Added value of chest CT in a machine learning-based prediction model to rule out COVID-19 before inpatient admission: A retrospective university network study.

PURPOSE: During the coronavirus disease 2019 (COVID-19) pandemic, hospitals still face the challenge of timely identification of infected individuals before inpatient admission. An artificial intelligence approach based on an established clinical network may improve prospective pandemic preparedness. METHOD: Supervised machine learning was used to construct diagnostic models to predict COVID-19. A pooled database was retrospectively generated from 4437 participant data that were collected between January 2017 and October 2020 at 12 German centers that belong to the radiological cooperative network of the COVID-19 (RACOON) consortium. A total of 692 (15.6 %) participants were COVID-19 positive according to the reference of the reverse transcription-polymerase chain reaction test. The diagnostic models included chest CT features (model R), clinical examination and laboratory test features (model CL), or all three feature categories (model RCL). Performance outcomes included accuracy, sensitivity, specificity, negative and positive predictive value, and area under the receiver operating curve (AUC). RESULTS: Performance of predictive models improved significantly by adding chest CT features to clinical evaluation and laboratory test features. Without (model CL) and with inclusion of chest CT (model RCL), sensitivity was 0.82 and 0.89 (p < 0.0001), specificity was 0.84 and 0.89 (p < 0.0001), negative predictive value was 0.96 and 0.97 (p < 0.0001), AUC was 0.92 and 0.95 (p < 0.0001), and proportion of false negative classifications was 2.6 % and 1.7 % (p < 0.0001), respectively. CONCLUSIONS: Addition of chest CT features to machine learning-based predictive models improves the effectiveness in ruling out COVID-19 before inpatient admission to regular wards.

The Role of Serum Cathepsin S as Predictors of COVID-19 severity

COVID-19 is a rapidly growing pandemic with its first case identified during December 2019 in Wuhan, Hubei Province, China. Due to the rampant rise in the number of cases in China and globally, WHO declared COVID-19 as a pandemic on 11th March 2020. The disease is transmitted via respiratory droplets of infected patients during coughing or sneezing and affects primarily the lung parenchyma. The spectrum of clinical manifestations can be seen in COVID-19 patients ranging from asymptomatic infections to severe disease resulting in mortality. Although respiratory involvement is most common in COVID-19 patients, the virus can affect other organ systems as well. The systemic inflammation induced by the disease along with multisystem expression of Angiotensin Convertin Enzyme 2 (ACE2), a receptor which allows viral entry into cells, explains the manifestation of extra-pulmonary symptoms affecting the gastrointestinal, cardiovascular, hematological, renal, musculoskeletal, and endocrine system. To date, many biomarkers reflecting the main pathophysiological characteristics of the disease have been identified and associated with the risk of developing severe disease. Proteolytic enzymes, or proteases, are known to play important roles in the maintenance of pulmonary homeostasis. However, during disease, proteolytic activity can become dysregulated and cause damage to the lung, contributing to the pathology of conditions like cystic fibrosis, chronic obstructive pulmonary disease, asthma, pulmonary fibrosis and ARDS. we first evaluated the status of CTSS in the context of ARDS and models of ARDS. These investigations revealed that CTSS levels and activity were elevated in the lungs of patients with ARDS, and that elevated CTSS activity was also detectable in the plasma of these patients. Altogether, these findings support a role for CTSS in the pathogenesis of ARDS and the fact that Corona virus infects the respiratory system and the severity of the infection increases with the increase in the severity of the inflammation.

D-dimer testing in clinical practice in the era of COVID-19.

D-dimer is a fragment of crosslinked fibrin resulting from plasmin cleavage of fibrin clots and hence an indirect biomarker of the hemostatic system activation. Early in the coronavirus disease 2019 (COVID-19) pandemic, several studies described coagulation disorders in affected patients, including high D-dimer levels. Consequently, D-dimer has been widely used in not-yet-approved indications. Ruling out pulmonary embolism and deep vein thrombosis in patients with low or intermediate clinical suspicion is the main application of D-dimer. D-dimer is also used to estimate the risk of venous thromboembolism recurrence and is included in the ISTH algorithm for the diagnosis of disseminated intravascular coagulation. Finally, numerous studies identified high D-dimer levels as a biomarker of poor prognosis in hospitalized patients with COVID-19. This report focuses on validated applications of D-dimer testing in patients with and without COVID-19.

