Objectives To evaluate how UK guidelines for individual health conditions consider coexisting multiple long term conditions and to propose improvements to guideline development processes so that guidelines appropriately account for and consider coexisting multiple long term conditions.Design Analysis and recommendations from review of single condition guidelines.Setting Clinical guidelines developed by the National Institute for Health and Care Excellence (NICE), UK, 1 January 2013 to 31 December 2024.Population 56 clinical guidelines developed by NICE covering a broad range of long term conditions.Main outcome measures The extent to which guideline recommendations consider multiple long term conditions and coexisting conditions, distinguishing between concordant conditions (those affecting the same organ system as the index condition) and discordant conditions (those affecting different systems).Results All but one of the NICE guidelines (n=55, 98%) included some advice on managing the index condition in the presence of coexisting conditions, and 50 (89%) guidelines offered general guidance on tailoring care. Only 11 (20%) guidelines, however, explicitly referred to multiple long term conditions, and none included a dedicated section on multiple long term conditions or how care should be adapted in this context. 19 (34%) guidelines featured sections looking at specific coexisting conditions or coexisting conditions generally. Coverage of coexisting conditions varied widely across categories of conditions, with mental health guidelines dealing with the most coexisting conditions (median 10, interquartile range (IQR) 4.5-14.75) in contrast with guidelines on cancer and eye disease covering the fewest conditions (median 3, 1-4.5; median 3, 2.25-2.75, respectively). Of the 397 possible concordant pairings, 120 (30%) were referenced, whereas of the 3859 possible discordant pairings, 259 (7%) were referenced, indicating greater coverage of same system combinations. Data on the composition of guideline committees showed wide variation in size, disciplinary diversity, inclusion of generalist clinicians (eg, general practitioners, general physicians, or others with no single specialty focus), and public contributors, although lived experience of multiple long term conditions was rarely specified.Conclusions Despite widespread acknowledgement of coexisting or multiple long term conditions, NICE guidelines are predominantly condition specific and offer limited tailored support for the care of multiple long term conditions. Recommendations rarely considered common condition clusters or the cumulative effect of multiple long term conditions. Structured improvements, such as clearer guidance on adapting care, broader cross condition referencing, and more transparent inclusion of lived experience could enhance the relevance and usability of guidelines for clinicians managing patients with multiple long term conditions.

All doctors require broad-based clinical knowledge, skills, and attitudes, but no doctor can be independently competent in every aspect of clinical practice. Within cardiology, core training equips cardiologists with core cardiovascular competencies, but appropriate training, assessment, and maintenance of post-certification competencies are required for cardiologists to function as part of a comprehensive multidisciplinary Heart Team across the full spectrum of cardiovascular practice required by our patients. This position statement describes the role of post-certification competencies in the delivery of cardiovascular care.

Summary Objectives To estimate the prevalence of multiple long-term conditions (MLTC) among people accessing hospital care in North East England, and to assess associations with age, sex, ethnicity and neighbourhood deprivation. Design Analysis of electronic health records. Setting Secondary care. Participants All adults, 18 years and above, with ≥ 1 admission to Newcastle upon Tyne Hospitals NHS Foundation Trust (NuTH) between July 2018 and June 2019 (N = 88,117, 51% women) or between July 2021 and June 2022 (N = 83,036, 51% women). Main outcome measures MLTC; ≥2 long-term conditions. Results Between July 2018 and June 2019, overall prevalence of MLTC was 49.6%, and between July 2021 and June 2022 it was 61.0%. Older age and living in a more deprived neighbourhood were consistently associated with increased risk of MLTC, whereby people living in the most deprived neighbourhoods had a prevalence of MLTC equivalent to people from the least deprived neighbourhoods a decade older. Associations between neighbourhood deprivation and increased MLTC risk were stronger in younger adults; relative risk of MLTC was 1.74 (95% confidence interval: 1.49–2.02) when comparing people aged 30–39 living in the most and least deprived neighbourhoods. Conclusions Among adults accessing inpatient hospital care in North East England, the prevalence of MLTC is high in all age groups, has increased since the COVID-19 pandemic and has a greater impact on younger adults from more deprived neighbourhoods. This highlights the sheer scale of the challenge that MLTC present in hospitals and the value of harnessing information at a local level as we look to redesign hospital care fit for the future.

