BACKGROUND:Heart failure with preserved ejection fraction (HFpEF) is characterised by systemic congestion and elevated left ventricular filling pressures. Pulmonary transit time (PTT) measured by cardiovascular magnetic resonance (CMR) provides a non-invasive, integrated assessment of cardiopulmonary haemodynamics, but its prognostic value in patients with HFpEF remains uncertain. We aimed to determine whether prolonged PTT independently predicts adverse clinical outcomes in HFpEF. METHODS:Adult patients with HFpEF were prospectively recruited and underwent comprehensive phenotyping including blood sampling, 12-lead electrocardiography, 6-min walk testing, echocardiography and multiparametric CMR (NCT03050593). PTT was derived from rest first-pass perfusion imaging and normalised to cardiac cycle length. An abnormal PTT threshold was defined as >95th percentile of asymptomatic controls. The primary endpoint was a composite of heart failure hospitalisation or all-cause mortality. Multivariable Cox proportional hazard regression (HR) models were used to investigate associations with clinical outcome. RESULTS:One-hundred and eighteen HFpEF patients were studied (median follow-up 7.7 years). Eighty-one composite events occurred (50 heart failure hospitalisations and 31 deaths). Prolonged PTT independently predicted the composite outcome across four separate models: clinical (HR 2.222 (1.222, 4.041), p=0.009), blood biomarker (HR 1.776 (1.022, 3.087), p=0.042), imaging (HR 2.108 (1.115, 3.983), p=0.020) and a combined model incorporating the strongest markers (HR 2.989 (1.612, 5.543), p<0.001). CONCLUSION:Prolonged PTT is an independent predictor of death or heart failure hospitalisation in HFpEF. As an easily obtainable and integrative CMR biomarker of cardiopulmonary haemodynamics, PTT may improve risk stratification in HFpEF, although its clinical application requires further investigation.

BACKGROUND AND AIMS:Frailty is increasingly recognized as an important determinant of adverse outcomes in older adults with acute myocardial infarction (AMI), but its impact in younger patients remains underexplored. The aims of this study were to evaluate the association of frailty with adverse outcomes in AMI patients, stratified by age. METHODS:This population-based epidemiological study utilized linked national administrative data from England and Wales. Patients were stratified into three age groups: <55 years, 55-74 years, and ≥75 years. Frailty was assessed using the Secondary Care Administrative Records Frailty index with patients categorized into fit, mild, moderate, and severe groups. All-cause mortality at 1 year was the primary outcome. Secondary outcomes were cardiovascular and bleeding-related events. RESULTS:A total of 931 133 patients were included of which 13% of patients were severely frail. In patients with severe frailty, adjusted hazard ratios for all-cause mortality were 6.69 [95% confidence interval (CI) 5.76-7.76] for young patients, 4.33 (95% CI 4.11-4.57) for middle-aged patients, and 2.31 (95% CI 2.23-2.39) for older patients. The interaction between age and frailty revealed that younger patients with severe frailty had a 3.51-fold (95% CI 3.11-3.96) higher risk of all-cause mortality compared with older patients with severe frailty (P < .001). CONCLUSIONS:Frailty is independently associated with poor outcomes after AMI, with the strongest impact observed in younger patients, highlighting the need for frailty assessment across all age groups.

