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Timely identification of COVID-19 patients at high risk of mortality can significantly improve patient management and resource allocation within hospitals. This study seeks to develop and validate a data-driven personalized mortality risk calculator for hospitalized COVID-19 patients. De-identified data was obtained for 3,927 COVID-19 positive patients from six independent centers, comprising 33 different hospitals. Demographic, clinical, and laboratory variables were collected at hospital admission. The COVID-19 Mortality Risk (CMR) tool was developed using the XGBoost algorithm to predict mortality. Its discrimination performance was subsequently evaluated on three validation cohorts. The derivation cohort of 3,062 patients has an observed mortality rate of 26.84%. Increased age, decreased oxygen saturation (≤ 93%), elevated levels of C-reactive protein (≥ 130 mg/L), blood urea nitrogen (≥ 18 mg/dL), and blood creatinine (≥ 1.2 mg/dL) were identified as primary risk factors, validating clinical findings. The model obtains out-of-sample AUCs of 0.90 (95% CI, 0.87–0.94) on the derivation cohort. In the validation cohorts, the model obtains AUCs of 0.92 (95% CI, 0.88–0.95) on Seville patients, 0.87 (95% CI, 0.84–0.91) on Hellenic COVID-19 Study Group patients, and 0.81 (95% CI, 0.76–0.85) on Hartford Hospital patients. The CMR tool is available as an online application at covidanalytics.io/mortality_calculator and is currently in clinical use. The CMR model leverages machine learning to generate accurate mortality predictions using commonly available clinical features. This is the first risk score trained and validated on a cohort of COVID-19 patients from Europe and the United States. [ABSTRACT FROM AUTHOR]

Objectives: Work-related musculoskeletal disorders (MSD) are quite frequent in healthcare workers (HCWs), but data about MSD in home-based healthcare workers (HHWs) are lacking. In this study we describe the prevalence of MSD among Italian HHWs.Material and Methods: A case-control study was carried out among 300 random-selected female HCWs, the sample comprising 100 HHWs, 100 HCWs with a low exposure to patient handling (MAPO - Movimentazione e Assistenza Pazienti Ospedalizzati - Movement and Assistance of Hospital Patients index 0-5) and 100 HCWs with high exposure to patient handling (MAPO index ≥ 5.01). As a negative control group, 200 visual display unit workers were also randomly selected. Musculoskeletal disorder cases were collected using a standardized case definition. A multivariate logistic regression analysis was performed comparing the MSD prevalence in the 4 groups.Results: The overall prevalence of MSD was 17% in the reference group and 28.3% for HCWs. HHWs and HCWs with MAPO index ≥ 5.01 had similar prevalence of neck pain (9% and 11%, respectively), whereas lumbosacral pain prevalence was higher in the HHWs group (31%), with similar results in residential HCWs groups (21% in MAPO index 0-5 group and 25% in MAPO index ≥ 5.01 group). HCWs of group MAPO index ≥ 5.01 and HHWs showed the higher prevalence of upper limb complaints, with a prevalence of 20% and 10%, respectively. In multivariate regression analysis, prevalence of MSD complaints was quite similar in HHWs (adjusted odds ratio (ORadj) = 2.335, 95% confidence interval (CI): 1.318-4.138) and in HCWs of the group MAPO ≥ 5.01 (ORadj = 2.729, 95% CI: 1.552-4.797).Conclusions: The prevalence of MSD in the examined HCWs was relatively high, with HHWs appearing as a particularly high-risk group for lumbosacral back pain. In higher exposed HCWs, upper-limb symptoms were particularly frequent, probably reflecting the different tasks required to manage residential and homebased patients. In conclusion, this study reaffirms the high prevalence of MSD in HHWs, seemly similar to high-risk residential groups. Int J Occup Med Environ Health 2017;30(2):291-304. [ABSTRACT FROM AUTHOR]

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