Trajectory of cardiac troponin T following moderate-to-severe COVID-19 and the association with cardiac abnormalities.

BACKGROUND: COVID-19 has been associated with cardiac troponin T (cTnT) elevations and changes in cardiac structure and function, but the link between cardiac dysfunction and high-sensitive cardiac troponin T (hs-cTnT) in the acute and convalescent phase is unclear. OBJECTIVE: To assess whether hs-cTnT concentrations are associated with cardiac dysfunction and structural abnormalities after hospitalization for COVID-19, and to evaluate the performance of hs-cTnT to rule out cardiac pathology. METHODS: Patients hospitalized with COVID-19 had hs-cTnT measured during the index hospitalization and after 3-and 12 months, when they also underwent an echocardiographic study. A subset also underwent cardiovascular magnetic resonance imaging (CMR) after 6 months. Cardiac abnormalities were defined as left ventricular hypertrophy or dysfunction, right ventricular dysfunction, or CMR late gadolinium. RESULTS: We included 189 patients with hs-cTnT concentrations measured during hospitalization for COVID-19, and after 3-and 12 months: Geometric mean (95%CI) 13 (11-15) ng/L, 7 (6-8) ng/L and 7 (6-8) ng/L, respectively. Cardiac abnormalities after 3 months were present in 45 (30%) and 3 (8%) of patients with hs-cTnT ≥ and < 5 ng/L at 3 months, respectively (negative predictive value 92.3% [95%CI 88.5-96.1%]). The performance was similar in patients with and without dyspnea. Hs-cTnT decreased from hospitalization to 3 months (more pronounced in intensive care unit-treated patients) and remained unchanged from 3 to 12 months, regardless of the presence of cardiac abnormalities. CONCLUSION: Higher hs-cTnT concentrations in the convalescent phase of COVID-19 are associated with the presence of cardiac pathology and low concentrations (< 5 ng/L) may support in ruling out cardiac pathology following the infection.

Intelligent diagnosis of the severity of disease conditions in COVID-19 patients based on the LASSO method.

Purpose: The purpose of this study is to develop an intelligent diagnosis model based on the LASSO method to predict the severity of COVID-19 patients. Methods: The study uses the clinical data of 500 COVID-19 patients from a designated hospital in Guangzhou, China, and selects eight features, including age, sex, dyspnea, comorbidity, complication, lymphocytes (LYM), CRP, and lung injury score, as the most important predictors of COVID-19 severity. The study applies the LASSO method to perform feature selection and regularization, and compares the LASSO method with other machine learning methods, such as ridge regression, support vector machine, and random forest. Results: The study finds that the ridge regression model has the best performance among the four models, with an AUROC of 0.92 in the internal validation and 0.91 in the external validation. Conclusion: The study provides a simple, robust, and interpretable model for the intelligent diagnosis of COVID-19 severity, and a convenient and practical tool for the public and the health care workers to assess COVID-19 severity. However, the study also has some limitations and directions for future research, such as the need for more data from different sources and settings, and from prospective, longitudinal, multi-class classification models. The study hopes to contribute to the prevention and control of COVID-19, and to the improvement of the diagnosis and treatment of COVID-19 patients.

Beyond prediction: unveiling the prognostic power of µ-opioid and cannabinoid receptors, alongside immune mediators, in assessing the severity of SARS-CoV-2 infection.

BACKGROUND: This study aims to explore the potential of utilizing the expression levels of cannabinoid receptor 2 (CB2), µ-opioid receptor (MOR), MCP-1, IL-17, IFN-γ, and osteopontin as predictors for the severity of SARS-CoV-2 infection. The overarching goal is to delineate the pathogenic mechanisms associated with SARS-CoV-2. METHODS: Using quantitative Real-time PCR, we analyzed the gene expression levels of CB2 and MOR in nasopharynx specimens obtained from patients diagnosed with SARS-CoV-2 infection, with 46 individuals classified as having severe symptoms and 46 as non-severe. Additionally, we measured the circulating levels of MCP-1, IL-17, IFN-γ, and osteopontin using an ELISA assay. We examined the predictive capabilities of these variables and explored their correlations across all patient groups. RESULTS: Our results demonstrated a significant increase in MOR gene expression in the epithelium of patients with severe infection. The expression of CB2 receptor was also elevated in both male and female patients with severe symptoms. Furthermore, we observed concurrent rises in MCP-1, IL-17, IFN-γ, and osteopontin levels in patients, which were linked to disease severity. CB2, MOR, MCP-1, IL-17, IFN-γ, and osteopontin showed strong predictive abilities in distinguishing between patients with varying degrees of SARS-CoV-2 severity. Moreover, we identified a significant correlation between CB2 expression and the levels of MOR, MCP-1, osteopontin, and IFN-γ. CONCLUSIONS: These results underline the interconnected nature of molecular mediators in a sequential manner, suggesting that their overexpression may play a role in the development of SARS-CoV-2 infections.

