Risultati per: Indicazioni per gli screening oncologici

Circulation, Volume 146, Issue Suppl_1, Page A10856-A10856, November 8, 2022. Introduction:Prior AF screening trials demonstrated low yield, highlighting the need for more targeted approaches. An AI algorithm was developed to identify ECG signatures of AF risk during normal sinus rhythm, which has been validated in diverse external populations.Hypothesis:An AI-guided, targeted screening approach could improve the diagnosis of AF.MethodsWe conducted a pragmatic decentralized trial to prospectively recruit patients with stroke risk factors but no prior AF. The AI algorithm was applied to the ECGs performed in routine practice and divided patients into high- or low-risk groups. The primary endpoint was AF lasting ≥ 30 seconds on a subsequent 30-day continuous cardiac rhythm monitor. In a secondary analysis, trial participants were 1:1 propensity score-matched to real-world controls derived from the eligible but unenrolled population.ResultsA total of 1,003 patients from 40 U.S. states completed the study, with a mean age of 74 [SD 8.8] years. Over a mean of 22.3 days of continuous monitoring, AF was detected in 6 (1.6%) of low-risk patients and 48 (7.6%) of high-risk patients (OR 4.98 [2.11-11.75], p

Circulation, Volume 146, Issue Suppl_1, Page A14221-A14221, November 8, 2022. Introduction:Cardiac amyloidosis (CA) is an increasingly recognized cause of heart failure. Novel therapies for transthyretin (TTR) CA elevate the need for early identification when treatment has the greatest efficacy. The TTR CA score (TCAS) was recently developed to predict the likelihood of TTR CA in patients undergoing 99mTc-pyrophosphate scintigraphy (PYP) scanning. Its simple inputs could be easily extracted from the electronic health record (EHR), suggesting possible use as a quick, EHR-based screening tool. We perform the first external validation of the TCAS using only EHR-extracted data. We hypothesized that a screening algorithm like TCAS could be generalizable and feasible to implement using our EHR.Methods:EHR data were extracted on all patients at a large academic medical center who underwent PYP scans between 2017 and 2022. PYP scan was considered positive if the patient was part of our institution’s registry of patients with confirmed CA. Inputs – age, sex, echocardiogram wall thickness and ejection fraction, and hypertension diagnosis codes – were converted to TCAS scores. Area under the receiver operating characteristic curve (AUROC) was calculated to analyze predictive performance. Using a TCAS ≥6 as the threshold for high-risk, performance characteristics were calculated.Results:Of 365 patients who underwent PYP scan, 335 had sufficient records to calculate a TCAS. Of these 335 patients, 69 (20.6%) had positive PYP scans. Median TCAS was 5 (interquartile range 4,7). The AUROC was 0.826, with a sensitivity of 87.0%, specificity of 63.9%, positive predictive value of 38.5%, and negative predictive value (NPV) of 95.0%.Conclusions:External validation of the TCAS supports its strong predictive performance with comparable AUROCs to the initial study (0.84-0.89). Clinically, with its high NPV, the TCAS has potential to serve as a simple, low-cost screen to avoid costly PYP scans. We demonstrate the ability to extract all inputs from the EHR, without need for manual chart review or calculation, suggesting that the TCAS could function as an EHR-based screening tool. Low-cost screening tools are needed to identify patients who would benefit from TTR CA workup with PYP scanning, facilitating treatment at earlier disease stages.

Circulation, Volume 146, Issue Suppl_1, Page A13817-A13817, November 8, 2022. Previous studies from India reported management of diabetes (diagnosed: 53%, treated: 41%) among adults 15-49 years, a fraction of those suffering from disease. This study aimed to provide nationally-representative estimates of (i) the proportion of all adults (18+ years) with prediabetes and diabetes mellitus (DM), and (ii) the heterogeneity in their cascade of care by natal sex, age, and urbanicity.Methods:Using data from non-pregnant women (n = 959,468) and men (n = 935,829) in the National Family Health Survey-V (2019-21), we estimated the sex-specific prevalence of prediabetes and DM (see footnotes ofFigure), and among adults with diabetes, the self-reported care cascade (ever screened, diagnosed, taking medication, under control defined by normoglycemia). All estimates incorporated the complex survey design and were stratified by urban versus rural or by age group (18-39, 40-64, 65+).Results:Nationally, the prevalence of prediabetes and DM were 11.3% (95%CI: 11.1,11.4) and 6.5% (95%CI: 6.4, 6.6) respectively. Prediabetes was similar in urban areas (vs rural areas) among men (%; 12.9 vs 12.0) and women (%; 11.1 vs 10.1). DM was higher in urban areas (vs rural areas) among men (%; 8.8 vs 5.6) and women (9.0 vs 5.3). Both prediabetes and DM were higher with increasing age. Screening in the total population was 30%, and was higher in urban areas, among women and higher with age. Among those with DM, only 72%, 51%, and 29% reported being diagnosed, taking medication, and under control, respectively. Diagnosis, treatment and control were also higher in urban areas, women, and older age groups (Figure).Conclusions:Despite the high prevalence of diabetes, there is a high unmet need in screening, diagnosis, treatment and control of disease nationally, and this is pronounced in rural adults. Moreover, the prevalence of prediabetes is high in rural areas, and those under 40 years, requiring a comprehensive approach for prevention and management.

