Frequency of Screening and Spontaneous Breathing Trial Techniques: A Randomized Clinical Trial.

Importance: The optimal screening frequency and spontaneous breathing trial (SBT) technique to liberate adults from ventilators are unknown. Objective: To compare the effects of screening frequency (once-daily screening vs more frequent screening) and SBT technique (pressure-supported SBT with a pressure support level that was >0-≤8 cm H2O and a positive end-expiratory pressure [PEEP] level that was >0-≤5 cm H2O vs T-piece SBT) on the time to successful extubation. Design, Setting, and Participants: Randomized clinical trial with a 2 × 2 factorial design including critically ill adults who were receiving invasive mechanical ventilation for at least 24 hours, who were capable of initiating spontaneous breaths or triggering ventilators, and who were receiving a fractional concentration of inspired oxygen that was 70% or less and a PEEP level of 12 cm H2O or less. Recruitment was between January 2018 and February 2022 at 23 intensive care units in North America; last follow-up occurred October 18, 2022. Interventions: Participants were enrolled early to enable protocolized screening (more frequent vs once daily) to identify the earliest that patients met criteria to undergo pressure-supported or T-piece SBT lasting 30 to 120 minutes. Main Outcome and Measures: Time to successful extubation (time when unsupported, spontaneous breathing began and was sustained for ≥48 hours after extubation). Results: Of 797 patients (198 in the once-daily screening and pressure-supported SBT group, 204 in once-daily screening and T-piece SBT, 195 in more frequent screening and pressure-supported SBT, and 200 in more frequent screening and T-piece SBT), the mean age was 62.4 (SD, 18.4) years and 472 (59.2%) were men. There were no statistically significant differences by screening frequency (hazard ratio [HR], 0.88 [95% CI, 0.76-1.03]; P = .12) or by SBT technique (HR, 1.06 [95% CI, 0.91-1.23]; P = .45). The median time to successful extubation was 2.0 days (95% CI, 1.7-2.7) for once-daily screening and pressure-supported SBT, 3.1 days (95% CI, 2.7-4.8) for once-daily screening and T-piece SBT, 3.9 days (95% CI, 2.9-4.7) for more frequent screening and pressure-supported SBT, and 2.9 days (95% CI, 2.0-3.1) for more frequent screening and T-piece SBT. An unexpected interaction between screening frequency and SBT technique required pairwise contrasts that revealed more frequent screening (vs once-daily screening) and pressure-supported SBT increased the time to successful extubation (HR, 0.70 [95% CI, 0.50-0.96]; P = .02). Once-daily screening and pressure-supported SBT (vs T-piece SBT) did not reduce the time to successful extubation (HR, 1.30 [95% CI, 0.98-1.70]; P = .08). Conclusions and Relevance: Among critically ill adults who received invasive mechanical ventilation for more than 24 hours, screening frequency (once-daily vs more frequent screening) and SBT technique (pressure-supported vs T-piece SBT) did not change the time to successful extubation. However, an unexpected and statistically significant interaction was identified; protocolized more frequent screening combined with pressure-supported SBTs increased the time to first successful extubation. Trial Registration: ClinicalTrials.gov Identifiers: NCT02399267 and NCT02969226.

INhaled Sedation versus Propofol in REspiratory failure in the Intensive Care Unit (INSPiRE-ICU1): protocol for a randomised, controlled trial.

INTRODUCTION: Sedation 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 ANALYSIS: INhaled 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 DISSEMINATION: Trial 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 NUMBER: NCT05312385.

Comparing the impact of targeting limited driving pressure to low tidal volume ventilation on mortality in mechanically ventilated adults with COVID-19 ARDS: an exploratory target trial emulation.

BACKGROUND: An 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. METHODS: To 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. RESULTS: Among 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. INTERPRETATION: Limiting ∆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.

Pharmacologic Treatment Strategies for Delirium in Hospitalized Adults: Past, Present, and Future.

