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Triage is crucial for patient's management and estimation of the required intensive care unit (ICU) beds is fundamental for health systems during the COVID-19 pandemic. We assessed whether chest computed tomography (CT) of COVID-19 pneumonia has an incremental role in predicting patient's admission to ICU. We performed volumetric and texture analysis of the areas of the affected lung in CT of 115 outpatients with COVID-19 infection presenting to the emergency room with dyspnea and unresponsive hypoxyemia. Admission blood laboratory including lymphocyte count, serum lactate dehydrogenase, D-dimer and C-reactive protein and the ratio between the arterial partial pressure of oxygen and inspired oxygen were collected. By calculating the areas under the receiver-operating characteristic curves (AUC), we compared the performance of blood laboratory-arterial gas analyses features alone and combined with the CT features in two hybrid models (Hybrid radiological and Hybrid radiomics)for predicting ICU admission. Following a machine learning approach, 63 patients were allocated to the training and 52 to the validation set. Twenty-nine (25%) of patients were admitted to ICU. The Hybrid radiological model comprising the lung %consolidation performed significantly (p = 0.04) better in predicting ICU admission in the validation (AUC = 0.82; 95% confidence interval 0.73–0.97) set than the blood laboratory-arterial gas analyses features alone (AUC = 0.71; 95% confidence interval 0.56–0.86). A risk calculator for ICU admission was derived and is available at: https://github.com/cgplab/covidapp. The volume of the consolidated lung in CT of patients with COVID-19 pneumonia has a mild but significant incremental value in predicting ICU admission. [ABSTRACT FROM AUTHOR]

Water immersion during labour and birth has become increasingly popular and widespread in many countries, in particular in midwifery-led care settings. Nevertheless, there is a dearth of quality data about waterbirth, with currently available findings mostly arising from observational studies and case series. The lack of high-quality evidence and the controversial results reported by different studies determined a "behavioral gap" without clearly objective, consistent indications allowing for a sound and evidence-based decision making process. Although water immersion in the first stage of labour is generally considered a safe and cost-effective method of pain management for women in labor, concerns still linger as to the safety of immersion during the second stage of labor and delivery, particularly in terms of neonatal risks and medico-legal implications.

The Italian National Outcome Programme has assessed the performance of Italian hospitals regarding several clinical performance indicators, including 30-daymortality after admission for acute myocardial infarction. Risk adjustment was obtained using demographic and comorbidity data based on the hospital discharge databases in the index admission, as well as in those of the previous two years. Noticeably, the ICD-9-CM 410.7* classification coding for NSTEMI (Non-ST elevation myocardial infarction)myocardial infarction, i.e. the less severe form, was not used, due to known variability in its use. We found that hospital-specific adjusted relative risk of death versus the national mean, as computed by the programme, is negatively associated with the proportion of NSTEMI infarctions at each Tuscan and Florentine hospital, coherently with the hypothesis of a selection by the emergency network, which addresses STEMI (ST elevation myocardial infarction) patients to hospitals offering haemodynamic laboratory with reperfusive services. Individual level clinical data of 3,200 patients in the AMI-Florence study in the period April 2008-March 2010 found that ICD-9-CM410.7* is underused. The analysis based on hospital discharge diagnoses (410.7* vs. other 410* codes) cannot explain differences in mortality among Florentine hospitals, as opposed to the use of a classification of myocardial infarction type (STEMI vs. NSTEMI) based on clinical data collected in AMI-Florence.