Correction: Contact tracing of COVID-19 in Karnataka, India: Superspreading and determinants of infectiousness and symptomatic infection.

[This corrects the article DOI: 10.1371/journal.pone.0270789.].

Predictors and Outcomes of Sudden Cardiac Arrest in Heart Failure With Preserved Ejection Fraction: A Nationwide Inpatient Sample Analysis.

Sudden cardiac arrest (SCA) is the leading cause of cardiovascular mortality in heart failure with preserved ejection fraction (HFpEF), contributing to around 25% of deaths observed in pivotal HFpEF trials. However, predictors and outcomes of in-hospital SCA in HFpEF have not been well characterized. We queried the Nationwide Inpatient Sample (2016 to 2017) to identify adult hospitalizations with a diagnosis of HFpEF. Patients with acute or chronic conditions associated with SCA (e.g., acute myocardial infarction, acute pulmonary embolism, sarcoidosis) were excluded. We ascertained whether SCA occurred during these hospitalizations, identified predictors of SCA using multivariate logistic regression, and determined outcomes of SCA in HFpEF. Of 2,909,134 hospitalizations, SCA occurred in 1.48% (43,105). The mean age of the SCA group was 72.3 ± 12.4 years, 55.8% were women, and 66.4% were White. Presence of third-degree atrioventricular block (odds ratio [OR] 5.95, 95% confidence interval [CI] 5.31 to 6.67), left bundle branch block (OR 1.96, 95% CI 1.72 to 2.25), and liver disease (OR 1.87, 95% CI 1.73 to 2.02) were the leading predictors of SCA in HFpEF. After excluding patients with do-not-resuscitate status, the SCA group versus those without SCA had higher mortality (25.9% vs 1.6%), major bleeding complications (4.1% vs 1.7%), increased use of percutaneous coronary intervention (2.5% vs 0.7%), and mechanical circulatory assist device (1.2% vs 0.1%). These observational inpatient data suggest identifiable risk factors for SCA in HFpEF including cardiac arrhythmias. Further research is warranted to identify the best tools to risk-stratify patients with HFpEF to implement targeted SCA prevention strategies.

Comparing eligibility for statin therapy for primary prevention under 2022 USPSTF recommendations and the 2018 AHA/ACC/ multi-society guideline recommendations: From National Health and Nutrition Examination Survey.

INTRODUCTION: The United States Preventive Services Taskforce (USPSTF) recently released recommendations for statin therapy eligibility for the primary prevention of cardiovascular disease (CVD). We report the proportion and the absolute number of US adults who would be eligible for statin therapy under these recommendations and compare them with the previously published 2018 American Heart Association (AHA)/ American College of Cardiology (ACC)/ Multisociety (MS) Cholesterol guidelines. METHODS: We used data from the National Health and Nutrition Examination Survey (NHANES) 2017-2020 of adults aged 40-75 years without prevalent self-reported atherosclerotic CVD (ASCVD) and low-density lipoprotein-cholesterol <190 mg/dL. The 2022 USPSTF recommends statin therapy for primary prevention in those with a 10-year ASCVD risk of ≥10% and ≥ 1 CVD risk factor (diabetes mellitus, dyslipidemia, hypertension, or smoking). The 2018 AHA/ ACC/ MS Cholesterol guideline recommends considering statin therapy for primary prevention for those with diabetes mellitus, or 10-year ASCVD risk ≥20% or 10-year ASCVD risk 7.5 to <20% after accounting for risk-enhancers and shared decision making. Survey recommended weights were used to project these proportions to national estimates. RESULTS: Among 1799 participants eligible for this study, the weighted mean age was 56.0 ± 0.5 years, with 53.0% women (95% confidence interval [CI] 49.7, 56.3), and 10.6% self-reported NH Black individuals (95% CI 7.7, 14.3). The weighted mean 10-year ASCVD risk was 9.6 ± 0.3%. The 2022 USPSTF recommendations and the 2018 AHA/ ACC/ MS Cholesterol guidelines indicated eligibility for statin therapy in 31.8% (95% CI 28.6, 35.1) and 46.8% (95% CI 43.0, 50.5) adults, respectively. These represent 33.7 million (95% CI 30.4, 37.2) and 49.7 million (95% CI 45.7, 53.7) adults, respectively. For those with diabetes mellitus, 2022 USPSTF recommended statin therapy in 63.0% (95% CI 52.1, 72.7) adults as compared with all adults with diabetes aged 40-75 years under the 2018 AHA/ ACC/ MS Cholesterol guidelines. CONCLUSION: In this analysis of the nationally representative US population from 2017 to 2020, approximately 15% (~16.0 million) fewer adults were eligible for statin therapy for primary prevention under the 2022 USPSTF recommendations as compared to the 2018 AHA/ ACC/ MS Cholesterol guideline.

Contact tracing of COVID-19 in Karnataka, India: Superspreading and determinants of infectiousness and symptomatic infection.

