Determinants of myocardial conduction velocity: implications for arrhythmogenesis.

Slowed myocardial conduction velocity (θ) is associated with an increased risk of re-entrant excitation, predisposing to cardiac arrhythmia. θ is determined by the ion channel and physical properties of cardiac myocytes and by their interconnections. Thus, θ is closely related to the maximum rate of action potential (AP) depolarisation ((dV/dt)max), as determined by the fast Na+ current (INa); the axial resistance (ra) to local circuit current flow between cells; their membrane capacitances (cm); and to the geometrical relationship between successive myocytes within cardiac tissue. These determinants are altered by a wide range of pathophysiological conditions. Firstly, INa is reduced by the impaired Na+ channel function that arises clinically during heart failure, ischaemia, tachycardia, and following treatment with class I antiarrhythmic drugs. Such reductions also arise as a consequence of mutations in SCN5A such as those occurring in Lenègre disease, Brugada syndrome, sick sinus syndrome, and atrial fibrillation (AF). Secondly, ra, may be increased due to gap junction decoupling following ischaemia, ventricular hypertrophy and heart failure, or as a result of mutations in CJA5 found in idiopathic AF and atrial standstill. Finally, either ra or cm could potentially be altered by fibrotic change through the resultant decoupling of myocyte-myocyte connections and coupling of myocytes with fibroblasts. Such changes are observed in myocardial infarction and cardiomyopathy or following mutations in MHC403 and SCN5A resulting in hypertrophic cardiomyopathy or Lenègre disease respectively. This review defines and quantifies the determinants of θ and summarises experimental evidence that links changes in these determinants with reduced myocardial θ and arrhythmogenesis. It thereby identifies the diverse pathophysiological conditions in which abnormal θ may contribute to arrhythmia

Personalising monitoring for chemotherapy patients through predicting deterioration in renal and hepatic function

BackgroundIn those receiving chemotherapy, renal and hepatic dysfunction can increase the risk of toxicity and should therefore be monitored. We aimed to develop a machine learning model to identify those patients that need closer monitoring, enabling a safer and more efficient service.MethodsWe used retrospective data from a large academic hospital, for patients treated with chemotherapy for breast cancer, colorectal cancer and diffuse-large B-cell lymphoma, to train and validate a Multi-Layer Perceptrons (MLP) model to predict the outcomes of unacceptable rises in bilirubin or creatinine. To assess the performance of the model, validation was performed using patient data from a separate, independent hospital using the same variables. Using this dataset, we evaluated the sensitivity and specificity of the model.Results1214 patients in total were identified. The training set had almost perfect sensitivity and specificity of >0.95; the area under the curve (AUC) was 0.99 (95% CI 0.98–1.00) for creatinine and 0.97 (95% CI: 0.95–0.99) for bilirubin. The validation set had good sensitivity (creatinine: 0.60, 95% CI: 0.55–0.64, bilirubin: 0.54, 95% CI: 0.52–0.56), and specificity (creatinine 0.98, 95% CI: 0.96–0.99, bilirubin 0.90, 95% CI: 0.87–0.94) and area under the curve (creatinine: 0.76, 95% CI: 0.70, 0.82, bilirubin 0.72, 95% CI: 0.68–0.76).ConclusionsWe have demonstrated that a MLP model can be used to reduce the number of blood tests required for some patients at low risk of organ dysfunction, whilst improving safety for others at high risk