Impaired platelet reactivity in patients with septic shock: a proof-of-concept study

Coagulation disorders and thrombocytopenia are common in patients with septic shock, but only few studies have focused on platelet variables beyond platelet count. The aim of this study was to evaluate whether platelets reactivity predicts sepsis-induced thrombocytopenia in patients with septic shock. We therefore enrolled consecutive patients with septic shock and platelets count >150*103/μL on the day of the diagnosis. Platelets reactivity tests were performed daily from the diagnosis of septic shock until day five; platelet volume distribution and mean platelet volume were also recorded daily. Sepsis-induced thrombocytopenia was defined as a platelet count <150*103/μL. Thirty patients were included; sepsis-induced thrombocytopenia occurred in 11 (31%) patients. Platelets reactivity and platelet count at day of septic shock diagnosis were not correlated. Patients who experienced thrombocytopenia had lower maximal aggregation at diagnosis than others. Maximal aggregation tests were predictors of thrombocytopenia (AUROC from 0.756 to 0.797, depending on the agonist used). Both platelet volume distribution width and mean platelet volume were predictors of 90-day mortality (AUROC 0.866 and 0.735, respectively). In this pilot study, impaired platelets reactivity was more common in patients who subsequently developed sepsis-induced thrombocytopenia; also, platelet volume distribution width and mean platelet volume were predictors of 90-day mortality

Cerebro-spinal fluid glucose and lactate concentrations changes in response to therapies in patIents with primary brain injury: the START-TRIP study

Abstract Introduction Altered levels of cerebrospinal fluid (CSF) glucose and lactate concentrations are associated with poor outcomes in acute brain injury patients. However, no data on changes in such metabolites consequently to therapeutic interventions are available. The aim of the study was to assess CSF glucose-to-lactate ratio (CGLR) changes related to therapies aimed at reducing intracranial pressure (ICP). Methods A multicentric prospective cohort study was conducted in 12 intensive care units (ICUs) from September 2017 to March 2022. Adult (> 18 years) patients admitted after an acute brain injury were included if an external ventricular drain (EVD) for intracranial pressure (ICP) monitoring was inserted within 24 h of admission. During the first 48–72 h from admission, CGLR was measured before and 2 h after any intervention aiming to reduce ICP (“intervention”). Patients with normal ICP were also sampled at the same time points and served as the “control” group. Results A total of 219 patients were included. In the intervention group (n = 115, 53%), ICP significantly decreased and CPP increased. After 2 h from the intervention, CGLR rose in both the intervention and control groups, although the magnitude was higher in the intervention than in the control group (20.2% vs 1.6%; p = 0.001). In a linear regression model adjusted for several confounders, therapies to manage ICP were independently associated with changes in CGLR. There was a weak inverse correlation between changes in ICP and CGRL in the intervention group. Conclusions In this study, CGLR significantly changed over time, regardless of the study group. However, these effects were more significant in those patients receiving interventions to reduce ICP

COVID-19 ICU mortality prediction: a machine learning approach using SuperLearner algorithm

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 model