (9:30 p.m. to 6:15 a.m.) intravenous dexmedetomidine (0.2 μg/kg/h, titrated by 0.1 μg /kg/h every 15 min until a goal Richmond Agitation and Sedation Scale score of -1 or maximum rate of 0.7 μg/kg/h was reached) or placebo until ICU discharge. During study infusions, all sedatives were halved; opioids were unchanged. Delirium was assessed using the Intensive Care Delirium Screening Checklist every 12 hours throughout the ICU admission. Sleep was evaluated each morning by the Leeds Sleep Evaluation Questionnaire. Nocturnal dexmedetomidine (vs. placebo) was associated with a greater proportion of patients who remained delirium-free during the ICU stay (dexmedetomidine [40 (80%) of 50 patients] vs. placebo [27 (54%) of 50 patients]; relative risk, 0.44; 95% confidence interval, 0.23-0.82; P = 0.006). The average Leeds Sleep Evaluation Questionnaire score was similar (mean difference, 0.02; 95% confidence interval, 0.42-1.92) between the 34 dexmedetomidine (average seven assessments per patient) and 30 placebo (six per patient) group patients able to provide one or more assessments. Incidence of hypotension, bradycardia, or both did not differ significantly between groups. Nocturnal administration of low-dose dexmedetomidine in critically ill adults reduces the incidence of delirium during the ICU stay; patient-reported sleep quality appears unchanged. Clinical trial registered with www.clinicaltrials.gov (NCT01791296).

BackgroundAn association between driving pressure (∆P) and the outcomes of invasive mechanical ventilation (IMV) may exist. However, the effect of a sustained limitation of ∆P on mortality in patients with acute respiratory distress syndrome (ARDS), including patients with COVID-19 (COVID-19-related acute respiratory distress syndrome (C-ARDS)) undergoing IMV, has not been rigorously evaluated. The use of emulations of a target trial in intensive care unit research remains in its infancy. To inform future, large ARDS target trials, we explored using a target trial emulation approach to analyse data from a cohort of IMV adults with C-ARDS to determine whether maintaining daily ∆p<15 cm H2O (in addition to traditional low tidal volume ventilation (LTVV) (tidal volume 5–7 cc/PBW+plateau pressure (Pplat) ≤30 cm H2O), compared with LTVV alone, affects the 28-day mortality.MethodsTo emulate a target trial, adults with C-ARDS requiring >24 hours of IMV were considered to be assigned to limited ∆P or LTVV. Lung mechanics were measured twice daily after ventilator setting adjustments were made. To evaluate the effect of each lung-protective ventilation (LPV) strategy on the 28-day mortality, we fit a stabilised inverse probability weighted marginal structural model that adjusted for baseline and time-varying confounders known to affect protection strategy use/adherence or survival.ResultsAmong the 92 patients included, 27 (29.3%) followed limited ∆P ventilation, 23 (25.0%) the LTVV strategy and 42 (45.7%) received no LPV strategy. The adjusted estimated 28-day survival was 47.0% (95% CI 23%, 76%) in the limited ∆P group, 70.3% in the LTVV group (95% CI 37.6%, 100%) and 37.6% (95% CI 20.8%, 58.0%) in the no LPV strategy group.InterpretationLimiting ∆P may not provide additional survival benefits for patients with C-ARDS over LTVV. Our results help inform the development of future target trial emulations focused on evaluating LPV strategies, including reduced ∆P, in adults with ARDS.