Benefits, risks, and cost-effectiveness of COVID-19 self-tests from a consumer’s perspective

Background The aim of this study is to quantify the health benefits, risks, and cost-effectiveness of COVID-19 self-tests from a consumer’s perspective in Germany. Methods The analysis is based on a modelling approach using secondary data. The clinical endpoints considered in this analysis are avoided SARS-CoV-2 infections and secondary severe clinical events (death, intensive care unit (ICU) admission, and long COVID syndrome). The study determines the number of self-tests that need to be conducted under a 7-day incidence of 75 per 100,000 population to prevent one infection or severe clinical event. Furthermore, the study calculates the cost of testing per avoided clinical event and quality-adjusted life year (QALY) gained from a consumer perspective. Results Disregarding the rate of unreported COVID-19 cases, 4556 self-tests need to be conducted (over 12 years) in order to avoid one undesirable event (death, intensive care unit stay, or long COVID syndrome). Ninety percent of infections are not avoided among direct contacts but along the chain of infection. The costs per quality-adjusted life year gained from a consumer’s perspective are €5870. This ratio is particularly sensitive to the 7-day incidence, effective reproduction number, and the age of contacts. Conclusions The benefits of self-testing in the general population at a 7-day incidence rate of 75 per 100,000 appear to be minor. Nevertheless, cost-effectiveness may still be acceptable in the presence of higher-risk contacts given the low costs of self-test kits in Germany.

A systematic review of the evidence supporting post-operative antithrombotic use following cardiopulmonary bypass in children with CHD

Objectives: To determine the optimal antithrombotic agent choice, timing of initiation, dosing and duration of therapy for paediatric patients undergoing cardiac surgery with cardiopulmonary bypass. Methods: We used PubMed and EMBASE to systematically review the existing literature of clinical trials involving antithrombotics following cardiac surgery from 2000 to 2020 in children 0–18 years. Studies were assessed by two reviewers to ensure they met eligibility criteria. Results: We identified 10 studies in 1929 children across three medications classes: vitamin K antagonists, cyclooxygenase inhibitors and indirect thrombin inhibitors. Four studies were retrospective, five were prospective observational cohorts (one of which used historical controls) and one was a prospective, randomised, placebo-controlled, double-blind trial. All included were single-centre studies. Eight studies used surrogate biomarkers and two used clinical endpoints as the primary endpoint. There was substantive variability in response to antithrombotics in the immediate post-operative period. Studies of warfarin and aspirin showed that laboratory monitoring levels were frequently out of therapeutic range (variably defined), and findings were mixed on the association of these derangements with bleeding or thrombotic events. Heparin was found to be safe at low doses, but breakthrough thromboembolic events were common. Conclusion: There are few paediatric prospective randomised clinical trials evaluating antithrombotic therapeutics post-cardiac surgery; most studies have been observational and seldom employed clinical endpoints. Standardised, validated endpoints and pragmatic trial designs may allow investigators to determine the optimal drug, timing of initiation, dosing and duration to improve outcomes by limiting post-operative morbidity and mortality related to bleeding or thrombotic events.

Comparison of CINtec PLUS cytology and cobas HPV test for triaging Canadian patients with LSIL cytology referred to colposcopy: A two-year prospective study

OBJECTIVES & METHODS: CINtec PLUS and cobas HPV tests were compared for triaging patients referred to colposcopy with a history of LSIL cytology in a 2-year prospective study. Cervical specimens were tested once at enrollment, and test positivity rates determined. Test performance was ascertained with cervical intraepithelial neoplasia grade 2 or worse (CIN2+) and CIN3 or worse (CIN3+) serving as clinical endpoints. RESULTS: In all ages, (19–76 years, n= 598), 44.3% tested CINtec PLUS positive vs. 55.4% HPV positive (p< 0.001). To detect CIN2+ (n= 99) CINtec PLUS was 81.8% sensitive vs. 93.9% for HPV testing (p= 0.009); genotype 16/18-specific sensitivity was 46.5%. Specificity was 52.9% vs. 36.6%, respectively (p< 0.001). In all ages, to detect CIN3+ (n= 44), sensitivity was 93.2% for both tests; genotype 16/18-specific sensitivity was 52.3%. Specificity was 48.4% for CINtec PLUS vs. 31.1% for HPV testing (p< 0.001). In patients < 30 years, CINtec was 91.7% sensitive vs 95.8% for HPV testing (p= 0.549). CONCLUSIONS: CINtec PLUS or cobas HPV test could serve as a predictor of CIN3+ with high sensitivity in patients referred to colposcopy with a history of LSIL regardless of age while significantly reducing the number of LSIL referral patients requiring further investigations and follow-up in colposcopy clinics.

