A Machine Learning Approach for Mortality Prediction in COVID-19 Pneumonia: Development and Evaluation of the Piacenza Score.

BACKGROUND: Several models have been developed to predict mortality in patients with COVID-19 pneumonia, but only a few have demonstrated enough discriminatory capacity. Machine learning algorithms represent a novel approach for the data-driven prediction of clinical outcomes with advantages over statistical modeling. OBJECTIVE: We aimed to develop a machine learning-based score-the Piacenza score-for 30-day mortality prediction in patients with COVID-19 pneumonia. METHODS: The study comprised 852 patients with COVID-19 pneumonia, admitted to the Guglielmo da Saliceto Hospital in Italy from February to November 2020. Patients' medical history, demographics, and clinical data were collected using an electronic health record. The overall patient data set was randomly split into derivation and test cohorts. The score was obtained through the naïve Bayes classifier and externally validated on 86 patients admitted to Centro Cardiologico Monzino (Italy) in February 2020. Using a forward-search algorithm, 6 features were identified: age, mean corpuscular hemoglobin concentration, PaO2/FiO2 ratio, temperature, previous stroke, and gender. The Brier index was used to evaluate the ability of the machine learning model to stratify and predict the observed outcomes. A user-friendly website was designed and developed to enable fast and easy use of the tool by physicians. Regarding the customization properties of the Piacenza score, we added a tailored version of the algorithm to the website, which enables an optimized computation of the mortality risk score for a patient when some of the variables used by the Piacenza score are not available. In this case, the naïve Bayes classifier is retrained over the same derivation cohort but using a different set of patient characteristics. We also compared the Piacenza score with the 4C score and with a naïve Bayes algorithm with 14 features chosen a priori. RESULTS: The Piacenza score exhibited an area under the receiver operating characteristic curve (AUC) of 0.78 (95% CI 0.74-0.84, Brier score=0.19) in the internal validation cohort and 0.79 (95% CI 0.68-0.89, Brier score=0.16) in the external validation cohort, showing a comparable accuracy with respect to the 4C score and to the naïve Bayes model with a priori chosen features; this achieved an AUC of 0.78 (95% CI 0.73-0.83, Brier score=0.26) and 0.80 (95% CI 0.75-0.86, Brier score=0.17), respectively. CONCLUSIONS: Our findings demonstrated that a customizable machine learning-based score with a purely data-driven selection of features is feasible and effective for the prediction of mortality among patients with COVID-19 pneumonia.

Scattering in Terms of Bohmian Conditional Wave Functions for Scenarios with Non-Commuting Energy and Momentum Operators.

Without access to the full quantum state, modeling quantum transport in mesoscopic systems requires dealing with a limited number of degrees of freedom. In this work, we analyze the possibility of modeling the perturbation induced by non-simulated degrees of freedom on the simulated ones as a transition between single-particle pure states. First, we show that Bohmian conditional wave functions (BCWFs) allow for a rigorous discussion of the dynamics of electrons inside open quantum systems in terms of single-particle time-dependent pure states, either under Markovian or non-Markovian conditions. Second, we discuss the practical application of the method for modeling light-matter interaction phenomena in a resonant tunneling device, where a single photon interacts with a single electron. Third, we emphasize the importance of interpreting such a scattering mechanism as a transition between initial and final single-particle BCWF with well-defined central energies (rather than with well-defined central momenta).

Education in anesthesia: three years of online logbook implementation in an Italian school.

BACKGROUND: The progress of physicians through residency training in anesthesiology can be monitored using an online logbook. The aim of this investigation was to establish how residents record clinical activities in their computerized web-based logbooks during their first years of anesthesiology training. METHODS: For this retrospective observational trial, the ESSE 3(©) digital registry of the University of Modena and Reggio Emilia, Italy was used to record all anesthesia-related activities performed by three consecutive year-groups of residents (Groups A, B and C) between 2009 and 2012. The ratio of activities to sessions was chosen as a surrogate measure of compliance. RESULTS: A total of 41,348 actions were analyzed. The ratio of activities to sessions showed a statistically significant decline for all activities concerning the perioperative management of anesthesia, with a steady reduction from the first to the last year-group (Group A 23.7, Group B 14.1 and Group C 2.2; p = 0.003). CONCLUSIONS: An online activities logbook is a useful tool for recording and assessing the clinical activities undertaken by each resident during residency training in anesthesiology.

Cost-effectiveness and diagnostic accuracy of focused cardiac ultrasound in the pre-participation screening of athletes: the SPORT-FoCUS study.