Utilizing Clustering FCM And Contrast Based Enhancement to Isolate Covid-19 Infected Regions in CT Lung Images

Covid-19 disease that directly affecting lungs is an acute disease caused death of many people around word, so the early detecting of it and asses the relative ratio of the lung infection is a vital need. In this work, Histogram based contrast adjustment was implemented to enhance four lung abnormal CT scan images to highlight the abnormal regions within the experimental images. Fuzzy c-mean algorithm then was applied to segment the images in order to detect and isolate the infected regions. Besides, several morphological operations were employed to extract the refined infected Covid-19 areas effectively with accuracy of 96%.

Sustainable Estimation of Cardiovascular Risk with the Predictive Gaziano's Tables without Laboratory

Background: identifying cardiovascular risk factors is very important, because by taking actions to counteract them, the probability of presenting cardiovascular and cerebrovascular diseases can decrease significantly. Objective: to demonstrate the feasibility of using the Gaziano's tables without a laboratory to estimate cardiovascular risk. Method: 72 health workers linked to the University of Medical Sciences of Camaguey were interviewed during November 2021, in addition, their blood pressure, weight and height were measured and they were asked in the survey: age, if they were diabetic and if they smoked. These people underwent Cronbach's Alpha reliability test. They were asked if they had other modifiable habits (frequency of physical exercise, consumption of alcoholic beverages, if they have snacks between meals and if they add salt to them). Cardiovascular risk was determined with the Gaziano's tables without laboratory. Results: 21 % of the interviewees presented high and very high risk. Of the study participants, 40 % declared they were hypertensive. The mean body mass index was 29.3 +/- 4 kg/m(2) and there was a correlation between this and age, with high and very high risks. Conclusions: Gaziano's predictive tables without laboratory were feasible to apply, their use could be extended to the first level of health care, as it is a sustainable, non-invasive and fast method in times of the COVID-19 pandemic. Knowledge of its use in undergraduate and postgraduate teaching should be transmitted.

Machine Learning-Based Prediction Models for Depression Symptoms Among Chinese Healthcare Workers During the Early COVID-19 Outbreak in 2020: A Cross-Sectional Study.

Background: The 2019 novel coronavirus (COVID-19)-related depression symptoms of healthcare workers have received worldwide recognition. Although many studies identified risk exposures associated with depression symptoms among healthcare workers, few have focused on a predictive model using machine learning methods. As a society, governments, and organizations are concerned about the need for immediate interventions and alert systems for healthcare workers who are mentally at-risk. This study aims to develop and validate machine learning-based models for predicting depression symptoms using survey data collected during the COVID-19 outbreak in China. Method: Surveys were conducted of 2,574 healthcare workers in hospitals designated to care for COVID-19 patients between 20 January and 11 February 2020. The patient health questionnaire (PHQ)-9 was used to measure the depression symptoms and quantify the severity, a score of ≥5 on the PHQ-9 represented depression symptoms positive, respectively. Four machine learning approaches were trained (75% of data) and tested (25% of data). Cross-validation with 100 repetitions was applied to the training dataset for hyperparameter tuning. Finally, all models were compared to evaluate their predictive performances and screening utility: decision tree, logistics regression with least absolute shrinkage and selection operator (LASSO), random forest, and gradient-boosting tree. Results: Important risk predictors identified and ranked by the machine learning models were highly consistent: self-perceived health status factors always occupied the top five most important predictors, followed by worried about infection, working on the frontline, a very high level of uncertainty, having received any form of psychological support material and having COVID-19-like symptoms. The area under the curve [95% CI] of machine learning models were as follows: LASSO model, 0.824 [0.792-0.856]; random forest, 0.828 [0.797-0.859]; gradient-boosting tree, 0.829 [0.798-0.861]; and decision tree, 0.785 [0.752-0.819]. The calibration plot indicated that the LASSO model, random forest, and gradient-boosting tree fit the data well. Decision curve analysis showed that all models obtained net benefits for predicting depression symptoms. Conclusions: This study shows that machine learning prediction models are suitable for making predictions about mentally at-risk healthcare workers predictions in a public health emergency setting. The application of multidimensional machine learning models could support hospitals' and healthcare workers' decision-making on possible psychological interventions and proper mental health management.

