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BACKGROUND: The characteristics of electrocardiogram (ECG) abnormalities related to cardiac channelopathies potentially linked to sudden cardiac death (SCD) are not widely recognized in Iran. We examined the prevalence of such ECG patterns and their related factors among adult residents of Tehran, Iran. METHODS: The clinical characteristics and 12-lead ECGs of Tehran Cohort Study participants were examined. Long QT intervals, short QT intervals, Brugada syndrome (BrS) patterns, and early repolarization (ER) were evaluated using computer-based assessment software validated by cardiologists. Logistic regression models were employed to identify the factors associated with the prevalence of different ECG patterns. RESULTS: Out of 7678 available ECGs, 7350 were included in this analysis. Long QT interval, ER pattern, BrS patterns, and short QT interval were found in 3.08%, 1.43%, 0.31%, and 0.03% of participants, respectively. The prevalence of long QT interval increased with age, opium consumption, and presence of hypertension. Younger age, lower body mass index (BMI), alcohol use and male sex were independently linked to an elevated prevalence of ER pattern. Most individuals with BrS patterns were men (95%) and had lower BMI, high- and low-density lipoprotein, and total cholesterol compared to those without the BrS pattern. At a mean follow-up of 30.2 ± 5.5 months, all-cause mortality in the group exhibiting abnormal ECG patterns (6.3%) was approximately twice as high as that in the group without such patterns (2.96%). CONCLUSION: Abnormal ECG patterns corresponding to channelopathies were relatively rare among adult residents of the Tehran population, and their prevalence was influenced by various factors. CLINICAL TRIAL NUMBER: Not applicable.

BACKGROUND: Standard-of-care biomarkers for renal allograft rejection are lagging indicators, signaling existing organ injury. This precludes early intervention, when immunological cascades leading to rejection are most susceptible. Donor-derived cell-free DNA (dd-cfDNA) shows promise as an early indicator of rejection, allowing earlier and possibly more effective treatment. This analysis was designed to assess this promise using real-world dd-cfDNA testing evidence. METHODS: This retrospective analysis of the prospective, observational ProActive registry study (NCT04091984) assessed dd-cfDNA and serum creatinine levels before biopsy in 424 patients with ≥1 dd-cfDNA test (n = 1013) in the 6 mo before biopsy. RESULTS: Of 4667 enrolled patients, 1631 patients had ≥18 mo of follow-up data, of which 424 had a biopsy and were included in this analysis. Twenty-six biopsies showed antibody-mediated rejection (ABMR), 62 showed T cell-mediated rejection, and 336 showed nonrejection; each from a unique patient. dd-cfDNA fractions were significantly elevated 5 mo before ABMR biopsies, and 2 mo before T cell-mediated rejection biopsies, compared with nonrejection biopsies. In contrast, serum creatinine did not discriminate between rejection and nonrejection in advance, or concurrent with biopsy. Among patients with nonrejection biopsies, estimated glomerular filtration rate was significantly lower in cases with ≥2 increased dd-cfDNA results (≥1%), compared with those with 0 or 1 increased dd-cfDNA result. CONCLUSIONS: These data indicate that dd-cfDNA is an early indicator of biopsy-proven rejection, especially ABMR, suggesting a greater role for dd-cfDNA in surveillance to identify patients at high risk of ongoing or future rejection, thus requiring closer monitoring, biopsy, or other management changes.

BACKGROUND: Cerebrospinal fluid (CSF) oligoclonal bands (OCB) are a diagnostic biomarker in multiple sclerosis (MS). The central vein sign (CVS) is an imaging biomarker for MS that may improve diagnostic accuracy. OBJECTIVES: The objective of the study is to examine the diagnostic performance of simplified CVS methods in comparison to OCB in participants with clinical or radiological suspicion for MS. METHODS: Participants from the CentrAl Vein Sign in MS (CAVS-MS) pilot study with CSF testing were included. Select-3 and Select-6 (counting up to three or six CVS+ lesions per scan) were rated on post-gadolinium FLAIR* images. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value for Select-3, Select-6, OCB, and combinations thereof were calculated for MS diagnosis at baseline and at 12 months. RESULTS: Of 53 participants, 25 were OCB+. At baseline, sensitivity for MS diagnosis was 0.75 for OCB, 0.83 for Select-3, and 0.71 for Select-6. Specificity for MS diagnosis was 0.76 for OCB, 0.48 for Select-3, and 0.86 for Select-6. At 12 months, PPV for MS diagnosis was 0.95 for Select-6 and 1.00 for Select-6 with OCB+ status. DISCUSSION: Results suggest similar diagnostic performance of simplified CVS methods and OCB. Ongoing studies will refine whether CVS could be used in replacement or in conjunction with OCB.

Ewing sarcoma (ES) is an uncommon mesenchymal neoplasm that typically develops as a bone mass, although up to 30% arise in extraskeletal sites. ES of the gastrointestinal (GI) and hepatobiliary tract is rare and may be misdiagnosed as other, more common neoplasms that occur in these sites. However, the correct classification of extraskeletal ES is important for timely clinical management and prognostication. We reviewed our experience of ES in the GI and hepatobiliary tract in order to further highlight the clinicopathologic features of these neoplasms and document the potential for misdiagnosis in this setting. The archives and consultation files of 6 academic institutions were retrospectively queried for cases of ES occurring in the GI and hepatobiliary tract. The histologic slides and ancillary studies were reviewed and clinical data were retrieved for each case through the electronic medical records, when available. Twenty-three patients with ES in the GI and/or hepatobiliary tract were identified from 2000 to 2022. Of these, 11 were women and 12 were men with a median age of 38 years (range, 2 to 64). Tumor locations included the pancreas (n=5), liver (n=2), stomach (n=3), colorectum (n=3), and small intestine (n=5), as well as tumors involving multiple organs, pelvis and retroperitoneum (n=5). Tumor size varied between 2 cm and 18 cm. Twenty were primary and 3 were metastases. Of the 23 cases, only 17% were initially diagnosed as ES. The most common misdiagnoses involved various forms of neuroendocrine neoplasia due to expression of synaptophysin and other neuroendocrine markers (22%). A wide variety of diagnoses including GI stromal tumor was considered due to aberrant CD117 expression (4%). The diagnosis of ES was ultimately confirmed by detection of the EWSR1 rearrangement in 22 cases. The remaining case was diagnosed using traditional immunohistochemistry. Follow-up information was available in 20 cases, with follow-up time varying between 2 and 256 months. Six patients with follow-up died of disease between 6 and 60 months following initial presentation. Our data indicate ES in the GI and hepatobiliary tract is commonly misdiagnosed leading to a delay in therapy. In light of the attendant therapeutic and prognostic implications, ES should be considered in the differential diagnosis of any GI or hepatobiliary tumor with epithelioid and/or small round cell morphology.

BACKGROUND: Renal dysfunction is a common complication following liver transplantation (LT). This study aimed to determine whether a comprehensive assessment of kidney function using nineteen serum and urinary biomarkers (BMs) within the first 48 h post-LT could enhance the prediction of severe acute kidney injury (AKI) and the need of kidney replacement therapy (KRT) during the first postoperative week. METHODS: Blood and urine (U) samples were collected during the pre- and postoperative periods. Nineteen BMs were evaluated to assess kidney health in the first 48 h after LT. Classification and regression tree (CART) cross-validation identified key predictors to determine the best BM combination for predicting outcomes. RESULTS: Among 100 LT patients, 36 developed severe AKI, and 34 required KRT within the first postoperative week. Preoperative assessment of U neutrophil gelatinase-associated lipocalin (NGAL) and liver-type fatty acid-binding protein (L-FABP) predicted the need for KRT with 75% accuracy. The combined assessment of U osmolality (OSM), U kidney injury molecule 1 (KIM-1), and tissue inhibitor of metalloproteinase (TIMP-1) within 48 h post-LT predicted severe AKI with 80% accuracy. U-OSM alone, measured within 48 h post-LT, had an accuracy of 83% for predicting KRT need, outperforming any BM combination. CONCLUSIONS: Combined BM analysis can accurately predict severe AKI and KRT needs in the perioperative period of LT. U-OSM alone proved to be an effective tool for monitoring the risk of severe AKI, available in most centers. Further studies are needed to assess its impact on AKI progression postoperatively.Registered at Clinical Trials (clinicaltrials.gov) in March 24th, 2014 by title Acute Kidney Injury Biomarkers: Diagnosis and Application in Pre-operative Period of Liver Transplantation (AKIB) and identifier NCT02095431.

