Mohammad Haris

Background and Aims: Intravascular ultrasound (IVUS)-guided percutaneous coronary intervention (PCI) has improved outcomes in randomized trials, yet some recent European trials reported neutral results despite positive findings from East Asian studies. This study assessed whether benefit was consistent across settings. Methods: Randomized trials comparing IVUS-guided with angiography-guided drug-eluting stent PCI were systematically reviewed and meta-analysed. Pooled risk ratios (RRs) used restricted maximum likelihood random-effects models with Hartung–Knapp–Sidik–Jonkman adjustment. Geographic effect modification was pre-specified as the primary hypothesis, and the interaction test for cardiac death was the primary analysis. Results: Seventeen trials (14 033 patients; 10 East Asian [10 155], 7 non-Asian [3878]) were included. The pooled RR for cardiac death was.71 (95% confidence interval [CI] .53–.94, P = .022), with a significant geographic interaction: East Asian RR.56 (.46–.69) vs non-Asian RR 1.23 (.85–1.76; interaction P < .001). Treatment effects diverged for target-vessel myocardial infarction (TV-MI; interaction P = .011), target-vessel revascularization (TVR; interaction P = .004), and major adverse cardiovascular events (MACE; interaction P = .040). IVUS guidance reduced definite stent thrombosis (RR .39, 95% CI .19–.79; interaction P = .433). All-cause death was similar (RR .89, 95% CI .76–1.04). Conclusions: Considering all studies, IVUS-guided PCI reduced cardiac death, TV-MI, TVR, MACE, and stent thrombosis. However, significant interactions were present such that the relative reductions in cardiac death, TV-MI, TVR, and MACE were greater in the Asian compared with non-Asian trials (although stent thrombosis was reduced consistently across geographies). Differences in how the information derived from IVUS was used to optimize stent implantation may contribute to this geographic heterogeneity.

Aims: Pharmacological therapies that treat cardiovascular, kidney, metabolic, and pulmonary (CKMP) risk factors reduce the risk of major adverse cardiovascular events, but community-dwelling individuals may not receive all treatments to which they are eligible from non-specialists. This trial investigated if specialist-led care in the community increases the utilization of CKMP pharmacological interventions compared with usual care. Methods and results: This pragmatic prospective randomized open-label blinded endpoint trial included community-dwelling high-risk adults aged ≥30 years from 15 primary care sites in England. Participants were randomized 1:1 to an intervention of two clinic appointments (at baseline and ∼3 months) with a cardiology specialist at their primary care site to review and optimize CKMP risk factors in accordance with UK guidelines, or usual care. The primary outcome was the proportion of participants with initiation and/or increase in the dose of guideline-directed CKMP pharmacological therapies at 6 months after date of randomization. Between March 2025 and July 2025, 138 participants were randomized (68 intervention, 70 control); the median age was 76.6 years, and 52.9% were women. At 6 months after randomization, in the intervention arm, 42 of 68 (61.8%) patients received a CKMP pharmacological intervention compared with 16 of 70 patients (22.9%) in the control arm (odds ratio 5.4, 95% CI 2.5–12.3, P < 0.0001, absolute risk difference 38.9%, 95% CI 23.7–54.1%). Conclusion: Specialist-led care in the community for high-risk individuals increased the use of pharmacological interventions for CKMP risk factors compared to usual care.