Should COVID-19 symptoms be used to cohort patients in the emergency department? A retrospective analysis.

OBJECTIVE: To determine how cohorting patients based on presenting complaints affects risk of nosocomial infection in crowded Emergency Departments (EDs) under conditions of high and low prevalence of COVID-19. METHODS: This was a retrospective analysis of presenting complaints and PCR tests collected during the COVID-19 epidemic from 4 EDs from a large hospital system in Bronx County, NY, from May 1, 2020 to April 30, 2021. Sensitivity, specificity, positive and negative predictive value (PPV, NPV) were calculated for a symptom screen based on the CDC list of COVID-19 symptoms: fever/chills, shortness of breath/dyspnea, cough, muscle or body ache, fatigue, headache, loss of taste or smell, sore throat, nasal congestion/runny nose, nausea, vomiting, and diarrhea. PPV was calculated for varying values of prevalence. RESULTS: There were 80,078 visits with PCR tests. The sensitivity of the symptom screen was 64.7% (95% CI: 63.6, 65.8), specificity 65.4% (65.1, 65.8). PPV was 16.8% (16.5, 17.0) and NPV was 94.5% (94.4, 94.7) when the observed prevalence of COVID-19 in the ED over the year was 9.7%. The PPV of fever/chills, cough, body and muscle aches and nasal congestion/runny nose were each approximately 25% across the year, while diarrhea, nausea, vomiting and headache were less predictive, (PPV 4.7%-9.6%) The combinations of fever/chills, cough, muscle/body aches, and shortness of breath had PPVs of 40-50%. The PPV of the screen varied from 3.7% (3.6, 3.8) at 2% prevalence of COVID-19 to 44.3% (44.0, 44.7) at 30% prevalence. CONCLUSION: The proportion of patients with a chief complaint of COVID-19 symptoms and confirmed COVID-19 infection was exceeded by the proportion without actual infection. This was true when prevalence in the ED was as high as 30%. Cohorting of patients based on the CDC's list of COVID-19 symptoms will expose many patients who do not have COVID-19 to risk of nosocomially acquired COVID-19. EDs should not use the CDC list of COVID-19 symptoms as the only strategy to minimize exposure.

Efficiencies of Laboratory Parameters in Covid-19 Patients Follow up: Turkish Journal of Medical and Surgical Intensive Care Medicine

Introduction: Diagnostic efficiencies of laboratory parameters used in COVID-19 patients and their association with disease severity were evaluated. Materials and Methods: Laboratory parameters of COVID-19 patients hospitalized in Dr. Lütfi Kırdar Kartal City Hospital between March and August 2020 were evaluated. The patients were grouped as non-severe and severe according to the interim guidance of the World Health Organization (WHO). The diagnostic performances of NLR, D-dimer, CRP, procalcitonin, IL-6, LDH, and ferritin in discrimination of severe cases were evaluated by Receiver operator’s characteristics (ROC) analysis. Generalized lineer model Analysis (GLM) was performed with mortality as a dependent variable and age, gender, NLR, D-dimer, CRP, Procalcitonin, IL-6, LDH, and ferritin as an independent variables. Results: A total of 257 patients were evaluated and there was a significant difference between non-severe and severe cases in terms of NLR, D-dimer, CRP, Procalcitonin, IL-6, LDH, and Ferritin values. All the parameters showed comparable performances in discriminating severe disease;D-dimer with the least (AUC 73.5%), and NLR with the highest (AUC 80.7%) efficiency. Values above 4.5 for NLR, 930 ug/L for D-dimer, 64 mg/L for CRP, 0.136 ug/L for procalcitonin, 44.3 pg/mL for IL-6, 304 IU for LDH, and 312 ug/L for ferritin were associated with severe disease. Contribution of age, NLR, D-dimer, and CRP were found significant on the model. Conclusions: NLR, D-dimer, CRP, procalcitonin, IL-6, LDH, and ferritin showed comparable performances in discriminating severe cases with predefined cut-offs. Age, NLR, D-dimer, and CRP may be considered as predictors of mortality in COVID-19 patients.