Purpose To clarify the predominant causative plaque constituent for periprocedural myocardial injury (PMI) following percutaneous coronary intervention: (a) erythrocyte-derived materials, indicated by a high plaque-to-myocardium signal intensity ratio (PMR) at coronary atherosclerosis T1-weighted characterization (CATCH) MRI, or (b) lipids, represented by a high maximum 4-mm lipid core burden index (maxLCBI4 mm) at near-infrared spectroscopy intravascular US (NIRS-IVUS). Materials and Methods This retrospective study included consecutive patients who underwent CATCH MRI before elective NIRS-IVUS-guided percutaneous coronary intervention at two facilities. PMI was defined as post-percutaneous coronary intervention troponin T values greater than five times the upper reference limit. Multivariable analysis was performed to identify predictors of PMI. Finally, the predictive capabilities of MRI, NIRS-IVUS, and their combination were compared. Results A total of 103 lesions from 103 patients (median age, 72 years [IQR, 64-78]; 78 male patients) were included. PMI occurred in 36 lesions. In multivariable analysis, PMR emerged as the strongest predictor (P = .001), whereas maxLCBI4 mm was not a significant predictor (P = .07). When PMR was excluded from the analysis, maxLCBI4 mm emerged as the sole independent predictor (P = .02). The combination of MRI and NIRS-IVUS yielded the largest area under the receiver operating curve (0.86 [95% CI: 0.64, 0.83]), surpassing that of NIRS-IVUS alone (0.75 [95% CI: 0.64, 0.83]; P = .02) or MRI alone (0.80 [95% CI: 0.68, 0.88]; P = .30). Conclusion Erythrocyte-derived materials in plaques, represented by a high PMR at CATCH MRI, were strongly associated with PMI independent of lipids. MRI may play a crucial role in predicting PMI by offering unique pathologic insights into plaques, distinct from those provided by NIRS. Keywords: Coronary Plaque, Periprocedural Myocardial Injury, MRI, Near-Infrared Spectroscopy Intravascular US Supplemental material is available for this article. © RSNA, 2024.

BackgroundOne challenge for primary care providers caring for patients with nonalcoholic fatty liver disease is to identify those at the highest risk for clinically significant liver disease.AimTo derive a risk stratification tool using variables from structured electronic health record (EHR) data for use in populations which are disproportionately affected with obesity and diabetes.MethodsWe used data from 344 participants who underwent Fibroscan examination to measure liver fat and liver stiffness measurement [LSM]. Using two approaches, multivariable logistic regression and random forest classification, we assessed risk factors for any hepatic fibrosis (LSM > 7 kPa) and significant hepatic fibrosis (> 8 kPa). Possible predictors included data from the EHR for age, gender, diabetes, hypertension, FIB-4, body mass index (BMI), LDL, HDL, and triglycerides.ResultsOf 344 patients (56.4% women), 34 had any hepatic fibrosis, and 15 significant hepatic fibrosis. Three variables (BMI, FIB-4, diabetes) were identified from both approaches. When we used variable cut-offs defined by Youden's index, the final model predicting any hepatic fibrosis had an AUC of 0.75 (95% CI 0.67-0.84), NPV of 91.5% and PPV of 40.0%. The final model with variable categories based on standard clinical thresholds (i.e., BMI ≥ 30 kg/m2; FIB-4 ≥ 1.45) had lower discriminatory ability (AUC 0.65), but higher PPV (50.0%) and similar NPV (91.3%). We observed similar findings for predicting significant hepatic fibrosis.ConclusionsOur results demonstrate that standard thresholds for clinical risk factors/biomarkers may need to be modified for greater discriminatory ability among populations with high prevalence of obesity and diabetes.

Coronary artery calcification is a significant predictor of cardiovascular disease, with current detection methods like Agatston scoring having limitations in sensitivity. This study aimed to evaluate the effectiveness of a novel CAC quantification method using dual-energy material decomposition, particularly its ability to detect low-density calcium and microcalcifications. A simulation study was conducted comparing the dual-energy material decomposition technique against the established Agatston scoring method and the newer volume fraction calcium mass technique. Detection accuracy and calcium mass measurement were the primary evaluation metrics. The dual-energy material decomposition technique demonstrated fewer false negatives than both Agatston scoring and volume fraction calcium mass, indicating higher sensitivity. In low-density phantom measurements, material decomposition resulted in only 7.41% false-negative (CAC = 0) measurements compared to 83.95% for Agatston scoring. For high-density phantoms, false negatives were removed (0.0%) compared to 20.99% in Agatston scoring. The dual-energy material decomposition technique presents a more sensitive and reliable method for CAC quantification.

This study compared the prognostic value of quantified thoracic artery calcium (TAC) including aortic arch on chest CT and coronary artery calcium (CAC) score on ECG-gated cardiac CT.MethodsA total of 2412 participants who underwent both chest CT and ECG-gated cardiac CT at the same period were included in the Multi-Ethnic Study of Atherosclerosis Exam 5. All participants were monitored for incident atherosclerotic cardiovascular disease (ASCVD) events. TAC is defined as calcification in the ascending aorta, aortic arch and descending aorta on chest CT. The quantification of TAC was measured using the Agatston method. Time-dependent receiver-operating characteristic (ROC) curves were used to compare the prognostic value of TAC and CAC scores.ResultsParticipants were 69±9 years of age and 47% were male. The Spearman correlation between TAC and CAC scores was 0.46 (p

IntroductionCoronary artery calcium (CAC) scans contain useful information beyond the Agatston CAC score that is not currently reported. We recently reported that artificial intelligence (AI)-enabled cardiac chambers volumetry in CAC scans (AI-CAC™) predicted incident atrial fibrillation in the Multi-Ethnic Study of Atherosclerosis (MESA). In this study, we investigated the performance of AI-CAC cardiac chambers for prediction of incident heart failure (HF).MethodsWe applied AI-CAC to 5750 CAC scans of asymptomatic individuals (52% female, White 40%, Black 26%, Hispanic 22% Chinese 12%) free of known cardiovascular disease at the MESA baseline examination (2000-2002). We used the 15-year outcomes data and compared the time-dependent area under the curve (AUC) of AI-CAC volumetry versus NT-proBNP, Agatston score, and 9 known clinical risk factors (age, gender, diabetes, current smoking, hypertension medication, systolic and diastolic blood pressure, LDL, HDL for predicting incident HF over 15 years.ResultsOver 15 years of follow-up, 256 HF events accrued. The time-dependent AUC [95% CI] at 15 years for predicting HF with AI-CAC all chambers volumetry (0.86 [0.82,0.91]) was significantly higher than NT-proBNP (0.74 [0.69, 0.77]) and Agatston score (0.71 [0.68, 0.78]) (p ​

BackgroundOxygen saturation (Spo2) screening has not led to earlier detection of critical congenital heart disease (CCHD). Adding pulse oximetry features (ie, perfusion data and radiofemoral pulse delay) may improve CCHD detection, especially coarctation of the aorta (CoA). We developed and tested a machine learning (ML) pulse oximetry algorithm to enhance CCHD detection.Methods and resultsSix sites prospectively enrolled newborns with and without CCHD and recorded simultaneous pre- and postductal pulse oximetry. We focused on models at 1 versus 2 time points and with/without pulse delay for our ML algorithms. The sensitivity, specificity, and area under the receiver operating characteristic curve were compared between the Spo2-alone and ML algorithms. A total of 523 newborns were enrolled (no CHD, 317; CHD, 74; CCHD, 132, of whom 21 had isolated CoA). When applying the Spo2-alone algorithm to all patients, 26.2% of CCHD would be missed. We narrowed the sample to patients with both 2 time point measurements and pulse-delay data (no CHD, 65; CCHD, 14) to compare ML performance. Among these patients, sensitivity for CCHD detection increased with both the addition of pulse delay and a second time point. All ML models had 100% specificity. With a 2-time-points+pulse-delay model, CCHD sensitivity increased to 92.86% (P=0.25) compared with Spo2 alone (71.43%), and CoA increased to 66.67% (P=0.5) from 0. The area under the receiver operating characteristic curve for CCHD and CoA detection significantly improved (0.96 versus 0.83 for CCHD, 0.83 versus 0.48 for CoA; both P=0.03) using the 2-time-points+pulse-delay model compared with Spo2 alone.ConclusionsML pulse oximetry that combines oxygenation, perfusion data, and pulse delay at 2 time points may improve detection of CCHD and CoA within 48 hours after birth.RegistrationURL: https://www.clinicaltrials.gov/study/NCT04056104?term=NCT04056104&rank=1; Unique identifier: NCT04056104.

BackgroundIschemic cardiomyopathy is the leading cause of heart failure (HF). Most patients do not undergo coronary assessment after HF diagnosis. There are no randomized clinical trials of coronary assessment after HF diagnosis.MethodsUsing an electronic health record cohort of all individuals with HF within the San Francisco Health Network from 2001 to 2019, we identified factors associated with coronary assessment. Then, we studied the association of coronary assessment within 30 days of HF diagnosis with all-cause mortality and a composite of mortality and emergent angiography using a target trial emulation observational comparative-effectiveness approach. Target trial emulation is an approach to causal inference based on creating a hypothetical randomized clinical trial protocol and using observational data to emulate the protocol. We used propensity scores for covariate adjustment. We used national death records to improve the ascertainment of mortality and included falsification end points for the cause of death.ResultsAmong 14 829 individuals with HF (median, 62 years old; 5855 [40%] women), 3987 (26.9%) ever completed coronary assessment, with 2467/13 301 (18.5%) with unknown coronary artery disease status at HF diagnosis assessed. Women, older individuals, and people without stable housing were less likely to complete coronary assessment. Among 5972 eligible persons of whom 627 underwent early elective coronary assessment, coronary assessment was associated with lower mortality (hazard ratio, 0.84 [95% CI, 0.72-0.97]; P=0.025), reduced risk of the composite outcome (hazard ratio, 0.86 [95% CI, 0.73-1.00]), higher rates of revascularization (odds ratio, 7.6 [95% CI, 5.4-10.6]), and higher use of medical therapy (odds ratio, 2.5 [95% CI, 1.7-3.6]), but not the falsification end points.ConclusionsIn a safety-net population, disparities in coronary assessment after HF diagnosis are not fully explained by coronary artery disease risk factors. Early coronary assessment is associated with improved HF outcomes possibly related to higher rates of revascularization and guideline-directed medical therapy but with low certainty that this finding is not attributable to unmeasured confounding.

