Introduction Brain multimodal monitoring including intracranial pressure (ICP) and brain tissue oxygen pressure (PbtO2) is more accurate than ICP alone in detecting cerebral hypoperfusion after traumatic brain injury (TBI). No data are available for the predictive role of a dynamic hyperoxia test in brain-injured patients from diverse etiology. Aim To examine the accuracy of ICP, PbtO2 and the oxygen ratio (OxR) in detecting regional cerebral hypoperfusion, assessed using perfusion cerebral computed tomography (CTP) in patients with acute brain injury. Methods Single-center study including patients with TBI, subarachnoid hemorrhage (SAH) and intracranial hemorrhage (ICH) undergoing cerebral blood flow (CBF) measurements using CTP, concomitantly to ICP and PbtO2 monitoring. Before CTP, FiO2 was increased directly from baseline to 100% for a period of 20 min under stable conditions to test the PbtO2 catheter, as a standard of care. Cerebral monitoring data were recorded and samples were taken, allowing the measurement of arterial oxygen pressure (PaO2) and PbtO2 at FiO2 100% as well as calculation of OxR (= ΔPbtO2/ΔPaO2). Regional CBF (rCBF) was measured using CTP in the tissue area around intracranial monitoring by an independent radiologist, who was blind to the PbtO2 values. The accuracy of different monitoring tools to predict cerebral hypoperfusion (i.e., CBF < 35 mL/100 g × min) was assessed using area under the receiver-operating characteristic curves (AUCs). Results Eighty-seven CTPs were performed in 53 patients (median age 52 [41–63] years—TBI, n = 17; SAH, n = 29; ICH, n = 7). Cerebral hypoperfusion was observed in 56 (64%) CTPs: ICP, PbtO2 and OxR were significantly different between CTP with and without hypoperfusion. Also, rCBF was correlated with ICP (r = − 0.27; p = 0.01), PbtO2 (r = 0.36; p < 0.01) and OxR (r = 0.57; p < 0.01). Compared with ICP alone (AUC = 0.65 [95% CI, 0.53–0.76]), monitoring ICP + PbO2 (AUC = 0.78 [0.68–0.87]) or ICP + PbtO2 + OxR (AUC = 0.80 (0.70–0.91) was significantly more accurate in predicting cerebral hypoperfusion. The accuracy was not significantly different among different etiologies of brain injury. Conclusions The combination of ICP and PbtO2 monitoring provides a better detection of cerebral hypoperfusion than ICP alone in patients with acute brain injury. The use of dynamic hyperoxia test could not significantly increase the diagnostic accuracy.
Background Since the beginning of coronavirus disease 2019 (COVID-19), the development of predictive models has sparked relevant interest due to the initial lack of knowledge about diagnosis, treatment, and prognosis. The present study aimed at developing a model, through a machine learning approach, to predict intensive care unit (ICU) mortality in COVID-19 patients based on predefined clinical parameters. Results Observational multicenter cohort study. All COVID-19 adult patients admitted to 25 ICUs belonging to the VENETO ICU network (February 28th 2020-april 4th 2021) were enrolled. Patients admitted to the ICUs before 4th March 2021 were used for model training (“training set”), while patients admitted after the 5th of March 2021 were used for external validation (“test set 1”). A further group of patients (“test set 2”), admitted to the ICU of IRCCS Ca’ Granda Ospedale Maggiore Policlinico of Milan, was used for external validation. A SuperLearner machine learning algorithm was applied for model development, and both internal and external validation was performed. Clinical variables available for the model were (i) age, gender, sequential organ failure assessment score, Charlson Comorbidity Index score (not adjusted for age), Palliative Performance Score; (ii) need of invasive mechanical ventilation, non-invasive mechanical ventilation, O2 therapy, vasoactive agents, extracorporeal membrane oxygenation, continuous venous-venous hemofiltration, tracheostomy, re-intubation, prone position during ICU stay; and (iii) re-admission in ICU. One thousand two hundred ninety-three (80%) patients were included in the “training set”, while 124 (8%) and 199 (12%) patients were included in the “test set 1” and “test set 2,” respectively. Three different predictive models were developed. Each model included different sets of clinical variables. The three models showed similar predictive performances, with a training balanced accuracy that ranged between 0.72 and 0.90, while the cross-validation performance ranged from 0.75 to 0.85. Age was the leading predictor for all the considered models. Conclusions Our study provides a useful and reliable tool, through a machine learning approach, for predicting ICU mortality in COVID-19 patients. In all the estimated models, age was the variable showing the most important impact on mortality.
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