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Papers by Pier Francesco Caruso

Research paper thumbnail of Contribution of clinical course to outcome after traumatic brain injury: mining patient trajectories from European intensive care unit data

arXiv (Cornell University), Mar 8, 2023

Research paper thumbnail of Machine Learning for Early Outcome Prediction in Septic Patients in the Emergency Department

Algorithms

Background: Sepsis is one of the major causes of in-hospital death, and is frequent in patients p... more Background: Sepsis is one of the major causes of in-hospital death, and is frequent in patients presenting to the emergency department (ED). Early identification of high-risk septic patients is critical. Machine learning (ML) techniques have been proposed for identification and prognostication of ED septic patients, but these models often lack pre-hospital data and lack validation against early sepsis identification scores (such as qSOFA) and scores for critically ill patients (SOFA, APACHE II). Methods We conducted an electronic health record (EHR) study to test whether interpretable and scalable ML models predict mortality in septic ED patients and compared their performance with clinical scores. Consecutive adult septic patients admitted to ED over 18 months were included. We built ML models, ranging from a simple-classifier model, to unbalanced and balanced logistic regression, and random forest, and compared their performance to qSOFA, SOFA, and APACHE II scores. Results: We in...

Research paper thumbnail of REVersal of nEuromusculAr bLocking Agents in Patients Undergoing General Anaesthesia (REVEAL Study)

Journal of Clinical Medicine

Background: Neuromuscular blocking agent (NMBA) monitoring and reversals are key to avoiding resi... more 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. Methods: We conducted an electronic health record analysis study, including all adult surgical patients undergoing general anesthesia with orotracheal intubation, from January 2016 to December 2019, to describe changes and temporal trends of NMBAs and NMBA reversals administration. Results: From an initial population of 115,046 surgeries, we included 37,882 procedures, with 24,583 (64.9%) treated with spontaneous recovery from neuromuscular block and 13,299 (35.1%) with NMBA reversals. NMBA reversals use doubled over 4 years from 25.5% to 42.5%, mainly driven by sugammadex use, which increased from 17.8% to 38.3%. Rocuronium increased from 58.6% (2...

Research paper thumbnail of Implementing Artificial Intelligence

Critical Care Clinics, Apr 1, 2023

Research paper thumbnail of 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

International Journal of Medical Informatics

Research paper thumbnail of Artificial intelligence in the intensive care unit

Research paper thumbnail of Artificial Intelligence to Predict Mortality in Critically ill COVID-19 Patients Using Data from the First 24h: A Case Study from Lombardy Outbreak

Introduction: SARS-CoV-2 infection was first identified at the end of 2019 in China, and subseque... more Introduction: SARS-CoV-2 infection was first identified at the end of 2019 in China, and subsequently spread globally. 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, considering that baseline comorbidities, age, and patient conditions at admission have been associated to poorer outcomes. Supervised machine learning techniques are increasingly diffuse in clinical medicine and can predict mortality and test associations reaching high predictive performance. We assessed performances of a machine learning approach to predict mortality in COVID-19 patients admitted to ICU using data from the Lombardy ICU Network.Methods: this is a secondary analysis of prospectively collected data from Lombardy ICU network. To predict survival at 7-,14- and 28 days w...

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

Journal of Clinical Monitoring and Computing

The Lombardy SARS-CoV-2 outbreak in February 2020 represented the beginning of COVID-19 epidemic ... more 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. We conducted an electronic health record study, including all confirmed COVID-19 patients hospitalized from February 21st, 2020 to April 21st, 2020 as cases, and all non-COVID-19 patients hospitalized in the same wards from January 1st, 2018 to December 31st, 2018. All data on temperature, peripheral oxygen saturation, respiratory rate, arterial blood pressure, and heart rate were retrieved. Derangement of vital signs was defined according to predefined thresholds. 470 COVID-19 patients and 9241 controls were included. Cases were older than controls, with a median age of 79 vs 76 years in non survivors (p = < 0.002). Gender was not associated with mortality. Overall mortality in COVID-19 hospitalized patients was 18%, ranging from 1.4% in patients below 65 years to about 30% in patients over 65 years. Heat maps analysis demonstrated that COVID-19 patients had an increased frequency in episodes of compromised respiratory rate, acute desaturation, and fever. COVID-19 epidemic profoundly affected the incidence of severe derangements in vital signs in a large academic hospital. We validated heat maps as a method to analyze the clinical stability of hospitalized patients. This method may help to improve resource allocation according to patient characteristics.

Research paper thumbnail of Early prediction of SARS-CoV-2 reproductive number from environmental, atmospheric and mobility data: a supervised machine learning approach

International Journal of Medical Informatics, 2022

Research paper thumbnail of Barotrauma in mechanically-ventilated patients with coronavirus disease 2019: a survey of 38 hospitals in Lombardy, Italy

Minerva anestesiologica, 2020

BACKGROUND The aim was to describe the incidence and risk factors of barotrauma in patients with ... more 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 27th to May 2nd, 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. RESULTS The response rate was 38/61 (62%). The incidence of barotrauma was 145/2041 (7.1%; 95%-CI: 6.1-8.3%). Only a few cases occurred with ventilatory settings that may be considered non-protective such as a plateau airway pressure >35 cmH2O (2/113 [2%]), a driving airway pressure >15 cmH2O (30/113 [27%]), or a tidal volume >8 ml/kg of ideal body weight and a plateau airway pressure >30 cmH2O (12/134 [9%]). CONCLUSIONS within the limits of a survey, patients with COVID-19 might be at high risk f...