Despite the use of multidomain prevention strategies, delirium still frequently occurs in hospitalized adults. With delirium often associated with undesirable symptoms and deleterious outcomes, including cognitive decline, treatment is important. Risk-factor reduction and the protocolized use of multidomain, nonpharmacologic bundles remain the mainstay of delirium treatment. There is a current lack of strong evidence to suggest any pharmacologic intervention to treat delirium will help resolve it faster, reduce its symptoms (other than agitation), facilitate hospital throughput, or improve post-hospital outcomes including long-term cognitive function. With the exception of dexmedetomidine as a treatment of severe delirium-associated agitation in the ICU, current practice guidelines do not recommend the routine use of any pharmacologic intervention to treat delirium in any hospital population. Future research should focus on identifying and evaluating new pharmacologic delirium treatment interventions and addressing key challenges and gaps surrounding delirium treatment research.

Cognitive and functional change in skilled nursing facilities: Differences by delirium and Alzheimer's disease and related dementias.

BACKGROUND: Whether cognitive and functional recovery in skilled nursing facilities (SNF) following hospitalization differs by delirium and Alzheimer's disease related dementias (ADRD) has not been examined. OBJECTIVE: To compare change in cognition and function among short-stay SNF patients with delirium, ADRD, or both. DESIGN: Retrospective cohort study using claims data from 2011 to 2013. SETTING: Centers for Medicare and Medicaid certified SNFs. PARTICIPANTS: A total of 740,838 older adults newly admitted to a short-stay SNF without prevalent ADRD who had at least two assessments of cognition and function. MEASUREMENTS: Incident delirium was measured by the Minimum Data Set (MDS) Confusion Assessment Method and ICD-9 codes, and incident ADRD by ICD-9 codes and MDS diagnoses. Cognitive improvement was a better or maximum score on the MDS Brief Interview for Mental Status, and functional recovery was a better or maximum score on the MDS Activities of Daily Living Scale. RESULTS: Within 30 days of SNF admission, the rate of cognitive improvement in patients with both delirium/ADRD was half that of patients with neither delirium/ADRD (HR = 0.45, 95% CI:0.43, 0.46). The ADRD-only and delirium-only groups also were 43% less likely to have improved cognition or function compared to those with neither delirium/ADRD (HR = 0.57, 95% CI:0.56, 0.58 and HR = 0.57, 95% CI:0.55, 0.60, respectively). Functional improvement was less likely in patients with both delirium/ADRD, as well (HR = 0.85, 95% CI:0.83, 0.87). The ADRD only and delirium only groups were also less likely to improve in function (HR = 0.93, 95% CI:0.92, 0.94 and HR = 0.92, 95% CI:0.90, 0.93, respectively) compared to those with neither delirium/ADRD. CONCLUSIONS: Among older adults without dementia admitted to SNF for post-acute care following hospitalization, a positive screen for delirium and a new diagnosis of ADRD, within 7 days of SNF admission, were both significantly associated with worse cognitive and functional recovery. Patients with both delirium and new ADRD had the worst cognitive and functional recovery.

A common data model for the standardization of intensive care unit medication features.

Objective: Common data models provide a standard means of describing data for artificial intelligence (AI) applications, but this process has never been undertaken for medications used in the intensive care unit (ICU). We sought to develop a common data model (CDM) for ICU medications to standardize the medication features needed to support future ICU AI efforts. Materials and Methods: A 9-member, multi-professional team of ICU clinicians and AI experts conducted a 5-round modified Delphi process employing conference calls, web-based communication, and electronic surveys to define the most important medication features for AI efforts. Candidate ICU medication features were generated through group discussion and then independently scored by each team member based on relevance to ICU clinical decision-making and feasibility for collection and coding. A key consideration was to ensure the final ontology both distinguished unique medications and met Findable, Accessible, Interoperable, and Reusable (FAIR) guiding principles. Results: Using a list of 889 ICU medications, the team initially generated 106 different medication features, and 71 were ranked as being core features for the CDM. Through this process, 106 medication features were assigned to 2 key feature domains: drug product-related (n = 43) and clinical practice-related (n = 63). Each feature included a standardized definition and suggested response values housed in the electronic data library. This CDM for ICU medications is available online. Conclusion: The CDM for ICU medications represents an important first step for the research community focused on exploring how AI can improve patient outcomes and will require ongoing engagement and refinement.