BACKGROUND:Serum troponin measurement forms a cornerstone of acute myocardial infarction (AMI) diagnosis. A major challenge is interpretation of an elevated first troponin in patients with impaired renal function. We aimed to (1) evaluate the relationship between estimated glomerular filtration rate (eGFR) and first troponin, (2) characterise the performance of different troponin assays for diagnosing AMI and (3) derive eGFR-specific thresholds for potential clinical use. METHODS:We analysed the distribution of troponin values stratified by eGFR and AMI. Diagnostic performance was analysed using the C-statistic. Test detection rate, false positive rate and positive predictive value were calculated for different cut-offs. RESULTS:We included 221 175 patients between 2010 and 2017 from four acute tertiary care hospitals in London, UK, with a median age of 65 years (IQR 49-79). eGFR was<60 mL/min/1.73 m2 in 20.6% of patients and 6.4% of patients had a diagnosis of AMI. In patients without AMI, we observed an inverse log-linear relationship between eGFR and troponin. Diagnostic performance for AMI was best in patients with eGFR>90 (C-statistic 0.93) and worst in eGFR<15 (C-statistic 0.81). For high-sensitive troponin T, using the conventional cut-off of 14 ng/L, false positive rates ranged from 68-93% for eGFRs between 15 and 60 mL/min/1.73 m2. Restricting the false positive rate to 15% yields eGFR specific cut-offs of 73, 112 and 184 ng/L, with detection rates of 73%, 70% and 68% in patients with an eGFR of 45-60, 30-45 or 15-30 mL/min/1.73 m2. CONCLUSIONS:The diagnostic performance of an unadjusted troponin cut-off for AMI falls with worsening renal function. We propose consideration of eGFR specific cut-offs to support more effective triage and early management of suspected AMI in patients with renal impairment. TRIAL REGISTRATION NUMBER:NCT03507309.

Aims:Coronary artery disease (CAD) remains a leading cause of morbidity and mortality worldwide, necessitating accurate diagnostic strategies and robust risk stratification. The FINESSE (Artificial Intelligence Stress Echo) study aims to develop and validate machine learning-based models to improve risk prediction in patients undergoing stress echocardiography (SE) for the assessment of inducible myocardial ischaemia, and to implement a structured AI-driven risk reclassification framework to support clinical decision-making. Methods and analysis:This is a retrospective observational study on prospectively recruited patients referred for SE with suspected CAD. Clinical, demographic, haemodynamic, and echocardiographic data will be extracted from institutional electronic health records and linked with longitudinal national datasets. The study employs a three-stage ML framework: (1) baseline risk estimation using QRISK3, (2) development and optimization of supervised ML models (including ensemble methods, support vector machines, and neural networks), and (3) longitudinal risk prediction using linked NHS England data (such as all-cause mortality and major adverse cardiovascular events, including cardiovascular death, non-fatal myocardial infarction, stroke, unplanned revascularization). Model performance will be evaluated using discrimination (AUC), calibration, and reclassification metrics, and will be externally validated using independent multicentre SE datasets. Conclusion:The FINESSE study will evaluate the incremental value of machine learning in SE by integrating multimodal data into a clinically actionable risk reclassification framework. This approach has the potential to improve personalized risk stratification, optimize diagnostic pathways, and support precision cardiovascular care. Protocol registration:Ethical approval has been obtained from the UK Health Research Authority and relevant research ethics committees, with appropriate data governance approvals for the use of routinely collected healthcare data. The FINESSE study is registered on ClinicalTrials.gov (Identifier: NCT07432620), ensuring transparency and public accessibility of the study protocol.

INTRODUCTION:Heart failure (HF) hospitalizations are frequent and lengthy, and usually involve treatment with intravenous diuretics to relieve congestion. SUBCUT HF II is evaluating the safety and efficacy of an alternative ambulatory care strategy using a novel subcutaneous formulation of furosemide delivered via a wearable pump. METHODS:The SUBCUT HF II trial is a multicentre, randomized, active comparator trial involving 20 hospitals in the UK. Eligible participants are patients with HF receiving inpatient treatment with intravenous loop diuretic. Patients are randomized to either early supported discharge, using a novel formulation of subcutaneous furosemide (SQIN-Furosemide) administered by a wearable pump (SQIN-Infusor), or continued inpatient treatment using intravenous furosemide. RESULTS:The primary endpoint is days spent alive and out of hospital at 30 days. As of October 2025, 168 of 170 patients have been randomized. CONCLUSION:The SUBCUT HF II trial is testing the safety and efficacy of an ambulatory care approach to managing patients presenting to the hospital with HF. TRIAL REGISTRATION:ClinicalTrials.gov identifier NCT05419115.