BackgroundLow grip strength and gait speed are associated with mortality. However, investigation of the additional mortality risk explained by these measures, over and above other factors, is limited.AimWe examined whether grip strength and gait speed improve discriminative capacity for mortality over and above more readily obtainable clinical risk factors.MethodsParticipants from the Health, Aging and Body Composition Study, Osteoporotic Fractures in Men Study, and the Hertfordshire Cohort Study were analysed. Appendicular lean mass (ALM) was ascertained using DXA; muscle strength by grip dynamometry; and usual gait speed over 2.4-6 m. Verified deaths were recorded. Associations between sarcopenia components and mortality were examined using Cox regression with cohort as a random effect; discriminative capacity was assessed using Harrell's Concordance Index (C-index).ResultsMean (SD) age of participants (n = 8362) was 73.8(5.1) years; 5231(62.6%) died during a median follow-up time of 13.3 years. Grip strength (hazard ratio (95% CI) per SD decrease: 1.14 (1.10,1.19)) and gait speed (1.21 (1.17,1.26)), but not ALM index (1.01 (0.95,1.06)), were associated with mortality in mutually-adjusted models after accounting for age, sex, BMI, smoking status, alcohol consumption, physical activity, ethnicity, education, history of fractures and falls, femoral neck bone mineral density (BMD), self-rated health, cognitive function and number of comorbidities. However, a model containing only age and sex as exposures gave a C-index (95% CI) of 0.65(0.64,0.66), which only increased to 0.67(0.67,0.68) after inclusion of grip strength and gait speed.ConclusionsGrip strength and gait speed may generate only modest adjunctive risk information for mortality compared with other more readily obtainable risk factors.

BACKGROUND: Pulsatile tinnitus (PT) may be due to a spectrum of cerebrovascular etiologies, ranging from benign venous turbulence to life threatening dural arteriovenous fistulas. A focused clinical history and physical examination provide clues to the ultimate diagnosis; however, the predictive accuracy of these features in determining PT etiology remains uncertain. METHODS: Patients with clinical PT evaluation and DSA were included. The final etiology of PT after DSA was categorized as shunting, venous, arterial, or non-vascular. Clinical variables were compared between etiologies using multivariate logistic regression, and performance at predicting PT etiology was determined by area under the receiver operating curve (AUROC). RESULTS: 164 patients were included. On multivariate analysis, patient reported high pitch PT (relative risk (RR) 33.81; 95% CI 3.81 to 882.80) compared with exclusively low pitch PT and presence of a bruit on physical examination (9.95; 2.04 to 62.08; P=0.007) were associated with shunting PT. Hearing loss was associated with a lower risk of shunting PT (0.16; 0.03 to 0.79; P=0.029). Alleviation of PT with ipsilateral lateral neck pressure was associated with a higher risk of venous PT (5.24; 1.62 to 21.01; P=0.010). An AUROC of 0.882 was achieved for predicting the presence or absence of a shunt and 0.751 for venous PT. CONCLUSION: In patients with PT, clinical history and physical examination can achieve high performance at detecting a shunting lesion. Potentially treatable venous etiologies may also be suggested by relief with neck compression.

OBJECTIVES: Coronary computed tomography angiography (CCTA) has higher diagnostic accuracy than coronary artery calcium (CAC) score for detecting obstructive coronary artery disease (CAD) in patients with stable chest pain, while the added diagnostic value of combining CCTA with CAC is unknown. We investigated whether combining coronary CCTA with CAC score can improve the diagnosis of obstructive CAD compared with CCTA alone. METHODS: A total of 2315 patients (858 women, 37%) aged 61.1 ± 10.2 from 29 original studies were included to build two CAD prediction models based on either CCTA alone or CCTA combined with the CAC score. CAD was defined as at least 50% coronary diameter stenosis on invasive coronary angiography. Models were built by using generalized linear mixed-effects models with a random intercept set for the original study. The two CAD prediction models were compared by the likelihood ratio test, while their diagnostic performance was compared using the area under the receiver-operating-characteristic curve (AUC). Net benefit (benefit of true positive versus harm of false positive) was assessed by decision curve analysis. RESULTS: CAD prevalence was 43.5% (1007/2315). Combining CCTA with CAC improved CAD diagnosis compared with CCTA alone (AUC: 87% [95% CI: 86 to 89%] vs. 80% [95% CI: 78 to 82%]; p

PURPOSE: Left ventricular (LV) fibrosis has a key role in arrhythmogenesis in patients with mitral valve prolapse (MVP). Cardiac magnetic resonance identifies LV fibrosis by using late gadolinium enhancement (LGE) technique. LGE assessment and quantification in patients with MVP lacks of standardization protocols. METHODS: 66 MVP patients with normal systolic function and without significant regurgitation were enrolled. Semi-automated gray-scale thresholding techniques using full width at half maximum (FWHM) and 2, 3 and 5 standard deviation (SD) above the remote myocardium were used and compared with the visual assessment, considered as the gold standard. RESULTS: LGE was identified in 41 MVP patients (62%) and quantified. The mean quantity of LGE visually assessed was 2.40 ± 1.07% or 1.40 ± 0.82 g. With FWHM, LGE resulted 3.56 ± 1.23% or 1.99 ± 1.13 g. Using thresholding, the mean LGE quantity was 9.2 ± 3.1% or 4.82 ± 2.28 g for 2-SD, 5.72 ± 1.75% or 3.06 ± 1.47 g for 3-SD and 2.36 ± 0.99% or 1.29 ± 0.79 g for 5-SD. The 5-SD measurement in percentage demonstrated a good correlation with LGE quantification visually assessed (2.40 ± 1.07 vs. 2.363 ± 0.9909, p = 0.543). When compared with the gold standard, the 5-SD threshold quantification, both in percentage and in grams, revealed the least intra-observer (respectively, ICC: 0.976 and 0.966) and inter-observer variability (respectively ICC: 0.948 and 0.935). CONCLUSION: The 5-SD gray-scale threshold technique in percentage revealed the best correlation with the visual assessment and an optimal reproducibility in MVP patient.

Background and purpose: Public medical insurance data are increasingly used to study myasthenia gravis (MG); however, a validated case definition is lacking. We assessed the clinical characteristics of patients identified according to previously used case definitions. Methods: Patients with diagnosis code G70.0 (myasthenia gravis) who visited Severance Hospital between January 2019 and December 2020 were retrospectively analyzed. Three case definitions used in previous epidemiologic studies were applied to the included patients, and their performance was evaluated based on sensitivity, specificity, and positive predictive value (PPV). Results: A total of 843 patients with diagnosis code G70.0 were identified, and 796 (96.7%) met the clinical diagnostic criteria for MG. All three previously-used case definitions showed a PPV of >98% in identifying clinically diagnosed MG, with sensitivities ranging from 68.7 to 71.1%. Case definition #1 was unable to identify 230 patients with MG: 42.6% were in remission, 23.5% were lost to follow-up, and 25.2% were receiving prescriptions elsewhere. A similar pattern was observed for definitions #2 and #3: Patients with thymoma-associated MG who were followed-up by thoracic surgeons were frequently missed based on the previously used case definitions. A proposed case definition that additionally includes a history of thymoma enhanced the sensitivity to 77.9%, while maintaining a PPV of 99.4%. Conclusion: The previously used case definitions showed a high PPV but were unable to identify patients in remission or followed-up elsewhere. These limitations can be overcome by utilizing a new case definition and expanding the study duration. [ABSTRACT FROM AUTHOR]

This review systematically investigates the role of radiomics in radiotherapy, with a particular emphasis on the use of quantitative imaging biomarkers for predicting clinical outcomes, assessing toxicity, and optimizing treatment planning. While the review encompasses various applications of radiomics in radiotherapy, it particularly highlights its potential for guiding reirradiation of recurrent cancers. A systematic review was conducted based on a Medline search with the search engine PubMed using the keywords "radiomics or radiomic" and "radiotherapy or reirradiation". Out of 189 abstracts reviewed, 147 original articles were included in the analysis. These studies were categorized by tumor localization, imaging modality, study objectives, and performance metrics, with a particular emphasis on the inclusion of external validation and adherence to a standardized radiomics pipeline. The review identified 14 tumor localizations, with the majority of studies focusing on lung (33 studies), head and neck (27 studies), and brain (15 studies) cancers. CT was the most frequently employed imaging modality (77 studies) for radiomics, followed by MRI (46 studies) and PET (13 studies). The overall AUC across all studies, primarily focused on predicting the risk of recurrence (94 studies) or toxicity (41 studies), was 0.80 (SD = 0.08). However, only 24 studies (16.3%) included external validation, with a slightly lower AUC compared to those without it. For studies using CT versus MRI or PET, both had a median AUC of 0.79, with IQRs of 0.73–0.86 for CT and 0.76–0.855 for MRI/PET, showing no significant differences in performance. Five studies involving reirradiation reported a median AUC of 0.81 (IQR: 0.73–0.825). Radiomics demonstrates considerable potential in personalizing radiotherapy by improving treatment precision through better outcome prediction and treatment planning. However, its clinical adoption is hindered by the lack of external validation and variability in study designs. Future research should focus on implementing rigorous validation methods and standardizing imaging protocols to enhance the reliability and generalizability of radiomics in clinical radiotherapy, with particular attention to its application in reirradiation. [ABSTRACT FROM AUTHOR]