BACKGROUND: Screening for atrial fibrillation (AF) on the basis of AF risk may be more effective. We aimed to develop, externally validate, and prospectively test a machine learning prediction model using electronic health records (EHRs) to guide AF screening. METHODS: We developed and validated a random forest prediction model for new AF within 6 months, using age, sex, and 10 comorbidities (Future Innovations in Novel Detection of Atrial Fibrillation [FIND-AF] 2.0) in EHRs in the United Kingdom (n=2 081 139), Japan (n=7 795 244), Israel (n=2 166 795), Canada (n=627 919), and China (n=149 145). We conducted a prospective study where participants ≥30 years old without AF and with a CHA ₂ DS ₂ -VASc score ≥2 in men and ≥3 in women, stratified by FIND-AF 2.0 into high and low risk, undertook 4 ECG recordings per day for 3 weeks using a handheld ECG recorder, with a primary outcome of newly diagnosed AF. We estimated stroke risk associated with nonanticoagulated AF in patients with high FIND-AF 2.0 risk in the FinACAF (Finnish Anticoagulation in Atrial Fibrillation) registry of patients with AF (n=229 565). RESULTS: FIND-AF 2.0 was applicable to all EHRs and showed good to excellent prediction performance (United Kingdom: area under the receiver operating characteristic curve [AUROC], 0.819 [95% CI, 0.809–0.829]; Israel: AUROC, 0.835 [95% CI, 0.828–0.842]; Japan: AUROC, 0.751 [95% CI, 0.745–0.757]; Canada: AUROC, 0.747 [95% CI, 0.741–0.753]; China: AUROC, 0.753 [95% CI, 0.725–0.771]), with AUROC>0.7 in men and women in all cohorts, and improved performance compared with CHA ₂ DS ₂ -VASc and C ₂ HEST (coronary artery disease or chronic obstructive pulmonary disease [1 point each]; hypertension [1 point]; elderly [age ≥75 years, 2 points]; systolic HF [2 points]; thyroid disease [hyperthyroidism, 1 point]). Of 1923 participants from 15 sites in the prospective study (mean age, 70.2 [SD 9.4] years), with a mean of 74.8 (SD, 19.4) ECG recordings, AF was diagnosed in 5 of 902 (0.6%) with low FIND-AF 2.0 risk and 46 of 1021 (4.5%) with high FIND-AF 2.0 risk (odds ratio, 8.46 [95% CI, 3.35–21.40], P <0.001). Median AF burden among high FIND-AF 2.0 risk–detected cases was 33.4% (interquartile range, 5.1%–91.6%), and 96.1% initiated oral anticoagulants. In the FinACAF registry, the rate of ischemic stroke for patients with high FIND-AF 2.0 risk, AF, and no anticoagulants was 6.0 events per 100 patient-years. CONCLUSIONS: The EHR-based machine learning model, FIND-AF 2.0, identifies a high-risk subpopulation for AF diagnosis among patients at elevated risk of stroke and could enable scalable, EHR-driven, risk-guided AF screening.

Background and Aims: Valvular heart disease (VHD) conveys a high burden of morbidity and mortality and, given the increasing armamentarium of therapeutic interventions, earlier diagnosis is important. This meta-analysis aimed to evaluate the potential of tools in routine health data to identify individuals with undiagnosed VHD in community or population-based settings. Methods: MEDLINE and Embase were searched from inception to 6 October 2025, for studies describing tools derived, validated or augmented to identify VHD in community populations. Potential measures (C-statistics) were pooled using Bayesian meta-analysis stratified by valve class and data modality with potential data from ≥3 cohorts, with heterogeneity assessed using 95% prediction intervals. Risk of bias was evaluated using the Prediction model Risk Of Bias Assessment Tool (PROBAST). Results: Nineteen studies including 33 prediction tools were included; 18% had a high risk of bias. Imaging- and electrocardiogram-based models demonstrated excellent potential [C-statistic 0.861 (95% CI 0.827–0.889; I2 = 96%) and 0.830 (0.802–0.855; I2 = 100%)], while multivariable clinical models displayed good potential and biomarker-based models adequate potential [0.786 (0.663–0.872; I2 = 99%) and 0.699 (0.629–0.760; I2 = 0%)]. Models targeting right-sided and mitral valve disease showed excellent potential [0.885 (0.837–0.920; I2 = 98%), 0.851 (0.819–0.879; I2 = 99%)]. Models targeting aortic valve disease displayed good potential [0.797 (0.765–0.826; I2 = 100%)]. External validation was limited, only one study assessed clinical utility, and no study prospectively validated a model. Conclusions: Models using routine healthcare data to identify risk of undiagnosed VHD show potential but comprehensive external and prospective validation is required before consideration of implementation.

