Cardiac Condition Evaluation

𝗔𝗜 𝗖𝗮𝗻 𝗡𝗼𝘄 𝗣𝗿𝗲𝗱𝗶𝗰𝘁 𝗛𝗲𝗮𝗿𝘁 𝗙𝗮𝗶𝗹𝘂𝗿𝗲 𝗙𝗶𝘃𝗲 𝗬𝗲𝗮𝗿𝘀 𝗕𝗲𝗳𝗼𝗿𝗲 𝗜𝘁 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝘀 A fascinating paper published this week in the The American Journal of Cardiology. The important question it asked was the following. Can we predict heart failure before the heart has already begun to fail? The answer, it now appears, is yes. A team led by Prof Charalambos Antoniades MD PhD FRCP FMedSci at the University of Oxford has developed an AI tool that analyses the fat surrounding the heart from routine cardiac CT scans, predicting a patient's risk of developing heart failure up to five years before any clinical signs appear. Epicardial adipose tissue (EAT) is a metabolically active visceral fat depot that is both a sensor and a modulator of myocardial biology and changes its composition in response to paracrine signals from the myocardium. The team hypothesised that radiomic characterization of EAT from routine coronary computed tomographic angiography (CCTA) can noninvasively capture this adverse remodeling and enable early heart failure (HF) risk stratification. The study involved over 72,000 patients across nine NHS centres, followed for up to a decade. The fat around the heart, it turns out, acts as a potential biological sensor. Patients in the highest risk group were twenty times more likely to develop heart failure than those in the lowest. The tool predicted five-year risk with 86% accuracy, outperforming models built on traditional risk factors alone. What is striking is the conceptual shift this represents. We have spent decades in cardiovascular medicine treating disease that has already declared itself, responding to symptoms, managing complications, optimising a heart already under strain. We have been using risk stratification of cardiac disease using various methods like calcium scores. The team are now seeking NHS regulatory approval and adapting the tool for any CT scan of the chest, not just cardiac ones. Every scan, for any reason, could soon carry an embedded layer of cardiac risk intelligence. As the NHS shifts into prevention as part of the long term plan these tools become more important.

New research in JACC: Advances shows that the eye may offer a powerful, noninvasive window into coronary artery disease detection. In a multicenter study of 383 patients, deep learning models trained on retinal images were able to identify CAD with strong performance, outperforming traditional clinical risk scores, particularly in intermediate risk patients where clinical uncertainty is highest. When retinal imaging was combined with clinical indicators using a multimodal AI approach, diagnostic accuracy improved further, achieving an AUC of 0.91 with over 92 percent sensitivity. Because retinal and coronary vessels share similar vascular origins, microvascular changes captured by OCT and OCTA appear to reflect underlying coronary disease. AI enables these subtle patterns to be translated into scalable, radiation free screening and risk stratification tools. This work points toward a future where cardiovascular risk can be assessed earlier, more safely, and more equitably, especially in settings where invasive testing is limited. Multimodal AI may be key to shifting CAD detection upstream and personalizing prevention before clinical events occur. 🔗 https://lnkd.in/gWJUU447 Follow Zain Khalpey, MD, PhD, FACS for more on Ai & Healthcare. #AIinHealthcare #Cardiology #CoronaryArteryDisease #PreventiveCardiology #DigitalHealth #MedicalAI #MultimodalAI #DeepLearning #NonInvasiveDiagnostics #RetinalImaging #OCTA #OCT #CardiovascularHealth #RiskStratification #PrecisionMedicine #ClinicalInnovation #HealthEquity #CVImaging

