Stringent variant interpretation guidelines can lead to high rates of variants of uncertain significance (VUS) for genetically heterogeneous disease like long QT syndrome (LQTS) and Brugada syndrome (BrS). Quantitative and disease-specific customization of

Background Congenital long QT syndrome (LQTS) is a rare heart disease caused by various underlying mutations. Most general cardiologists do not routinely see patients with congenital LQTS and may not always recognize the accompanying ECG features. In addition, a proportion of disease carriers do not display obvious abnormalities on their ECG. Combined, this can cause underdiagnosing of this potentially life-threatening disease. Methods This study presents 1D convolutional neural network models trained to identify genotype positive LQTS patients from electrocardiogram as input. The deep learning (DL) models were trained with a large 10-s 12-lead ECGs dataset provided by Amsterdam UMC and externally validated with a dataset provided by University Hospital Leuven. The Amsterdam dataset included ECGs from 10000 controls, 172 LQTS1, 214 LQTS2, and 72 LQTS3 patients. The Leuven dataset included ECGs from 2200 controls, 32 LQTS1, and 80 LQTS2 patients. The performance of the DL models was compared with conventional QTc measurement and with that of an international expert in congenital LQTS (A.A.M.W). Lastly, an explainable artificial intelligence (AI) technique was used to better understand the prediction models. Results Overall, the best performing DL models, across 5-fold cross-validation, achieved on average a sensitivity of 84 ± 2%, 90 ± 2% and 87 ± 6%, specificity of 96 ± 2%, 95 ± 1%, and 92 ± 4%, and AUC of 0.90 ± 0.01, 0.92 ± 0.02, and 0.89 ± 0.03, for LQTS 1, 2, and 3 respectively. The DL models were also shown to perform better than conventional QTc measurements in detecting LQTS patients. Furthermore, the performances held up when the DL models were validated on a novel external cohort and outperformed the expert cardiologist in terms of specificity, while in terms of sensitivity, the DL models and the expert cardiologist in LQTS performed the same. Finally, the explainable AI technique identified the onset of the QRS complex as the most informative region to classify LQTS from non-LQTS patients, a feature previously not associated with this disease. Conclusions This study suggests that DL models can potentially be used to aid cardiologists in diagnosing LQTS. Furthermore, explainable DL models can be used to possibly identify new features for LQTS on the ECG, thus increasing our understanding of this syndrome.

Background/Introduction Guidelines for variant interpretation in Mendelian disease set stringent criteria to report a variant as (likely) pathogenic, prioritising control of false positive rate over test sensitivity and diagnostic yield, and require customisation for the specific genetic characteristics of gene-disease dyads. Inherited arrhythmias like long QT syndrome (LQTS) and Brugada syndrome (BrS) are genetically heterogeneous, with missense variants constituting the preponderance of disease-causing variants. Evidence from family segregation or functional assays to confirm pathogenicity are often unavailable or impractical in clinical genetic testing, leading to high rates of variants of uncertain significance and false negative test results. Methods We compared rare variant frequencies from 1847 LQTS (KCNQ1, KCNH2, SCN5A) and 3335 BrS (SCN5A) cases from the International LQTS/BrS Genetics Consortia to population-specific gnomAD data. We propose disease-specific criteria for ACMG/AMP evidence classes – rarity (PM2/BS1 rules) and enrichment of individual (PS4) and domain-specific (PM1) variants in cases over controls. Results Rare SCN5A variant prevalence differed between BrS cases with spontaneous (28.7%) versus induced (15.8%) type 1 Brugada ECG (p=1.3x10–13) and between European (20.8%) and Japanese (8.9%) patients (p=8.8x10–18). Transmembrane regions and specific N-terminus (KCNH2) and C-terminus (KCNQ1/KCNH2) domains were characterised by high enrichment of case variants and >95% probability of pathogenicity. Applying the customised rules, 17.5% of European BrS cases and 73.7% of European LQTS cases had variants classified as (likely) pathogenic, compared to estimated diagnostic yields (case excess over gnomAD) of 19.3%/82.6%. Conclusions Large case/control datasets enable quantitative implementation of ACMG/AMP guidelines and increased sensitivity for inherited arrhythmia genetic testing. Classification of Brugada/LQTS variants Funding Acknowledgement Type of funding source: Foundation. Main funding source(s): Dutch Heart Foundation, Netherlands Organisation for Scientific Research

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BACKGROUND: Return to work (RTW) is an important outcome in Total Knee Arthroplasty (TKA). At present, 70–80%of TKA patients return to work within three to six months. OBJECTIVE: What are patients’ perspectives regarding beneficial and limiting factors in RTW after TKA? METHODS: Focus groups were formed in accordance with the Consolidated Criteria for Reporting Qualitative Research (COREQ) checklist. Three major topics were explored: 1. What was beneficial for RTW after TKA; 2. What was limiting for RTW after TKA; and 3. What additional care would benefit RTW after TKA? RESULTS: Data saturation was reached after four focus groups, comprising 17 participants—nine men and eight women (median age 58, range 52–65). The focus group study identified four main themes that contributed to a successful RTW namely rehabilitation (medical) like post-operative physical therapy, patient characteristics (personal), like motivation to RTW, occupational characteristics (work-related) like build-up in work tasks and medical support (medical) like availability of a walker or crutches. CONCLUSION: According to participants, factors within the following four themes can contribute to a successful return to work: occupational, patient, rehabilitation and medical care. Incorporating these factors into the integrated care pathway for the “young” TKA patients may increase the chances of a successful RTW.