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Atherosclerotic cardiovascular disease (ASCVD) remains a leading cause of morbidity and mortality in individuals with diabetes, partly driven by dyslipidemia. While low-density lipoprotein cholesterol (LDL-C) reduction is the primary target of lipid management, many patients with diabetes exhibit mixed dyslipidemia characterised by elevated triglycerides and increased concentrations of atherogenic remnant lipoproteins, which are more comprehensively captured by non-high-density lipoprotein cholesterol (non-HDL-C). Current guidelines from international societies, including the American Diabetes Association (ADA), the American Association of Clinical Endocrinology (AACE), and the European Society of Cardiology (ESC), recommend LDL-C and non-HDL-C targets based on individual cardiovascular risk profiles. Despite clear therapeutic algorithms, lipid target attainment remains suboptimal in routine clinical practice, necessitating more intensive and individualised treatment strategies. Lipid-lowering therapies, including statins, ezetimibe, bempedoic acid and PCSK9 inhibitors, effectively reduce LDL-C and non-HDL-C, significantly lowering cardiovascular risk. Triglyceride-lowering therapies, including omega-3 fatty acids and fibrates, have demonstrated substantial reductions in triglyceride levels, but their impact on cardiovascular outcomes remains uncertain. Given the heterogeneity of dyslipidemia in diabetes, non-HDL-C and apolipoprotein B (apoB) have emerged as superior markers for assessing atherogenic burden. While LDL-C reduction remains central, additional efforts are needed to optimise the management of residual atherogenic lipoprotein particles in diabetes. Future research should focus on refining risk stratification, improving lipid target attainment, and integrating novel lipid-modifying agents to enhance cardiovascular outcomes in this high-risk population.

Diagnosis of heart failure is complex but crucial for patient outcomes and often hindered by the untapped potential of unstructured clinical notes. We introduce a novel end-to-end pipeline for heart failure diagnosis, leveraging electronic health records (EHR) and German clinical notes from 846 patients. Our pipeline synthesizes abbreviation disambiguation, translation of German clinical notes to English, medical entity linking to SNOMED-CT, and subsequent classification. The classification was performed using a Support Vector Machine (SVM) and compared against a fine-tuned medBERT.de neural baseline. We reduced the reliance on training data with zero-shot learning to address limitations with abbreviation disambiguation and entity linking approaches. Validation against benchmark datasets and cardiologists demonstrates high accuracy for real clinical use. Abbreviation disambiguation achieved an accuracy of up to 96.1%. Entity linking achieved competitive performance compared to state-of-the-art approaches on selected evaluation datasets. The SVM classification approach utilizing SNOMED-CT concepts and EHR data achieved an F1-score of 65.3%, on par with the medBERT.de neural baseline using clinical notes and EHR data. Despite challenges regarding limited language-specific resources and reference dataset availability for SNOMED-CT annotations in German, our pipeline demonstrates high potential for real-world clinical use and clinical decision support grounded in the standardized SNOMED-CT ontology.

Objective This review aims to systematically map and categorize the current state of wearable applications among oncology patients and to identify determinants impeding clinical implementation. Methods A Medline, Embase and clinicaltrials.gov search identified journal articles, conference abstracts, letters, reports, dissertations and registered studies on the use of wearables in patients with malignancies published up to 10 November 2021. Results Of 2509 records identified, 112 met the eligibility criteria. Of these, 9.8% (11/112) were RCTs and 47.3% (53/112) of publications were observational. Wearables were investigated pre-treatment (2.7%; 3/112), during treatment (34.8%; 39/112), post-treatment (17.9%; 20/112), in survivors (27.7%; 31/112) and in non-specified or multiple treatment phases (17.0%; 19/112). Medical-grade wearables were applied in 22.3% (25/112) of publications. Primary objectives ranged from technical feasibility (8.0%; 9/112), user feasibility (42.9%; 48/112) and correlational analysis (40.2%; 45/112) to outcome change analysis (8.9%; 10/112). Outcome change was mostly investigated regarding physical activity improvement (80.0%; 8/10). Most publications (42.9%; 48/112) and registered studies (39.3%; 24/61) featured multiple cancer types, with breast cancer as the most prevalent specific type (22.3% in publications, 16.4% in registered studies). Conclusions Most studies among oncology patients using wearables are focused on assessing the user feasibility of consumer-grade wearables, whereas rates of RCTs assessing clinical efficacy are low. Substantial improvements in clinically relevant endpoints by the use of wearables, such as morbidity and mortality are yet to be demonstrated.

