Does pain optimisation impact delirium outcomes in critically ill patients? A systematic review and meta-analysis protocol.

BACKGROUND: Untreated pain is associated with short-term and long-term consequences, including post-traumatic stress disorder and insomnia. Side effects of some analgesic medications include dysphoria, hallucinations and delirium. Therefore, both untreated pain and analgesic medications may be risk factors for delirium. Delirium is associated with longer length of stay or cognitive impairment. Our systematic review and meta-analysis will examine the relationship between pain or analgesic medications with delirium occurrence, duration and severity among critically ill adults. METHODS AND ANALYSIS: MEDLINE, EMBASE, CINAHL, the Cochrane Central Register of controlled trials and a review of recent conference abstracts will be searched without restriction from inception to 15 May 2023. Study inclusion criteria are: (1) age≥18 years admitted to intensive care; (2) report a measure of pain, analgesic medications and delirium; (3) study design-randomised controlled trial, quasiexperimental designs and observational cohort and case-control studies excluding case reports. Study exclusion criteria are: (1) alcohol withdrawal delirium or delirium tremens; or (2) general anaesthetic emergence delirium; or (3) lab or animal studies. Risk of bias will be assessed with the Risk of Bias V.2 and risk of bias in non-randomised studies tools. There is no language restriction. Occurrence estimates will be transformed using the Freeman-Tukey double arcsine. Point estimates will be pooled using Hartung-Knapp Sidik-Jonkman random effects meta-analysis to estimate a pooled risk ratio. Statistical heterogeneity will be estimated with the I2 statistic. Risk of small study effects will be assessed using funnel plots and Egger test. Studies will be analysed for time-varying and unmeasured confounding using E values. ETHICS AND DISSEMINATION: Ethical approval is not required as this is an analysis of published aggregated data. We will share our findings at conferences and in peer-reviewed journals. PROSPERO REGISTRATION NUMBER: The finalised protocol was submitted to the International Prospective Register of Systematic Reviews (PROSPERO ID: CRD42022367715).

Endotyping in ARDS: one step forward in precision medicine.

BACKGROUND: The Berlin definition of acute respiratory distress syndrome (ARDS) includes only clinical characteristics. Understanding unique patient pathobiology may allow personalized treatment. We aimed to define and describe ARDS phenotypes/endotypes combining clinical and pathophysiologic parameters from a Canadian ARDS cohort. METHODS: A cohort of adult ARDS patients from multiple sites in Calgary, Canada, had plasma cytokine levels and clinical parameters measured in the first 24 h of ICU admission. We used a latent class model (LCM) to group the patients into several ARDS subgroups and identified the features differentiating those subgroups. We then discuss the subgroup effect on 30 day mortality. RESULTS: The LCM suggested three subgroups (n1 = 64, n2 = 86, and n3 = 30), and 23 out of 69 features made these subgroups distinct. The top five discriminating features were IL-8, IL-6, IL-10, TNF-a, and serum lactate. Mortality distinctively varied between subgroups. Individual clinical characteristics within the subgroup associated with mortality included mean PaO2/FiO2 ratio, pneumonia, platelet count, and bicarbonate negatively associated with mortality, while lactate, creatinine, shock, chronic kidney disease, vasopressor/ionotropic use, low GCS at admission, and sepsis were positively associated. IL-8 and Apache II were individual markers strongly associated with mortality (Area Under the Curve = 0.84). PERSPECTIVE: ARDS subgrouping using biomarkers and clinical characteristics is useful for categorizing a heterogeneous condition into several homogenous patient groups. This study found three ARDS subgroups using LCM; each subgroup has a different level of mortality. This model may also apply to developing further trial design, prognostication, and treatment selection.

Multicentre implementation of a quality improvement initiative to reduce delirium in adult intensive care units: An interrupted time series analysis.

PURPOSE: The ABCDEF bundle may improve delirium outcomes among intensive care unit (ICU) patients, however population-based studies are lacking. In this study we evaluated effects of a quality improvement initiative based on the ABCDEF bundle in adult ICUs in Alberta, Canada. MATERIAL AND METHODS: We conducted a pre-post, registry-based clinical trial, analysed using interrupted time series methodology. Outcomes were examined via segmented linear regression using mixed effects models. The main data source was a population-based electronic health record. RESULTS: 44,405 consecutive admissions (38,400 unique patients) admitted to 15 general medical/surgical and/or neurologic adult ICUs between 2014 and 2019 were included. The proportion of delirium days per ICU increased from 30.24% to 35.31% during the pre-intervention period. After intervention implementation it decreased significantly (bimonthly decrease of 0.34%, 95%CI 0.18-0.50%, p < 0.01) from 33.48% (95%CI 29.64-37.31%) in 2017 to 28.74% (95%CI 25.22-32.26%) in 2019. The proportion of sedation days using midazolam demonstrated an immediate decrease of 7.58% (95%CI 4.00-11.16%). There were no significant changes in duration of invasive ventilation, proportion of partial coma days, ICU mortality, or potential adverse events. CONCLUSIONS: An ABCDEF delirium initiative was implemented on a population-basis within adult ICUs and was successful at reducing the prevalence of delirium.