The risk of fractures, acute myocardial infarction, atrial fibrillation and ventricular arrhythmia in geriatric patients exposed to promethazine

AI-based disease risk score for community-acquired pneumonia hospitalization

Community-acquired pneumonia (CAP) is an acute infection involving the parenchyma of the lungs, which is acquired outside of the hospital. Population-wide real-world data and artificial intelligence (AI) were used to develop a disease risk score for CAP hospitalization among older individuals. The source population included residents in Denmark aged 65 years or older in the period January 1, 1996, to July 30, 2018. 137344 individuals were hospitalized for pneumonia during the study period for which, 5 controls were matched leading to a study population of 620908 individuals. The disease risk had an average accuracy of 0.79 based on 5-fold cross-validation in predicting CAP hospitalization. The disease risk score can be useful in clinical practice to identify individuals at higher risk of CAP hospitalization and intervene to minimize their risk of being hospitalized for CAP.

Machine learning techniques for arrhythmic risk stratification: a review of the literature

Ventricular arrhythmias (VAs) and sudden cardiac death (SCD) are significant adverse events that affect the morbidity and mortality of both the general population and patients with predisposing cardiovascular risk factors. Currently, conventional disease-specific scores are used for risk stratification purposes. However, these risk scores have several limitations, including variations among validation cohorts, the inclusion of a limited number of predictors while omitting important variables, as well as hidden relationships between predictors. Machine learning (ML) techniques are based on algorithms that describe intervariable relationships. Recent studies have implemented ML techniques to construct models for the prediction of fatal VAs. However, the application of ML study findings is limited by the absence of established frameworks for its implementation, in addition to clinicians’ unfamiliarity with ML techniques. This review, therefore, aims to provide an accessible and easy-to-understand summary of the existing evidence about the use of ML techniques in the prediction of VAs. Our findings suggest that ML algorithms improve arrhythmic prediction performance in different clinical settings. However, it should be emphasized that prospective studies comparing ML algorithms to conventional risk models are needed while a regulatory framework is required prior to their implementation in clinical practice.

The PHARMACOM-EPI Framework for Integrating Pharmacometric Modelling Into Pharmacoepidemiological Research Using Real-World Data: Application to Assess Death Associated With Valproate

In pharmacoepidemiology, it is usually expected that the observed association should be directly or indirectly related to the pharmacological effects of the drug/s under investigation. Pharmacological effects are, in turn, strongly connected to the pharmacokinetic and pharmacodynamic properties of a drug, which can be characterized and investigated using pharmacometric models. Recently, the use of pharmacometrics has been proposed to provide pharmacological substantiation of pharmacoepidemiological findings derived from real-world data. However, validated frameworks suggesting how to combine these two disciplines for the aforementioned purpose are missing. Therefore, we propose PHARMACOM-EPI, a framework that provides a structured approach on how to identify, characterize, and apply pharmacometric models with practical details on how to choose software, format dataset, handle missing covariates/dosing data, how to perform the external evaluation of pharmacometric models in real-world data, and how to provide pharmacological substantiation of pharmacoepidemiological findings. PHARMACOM-EPI was tested in a proof-of-concept study to pharmacologically substantiate death associated with valproate use in the Danish population aged ≥ 65 years. Pharmacological substantiation of death during a follow-up period of 1 year showed that in all individuals who died (n = 169) individual predictions were within the subtherapeutic range compared with 52.8% of those who did not die (n = 1,084). Of individuals who died, 66.3% (n = 112) had a cause of death possibly related to valproate and 33.7% (n = 57) with well-defined cause of death unlikely related to valproate. This proof-of-concept study showed that PHARMACOM-EPI was able to provide pharmacological substantiation for death associated with valproate use in the study population.

The Relationship between Timing of Pretransplant Kidney Biopsy, Graft Loss, and Survival in Kidney Transplantation: An Italian Cohort Study

INTRODUCTION

Kidney biopsy is performed to assess if an extended criteria graft can be used for transplantation. It may be performed before or after cross-clamping during organ procurement. This study aims to evaluate whether the timing of biopsy may modify cold ischemia times (CIT) and/or graft outcomes.