Circulation, Volume 146, Issue Suppl_1, Page A14935-A14935, November 8, 2022. Introduction:The electrocardiogram (ECG) contains information about age-related changes in cardiovascular physiology, which have been linked with the frailty syndrome.Hypothesis:We sought to develop and validate a predictive model leveraging the 12-lead ECG to screen for frailty as defined by a prospective reference standard.Methods:We conducted a population-based cohort study using data from the Canadian Longitudinal Study of Aging (CLSA). From 2010-2015, the CLSA enlisted a diverse and multi-ethnic sample of community-dwelling adults 45-85 years of age. Comprehensive phenotyping was performed through interviews at participants’ homes and assessments at data collection sites. Frailty was quantified by the 110-item Frailty Index (FI) Composite, consisting of self-reported comorbidities, blood tests, physical performance tests, body composition tests, cardiovascular and pulmonary tests, cognitive and sensory tests. After dividing our sample into training (80%) and test (20%) sets, we developed an end-to-end deep neural network to predict the FI score based on the 12-lead ECG time series.Results:A total of 26,700 ECGs with paired FI scores were evaluated. For classification of FI quintiles, a bidirectional long short-term memory (BiLSTM) neural network with a cross-entropy loss function achieved a 5-fold mean area under the receiver operating characteristics curve (AUROC) of 0.70 and area under the precision-recall curve (AUPRC) of 0.36. Predictive performance was superior for classification of the first (most robust) quintile that had AUROC 0.79 and AUPRC 0.46, and the fifth (most frail) quintile that had AUROC 0.79 and AUPRC 0.56, as compared to the middle quintiles that had AUROC 0.60-0.69 and AUPRC 0.27-0.29.Conclusions:Our deep learning model can be used to screen for high or low levels of frailty based on the readily available 12-lead ECG. Additional research is underway to gain insights into other representations of the ECG signal and relative importance of the ECG features.

Circulation, Volume 146, Issue Suppl_1, Page A10560-A10560, November 8, 2022. Introduction.The mHealth Screening to Prevent Strokes (mSToPS) study reported that, compared with standard care, screening older Americans for AF using 2-week Zio patch monitors increased AF diagnosis and oral anticoagulant prescription within one year. The monitored group was also observed to have fewer strokes and deaths at 3 years. The cost-effectiveness of AF screening in this manner has not been explored.Methods.We conducted a health economic analysis of AF screening with Zio patch monitors using patient-level data from the mSToPS study. Clinical outcomes and costs from the payer perspective were obtained from enrollment through 3 years using Aetna claims data. Individual costs, survival and quality-adjusted survival (QALYs) were projected over a lifetime horizon using regression modeling, US life tables and external literature where needed. Potential imbalances between groups were adjusted for with propensity score bin bootstrapping.Results.Study group participants (mean age 74 years, 41% female, median CHA2DS2-VASC score 3) wore an average of 1.7 two-week monitors at an average cost of $601/person. Over 3 years, monitored individuals were more likely than unmonitored to have outpatient visits, including to cardiology, but less likely to require emergency department visits or hospitalization (see Table). Pharmacy costs over 3 years were similar between groups. Total adjusted 3-year costs, including monitors, were slightly higher (difference $1,170, 95% CI -1315 to 3657) in the monitoring group. In patient-level projections, the monitoring group had slightly better total and quality-adjusted survival (11.91 vs. 11.82 life years, 9.38 vs. 9.30 QALYs) and slightly higher lifetime costs, resulting in an incremental cost-effectiveness ratio of $16,978/QALY gained.Conclusions.Based on lifetime projections derived from the mSToPS study, we found AF screening using 2-week Zio patch monitors to provide high value from a health economic perspective.