Predicting COVID-19 outcomes with the Edmonton Obesity Staging System.

BACKGROUND: Multiple studies have demonstrated a correlation between a high body mass index and discriminatory COVID-19 outcomes. Studies appear to indicate that there is a correlation between obesity-related comorbidities and less favorable outcomes. OBJECTIVES: The primary aim of the current investigation is to conduct a thorough assessment of the correlation between BMI and comorbidities associated with obesity, and their potential impact on the severity and consequences of COVID-19 infection among patients receiving care in a tertiary healthcare setting. DESIGN: Retrospective cohort. SETTINGS: Tertiary rehabilitation center, Riyadh, Saudi Arabia. PATIENTS AND METHODS: The study included all individuals who received medical treatment and tested positive for COVID-19 by means of RT-PCR during the period from March to September 2020. COVID-19 patients were classified using Edmonton Obesity Staging System (EOSS). MAIN OUTCOME MEASURES: COVID-19-related complications, including pneumonia and cytokine release syndrome, as well as the time length to COVID-19 negativization. SAMPLE SIZE: 315 patients. RESULTS: The median (25th-75th percentiles) age of the patients was 38 (31.5-49) years old. Males outnumbered females, and 66% of patients were non-Saudis. Forty-eight patients (15.2%) had obesity class I, whereas 13 patients (4.1%) had class II. Thirty-two patients (10.2%) were classified as EOSS stage 1, 105 patients (33.3%) were classified as EOSS stage 2, and 25 patients (7.9%) were assigned to EOSS stage 3. Males predominated in EOSS stages 1 and 2, whereas females predominated in stage 3. In EOSS stage 3, 52% of cases had moderate severity and 48% had severe illness. CONCLUSIONS: EOSS distinguishes the COVID-19 risks of poor outcomes beyond BMI. Patients who were overweight or obese but remained in the stage 1 of the EOSS had a lower risk of a poor COVID-19 outome than normal-weight patients. The health status of obese patients is a more precise indicator of the progression of COVID-19 during hospitalization than BMI alone. LIMITATIONS: Given the limited capacity of urgent care facilities to conduct a comprehensive evaluation of comorbidities and other relevant outcomes in all patients, it is plausible that certain patients may have been erroneously classified with an EOSS stage 2 diagnosis, when in fact they ought to have been assigned a stage 3 diagnosis.

COVID-related disruptions to colorectal cancer screening, diagnosis, and treatment could increase cancer Burden in Australia and Canada: A modelling study.

COVID-19 disrupted cancer control worldwide, impacting preventative screening, diagnoses, and treatment services. This modelling study estimates the impact of disruptions on colorectal cancer cases and deaths in Canada and Australia, informed by data on screening, diagnosis, and treatment procedures. Modelling was used to estimate short- and long-term effects on colorectal cancer incidence and mortality, including ongoing impact of patient backlogs. A hypothetical mitigation strategy was simulated, with diagnostic and treatment capacities increased by 5% from 2022 to address backlogs. Colorectal cancer screening dropped by 40% in Canada and 6.3% in Australia in 2020. Significant decreases to diagnostic and treatment procedures were also observed in Australia and Canada, which were estimated to lead to additional patient wait times. These changes would lead to an estimated increase of 255 colorectal cancer cases and 1,820 colorectal cancer deaths in Canada and 234 cases and 1,186 deaths in Australia over 2020-2030; a 1.9% and 2.4% increase in mortality, respectively, vs a scenario with no screening disruption or diagnostic/treatment delays. Diagnostic and treatment capacity mitigation would avert 789 and 350 deaths in Canada and Australia, respectively. COVID-related disruptions had a significant impact on colorectal cancer screening, diagnostic, and treatment procedures in Canada and Australia. Modelling demonstrates that downstream effects on disease burden could be substantial. However, backlogs can be managed and deaths averted with even small increases to diagnostic and treatment capacity. Careful management of resources can improve patient outcomes after any temporary disruption, and these results can inform targeted approaches early detection of cancers.