Aims: Heart failure (HF) is a common sequela of type 2 diabetes (diabetes), and models have been developed for its prediction. We aimed to synthesize the available evidence by performing a systematic review and meta-analysis of models predicting incident HF and HF hospitalization (HFH) in individuals with diabetes. Methods: We searched MEDLINE and EMBASE for multivariable models predicting HF or HFH in patients with diabetes from inception to 2 December 2025. Discrimination metrics from models validated in ≥3 cohorts were pooled using Bayesian meta-analysis. Heterogeneity was assessed using 95% prediction intervals (PI), and risk of bias with PROBAST. Results: In total, 65 studies describing 56 HF and 44 HFH prediction models were included. Following exclusion of studies at high risk of bias, two HF models (RECODe (0.711, 95% CI 0.651–0.767) and DMRS (0.758, 95% CI 0.684–0.827)) and two HFH models (WATCH-DM 2022 (r) (0.705, 95% CI, 0.624–0.795), WATCH-DM 2022(i) (0.718, 95% CI 0.587–0.850)) had acceptable prediction performance, while one HFH model had good performance (DM-CURE (0.837, 95% CI 0.757–0.913). Of these, none reported a prediction horizon of less than ten years. For all models, regardless of risk of bias, none were prospectively tested, and only three underwent clinical utility analysis. Conclusion: While some HF and HFH models in diabetes show acceptable/good long-term discriminative performance, the scarcity of utility analyses and prospective testing limits translation to clinical settings. Prospective evaluation of these models is required to establish clinical utility.

Aims: Electronic health records (EHRs) can be used to target atrial fibrillation (AF) screening. We evaluated the performance of risk prediction models scalable across nationwide EHRs. Methods and results: This retrospective cohort study included individuals aged ≥30 years without diagnosed AF in the Clalit Health Services (Israel) EHR dataset between 1 January 2019 and 30 June 2019. The primary outcome was a diagnosis of AF or atrial flutter within 6 months. The FIND-AF, CHA2DS2-VASc, and C2HEST scores were evaluated, with prediction performance assessed overall and by sex. The optimum threshold to apply in prospective screening was determined with a lift analysis. Of 2 166 795 individuals in the cohort, 4275 developed AF within 6 months. Prediction performance was strongest for FIND-AF [area under the receiver operating characteristic curve (AUROC) 0.871, 95% confidence interval (CI) 0.864–0.877; calibration slope 0.73, 95% CI 0.67–0.79] compared with CHA2DS2-VASc (AUROC 0.838, 95% CI 0.831–0.845; calibration slope 0.63, 95% CI 0.60–0.67) and C2HEST scores (AUROC 0.834, 95% CI 0.823–0.844; calibration slope 0.62, 95% CI 0.58–0.65), including in women (FIND-AF AUROC 0.883, 95% CI 0.876–0.889; CHA2DS2-VASc AUROC 0.865, 95% CI 0.858–0.872; C2HEST AUROC 0.853, 95% CI 0.846–0.861) and men (FIND-AF AUROC 0.857, 95% CI 0.850–0.864; CHA2DS2-VASc AUROC 0.835, 95% CI 0.828–0.843; C2HEST AUROC 0.814, 95% CI, 0.806–0.822). Lift analysis suggested that screening the top 15% of FIND-AF risk compared with screening by age would identify 72% compared with 63% of AF diagnoses. Conclusion: The FIND-AF machine learning algorithm was scalable in routine EHR data with good discrimination for incident AF. Prospective evaluation is now required to evaluate risk-guided AF screening.

