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Interventions for preventing delirium in hospitalised non-ICU patients.

ABSTRACT:

SUBMITTER: Siddiqi N

PROVIDER: S-EPMC10431752 | biostudies-literature | 2016 Mar

REPOSITORIES: biostudies-literature

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Interventions for preventing delirium in hospitalised non-ICU patients.

Siddiqi Najma N Harrison Jennifer K JK Clegg Andrew A Teale Elizabeth A EA Young John J Taylor James J Simpkins Samantha A SA

The Cochrane database of systematic reviews 20160311

<h4>Background</h4>Delirium is a common mental disorder, which is distressing and has serious adverse outcomes in hospitalised patients. Prevention of delirium is desirable from the perspective of patients and carers, and healthcare providers. It is currently unclear, however, whether interventions for preventing delirium are effective.<h4>Objectives</h4>To assess the effectiveness of interventions for preventing delirium in hospitalised non-Intensive Care Unit (ICU) patients.<h4>Search methods< ...[more]

PMID: 26967259

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Project description:To compare the efficacy and safety of scheduled low-dose haloperidol versus placebo for the prevention of delirium (Intensive Care Delirium Screening Checklist ? 4) administered to critically ill adults with subsyndromal delirium (Intensive Care Delirium Screening Checklist = 1-3).Randomized, double-blind, placebo-controlled trial.Three 10-bed ICUs (two medical and one surgical) at an academic medical center in the United States.Sixty-eight mechanically ventilated patients with subsyndromal delirium without complicating neurologic conditions, cardiac surgery, or requiring deep sedation.Patients were randomly assigned to receive IV haloperidol 1 mg or placebo every 6 hours until delirium occurred (Intensive Care Delirium Screening Checklist ? 4 with psychiatric confirmation), 10 days of therapy had elapsed, or ICU discharge.Baseline characteristics were similar between the haloperidol (n = 34) and placebo (n = 34) groups. A similar number of patients given haloperidol (12/34 [35%]) and placebo (8/34 [23%]) developed delirium (p = 0.29). Haloperidol use reduced the hours per study day spent agitated (Sedation Agitation Scale ? 5) (p = 0.008), but it did not influence the proportion of 12-hour ICU shifts patients spent alive without coma (Sedation Agitation Scale ? 2) or delirium (p = 0.36), the time to first delirium occurrence (p = 0.22), nor delirium duration (p = 0.26). Days of mechanical ventilation (p = 0.80), ICU mortality (p = 0.55), and ICU patient disposition (p = 0.22) were similar in the two groups. The proportion of patients who developed corrected QT-interval prolongation (p = 0.16), extrapyramidal symptoms (p = 0.31), excessive sedation (p = 0.31), or new-onset hypotension (p = 1.0) that resulted in study drug discontinuation was comparable between the two groups.Low-dose scheduled haloperidol, initiated early in the ICU stay, does not prevent delirium and has little therapeutic advantage in mechanically ventilated, critically ill adults with subsyndromal delirium.

Project description:IntroductionDelirium occurrence is common and preventive strategies are resource intensive. Screening tools can prioritize patients at risk. Using machine learning, we can capture time and treatment effects that pose a challenge to delirium prediction. We aim to develop a delirium prediction model that can be used as a screening tool.MethodsFrom the eICU Collaborative Research Database (eICU-CRD) and the Medical Information Mart for Intensive Care version III (MIMIC-III) database, patients with one or more Confusion Assessment Method-Intensive Care Unit (CAM-ICU) values and intensive care unit (ICU) length of stay greater than 24 h were included in our study. We validated our model using 21 quantitative clinical parameters and assessed performance across a range of observation and prediction windows, using different thresholds and applied interpretation techniques. We evaluate our models based on stratified repeated cross-validation using 3 algorithms, namely Logistic Regression, Random Forest, and Bidirectional Long Short-Term Memory (BiLSTM). BiLSTM represents an evolution from recurrent neural network-based Long Short-Term Memory, and with a backward input, preserves information from both past and future. Model performance is measured using Area Under Receiver Operating Characteristic, Area Under Precision Recall Curve, Recall, Precision (Positive Predictive Value), and Negative Predictive Value metrics.ResultsWe evaluated our results on 16 546 patients (47% female) and 6294 patients (44% female) from eICU-CRD and MIMIC-III databases, respectively. Performance was best in BiLSTM models where, precision and recall changed from 37.52% (95% confidence interval [CI], 36.00%-39.05%) to 17.45 (95% CI, 15.83%-19.08%) and 86.1% (95% CI, 82.49%-89.71%) to 75.58% (95% CI, 68.33%-82.83%), respectively as prediction window increased from 12 to 96 h. After optimizing for higher recall, precision and recall changed from 26.96% (95% CI, 24.99%-28.94%) to 11.34% (95% CI, 10.71%-11.98%) and 93.73% (95% CI, 93.1%-94.37%) to 92.57% (95% CI, 88.19%-96.95%), respectively. Comparable results were obtained in the MIMIC-III cohort.ConclusionsOur model performed comparably to contemporary models using fewer variables. Using techniques like sliding windows, modification of threshold to augment recall and feature ranking for interpretability, we addressed shortcomings of current models.

Project description:IntroductionIntensive care unit (ICU) delirium is an acute brain dysfunction that affects up to 7 out of 10 patients admitted to ICUs. Patients who develop ICU delirium cannot think clearly, have trouble paying attention, do not understand what is happening around them and may see or hear things that are not there. ICU delirium increases the time patients spend in ICUs and hospitals and therefore healthcare costs. ICU delirium is also associated with increased mortality and dementia in the longer term. ICU delirium prevention and management strategies are likely to include both pharmacological and non-pharmacological components as part of a complex intervention, but it is unclear which components should be included. The objective of this meta-review is to systematically map the quantity and certainty of the available evidence from reviews and meta-analyses of randomised controlled trials (RCTs) of pharmacological and non-pharmacological interventions, which will be used to design a multicomponent intervention to prevent and manage ICU delirium.Methods and analysisA systematic search strategy was performed in MEDLINE (Ovid), Embase (Elsevier), Cochrane Database of Systematic Reviews, Scopus, Cumulative Index to Nursing and Allied Health Literature (CINAHL), PsycINFO and Web of Science (from inception to 26 September 2023), as well as Epistemonikos (from inception to 19 July 2023). We will include all critically ill adults (aged≥18 years) and any ICU delirium prevention or management intervention (pharmacological or non-pharmacological). For pharmacological interventions, we will include reviews of RCTs. For non-pharmacological interventions, we will consider reviews of RCTs, quasi-experimental and cohort studies. We will use the International Consensus Study (Del-COrS) core outcome set for research evaluating interventions to prevent or manage ICU delirium and synthesise our findings using quantitative data description methods. We will involve our Patient and Public Involvement group of people who experienced ICU delirium to develop and comment on such aspects as the research question, methodology and which outcomes are most important.Ethics and disseminationNo ethical approval is required for this study. The results of this meta-review will be disseminated through peer-reviewed publications and conferences. They will also form part of an evidence map and logic model for the prevention and management of ICU delirium.Prospero registration numberCRD42023473260.

Dataset Information

Interventions for preventing delirium in hospitalised non-ICU patients.

Publications

Interventions for preventing delirium in hospitalised non-ICU patients.

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