Unsupervised machine learning analysis to identify patterns of ICU medication use for fluid overload prediction.

INTRODUCTION: Intravenous (IV) medications are a fundamental cause of fluid overload (FO) in the intensive care unit (ICU); however, the association between IV medication use (including volume), administration timing, and FO occurrence remains unclear. METHODS: This retrospective cohort study included consecutive adults admitted to an ICU ≥72 hours with available fluid balance data. FO was defined as a positive fluid balance ≥7% of admission body weight within 72 hours of ICU admission. After reviewing medication administration record (MAR) data in three-hour periods, IV medication exposure was categorized into clusters using principal component analysis (PCA) and Restricted Boltzmann Machine (RBM). Medication regimens of patients with and without FO were compared within clusters to assess for temporal clusters associated with FO using the Wilcoxon rank sum test. Exploratory analyses of the medication cluster most associated with FO for medications frequently appearing and used in the first 24 hours was conducted. RESULTS: FO occurred in 127/927 (13.7%) of the patients enrolled. Patients received a median (IQR) of 31 (13-65) discrete IV medication administrations over the 72-hour period. Across all 47,803 IV medication administrations, ten unique IV medication clusters were identified with 121-130 medications in each cluster. Among the ten clusters, cluster 7 had the greatest association with FO; the mean number of cluster 7 medications received was significantly greater in patients in the FO cohort compared to patients who did not experience FO (25.6 vs.10.9. p<0.0001). 51 of the 127 medications in cluster 7 (40.2%) appeared in > 5 separate 3-hour periods during the 72-hour study window. The most common cluster 7 medications included continuous infusions, antibiotics, and sedatives/analgesics. Addition of cluster 7 medications to a prediction model with APACHE II score and receipt of diuretics improved the ability for the model to predict fluid overload (AUROC 5.65, p =0.0004). CONCLUSIONS: Using ML approaches, a unique IV medication cluster was strongly associated with FO. Incorporation of this cluster improved the ability to predict development of fluid overload in ICU patients compared with traditional prediction models. This method may be further developed into real-time clinical applications to improve early detection of adverse outcomes.

The Delphi Delirium Management Algorithms. A practical tool for clinicians, the result of a modified Delphi expert consensus approach.

Delirium is common in hospitalised patients, and there is currently no specific treatment. Identifying and treating underlying somatic causes of delirium is the first priority once delirium is diagnosed. Several international guidelines provide clinicians with an evidence-based approach to screening, diagnosis and symptomatic treatment. However, current guidelines do not offer a structured approach to identification of underlying causes. A panel of 37 internationally recognised delirium experts from diverse medical backgrounds worked together in a modified Delphi approach via an online platform. Consensus was reached after five voting rounds. The final product of this project is a set of three delirium management algorithms (the Delirium Delphi Algorithms), one for ward patients, one for patients after cardiac surgery and one for patients in the intensive care unit.

Clinical Impact of the Implementation Strategies Used to Apply the 2013 Pain, Agitation/Sedation, Delirium or 2018 Pain, Agitation/Sedation, Delirium, Immobility, Sleep Disruption Guideline Recommendations: A Systematic Review and Meta-Analysis.