Background/Objectives: MRI is the gold standard for detecting meniscal tears; however, ultrasound may readily detect meniscal changes, obviating the need for MRI. We aim to (1) determine ultrasound sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy in detecting meniscal changes, and (2) describe characteristic meniscal changes in US and their prevalence. Methods: A retrospective analysis of knee ultrasound scans for the presence of medial and lateral meniscal tears was conducted. Meniscal changes were characterized into five US appearances (cleft, diminutive, cyst, displaced fragment, and extrusion) by the consensus of two musculoskeletal radiologists. Ultrasound findings were then compared to MRI results. Results: In total, 249 patients were included. Ultrasound sensitivity, specificity, PPV, NPV, and accuracy for medial meniscal tears were 79%, 97.3%, 95.3%, 86.6%, and 90%, respectively, and for lateral meniscal tears the ultrasound sensitivity, specificity, PPV, NPV, and accuracy were 63%, 99.5%, 96%, 93%, and 93.6%, respectively. The false negative and false positive rates for medial meniscal tears were 13.4% and 4.7%, respectively, and for the lateral meniscus, the false negative and false positive rates were 6.7% and 3.8%, respectively. Meniscal clefts were the most prevalent appearance in the medial meniscus followed by extrusions. Meniscal extrusions were the most prevalent appearance in the lateral meniscus followed by clefts. Conclusions: Community-based US is highly accurate in the detection of meniscal tears when compared with MRI, making it a valuable diagnostic imaging tool for detecting meniscal tears in a community setting where accessibility to MRI is limited or if there are MRI contraindications. [ABSTRACT FROM AUTHOR]

There is a need for refined methods to detect and quantify brain injuries that may be undetectable by magnetic resonance imaging and neurologic examination. This review evaluates the potential efficacy of circulating brain injury biomarkers for predicting outcomes following elective neurosurgical procedures. A comprehensive search was conducted using the Cochrane, PubMed, and Scopus databases. Analysis of 23 relevant studies revealed that specific biomarkers, including glial fibrillary acidic protein, neurofilament light chain, neuron-specific enolase, S100B, and tau, are significantly associated with the extent of brain injury and could potentially predict postsurgical outcomes. The evaluated studies described intracranial tumor surgeries and miscellaneous neurosurgical interventions and demonstrated the complex relationship between biomarker levels and patient outcomes. Circulating brain injury biomarkers show promise for providing objective insights into the extent of perioperative brain injury and improving prognostication of postsurgical outcomes. However, the heterogeneity in study designs and outcomes along with the lack of standardized biomarker thresholds underscore the need for further research. [ABSTRACT FROM AUTHOR]

Background: Timely and accurate assessment of coagulopathy is crucial for the management of primary postpartum hemorrhage (PPH). Thromboelastography (TEG) provides a comprehensive assessment of coagulation status and is useful for guiding the treatment of hemorrhagic events in various diseases. This study aimed to evaluate the role of TEG in predicting hypofibrinogenemia in emergency department (ED) patients with primary PPH. Methods: We conducted a retrospective observational study in the ED of a university-affiliated tertiary hospital between November 2015 and August 2023. TEG was performed upon admission. The cutoff value for hypofibrinogenemia was 200 mg/dL. The primary outcome was the presence of hypofibrinogenemia. Results: Among the 174 patients, 73 (42.0%) had hypofibrinogenemia. The need for massive transfusion was higher in the hypofibrinogenemia group (37.0% vs. 5.0%, p < 0.001). Among the TEG parameters, all values were significantly different between the groups, except for lysis after 30 min, suggesting a tendency toward hypocoagulability. Multivariable analysis revealed that the alpha angle (odds ratio (OR) 0.924, 95% confidence interval (CI) 0.876–0.978) and maximum amplitude (MA) (OR 0.867, 95% CI 0.801–0.938) were independently associated with hypofibrinogenemia. The optimal cutoff values for the alpha angle and maximum amplitude (MA) for hypofibrinogenemia were 63.8 degrees and 56.1 mm, respectively. Conclusion: Point-of-care TEG could be a valuable tool for the early identification of hypofibrinogenemia in ED patients with primary PPH. [ABSTRACT FROM AUTHOR]

OBJECTIVE: This study aimed to establish normative values for maximum inspiratory pressure and maximal expiratory pressure in the Turkish population while creating specific equations to calculate these values. MATERIAL AND METHODS: The study involved 219 healthy adults, with a minimum of 50 individuals in specific age ranges: 20-29, 30-39, 40-49, and 50-60 years. Each age group comprised at least 25 males and 25 females. Participants were required to be free from health conditions influencing respiratory muscle strength and non-smokers. Measurements of maximum inspiratory pressure and maximal expiratory pressure were recorded for all participants. RESULTS: As a result of the regression analysis performed for the maximum inspiratory pressure values, the model P value was < .001, and the R2 value was found to be 0.261. The equation obtained as a result of the model was: 82.583 - 3.218 × gender - 0.093 × age + 9.534 × height + 0.343 × weight. As a result of the regression analysis performed for maximal expiratory pressure values, the model P value was <.001, and the R2 value was found to be 0.285. The equation obtained as a result of the model was: 157.165 - 35.522 × gender - 0.271 × age-42.036 × height + 0.787 × weight. CONCLUSIONS: The newly developed equations offer valuable tools for evaluating respiratory muscle strength in the Turkish population. These results confirm the importance of using maximum inspiratory pressure and maximal expiratory pressure to monitor changes in each patient, while also emphasizing the necessity of reliable reference equations. [ABSTRACT FROM AUTHOR]

Abstract Background Timely and accurate assessment of coagulopathy is crucial for the management of primary postpartum hemorrhage (PPH). Thromboelastography (TEG) provides a comprehensive assessment of coagulation status and is useful for guiding the treatment of hemorrhagic events in various diseases. This study aimed to evaluate the role of TEG in predicting hypofibrinogenemia in emergency department (ED) patients with primary PPH. Methods We conducted a retrospective observational study in the ED of a university-affiliated tertiary hospital between November 2015 and August 2023. TEG was performed upon admission. The cutoff value for hypofibrinogenemia was 200 mg/dL. The primary outcome was the presence of hypofibrinogenemia. Results Among the 174 patients, 73 (42.0%) had hypofibrinogenemia. The need for massive transfusion was higher in the hypofibrinogenemia group (37.0% vs. 5.0%, p

Background & aimsGaps remain in understanding the epidemiology of eosinophilic esophagitis (EoE). Our aim was to identify and validate a national cohort of individuals with EoE using Veterans Health Administration (VHA) data.MethodsWe used 2 validation strategies to develop algorithms that identified adults with EoE between 1999 and 2020. The first validation strategy applied International Classification of Diseases (ICD) code algorithms to a base cohort of individuals who underwent esophagogastroduodenoscopy with esophageal biopsy specimens. The second applied ICD code algorithms to a base cohort of all individuals in the VHA. For each ICD algorithm applied, a random sample of candidate EoE cases and non-EoE controls were selected and the charts were reviewed manually by a blinded reviewer. Each algorithm was modified iteratively until the prespecified diagnostic accuracy end point (95% confidence lower bound for a positive predictive value [PPV], >88%) was achieved. We compiled individuals from each strategy's maximum performance algorithm to construct the Veterans Affairs Eosinophilic Esophagitis Cohort.ResultsThe maximum performance algorithm from the first validation strategy included 2 or more ICD code encounters for EoE separated by more than 30 days and achieved a 93.3% PPV (lower bound, 88.1%) for identifying true EoE cases. The maximum performance algorithm from the second validation strategy included 4 or more ICD code encounters for EoE in which 2 codes were separated by at least 30 days, and similarly achieved a 93.3% PPV (lower bound, 88.1%). Combining both strategies yielded 6637 individuals, which comprised the Veterans Affairs Eosinophilic Esophagitis Cohort.ConclusionsWe developed and validated 2 highly accurate coding algorithms for EoE and established a nationwide VHA cohort of adults with EoE for future studies.

AimsStudies have indicated inconsistent results regarding the association between plasma levels of Lipoprotein(a) [Lp(a)] and coronary artery calcification (CAC). We performed a systematic review and meta-analysis to investigate the association between elevated levels of Lp(a) and risk of CAC in populations free of cardiovascular disease (CVD) symptoms.Data synthesisPubMed, Web of Science, Embase, and Scopus were searched up to July 2022 and the methodological quality was assessed using Newcastle-Ottawa Scale (NOS) scale. Random-effects meta-analysis was used to estimate pooled odds ratio (OR) and 95% confidence interval. Out of 298 studies, data from 8 cross-sectional (n = 18,668) and 4 cohort (n = 15,355) studies were used in meta-analysis. Cohort studies demonstrated a positive significant association between Lp(a) and CAC, so that individuals with Lp(a)≥30-50 exposed to about 60% risk of CAC incidence compared to those with lower Lp(a) concentrations in asymptomatic CVD subjects (OR, 1.58; 95% CI, 1.38-1.80; l2, 0.0%; P, 0.483); Subgroup analysis showed that a cut-off level for Lp(a) measurement could not statistically affect the association, but race significantly affected the relationship between Lp(a) and CAC (OR,1.60; 95% CI, 1.41-1.81). Analyses also revealed that both men and women with higher Lp(a) concentrations are at the same risk for increased CAC.ConclusionsBlood Lp(a) level was significantly associated with CAC incidence in asymptomatic populations with CVD, indicating that measuring Lp(a) may be a useful biomarker for diagnosing subclinical atherosclerosis in individuals at higher risk of CAC score.Prospero registration numberCRD42022350297.