Objectives Living with multiple long-term conditions (MLTC) is increasingly common, posing challenges for health-care systems and individuals alike. Identifying clusters of co-occurring conditions has been proposed as key to understanding disease patterns and supporting patient-centered coordinated care. Latent class analysis (LCA) has been suggested as the optimal clustering method for MLTC, based on simulation studies where condition prevalence and correlations were assumed to be constant across clusters. However, real-world data demonstrate substantial variation in both prevalence and intercondition correlations, particularly when highly prevalent conditions, such as hypertension, coexist with much rarer conditions. The objective of this methodological study was to evaluate the performance and robustness of LCA using real-world data from routinely collected electronic health records (EHRs) for people hospitalized in North-East England. Study Design and Setting We investigated the performance and robustness of LCA using information on 60 long-term conditions (LTC). Four analytical approaches were assessed: (1) including all LTC, (2) excluding the most prevalent condition, (3) restricting analyses to the population living with the most prevalent condition and applying LCA to the remaining 59 conditions, and (4) restricting analyses to the population without the most prevalent condition and applying LCA to the remaining 59 conditions. LCA performance was examined using criteria including patient partitioning, condition clustering patterns, and dispersion of condition prevalence across clusters, with further assessment through bootstrap sampling to evaluate reproducibility. Results Across all approaches, LCA consistently demonstrated strong performance according to these criteria. Excluding or stratifying by the most prevalent condition led to only marginal improvements in the clustering accuracy and stability of the remaining conditions. Conclusion These findings confirm that LCA remains a robust and reliable method for MLTC clustering in realistic settings where prevalence of different LTC varies markedly and a single dominant LTC is observed. This supports the continued use of LCA to understand complex disease patterns and guide future MLTC research. Plain Language Summary Many people live with two or more long-term health conditions, which can make their care more complex. Researchers often group patients based on patterns of coexisting conditions to better understand these complexities and improve care planning. One commonly used method for this is called LCA. Previous research has suggested that LCA is a useful way of identifying groups of patients with similar patterns of health conditions. However, this research made assumptions that do not reflect the complexity of real-world health data. In reality, some conditions are very common, while others are rare, and the relationships between different conditions may vary. In this study, we used data from EHRs from a hospital in the UK to test how well LCA performs under more realistic conditions. We also explored whether very common conditions, such as hypertension, affect the results. We found that LCA performs well even when there are large differences in how common conditions are and how they are related to each other. We found no evidence that the presence of one condition, that is much more common than others, influences the results. These findings suggest that LCA is a reliable method for identifying groups of patients with similar patterns of health conditions in real-world data. This can help researchers and clinicians better understand disease patterns and support more personalized and coordinated care for people living with MLTC.

This 2026 update of the European Society of Cardiology (ESC) Core Curriculum for the Cardiologist reflects contemporary and emerging requirements for the practice of cardiology and the resulting training needs. The document has three main parts:Section 1 outlining the main training requirements and their presentation in entrustable professional activities (EPAs).Section 2 explaining the requirements for training centres.Section 3 outlining the general skills, knowledge, and attitudes required in cardiology (chapter 1) - and eight chapters outlining EPAs in the different areas of cardiology (chapter 2 to 9). This update has been written over 9 months in a iterative process, involving over 90 representatives as per the initial curriculum, similar to that used for the previous iteration of the core curriculum in 2020. Though much of the 2020 core curriculum remains relevant and unchanged, there was a clear need to amend this previous curriculum to reflect the expanding roles of some areas of cardiology such as data science, digital health, and artificial intelligence. These items have now been specifically included along with general updates to various clinical EPAs due to changes in practice or evidence base.

BACKGROUND:The complication risk of procedures may be influenced by operator and institutional characteristics. Our aim was to assess whether supervising consultant seniority and operative volume, and hospital volume were associated with the risk of reintervention following complex device implantation. METHODS:A nationwide population-based study was performed using the National Institute for Cardiovascular Outcomes Research registry including all patients receiving their first transvenous implantable cardioverter defibrillator or cardiac resynchronisation therapy (CRT) implant in England over 5 years (April 2014-March 2019). The primary endpoint was 1-year reintervention. We evaluated the association between reintervention and supervising consultant annualised complex device volume, supervising consultant seniority and hospital annualised complex device volume, using multilevel logistic regression. RESULTS:47 630 implants were included. The 1-year reintervention rate was 6.1% (N=2916). There was no difference in reintervention risk with increasing supervising consultant volume (OR 0.89 Q4 vs Q1; 95% CI 0.76 to 1.05, p=0.17). When CRT-pacemakers/defibrillators implants were analysed separately (N=26 108), there was an association between operator volume and 1-year reintervention, but this was of borderline statistical significance and only evident in the highest compared with the lowest volume quartile of operators (adjusted OR 0.79 Q4 vs Q1; 95% CI 0.63 to 0.98, p=0.03). There was a non-linear relationship between reintervention risk and supervising consultant seniority, with the operators in the middle two quartiles of seniority having a lower risk (OR 0.87 Q2 vs Q1, p=0.02; OR 0.81 Q3 vs.Q1; p=0.003) while the most and least senior operators had a similar reintervention risk (OR 0.93 Q4 vs Q1, p=0.31). Hospital volume was not associated with 1-year reintervention. CONCLUSIONS:There is a U-shaped curve between operator seniority and reintervention risk for complex devices. Although there are several potential explanations, these data suggest that while newly qualified consultants may benefit from mentoring, all operators should continuously evaluate their outcomes and share them within their centre and more widely through the national audit.