Background: Many patients with heart failure (HF) remain undiagnosed until acute hospital admission for decompensation. Think-HF is a clinical decision support tool (CDST) designed to identify possible undiagnosed HF in primary care and trigger timely, guideline recommended assessment. Methods: Think-HF was developed through epidemiological evidence and co-design. Integrated within SystmOne, it analyses routine primary care data (codes, medications, test results, free text) to detect missed or emerging HF signals. When a record is opened, if a patient has two or more comorbidity indicators plus an HF-suggestive symptom, an alert is triggered and a structured template opens with one-click options for natriuretic peptide (NP) testing, echocardiography, specialist referral and coding. Additional algorithms identify unresolved investigations, coding inconsistencies and medication-based signals, generating a marker on the clinical record. A mixed-methods feasibility study across six primary care practices assessed reach, usability, acceptability, and early implementation signals, using the RE-AIM framework. Results: Six socioeconomically and ethnically diverse general practices participated (63 GPs; 78,640 patients [47.5% women, 59.4% White, 17.8% South Asian, 8.5% Black, 3% Chinese and 2.8% mixed ethnicities]). At baseline, 876 patients (1.1%) met the main alert criteria and 2,805 (3.6%) met additional algorithm criteria. Of 801 patients on the HF register, 665 (83%) lacked a refined HF left ventricular phenotype code. During four months of testing, Think-HF generated 299 clinically relevant main trigger alerts (75% during consultations). Early improvements included (i) 31 new HF diagnoses (4.2% increase), with higher gains (10%) in practices with lower baseline prevalence; (ii) a 14% increase in refined HF phenotype coding; (iii) fewer raised NT-proBNP results without follow-up (7% decrease); and (iv) fewer patients prescribed loop diuretics without NP testing (14% decrease, up to 45% decrease in one practice with pharmacist-supported review). Clinicians reported improved awareness, more systematic assessment, and better follow-up of missed investigations or coding anomalies. Identified gaps aligned with patient-reported delays and misattributed symptoms. Conclusions: Think-HF is feasible, acceptable and well aligned with routine primary care workflows. Early gains in diagnostic processes and coding accuracy highlight its potential to improve patient care. Team-based implementation will be essential for scale-up. Larger evaluation is required to assess clinical impact. ### Competing Interest Statement CL has acted as a speaker for Boehringer Ingelheim. KK has acted as a consultant, speaker or received grants for investigator-initiated studies for AstraZeneca, Boehringer Ingelheim, Lilly, MSD, Novo Nordisk, Sanofi, Servier, Oramed Pharmaceuticals, Roche, Daiichi-Sankyo, Applied Therapeutics, consulting fees from Amgen, AstraZeneca, Bristol Myers Squibb, Boehringer Ingelheim, Lilly, Novo Nordisk, Sanofi, Servier, Pfizer, Roche, Daiichi-Sankyo, Embecta and Nestle Health Science and payment or honoraria from Amgen, AstraZeneca, Bristol Myers Squibb, Boehringer Ingelheim, Lilly, Novo Nordisk, Sanofi, Servier, Pfizer, Roche, Daiichi-Sankyo, Embecta and Nestle Health Science. CJT has received grants from the British Heart Foundation, NIHR, consultancy and speaker fees from Astra Zeneca, Roche, Edwards and Bayer, and an research grant from Bayer. CD has research funding from NIHR. ### Funding Statement This is independent research funded by the National Institute for Health Research (NIHR-206318) and carried out at the National Institute for Health and Care Research (NIHR) Leicester Biomedical Research Centre (BRC). The views expressed are those of the author(s) and not necessarily those of the NIHR. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Ethics approval was granted by the Health and Social Care Research Ethics Committee A (HSC REC A) [23/NI/0156]. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes The data underlying this article cannot be shared publicly due to the privacy of individuals that participated in the study. The anonymised data, codelists and study protocol will be shared on reasonable request to the corresponding author.

