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A study published in the European Journal of Heart Failure evaluated the readability and quality of responses provided by ChatGPT, a large language model, to hypothetical questions from a patient with heart failure. The study found that the responses required a college-level reading level, indicating poor readability for the average adult. The responses were generally accurate, with minimal factual errors, but lacked emphasis on key learning points and contained jargon that was not always explained. The study highlights the need for healthcare professionals to guide patients on where to find appropriate and readable healthcare information. [Extracted from the article]

Objective: Following acute coronary syndrome (ACS), patients are managed long-term in the community, yet few tools are available to guide patient-clinician communication about risk management in that setting. We developed a score for predicting cardiovascular disease (CVD) risk among patients managed in the community after ACS.Methods: Adults aged 30-79 years with prior ACS were identified from a New Zealand primary care CVD risk management database (PREDICT) with linkage to national mortality, hospitalisation, pharmaceutical dispensing and regional laboratory data. A Cox model incorporating clinically relevant factors was developed to estimate the time to a subsequent fatal or non-fatal CVD event and transformed into a 5-year risk score. External validation was performed in patients (Coronary Disease Cohort Study) assessed 4 months post-ACS.Results: The PREDICT-ACS cohort included 13 703 patients with prior hospitalisation for ACS (median 1.9 years prior), 69% men, 58% European, median age 63 years, who experienced 3142 CVD events in the subsequent 5 years. Median estimated 5 year CVD risk was 24% (IQR 17%-35%). The validation cohort consisted of 2014 patients, 72% men, 92% European, median age 67 years, with 712 CVD events in the subsequent 5 years. Median estimated 5-year risk was 33% (IQR 24%-51%). The risk score was well calibrated in the derivation and validation cohorts, and Harrell's c-statistic was 0.69 and 0.68, respectively.Conclusions: The PREDICT-ACS risk score uses data routinely available in community care to predict the risk of recurrent clinical events. It was derived and validated in real-world contemporary populations and can inform management decisions with patients living in the community after experiencing an ACS. [ABSTRACT FROM AUTHOR]

Background: To review long QT syndrome molecular autopsy results in sudden unexplained death in young (SUDY) between 2006 and 2013 in New Zealand. Methods: Audit of the LQTS molecular autopsy results, cardiac investigations and family screening data from gene-positive families. Results: During the study period, 365 SUDY cases were referred for molecular autopsy. 128 cases (35%) underwent LQTS genetic testing. 31 likely pathogenic variants were identified in 27 cases (21%); SCN5A (14/31, 45%), KCNH2 (7/31, 22%), KCNQ1 (4/31, 13%), KCNE2 (3/31, 10%), KCNE1 (2/31, 7%), KCNJ2 (1/31, 3%). Thirteen variants (13/128, 10%) were ultimately classified as pathogenic. Most deaths (63%) occurred during sleep. Gene variant carriage was more likely with a positive medical history (mostly seizures, 63% vs 36%, p = 0.01), amongst females (36% vs 12%, p = 0.001) and whites more than Maori (31% vs 0, p = 0.0009). Children 1–12 years were more likely to be gene-positive (33% vs 14%, p = 0.02). Family screening identified 42 gene-positive relatives, 18 with definitive phenotypic expression of LQTS/Brugada. 76% of the variants were maternally inherited (p = 0.007). Further family investigations and research now support pathogenicity of the variant in 13/27 (48%) of gene-positive cases. Conclusion: In New Zealand, variants in SCN5A and KCNH2, with maternal inheritance, predominate. A rare variant in LQTS genes is more likely in whites rather than Maori, females, children 1–12 years and those with a positive personal and family history of seizures, syncope or SUDY. Family screening supported the diagnosis in a third of the cases. The changing classification of variants creates a significant challenge. [ABSTRACT FROM AUTHOR]

The article discusses the results of a study on the use of the Chronic Kidney Disease-Epidemiology Collaboration Group (CKD-EPI) and the Modification of Diet in Renal Disease (MDRD) equations in assessing the frequency of renal dysfunction in patients with heart failure (HF). The CKD-EPI is more accurate than MDRD equation in calculating the estimated glomerular filtration rate (eGFR). It allows higher estimation of renal dysfunction and accurate classification of mortality risk in HF.