There is high mortality among intensive care unit (ICU) patients with acute respiratory distress syndrome (ARDS) caused by coronavirus disease (COVID-19). Important factors for COVID-19 mortality are diabetes status and elevated fasting plasma glucose (FPG). However, the effect of glycaemic variability on survival has not been explored in patients with COVID-19 and ARDS. This single-centre cohort study compared several metrics of glycaemic variability for goodness-of-fit in patients requiring mechanical ventilation due to COVID-19 ARDS in the ICU at University Hospital Aachen, Germany. 106 patients had moderate to severe ARDS (P/F ratio median [IQR]: 112 [87–148] mmHg). Continuous HRs showed a proportional increase in mortality risk with daily glycaemic variability (DGV). Multivariable unadjusted and adjusted Cox-models showed a statistically significant difference in mortality for DGV (HR: 1.02, (P) < 0.001, LR(P) < 0.001; HR: 1.016, (P) = 0.001, LR(P) < 0.001, respectively). Kaplan–Meier estimators yielded a shorter median survival (25 vs. 87 days) and a higher likelihood of death (75% vs. 31%) in patients with DGV ≥ 25.5 mg/dl ( P  < 0.0001). High glycaemic variability during ICU admission is associated with significant increase in all-cause mortality for patients admitted with COVID-19 ARDS to the ICU. This effect persisted even after adjustment for clinically predetermined confounders, including diabetes, median procalcitonin and FPG.

IntroductionCardiovascular disease (CVD) represents a public health burden, with high prevalence and significant morbidity and mortality. Although evidence-based interventions exist, there is a need for more individualised care. The European project Individualised care from early risk of cardiovascular disease to established heart failure (iCARE4CVD) aims to personalise CVD prevention and treatment. Participatory health research, which actively involves patients in the planning, implementation and evaluation of projects, plays a crucial role here. However, patient participation is often unsuccessful due to the lack of a representative patient sample who is involved throughout the project’s duration, has knowledge of the project and can contribute their experience.Methods and analysisParticipative Research for Individualised Care in Cardiovascular Diseases is a non-interventional, non-randomised, multicentre mixed-methods study. The aim is to incorporate patients’ insights into several key activities within iCARE4CVD by establishing country-specific patient panels in Belgium, Germany, Ireland and the UK. The primary objective is to identify patients’ preferences, experiences, requirements and needs for better diagnosis, treatment and self-care of CVD. Therefore, 10–12 patients across the CVD spectrum, from early risk to established CVD and heart failure, will be included in each country (40–48 in total). Over 3.5 years, patient panel members are required to complete four tasks: (1) identification of meaningful Patient-Reported Outcome and Experiences Measures, (2) development of a motivational model to increase adherence, (3) feedback on CVD care processes and (4) usability testing of new digital tools developed within iCARE4CVD. These tasks comprise eight activities in the form of paper-based or digital exercises, telephone surveys, written surveys and in-person focus groups. The results will be continuously incorporated into iCARE4CVD.Ethics and disseminationThis study received ethical approval by the Ethics Committee at the Faculty of Medicine of RWTH Aachen University (EK 24-172) and St. Vincent’s University Hospital (RS24-027), Research Ethics Committee. In Geel and Belfast, positive ethics approval is pending. All participants will provide written informed consent prior to enrolment in the study and participation in the first patient panel task. Results will be published in peer-reviewed journals and presented at scientific conferences.Trial registration numberDRKS00034899.Protocol versionV2.1, 6 June 2024.

ObjectiveHeart failure is a growing challenge to healthcare systems worldwide. Technological solutions have the potential to improve the health of patients and help to reduce costs. Acceptability is a prerequisite for the use and a successful implementation of new disruptive technologies. This qualitative study aimed to explore determinants that influence the acceptance of patients and their informal caregivers regarding a patient-oriented digital decision-making solution—a doctor-at-home system.DesignWe applied a semistructured design using an interview guide that was based on a theoretical framework influenced by established acceptance theories. The interviews were analysed using a content analysis.SettingA multicentred study in four European countries.ParticipantsWe interviewed 49 patients and 33 of their informal caregivers. Most of the patients were male (76%) and aged between 60 and 69 years (43%). Informal caregivers were mostly female (85%). The majority of patients (55%) suffered from heart failure with mild symptoms.ResultsFour main categories emerged from the data: needs and expectations, preferences regarding the care process, perceived risk and trust. Participants expressed clear wishes and expectations regarding a doctor-at-home, especially the need for reassurance and support in the management of heart failure. They were receptive to changes to the current healthcare processes. However, trust was identified as an important basis for acceptance and use. Finally, perceived risk for decision-making errors is a crucial topic in need of attention.ConclusionPatients and informal caregivers see clear benefits of digitalisation in healthcare. They perceive that an interactive decision-making system for patients could empower and enable effective self-care. Our results provide important insights for development processes of patient-centred decision-making systems by identifying facilitators and barriers for acceptance. Further research is needed, especially regarding the influence and mitigation of patients and informal caregivers’ perceived risks.