BACKGROUND:The National Early Warning Score 2 (NEWS2) has been widely adopted for predicting patient deterioration in health care settings using routinely collected physiological observations. The use of NEWS2 has been shown to reduce in-hospital mortality, but it has limited accuracy in the prediction of clinically important outcomes, especially over longer time periods. OBJECTIVE:This project aims to improve the predictive accuracy of the NEWS2 scoring system, particularly its accuracy over more than 24 hours and its predictive value in older patients and children. It will investigate whether using the currently collected data differently and the inclusion of additional data would result in an improved algorithm. METHODS:The study will use historical patient data from the Newcastle upon Tyne Hospitals NHS Foundation Trust, including observational data (eg, vital signs), BMI- related data, and other outcome-related variables (eg, mortality rates) to train and test an algorithm to predict the risk of key clinical outcomes, including mortality, intensive therapy unit admission, sepsis, and cardiac arrest, to demonstrate a proof of concept for a modified scoring system. The algorithm's performance will be assessed based on its accuracy, precision, F1-score, area under the curve, and receiver operating characteristic curve. RESULTS:The study is expected to start in April 2025. The findings are expected to be produced by the end of 2026 and will be disseminated at symposia, conferences, and in journal publications. CONCLUSIONS:The refined NEWS2 algorithm will address limited accuracy in predicting clinical deterioration beyond 24 hours in the original system by incorporating additional variables. Improved accuracy in the early detection of deterioration can lead to timely interventions, potentially reducing mortality and adverse clinical events. The enhanced algorithm also has the potential to be integrated into existing clinical decision support systems to facilitate health care professionals' decision-making. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID):PRR1-10.2196/70303.

Over 1 million cases of prostate cancer are reported every year, and it is the second most common cancer in men. Androgen deprivation therapy (ADT) is a hallmark treatment for prostate cancer but is associated with the development or exacerbation of cardiovascular disease. The most common cause of non-cancer death in patients with prostate cancer is cardiovascular disease. Thus, a better understanding of the prevalence of cardiovascular toxicity across all therapies, management of potential cardiovascular complications, and prevention of cardiovascular events is essential as treatments continue to evolve. In this article, the first in a 2-part series, we provide a review of the current landscape of ADT therapy and its association with cardiovascular disease, summarize recent clinical trial data evaluating cardiovascular outcomes, and provide insights on the management of cardiovascular risk factors and adverse events for clinicians managing this high-risk population of men undergoing potentially cardiotoxic treatment for prostate cancer.