Artificial Neural Network Prediction of COVID-19 Daily Infection Count.

This study addresses COVID-19 testing as a nonlinear sampling problem, aiming to uncover the dependence of the true infection count in the population on COVID-19 testing metrics such as testing volume and positivity rates. Employing an artificial neural network, we explore the relationship among daily confirmed case counts, testing data, population statistics, and the actual daily case count. The trained artificial neural network undergoes testing in in-sample, out-of-sample, and several hypothetical scenarios. A substantial focus of this paper lies in the estimation of the daily true case count, which serves as the output set of our training process. To achieve this, we implement a regularized backcasting technique that utilize death counts and the infection fatality ratio (IFR), as the death statistics and serological surveys (providing the IFR) as more reliable COVID-19 data sources. Addressing the impact of factors such as age distribution, vaccination, and emerging variants on the IFR time series is a pivotal aspect of our analysis. We expect our study to enhance our understanding of the genuine implications of the COVID-19 pandemic, subsequently benefiting mitigation strategies.

Correlation of SARS-CoV-2 in Wastewater and Individual Testing Results in a Jail, Atlanta, Georgia, USA.

Institution-level wastewater-based surveillance was implemented during the COVID-19 pandemic, including in carceral facilities. We examined the relationship between COVID-19 diagnostic test results of residents in a jail in Atlanta, Georgia, USA (average population ≈2,700), and quantitative reverse transcription PCR signal for SARS-CoV-2 in weekly wastewater samples collected during October 2021‒May 2022. The jail offered residents rapid antigen testing at entry and periodic mass screenings by reverse transcription PCR of self-collected nasal swab specimens. We aggregated individual test data, calculated the Spearman correlation coefficient, and performed logistic regression to examine the relationship between strength of SARS-CoV-2 PCR signal (cycle threshold value) in wastewater and percentage of jail population that tested positive for COVID-19. Of 13,745 nasal specimens collected, 3.9% were COVID-positive (range 0%-29.5% per week). We observed a strong inverse correlation between diagnostic test positivity and cycle threshold value (r = -0.67; p<0.01). Wastewater-based surveillance represents an effective strategy for jailwide surveillance of COVID-19.

Race, Ethnicity, and Delayed Time to COVID-19 Testing Among US Health Care Workers.

Importance: Access to COVID-19 testing is critical to reducing transmission and supporting early treatment decisions; when made accessible, the timeliness of testing may also be an important metric in mitigating community spread of the infection. While disparities in transmission and outcomes of COVID-19 have been well documented, the extent of timeliness of testing and the association with demographic factors is unclear. Objectives: To evaluate demographic factors associated with delayed COVID-19 testing among health care personnel (HCP) during the COVID-19 pandemic. Design, Setting, and Participants: This cross-sectional study used data from the Preventing Emerging Infections Through Vaccine Effectiveness Testing study, a multicenter, test-negative, case-control vaccine effectiveness study that enrolled HCP who had COVID-19 symptoms and testing between December 2020 and April 2022. Data analysis was conducted from March 2022 to Junne 2023. Exposure: Displaying COVID-19-like symptoms and polymerase chain reaction testing occurring from the first day symptoms occurred up to 14 days after symptoms occurred. Main Outcomes and Measures: Variables of interest included patient demographics (sex, age, and clinical comorbidities) and COVID-19 characteristics (vaccination status and COVID-19 wave). The primary outcome was time from symptom onset to COVID-19 testing, which was defined as early testing (≤2 days) or delayed testing (≥3 days). Associations of demographic characteristics with delayed testing were measured while adjusting for clinical comorbidities, COVID-19 characteristics, and test site using multivariable modeling to estimate relative risks and 95% CIs. Results: A total of 5551 HCP (4859 female [82.9%]; 1954 aged 25-34 years [35.2%]; 4233 non-Hispanic White [76.3%], 370 non-Hispanic Black [6.7%], and 324 non-Hispanic Asian [5.8%]) were included in the final analysis. Overall, 2060 participants (37.1%) reported delayed testing and 3491 (62.9%) reported early testing. Compared with non-Hispanic White HCP, delayed testing was higher among non-Hispanic Black HCP (adjusted risk ratio, 1.18; 95%CI, 1.10-1.27) and for non-Hispanic HCP of other races (adjusted risk ratio, 1.17; 95% CI, 1.03-1.33). Sex and age were not associated with delayed testing. Compared with clinical HCP with graduate degrees, all other professional and educational groups had significantly delayed testing. Conclusions and Relevance: In this cross-sectional study of HCP, compared with non-Hispanic White HCP and clinical HCP with graduate degrees, non-Hispanic Black HCP, non-Hispanic HCP of other races, and HCP all other professional and education backgrounds were more likely to have delayed COVID-19 testing. These findings suggest that time to testing may serve as a valuable metric in evaluating sociodemographic disparities in the response to COVID-19 and future health mitigation strategies.