OBJECTIVES: To summarize the effectiveness of implementation strategies for ICU execution of recommendations from the 2013 Pain, Agitation/Sedation, Delirium (PAD) or 2018 PAD, Immobility, Sleep Disruption (PADIS) guidelines. DATA SOURCES: PubMed, CINAHL, Scopus, and Web of Science were searched from January 2012 to August 2023. The protocol was registered with PROSPERO (CRD42020175268). STUDY SELECTION: Articles were included if: 1) design was randomized or cohort, 2) adult population evaluated, 3) employed recommendations from greater than or equal to two PAD/PADIS domains, and 4) evaluated greater than or equal to 1 of the following outcome(s): short-term mortality, delirium occurrence, mechanical ventilation (MV) duration, or ICU length of stay (LOS). DATA EXTRACTION: Two authors independently reviewed articles for eligibility, number of PAD/PADIS domains, quality according to National Heart, Lung, and Blood Institute assessment tools, implementation strategy use (including Assess, prevent, and manage pain; Both SAT and SBT; Choice of analgesia and sedation; Delirium: assess, prevent, and manage; Early mobility and exercise; Family engagement and empowerment [ABCDEF] bundle) by Cochrane Effective Practice and Organization of Care (EPOC) category, and clinical outcomes. Certainty of evidence was assessed using Grading of Recommendations Assessment, Development, and Evaluation. DATA SYNTHESIS: Among the 25 of 243 (10.3%) full-text articles included ( n = 23,215 patients), risk of bias was high in 13 (52%). Most studies were cohort ( n = 22, 88%). A median of 5 (interquartile range [IQR] 4-7) EPOC strategies were used to implement recommendations from two (IQR 2-3) PAD/PADIS domains. Cohort and randomized studies were pooled separately. In the cohort studies, use of EPOC strategies was not associated with a change in mortality (risk ratio [RR] 1.01; 95% CI, 0.9-1.12), or delirium (RR 0.92; 95% CI, 0.82-1.03), but was associated with a reduction in MV duration (weighted mean difference [WMD] -0.84 d; 95% CI, -1.25 to -0.43) and ICU LOS (WMD -0.77 d; 95% CI, -1.51 to 0.04). For randomized studies, EPOC strategy use was associated with reduced mortality and MV duration but not delirium or ICU LOS. CONCLUSIONS: Using multiple implementation strategies to adopt PAD/PADIS guideline recommendations may reduce mortality, duration of MV, and ICU LOS. Further prospective, controlled studies are needed to identify the most effective strategies to implement PAD/PADIS recommendations.

Relationship Between Pain and Delirium in Critically Ill Adults.

OBJECTIVES: Although opioids are frequently used to treat pain, and are an important risk for ICU delirium, the association between ICU pain itself and delirium remains unclear. We sought to evaluate the relationship between ICU pain and delirium. DESIGN: Prospective cohort study. SETTING: A 32-bed academic medical-surgical ICU. PATIENTS: Critically ill adults (n = 4,064) admitted greater than or equal to 24 hours without a condition hampering delirium assessment. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Daily mental status was classified as arousable without delirium, delirium, or unarousable. Pain was assessed six times daily in arousable patients using a 0-10 Numeric Rating Scale (NRS) or the Critical Care Pain Observation Tool (CPOT); daily peak pain score was categorized as no (NRS = 0/CPOT = 0), mild (NRS = 1-3/CPOT = 1-2), moderate (NRS = 4-6/CPOT = 3-4), or severe (NRS = 7-10/CPOT = 5-8) pain. To address missingness, a Multiple Imputation by Chained Equations approach that used available daily pain severity and 19 pain predictors was used to generate 25 complete datasets. Using a first-order Markov model with a multinomial logistic regression analysis, that controlled for 11 baseline/daily delirium risk factors and considered the competing risks of unarousability and ICU discharge/death, the association between peak daily pain and next-day delirium in each complete dataset was evaluated. RESULTS: Among 14,013 ICU days (contributed by 4,064 adults), delirium occurred on 2,749 (19.6%). After pain severity imputation on 1,818 ICU days, mild, moderate, and severe pain were detected on 2,712 (34.1%), 1,682 (21.1%), and 894 (11.2%) of the no-delirium days, respectively, and 992 (36.1%), 513 (18.6%), and 27 (10.1%) of delirium days (p = 0.01). The presence of any pain (mild, moderate, or severe) was not associated with a transition from awake without delirium to delirium (aOR 0.96; 95% CI, 0.76-1.21). This association was similar when days with only mild, moderate, or severe pain were considered. All results were stable after controlling for daily opioid dose. CONCLUSIONS: After controlling for multiple delirium risk factors, including daily opioid use, pain may not be a risk factor for delirium in the ICU. Future prospective research is required.