Introduction: Heart failure (HF) is a common sequela of diabetes and obesity. Glucagon-like peptide-1 (GLP-1) receptor agonists may prevent HF events. This meta-analysis estimates absolute risk reduction (ARR) and number needed to treat (NNT) for GLP-1 receptor agonists to prevent one HF event in patients with type 2 diabetes and/or obesity, including those without baseline HF. Methods: The Medline, Embase, and Cochrane Central databases were searched to 04 April 2025 for placebo-controlled randomized controlled trials (RCTs) of GLP-1 receptor agonists in a type 2 diabetes and/or obesity indication with a prespecified HF event endpoint. Random effects meta-analysis using the Mantel-Haenszel Method was performed to synthesize risk ratios (RR), ARRs, and NNTs with 95% confidence intervals (CI). Results: Twelve placebo-controlled RCTs involving 95 023 patients were included. GLP-1 receptor agonists reduced HF events by 12% (RR 0.88, 95% CI 0.82–0.95; ARR 0.42%, 95% CI 0.17%–0.62%; NNT 238, 95% CI 161–588), and, in those without baseline HF, by 19% (RR 0.81, 95% CI 0.72–0.90; ARR 0.60%, 95% CI 0.32%-0.89%; NNT 167, 95% CI 113–313). Semaglutide reduced the risk of HF events by 16% (RR 0.84, 95% CI 0.74–0.95; ARR 0.62%, 95% CI 0.19%-1.00%; NNT 161, 95% CI 100–526), and by 31% in those without baseline HF (RR 0.69, 95% CI 0.55–0.88; ARR 1.25%, 95% CI 0.48%-1.82%; NNT 80, 95% CI 55–208). Conclusion: GLP-1 receptor agonists have limited absolute benefit for preventing HF events in patients with type 2 diabetes and/or obesity, including in those without baseline HF. Systematic review registration: PROSPERO: CRD420251074882

Aims: Stroke is the second leading cause of death and the third leading cause of disability worldwide. We performed a systematic review and meta-analysis of multivariable models applicable to the prediction of incident stroke in community cohorts. Methods and results: Ovid Medline and Embase were searched for studies related to stroke and prediction models from inception to 3 November 2025. Measures of discrimination were extracted and pooled by Bayesian meta-analysis, with heterogeneity assessed through a 95% prediction interval (PI). Risk of bias was assessed using the Prediction model Risk Of Bias Assessment Tool and certainty in effect estimates by Grading of Recommendations, Assessment, Development and Evaluation. Forty-one studies met the inclusion criteria, describing 80 prediction models, with two (R-FSRS and Basic IS) eligible for meta-analysis, including 969 514 participants. Both R-FSRS (summary c-statistic 0.714, 95% CI 0.681–0.747) and Basic IS (0.709, 95% CI 0.647–0.769) showed acceptable discrimination performance. Risk of bias was high in 66% of models, and both models showed reduced performance when excluding development cohorts and studies at high risk of bias (R-FSRS, 0.667, 95% CI 0.604–0.727; Basic IS 0.701; 95% CI 0.583–0.807). Only 43% of studies reported calibration, and no model underwent clinical utility analysis or a clinical impact study. Conclusion: Many models have been derived for stroke prediction, however, they are rarely externally validated, and studies are limited by a high risk of bias, poor reporting of calibration and a lack of clinical utility analysis or prospective validation. Thus, the evidence base is insufficient to translate these models to clinical practice.