METHODS

Kidney transplants performed in our center from January 2007 to December 2017 were analyzed. Grafts with preimplantation kidney biopsy were included. Biopsies were performed during surgical back table (ex situ kidney biopsy [ESKB]) until 2012 and since then before the aortic cross-clamping (in situ kidney biopsy [ISKB]). To overcome biases owing to different distributions, a propensity score model was developed. The study population consists in 322 patients, 115 ESKB, and 207 ISKB.

RESULTS

CIT was significantly lower for ISKB (730 min ISKB vs. 840 min ESKB, p value = 0.001). In both crude (OR 0.27; 95% confidence interval, 95% CI 0.12-0.60; p value = 0.002) and adjusted analyses (OR 0.37; 95% CI 0.14-0.94; p value = 0.039), ISKB was associated with a reduced odd of graft loss when compared to ESKB.

DISCUSSION/CONCLUSION

Performing preimplantation kidney biopsy during the recovery, prior to the aortic cross-clamping, may be a strategy to reduce CIT and improve transplant outcomes.

The Relationship Between Valproate and Lamotrigine/Levetiracetam Use and Prognosis in Patients With Epilepsy and Heart Failure: A Danish Register-Based Study

OBJECTIVE

To compare the hazard for all-cause mortality and mortality due to heart failure (HF) between valproate (VPA) and levetiracetam (LEV)/lamotrigine (LTG) users in patients aged ≥ 65 with comorbidities of epilepsy and HF.

METHODS

This was a cohort study using Danish registers during the period from January 1996 to July 2018. The study population included new users of LTG, LEV or VPA. A Cox regression model was used to compute crude and adjusted hazard ratios for the outcome, using an intention-to-treat approach. Average treatment effects (eg, 1-year absolute risks), risk differences and the ratio of risks were computed using the G-formula based on a Cox regression model for the outcomes at the end of the follow-up period.

RESULTS

We included 1345 subjects in the study population. VPA users (n = 696), when compared to LTG/LEV users (n = 649), had an increased hazard of mortality due to HF (hazard ratio [HR] 2.39; 95% CI 1.02-5.60) and to all-cause mortality (HR 1.37; 95% CI 1.01-1.85) in both crude and adjusted analyses. The 1-year absolute risks for all-cause mortality were 29% (95% CI 25%-33%) and 22% (95% CI 18%-26%) for VPA and LTG/LEV users. For mortality due to HF, 1-year absolute risks were 5% (95% CI 3%-7%) and 2% (95% CI 1%-4%) for VPA and LTG/LEV users. The average risk ratio, with LTG/LEV as the reference group, was 1.31 (95% CI 1.02-1.71) for all-cause mortality and 2.35 (95% CI 1.11-5.76) for HF mortality.

CONCLUSION

In older people with HF and epilepsy, treatment with VPA was associated with a higher risk of all-cause and HF mortality compared to treatment with LTG and LEV.

Utilizing drug-target-event relationships to unveil safety patterns in pharmacovigilance

INTRODUCTION

Signal detection is the most pivotal activity of signal management to guarantee that drugs maintain a positive risk-benefit ratio during their lifetime on the market. Signal detection is based on the systematic evaluation of available data sources, which have recently been extended in order to improve timely and comprehensive signal detection of drug safety problems.

AREAS COVERED

In recent years, attempts have been made to incorporate pharmacological data for the prediction of safety signals. Previous studies have shown that data on the pharmacological targets of drugs are predictive of post-marketing adverse events. However, current approaches limit such predictions to adverse events expected from the interaction of a drug with the main pharmacological target and do not take off-target interactions into consideration.

EXPERT OPINION

The authors propose the application of predictive modeling techniques utilizing pharmacological data from public databases for predicting drug-target-event relationships deriving from main- and off-target binding and from which potential safety signals can be deduced. Additionally, they provide an operative procedure for the identification of clinically relevant subgroups for predicted safety signals.