MRI Radiomics in Prostate Cancer: A Reliability Study

BackgroundRadiomics can provide quantitative features from medical imaging that can be correlated to clinical endpoints. The challenges relevant to robustness of radiomics features have been analyzed by many researchers, as it seems to be influenced by acquisition and reconstruction protocols, as well as by the segmentation of the region of interest (ROI). Prostate cancer (PCa) represents a difficult playground for this technique, due to discrepancies in the identification of the cancer lesion and the heterogeneity of the acquisition protocols. The aim of this study was to investigate the reliability of radiomics in PCa magnetic resonance imaging (MRI).MethodsA homogeneous cohort of patients with a PSA rise that underwent multiparametric MRI imaging of the prostate before biopsy was tested in this study. All the patients were acquired with the same MRI scanner, with a standardized protocol. The identification and the contouring of the region of interest (ROI) of an MRI suspicious cancer lesion were done by two radiologists with great experience in prostate cancer (&gt;10 years). After the segmentation, the texture features were extracted with LIFEx. Texture features were then tested with intraclass coefficient correlation (ICC) analysis to analyze the reliability of the segmentation.ResultsForty-four consecutive patients were included in the present analysis. In 26 patients (59.1%), the prostate biopsy confirmed the presence of prostate cancer, which was scored as Gleason 6 in 6 patients (13.6%), Gleason 3 + 4 in 8 patients (18.2%), and Gleason 4 + 3 in 12 patients (27.3%). The reliability analysis conversely showed poor reliability in the majority of the MRI acquisition (61% in T2, 89% in DWI50, 44% in DWI400, and 83% in DWI1,500), with ADC acquisition only showing better reliability (poor reliability in only 33% of the texture features).ConclusionsThe low ratio of reliability in a monoinstitutional homogeneous cohort represents a significant alarm bell for the application of MRI radiomics in the field of prostate cancer. More work is needed in a clinical setting to further study the potential of MRI radiomics in prostate cancer.

A reusable benchmark of brain-age prediction from M/EEG resting-state signals

Population-level modeling can define quantitative measures of individual aging by applying machine learning to large volumes of brain images. These measures of brain age, obtained from the general population, helped characterize disease severity in neurological populations, improving estimates of diagnosis or prognosis. Magnetoencephalography (MEG) and Electroencephalography (EEG) have the potential to further generalize this approach towards prevention and public health by enabling assessments of brain health at large scales in socioeconomically diverse environments. However, more research is needed to define methods that can handle the complexity and diversity of M/EEG signals across diverse real-world contexts. To catalyse this effort, here we propose reusable benchmarks of competing machine learning approaches for brain age modeling. We benchmarked popular classical machine learning pipelines and deep learning architectures previously used for pathology decoding or brain age estimation in 4 international M/EEG cohorts from diverse countries and cultural contexts, including recordings from more than 2500 participants. Our benchmarks were built on top of the M/EEG adaptations of the BIDS standard, providing tools that can be applied with minimal modification on any M/EEG dataset provided in the BIDS format. Our results suggest that, regardless of whether classical machine learning or deep learning was used, the highest performance was reached by pipelines and architectures involving spatially aware representations of the M/EEG signals, leading to R^2 scores between 0.60-0.71. Hand-crafted features paired with random forest regression provided robust benchmarks even in situations in which other approaches failed. Taken together, this set of benchmarks, accompanied by open-source software and high-level Python scripts, can serve as a starting point and quantitative reference for future efforts at developing M/EEG-based measures of brain aging. The generality of the approach renders this benchmark reusable for other related objectives such as modeling specific cognitive variables or clinical endpoints.

Artificial intelligence-aided clinical annotation of a large multi-cancer genomic dataset

To accelerate cancer research that correlates biomarkers with clinical endpoints, methods are needed to ascertain outcomes from electronic health records at scale. Here, we train deep natural language processing (NLP) models to extract outcomes for participants with any of 7 solid tumors in a precision oncology study. Outcomes are extracted from 305,151 imaging reports for 13,130 patients and 233,517 oncologist notes for 13,511 patients, including patients with 6 additional cancer types. NLP models recapitulate outcome annotation from these documents, including the presence of cancer, progression/worsening, response/improvement, and metastases, with excellent discrimination (AUROC > 0.90). Models generalize to cancers excluded from training and yield outcomes correlated with survival. Among patients receiving checkpoint inhibitors, we confirm that high tumor mutation burden is associated with superior progression-free survival ascertained using NLP. Here, we show that deep NLP can accelerate annotation of molecular cancer datasets with clinically meaningful endpoints to facilitate discovery.