Introduction: The second National Early Warning Score (NEWS2) is a widely used tool for the systematic identification and documentation of clinical deterioration. It is effective at reducing in-hospital mortality,1,2 facilitates effective communication between clinicians and enables timely interventions to improve patient outcomes, but NEWS2 has limited positive and negative predictive accuracy,3 particularly in predicting adverse events beyond 24 h.4The use of digital technologies in healthcare presents an opportunity to evaluate whether the inclusion of additional routinely collected variables, and/or changes in the use of existing data, would improve the predictive accuracy of an early warning score and, thus, reduce patient risk.1,5Older adults are particularly vulnerable to sudden changes in physiological status,6 but this group was not included in the development of NEWS2.7 Improved predictive accuracy in this growing population group could significantly improve patient outcomes.8 Methods and Methods: The project is a retrospective cohort study using ∼8 years (2017–2024) of anonymised patient data from the Newcastle upon Tyne Hospitals NHS Foundation Trust (NUTH). The study includes any patients who were admitted to NUTH between 2017 and 2024 and had physiological observations recorded in the form of NEWS2. People who had opted out of the use of their de-identified data for research either locally or nationally, children (age <16 years old) and maternity admissions were excluded (Fig 1).A scoping review of six databases (CINAHL, PubMed, Embase, ScienceDirect, Cochrane Library and Web of Science) was conducted9 to determine which variables may improve the risk prediction of NEWS2. Data analysis: De-identified data from the Trust’s clinical data warehouse will be divided into training and testing subsets. Using these datasets, we will train and test an algorithm that optimises the variables and their weightings to predict the risk of key clinical outcomes, including mortality, intensive care unit admission, sepsis and cardiac arrest, to demonstrate a proof of concept for a modified scoring system. Intended outputs: By June 2025, an algorithm based on 8 years of historical patient data will have been trained and tested on NUTH datasets to create a new tool that is of national and international importance. The aim of this project is to improve the predictive accuracy of the NEWS2 scoring system, particularly over more than 24-h, and in older patients. If the new system performs better than NEWS2, we will conduct implementation studies in Newcastle and then in other NHS Trusts to assess adoption of the system and gather initial data on patient outcomes to construct a learning healthcare system to evaluate real-word performance of the algorithm and facilitate continual improvement.10 Ethics: An application for the ethical approval of this study has been submitted to the UK The Health Research Authority Integrated Research Approval System and has undergone proportionate review.

Background Multiple long-term conditions (MLTCs; commonly referred to as multimorbidity) are highly prevalent among people admitted to hospital and are therefore of critical importance to hospital-based healthcare systems. To date, most research on MLTCs has been conducted in primary care or the general population with comparatively little work undertaken in the hospital setting. Purpose To describe the rationale and content of ADMISSION: a four-year UK Research and Innovation and National Institute of Health and Care Research funded interdisciplinary programme that seeks, in partnership with public contributors, to transform care for people living with MLTCs admitted to hospital. Research design Based across five UK academic centres, ADMISSION combines expertise in clinical medicine, epidemiology, informatics, computing, biostatistics, social science, genetics and care pathway mapping to examine patterns of conditions, mechanisms, consequences and pathways of care for people with MLTCs admitted to hospital. Data collection The programme uses routinely collected electronic health record data from large UK teaching hospitals, population-based cohort data from UK Biobank and routinely collected blood samples from The Scottish Health Research Register and Biobank (SHARE). These approaches are complemented by focused qualitative work exploring the perspectives of healthcare professionals and the lived experience of people with MLTCs admitted to hospital. Conclusion ADMISSION will provide the necessary foundations to develop novel ways to prevent and treat MLTCs and their consequences in people admitted to hospital and to improve care systems and the quality of care for this underserved group.

Introduction The second iteration of the National Early Warning Score has been adopted widely within the UK and internationally. It uses routinely collected physiological measurements to standardise the assessment and response to acute illness. Its use is associated with reduced mortality but has limited positive and negative predictive accuracy. There is a growing body of research demonstrating the effectiveness of artificial intelligence (AI) in predicting clinical deterioration, but there is limited evidence to show which aspect of AI is best suited to this task. This systematic review aims to establish which AI or machine learning algorithm is best suited to analysing physiological data sets to predict patient deterioration in a hospital setting.Methods and analysis A systematic review will be conducted in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) and the PICOS (Population, Intervention, Comparator, Outcome and Study) frameworks. Eight databases (PubMed, Embase, CINAHL, Cochrane Library, Web of Science, Scopus, IEEE Xplore and ACM Digital Library) will be used to search for studies published from 2007 to the present that meet the inclusion criteria. Two reviewers will screen the studies identified and extract data independently, with any discrepancies resolved by discussion. The review is expected to be completed by January 2026, and the results will be presented in publication by June 2026.Ethics and dissemination Ethical approval is not required as data will be obtained from published sources. Findings from this study will be disseminated via publication in a peer-reviewed journal.