Prevalence and duration of SARS-CoV-2 fecal shedding in breastfeeding dyads following maternal COVID-19 diagnosis.

Background: There is a paucity of data on the presence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in feces of lactating women with coronavirus disease 2019 (COVID-19) and their breastfed infants as well as associations between fecal shedding and symptomatology. Objective: We examined whether and to what extent SARS-CoV-2 is detectable in the feces of lactating women and their breastfed infants following maternal COVID-19 diagnosis. Methods: This was a longitudinal study carried out from April 2020 to December 2021 involving 57 breastfeeding maternal-infant dyads: 33 dyads were enrolled within 7 d of maternal COVID-19 diagnosis, and 24 healthy dyads served as controls. Maternal/infant fecal samples were collected by participants, and surveys were administered via telephone over an 8-wk period. Feces were analyzed for SARS-CoV-2 RNA. Results: Signs/symptoms related to ears, eyes, nose, and throat (EENT); general fatigue/malaise; and cardiopulmonary signs/symptoms were commonly reported among mothers with COVID-19. In infants of mothers with COVID-19, EENT, immunologic, and cardiopulmonary signs/symptoms were most common, but prevalence did not differ from that of infants of control mothers. SARS-CoV-2 RNA was detected in feces of 7 (25%) women with COVID-19 and 10 (30%) of their infants. Duration of fecal shedding ranged from 1-4 wk for both mothers and infants. SARS-CoV-2 RNA was sparsely detected in feces of healthy dyads, with only one mother's and two infants' fecal samples testing positive. There was no relationship between frequencies of maternal and infant SARS-CoV-2 fecal shedding (P=0.36), although presence of maternal or infant fever was related to increased likelihood (7-9 times greater, P≤0.04) of fecal shedding in infants of mothers with COVID-19.

Can the ADO Index Be Used as a Predictor of Mortality from COVID-19 in Patients with COPD?

Background: Several studies have shown that the risk of mortality due to COVID-19 is high in patients with COPD. However, evidence on factors predicting mortality is limited. Research Question: Are there any useful markers to predict mortality in COVID-19 patients with COPD?. Study Design and Methods: A total of 689 patients were included in this study from the COPET study, a national multicenter observational study investigating COPD phenotypes consisting of patients who were followed up with a spirometry-confirmed COPD diagnosis. Patients were also retrospectively examined in terms of COVID-19 and their outcomes. Results: Among the study patients, 105 were diagnosed with PCR-positive COVID-19, and 19 of them died. Body mass index (p= 0.01) and ADO (age, dyspnoea, airflow obstruction) index (p= 0.01) were higher, whereas predicted FEV1 (p< 0.001) and eosinophil count (p= 0.003) were lower in patients who died of COVID-19. Each 0.755 unit increase in the ADO index increased the risk of death by 2.12 times, and each 0.007 unit increase in the eosinophil count decreased the risk of death by 1.007 times. The optimum cut-off ADO score of 3.5 was diagnostic with 94% sensitivity and 40% specificity in predicting mortality. Interpretation: Our study suggested that the ADO index recorded in the stable period in patients with COPD makes a modest contribution to the prediction of mortality due to COVID-19. Further studies are needed to validate the use of the ADO index in estimating mortality in both COVID-19 and other viral respiratory infections in patients with COPD.

Detection of SARS-CoV-2 using machine learning-enabled paper-assisted ratiometric fluorescent sensors based on target-induced magnetic DNAzyme.