I’ve seen 35-year-olds with the arteries of 70-year-olds, and they had no idea. That’s the scary thing about ageing. It doesn’t always show on the outside. You can look fit, go to the gym, eat “healthy”… and still be ageing far faster inside your arteries, your metabolism, and your cells. And most people don’t find out until it’s too late. That’s why prevention and longevity testing is must. Because the only way to know your real biological age is to measure it. Here are 10 markers I look at when assessing someone’s true healthspan: 1️⃣ Biological age blood panels – reveal if your body is ageing faster or slower than your years. 2️⃣ Epigenetic clocks – DNA tests that pick up cardiovascular risk years in advance. 3️⃣ Inflammation markers – going deeper than CRP, to uncover hidden vascular inflammation. 4️⃣ Lipoprotein(a) & ApoB – stronger predictors of heart disease than standard cholesterol. 5️⃣ Multi-cancer blood tests – early detection, long before symptoms. 6️⃣ Omega-3 levels – higher levels are linked with longer lifespan, yet most UK adults are deficient. 7️⃣ RDW – a simple blood count marker that predicts mortality. 8️⃣ HbA1c variability – sugar swings that silently age your arteries. 9️⃣ ST2 & Galectin-3 – markers of vascular stiffness and early heart failure risk. 🔟 AI-driven risk scores – combining genetics and labs into a personal “longevity map.” 💡 Practical tips: – Ask for ApoB and Lp(a), not just cholesterol. – Track HbA1c trends, not just one-off results. – Don’t ignore omega-3, diet or supplements. Because longevity isn’t about adding years at the end of life. It’s about protecting the years you have right now. And when I see a 35-year-old with the arteries of a 70-year-old, it’s a reminder: We can’t wait for symptoms. We need to act before disease shows up. That’s how you truly add life to years, not just years to life.

🧪 Urine ACR: The Unsung Hero of Cardiovascular, Kidney & Metabolic Risk Prediction 💥 A landmark state-of-the-art review in Circulation confirms what many of us in primary and specialist care already suspect: urinary albumin-to-creatinine ratio (UACR) is one of the most powerful and underutilised biomarkers in modern medicine. Whether in the context of diabetes, CKD, hypertension, heart failure, or metabolic syndrome, albuminuria is a strong, independent predictor of cardiovascular and renal outcomes—even below the traditional threshold of 3.4 mg/mmol (30 mg/g). 📌 Key Takeaways: 🔹 Albuminuria reflects widespread vascular dysfunction, not just kidney damage 🔸 Even low-grade albuminuria (UACR ~0.8–1.1 mg/mmol) is associated with increased risk of CVD, CKD, and all-cause mortality 🔹 UACR is non-invasive, inexpensive, widely available, and provides prognostic insight well before eGFR declines 🔸 Cardio–renal–metabolic therapies (SGLT2is, GLP-1 RAs, MRAs) are most impactful when guided by early UACR measurement 🚨 Still Underused Despite strong recommendations from NICE, ESC, KDIGO, and ADA, UACR screening remains infrequently performed, particularly in primary care. 💬 The authors call for: ✅ Wider and earlier ACR testing, not just in diabetes and CKD, but also in hypertension, obesity, liver disease, and CVD ✅ Routine UACR use in cardiology and metabolic clinics ✅ Recognition of albuminuria as a core CKM biomarker, not just a “renal test” 👉 Bottom line: If you’re not checking urine ACR, you’re missing a critical early signal. Let’s stop waiting for eGFR to fall—UACR reveals risk earlier and enables earlier, more effective action. 💭 Are you using ACR routinely in your practice? What are the barriers? 🔗 https://lnkd.in/eBgPfdB8 Kevin FernandoKevin LeeSarah DaviesSarah Jarvis, MBEBeverley Bostock RGN MSc MA Queen's NurseAhmet FuatProf Derek ConnollyBethany Kelly RN, QN, MSc, PgDipNadia Malik MRPharm BSc (hons)Hannah BebaPhilip Newland-Jones

Predicting a heart attack 15 years out — using proteins and metabolites in your blood today. A new study in Nature Communications introduces CardiOmicScore, a deep learning framework trained on UK Biobank data that learns separate risk scores from 2,920 proteins and 168 metabolites for six types of cardiovascular disease. On their own, the proteomic scores hit a C-index (CVD risk index) of up to 0.82. Combined with standard clinical data, they push prediction accuracy significantly higher — up to 15 years before disease onset. Critically, the model identifies which proteins and metabolites matter most, generating concrete leads for new biomarkers and drug targets. The code enables a shift from generic population risk tables to molecular-level, personalized cardiovascular prevention. #Proteomics #MultiOmics #CardiovascularDisease #DeepLearning #PrecisionMedicine https://lnkd.in/e5A6Nb9H