Decompressive craniectomy for the treatment of high intracranial pressure in closed traumatic brain injury

Decompressive Craniectomy for Traumatic Brain Injury: In-hospital Mortality-Associated Factors

Objective  Determine predictors of in-hospital mortality in patients with severe traumatic brain injury (TBI) who underwent decompressive craniectomy. Materials and Methods  This retrospective study reviewed consecutive patients who underwent a decompressive craniectomy between March 2017 and March 2020 at our institution, and analyzed clinical characteristics, brain tomographic images, surgical details and morbimortality associated with this procedure. Results  Thirty-three (30 unilateral and 3 bifrontal) decompressive craniectomies were performed, of which 27 patients were male (81.8%). The mean age was 52.18 years, the mean Glasgow coma scale (GCS) score at admission was 9, and 24 patients had anisocoria (72.7%). Falls were the principal cause of the trauma (51.5%), the mean anterior-posterior diameter (APD) of the bone flap in unilateral cases was 106.81 mm (standard deviation [SD] 20.42) and 16 patients (53.3%) underwent a right-sided hemicraniectomy. The temporal bone enlargement was done in 20 cases (66.7%), the mean time of surgery was 2 hours and 27 minutes, the skull flap was preserved in the subcutaneous layer in 29 cases (87.8%), the mean of blood loss was 636.36 mL,and in-hospital mortality was 12%. Univariate analysis found differences between the APD diameter (120.3 mm vs. 85.3 mm; p = 0.003) and the presence of midline shift > 5 mm ( p = 0.033). Conclusion  The size of the skull flap and the presence of midline shift > 5 mm were predictors of mortality. In the absence of intercranial pressure (ICP) monitoring, clinical and radiological criteria are mandatory to perform a decompressive craniectomy.

Timing of surgical intervention for compartment syndrome in different body region: systematic review of the literature

Compartment syndrome can occur in many body regions and may range from homeostasis asymptomatic alterations to severe, life-threatening conditions. Surgical intervention to decompress affected organs or area of the body is often the only effective treatment, although evidences to assess the best timing of intervention are lacking. Present paper systematically reviewed the literature stratifying timings according to the compartmental syndromes which may beneficiate from immediate, early, delayed, or prophylactic surgical decompression. Timing of decompression have been stratified into four categories: (1) immediate decompression for those compartmental syndromes whose missed therapy would rapidly lead to patient death or extreme disability, (2) early decompression with the time burden of 3-12 h and in any case before clinical signs of irreversible deterioration, (3) delayed decompression identified with decompression performed after 12 h or after signs of clinical deterioration has occurred, and (4) prophylactic decompression in those situations where high incidence of compartment syndrome is expected after a specific causative event.

Intracranial Pressure Monitoring Signals After Traumatic Brain Injury: A Narrative Overview and Conceptual Data Science Framework

Continuous intracranial pressure (ICP) monitoring is a cornerstone of neurocritical care after severe brain injuries such as traumatic brain injury and acts as a biomarker of secondary brain injury. With the rapid development of artificial intelligent (AI) approaches to data analysis, the acquisition, storage, real-time analysis, and interpretation of physiological signal data can bring insights to the field of neurocritical care bioinformatics. We review the existing literature on the quantification and analysis of the ICP waveform and present an integrated framework to incorporate signal processing tools, advanced statistical methods, and machine learning techniques in order to comprehensively understand the ICP signal and its clinical importance. Our goals were to identify the strengths and pitfalls of existing methods for data cleaning, information extraction, and application. In particular, we describe the use of ICP signal analytics to detect intracranial hypertension and to predict both short-term intracranial hypertension and long-term clinical outcome. We provide a well-organized roadmap for future researchers based on existing literature and a computational approach to clinically-relevant biomedical signal data.