BACKGROUND: India has experienced the second largest outbreak of COVID-19 globally, yet there is a paucity of studies analysing contact tracing data in the region which can optimise public health interventions (PHI's). METHODS: We analysed contact tracing data from Karnataka, India between 9 March and 21 July 2020. We estimated metrics of transmission including the reproduction number (R), overdispersion (k), secondary attack rate (SAR), and serial interval. R and k were jointly estimated using a Bayesian Markov Chain Monte Carlo approach. We studied determinants of risk of further transmission and risk of being symptomatic using Poisson regression models. FINDINGS: Up to 21 July 2020, we found 111 index cases that crossed the super-spreading threshold of ≥8 secondary cases. Among 956 confirmed traced cases, 8.7% of index cases had 14.4% of contacts but caused 80% of all secondary cases. Among 16715 contacts, overall SAR was 3.6% [95% CI, 3.4-3.9] and symptomatic cases were more infectious than asymptomatic cases (SAR 7.7% vs 2.0%; aRR 3.63 [3.04-4.34]). As compared to infectors aged 19-44 years, children were less infectious (aRR 0.21 [0.07-0.66] for 0-5 years and 0.47 [0.32-0.68] for 6-18 years). Infectors who were confirmed ≥4 days after symptom onset were associated with higher infectiousness (aRR 3.01 [2.11-4.31]). As compared to asymptomatic cases, symptomatic cases were 8.16 [3.29-20.24] times more likely to cause symptomatic infection in their secondary cases. Serial interval had a mean of 5.4 [4.4-6.4] days, and case fatality rate was 2.5% [2.4-2.7] which increased with age. CONCLUSION: We found significant heterogeneity in the individual-level transmissibility of SARS-CoV-2 which could not be explained by the degree of heterogeneity in the underlying number of contacts. To strengthen contact tracing in over-dispersed outbreaks, testing and tracing delays should be minimised and retrospective contact tracing should be implemented. Targeted measures to reduce potential superspreading events should be implemented. Interventions aimed at children might have a relatively small impact on reducing transmission owing to their low symptomaticity and infectivity. We propose that symptomatic cases could cause a snowballing effect on clinical severity and infectiousness across transmission generations; further studies are needed to confirm this finding.

Adjusting Reported COVID-19 Deaths for the Prevailing Routine Death Surveillance in India.

In India, the "low mortality" narrative based on the reported COVID-19 deaths may be causing more harm than benefit. The extent to which COVID-19 deaths get reported depends on the coverage of routine death surveillance [death registration along with medical certification of cause of death (MCCD)] and the errors in MCCD. In India, the coverage of routine death surveillance is 18.1%. This is compounded by the fact that COVID-19 death reporting is focused among reported cases and the case detection ratio is low. To adjust for the coverage of routine death surveillance and errors in MCCD, we calculated a correction (multiplication) factor at national and state level to produce an estimated number of COVID-19 deaths. As on July 31, 2020, we calculated the infection fatality ratio (IFR) for India (0.58:100-1.16:100) using these estimated COVID-19 deaths; this is comparable with the IFR range in countries with near perfect routine death surveillance. We recommend the release of excess deaths data during COVID-19 (at least in states with high death registration) and post-mortem COVID-19 testing as a surveillance activity for a better understanding of under-reporting. In its absence, we should adjust reported COVID-19 deaths for the coverage of routine death surveillance and errors in MCCD. This way we will have a clear idea of the true burden of deaths and our public health response will never be inadequate. We recommend that "reported" or "estimated" is added before the COVID-19 death data and related indicators for better clarity and interpretation.

The association of D-dimers with mortality, intensive care unit admission or acute respiratory distress syndrome in patients hospitalized with coronavirus disease 2019 (COVID-19): A systematic review and meta-analysis.

AIM: To determine if D-dimers are elevated in individuals with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection who have adverse clinical outcomes including all-cause mortality, intensive care unit (ICU) admission or acute respiratory distress syndrome (ARDS). METHODS: We conducted a systematic review and meta-analysis of the published literature in PubMed, Embase and Cochrane databases through April 9, 2020 for studies evaluating D-dimer levels in SARS-COV-2 infected patients with and without a composite clinical endpoint, defined as the presence of all-cause of mortality, Intensive care unit (ICU) admission or acute respiratory distress syndrome (ARDS). A total of six studies were included in the meta-analysis. RESULTS: D-dimers were significantly increased in patients with the composite clinical end point than in those without (SMD, 1.67 ug/ml (95% CI, 0.72-2.62 ug/ml). The SMD of the studies (Tang et al, Zhou et al, Chen et al), which used only mortality as an outcome measure was 2.5 ug/mL (95% CI, 0.62-4.41 ug/ml). CONCLUSION: We conclude that SARS-CoV-2 infected patients with elevated D-dimers have worse clinical outcomes (all-cause mortality, ICU admission or ARDS) and thus measurement of D-dimers can guide in clinical decision making.

Transmission dynamics of the COVID-19 epidemic in India and modeling optimal lockdown exit strategies.

