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

Prosafe: a european endeavor to improve quality of critical care medicine in seven countries

BACKGROUND: long-lasting shared research databases are an important source of epidemiological information and can promote comparison between different healthcare services. Here we present ProsaFe, an advanced international research network in intensive care medicine, with the focus on assessing and improving the quality of care. the project involved 343 icUs in seven countries. all patients admitted to the icU were eligible for data collection. MetHoDs: the ProsaFe network collected data using the same electronic case report form translated into the corresponding languages. a complex, multidimensional validation system was implemented to ensure maximum data quality. individual and aggregate reports by country, region, and icU type were prepared annually. a web-based data-sharing system allowed participants to autonomously perform different analyses on both own data and the entire database. RESULTS: The final analysis was restricted to 262 general ICUs and 432,223 adult patients, mostly admitted to Italian units, where a research network had been active since 1991. organization of critical care medicine in the seven countries was relatively similar, in terms of staffing, case mix and procedures, suggesting a common understanding of the role of critical care medicine. conversely, icU equipment differed, and patient outcomes showed wide variations among countries. coNclUsioNs: ProsaFe is a permanent, stable, open access, multilingual database for clinical benchmarking, icU self-evaluation and research within and across countries, which offers a unique opportunity to improve the quality of critical care. its entry into routine clinical practice on a voluntary basis is testimony to the success and viability of the endeavor

American College of Cardiology; American Heart Association Task Force; European Society of Cardiology Committee for Practice Guidelines. ACC/AHA/ESC 2006 guidelines for management of patients with ventricular arrhythmias and the prevention of sudden cardiac death: a report of the American College of Cardiology/American Heart Association Task Force and the European Society of Cardiology Committee for Practice Guidelines (Writing Committee to Develop Guidelines for Management of Patients With Ventricular Arrhythmias and the Prevention of Sudden Cardiac Death).

The purpose this document is to update and combine the previously published recommendations into one source approved by the major cardiology organizations in the United States and Europe. We have consciously attempted to create a streamlined document, not a textbook, that would be useful specifically to locate recommendations on the evaluation and treatment of patients who have or may be at risk for ventricular arrhythmias. Thus, sections on epidemiology, mechanisms and substrates, and clinical presentations are brief, because there are no recommendations for those sections. For the other sections, the wording has been kept to a minimum, and clinical presentations have been confined to those aspects relevant to forming recommendations