BackgroundSystematic reviews show palliative care improves outcomes in patients with heart failure (HF), but prior meta-analyses were not HF-specific, or explored how intervention and patient characteristics influence effectiveness, whilst new randomized controlled trials(RCTs) have been published.MethodsWe conducted a systematic review and meta-analysis of RCTs exploring PC in HF. PubMed, EMBASE, CENTRAL and CINAHL were searched until January 24th 2025(PROSPERO ID:CRD42024607104). The primary outcome was hospitalisations. Secondary outcomes included change in quality of life(QoL), assessed by Kansas-City-Cardiomyopathy Questionnaire (KCCQ) and Functional Assessment of Chronic Illness Therapy-Palliative Care(FACIT-Pal) and mental health, assessed by Hospital-Anxiety-Depression-Scale(HADS). Subgroup analyses were conducted based on intervention characteristics (mode and duration), and patient characteristics (gender distribution and HF symptom severity, measured by NYHA class).ResultsTwenty-one RCTs were identified; thirteen were included in meta-analyses(n = 1919). PC was associated with reduced hospitalisation [log OR -0.6 (95% CI -1.14, -0.07); I2 = 69%], and improvement in QoL [mean difference(MD) in KCCQ: 3.09 [95% CI 1.43, 4.75], I2 = 35%] and depression [MD HADS-D: -0.44 (95% CI -0.75 to -0.13)]. No clear differences were observed between intervention modes. Longer interventions (>12 weeks) and interventions targeting patients with advanced HF symptoms (NYHA III/IV ≥70%) were associated with greater improvement in KCCQ and reduction in hospitalisation respectively. No differences in outcomes were observed by gender distribution.ConclusionsPalliative care reduces hospitalisations, improves QoL and mental health in HF. Longer interventions and those targeting patients with advanced symptoms were linked to differences in outcomes, whereas mode of intervention and gender did not appear to impact outcomes.

Nonadherence to antihypertensive medications is a major concern, with significant implications for healthcare systems and patient outcomes. Chemical adherence testing (CAT) is a recommended method to assess nonadherence objectively by detecting drugs or their metabolites in blood or urine samples. Interpretation of CAT results require relevant pharmacokinetic parameters. However, pharmacokinetic data in established sources are not graded for quality and not compiled into a single resource. We systematically collated and graded the literature on pharmacokinetic parameters for 20 commonly prescribed antihypertensive agents. A total of 53 suitable manuscripts were included. Pharmacokinetic data, including half-life and maximum concentration, were collated. Also, minimum concentration at regular dosing intervals was calculated. This resource allows clinicians and researchers to interpret CAT results with more confidence and accuracy and will help facilitate the interpretation of CAT. It also shows that there are significant gaps in available literature, and further research is needed.

