Big Data Collection and Traumatic Brain Injury

Dolmans, Rianne G F; Stopa, Brittany M.; Broekman, Marike L. D.

doi:10.1007/978-3-030-78075-3_5

Cited by 1 publication

(2 citation statements)

References 17 publications

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“…Two larger surveys on sedation protocols in the neurointensive care population showing an association between aSAH severity and timing of IS support our data (10,11). In one study, the duration of prolonged sedation for patients with unfavorable biomarkers, such as therapy refractory ICP or radiological surrogates for elevated ICP, was reported with a mean of 4.5 days (SD 1.8) for good-grade SAH and 5.6 days (SD 2.8) for poor-grade SAH (10).…”

Section: Discussionsupporting

confidence: 86%

“…As neurointensivists are therefore hesitant to adopt the practice of daily IS and spontaneous breathing trials, prolonged sedation is often only terminated after days in pathologies with relevant ICP elevation, such as aSAH. However, data regarding the optimal timing and clinical biomarkers predicting adverse events during IS after prolonged sedation are scarce, leading to uncertainty and heterogeneous management (10)(11)(12).…”

Section: Introductionmentioning

confidence: 99%

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Risk factors and outcomes after interruption of sedation in subarachnoid hemorrhage (ROUTINE-SAH)—a retrospective cohort study

Schmidbauer,

Läufer,

Maskos

et al. 2024

Front. Neurol.

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IntroductionAneurysmal subarachnoid hemorrhage (aSAH) often necessitates prolonged sedation to manage elevated intracranial pressure (ICP) and to prevent secondary brain injury. Optimal timing and biomarkers for predicting adverse events (AEs) during interruption of sedation (IS) after prolonged sedation are not well established. To guide sedation management in aSAH, we aimed to explore the frequency, risk factors, and outcomes of IS in aSAH.MethodsIn a retrospective cohort study, a total of 148 patients with aSAH from January 2015 to April 2020 were screened. In total, 30 patients accounting for 42 IS were included in the analysis. Adverse events (AEs) during IS were used as core outcome measures and were categorized into neurological and non-neurological AEs. Baseline characteristics, clinical parameters before IS, AEs, and functional outcomes were collected using health records. Statistical analysis used generalized linear mixed-effects models with regularization to identify candidate predictors with subsequent bootstrapping to test model stability. As an exploratory analysis, multivariate linear and logistic regression was used to analyze the association between IS and intensive care unit length of stay, duration of mechanical ventilation, and functional outcomes.ResultsThe mean age was 56.9 (SD 14.8) years, and a majority of the patients presented with poor-grade SAH (16/30, 53.3%). Neurological and non-neurological AEs occurred in 60.0% (18/30) of the patients. Timing, number of IS attempts, ICP burden, craniectomy status, level of consciousness, heart rate, cerebral perfusion pressure, oxygen saturation, fraction of inspired oxygen, and temperature were selected as candidate predictors. Through bootstrapping, elapsed time since disease onset (OR 0.85, 95% confidence interval (95% CI) 0.75–0.97), ICP burden (OR 1.24, 95% CI 1.02–1.52), craniectomy (OR 0.68, 95% CI 0.48–0.69), and oxygen saturation (OR, 0.80 0.72–0.89) were revealed as relevant biomarkers for neurological AEs, while none of the pre-selected predictors was robustly associated with non-neurological AEs.ConclusionIn aSAH, complications during the definite withdrawal of sedation are frequent but can potentially be predicted using clinical parameters available at the bedside. Prospective multicenter studies are essential to validate these results and further investigate the impact of IS complications.

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Section: Discussionsupporting

confidence: 86%

Section: Introductionmentioning

confidence: 99%