Circulation, Volume 146, Issue Suppl_1, Page A9822-A9822, November 8, 2022. Background:Data mining of electronic health records (EHR) has been used as a strategy to identify patients with undiagnosed familial hypercholesterolemia (FH). Most studies have been limited by the absence of both phenotypic and genotypic data in the same cohort.Methods:Using a subset of the Geisinger MyCode Community Health Initiative (MyCode) cohort with both exome sequencing and EHR data (n=130,257), we ran two FH screening algorithms to determine genetic and phenotypic diagnostic yields: the Mayo Clinic algorithm (Mayo), which identifies those with LDL-C levels >190 mg/dL, and FIND FH®, the Family Heart Foundation’s machine learning model, to identify individuals with phenotypes suggestive of FH. With 29,243 excluded by Mayo (for secondary causes of hypercholesterolemia, no lipid value in EHR), 52,034 excluded by FIND-FH (insufficient data to run the model), and 187 excluded for prior FH diagnosis, a final cohort of 59,729 participants screened by both algorithms was created. Genetic diagnosis was based on the presence of a pathogenic or likely pathogenic (P/LP) variant in 3 genes implicated in FH via genomic screening. Charts from 180 variant negative participants (60 controls; 120 identified by FIND FH and/or Mayo) were reviewed to calculate Dutch Lipid Clinic Network scores; a score >5 defined probable or definite FH.Results:Mayo flagged 10,415 subjects; 164 (1.6%) had an FH P/LP variant. FIND-FH flagged 573; 28 (4.9%) had an FH P/LP variant giving a net yield from both algorithms of 167/240 (70%). Confirmation of a phenotypic diagnosis was constrained by lack of EHR data on physical findings or family history (high cholesterol, premature atherosclerotic disease) required for score calculation. Phenotypic FH by chart review was present by Mayo and/or FIND-FH in 13/120 vs 2/60 not flagged by either (p< 0.09).Conclusion:After excluding those with a prior FH diagnosis, applying two recognized phenotypic FH screening algorithms to the eligible MyCode cohort identified 70% of those with a P/LP FH variant. Limitations to this approach include participant exclusions for each algorithm, a low yield of positive genomic screening for Mayo, and a low yield of participants for FIND FH. Phenotypic diagnosis was rarely achievable due to missing data.

Circulation, Volume 146, Issue Suppl_1, Page A13601-A13601, November 8, 2022. Introduction:Truncating variants inTTN(TTNtvs) are the largest genetic cause of dilated cardiomyopathies (DCM). In populations, genetic variation inTTNis pervasive and penetrance estimates for DCM are low, even when carriers are limited to those withTTNtvs in exons with “percentage spliced in” index > 90 (hiPSI), a representation of constitutive cardiac expression. Patients with cardiomyopathies (CM) often carry a large number of other cardiac conditions, such as atrial fibrillation (Afib). We sought to confirm this association and determine whether the presence of Afib and a hiPSITTNtv predicted CM.Results:Leveraging clinicogenomic data from ~450,000 individuals in two health systems, we show support for associations with both CM and Afib at the population level. We perform a sliding window analysis ofTTNtvs and confirm the association is specific to hiPSI exons, with no meaningful associations in exons with less cardio expression. The combination of hiPSITTNtv carrier status and early Afib diagnosis (dx before age 60) finds a subset ofTTNcarriers at high risk for CM (34% prevalence) – this risk is 3.5 fold higher than that of all hiPSITTNtv carriers (9% prevalence) and 5-fold higher than non-carriers with early Afib (5% prevalence, p=4.8e-56 after controlling for age and sex). Further, Afib either predates or is concurrently diagnosed with CM in 72% of those with both diagnoses.Conclusion:CM and Afib are linked in hiPSITTNtv carriers and may represent progressive manifestations of structurally-based heart failure. Our retrospective analysis suggests hiPSITTNtv screening (~0.5% of cohorts) in conjunction with routine monitoring for arrhythmias may be an effective strategy to improve outcomes and reduce the incidence of severe cardio outcomes in the population.

Purpose
Risk factor-based models struggle to accurately predict the development of cardiovascular disease (CVD) at the level of the individual. Ways of identifying people with low predicted risk who will develop CVD would allow stratified advice and support informed treatment decisions about the initiation or adjustment of preventive medication, and this is the aim of this prospective cohort study.

Participants
The Tayside Screening for Cardiac Events (TASCFORCE) study recruited men and women aged≥40 years, free from known CVD, with a predicted 10-year risk of coronary heart disease

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Annals of Internal Medicine, Volume 175, Issue 10, Page W115-W116, October 2022.

Annals of Internal Medicine, Volume 175, Issue 10, Page W115, October 2022.

This systematic review to support the 2022 US Preventive Services Task Force Recommendation Statement on screening for depression and suicide risk in children and adolescents summarizes published evidence on the benefits and harms of screening for and treatment of depression and suicide risk in children and adolescents 18 years or younger.

Suicidal behavior is among the most critical of medical emergencies for adolescents. Among US youth aged 15 to 24 years, intentional self-harm (suicide) is the second leading cause of death and accounted for 6807 deaths in 2018. Recent statistics are ominous regarding significant increases in suicidal behavior among adolescents; from 2009 and 2019, there were significant increases in the prevalence of those who reported having seriously considered attempting suicide (13.8% to 18.8%) and having attempted suicide (6.3% to 8.9%). These increases occurred prior to the COVID-19 pandemic. A study that evaluated emergency department visits for suspected suicidal behavior among persons aged 12 to 25 years before and during the COVID-19 pandemic found that the mean number of weekly visits for suspected suicide attempts increased from February through March 2021, compared with the same period in 2019, with a 50.6% increase among girls and a 3.7% increase among boys.

This 2022 Recommendation Statement from the US Preventive Services Task Force recommends screening for major depressive disorder (MDD) in adolescents aged 12 to 18 years (B recommendation) and concludes that current evidence is insufficient to assess the balance of benefits and harms of screening for MDD in children 11 years or younger (I statement) and of screening for suicide risk in children and adolescents (I statement).