Lowering the levels of LDL cholesterol in the plasma has been shown to reduce the risk of atherosclerotic cardiovascular disease (ASCVD). Several other lipoproteins, such as triglyceride-rich lipoproteins, HDL and lipoprotein(a) are associated with atherosclerosis and ASCVD, with strong evidence supporting causality for some. In this Review, we discuss novel and upcoming therapeutic strategies targeting different pathways in lipid metabolism to potentially attenuate the risk of cardiovascular events. Key proteins involved in lipoprotein metabolism, such as PCSK9, angiopoietin-related protein 3, cholesteryl ester transfer protein and apolipoprotein(a), have been identified as viable targets for therapeutic intervention through observational and genetic studies. These proteins can be targeted using a variety of approaches, such as protein inhibition or interference, inhibition of translation at the mRNA level (with the use of antisense oligonucleotides or small interfering RNA), and the introduction of loss-of-function mutations through base editing. These novel and upcoming strategies are complementary to and could work synergistically with existing therapies, or in some cases could potentially replace therapies, offering unprecedented opportunities to prevent ASCVD. Moreover, a major challenge in the prevention and treatment of non-communicable diseases is how to achieve safe, long-lasting reductions in causal exposures. This challenge might be overcome with approaches such as small interfering RNAs or genome editing, which shows how far the field has advanced from when the burden of achieving this goal was placed upon patients through rigorous adherence to daily small-molecule drug regimens.In this Review, the authors discuss current treatment regimens for lowering plasma LDL cholesterol levels to reduce the risk of cardiovascular disease, highlight treatment gaps and challenges, as well as describe opportunities raised by novel available therapies and potential future therapeutic approaches.

Serious clinical complications (SCC; CTCAE grade ≥ 3) occur frequently in patients treated for hematological malignancies. Early diagnosis and treatment of SCC are essential to improve outcomes. Here we report a deep learning model-derived SCC-Score to detect and predict SCC from time-series data recorded continuously by a medical wearable. In this single-arm, single-center, observational cohort study, vital signs and physical activity were recorded with a wearable for 31,234 h in 79 patients (54 Inpatient Cohort (IC)/25 Outpatient Cohort (OC)). Hours with normal physical functioning without evidence of SCC (regular hours) were presented to a deep neural network that was trained by a self-supervised contrastive learning objective to extract features from the time series that are typical in regular periods. The model was used to calculate a SCC-Score that measures the dissimilarity to regular features. Detection and prediction performance of the SCC-Score was compared to clinical documentation of SCC (AUROC ± SD). In total 124 clinically documented SCC occurred in the IC, 16 in the OC. Detection of SCC was achieved in the IC with a sensitivity of 79.7% and specificity of 87.9%, with AUROC of 0.91 ± 0.01 (OC sensitivity 77.4%, specificity 81.8%, AUROC 0.87 ± 0.02). Prediction of infectious SCC was possible up to 2 days before clinical diagnosis (AUROC 0.90 at −24 h and 0.88 at −48 h). We provide proof of principle for the detection and prediction of SCC in patients treated for hematological malignancies using wearable data and a deep learning model. As a consequence, remote patient monitoring may enable pre-emptive complication management.

ZusammenfassungIn den letzten Jahren hat sich die Therapie des Typ-2-Diabetes durch die Einführung neuer oraler Antidiabetika komplett geändert. Kardiovaskuläre Endpunktstudien belegen die Sicherheit der Dipeptidylpeptidase(DPP)-4-Hemmer und eine kardiovaskuläre Protektion bei GLP(„glucagon-like peptide“)-1-Rezeptor-Agonisten und SGLT2(„sodium-glucose linked transporter 2“)-Hemmern. Die SGLT2-Hemmer reduzieren zusätzlich das Risiko für eine Herzinsuffizienz und haben einen renoprotektiven Effekt. Dies hat zu neuen klinischen Empfehlungen und Leitlinien geführt. Bei Patienten mit hohem und sehr hohem kardiorenalen Risiko werden zur Risikoprotektion unabhängig vom glykosylierten Hämoglobin (HbA1c) SGLT2-Hemmer oder GLP-1-Rezeptor-Agonisten empfohlen, bei bestehendem oder hohem Risiko für eine Herzinsuffizienz SGLT2-Hemmer. Damit steht bei der Wahl der antidiabetischen Therapiestrategie nicht mehr allein die Höhe des HbA1c, sondern v. a. das kardiorenale Risiko im Vordergrund.

Purpose of Review This review explores the relationship between lipid-lowering therapies, particularly statins, and the risk of new-onset diabetes (NOD). It examines the underlying mechanisms and evaluates whether other lipid-lowering agents present similar risks. Recent Findings Recent meta-analyses further underscore a dose-dependent increase in NOD risk with statin therapy, particularly with high-intensity statins. In contrast to other LDL-cholesterol lowering drugs and their impact on lipid metabolism in the liver, genetic and experimental studies indicate that statins may impair insulin secretion through various mechanisms, including alterations in small G protein function, calcium signaling, and cholesterol homeostasis in pancreatic beta cells. This might contribute to the increased risk of NOD. Summary Statins effectively reduce cardiovascular events but increase the risk of NOD, potentially via intracellular pathways affecting liver and beta-cell function. Despite the cardiovascular benefits of statins, personalized treatment strategies and alternative lipid-lowering therapies may offer safer options for patients at risk of diabetes, potentially shaping future clinical guidelines and therapeutic approaches.