Biomarkers of the Physical Function Mobility Domains Among Patients Hospitalized in Internal Medicine

Hospitalized patients in internal medicine have an increased risk of low physical reserve which further declines during the hospital stay. The diagnosis requires bed-side testing of functional domains or more complex investigations of the muscle mass. Clinically useful biomarkers of functional status are needed, thus we aimed to explore the potential of microRNAs. Among hospitalized patients, we recorded the basic demographics, anthropometrics, nutritional status, and physical function domains: hand-grip strength (HGS, abnormal values M<30 kg, W<20 kg), balance (<30 s), chair-stands speed (CHSS<0.5/s) and gait speed (GS<0.8 m/s). A panel of five micro-RNAs (miRNA 1, miRNA 133a, miRNA 133b, miRNA 29a, miRNA 29b) and basic blood biochemistry and vitamin D values were recorded. We enrolled 80 patients (M40, W40), with a mean age of 68.8±8.4 years. Obesity was observed in 27.5 % and 30 %, low HGS and low CHSS in 65.0, 77.5 %, and 80, 90 % of men and women respectively. The median hospital stay was 6.5 days. MiRNA29a and miRNA29b have the strongest correlation with the triceps skinfold (miRNA 29b, r=0.377, p=0.0006) and CHSS (miRNA 29a, r=0.262, p=0.02). MiRNA 29a, miRNA 29b and 133a levels were significantly higher in patients with CHSS<0.5/s. Other anthropometric parameters, mobility domains, or vitamin D did not correlate. All miRNAs except of miRNA 1, could predict low CHSS (miRNA29b, AUROC=0.736 CI 0.56-0.91, p=0.01), particularly in patients with low HGS (miRNA 29b, AUROC=0.928 CI 0.83-0.98). Among hospitalized patients in internal medicine, low functional status was frequent. MicroRNAs were fair biomarkers of the antigravity domain, but not other domains. Larger studies with clinical endpoints are needed.

Relationship Between Diabetes Mellitus and the Prognosis of Ischemic Heart Failure

Background: Ischemic heart failure (IHF) is an important type of heart failure (HF). The aim of this study is to analyze the clinical features of IHF, investigate the prognostic value of clinical risk factors, especially DM-related factors, on the IHF patients, so as to provide reference for the high-quality health management of IHF patients.Methods: A total of 430 IHF patients were divided into comorbid (IHF+ DM) group or IHF group according to whether they had DM in this cross sectional and prospective study. The patients were collected for their clinical information, and were followed up for 3.3 years to record the composite clinical endpoints including all-cause death, stroke, myocardial infarction and decompensated heart failure. The prognostic value of DM-related and other traditional risk factors with IHF were evaluated. Results: Among 430 patients with IHF, 176 had DM. Compared with the IHF group, the IHF + DM group had higher prevalence of hypertension and overweight, higher levels of FPG and HbA1c, higher proportion of patients in NYHA III – IV grade, larger left ventricular end diastolic diameter (LVEDD) in male group, higher Gensini score and shorter distance of 6-minute walk test (6-MWT). After a median follow-up of 3.3 years, a total of 156 patients (36.3%) had experienced clinical endpoint events. Kaplan Meier analysis showed that the IHF patients with DM history, and higher HbA1c or FPG level would significantly reduce the cumulative survival rates of composite clinical endpoints, as well as the cumulative survival rates of all-cause death and cardiovascular death (all P < 0.05). Cox regression analysis indicated that DM history and higher PFG or HbA1c levels were independently associated with the all-cause death, cardiovascular death, and composite endpoints (all P < 0.05). The incorporation of diabetes related factors (diabetes history, FPG and HbA1c) significantly increased the area under the receiver operating characteristic (ROC) curve of the risk model established by the traditional risk factors (0.803 vs. 0.775, P= 0.039). In addition to DM history, male sex, uric acid ≥ 400 μmol / L, creatinine ≥ 100 μmol / L, Gensini score > 80 and ACEF score were all independent risk factors for the occurrence of composite clinical endpoints in IHF patients.Conclusion: There existed a high incidence rate of DM in the IHF population. The diabetes related factors (diabetes history, FPG, HbA1c) significantly increased the predictive value of the risk model made by the basic risk factors in predicting the IHF outcomes. DM history, male, uric acid ≥ 400 μmol / L, creatinine ≥ 100 μmol / L, Gensini score > 80 and ACEF score were independent risk factors for the occurrence of composite clinical endpoints in IHF patients.

