Sustainability in Internal Medicine: A Year-Long Ward-Wide Observational Study.

Population aging and multimorbidity challenge health system sustainability, but the role of assistance-related variables rather than individual pathophysiological factors in determining patient outcomes is unclear. To identify assistance-related determinants of sustainable hospital healthcare, all patients hospitalised in an Internal Medicine Unit (n = 1073) were enrolled in a prospective year-long observational study and split 2:1 into a training (n = 726) and a validation subset (n = 347). Demographics, comorbidities, provenance setting, estimates of complexity (cumulative illness rating scale, CIRS: total, comorbidity, CIRS-CI, and severity, CIRS-SI subscores) and intensity of care (nine equivalents of manpower score, NEMS) were analysed at individual and Unit levels along with variations in healthcare personnel as determinants of in-hospital mortality, length of stay and nosocomial infections.

Implementing Artificial Intelligence: Assessing the Cost and Benefits of Algorithmic Decision-Making in Critical Care.

This article provides an overview of the most useful artificial intelligence algorithms developed in critical care, followed by a comprehensive outline of the benefits and limitations. We begin by describing how nurses and physicians might be aided by these new technologies. We then move to the possible changes in clinical guidelines with personalized medicine that will allow tailored therapies and probably will increase the quality of the care provided to patients.

Mining the contribution of intensive care clinical course to outcome after traumatic brain injury.

Existing methods to characterise the evolving condition of traumatic brain injury (TBI) patients in the intensive care unit (ICU) do not capture the context necessary for individualising treatment. Here, we integrate all heterogenous data stored in medical records (1166 pre-ICU and ICU variables) to model the individualised contribution of clinical course to 6-month functional outcome on the Glasgow Outcome Scale -Extended (GOSE). On a prospective cohort (n = 1550, 65 centres) of TBI patients, we train recurrent neural network models to map a token-embedded time series representation of all variables (including missing values) to an ordinal GOSE prognosis every 2 h.

REVersal of nEuromusculAr bLocking Agents in Patients Undergoing General Anaesthesia (REVEAL Study).

Background: Neuromuscular blocking agent (NMBA) monitoring and reversals are key to avoiding residual curarization and improving patient outcomes. Sugammadex is a NMBA reversal with favorable pharmacological properties. There is a lack of real-world data detailing how the diffusion of sugammadex affects anesthetic monitoring and practice.

Outcome prediction during an ICU surge using a purely data-driven approach: A supervised machine learning case-study in critically ill patients from COVID-19 Lombardy outbreak.

Purpose: COVID-19 disease frequently affects the lungs leading to bilateral viral pneumonia, progressing in some cases to severe respiratory failure requiring ICU admission and mechanical ventilation. Risk stratification at ICU admission is fundamental for resource allocation and decision making. We assessed performances of three machine learning approaches to predict mortality in COVID-19 patients admitted to ICU using early operative data from the Lombardy ICU Network.

Early prediction of SARS-CoV-2 reproductive number from environmental, atmospheric and mobility data: A supervised machine learning approach.

Introduction: SARS-CoV-2 was declared a pandemic by the WHO on March 11th, 2020. Public protective measures were enforced in every country to limit the diffusion of SARS-CoV-2. Its transmission, mainly by droplets, has been measured by the effective reproduction number (Rt) that counts the number of secondary cases caused in a population by an average infectious individual at time t.

The effect of COVID-19 epidemic on vital signs in hospitalized patients: a pre-post heat-map study from a large teaching hospital.

The Lombardy SARS-CoV-2 outbreak in February 2020 represented the beginning of COVID-19 epidemic in Italy. Hospitals were flooded by thousands of patients with bilateral pneumonia and severe respiratory, and vital sign derangements compared to the standard hospital population. We propose a new visual analysis technique using heat maps to describe the impact of COVID-19 epidemic on vital sign anomalies in hospitalized patients.

Barotrauma in mechanically ventilated patients with Coronavirus disease 2019: a survey of 38 hospitals in Lombardy, Italy.

Background: The aim was to describe the incidence and risk factors of barotrauma in patients with the Coronavirus disease 2019 (COVID-19) on invasive mechanical ventilation, during the outbreak in our region (Lombardy, Italy).

Methods: The study was an electronic survey open from March 27 to May 2, 2020. Patients with COVID-19 who developed barotrauma while on invasive mechanical ventilation from 61 hospitals of the COVID-19 Lombardy Intensive Care Unit network were involved.

Artificial Intelligence in the Intensive Care Unit.

The diffusion of electronic health records collecting large amount of clinical, monitoring, and laboratory data produced by intensive care units (ICUs) is the natural terrain for the application of artificial intelligence (AI). AI has a broad definition, encompassing computer vision, natural language processing, and machine learning, with the latter being more commonly employed in the ICUs. Machine learning may be divided in supervised learning models (i.