AIMS: The role of pre-participation screening (PPS) modalities in preventing sudden cardiac death (SCD) in athletes is debated due to a high false-positive rate. Focused cardiac ultrasound (FoCUS) has shown higher sensitivity and specificity, but its cost-effectiveness remains uncertain. This study aimed to determine the diagnostic performance and cost-effectiveness of FoCUS use in PPS. METHODS AND RESULTS: A total of 2111 athletes (77.4% male, mean age 24.9 ± 15.2years) underwent standardized family and medical history collection, physical examination, resting electrocardiography (ECG), FoCUS (10 min/5 views protocol), comprehensive echocardiography and exercise stress test. We prospectively evaluated three PPS incremental models: Model A, standardized medical history and physical examination Model B, Model A plus resting and stress ECG and Model C, Model B plus FoCUS (10 min/5 views protocol). We determined their incremental diagnostic accuracy and cost-effectiveness ratio. A total of 30 athletes were diagnosed with a cardiac condition associated with SCD: 3 were identified by Model A, 14 by Model B, and 13 athletes by Model C. The introduction of FoCUS markedly increased the sensitivity of PPS, compared with Model A and Model B (sensitivity 94% vs. 19% vs. 58% specificity 93% vs. 93% vs. 92%). The total screening costs were as follows: Model A 35.64 euros, Model B 87.68 euros, and Model C 120.89 euros. Considering the sole conditions at risk of SCD, the incremental cost-effectiveness ratio was 135.62 euros for Model B and 114.31 for Model C. CONCLUSIONS: The implementation of FoCUS into the PPS allows to identify a significantly greater number of athletes at risk of SCD and markedly lowers the false negative rate. Furthermore, the incorporation of FoCUS into the screening process has shown to be cost-effective.

A significant electrocardiography false-positive rate makes pre-participation screening (PPS) for sudden cardiac death (SCD) in athletes controversial. Focused cardiac ultrasound (FoCUS) may increase sensitivity and specificity however, its cost-effectiveness is unknown. This study evaluates the diagnostic performance and cost-effectiveness of FoCUS in PPS. Incorporating a simplified echocardiographic exam called FoCUS into PPS resulted in higher diagnostic reliability, with a lower rate of false negatives and a higher number of athletes at risk for SCD identified.The integration of FoCUS into the screening process resulted to be cost-effective in our athletes' cohort.

Description, Organization, and Individual Postgraduate Perspectives of One Italian School of Anesthesia and Intensive Care.

INTRODUCTION: The study aims to describe the organization of one accredited school of Anesthesia and Intensive Care of University of Modena and Reggio Emilia, Italy. The analysis of the post-graduation period aims to measure the time-to-first job, the perceived challenges, what postgraduate residents choose as first employ, and the overall satisfaction rating of a cohort of residents completing their training until 2017 with the usual and standard training program. METHODS: We collected organization and administrative records of the five-year program of the A-IC School of 4 cohorts of residents who joined from 2009 to 2012 and we performed a survey. We also analyzed the differences among school cohorts during the medical training. In the end, it was investigated as a reason to choose hub hospitals or not. RESULTS: The focus of the training activities revolved around the operating room with a mean of 30.41 ± 6.6 (sd), months followed by Intensive care with 17.29 ± 4.49 (sd) months. Although 7.5% of the respondents were not fully satisfied of the school's program, 89.7% of residents rated their training as adequate. In fact, 97.2% respondents reported they could overcome the professional challenges they faced after graduation. The multiple variables logistic regression showed a correlation among working in hub hospitals and training performed in university hospitals with a p value of 0.015. CONCLUSION: This paper describes the postgraduation period. This point should be examined as an integral part of the accreditation procedure. Knowing the satisfaction rate, perception autonomy, and which type of hospitals are preferred can measure the education training capacity of a postgraduation school.

How Artificial Intelligence and New Technologies Can Help the Management of the COVID-19 Pandemic.

The COVID-19 pandemic has worked as a catalyst, pushing governments, private companies, and healthcare facilities to design, develop, and adopt innovative solutions to control it, as is often the case when people are driven by necessity. After 18 months since the first case, it is time to think about the pros and cons of such technologies, including artificial intelligence-which is probably the most complex and misunderstood by non-specialists-in order to get the most out of them, and to suggest future improvements and proper adoption. The aim of this narrative review was to select the relevant papers that directly address the adoption of artificial intelligence and new technologies in the management of pandemics and communicable diseases such as SARS-CoV-2: environmental measures; acquisition and sharing of knowledge in the general population and among clinicians; development and management of drugs and vaccines; remote psychological support of patients; remote monitoring, diagnosis, and follow-up; and maximization and rationalization of human and material resources in the hospital environment.