Aims: Health-related quality of life (HRQoL) is an important measure of disease status and represents a holistic approach to delivering patient-centered care. We conducted a scoping review of HRQoL patient reported outcome measures (PROMs) for cardiovascular diseases (CVDs) and evaluated their psychometric properties. Methods and results: Randomized trials and observational studies that developed and validated HRQoL PROMs for adults with ischaemic heart disease (IHD), aortic stenosis (AS), atrial fibrillation (AF), heart failure (HF), or generic CVD were included, published from inception of databases to 8 February 2025 using PubMed, Web of Science, and Embase. Independent reviewers selected and extracted the psychometric properties of each PROM in accordance with the Consensus-based Standards for the selection of health Measurement Instruments (COSMIN) checklist: content validity, reliability, internal consistency, structural validity, criterion/convergent, cross-cultural validity, measurement error, hypothesis testing, and responsiveness. Each PROM was graded using the Grading of Recommendations Assessment, Development, and Evaluation approach. Of 9430 articles, 220 studies for 38 different PROMs were included (HF n = 17, 45%; AF n = 11, 29%; IHD n = 7, 18%; generic n = 2, 5%; AS n = 1, 3%). Eleven PROMs (29%) satisfied all nine COSMIN criteria; the majority (n = 19, 50%) required further validation and eight were deemed inadequate for clinical use (21%). Conclusion: This scoping review of HRQoL PROMs in individuals with common CVDs found evidence that many PROMs do not fulfill all nine COSMIN criteria for methodological quality, and for some CVDs there is a limited choice of suitable PROMs for HRQoL measurement. There is an opportunity to improve HRQoL evaluation for use within routine care and research.

Background: Screening for silent AF is becoming increasingly important (1, 2), and the FIND-AF algorithms can use routinely-collected data to identify individuals at higher risk for undiagnosed AF (3). There may be a much higher prevalence of patients at risk for AF and its complications in a hospital environment compared with the community (4). Purpose: We prospectively implemented the FIND-AF algorithm within cardiology and geriatric services to quantify the burden of AF risk amongst all-comers, and to understand their outcomes over the next 6 months. Methods: We prospectively implemented the FIND-AF algorithm in the cardiology and geriatric departments of three UK hospitals for consecutive inpatient or outpatient attendances from February 1 to July 13th 2023. We assessed the number of individuals attending each service eligible for an AF screening protocol (exclusion criteria: age <30 years, history of AF, on a palliative care pathway, use of oral anticoagulation), and calculated the burden of AF risk in each service. Patients were followed up for 6 months for the primary outcome of incident AF, heart failure (HF), stroke/transient ischaemic attack (TIA)/systemic embolism, and death. Incident diagnoses were the first record of that condition in primary or secondary care records. Rate ratios (RRs) with 95% confidence intervals (CIs) for combined incident cardiovascular outcomes or death were derived through Poisson regression for predicted higher versus lower AF risk. Results: Of 467 consecutive patients, 364 were eligible for AF screening. 66 were excluded due to a previous diagnosis of AF, 25 were already prescribed a long-term oral anticoagulant, 5 were on a palliative care pathway and 7 were aged <30 years. Baseline characteristics of each cohort are summarised in Table 1. Of patients eligible for AF screening, those attending geriatrics were at higher risk of incident AF than patients attending cardiology (median FIND-AF score: geriatrics, 0.015, 95% CI 0.008 - 0.024; cardiology, 0.005, 95% CI 0.001-0.015; p<0.05; distributions of risk summarised in Figure 1) and had a higher stroke risk. Absolute risk of AF was greater than 5% within the next 6 months for 11.6% and 9.1% of geriatric and cardiology patients, respectively. Of the eligible cohort, 328 had follow up at 6 months, with 46 experiencing the primary outcome (9, 7, 3, and 27 recorded cases of AF, HF, stroke/TIA, and death, respectively). The higher predicted risk cohort, compared to lower predicted risk, had a higher risk of combined incident AF, heart failure, or stroke/TIA/systemic embolism (Rate ratio: 5.59, 95% CI 1.13-101.01) and death (RR 9.68, 2.06-172.85). Conclusions: The FIND-AF algorithm can be prospectively implemented in hospital records to identify cardiology and geriatric patients at risk of incident AF, heart failure, thromboembolic disease and death. There is an opportunity to conduct risk-guided screening in the context of hospital attendances.