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Background: Adverse drug reactions (ADRs) are common and a leading cause of injury. However, information on ADR risks of individual medicines is often limited. The aim of this hypothesis-generating study was to assess the relative importance of ADR-related and emergency hospital admission for large group of medication classes., Methods: This study was a propensity-matched case-control study in English primary care. Data sources were Clinical Practice Research Databank and Aurum with longitudinal, anonymized, patient level electronic health records (EHRs) from English general practices linked to hospital records. Cases aged 65-100 with ADR-related or emergency hospital admission were matched to up to six controls by age, sex, morbidity and propensity scores for hospital admission risk. Medication groups with systemic administration as listed in the British National Formulary (used by prescribers for medication advice). Prescribing in the 84 days before the index date was assessed. Only medication groups with 50+ cases exposed were analysed. The outcomes of interest were ADR-related and emergency hospital admissions. Conditional logistic regression estimated odds ratios (ORs) and 95% confidence intervals (CI)., Results: The overall population included 121 546 cases with an ADR-related and 849 769 cases with emergency hospital admission. The percentage of hospitalizations with an ADR-related code for admission diagnosis was 1.83% and 6.58% with an ADR-related code at any time during hospitalization. A total of 137 medication groups was included in the main ADR analyses. Of these, 13 (9.5%) had statistically non-significant adjusted ORs, 58 (42.3%) statistically significant ORs between 1.0 and 1.5, 37 (27.0%) between 1.5-2.0, 18 (13.1%) between 2.0-3.0 and 11 (8.0%) 3.0 or higher. Several classes of antibiotics (including penicillins) were among medicines with largest ORs. Evaluating the 14 medications most often associated with ADRs, a strong association was found between the number of these medicines and the risk of ADR-related hospital admission (adjusted OR of 7.53 (95% CI 7.15-7.93) for those exposed to 6+ of these medicines)., Conclusions and Relevance: There is a need for a regular systematic assessment of the harm-benefit ratio of medicines, harvesting the information in large healthcare databases and combining it with causality assessment of individual case histories., (© 2023 The Authors. Pharmacoepidemiology and Drug Safety published by John Wiley & Sons Ltd.)

This study evaluated drug-drug interactions (DDIs) between antibiotic and nonantibiotic drugs listed with warnings of severe outcomes in the British National Formulary based on adverse drug reaction (ADR) detectable with routine International Classification of Diseases, Tenth Revision coding. Data sources were Clinical Practice Research Databank GOLD and Aurum anonymized electronic health records from English general practices linked to hospital admission records. In propensity-matched case-control study, outcomes were ADR or emergency admissions. Analyzed were 121,546 ADR-related admission cases matched to 638,238 controls. For most antibiotics, adjusted odds ratios (aORs) for ADR-related hospital admission were large (aOR for trimethoprim 4.13; 95% confidence interval (CI), 3.97-4.30). Of the 51 DDIs evaluated for ADR-related admissions, 38 DDIs (74.5%) had statistically increased aORs of concomitant exposure compared with nonexposure (mean aOR 3.96; range 1.59-11.42); for the 89 DDIs for emergency hospital admission, the results were 75 (84.3%) and mean aOR 2.40; range 1.43-4.17. Changing reference group to single antibiotic exposure reduced aORs for concomitant exposure by 76.5% and 83.0%, respectively. Medicines listed to cause nephrotoxicity substantially increased risks that were related to number of medicines (aOR was 2.55 (95% CI, 2.46-2.64) for current use of 1 and 10.44 (95% CI, 7.36-14.81) for 3 or more medicines). In conclusion, no evidence of substantial risk was found for multiple DDIs with antibiotics despite warnings of severe outcomes in a national formulary and flagging in electronic health record software. It is proposed that the evidence base for inclusion of DDIs in national formularies be strengthened and made publicly accessible and indiscriminate flagging, which compounds alert fatigue, be reduced., (© 2022 The Authors. Clinical Pharmacology & Therapeutics published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)