The development of an advanced analytical platform with regard to SARS-CoV-2 is crucial for public health. Herein, we present a machine learning platform based on paper-assisted ratiometric fluorescent sensors for highly sensitive detection of the SARS-CoV-2 RdRp gene. The assay involves target-induced rolling circle amplification to generate magnetic DNAzyme, which is then detectable using the paper-assisted ratiometric fluorescent sensor. This sensor detects the SARS-CoV-2 RdRp gene with a visible-fluorescence color response. Moreover, leveraging different fluorescence responses, the ResNet algorithm of machine learning assists in accurately identifying fluorescence images and differentiating the concentration of the SARS-CoV-2 RdRp gene with over 99% recognition accuracy. The machine learning platform exhibits exceptional sensitivity and color responsiveness, achieving a limit of detection of 30 fM for the SARS-CoV-2 RdRp gene. The integration of intelligent artificial vision with the paper-assisted ratiometric fluorescent sensor presents a novel approach for the on-site detection of COVID-19 and holds potential for broader use in disease diagnostics in the future.

Patient-perspective and feasibility of home finger-prick testing to complement and facilitate large-scale research in rheumatology.

BACKGROUND: During the COVID-19 pandemic, we developed a digital research platform to longitudinally investigate COVID-19-related outcomes in patients with rheumatic diseases and healthy controls. We used home finger-prick testing in order to collect serum samples remotely and increase the overall efficiency of the platform. The aim of the present study was to evaluate the success rate of the finger prick and patients' perspective towards the finger prick. METHODS: Serum samples were collected up to five times during follow-up, either via a venepuncture at the research institute or a finger prick from participants' home. Participants were asked to complete a digital evaluation questionnaire of the finger prick after their attempts. RESULTS: A total of 2135 patients and 899 controls performed at least one finger prick and were included in this study. The first finger prick was successfully done by 92% (95% CI: 90% to 93%) of patients, 94% (95% CI: 92% to 95%) of controls, 93% (95% CI: 92% to 94%) of all participants aged ≤70 years and 89% (95% CI: 86% to 92%) of all participants aged >70 years. Sex did not impact these success rates. Repeated failure occurred in 11/439 (0.8%) patients and 4/712 (0.6%) controls. Both patients and controls were less willing to perform a finger prick for individual healthcare compared with scientific research. CONCLUSION: The vast majority of participants, among which elderly and patients with rheumatic diseases, were able to successfully draw the required amount of blood for serological analyses. This shows that finger-prick testing is suitable for a high-throughput implementation to monitor patients remotely.

Impacto de la pandemia por SARS-CoV-2 en el índice de masa corporal de niños asistidos en la Ciudad Autónoma de Buenos Aires / Impact of the SARS-CoV-2 pandemic on the body mass index of children seen in the City of Buenos Aires

Introducción. El sobrepeso y la obesidad infantil constituyen un problema de salud pública. El inicio de la pandemia por COVID-19 pudo haber favorecido esta patología. El puntaje Z del índice de masa corporal (Z-IMC) es un indicador aceptado para su diagnóstico y seguimiento. Objetivo. Evaluar si la prevalencia de sobrepeso y obesidad, y el Z-IMC en niños de 2 a 5 años aumentó durante la pandemia. Población y métodos. Estudio de cohorte retrospectiva. Se incluyeron pacientes asistidos en efectores públicos de salud del Gobierno de la Ciudad Autónoma de Buenos Aires (GCABA), de 2 a 5 años de edad, con registro de peso y talla en dos consultas, antes y después de haber comenzado el aislamiento social preventivo y obligatorio (ASPO). Se registró estado nutricional (Z-IMC) y variación del Z-IMC entre ambas consultas. Resultados. Se evaluaron 3866 sujetos, edad promedio 3,4 ± 0,8 años; el 48,1 % fueron mujeres. El intervalo promedio entre consultas fue 14,3 ± 2,5 meses. La prevalencia de sobrepeso/obesidad aumentó del 12,6 % (IC95% 11,6-13,6) al 20,9 % (IC95% 19,6-22-2); p <0,001, al igual que el Z-IMC (0,4 ± 1,1 vs. 0,8 ± 1,3; p <0,001). Conclusión. La prevalencia de sobrepeso y obesidad, y el Z-IMC en niños de 2 a 5 años aumentó significativamente durante la pandemia.