Background:Biochemical urine analysis by liquid chromatography-mass spectrometry analysis (ie, chemical adherence testing [CAT]) is an objective method of detecting non-adherence to antihypertensive treatment. We aimed to assess whether an intervention based on providing non-adherent patients with hypertension with information on their urine analysis results combined with a discussion of the main reasons for non-adherence (CAT-guided intervention) would lead to a cost-effective improvement in adherence, blood pressure, and urinary excretion of albumin. Methods:OUTREACH was a multicentre, randomised controlled trial in 12 UK secondary or tertiary outpatient centres and primary care services. We recruited non-pregnant patients with hypertension who were older than 18 years and were on at least two antihypertensive medications. Participants who were non-adherent to antihypertensive treatment based on the results of their first urine CAT were randomly assigned (1:1) either to the intervention (discussion of the results of the urine CAT; group A) or standard of care (group B) after visit 2. The sequence of randomisation was computer-generated through an automated randomisation service (Sealed Envelope), which used minimisation as a method of allocation based on recruitment site, baseline systolic blood pressure, age, sex, number of antihypertensive medications prescribed at visit 1, the improvement in biochemical adherence to antihypertensive treatment between visits 1 and 2, and a random element to ensure the unpredictability of the assignment. The primary outcome was the mean clinic systolic blood pressure measured at visit 4. All analyses were based on the intention-to-treat principle. The trial was registered with ClinicalTrials.gov (NCT03293147) and is completed. Findings:Between Feb 4, 2019, and Feb 1, 2023, 879 patients were assessed for eligibility, 748 of whom were excluded after visit 1; 720 because they were either adherent or had unconfirmed adherence status on urine CAT. 70 adherent individuals were retained on the study for masking purposes but were not a part of the two-arm randomised component of the study. 130 non-adherent patients included in the study were randomly assigned to the intervention group (A; n=65) or the standard of care group (B; n=65). 71 (55%) participants were male and 59 (45%) were female; 57 (44%) were White, 22 (17%) were Asian or Asian British, 49 (38%) were Black, African, Caribbean, or Black British, and two (2%) were other ethnicities. The median follow-up after the intervention to visit 4 was 2·8 months (IQR 2·2-4·3). Mean clinic systolic blood pressure at visit 4 was 150·9 mm Hg (SD 24·7) in 56 participants in group A and 151·1 mm Hg (24·4) in 55 participants in group B (adjusted mean difference -5·1 mm Hg [95% CI -12·7 to 2·5]; p=0·19). 33 adverse events were reported (11 in group A and 22 in group B). 12 serious adverse events were recorded during the trial, occurring in five (8%) of 65 participants in group A and five (8%) of 65 participants in group B (two participants in group B had two serious adverse events each). Interpretation:CAT-guided intervention did not show a significant effect on clinic systolic blood pressure, but the study was underpowered. Larger studies are required to better understand the effect of urine CAT-guided interventions on blood pressure. Funding:British Heart Foundation, National Institute for Health and Care Research Manchester Biomedical Research Centre, Manchester Academic Health Science Centre, and Omron.

Purpose To compare left ventricular (LV) peak early diastolic strain rate (PEDSR) and peak late diastolic strain rate (PLDSR) using cardiac MRI feature tracking (FT) across a spectrum of diastolic dysfunction and determine the association between diastolic strain rates and cardiac remodeling. Materials and Methods Between October 2008 and December 2022, cardiac MRI and echocardiography were performed in prospectively recruited cohorts with type 2 diabetes mellitus, heart failure with preserved ejection fraction, and severe aortic stenosis, as well as asymptomatic participants without diabetes. Diastolic dysfunction was classified using established echocardiography guidelines. Global circumferential and longitudinal PEDSR and PLDSR were measured at cardiac MRI. Linear regression was performed to identify independent associations between LV diastolic strain rates and remodeling. Results A total of 600 participants (mean age, 65.2 years ± 8.4 [SD]; 361 of 600 male participants [60%]) were included. Proportions of participants with normal diastolic function and those with grade 1, indeterminate, and grade 2 or 3 diastolic dysfunction were 92 of 600 (15%), 401 of 600 (67%), 85 of 600 (14%), and 22 of 600 (4%), respectively. Compared with participants who had normal function, PEDSR decreased in those with grade 1 dysfunction (circumferential PEDSR, 0.99 sec-1 ± 0.22 vs 0.81 sec-1 ± 0.24 [P < .001]; longitudinal PEDSR, 0.79 sec-1 ± 0.19 vs 0.60 sec-1 ± 0.19 [P < .001]) and remained low throughout worsening stages of diastolic dysfunction. In contrast, compared with participants who had normal diastolic function, PLDSR increased in those with grade 1 dysfunction (circumferential PLDSR, 0.70 sec-1 ± 0.17 vs 0.82 sec-1 ± 0.23 [P < .001]; longitudinal PLDSR, 0.73 sec-1 ± 0.18 vs 0.80 sec-1 ± 0.27 [P < .001]) and declined progressively with worsening diastolic dysfunction. After multivariable adjustment for risk factors, inverse associations persisted between PEDSR and PLDSR with cardiac remodeling. Conclusion A distinctive pattern of cardiac MRI FT early and late diastolic strain rates was observed across the range of diastolic dysfunction. Keywords: Diastolic Dysfunction, Peak Early Diastolic Strain Rate, Peak Late Diastolic Strain Rate, Feature Tracking Supplemental material is available for this article. © The Author(s) 2025. Published by the Radiological Society of North America under a CC BY 4.0 license.

