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1.
Sci Rep ; 11(1): 21136, 2021 10 27.
Article in English | MEDLINE | ID: covidwho-1493228

ABSTRACT

The COVID-19 pandemic is impressively challenging the healthcare system. Several prognostic models have been validated but few of them are implemented in daily practice. The objective of the study was to validate a machine-learning risk prediction model using easy-to-obtain parameters to help to identify patients with COVID-19 who are at higher risk of death. The training cohort included all patients admitted to Fondazione Policlinico Gemelli with COVID-19 from March 5, 2020, to November 5, 2020. Afterward, the model was tested on all patients admitted to the same hospital with COVID-19 from November 6, 2020, to February 5, 2021. The primary outcome was in-hospital case-fatality risk. The out-of-sample performance of the model was estimated from the training set in terms of Area under the Receiving Operator Curve (AUROC) and classification matrix statistics by averaging the results of fivefold cross validation repeated 3-times and comparing the results with those obtained on the test set. An explanation analysis of the model, based on the SHapley Additive exPlanations (SHAP), is also presented. To assess the subsequent time evolution, the change in paO2/FiO2 (P/F) at 48 h after the baseline measurement was plotted against its baseline value. Among the 921 patients included in the training cohort, 120 died (13%). Variables selected for the model were age, platelet count, SpO2, blood urea nitrogen (BUN), hemoglobin, C-reactive protein, neutrophil count, and sodium. The results of the fivefold cross-validation repeated 3-times gave AUROC of 0.87, and statistics of the classification matrix to the Youden index as follows: sensitivity 0.840, specificity 0.774, negative predictive value 0.971. Then, the model was tested on a new population (n = 1463) in which the case-fatality rate was 22.6%. The test model showed AUROC 0.818, sensitivity 0.813, specificity 0.650, negative predictive value 0.922. Considering the first quartile of the predicted risk score (low-risk score group), the case-fatality rate was 1.6%, 17.8% in the second and third quartile (high-risk score group) and 53.5% in the fourth quartile (very high-risk score group). The three risk score groups showed good discrimination for the P/F value at admission, and a positive correlation was found for the low-risk class to P/F at 48 h after admission (adjusted R-squared = 0.48). We developed a predictive model of death for people with SARS-CoV-2 infection by including only easy-to-obtain variables (abnormal blood count, BUN, C-reactive protein, sodium and lower SpO2). It demonstrated good accuracy and high power of discrimination. The simplicity of the model makes the risk prediction applicable for patients in the Emergency Department, or during hospitalization. Although it is reasonable to assume that the model is also applicable in not-hospitalized persons, only appropriate studies can assess the accuracy of the model also for persons at home.


Subject(s)
COVID-19/mortality , Machine Learning , Pandemics , SARS-CoV-2 , Aged , Aged, 80 and over , Blood Cell Count , Blood Chemical Analysis , COVID-19/blood , Cohort Studies , Female , Hospital Mortality , Humans , Male , Middle Aged , Models, Statistical , Multivariate Analysis , Oxygen/blood , Pandemics/statistics & numerical data , ROC Curve , Risk Factors , Rome/epidemiology
2.
Med Lav ; 112(4): 320-326, 2021 Aug 26.
Article in English | MEDLINE | ID: covidwho-1377151

ABSTRACT

BACKGROUND: Occupational hand dermatitis (OHD) is a skin disease occurring on employees' hands in certain jobs. Little is known about prevalence, incidence and characteristics of this adverse skin reaction and its associated risk factors during COVID-19 pandemic. To evaluate both prevalence and incidence of OHD and associated risk factors in Italian clinicians. METHODS: A cross-sectional study was performed using a self-report questionnaire. RESULTS: Two hundred and thirty clinicians responded to the survey and 82% of responders did not report previous OHD history before the COVID-19 pandemic. Daily use of gloves was reported by 80% of responders. OHD prevalence was 18%, while incidence was 80%. We found a protective effect on symptom occurrence for vinyl/nitrile gloves if the time with gloves was ≥ 6 hours per day. CONCLUSIONS: This survey reveals a high OHD incidence in an Italian population of clinicians. Furthermore, wearing vinyl/nitrile gloves for at least 6 hours a day had a protective effect on symptom onset.


Subject(s)
COVID-19 , Dermatitis, Occupational , Hand Dermatoses , Cross-Sectional Studies , Dermatitis, Occupational/epidemiology , Dermatitis, Occupational/etiology , Gloves, Protective , Hand Dermatoses/epidemiology , Hand Dermatoses/etiology , Hospitals , Humans , Pandemics , SARS-CoV-2 , Surveys and Questionnaires
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