Jianhua Wu

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.

Objectives: To describe the clinical characteristics and dental treatment for patients treated at a dental hospital in the United Kingdom. Material and Methods: This descriptive study utilized anonymized routinely collected electronic health record data from dental patients who attended Leeds Dental Institute, collected from 2014 to 2023. Patient characteristics, comorbidities and completion of dental treatment were reported through descriptive statistics (mean and standard deviation [SD]; percentage) and by clinic attended. Results: A total of 109,718 patients were included in this study, of whom 58,964 (54%) were female, and 43,828 (40%) lived in the most deprived areas in England. The most frequently recorded comorbidities were joint or bone problems ( n = 4913, 5%), chest or breathing problems ( n = 4619, 4%), and heart problems ( n = 4455, 4%). Across clinic groups, the proportion of patients with at least one recorded comorbidity was highest among those attending periodontal/restorative clinics (24%) and acute dental care clinics (19%). Less than half of all patients ( n = 52,655, 48%) completed their dental treatment. Oral surgery had the highest percentage of patients who completed their treatment ( n = 10,459, 71%). Conclusions: The sociodemographic and clinical characteristics reported in this study reflect a large population of dental patients from Leeds and the surrounding region. The findings of this study are consistent with literature showing sociodemographic trends in dental attendance. Future linkage of dental electronic health records with hospital records could improve understanding of the complexities of oral‐systemic associations further.

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: Co-existence of atrial cardiomyopathy (AtCM) and heart failure with reduced ejection fraction (HFrEF) has prognostic implications. We aimed to quantify the association of a health records data biomarker, FIND-AF, with outcomes in patients with HFrEF and AtCM without atrial fibrillation (AF). Methods: Patients with HFrEF and AtCM without AF at baseline were identified from the ECG-HF registry (2010–6) and linked to the territory-wide Clinical Data Analysis and Reporting System. AtCM was operationalized using a pragmatic multi-domain framework based on the 2025 clinical consensus statement of the Heart Failure Association of the ESC, using electrocardiographic and echocardiographic parameters. Patients were stratified into high and low risk by sex-specific FIND-AF thresholds. The primary outcome was a composite of incident AF, ischaemic stroke, and all-cause mortality. All patients were followed until 31 December 2019. Results: We included 164 patients (mean age 62.3 ± 13.0 years, 73.8% men). The primary outcome occurred in 23/32 (71.9%) high-risk vs 67/132 (50.8%) low-risk patients (OR 2.48, 95% CI 1.07–5.76; P = .035). In the model including both FIND-AF risk group and AtCM severity, high FIND-AF risk remained associated with the composite outcome (adjusted OR 2.70, 95% CI 1.14–6.40; P = .024). Compared with CHA2DS2-VASc, FIND-AF showed superior discrimination for the composite outcome (AUC .735 vs .641; DeLong P = .007) and ischaemic stroke (AUC .700 vs .575; DeLong P = .008). Conclusions: High FIND-AF risk is associated with adverse prognosis in a cohort of patients with HFrEF and AtCM but without baseline AF. These exploratory data require external validation and a prospective study.

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.