Interpretation of electrocardiograms in primary care

Abstract

get to help us identify who's more at risk than the next person.QOF encouraged us to identify chronic kidney disease (CKD), and now overburdened by its commonness, we are at risk of throwing away all we have achieved. Few diagnoses are predictably associated with such a dramatic increase in cardiovascular risk and none are so easily identified by a cheap and easily available blood test. 2,3The clustering of vascular pathologies with diabetes and hypertension makes this burden of disease the greatest challenge for the next generation of patients and doctors.We investigated the reassurance given by NICE, that the previous decade's CKD QOF initiative had improved the identification and management of CKD. In a practice population of 10 264, 9% of adults aged ≥18 years had a diagnosis of CKD on repeated testing. Despite this remarkable prevalence (usual estimates 3-6%), a total of 75% of these patients with CKD were unaware of the diagnosis, and in more than 25% both GP and patients were unaware of the condition. The results demonstrated that this lack of awareness was not limited to those with mild renal compromise but applied to onethird of patients with CKD stage 4. Our short intervention, either by phone or letter, significantly improved attainment of NICE (health indicator and education) criteria but also identified the continued confusion between CKD and lower urinary tract symptoms.It's time to remember why we estimated renal function in the first place. In a world of uncertainty, this is information for free. When associated with hypertension, diabetes, proteinuria, and vascular disease -pause -look again and feel confident that CKD means something. The lower the number the higher the risk.

Una experiencia en la evaluación de competencias en la lectura de electrocardiogramas por parte de posgrados de Pediatría

“…No hay estudios realizados en nuestro país que compare lectura de ECG entre posgrado de Pediatría y cardiólogo pediatra. A diferencia de algunos estudios realizados en enfermeros (7) y en adultos por médicos de familia y cardiólogos, en este trabajo se obtuvo una aceptable concordancia en la mayoría de las variables (2)(3)(4)8) . El índice de concordancia superó el 73% en todas las variables, alcanzando coincidencia del orden de 95%.…”

Artificial intelligence for atrial fibrillation detection, prediction, and treatment: A systematic review of the last decade (2013–2023)

“…In primary care and emergency units, the interpretation of complex ECGs and rapid diagnosis may be hindered by the absence of specialized personnel. Even with current computer interpretation technology, the combined accuracy of practitioners in these settings for AF diagnosis remains inadequate (Sahota & Taggar, 2016). Current challenges for AI‐supported AF detection, predication, and treatment are listed as follows: Asymptomatic Cases : AF can be asymptomatic, making it challenging to detect through traditional means, such as patient‐reported symptoms alone. Intermittent Nature : AF episodes can be sporadic, making it difficult to capture irregular heart rhythms during routine medical check‐ups. False Positives : Screening tools may generate false‐positive results, leading to unnecessary anxiety and follow‐up tests. Variability in Data Sources : ECG, PPG, and wearable sensors can produce vast amounts of data, and the variability in data quality and sources can pose challenges in standardization and interpretation. …”

“…In primary care and emergency units, the interpretation of complex ECGs and rapid diagnosis may be hindered by the absence of specialized personnel. Even with current computer interpretation technology, the combined accuracy of practitioners in these settings for AF diagnosis remains inadequate (Sahota & Taggar, 2016). Current challenges for AIsupported AF detection, predication, and treatment are listed as follows:…”

Diagnostic and prognostic value of ECG-predicted hypertension-mediated left ventricular hypertrophy using machine learning

“…However, accurate reporting of the ECG is challenging for clinicians, and any improvement in automated analysis could ensure timely diagnosis and treatment of hypertensive patients with LVH. [12][13][14] A machine learning (ML) tool to detect hypertension-mediated LVH phenotypes could reduce the number of unnecessary CMR scans, allowing them to be used more efficiently, thus reducing waiting times. This is also of clinical significance as the ECG features derived from classifying hypertension-mediated LVH phenotypes may be used as surrogate markers to predict clinical outcomes in hypertensives.…”