Prediction of 28-day mortality in critically ill patients with COVID-19: Development and internal validation of a clinical prediction model.

BACKGROUND: COVID-19 pandemic has rapidly required a high demand of hospitalization and an increased number of intensive care units (ICUs) admission. Therefore, it became mandatory to develop prognostic models to evaluate critical COVID-19 patients. MATERIALS AND METHODS: We retrospectively evaluate a cohort of consecutive COVID-19 critically ill patients admitted to ICU with a confirmed diagnosis of SARS-CoV-2 pneumonia. A multivariable Cox regression model including demographic, clinical and laboratory findings was developed to assess the predictive value of these variables. Internal validation was performed using the bootstrap resampling technique. The model's discriminatory ability was assessed with Harrell's C-statistic and the goodness-of-fit was evaluated with calibration plot. RESULTS: 242 patients were included [median age, 64 years (56-71 IQR), 196 (81%) males]. Hypertension was the most common comorbidity (46.7%), followed by diabetes (15.3%) and heart disease (14.5%). Eighty-five patients (35.1%) died within 28 days after ICU admission and the median time from ICU admission to death was 11 days (IQR 6-18). In multivariable model after internal validation, age, obesity, procaltitonin, SOFA score and PaO2/FiO2 resulted as independent predictors of 28-day mortality. The C-statistic of the model showed a very good discriminatory capacity (0.82). CONCLUSIONS: We present the results of a multivariable prediction model for mortality of critically ill COVID-19 patients admitted to ICU. After adjustment for other factors, age, obesity, procalcitonin, SOFA and PaO2/FiO2 were independently associated with 28-day mortality in critically ill COVID-19 patients. The calibration plot revealed good agreements between the observed and expected probability of death.

[Late occurrence of pulmonary embolsim in SARS-CoV-2 pneumonia: a case series]. / L'embolia polmonare come complicanza tardiva della polmonite da SARS-CoV-2: una serie di casi clinici.

Several studies suggested that the acute phase of SARS-CoV-2 infection may be associated with a hypercoagulable state and increased risk for venous thromboembolism but the incidence of thrombotic complications in the late phase of the disease is currently unknown. The present article describes three cases of patients with SARS-CoV-2 pneumonia and late occurrence of pulmonary embolism. Case 1: a 57-year-old man diagnosed with pulmonary embolism and type B aortic dissection after 12 days from SARS-CoV-2 pneumonia. Laboratory panel at the time of pulmonary embolism showed no signs of ongoing inflammation but only an elevated D-dimer. Case 2: a 76-year-old man with a diagnosis of SARS-CoV-2 pneumonia followed by pulmonary embolism 20 days later, high-resolution computed tomography on that time showed a partial resolution of crazy paving consolidation. Case 3: a 77-year-old man with SARS-CoV-2 pneumonia who developed a venous thromboembolic event despite thromboprophylaxis with low molecular weight heparin. Also in this patients no markers of inflammation were present at the time of complication.The present cases raise the possibility that in SARS-CoV-2 infection the hypercoagulable state may persist over the active inflammation phase and cytokine storm. These findings suggest a role for medium-long term therapeutic anticoagulation started at the time of SARS-CoV-2 pneumonia diagnosis.

[Thromboembolic risk in atrial fibrillation: new clinical perspectives]. / Il rischio tromboembolico della fibrillazione atriale: un tema sempre di attualità.

Prevention of embolic complications is one of the major goals of therapeutic strategy for atrial fibrillation. The embolic risk is related to the presence and nature of underlying heart disease; furthermore cerebrovascular accidents associated with atrial fibrillation occur in a higher percentage in the elderly, representing 6.7% of the total number of cerebrovascular accidents in the 50-to-59-year-old population and 36% in the 80-to-89-year-old population. Anticoagulation with oral vitamin K antagonists, such as warfarin, is highly effective in reducing ischemic stroke in these patients, but some concerns exist about its safety and tolerability, especially in older patients, because of the hemorrhagic risk. Several risk stratification schemes have been developed to evaluate the thromboembolic/hemorrhagic risk profile in the individual patient, but they have shown intrinsic limitations. In this short report we compare different risk stratification schemes to predict thromboembolic risk in atrial fibrillation patients, and we discuss the application of these schemes into clinical practice and the effects on clinical events.