BACKGROUND:Health inequalities in cardiovascular care have been identified in the UK. The sociodemographic characteristics of patients undergoing intervention for aortic stenosis (AS) in England, and the impact of COVID-19, is unknown. METHODS:National linked data sets identified all surgical aortic valve replacement (SAVR) and transcatheter aortic valve implantation (TAVI) for AS, and post-intervention cardiovascular mortality, between 2000 and 2023. RESULTS:Of 179 645 procedures, there were 139 990 SAVR (mean age 71±10.8 years, 64% male, 96.0% white) and 39 655 TAVI (mean age 81±7.7 years, 57% male, 95.7% white). Rates of SAVR declined during COVID-19 for all groups, but TAVI rates increased steadily. Women were older; ethnic minority groups and those from most deprived areas were younger, with greater comorbidities. Women and more deprived groups had lower rates of SAVR (age-standardised rates per 100 000 in 2020-2023: 17.07 vs 6.65 for men vs women; 9.82 vs 10.10 for Index of Multiple Deprivation (IMD)-1 vs IMD-5) and TAVI (20.20 vs 9.79 for men vs women; 9.55 vs 13.36 for IMD-1 vs IMD-5). These discrepancies widened over time. Ethnic differences were observed for SAVR, with the lowest rates in black patients. Cardiovascular mortality post-intervention was lower in female patients and with decreasing deprivation, with no ethnicity-based differences. CONCLUSIONS:There are differences in intervention rates for AS in England, with lower rates in female patients and to a lesser extent, those from the most deprived areas and ethnic minority groups. These variations have widened over time. Post-intervention cardiovascular mortality is lower in women and with decreasing deprivation. Public health measures and research are needed to identify the true prevalence of AS in different populations, and the reasons for potential inequalities.

Current Acute Coronary Syndromes (ACS) rule-out algorithms rely on a combination of clinical assessment and measuring troponin levels. It can take several hours for troponin levels to rise after a myocardial infarction, so initial testing may not show detectable levels of troponin. In order to rule out a false negative result, troponin levels are typically tested again several hours later to look for rising values meaning patients are admitted for observation which has a large resource implication. We developed a machine learning model aimed at improving early discharge at initial assessment. The study was conducted using data from the National Institute for Health Research Health Informatics Collaborative Cardiovascular dataset.(1,2) We trained and tuned a machine learning model (Rapid-RO) using patient data from two separate hospitals to rule-out ACS with simple routine demographic or clinical measurements. The model was then tested for its predictive accuracy in cohorts of patients at four different hospitals from separate time periods. The model was assessed against troponin threshold guided management as recommended by the European Society of Cardiology clinical guidelines. The patient cohorts of the six derived datasets are presented in Figure 1. On the left side are the training and tuning cohorts and the right side are the cohorts from which the derived model was tested. The Rapid-RO machine learning model included input from 11 inputs that had the highest feature importance, including troponin, age, C-reactive protein, urea, platelet count, eGFR, white cell count, haemoglobin, heart failure, diabetes, and hypertension. The Rapid-RO model identified 12037 (35.69%) very low risk patients on top of standard clinical assessment who could have been discharged early, compared with 8967 (26.58%) identified by a troponin threshold approach alone (Figure 2), with significantly fewer missed ACS cases (27 (0.22%) vs. 108 (1.20%)) and similar mortality rates (2 (0.02%) vs. 4 (0.04%) at 30 days). The Rapid-RO model demonstrated a consistently higher rule-out rate for ACS with a lower missed ACS rate across patient subsets, including patients with and without chest pain or COVID-19. The Rapid-RO machine learning model, which uses patient history and initial blood tests, offers a significant advancement in the risk stratification process, presenting a reliable tool for clinicians to rapidly rule out ACS and potentially reduce unnecessary hospital admissions. Its robust performance in diverse patient groups across different time periods, underscores its potential utility in a real-world clinical setting.Figure 1 Figure 2