Phosphatase and tensin homolog (PTEN) loss is associated with adverse outcomes in prostate cancer and can be measured via immunohistochemistry. The purpose of the study was to establish the clinical application of an in-house developed artificial intelligence (AI) image analysis workflow for automated detection of PTEN loss on digital images for identifying patients at risk of early recurrence and metastasis. Postsurgical tissue microarray sections from the Canary Foundation (n = 1264) stained with anti-PTEN antibody were evaluated independently by pathologist conventional visual scoring (cPTEN) and an automated AI-based image analysis pipeline (AI-PTEN). The relationship of PTEN evaluation methods with cancer recurrence and metastasis was analyzed using multivariable Cox proportional hazard and decision curve models. Both cPTEN scoring by the pathologist and quantification of PTEN loss by AI (high-risk AI-qPTEN) were significantly associated with shorter metastasis-free survival (MFS) in univariable analysis (cPTEN hazard ratio [HR], 1.54; CI, 1.07-2.21; P = .019; AI-qPTEN HR, 2.55; CI, 1.83-3.56; P < .001). In multivariable analyses, AI-qPTEN showed a statistically significant association with shorter MFS (HR, 2.17; CI, 1.49-3.17; P < .001) and recurrence-free survival (HR, 1.36; CI, 1.06-1.75; P = .016) when adjusting for relevant postsurgical clinical nomogram (Cancer of the Prostate Risk Assessment [CAPRA] postsurgical score [CAPRA-S]), whereas cPTEN does not show a statistically significant association (HR, 1.33; CI, 0.89-2; P = .2 and HR, 1.26; CI, 0.99-1.62; P = .063, respectively) when adjusting for CAPRA-S risk stratification. More importantly, AI-qPTEN was associated with shorter MFS in patients with favorable pathological stage and negative surgical margins (HR, 2.72; CI, 1.46-5.06; P = .002). Workflow also demonstrated enhanced clinical utility in decision curve analysis, more accurately identifying men who might benefit from adjuvant therapy postsurgery. This study demonstrates the clinical value of an affordable and fully automated AI-powered PTEN assessment for evaluating the risk of developing metastasis or disease recurrence after radical prostatectomy. Adding the AI-qPTEN assessment workflow to clinical variables may affect postoperative surveillance or management options, particularly in low-risk patients.

Artificial intelligence (AI) promises to revolutionize many fields, but its clinical implementation in cardiovascular imaging is still rare despite increasing research. We sought to facilitate discussion across several fields and across the lifecycle of research, development, validation, and implementation to identify challenges and opportunities to further translation of AI in cardiovascular imaging. Furthermore, it seemed apparent that a multidisciplinary effort across institutions would be essential to overcome these challenges. This paper summarizes the proceedings of the National Heart, Lung, and Blood Institute-led workshop, creating consensus around needs and opportunities for institutions at several levels to support and advance research in this field and support future translation.

Abstract This study evaluated the positive predictive value (PPV) of artificial intelligence (AI) in detecting pneumothorax on chest radiographs (CXRs) and its affecting factors. Patients determined to have pneumothorax on CXR by a commercial AI software from March to December 2021 were included retrospectively. The PPV was evaluated according to the true-positive (TP) and false-positive (FP) diagnosis determined by radiologists. To know the factors that might influence the results, logistic regression with generalized estimating equation was used. Among a total of 87,658 CXRs, 308 CXRs with 331 pneumothoraces from 283 patients were finally included. The overall PPV of AI about pneumothorax was 41.1% (TF:FP = 136:195). The PA view (odds ratio [OR], 29.837; 95% confidence interval [CI], 15.062–59.107), high abnormality score (OR, 1.081; 95% CI, 1.066–1.097), large amount of pneumothorax (OR, 1.005; 95% CI, 1.003–1.007), presence of ipsilateral atelectasis (OR, 3.508; 95% CI, 1.509–8.156) and a small amount of ipsilateral pleural effusion (OR, 5.277; 95% CI, 2.55–10.919) had significant effects on the increasing PPV. Therefore, PPV for pneumothorax diagnosis using AI can vary based on patients’ factors, image-acquisition protocols, and the presence of concurrent lesions on CXR.

This study evaluated the positive predictive value (PPV) of artificial intelligence (AI) in detecting pneumothorax on chest radiographs (CXRs) and its affecting factors. Patients determined to have pneumothorax on CXR by a commercial AI software from March to December 2021 were included retrospectively. The PPV was evaluated according to the true-positive (TP) and false-positive (FP) diagnosis determined by radiologists. To know the factors that might influence the results, logistic regression with generalized estimating equation was used. Among a total of 87,658 CXRs, 308 CXRs with 331 pneumothoraces from 283 patients were finally included. The overall PPV of AI about pneumothorax was 41.1% (TF:FP = 136:195). The PA view (odds ratio [OR], 29.837; 95% confidence interval [CI], 15.062–59.107), high abnormality score (OR, 1.081; 95% CI, 1.066–1.097), large amount of pneumothorax (OR, 1.005; 95% CI, 1.003–1.007), presence of ipsilateral atelectasis (OR, 3.508; 95% CI, 1.509–8.156) and a small amount of ipsilateral pleural effusion (OR, 5.277; 95% CI, 2.55–10.919) had significant effects on the increasing PPV. Therefore, PPV for pneumothorax diagnosis using AI can vary based on patients' factors, image-acquisition protocols, and the presence of concurrent lesions on CXR. [ABSTRACT FROM AUTHOR]

Objective: This study aims to develop and validate predictive models that assess the risk of leprosy development among contacts, contributing to an enhanced understanding of disease occurrence in this population. Methods: A cohort of 600 contacts of people with leprosy treated at the National Reference Center for Leprosy and Health Dermatology at the Federal University of Uberlândia (CREDESH/HC‐UFU) was followed up between 2002 and 2022. The database was divided into two parts: two‐third to construct the disease risk score and one‐third to validate this score. Multivariate logistic regression models were used to construct the disease score. Results: Of the four models constructed, model 3, which included the variables anti‐phenolic glycolipid I immunoglobulin M positive, absence of Bacillus Calmette‐Guérin vaccine scar and age ≥60 years, was considered the best for identifying a higher risk of illness, with a specificity of 89.2%, a positive predictive value of 60% and an accuracy of 78%. Conclusions: Risk prediction models can contribute to the management of leprosy contacts and the systematisation of contact surveillance protocols. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) systems are already being used in various healthcare areas. Similarly, they can offer many advantages in hospital emergency services. The objective of this work is to demonstrate that through the novel use of AI, a trained system can be developed to detect patients at potential risk of infection in a new pandemic more quickly than standardized triage systems. This identification would occur in the emergency department, thus allowing for the early implementation of organizational preventive measures to block the chain of transmission. Materials and Methods: In this study, we propose the use of a machine learning system in emergency department triage during pandemics to detect patients at the highest risk of death and infection using the COVID-19 era as an example, where rapid decision making and comprehensive support have becoming increasingly crucial. All patients who consecutively presented to the emergency department were included, and more than 89 variables were automatically analyzed using the extreme gradient boosting (XGB) algorithm. Results: The XGB system demonstrated the highest balanced accuracy at 91.61%. Additionally, it obtained results more quickly than traditional triage systems. The variables that most influenced mortality prediction were procalcitonin level, age, and oxygen saturation, followed by lactate dehydrogenase (LDH) level, C-reactive protein, the presence of interstitial infiltrates on chest X-ray, and D-dimer. Our system also identified the importance of oxygen therapy in these patients. Conclusions: These results highlight that XGB is a useful and novel tool in triage systems for guiding the care pathway in future pandemics, thus following the example set by the well-known COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

BACKGROUND: Atrial fibrillation (AF) is often asymptomatic and undiagnosed. As AF and hypertension often coexist, opportunistic AF detection during routine automated blood pressure (BP) measurement appears to be an attractive screening method. METHODS: A systematic literature search was conducted to identify studies assessing the diagnostic test accuracy of office, home, or 24-hour ambulatory BP measuring devices with AF detection algorithms versus reference electrocardiography. Analyses were performed per participant (AF status based on several BP readings; most office/home devices) or per reading (AF status based on individual readings; all ambulatory devices). A meta-analysis stratified by device type (office/home/ambulatory) was conducted to calculate pooled measures of diagnostic accuracy. Sensitivity/meta-regression analyses were also performed. RESULTS: Among 3096 records initially retrieved, 23 diagnostic test accuracy studies were included. Data derived from 11 093 individuals (weighted age 69 years, males 56%, hypertensives 79%, diabetics 24%, and AF prevalence 17%) indicated a pooled sensitivity 0.97 (95% CI, 0.92–0.99), specificity 0.93 (95% CI, 0.90–0.95), and accuracy 0.93 (95% CI, 0.89–0.95), with generally consistent results using office, home, or ambulatory BP devices (slightly lower specificity with the latter). The positive and negative predictive values were 0.70 (95% CI, 0.60–0.80) and 0.99 (95% CI, 0.98–1.00), respectively. Sensitivity analyses indicated lower specificity in studies implementing reading versus participant analyses. Most studies presented a low risk of bias and minor applicability concerns. CONCLUSIONS: There is considerable and consistent evidence suggesting high diagnostic accuracy of AF detection algorithms implemented in automated BP monitors during routine BP measurements in and out of the office. AF diagnosis requires verification (electrocardiography) before treatment is administered. [ABSTRACT FROM AUTHOR]

