Clinical Presentation of the SARS-CoV-2 Virus Infection and Predictive Validity of the PCR Test in Primary Health Care Worker Patients of the Spanish National Health System

Abstract

Background: Despite the impact that the SARS-CoV-2 virus infection has presented in Spain, data on the diagnostic capacity of the symptoms associated with this infection are limited, especially among patients with mild symptoms and who are detected in the primary care field (PC). The objective of the present study was to know the associated symptoms and their predictive criterial validity in SARS-CoV-2 infection among professionals working in PC. Methods: A cross-sectional, multicenter study was carried out in the Spanish National Health System, through an epidemiological survey directed to patients who underwent the PCR test for SARS-CoV-2 in the PC setting. Results: A total of 1612 patients participated, of which 86.6% were PC healthcare professionals, and of these, 67.4% family doctors. Hyposmia, with a sensitivity of 42.69% (95% CI: 37.30–48.08) and a specificity of 95.91% (95% CI: 94.78–97.03), and ageusia with a sensitivity of 39.47% (34.15–44.80) and a specificity of 95.20% (93.98–96.41) were the symptoms with the highest criteria validity indexes. Conclusions: This study identifies the specific symptoms of loss of smell or taste as the most frequently associated with SARS-CoV-2 infection, essential in the detection of COVID-19 given its high frequency and predictive capacity.

Machine learning and pre-exposure prophylaxis: A survey

“…Machine learning applications have been extensively utilized on clinical features for cancer and tumor prognosis prediction, in lung cancer and breast cancer. The authors in [42] suggested that Logistic Regression (LR), Gaussian Naive Bayes (GNB), Decision Trees (DT), K-nearest Neighbor (KNN), extreme Gradient Boosting (XGB), Random Forest (RF), and others when used on data from clinical care of HIV patients, they showed to be effective in modeling viral load and CD4 related outcomes. In addition, Xtreme Gradient Boost machine learning [43]- [47] has shown to be effective in prediction of the hospitalization outcome of HIV/AIDs patients with marneffei infection.…”

“…Researchers in [62] used machine learning [63] in risk score identification. ML algorithms employed on data from routine clinical care of HIV patients such as Logistic Regression (LR) [64], Gaussian Naive Bayes (GNB) [65], Decision Trees (DT) [66], K-nearest Neighbor (KNN) [67], eXtreme Gradient Boosting (XGB) [68], Random Forest (RF), and others were effective to model viral load and CD4-related outcomes [42].…”