Background: In response to the need for harmonisation of cardiology training in the Asia-Pacific region, the Asian Pacific Society of Cardiology (APSC) approached the European Society of Cardiology in 2020 to allow its members to take the European Examination in Core Cardiology (EECC). This article reports the examination results of the APSC examinees over the first 4 years of implementation, and compares their results with examinees from the rest of the world (non-APSC). Methods: This is a retrospective analysis of the performance of APSC examinees in the EECC from 2020 to 2023, using the scores of non-APSC examinees as a benchmark. Results: A total of 287 APSC examinees and 2,492 non-APSC examinees took the EECC from 2020 to 2023. The number of examinees from APSC member countries, as well as those from other countries/regions around the world, showed a steady annual increase. The overall pass rate of candidates from APSC member countries was 72.8%, with 83.9% of non-APSC candidates passing. The APSC pass rate increased steadily from 2020 to 2023 (from 60.5% in 2020 to 83.0% in 2023). The overall non-APSC pass rate fluctuated between 77.7% (lowest pass rate observed in 2021) and 90.6% (highest pass rate observed in 2023). Conclusion: While the pass rate of APSC examinees was lower than non-APSC examinees during the early rounds of the EECC, the gap between pass rates has reduced over time. The authors advocate the use of the EECC and its preparation course as the APSC Exit Examination across the Asia-Pacific region as part of the assessment of core knowledge for cardiology trainees, and as a contribution to the harmonisation of training and benchmarking of educational standards across the world.

The 2022 ESC Guideline[1] recommends cardiac troponin (cTn) monitoring during anthracycline chemotherapy to guide cardio-protection. However, it is unclear how consistently these recommendations can be implemented in routine practice when hospitals use a range of cTn assays. To evaluate 4 highly sensitive cTn assays commonly used in clinical practice, focusing on the ability to detect mild anthracycline-induced Cancer Therapy Related Cardiac Dysfunction (CTRCD). The PROACT trial[2] recruited 111 patients with breast cancer or non-Hodgkins’s lymphoma undergoing 6 cycles of anthracycline chemotherapy (planned ≥300mg/m² dox-equivalent). Blood samples were collected <72 hours before each cycle and 4 weeks post-therapy. Samples were centrally analysed at 4 NHS biochemistry departments using: Elecsys Troponin T Highly Sensitive Assay; Roche Diagnostics (cTnT), ARCHITECT STAT High Sensitivity Troponin I Assay; Abbott Laboratories (cTnI(A)), ADVIA Centaur High Sensitivity Troponin I Assay; Siemens Healthineers (cTnI(S)) and ACCESS High Sensitivity Troponin I Assay; Beckman Coulter (cTnI(BC)). The frequency of positive results (>ULN) by cycle was compared across assays. Units of change were compared by standardising peak cTn to the assay ULN, and averaging to baseline values. The potential impact on clinical decision making was assessed by comparing rates of mild CTRCD defined by each assay. Marked differences were observed between assays. Notably, cTnT and cTnI(S) showed a 43% absolute difference in the proportion of patients exhibiting myocardial injury(Tab. 1). Differences were also evident between cTnI assays, with cTnI(BC) results aligning more closely with cTnT, while cTnI(A) and cTnI(S) had a lower sensitivity in detecting myocardial injury(Fig 1). Consequently, diagnosis of mild CTRCD varied by assay. Using ECHO alone 18 (20%) patients were diagnosed (of 87 patients with complete GLS data). Combining ECHO with cTn the rates of CTRCD were: cTnT 76 (87%), cTnI(BC) 65 (75%), cTnI(A) 51 (59%) and cTnI(S) 43 (49%) patients. A limitation in comparing cumulative proportion of CTRCD was sample completeness, which was slightly lower for cTnI(S) (82%) and cTnI(BC) (83%) compared to original trial endpoints cTnT (92%) and cTnI(A) (88%). When standardised to ULN, cTnT and cTnI(BC) showed the greatest proportionate rises from baseline(Tab. 1). When standardised to baseline results, cTnI(A) and cTnI(S) had the greatest proportional rise, although this did not translate into more samples >ULN. The diagnosis of mild CTRCD varied significantly among commonly available cTn assays, reflecting differing sensitivities in detecting myocardial injury. These discrepancies highlight limitations in the current recommendation for guiding treatment decisions based on cTn >ULN in clinical practice. More research is required to understand the utility of different hs-Tn platforms and to standardise practice.Figure 1:Frequency of Positive Results Table 1:Comparison of Troponin Assays