India imposed one of the world's strictest population-wide lockdowns on March 25, 2020 for COVID-19. We estimated epidemiological parameters, evaluated the effect of control measures on the epidemic in India, and explored strategies to exit lockdown. We obtained patient-level data to estimate the delay from onset to confirmation and the asymptomatic proportion. We estimated the basic and time-varying reproduction number (R0 and Rt) after adjusting for imported cases and delay to confirmation using incidence data from March 4 to April 25, 2020. Using a SEIR-QDPA model, we simulated lockdown relaxation scenarios and increased testing to evaluate lockdown exit strategies. R0 for India was estimated to be 2·08, and the Rt decreased from 1·67 on March 30 to 1·16 on April 22. We observed that the delay from the date of lockdown relaxation to the start of the second wave increases as lockdown is extended farther after the first wave peak-this delay is longer if lockdown is relaxed gradually. Aggressive measures such as lockdowns may be inherently enough to suppress an outbreak; however, other measures need to be scaled up as lockdowns are relaxed. Lower levels of social distancing when coupled with a testing ramp-up could achieve similar outbreak control as an aggressive social distancing regime where testing was not increased.

Diagnostic accuracy of non-contact infrared thermometers and thermal scanners: a systematic review and meta-analysis.

Infrared thermal screening, via the use of handheld non-contact infrared thermometers (NCITs) and thermal scanners, has been widely implemented all over the world. We performed a systematic review and meta-analysis to investigate its diagnostic accuracy for the detection of fever. We searched PubMed, Embase, the Cochrane Library, medRxiv, bioRxiv, ClinicalTrials.gov, COVID-19 Open Research Dataset, COVID-19 research database, Epistemonikos, EPPI-Centre, World Health Organization International Clinical Trials Registry Platform, Scopus and Web of Science databases for studies where a non-contact infrared device was used to detect fever against a reference standard of conventional thermometers. Forest plots and Hierarchical Summary Receiver Operating Characteristics curves were used to describe the pooled summary estimates of sensitivity, specificity and diagnostic odds ratio. From a total of 1063 results, 30 studies were included in the qualitative synthesis, of which 19 were included in the meta-analysis. The pooled sensitivity and specificity were 0.808 (95%CI 0.656-0.903) and 0.920 (95%CI 0.769-0.975), respectively, for the NCITs (using forehead as the site of measurement), and 0.818 (95%CI 0.758-0.866) and 0.923 (95%CI 0.823-0.969), respectively, for thermal scanners. The sensitivity of NCITs increased on use of rectal temperature as the reference. The sensitivity of thermal scanners decreased in a disease outbreak/pandemic setting. Changes approaching statistical significance were also observed on the exclusion of neonates from the analysis. Thermal screening had a low positive predictive value, especially at the initial stage of an outbreak, whereas the negative predictive value (NPV) continued to be high even at later stages. Thermal screening has reasonable diagnostic accuracy in the detection of fever, although it may vary with changes in subject characteristics, setting, index test and the reference standard used. Thermal screening has a good NPV even during a pandemic. The policymakers must take into consideration the factors surrounding the screening strategy while forming ad-hoc guidelines.

Utility of Artificial Intelligence Amidst the COVID 19 Pandemic: A Review.

The term machine learning refers to a collection of tools used for identifying patterns in data. As opposed to traditional methods of pattern identification, machine learning tools relies on artificial intelligence to map out patters from large amounts of data, can self-improve as and when new data becomes available and is quicker in accomplishing these tasks. This review describes various techniques of machine learning that have been used in the past in the prediction, detection and management of infectious diseases, and how these tools are being brought into the battle against COVID-19. In addition, we also discuss their applications in various stages of the pandemic, the advantages, disadvantages and possible pit falls.

SARS-CoV-2 epidemic in India: epidemiological features and in silico analysis of the effect of interventions.

Background: After SARS-CoV-2 set foot in India, the Government took a number of steps to limit the spread of the virus in the country. This included restricted testing, isolation, contact tracing and quarantine, and enforcement of a nation-wide lockdown starting 25 March 2020. The objectives of this study were to i) describe the age, gender distribution, and mortality among COVID-19 patients identified till 14 April 2020 and predict the range of contact rate; and ii) predict the number of  COVID-19 infections after 40 days of lockdown. Methods: We used a cross-sectional descriptive design for the first objective and a susceptible-infected-removed model for in silico predictions. We collected data from government-controlled and crowdsourced websites. Results: Studying age and gender parameters of 1161 Indian COVID-19 patients, the median age was 38 years (IQR, 27-52) with 20-39 year-old males being the most affected group. The number of affected patients were 854 (73.6%) men and 307 (26.4%) women. If the current contact rate continues (0.25-27), India may have 110460 to 220575 infected persons at the end of 40 days lockdown. Conclusion: The disease is majorly affecting a younger age group in India. Interventions have been helpful in preventing the worst-case scenario in India but will be unable to prevent the spike in the number of cases.