Background: Long QT syndrome is a lethal arrhythmia syndrome, frequently caused by rare loss-of-function variants in the potassium channel encoded by KCNH2 . Variant classification is difficult, often because of lack of functional data. Moreover, variant-based risk stratification is also complicated by heterogenous clinical data and incomplete penetrance. Here we sought to test whether variant-specific information, primarily from high-throughput functional assays, could improve both classification and cardiac event risk stratification in a large, harmonized cohort of KCNH2 missense variant heterozygotes., Methods: We quantified cell-surface trafficking of 18 796 variants in KCNH2 using a multiplexed assay of variant effect (MAVE). We recorded KCNH2 current density for 533 variants by automated patch clamping. We calibrated the strength of evidence of MAVE data according to ClinGen guidelines. We deeply phenotyped 1458 patients with KCNH2 missense variants, including QTc, cardiac event history, and mortality. We correlated variant functional data and Bayesian long QT syndrome penetrance estimates with cohort phenotypes and assessed hazard ratios for cardiac events., Results: Variant MAVE trafficking scores and automated patch clamping peak tail currents were highly correlated (Spearman rank-order ρ=0.69; n=433). The MAVE data were found to provide up to pathogenic very strong evidence for severe loss-of-function variants. In the cohort, both functional assays and Bayesian long QT syndrome penetrance estimates were significantly predictive of cardiac events when independently modeled with patient sex and adjusted QT interval (QTc); however, MAVE data became nonsignificant when peak tail current and penetrance estimates were also available. The area under the receiver operator characteristic curve for 20-year event outcomes based on patient-specific sex and QTc (area under the curve, 0.80 [0.76-0.83]) was improved with prospectively available penetrance scores conditioned on MAVE (area under the curve, 0.86 [0.83-0.89]) or attainable automated patch clamping peak tail current data (area under the curve, 0.84 [0.81-0.88])., Conclusions: High-throughput KCNH2 variant MAVE data meaningfully contribute to variant classification at scale, whereas long QT syndrome penetrance estimates and automated patch clamping peak tail current measurements meaningfully contribute to risk stratification of cardiac events in patients with heterozygous KCNH2 missense variants.

Background: Long QT syndrome (LQTS) is a lethal arrhythmia syndrome, frequently caused by rare loss-of-function variants in the potassium channel encoded by KCNH2 . Variant classification is difficult, often owing to lack of functional data. Moreover, variant-based risk stratification is also complicated by heterogenous clinical data and incomplete penetrance. Here, we sought to test whether variant-specific information, primarily from high-throughput functional assays, could improve both classification and cardiac event risk stratification in a large, harmonized cohort of KCNH2 missense variant heterozygotes., Methods: We quantified cell-surface trafficking of 18,796 variants in KCNH2 using a Multiplexed Assay of Variant Effect (MAVE). We recorded KCNH2 current density for 533 variants by automated patch clamping (APC). We calibrated the strength of evidence of MAVE data according to ClinGen guidelines. We deeply phenotyped 1,458 patients with KCNH2 missense variants, including QTc, cardiac event history, and mortality. We correlated variant functional data and Bayesian LQTS penetrance estimates with cohort phenotypes and assessed hazard ratios for cardiac events., Results: Variant MAVE trafficking scores and APC peak tail currents were highly correlated (Spearman Rank-order ρ = 0.69). The MAVE data were found to provide up to pathogenic very strong evidence for severe loss-of-function variants. In the cohort, both functional assays and Bayesian LQTS penetrance estimates were significantly predictive of cardiac events when independently modeled with patient sex and adjusted QT interval (QTc); however, MAVE data became non-significant when peak-tail current and penetrance estimates were also available. The area under the ROC for 20-year event outcomes based on patient-specific sex and QTc (AUC 0.80 [0.76-0.83]) was improved with prospectively available penetrance scores conditioned on MAVE (AUC 0.86 [0.83-0.89]) or attainable APC peak tail current data (AUC 0.84 [0.81-0.88])., Conclusion: High throughput KCNH2 variant MAVE data meaningfully contribute to variant classification at scale while LQTS penetrance estimates and APC peak tail current measurements meaningfully contribute to risk stratification of cardiac events in patients with heterozygous KCNH2 missense variants.

Aims: Clinical registry-derived data are widely used to represent patient populations. In New Zealand (NZ), a national registry-the All New Zealand Acute Coronary Syndrome Quality Improvement (ANZACS-QI) registry-aims to include all patients undergoing coronary angiography; other acute coronary syndrome (ACS) patients are also registered but without complete capture. This study compares national hospitalization data of all first-time ACS admissions in NZ with patients in the ANZACS-QI registry, to investigate the use of clinical registry-derived data in research and in assessing clinical care., Methods and Results: Patients admitted with first-time ACS in the NZ National Hospitalisation Dataset between 1 January 2015 and 31 December 2016 were included. Clinical characteristics and time to 12-month clinical outcomes were compared between patients captured and not-captured in the registry. A total of 16 569 patients were admitted with first-time ACS, median age 69 years, 61% male; 60% (n = 9918) were enrolled in ANZACS-QI. Registry-captured patients were younger, more often male, and with a lower comorbidity burden than non-captured patients. Overall, 16% patients died within 12 months, 15% experienced a non-fatal cardiovascular (CV) readmission, and 28% either died or were readmitted. Patients not captured in the registry were more than twice as likely to have experienced death or a non-fatal CV readmission within 12 months as captured patients., Conclusions: First-time ACS patients captured in the ANZACS-QI registry had very different clinical characteristics and outcomes than those not captured. Cardiovascular registry-derived data are dependent on registry design and may not be representative of the wider patient population; this must be considered when using registry-derived data., (Published on behalf of the European Society of Cardiology. All rights reserved. © The Author(s) 2019. For permissions, please email: journals.permissions@oup.com.)