Machine learning vs. traditional regression analysis for fluid overload prediction in the ICU.

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.

Data Missingness Reporting and Use of Methods to Address It in Critical Care Cohort Studies.

IMPORTANCE: Failure to recognize and address data missingness in cohort studies may lead to biased results. Although Strengthening the Reporting of Observational Studies in Epidemiology reporting guidelines advocate data missingness reporting, the degree to which missingness is reported and addressed in the critical care literature remains unclear. OBJECTIVES: To review published ICU cohort studies to characterize data missingness reporting and the use of methods to address it. DESIGN SETTING AND PARTICIPANTS: We searched the 2022 table of contents of 29 critical care/critical care subspecialty journals having a 2021 impact factor greater than or equal to 3 to identify published prospective clinical or retrospective database cohort studies enrolling greater than or equal to 100 patients. MAIN OUTCOMES AND MEASURES: In duplicate, two trained researchers conducted a manuscript/supplemental material PDF word search for "missing*" and extracted study type, patient age, ICU type, sample size, missingness reporting, and the use of methods to address it. RESULTS: A total of 656 studies were reviewed. Of the 334 of 656 (50.9%) studies mentioning missingness, missingness was reported for greater than or equal to 1 variable in 234 (70.1%) and it exceeded 5% for at least one variable in 160 (47.9%). Among the 334 studies mentioning missingness, 88 (26.3%) used exclusion criteria, 36 (10.8%) used complete-case analysis, and 164 (49.1%) used a formal method to avoid missingness. In these 164 studies, imputation only was used in 100 (61.0%), an analytic strategy only in 24 (14.6%), and both in 40 (24.4%). Only missingness greater than 5% (in ≥ 1 variable) was independently associated with greater use of a missingness method (adjusted odds ratio 2.91; 95% CI, 1.85-4.60). Among 140 studies using imputation, multiple imputation was used in 87 studies (62.1%) and simple imputation in 49 studies (35.0%). For the 64 studies using an analytic method, 12 studies (18.8%) assigned missingness as an unknown category, whereas sensitivity analysis was used in 47 studies (73.4%). CONCLUSIONS AND RELEVANCE: Among published critical care cohort studies, only half mentioned result missingness, one-third reported actual missingness and only one-quarter used a method to manage missingness. Educational strategies to promote missingness reporting and resolution methods are required.

Frequency of screening and SBT Technique Trial-North American Weaning Collaboration (FAST-NAWC): an update to the protocol and statistical analysis plan.

BACKGROUND: This update summarizes key changes made to the protocol for the Frequency of Screening and Spontaneous Breathing Trial (SBT) Technique Trial-North American Weaning Collaborative (FAST-NAWC) trial since the publication of the original protocol. This multicenter, factorial design randomized controlled trial with concealed allocation, will compare the effect of both screening frequency (once vs. at least twice daily) to identify candidates to undergo a SBT and SBT technique [pressure support + positive end-expiratory pressure vs. T-piece] on the time to successful extubation (primary outcome) in 760 critically ill adults who are invasively ventilated for at least 24 h in 20 North American intensive care units. METHODS/DESIGN: Protocols for the pilot, factorial design trial and the full trial were previously published in J Clin Trials ( https://doi.org/10.4172/2167-0870.1000284 ) and Trials (https://doi: 10.1186/s13063-019-3641-8). As planned, participants enrolled in the FAST pilot trial will be included in the report of the full FAST-NAWC trial. In response to the onset of the coronavirus disease of 2019 (COVID-19) pandemic when approximately two thirds of enrollment was complete, we revised the protocol and consent form to include critically ill invasively ventilated patients with COVID-19. We also refined the statistical analysis plan (SAP) to reflect inclusion and reporting of participants with and without COVID-19. This update summarizes the changes made and their rationale and provides a refined SAP for the FAST-NAWC trial. These changes have been finalized before completion of trial follow-up and the commencement of data analysis. TRIAL REGISTRATION: Clinical Trials.gov NCT02399267.