Non‐invasive methods have largely replaced biopsy to identify advanced fibrosis in hepatitis C virus (HCV). Guidelines vary regarding testing strategy to balance accuracy, costs and loss to follow‐up. Although individual test characteristics are well‐described, data comparing the accuracy of using two tests together are limited. We calculated combined test characteristics to determine the utility of combined strategies. This study synthesizes empirical data from fibrosis staging trials and the literature to estimate test characteristics for Fibrosis‐4 (FIB4), APRI or a commercial serum panel (FibroSure®), followed by transient elastography (TE) or FibroSure®. We simulated two testing strategies: (1) second test only for those with intermediate first test results (staged approach), and (2) second test for all. We summarized empiric data with multinomial distributions and used this to estimate test characteristics of each strategy on a simulated population of 10,000 individuals with 4.2% cirrhosis prevalence. Negative predictive value (NPV) for cirrhosis from a single test ranged from 98.2% (95% CB 97.6–98.8%) for FIB‐4 to 99.4% (95% CB 99.0–99.8%) for TE. Using a staged approach with TE second, sensitivity for cirrhosis rose to 93.3–96.9%, NPV to 99.7–99.8%, while PPV dropped to <32%. Using TE as a second test for all minimally changed estimated test characteristics compared with the staged approach. Combining two non‐invasive fibrosis tests barely improves NPV and decreases or does not change PPV compared with a single test, challenging the utility of serial testing modalities. These calculated combined test characteristics can inform best methods to identify advanced fibrosis in various populations. [ABSTRACT FROM AUTHOR]

AIMS: The totality of atherosclerotic plaque derived from coronary computed tomography angiography (CCTA) emerges as a comprehensive measure to assess the intensity of medical treatment that patients need. This study examines the differences in age onset and prognostic significance of atherosclerotic plaque burden between sexes. METHODS AND RESULTS: From a large multi-center CCTA registry the Leiden CCTA score was calculated in 24 950 individuals. A total of 11 678 women (58.5 ± 12.4 years) and 13 272 men (55.6 ± 12.5 years) were followed for 3.7 years for major adverse cardiovascular events (MACE) (death or myocardial infarction). The age where the median risk score was above zero was 12 years higher in women vs. men (64-68 years vs. 52-56 years, respectively, P < 0.001). The Leiden CCTA risk score was independently associated with MACE: score 6-20: HR 2.29 (1.69-3.10); score > 20: HR 6.71 (4.36-10.32) in women, and score 6-20: HR 1.64 (1.29-2.08); score > 20: HR 2.38 (1.73-3.29) in men. The risk was significantly higher for women within the highest score group (adjusted P-interaction = 0.003). In pre-menopausal women, the risk score was equally predictive and comparable with men. In post-menopausal women, the prognostic value was higher for women [score 6-20: HR 2.21 (1.57-3.11); score > 20: HR 6.11 (3.84-9.70) in women; score 6-20: HR 1.57 (1.19-2.09); score > 20: HR 2.25 (1.58-3.22) in men], with a significant interaction for the highest risk group (adjusted P-interaction = 0.004). CONCLUSION: Women developed coronary atherosclerosis approximately 12 years later than men. Post-menopausal women within the highest atherosclerotic burden group were at significantly higher risk for MACE than their male counterparts, which may have implications for the medical treatment intensity.

AimsIcosapent ethyl (IPE) significantly reduced ischaemic events in statin-treated patients with atherosclerosis or diabetes and elevated triglycerides in REDUCE-IT, including large reductions in myocardial infarction and elective, urgent, and emergent coronary revascularization. However, the mechanisms driving this clinical benefit are not fully known. The EVAPORATE trial demonstrated that IPE significantly reduced plaque burden. No study to date has assessed the impact of IPE on coronary physiology. Fractional flow reserve (FFR) derived from coronary computed tomography angiography (CTA) data sets (FFRCT) applies computational fluid dynamics to calculate FFR values in epicardial coronary arteries. Our objective was to assess the impact of IPE on coronary physiology assessed by FFRCT using imaging data from EVAPORATE.Methods and resultsA total of 47 patients and of 507 coronary lesions at baseline, 9 months, and 18 months with coronary CTA and FFRCT were studied in a blinded core lab. The pre-specified primary endpoint was the FFRCT value in the distal coronary segment from baseline to follow-up in the most diseased vessel per patient using IPE compared with placebo. The pre-specified secondary endpoint was the change in translesional FFRCT (ΔFFRCT) across the most severe (minimum 30% diameter stenosis) coronary lesion per vessel. Baseline FFRCT was similar for IPE compared with placebo (0.83 ± 0.08 vs. 0.84 ± 0.08, P = 0.55). There was significant improvement in the primary endpoint, as IPE improved mean distal segment FFRCT at 9- and 18-month follow-up compared with placebo (0.01 ± 0.05 vs. -0.05 ± 0.09, P = 0.02, and -0.01 ± 0.09 vs. -0.09 ± 0.12, P = 0.03, respectively). ΔFFRCT in 140 coronary lesions was improved, although not statistically significant, with IPE compared with placebo (-0.06 ± 0.08 vs. -0.09 ± 0.1, P = 0.054).ConclusionIcosapent ethyl demonstrated significant benefits in coronary physiology compared with placebo. This early and sustained improvement in FFRCT at 9- and 18-month follow-up provides mechanistic insight into the clinical benefit observed in the REDUCE-IT trial. Furthermore, this is the first assessment of FFRCT to determine drug effect.

Reducing the age of first autism diagnosis facilitates access to critical early intervention services. A current "waitlist crisis" for autism diagnostic evaluation thus demands that we consider novel use of available clinical resources. Previous work has found that expert autism clinicians can identify autism in young children with high specificity after only a brief observation; rapid identification by non-experts remains untested. In the current study, 252 children ages 12-53 months presented for a comprehensive autism diagnostic evaluation. We found that junior clinicians in training to become autism specialists (n = 29) accurately determined whether or not a young child would be diagnosed with autism in the first five minutes of the clinic visit in 75% of cases. Specificity of brief observations was high (0.92), suggesting that brief observations may be an effective tool for triaging young children toward autism-specific interventions. In contrast, the lower negative predictive value (0.71) of brief observations, suggest that they should not be used to rule out autism. When trainees expressed more confidence in their initial impression, their impression was more likely to match the final diagnosis. These findings add to a body of literature showing that clinical observations of suspected autism should be taken seriously, but lack of clinician concern should not be used to rule out autism or overrule other indicators of likely autism, such as parent concern or a positive screening result.

Background The Danish National Patient Registry (DNPR) is a valuable resource for population-based research, but the validity of routine registration of advanced heart failure (HF) treatments within the registry is unknown. We, therefore, investigated the validity of HF, advanced HF treatments, and HF readmissions in the DNPR. Methods We randomly sampled patients registered at a Danish University Hospital during 2017-2021 from the DNPR. We identified 200 patients with first-time HF, 390 patients with one of eight advanced HF treatments, and 133 patients with HF admission after implantable cardioverter-defibrillator (ICD) or cardiac resynchronisation therapy (CRT). Compared with medical record reviews, we calculated positive predictive values (PPVs) with 95 confidence intervals (CIs). Results The PPV for first-time HF was 81 (95 CI: 74-86). For advanced HF treatments, the PPV was 97 (95 CI: 91-99) for ICD, 96 (95 CI: 86-100) for CRT-pacemaker, 88 (95 CI: 76-95) for CRT-defibrillator, 100 (95 CI: 83-100) for left ventricular assist device, 43 (95 CI: 18-71) for intra-aortic balloon pump, 38 (95 CI: 25-35) for impella, 100 (95 CI: 93-100) for cardiopulmonary support, and 100 (95 CI: 94-100) for heart transplantation. The PPV for HF admission after ICD was 25 (95 CI: 16-37) and 18 (95 CI: 9.2-30) after CRT. Conclusions The PPV of routine registrations in the DNPR was moderate for first-time HF, high for most advanced HF treatments, and low for HF admissions after ICD or CRT. Thus, the DNPR is a valuable data source for population-based research on first-time HF and many advanced HF treatments.