The ability to manage more complex coronary disease has evolved with the development of new technologies, techniques, and practitioner experience. The increasing technical difficulty of interventional procedures is known to be associated with an increased risk of complications. This can include coronary perforation, coronary dissection, coronary thrombosis, and aortic valve dysfunction. Permanent aortic valve damage caused by guide catheter, wire instrumentation or stent migration is a rare occurrence. We report the case of a woman in her 50s who developed acute severe aortic regurgitation requiring aortic valve replacement post-percutaneous coronary intervention.

Background:Heart failure is often diagnosed during unplanned admission to hospital, which is associated with poor outcomes. The aim of this study was to examine the potential for early heart failure identification in primary care, trends in diagnostic practices, sociodemographic inequalities, and the association between diagnostic pathways and outcomes. Methods:We conducted a retrospective cohort study using the Clinical Practice Research Datalink. Adults aged 18 years or older with newly diagnosed heart failure were identified from linked primary care and hospital records in England between Jan 1, 2000, and March 31, 2021. From primary care records, we analysed heart failure indicators (ie, breathlessness, ankle swelling, and loop diuretic use) recorded up to 5 years before diagnosis and diagnostic investigations (ie, natriuretic peptide tests, echocardiography, and specialist review) recorded within the previous 6 months. Trends over time and differences among sociodemographic groups are reported. Associations between diagnosis timing, investigation use, location (inpatient vs outpatient) and the primary outcome of 1-year survival were assessed, with analyses restricted to Jan 1, 2015, to Dec 31, 2019. Associations were adjusted for age, sex, ethnicity, socioeconomic status, year of diagnosis, systolic blood pressure, BMI, cholesterol, smoking, comorbidities, and prescribed drugs at diagnosis. Findings:Among 412 173 new heart failure diagnoses (median age 78·0 years [IQR 69·0-85·0]), 194 175 (47·1%) were women and 217 998 (52·9%) were men. In 407 622 participants with ethnicity data, ethnicity was recorded as White for 375 808 (92·2%), south Asian for 11 644 (2·9%), Black for 6994 (1·7%), other or mixed for 3622 (0·9%), and unknown for 9554 (2·3%). Although 274 228 (66·5%) of 412 173 patients had previous heart failure indicators, diagnostic timing worsened over the study period (2000-04 to 2015-19), with a median lag time increasing from 16·4 months (IQR 1·6-45·7) to 35·4 months (6·4-54·7) and the proportion of inpatient diagnoses rising from 30 560 (33·9%) of 90 136 patients to 55 905 (46·8%) of 119 355 patients. Among 80 824 individuals with previous indicators suggestive of heart failure diagnosed between Jan 1, 2015, and Dec 31, 2019, only 10 079 (12·5%) patients underwent natriuretic peptide testing, 15 986 (19·8%) had echocardiography, 27 804 (34·4%) were referred to specialists, and 42 302 (52·3%) had no diagnostic investigations recorded in primary care. Women, individuals living in deprived quintiles, and those with multiple long-term conditions had up to five times longer delays, lower investigation rates, and a higher likelihood of hospital diagnosis. Delays (adjusted hazard ratio [HR] 1·15 [95% CI 1·10-1·20]), absence of investigations (1·89 [1·83-1·95]), and inpatient diagnosis (2·58 [2·50-2·66]) were all associated with higher mortality. Mortality was lowest among outpatients who underwent primary care investigations (571 [5·5%] of 10 469 patients) and highest among inpatients with long-term loop diuretic use but no previous investigation (5429 [33·0%] of 16 458 patients; adjusted HR 5·29 [95% CI 4·83-5·79]). Interpretation:Most patients with heart failure show early signs in primary care, yet few receive timely diagnostic evaluation. Delays, missed investigations, and inpatient diagnoses are associated with poor outcomes and care inequities. Funding:British Heart Foundation and National Institute for Health Research.