Efficiencies of Laboratory Parameters in Covid-19 Patients Follow up

Introduction: Diagnostic efficiencies of laboratory parameters used in COVID-19 patients and their association with disease severity were evaluated. Materials and Methods: Laboratory parameters of COVID-19 patients hospitalized in Dr. Lutfi Kirdar Kartal City Hospital between March and August 2020 were evaluated. The patients were grouped as non-severe and severe according to the interim guidance of the World Health Organization (WHO). The diagnostic performances of NLR, D-dieter, CRP, procalcitonin, IL-6, LDH, and ferritin in discrimination of severe cases were evaluated by Receiver operator's characteristics (ROC) analysis. Generalized lineer model Analysis (GLM) was performed with mortality as a dependent variable and age, gender, NLR, D-dimer, CRP, Procalcitonin, IL-6, LDH, and ferritin as an independent variables. Results: A total of 257 patients were evaluated and there was a significant difference between non-severe and severe cases in terms of NLR, D-dimer, CRP, Procalcitonin, IL-6, LDH, and Ferritin values. All the parameters showed comparable perfoluiances in discriminating severe disease;D-dimer with the least (AUC 73.5%), and NLR with the highest (AUC 80.7%) efficiency. Values above 4.5 for NLR, 930 ug/L for D-dimer, 64 mg/L for CRP, 0.136 ug/L for procalcitonin, 44.3 pg/mL for IL-6, 304 IU for LDH, and 312 ug/L for ferritin were associated with severe disease. Contribution of age, NLR, D-dimer, and CRP were found significant on the model. Conclusions: NLR, D-dimer, CRP, procalcitonin, IL-6, LDH, and ferritin showed comparable performances in discriminating severe cases with predefined cut-offs. Age, NLR, D-dimer, and CRP may be considered as predictors of mortality in COVID-19 patients.

Prognostic Value of Inflammatory Biomarkers in Patients with Severe COVID-19: A Single-Center Retrospective Study.

BACKGROUND: The current knowledge about novel coronavirus-2019 (COVID-19) indicates that the immune system and inflammatory response play a crucial role in the severity and prognosis of the disease. In this study, we aimed to investigate prognostic value of systemic inflammatory biomarkers including C-reactive protein/albumin ratio (CAR), prognostic nutritional index (PNI), neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR), and platelet-to-lymphocyte ratio (PLR) in patients with severe COVID-19. METHODS: This single-center, retrospective study included a total of 223 patients diagnosed with severe COVID-19. Primary outcome measure was mortality during hospitalization. Multivariate logistic regression analyses were performed to identify independent predictors associated with mortality in patients with severe COVID-19. Receiver operating characteristic (ROC) curve was used to determine cut-offs, and area under the curve (AUC) values were used to demonstrate discriminative ability of biomarkers. RESULTS: Compared to survivors of severe COVID-19, non-survivors had higher CAR, NLR, and PLR, and lower LMR and lower PNI (P < .05 for all). The optimal CAR, PNI, NLR, PLR, and LMR cut-off values for detecting prognosis were 3.4, 40.2, 6. 27, 312, and 1.54 respectively. The AUC values of CAR, PNI, NLR, PLR, and LMR for predicting hospital mortality in patients with severe COVID-19 were 0.81, 0.91, 0.85, 0.63, and 0.65, respectively. In ROC analysis, comparative discriminative ability of CAR, PNI, and NLR for hospital mortality were superior to PLR and LMR. Multivariate analysis revealed that CAR (⩾0.34, P = .004), NLR (⩾6.27, P = .012), and PNI (⩽40.2, P = .009) were independent predictors associated with mortality in severe COVID-19 patients. CONCLUSIONS: The CAR, PNI, and NLR are independent predictors of mortality in hospitalized severe COVID-19 patients and are more closely associated with prognosis than PLR or LMR.