The National Early Warning Score 2 (NEWS2) has been adopted as the standard approach for early detection of deterioration in clinical settings in the UK, and is also used in many non-UK settings. Limitations have been identified, including a reliance on ‘normal’ physiological parameters without accounting for individual variation. This review aimed to map how the NEWS2 has been modified to improve its predictive accuracy while placing minimal additional burden on clinical teams. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA-ScR) and the Population, Intervention, Comparator, Outcome, and Study (PICOS) frameworks were followed to structure the review. Six databases (CINAHL, PubMed, Embase, ScienceDirect, Cochrane Library and Web of Science) were searched for studies which reported the predictive accuracy of a modified version of NEWS2. The references were screened based on keywords using EndNote 21. Title, abstract and full-text screening were performed by 2 reviewers independently in Rayyan. Data was extracted into a pre-established form and synthesised in a descriptive analysis. Twelve studies were included from 12,867 references. In 11 cases, modified versions of NEWS2 demonstrated higher predictive accuracy for at least one outcome. Modifications that incorporated demographic variables, trend data and adjustments to the weighting of the score’s components were found to be particularly conducive to enhancing the predictive accuracy of NEWS2. Three key modifications to NEWS2—incorporating age, nuanced treatment of FiO2 data and trend analysis—have the potential to improve predictive accuracy without adding to clinician burden. Future research should validate these modifications and explore their composite impact to enable substantial improvements to the performance of NEWS2.

supplementary data: Methods, tables, figure.

Mobile health (mHealth) solutions have the potential to improve self-management and clinical care. For successful integration into routine clinical practice, healthcare professionals (HCPs) need accepted criteria helping the mHealth solutions' selection, while patients require transparency to trust their use. Information about their evidence, safety and security may be hard to obtain and consensus is lacking on the level of required evidence. The new Medical Device Regulation is more stringent than its predecessor, yet its scope does not span all intended uses and several difficulties remain. The European Society of Cardiology Regulatory Affairs Committee set up a Task Force to explore existing assessment frameworks and clinical and cost-effectiveness evidence. This knowledge was used to propose criteria with which HCPs could evaluate mHealth solutions spanning diagnostic support, therapeutics, remote follow-up and education, specifically for cardiac rhythm management, heart failure and preventive cardiology. While curated national libraries of health apps may be helpful, their requirements and rigour in initial and follow-up assessments may vary significantly. The recently developed CEN-ISO/TS 82304-2 health app quality assessment framework has the potential to address this issue and to become a widely used and efficient tool to help drive decision-making internationally. The Task Force would like to stress the importance of co-development of solutions with relevant stakeholders, and maintenance of health information in apps to ensure these remain evidence-based and consistent with best practice. Several general and domain-specific criteria are advised to assist HCPs in their assessment of clinical evidence to provide informed advice to patients about mHealth utilization. Graphical Abstract

Background Cardiotoxicity is a concern for cancer survivors undergoing anthracycline chemotherapy. Enalapril has been explored for its potential to mitigate cardiotoxicity in cancer patients. The dose-dependent cardiotoxicity effects of anthracyclines can be detected early through the biomarker cardiac troponin. Objectives The PROACT (Preventing Cardiac Damage in Patients Treated for Breast Cancer and Lymphoma) clinical trial assessed the effectiveness of enalapril in preventing cardiotoxicity, manifesting as myocardial injury and cardiac function impairment, in patients undergoing high-dose anthracycline-based chemotherapy for breast cancer or non-Hodgkin lymphoma. Methods This prospective, multicenter, open-label, randomized controlled trial employed a superiority design with observer-blinded endpoints. A total of 111 participants, scheduled for 6 cycles of chemotherapy with a planned dose of ≥300 mg/m2 doxorubicin equivalents, were randomized to receive either enalapril (titrated up to 20 mg daily) or standard care without enalapril. Results Myocardial injury, indicated by cardiac troponin T (≥14 ng/L), during and 1 month after chemotherapy, was observed in 42 (77.8%) of 54 patients in the enalapril group vs 45 (83.3%) of 54 patients in the standard care group (OR: 0.65; 95% CI: 0.23-1.78). Injury detected by cardiac troponin I (>26.2 ng/L) occurred in 25 (47.2%) of 53 patients on enalapril compared with 24 (45.3%) of 53 in standard care (OR: 1.10; 95% CI: 0.50-2.38). A relative decline of more than 15% from baseline in left ventricular global longitudinal strain was observed in 10 (21.3%) of 47 patients on enalapril and 9 (21.9%) of 41 in standard care (OR: 0.95; 95% CI: 0.33-2.74). An absolute decline of >10% to <50% in left ventricular ejection fraction was seen in 2 (4.1%) of 49 patients on enalapril vs none in patients in standard care. Conclusions Adding enalapril to standard care during chemotherapy did not prevent cardiotoxicity in patients receiving high-dose anthracycline-based chemotherapy. (PROACT: Can we prevent Chemotherapy-related Heart Damage in Patients With Breast Cancer and Lymphoma?; NCT03265574)