Introduction. Childhood overweight and obesity are a public health problem. The onset of the COVID-19 pandemic may have contributed to this condition. The body mass index (BMI) Z-score has been accepted as an indicator for overweight and obesity diagnosis and follow-up. Objective. To assess whether the prevalence of overweight and obesity and the BMI Z-score in children aged 2 to 5 years increased during the pandemic. Population and methods. Retrospective, cohort study. Patients included were those seen at public health care facilities in the City of Buenos Aires (CABA), who were aged 2 to 5 years, had weight and height values recorded at 2 different visits, before and after the establishment of the preventive and mandatory social isolation policy. Patients' nutritional status (BMI Z-score) and the variation in this indicator between both visits were recorded. Results. A total of 3866 subjects were assessed; their average age was 3.4 ± 0.8 years; 48.1% were girls. The average interval between both visits was 14.3 ± 2.5 months. The prevalence of overweight/ obesity increased from 12.6% (95% CI: 11.6­13.6) to 20.9% (95% CI: 19.6­22.2), p < 0.001, and so did the BMI Z-score (0.4 ± 1.1 versus 0.8 ± 1.3, p < 0.001). Conclusion. The prevalence of overweight and obesity and the BMI Z-score in children aged 2 to 5 years increased significantly during the pandemic.

RNA-Based Sensor Systems for Affordable Diagnostics in the Age of Pandemics.

In the era of the COVID-19 pandemic, the significance of point-of-care (POC) diagnostic tools has become increasingly vital, driven by the need for quick and precise virus identification. RNA-based sensors, particularly toehold sensors, have emerged as promising candidates for POC detection systems due to their selectivity and sensitivity. Toehold sensors operate by employing an RNA switch that changes the conformation when it binds to a target RNA molecule, resulting in a detectable signal. This review focuses on the development and deployment of RNA-based sensors for POC viral RNA detection with a particular emphasis on toehold sensors. The benefits and limits of toehold sensors are explored, and obstacles and future directions for improving their performance within POC detection systems are presented. The use of RNA-based sensors as a technology for rapid and sensitive detection of viral RNA holds great potential for effectively managing (dealing/coping) with present and future pandemics in resource-constrained settings.

The role of C-reactive protein, procalcitonin, interleukin-6 and neutrophil / lymphocyte ratio as a laboratory biomarker in COVID-19.

Biomarkers such as Interleukin-6 (IL-6), Procalcitonin (PCT), C-reactive protein (CRP) and Neutrophil-Lymphocyte Ratio (NLR) have a role in the pathogenesis of severe coronavirus disease 2019 (COVID-19). The aim of this study was to explore the differences between serum levels of such biomarkers in severe and non-severe COVID-19 cases and compare them with normal people and to evaluate the sociodemographic variables and chronic diseases effect on the severity of COVID-19. The study included 160 subjects, divided into two groups, a case group of 80 patients, and a control group of 80 normal persons. The case group was divided into two subgroups: 40 severe COVID-19 patients and 40 patients with non-severe disease. Blood IL-6 was assessed by an enzyme-linked immunosorbent assay (ELISA), PCT by an immunoassay, CRP by an immunoturbidimetric assay and NLR from CBC. The levels of IL-6, PCT, CRP, and NLR were significantly higher in the case group than in control group (p= 0.001, for all). However, there was no difference between these biomarkers level in the non-severe COVID-19 subgroup and the control group (p>0.05 for all). The proportion of severe COVID-19 was significantly higher in patients aged >50 years, and in patients with chronic diseases (p=0.046 and p=0.001, respectively). We also found a strong correlation between such biomarkers and old age, and chronic diseases with the disease severity. There was a significant difference in the level of the three biomarkers (IL-6, PCT, CRP, and NLR) between patients' subgroups and the control group. In conclusion, since the levels of these biomarkers are correlated with the severity of the COVID-19 disease, and there was a difference in the levels between the groups with severe and non-severe symptoms, we suggest a role of these biomarkers in predicting the severity COVID-19 disease and its poor prognosis.

Accuracy of automated computer-aided risk scoring systems to estimate the risk of COVID-19: a retrospective cohort study.