Self-sampling for high-risk human papillomavirus as a follow-up alternative after treatment of high-grade cervical intraepithelial neoplasia.

Women treated for high-grade cervical-intraepithelial-neoplasia (CIN) require long-term follow-up with high-risk human-papillomavirus (HPV) testing. Self-sampling for HPV is well-accepted among these patients, but its role in follow-up for this group requires investigation. The present study examined how well HPV findings from self-sampled vaginal (VSS) and urine specimens correctly identified women from this cohort with recurrent CIN2+ compared with samples collected by clinicians. At 1st post-conization follow-up, 531 patients (99.8% participation) gave urine samples, performed VSS, underwent colposcopy with punch biopsy of visible lesions and clinician-collected cervical sampling for HPV analysis and liquid-based cytology. A total of 113 patients with positive HPV and/or abnormal cytology at 1st follow-up underwent 2nd follow-up. At 1st follow-up, all patients with recurrent CIN3 had positive HPV results by all methods. Clinician sampling and VSS revealed HPV16 positivity in 50% of recurrent cases and urine sampling revealed HPV16 positivity in 25% of recurrent cases. At 2nd follow-up, all 7 newly-detected CIN2/3 recurrences were associated with HPV positivity on VSS and clinician-samples. Only clinician-collected samples detected HPV positivity for two adenocarcinoma-in-situ recurrences, and both were HPV18 positive. A total of 77 patients had abnormal cytology at 1st follow-up, for which HPV positivity via VSS yielded highest sensitivity. The HPV findings were positive from VSS in 12 patients with high-grade squamous-intraepithelial-lesions (HSIL), and 11 patients with HSIL had positive HPV findings in clinician-collected and urine samples. All methods for assessing HPV presence yielded significant age-adjusted odds ratios for predicting abnormal lesions at 1st follow-up. For overall HPV results, Cohen's kappa revealed substantial agreement between VSS and clinician sampling, and moderate agreement between urine and clinician sampling. Clinician sampling and VSS were highly concordant for HPV16. Insofar as the pathology was squamous (not glandular), VSS appeared as sensitive as clinician sampling for HPV in predicting outcome among the present cohort. Since VSS can be performed at home, this option can maximize participation in the required long-term follow-up for these women at high-risk.

Clinical and chest CT features as a predictive tool for COVID-19 clinical progress: introducing a novel semi-quantitative scoring system.

OBJECTIVE: Proposing a scoring tool to predict COVID-19 patients' outcomes based on initially assessed clinical and CT features. METHODS: All patients, who were referred to a tertiary-university hospital respiratory triage (March 27-April 26, 2020), were highly clinically suggestive for COVID-19 and had undergone a chest CT scan were included. Those with positive rRT-PCR or highly clinically suspicious patients with typical chest CT scan pulmonary manifestations were considered confirmed COVID-19 for additional analyses. Patients, based on outcome, were categorized into outpatient, ordinary-ward admitted, intensive care unit (ICU) admitted, and deceased; their demographic, clinical, and chest CT scan parameters were compared. The pulmonary chest CT scan features were scaled with a novel semi-quantitative scoring system to assess pulmonary involvement (PI). RESULTS: Chest CT scans of 739 patients (mean age = 49.2 ± 17.2 years old, 56.7% male) were reviewed; 491 (66.4%), 176 (23.8%), and 72 (9.7%) cases were managed outpatient, in an ordinary ward, and ICU, respectively. A total of 439 (59.6%) patients were confirmed COVID-19 cases; their most prevalent chest CT scan features were ground-glass opacity (GGO) (93.3%), pleural-based peripheral distribution (60.3%), and multi-lobar (79.7%), bilateral (76.6%), and lower lobes (RLL and/or LLL) (89.1%) involvement. Patients with lower SpO2, advanced age, RR, total PI score or PI density score, and diffuse distribution or involvement of multi-lobar, bilateral, or lower lobes were more likely to be ICU admitted/expired. After adjusting for confounders, predictive models found cutoffs of age ≥ 53, SpO2 ≤ 91, and PI score ≥ 8 (15) for ICU admission (death). A combination of all three factors showed 89.1% and 95% specificity and 81.9% and 91.4% accuracy for ICU admission and death outcomes, respectively. Solely evaluated high PI score had high sensitivity, specificity, and NPV in predicting the outcome as well. CONCLUSION: We strongly recommend patients with age ≥ 53, SpO2 ≤ 91, and PI score ≥ 8 or even only high PI score to be considered as high-risk patients for further managements and care plans. KEY POINTS: • Chest CT scan is a valuable tool in prioritizing the patients in hospital triage. • A more accurate and novel 35-scale semi-quantitative scoring system was designed to predict the COVID-19 patients' outcome. • Patients with age ≥ 53, SpO2 ≤ 91, and PI score ≥ 8 or even only high PI score should be considered high-risk patients.