Introduction: The effectiveness of admission criteria in predicting academic performance is critical for optimizing student success in medical education. This study investigates how various admission standards, including FSc grades, entrance test scores, and merit criteria, correlate with academic outcomes in medical institutions in Khyber Pakhtunkhwa. Aims & Objectives: To examine the correlation between admission requirements and academic achievement among medical students across various medical teaching institutions in Khyber Pakhtunkhwa. Place and Duration of Study: It was conducted for over six months from January 2022 till June 2022 at the Department of Medical Education, Khyber Medical University, Peshawar after approval from the Institutional Review Board (IRB). Material & Method: A cross-sectional study using convenience sampling to include data from 10,061 students (5,657 male, 4,404 female) enrolled in public medical and dental colleges across Khyber Pakhtunkhwa (KP) under the provincial government’s administration. The study utilized SPSS version 25 for data analysis, employing descriptive statistics, linear regression analysis and t-tests to assess the relationships between predictor variables and professional examination performance. Results: Significant but weak correlations (p < 0.05 and p < 0.01) were observed between three predictor variables— F.Sc scores, merit criteria, and entrance test scores—and professional examination scores across ten medical colleges in Khyber Pakhtunkhwa. Regression analyses confirmed that F.Sc scores were the strongest predictor of academic performance from the 1st to the 4th year, whereas merit criteria, which include 40% of entrance test marks, emerged as the predominant predictor in the final professional year. Conclusion: The study revealed significant correlations between interpreters (F.Sc, entrance test, and merit scores) and MBBS examination scores across medical institutions. Stepwise regression analysis indicated F.Sc as the best predictor from the first to fourth year, with merit emerging as the top predictor in the final year.

Aim To evaluate diagnostic reliability of accessible laboratory findings in recognition of acute appendicitis (AA). Methods A retrospective study included children aged 0-15 years with abdominal pain that lasted less than 5 days with at least two of the signs/symptoms - abdominal pain, tenderness of the lower right quadrant of the abdomen, "return" sensitivity of the abdomen to palpation, loss of appetite, nausea, vomiting, body temperature>37.2°C. Values of procalcitonin (PCT), C-reactive protein (CRP) and the leukocyte count were analyzed in the peripheral blood. Results Among 114 children, 63 (58.2%) were boys and 50 (41.8%) girls; median age of 9.5 years. Elevated values of PCT were found in 74 (65.5%), CRP in 94 (83.1%), and leukocytes in 78 (69%) (65%) children. Almost uniform significance in the recognition of AA was found for pathological values of PCT and CRP with sensitivity of 65% and 83% and diagnostic accuracy of 63% and 59%, respectively, but somewhat lower sensitivity for leukocytes, 61%. A very high predictive value of 98% for PCT and CRP, and PCT with leukocytes was found; CRP with leukocytes had a negative predictive value of 100%. Conclusion PCT values have significant sensitivity, specificity and diagnostic accuracy in recognizing AA, while CRP and leukocytes, with high sensitivity, as non-specific markers can be a significant support for clinical assessment in the timely diagnosis of AA.

Body mass index is a widely used but poor predictor of adiposity in populations with excessive fat-free mass. Rigorous predictive models validated specifically in a nationally representative sample of the US population and that could be used for calibration purposes are needed. The objective of this study was to develop and validate prediction equations of body fat percentage obtained from Dual Energy X-ray Absorptiometry using body mass index (BMI) and socio-demographics. We used the National Health and Nutrition Examination Survey (NHANES) data from 5931 and 2340 adults aged 20 to 69 in 1999-2002 and 2003-2006, respectively. A supervised machine learning using ordinary least squares and a validation set approach were used to develop and select best models based on R2 and root mean square error. We compared our findings with other published models and utilized our best models to assess the amount of bias in the association between predicted body fat and elevated low-density lipoprotein (LDL). Three models included BMI, BMI2, age, gender, education, income, and interaction terms and produced R-squared values of 0.87 and yielded the smallest standard errors of estimation. The amount of bias in the association between predicted BF% and elevated LDL from our best model was -0.005. Our models provided strong predictive abilities and low bias compared to most published models. Its strengths rely on its simplicity and its ease of use in low-resource settings.

Background & aimsAchalasia is an esophageal motility disorder associated with significant morbidity, yet achalasia-associated risk factors and outcomes are not well-characterized. Our aim was to establish a national cohort of individuals with achalasia, utilizing Veterans Health Administration (VHA) data.MethodsWe iteratively developed combinations of International Classification of Diseases and Current Procedural Terminology code algorithms to validate an approach for identifying achalasia cases. We assessed algorithm accuracy for achalasia diagnosis through manual chart review of candidate achalasia cases and candidate non-achalasia controls. The prespecified end point chosen to establish algorithm performance success was achieving a 1-sided 95% confidence lower bound for a positive predictive value >85% for a random sample of 100 candidate achalasia cases. Once adequate performance was validated, we queried national VHA data to establish and characterize a cohort of individuals diagnosed with achalasia between 1999 and 2020.ResultsThree rounds of algorithm modification and validation were conducted to achieve the prespecified performance endpoint. In the final round, a combination of 3 or more International Classification of Diseases codes for achalasia in the subject's lifetime and a Current Procedural Terminology code for esophageal manometry achieved an observed 94% positive predictive value (1-sided 95% confidence lower bound of 88.5%) for identifying achalasia. Applying the algorithm to national VHA data identified a cohort of 2100 individuals with achalasia, with a median age 65 years and who were 93% male.ConclusionsUsing a rigorous validation approach, we established a national cohort of 2100 individuals with achalasia within the VHA, one of the largest established to date. This cohort can be utilized to study risk factors for achalasia and outcomes over time.

ObjectiveUtilization of evidence-based specialty guidelines is low in primary care settings. Early use of ankle-brachial index (ABI) testing and a validated wound classification system allows prompt referral of patients for specialty care. We implemented a program to teach providers ABI testing and the use of the Wound, Ischemia, and foot Infection (WIfI) classification tool. Here, we report program outcomes and provider perceptions.MethodsPhysicians and non-physicians from wound care centers, nursing and physician education programs, primary care offices, and federally qualified health centers were invited to participate in the educational program teaching ABI testing and the use of the WIfI tool. Pretest and posttest responses and intention to use content in the future were assessed with descriptive statistics.ResultsA total of 101 subjects completed the ABI module, and 84 indicated their occupation (59 physicians, 25 non-physicians). Seventy-nine subjects completed the WIfI module, and 89% indicated their occupation (50 physicians, 20 non-physicians). Physicians had lower pre-test knowledge scores for the ABI module than non-physicians (mean scores of 7.9 and 8.2, respectively). Both groups had improved knowledge scores on the post-test (physicians, 13.4; non-physicians, 13.8; P < .001). Non-physicians in practice longer than 10 years at wound care centers had the lowest baseline knowledge scores, whereas physicians in practice for over 10 years had the highest. In the ABI module, the largest knowledge gap included accurately calculating the ABI, followed by the correct use of the Doppler, and management of incompressible vessels. For the WIfI module, providers struggled to accurately score patients based on wound classification. The greatest barriers to the implementation of ABI testing were the availability of trained personnel, followed by limited time for testing. Barriers to the use of the WIfI tool for physicians included lack of time and national guideline support. For non-physicians, the most notable barrier was a lack of training.ConclusionsProvider understanding of ABI and WIfI tools are limited in wound care centers, primary care offices, and federally qualified health centers. Further barriers include a lack of training in the use of tools, limited potential for point-of-care testing reimbursement, and insufficient dissemination of WIfI guidelines. Such barriers discourage widespread adoption and result in delayed diagnosis of arterial insufficiency.

BackgroundCoronary artery calcification (CAC), often assessed by computed tomography (CT), is a powerful marker of coronary artery disease that can guide preventive therapies. Computed tomographies, however, are not always accessible or serially obtainable. It remains unclear whether other widespread tests such as transthoracic echocardiograms (TTEs) can be used to predict CAC.MethodsUsing a data set of 2,881 TTE videos paired with coronary calcium CTs, we trained a video-based artificial intelligence convolutional neural network to predict CAC scores from parasternal long-axis views. We evaluated the model's ability to classify patients from a held-out sample as well as an external site sample into zero CAC and high CAC (CAC ≥ 400 Agatston units) groups by receiver operating characteristic and precision-recall curves. We also investigated whether such classifications prognosticated significant differences in 1-year mortality rates by the log-rank test of Kaplan-Meier curves.ResultsTransthoracic echocardiogram artificial intelligence models had high discriminatory abilities in predicting zero CAC (receiver operating characteristic area under the curve [AUC] = 0.81 [95% CI, 0.74-0.88], F1 score = 0.95) and high CAC (AUC = 0.74 [0.68-0.8], F1 score = 0.74). This performance was confirmed in an external test data set of 92 TTEs (AUC = 0.75 [0.65-0.85], F1 score = 0.77; and AUC = 0.85 [0.76-0.93], F1 score = 0.59, respectively). Risk stratification by TTE-predicted CAC performed similarly to CT CAC scores in prognosticating significant differences in 1-year survival in high-CAC patients (CT CAC ≥ 400 vs CT CAC < 400, P = .03; TTE-predicted CAC ≥ 400 vs TTE-predicted CAC < 400, P = .02).ConclusionsA video-based deep learning model successfully used TTE videos to predict zero CAC and high CAC with high accuracy. Transthoracic echocardiography-predicted CAC prognosticated differences in 1-year survival similar to CT CAC. Deep learning of TTEs holds promise for future adjunctive coronary artery disease risk stratification to guide preventive therapies.