BACKGROUND: In the UK National Health Service (NHS), the patient's vital signs are monitored and summarised into a National Early Warning Score (NEWS) score. A set of computer-aided risk scoring systems (CARSS) was developed and validated for predicting in-hospital mortality and sepsis in unplanned admission to hospital using NEWS and routine blood tests results. We sought to assess the accuracy of these models to predict the risk of COVID-19 in unplanned admissions during the first phase of the pandemic. METHODS: Adult ( > = 18 years) non-elective admissions discharged (alive/deceased) between 11-March-2020 to 13-June-2020 from two acute hospitals with an index NEWS electronically recorded within ± 24 h of admission. We identified COVID-19 admission based on ICD-10 code 'U071' which was determined by COVID-19 swab test results (hospital or community). We assessed the performance of CARSS (CARS_N, CARS_NB, CARM_N, CARM_NB) for predicting the risk of COVID-19 in terms of discrimination (c-statistic) and calibration (graphically). RESULTS: The risk of in-hospital mortality following emergency medical admission was 8.4% (500/6444) and 9.6% (620/6444) had a diagnosis of COVID-19. For predicting COVID-19 admissions, the CARS_N model had the highest discrimination 0.73 (0.71 to 0.75) and calibration slope 0.81 (0.72 to 0.89) compared to other CARSS models: CARM_N (discrimination:0.68 (0.66 to 0.70) and calibration slope 0.47 (0.41 to 0.54)), CARM_NB (discrimination:0.68 (0.65 to 0.70) and calibration slope 0.37 (0.31 to 0.43)), and CARS_NB (discrimination:0.68 (0.66 to 0.70) and calibration slope 0.56 (0.47 to 0.64)). CONCLUSIONS: The CARS_N model is reasonably accurate for predicting the risk of COVID-19. It may be clinically useful as an early warning system at the time of admission especially to triage large numbers of unplanned admissions because it requires no additional data collection and is readily automated.

Prognostic value of the neutrophil-to-lymphocyte ratio and C-reactive-protein-to-prealbumin ratio in hospitalized older patients with coronavirus disease 2019.

The neutrophil-to-lymphocyte ratio (NLR) and C-reactive protein-to-prealbumin ratio (CPAR) are novel markers of inflammation. The CPAR is an indicator of inflammation and malnutrition. We evaluated NLR and CPAR in combination as indicators of disease severity and prognosis in hospitalized older patients with coronavirus disease 2019 (COVID-19). A total of 222 hospitalized patients with COVID-19 (aged > 60 years) were divided into non-severe and severe groups. The severe group was subdivided into the surviving and deceased subgroups. We retrospectively assessed the predictive power of NLR and CPAR in combination (NLR + CPAR) to determine the prognosis of hospitalized older patients with COVID-19. The NLR and CPAR were significantly higher in the severe group than in the non-severe group (P < .001). Furthermore, the NLR and CPAR were higher in the deceased subgroup than in the surviving subgroup (P < .001). Pearson correlation analysis showed a highly significant positive correlation between NLR and CPAR (P < .001, r = 0.530). NLR + CPAR showed an area under the curve of 0.827 and sensitivity of 83.9% in the severe group; the area under the curve was larger (0.925) and sensitivity was higher (87.1%) in the deceased subgroup. The receiver operating characteristic curve of NLR + CPAR was significantly different from the receiver operating characteristic curves of either biomarker alone (P < .001). Kaplan-Meier analysis showed that patients in the severe group with elevated NLR + CPAR had a significantly lower 90-day survival rate than patients who lacked this finding (odds ratio 7.87, P < .001). NLR + CPAR may enable early diagnosis and assessment of disease severity in hospitalized older patients with COVID-19. This may also enable the identification of high-risk older patients with COVID-19 at the time of admission.

Moving Biosurveillance Beyond Coded Data Using AI for Symptom Detection From Physician Notes: Retrospective Cohort Study.