Schrödinger's Worker: Are They Positive or Negative for SARS-CoV-2?

In these days of 2020, tests for the diagnosis of SARS-CoV-2, and their use in the context of health surveillance of workers, are becoming popular. Nevertheless, their sensitivity and specificity could vary on the basis of the type of test used and on the moment of infection of the subject tested. The aim of this viewpoint paper is to make employers, workers, occupational physicians, and public health specialists think about the limits of diagnostic tests currently available, and the possible implication related to the erroneous and incautious assignment of "immunity passports" or "risk-free certificates" to workers during screening campaigns in workplaces.

A predictive model and scoring system combining clinical and CT characteristics for the diagnosis of COVID-19.

OBJECTIVES: To develop a predictive model and scoring system to enhance the diagnostic efficiency for coronavirus disease 2019 (COVID-19). METHODS: From January 19 to February 6, 2020, 88 confirmed COVID-19 patients presenting with pneumonia and 80 non-COVID-19 patients suffering from pneumonia of other origins were retrospectively enrolled. Clinical data and laboratory results were collected. CT features and scores were evaluated at the segmental level according to the lesions' position, attenuation, and form. Scores were calculated based on the size of the pneumonia lesion, which graded at the range of 1 to 4. Air bronchogram, tree-in-bud sign, crazy-paving pattern, subpleural curvilinear line, bronchiectasis, air space, pleural effusion, and mediastinal and/or hilar lymphadenopathy were also evaluated. RESULTS: Multivariate logistic regression analysis showed that history of exposure (ß = 3.095, odds ratio (OR) = 22.088), leukocyte count (ß = - 1.495, OR = 0.224), number of segments with peripheral lesions (ß = 1.604, OR = 1.604), and crazy-paving pattern (ß = 2.836, OR = 2.836) were used for establishing the predictive model to identify COVID-19-positive patients (p < 0.05). In this model, values of area under curve (AUC) in the training and testing groups were 0.910 and 0.914, respectively (p < 0.001). A predicted score for COVID-19 (PSC-19) was calculated based on the predictive model by the following formula: PSC-19 = 2 × history of exposure (0-1 point) - 1 × leukocyte count (0-2 points) + 1 × peripheral lesions (0-1 point) + 2 × crazy-paving pattern (0-1 point), with an optimal cutoff point of 1 (sensitivity, 88.5%; specificity, 91.7%). CONCLUSIONS: Our predictive model and PSC-19 can be applied for identification of COVID-19-positive cases, assisting physicians and radiologists until receiving the results of reverse transcription-polymerase chain reaction (RT-PCR) tests. KEY POINTS: • Prediction of RT-PCR positivity is crucial for fast diagnosis of patients suspected of having coronavirus disease 2019 (COVID-19). • Typical CT manifestations are advantageous for diagnosing COVID-19 and differentiation of COVID-19 from other types of pneumonia. • A predictive model and scoring system combining both clinical and CT features were herein developed to enable high diagnostic efficiency for COVID-19.