Serial Assessment of High-Sensitivity Cardiac Troponin and the Effect of Dapagliflozin in Patients with Heart Failure with Reduced Ejection Fraction: An Analysis of the DAPA-HF Trial

Background: Circulating high-sensitivity cardiac troponin T (hsTnT) predominantly reflects myocardial injury, and higher levels are associated with a higher risk of worsening heart failure (HF) and death in patients with HF with reduced ejection fraction (HFrEF). Less is known about the prognostic significance of changes in hsTnT over time, the effects of dapagliflozin on clinical outcomes in relation to baseline hsTnT levels, and the effect of dapagliflozin on hsTnT levels. Methods: DAPA-HF was a randomized, double-blind, placebo-controlled trial of dapagliflozin (10 mg daily) in patients with NYHA class II-IV symptoms and left ventricular ejection fraction ≤40% (median follow-up = 18.2 months). hsTnT (Roche Diagnostics) was measured at baseline in 3,112 patients and at 1 year in 2,506 patients. The primary endpoint was adjudicated worsening HF or cardiovascular death. Clinical endpoints were analyzed according to baseline hsTnT and change in hsTnT from baseline to 1 year. Comparative treatment effects on clinical endpoints with dapagliflozin vs. placebo were assessed by baseline hsTnT. The effect of dapagliflozin on hsTnT was explored. Results: Median baseline hsTnT concentration was 20.0 (25th-75th percentile, 13.7 to 30.2) ng/L. Over 1 year, 67.9% of patients had a ≥10% relative increase or decrease in hsTnT concentrations, and 43.5% had a ≥20% relative change. A stepwise gradient of higher risk for the primary endpoint was observed across increasing quartiles of baseline hsTnT concentration (adjusted hazard ratio [aHR] Q4 vs. Q1, 5.10; 95% CI, 3.67-7.08). Relative and absolute increases in hsTnT over 1 year were associated with higher subsequent risk of the primary endpoint. The relative reduction in the primary endpoint with dapagliflozin was consistent across quartiles of baseline hsTnT (p-interaction = 0.55), but patients in the top quartile tended to have the greatest absolute risk reduction (absolute risk difference, 7.5%; 95% CI, 1.0% - 14.0%). Dapagliflozin tended to attenuate the increase in hsTnT over time compared to placebo (relative least squares mean reduction, −3% [-6% to 0%]; p=0.076). Conclusions: Higher baseline hsTnT and greater increase in hsTnT over 1 year are associated with worse clinical outcomes. Dapagliflozin consistently reduced the risk of the primary endpoint, irrespective of baseline hsTnT levels. Clinical Trial Registration: URL: https://www.clinicaltrials.gov. Unique Identifier: NCT03036124.

The Association of Plant-Based Diet With Cardiovascular Disease and Mortality: A Meta-Analysis and Systematic Review of Prospect Cohort Studies

Background: The association between plant-based diets and cardiovascular disease (CVD) remains poorly characterized. Given that diet represents an important and a modifiable risk factor, this study aimed to assess (1) the relationships between the impact of adherence to plant-based diets on cardiovascular mortality, incident CVD, and stroke; (2) if associations differed by adherence to healthful and less healthful plant-based diets.Methods and Findings: MEDLINE and EMBASE databases were searched up to May 2021. Studies assessing CVD outcomes with relation to plant-based dietary patterns or according to plant-based dietary indices (PDI) were included. A meta-analysis of hazard ratios (HR) was conducted using DerSimonian and Laird random effects model. Thirteen studies involving 410,085 participants were included. Greater adherence to an overall plant-based dietary pattern was significantly associated with a lower risk of cardiovascular mortality (pooled HR: 0.92, 95% CI: 0.86–0.99 p = 0.0193, I2 = 88.5%, N = 124,501) and a lower risk of CVD incidence (pooled HR: 0.90, 95% CI: 0.82–0.98, p = 0.0173, I2 = 87.2%, N = 323,854). Among the studies that used PDI, unhealthful plant-based diets were associated with increased risk of cardiovascular mortality (pooled HR: 1.05, 95% CI: 1.01–1.09, p = 0.0123, I2 = 0.00%, N = 18,966), but not CVD incidence. Conversely, healthful plant-based diets were associated with decreased CVD incidence (pooled HR: 0.87, 95% CI: 0.80–0.95, p = 0.0011, I2 = 57.5%, N = 71,301), but not mortality. Vegetarians also had significantly lower CVD incidence (HR: 0.81, 95% CI: 0.72–0.91, p = 0.0004, I2 = 22.2%, N = 16,254), but similar CVD mortality or stroke risk when compared to the meat-eaters.Conclusion: To date, this comprehensive study examines the effects of a plant-based diet on major clinical endpoints using more holistic PDIs. These findings highlight the favorable role of healthful plant-based foods in reducing cardiovascular mortality and CVD.