Coronary computed tomographic angiography (CCTA) is becoming the first-line investigation for establishing the presence of coronary artery disease and, with fractional flow reserve (FFRCT), its haemodynamic significance. In patients without significant epicardial obstruction, its role is either to rule out atherosclerosis or to detect subclinical plaque that should be monitored for plaque progression/regression following prevention therapy and provide risk classification. Ischaemic non-obstructive coronary arteries are also expected to be assessed by non-invasive imaging, including CCTA. In patients with significant epicardial obstruction, CCTA can assist in planning revascularisation by determining the disease complexity, vessel size, lesion length and tissue composition of the atherosclerotic plaque, as well as the best fluoroscopic viewing angle; it may also help in selecting adjunctive percutaneous devices (e.g., rotational atherectomy) and in determining the best landing zone for stents or bypass grafts.

Left ventricular mass is a risk marker for cardiovascular events, and may indicate an underlying cardiomyopathy. Cardiac magnetic resonance is the gold-standard for left ventricular mass estimation, but is challenging to obtain at scale. Here, we use deep learning to enable genome-wide association study of cardiac magnetic resonance-derived left ventricular mass indexed to body surface area within 43,230 UK Biobank participants. We identify 12 genome-wide associations (1 known at TTN and 11 novel for left ventricular mass), implicating genes previously associated with cardiac contractility and cardiomyopathy. Cardiac magnetic resonance-derived indexed left ventricular mass is associated with incident dilated and hypertrophic cardiomyopathies, and implantable cardioverter-defibrillator implant. An indexed left ventricular mass polygenic risk score ≥90th percentile is also associated with incident implantable cardioverter-defibrillator implant in separate UK Biobank (hazard ratio 1.22, 95% CI 1.05-1.44) and Mass General Brigham (hazard ratio 1.75, 95% CI 1.12-2.74) samples. Here, we perform a genome-wide association study of cardiac magnetic resonance-derived indexed left ventricular mass to identify 11 novel variants and demonstrate that cardiac magnetic resonance-derived and genetically predicted indexed left ventricular mass are associated with incident cardiomyopathy.

Kasper Bonnesen,1,2 Morten Schmidt1– 3 1Department of Clinical Epidemiology, Aarhus University and Aarhus University Hospital, Aarhus, Denmark; 2Department of Clinical Medicine, Aarhus University, Aarhus, Denmark; 3Department of Cardiology, Aarhus University Hospital, Aarhus, DenmarkCorrespondence: Kasper Bonnesen, Aarhus University, Olof Palmes Allé 43– 45, 8200 Aarhus N, Denmark, Tel +45 87 16 72 12, Email bonnesen@clin.au.dkPurpose: Hypertension is an important risk factor in cardio-epidemiological research, but data quality remains a concern. We validated different registry-based definitions of hypertension.Patients and Methods: The cohort included all first-time responders of the Danish National Health Surveys (2010, 2013, or 2017). Prescription-defined hypertension was defined as ≥ 1 or ≥ 2 filled prescriptions of antihypertensive specific drugs in ≥ 1 or ≥ 2 different antihypertensive drug classes within 90, 180, or 365 days before survey response. Hospital-diagnosed hypertension was defined from hypertension diagnoses within five years before the survey response. Considering self-reported hypertension as the reference, we calculated the positive predictive value (PPV), the negative predictive value (NVP), the sensitivity, and the specificity of prescription-defined and hospital-diagnosed hypertension.Results: Among 442,490 survey responders, 127,247 (29%) had self-reported hypertension. For prescription-defined hypertension with 365-day lookback, the PPV was highest for ≥ 2 prescriptions in ≥ 2 drug classes (94%) and lowest for ≥ 1 prescription in ≥ 1 drug class (85%). The NPV was highest for ≥ 1 prescription in ≥ 2 drug classes (94%) and lowest for ≥ 1 prescription in ≥ 2 drug classes (80%). The sensitivity was highest for ≥ 1 prescription in ≥ 1 drug class (79%) and lowest for ≥ 2 prescriptions in ≥ 2 drug classes (30%). The specificity was ≥ 94% for all algorithms. The PPV and specificity did not change noteworthy with length of lookback period, whereas the NPV and the sensitivity generally were higher for longer lookback. The algorithm ≥ 1 prescription in ≥ 2 drug classes with 365-day lookback was among the best balanced across all measures of validity (PPV=88%, NPV=94%, sensitivity=75%, specificity=96%). For hospital-diagnosed hypertension, the PPV was 90%, the NPV was 76%, the sensitivity was 22%, and the specificity was 99%.Conclusion: Compared with self-reported hypertension, the algorithms for prescription-defined and hospital-diagnosed hypertension had high predictive values and specificity, but low sensitivity.Keywords: epidemiologic studies, epidemiology, hypertension, predictive value of tests, sensitivity and specificity, validation study

Background and aim During the past decade, the frequency of extrapulmonary forms of tuberculosis (TB) has increased. These forms are often miss-diagnosed. This statement of the TB epidemiological profile modification, conduct us to reflect about the utility of the Tuberculin Skin Test (TST) in active TB detection. This study aimed to evaluate the diagnostic accuracy performance of the TST for active tuberculosis detection. Methods This was a case-control, multicenter study conducted in 11 anti-TB centers in Tunisia (June-November2014). The cases were adults aged between 18 and 55 years with newly diagnosed and confirmed tuberculosis. Controls were free from tuberculosis. A data collection sheet was filled out and a TST was performed for each participant. Diagnostic accuracy measures of TST were estimated using Receiver Operating Curve (ROC) curve and Area Under Curve (AUC) to estimate sensitivity and specificity of a determined cut-off point. Results Overall, 1050 patients were enrolled, composed of 336 cases and 714 controls. The mean age was 38.3±11.8 years for cases and 33.6±11 years for controls. The mean diameter of the TST induration was significantly higher among cases than controls (13.7mm vs.6.2mm; p=10 -6). AUC was 0.789 [95% CI: 0.758-0.819; p=0.01], corresponding to a moderate discriminating performance for this test. The most discriminative cut-off value of the TST, which was associated with the best sensitivity (73.7%) and specificity (76.6%) couple was ≥ 11 mm with a Youden index of 0.503. Positive and Negative predictive values were 3.11% and 99.52%, respectively. Conclusions TST could be a useful tool used for active tuberculosis detection, with a moderate global performance and accepted sensitivity and specificity at the cut-off point of 11 mm. However, it cannot be considered as a gold standard test due to its multiple disadvantages.

OBJECTIVE: Most health care personnel (HCP) reporting symptoms consistent with COVID-19 illness are assessed by high-accuracy SARS-CoV-2 assays performed in clinical laboratories, but the results of such assays typically are not available until the following day. METHODS: This is an observational study over 16 weeks of a rapid nucleic acid amplification test (NAAT) performed at point of contact. The benchmark for comparison was a simultaneously obtained specimen assayed by a routine NAAT assay performed in a clinical laboratory. RESULTS: There were 577 paired rapid and routine NAAT results. Rapid test positive predictive value was 90.0% (95% confidence interval = 88.8%-91.2%), and negative predictive value was 95.2% (95% confidence interval = 93.5%-96.9%). The rapid test avoided an estimated 160 to 184 lost work shifts over 4 months. CONCLUSIONS: A rapid NAAT test-based strategy proved effective in safely clearing symptomatic employees without infection for earlier return to work.

BackgroundThe heart has metabolic flexibility, which is influenced by fed/fasting states, and pathologies such as myocardial ischemia and hypertrophic cardiomyopathy (HCM). Hyperpolarized (HP) 13C-pyruvate MRI is a promising new tool for non-invasive quantification of myocardial glycolytic and Krebs cycle flux. However, human studies of HP 13C-MRI have yet to demonstrate regional quantification of metabolism, which is important in regional ischemia and HCM patients with asymmetric septal/apical hypertrophy.MethodsWe developed and applied methods for whole-heart imaging of 13C-pyruvate, 13C-lactate and 13C-bicarbonate, following intravenous administration of [1-13C]-pyruvate. The image acquisition used an autonomous scanning method including bolus tracking, real-time magnetic field calibrations and metabolite-specific imaging. For quantification of metabolism, we evaluated 13C metabolite images, ratio metrics, and pharmacokinetic modeling to provide measurements of myocardial lactate dehydrogenase (LDH) and pyruvate dehydrogenase (PDH) mediated metabolic conversion in 5 healthy volunteers (fasting & 30 min following oral glucose load).ResultsWe demonstrate whole heart coverage for dynamic measurement of pyruvate-to-lactate conversion via LDH and pyruvate-to-bicarbonate conversion via PDH at a resolution of 6 × 6 × 21 mm3 (13C-pyruvate) and 12 × 12 × 21 mm3 (13C-lactate, 13C-bicarbonate). 13C-pyruvate and 13C-lactate were detected simultaneously in the RV blood pool, immediately after intravenous injection, reflecting LDH activity in blood. In healthy volunteers, myocardial 13C-pyruvate-SNR, 13C-lactate-SNR, 13C-bicarbonate-SNR, 13C-lactate/pyruvate ratio, 13C-pyruvate-to-lactate conversion rate, kPL, and 13C-pyruvate-to-bicarbonate conversion rate, kPB, all had statistically significant increases following oral glucose challenge. kPB, reflecting PDH activity and pyruvate entering the Krebs Cycle, had the highest correlation with blood glucose levels and was statistically significant.ConclusionsWe demonstrate first-in-human regional quantifications of cardiac metabolism by HP 13C-pyruvate MRI that aims to reflect LDH and PDH activity.