Research paper thumbnail of Contribution of clinical course to outcome after traumatic brain injury: mining patient trajectories from European intensive care unit data

arXiv (Cornell University), Mar 8, 2023

Research paper thumbnail of Machine Learning for Early Outcome Prediction in Septic Patients in the Emergency Department

Algorithms

Background: Sepsis is one of the major causes of in-hospital death, and is frequent in patients p... more Background: Sepsis is one of the major causes of in-hospital death, and is frequent in patients presenting to the emergency department (ED). Early identification of high-risk septic patients is critical. Machine learning (ML) techniques have been proposed for identification and prognostication of ED septic patients, but these models often lack pre-hospital data and lack validation against early sepsis identification scores (such as qSOFA) and scores for critically ill patients (SOFA, APACHE II). Methods We conducted an electronic health record (EHR) study to test whether interpretable and scalable ML models predict mortality in septic ED patients and compared their performance with clinical scores. Consecutive adult septic patients admitted to ED over 18 months were included. We built ML models, ranging from a simple-classifier model, to unbalanced and balanced logistic regression, and random forest, and compared their performance to qSOFA, SOFA, and APACHE II scores. Results: We in...

Research paper thumbnail of REVersal of nEuromusculAr bLocking Agents in Patients Undergoing General Anaesthesia (REVEAL Study)

Journal of Clinical Medicine

Background: Neuromuscular blocking agent (NMBA) monitoring and reversals are key to avoiding resi... more 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. Methods: We conducted an electronic health record analysis study, including all adult surgical patients undergoing general anesthesia with orotracheal intubation, from January 2016 to December 2019, to describe changes and temporal trends of NMBAs and NMBA reversals administration. Results: From an initial population of 115,046 surgeries, we included 37,882 procedures, with 24,583 (64.9%) treated with spontaneous recovery from neuromuscular block and 13,299 (35.1%) with NMBA reversals. NMBA reversals use doubled over 4 years from 25.5% to 42.5%, mainly driven by sugammadex use, which increased from 17.8% to 38.3%. Rocuronium increased from 58.6% (2...

Research paper thumbnail of Implementing Artificial Intelligence

Critical Care Clinics, Apr 1, 2023

Research paper thumbnail of 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

International Journal of Medical Informatics

Research paper thumbnail of Artificial intelligence in the intensive care unit

Research paper thumbnail of Artificial Intelligence to Predict Mortality in Critically ill COVID-19 Patients Using Data from the First 24h: A Case Study from Lombardy Outbreak

Introduction: SARS-CoV-2 infection was first identified at the end of 2019 in China, and subseque... more Introduction: SARS-CoV-2 infection was first identified at the end of 2019 in China, and subsequently spread globally. 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, considering that baseline comorbidities, age, and patient conditions at admission have been associated to poorer outcomes. Supervised machine learning techniques are increasingly diffuse in clinical medicine and can predict mortality and test associations reaching high predictive performance. We assessed performances of a machine learning approach to predict mortality in COVID-19 patients admitted to ICU using data from the Lombardy ICU Network.Methods: this is a secondary analysis of prospectively collected data from Lombardy ICU network. To predict survival at 7-,14- and 28 days w...

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

Journal of Clinical Monitoring and Computing

The Lombardy SARS-CoV-2 outbreak in February 2020 represented the beginning of COVID-19 epidemic ... more 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. We conducted an electronic health record study, including all confirmed COVID-19 patients hospitalized from February 21st, 2020 to April 21st, 2020 as cases, and all non-COVID-19 patients hospitalized in the same wards from January 1st, 2018 to December 31st, 2018. All data on temperature, peripheral oxygen saturation, respiratory rate, arterial blood pressure, and heart rate were retrieved. Derangement of vital signs was defined according to predefined thresholds. 470 COVID-19 patients and 9241 controls were included. Cases were older than controls, with a median age of 79 vs 76 years in non survivors (p = < 0.002). Gender was not associated with mortality. Overall mortality in COVID-19 hospitalized patients was 18%, ranging from 1.4% in patients below 65 years to about 30% in patients over 65 years. Heat maps analysis demonstrated that COVID-19 patients had an increased frequency in episodes of compromised respiratory rate, acute desaturation, and fever. COVID-19 epidemic profoundly affected the incidence of severe derangements in vital signs in a large academic hospital. We validated heat maps as a method to analyze the clinical stability of hospitalized patients. This method may help to improve resource allocation according to patient characteristics.

Research paper thumbnail of Early prediction of SARS-CoV-2 reproductive number from environmental, atmospheric and mobility data: a supervised machine learning approach

International Journal of Medical Informatics, 2022

Research paper thumbnail of Barotrauma in mechanically-ventilated patients with coronavirus disease 2019: a survey of 38 hospitals in Lombardy, Italy

Minerva anestesiologica, 2020

BACKGROUND The aim was to describe the incidence and risk factors of barotrauma in patients with ... more 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 27th to May 2nd, 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. RESULTS The response rate was 38/61 (62%). The incidence of barotrauma was 145/2041 (7.1%; 95%-CI: 6.1-8.3%). Only a few cases occurred with ventilatory settings that may be considered non-protective such as a plateau airway pressure >35 cmH2O (2/113 [2%]), a driving airway pressure >15 cmH2O (30/113 [27%]), or a tidal volume >8 ml/kg of ideal body weight and a plateau airway pressure >30 cmH2O (12/134 [9%]). CONCLUSIONS within the limits of a survey, patients with COVID-19 might be at high risk f...