Flecainide administration in children: dosage, drug levels, and clinical effect.

Therapeutic drug monitoring of flecainide in children using plasma concentration measurements is undertaken by some clinicians. There is very little published evidence surrounding factors which influence plasma flecainide concentration, particularly in paediatric populations. We undertook a retrospective study of 45 children receiving flecainide to identify factors that influence its plasma concentration. Patients receiving a dose of 6 mg/kg/day had a higher mean plasma flecainide concentration than those receiving lower doses. Younger age and lighter weight were also associated with higher plasma flecainide concentrations. Children aged younger than 1 year receiving flecainide three times a day had a higher mean plasma flecainide concentration than older children who received flecainide twice a day. All supratherapeutic levels occurred in children aged less than 1 year who were receiving flecainide three times a day. Supratherapeutic levels were more common in those receiving 6 mg/kg/day while subtherapeutic levels were more common in those receiving 2 mg/kg/day. A supratherapeutic level did not correlate with adverse effects or clinical toxicity. Our results would suggest the need for a change of practice from prescribing flecainide at a frequency of three times a day in children aged younger than 1 year to twice a day in line with other ages.

Machine learning models improve prediction of large vessel occlusion and mechanical thrombectomy candidacy in acute ischemic stroke.

BACKGROUND AND PURPOSE: Early identification of large vessel occlusions (LVO) and timely recanalization are paramount to improved clinical outcomes in acute ischemic stroke. A stroke assessment that maximizes sensitivity and specificity for LVOs is needed to identify these cases and not overburden the health system with unnecessary transfers. Machine learning techniques are being used for predictive modeling in many aspects of stroke care and may have potential in predicting LVO presence and mechanical thrombectomy (MT) candidacy. METHODS: Ischemic stroke patients treated at Loyola University Medical Center from July 2018 to June 2019 (N = 286) were included. Thirty-five clinical and demographic variables were analyzed using machine learning algorithms, including logistic regression, extreme gradient boosting, random forest (RF), and decision trees to build models predictive of LVO presence and MT candidacy by area of the curve (AUC) analysis. The best performing model was compared with prior stroke scales. RESULTS: When using all 35 variables, RF best predicted LVO presence (AUC = 0.907 ± 0.856-0.957) while logistic regression best predicted MT candidacy (AUC = 0.930 ± 0.886-0.974). When compact models were evaluated, a 10-feature RF model best predicted LVO (AUC = 0.841 ± 0.778-0.904) and an 8-feature RF model best predicted MT candidacy (AUC = 0.862 ± 0.782-0.942). The compact RF models had sensitivity, specificity, negative predictive value and positive predictive value of 0.81, 0.87, 0.92, 0.72 for LVO and 0.87, 0.97, 0.97, 0.86 for MT, respectively. The 10-feature RF model was superior at predicting LVO to all previous stroke scales (AUC 0.944 vs 0.759-0.878) and the 8-feature RF model was superior at predicting MT (AUC 0.970 vs 0.746-0.834). CONCLUSION: Random forest machine learning models utilizing clinical and demographic variables predicts LVO presence and MT candidacy with a high degree of accuracy in an ischemic stroke cohort. Further validation of this strategy for triage of stroke patients requires prospective and external validation.