BACKGROUND: Real-time surveillance of emerging infectious diseases necessitates a dynamically evolving, computable case definition, which frequently incorporates symptom-related criteria. For symptom detection, both population health monitoring platforms and research initiatives primarily depend on structured data extracted from electronic health records. OBJECTIVE: This study sought to validate and test an artificial intelligence (AI)-based natural language processing (NLP) pipeline for detecting COVID-19 symptoms from physician notes in pediatric patients. We specifically study patients presenting to the emergency department (ED) who can be sentinel cases in an outbreak. METHODS: Subjects in this retrospective cohort study are patients who are 21 years of age and younger, who presented to a pediatric ED at a large academic children's hospital between March 1, 2020, and May 31, 2022. The ED notes for all patients were processed with an NLP pipeline tuned to detect the mention of 11 COVID-19 symptoms based on Centers for Disease Control and Prevention (CDC) criteria. For a gold standard, 3 subject matter experts labeled 226 ED notes and had strong agreement (F1-score=0.986; positive predictive value [PPV]=0.972; and sensitivity=1.0). F1-score, PPV, and sensitivity were used to compare the performance of both NLP and the International Classification of Diseases, 10th Revision (ICD-10) coding to the gold standard chart review. As a formative use case, variations in symptom patterns were measured across SARS-CoV-2 variant eras. RESULTS: There were 85,678 ED encounters during the study period, including 4% (n=3420) with patients with COVID-19. NLP was more accurate at identifying encounters with patients that had any of the COVID-19 symptoms (F1-score=0.796) than ICD-10 codes (F1-score =0.451). NLP accuracy was higher for positive symptoms (sensitivity=0.930) than ICD-10 (sensitivity=0.300). However, ICD-10 accuracy was higher for negative symptoms (specificity=0.994) than NLP (specificity=0.917). Congestion or runny nose showed the highest accuracy difference (NLP: F1-score=0.828 and ICD-10: F1-score=0.042). For encounters with patients with COVID-19, prevalence estimates of each NLP symptom differed across variant eras. Patients with COVID-19 were more likely to have each NLP symptom detected than patients without this disease. Effect sizes (odds ratios) varied across pandemic eras. CONCLUSIONS: This study establishes the value of AI-based NLP as a highly effective tool for real-time COVID-19 symptom detection in pediatric patients, outperforming traditional ICD-10 methods. It also reveals the evolving nature of symptom prevalence across different virus variants, underscoring the need for dynamic, technology-driven approaches in infectious disease surveillance.

Nirmatrelvir for Vaccinated or Unvaccinated Adult Outpatients with Covid-19.

BACKGROUND: Nirmatrelvir in combination with ritonavir is an antiviral treatment for mild-to-moderate coronavirus disease 2019 (Covid-19). The efficacy of this treatment in patients who are at standard risk for severe Covid-19 or who are fully vaccinated and have at least one risk factor for severe Covid-19 has not been established. METHODS: In this phase 2-3 trial, we randomly assigned adults who had confirmed Covid-19 with symptom onset within the past 5 days in a 1:1 ratio to receive nirmatrelvir-ritonavir or placebo every 12 hours for 5 days. Patients who were fully vaccinated against Covid-19 and who had at least one risk factor for severe disease, as well as patients without such risk factors who had never been vaccinated against Covid-19 or had not been vaccinated within the previous year, were eligible for participation. Participants logged the presence and severity of prespecified Covid-19 signs and symptoms daily from day 1 through day 28. The primary end point was the time to sustained alleviation of all targeted Covid-19 signs and symptoms. Covid-19-related hospitalization and death from any cause were also assessed through day 28. RESULTS: Among the 1296 participants who underwent randomization and were included in the full analysis population, 1288 received at least one dose of nirmatrelvir-ritonavir (654 participants) or placebo (634 participants) and had at least one postbaseline visit. The median time to sustained alleviation of all targeted signs and symptoms of Covid-19 was 12 days in the nirmatrelvir-ritonavir group and 13 days in the placebo group (P = 0.60). Five participants (0.8%) in the nirmatrelvir-ritonavir group and 10 (1.6%) in the placebo group were hospitalized for Covid-19 or died from any cause (difference, -0.8 percentage points; 95% confidence interval, -2.0 to 0.4). The percentages of participants with adverse events were similar in the two groups (25.8% with nirmatrelvir-ritonavir and 24.1% with placebo). In the nirmatrelvir-ritonavir group, the most commonly reported treatment-related adverse events were dysgeusia (in 5.8% of the participants) and diarrhea (in 2.1%). CONCLUSIONS: The time to sustained alleviation of all signs and symptoms of Covid-19 did not differ significantly between participants who received nirmatrelvir-ritonavir and those who received placebo. (Supported by Pfizer; EPIC-SR ClinicalTrials.gov number, NCT05011513.).