Clinical prediction models (CPMs) can predict clinically relevant outcomes or events. Typically, prognostic CPMs are derived to predict the risk of a single future outcome. However, there are many medical applications where two or more outcomes are of interest, meaning this should be more widely reflected in CPMs so they can accurately estimate the joint risk of multiple outcomes simultaneously. A potentially naïve approach to multi-outcome risk prediction is to derive a CPM for each outcome separately, then multiply the predicted risks. This approach is only valid if the outcomes are conditionally independent given the covariates, and it fails to exploit the potential relationships between the outcomes. This paper outlines several approaches that could be used to develop CPMs for multiple binary outcomes. We consider four methods, ranging in complexity and conditional independence assumptions: namely, probabilistic classifier chain, multinomial logistic regression, multivariate logistic regression, and a Bayesian probit model. These are compared with methods that rely on conditional independence: separate univariate CPMs and stacked regression. Employing a simulation study and real-world example, we illustrate that CPMs for joint risk prediction of multiple outcomes should only be derived using methods that model the residual correlation between outcomes. In such a situation, our results suggest that probabilistic classification chains, multinomial logistic regression or the Bayesian probit model are all appropriate choices. We call into question the development of CPMs for each outcome in isolation when multiple correlated or structurally related outcomes are of interest and recommend more multivariate approaches to risk prediction., (© 2020 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.)

There is growing interest in developing clinical prediction models (CPMs) to aid local healthcare decision-making. Frequently, these CPMs are developed in isolation across different populations, with repetitive de novo derivation a common modelling strategy. However, this fails to utilise all available information and does not respond to changes in health processes through time and space. Alternatively, model updating techniques have previously been proposed that adjust an existing CPM to suit the new population, but these techniques are restricted to a single model. Therefore, we aimed to develop a generalised method for updating and aggregating multiple CPMs. The proposed "hybrid method" re-calibrates multiple CPMs using stacked regression while concurrently revising specific covariates using individual participant data (IPD) under a penalised likelihood. The performance of the hybrid method was compared with existing methods in a clinical example of mortality risk prediction after transcatheter aortic valve implantation, and in 2 simulation studies. The simulation studies explored the effect of sample size and between-population-heterogeneity on the method, with each representing a situation of having multiple distinct CPMs and 1 set of IPD. When the sample size of the IPD was small, stacked regression and the hybrid method had comparable but highest performance across modelling methods. Conversely, in large IPD samples, development of a new model and the hybrid method gave the highest performance. Hence, the proposed strategy can inform the choice between utilising existing CPMs or developing a model de novo, thereby incorporating IPD, existing research, and prior (clinical) knowledge into the modelling strategy., (© 2017 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.)

Cardiac implantable electronic devices include remote monitoring tools intended to guide heart failure management. The monitoring focus has been on averting hospitalizations by predicting worsening heart failure. However, although device measurements including intrathoracic impedance correlate with risk of decompensation, they individually predict hospitalizations with limited accuracy. Current 'crisis detection' methods involve repeatedly screening for impending decompensation, and do not adhere to the principles of diagnostic testing. Complex substrate, limited test performance, low outcome incidence, and long test to outcome times inevitably generate low positive and high negative predictive values. When combined with spectrum bias, the generalizability, incremental value, and cost-effectiveness of device algorithms are questionable. To avoid these pitfalls, remote monitoring may need to shift from crisis detection to health maintenance, keeping the patient within an ideal physiological range through continuous 'closed loop' interaction and dynamic therapy adjustment. Test performance must also improve, possibly through combination with physiological sensors in different dimensions, static baseline characteristics, and biomarkers. Complex modelling may tailor monitoring to individual phenotypes, and thus realize a personalized medicine approach. Future randomized controlled trials should carefully consider these issues, and ensure that the interventions tested are generalizable to clinical practice., (© 2015 The Authors European Journal of Heart Failure © 2015 European Society of Cardiology.)

In many time-to-event studies, particularly in epidemiology, the time of the first observation or study entry is arbitrary in the sense that this is not a time of risk modification. We present a formal argument that, in these situations, it is not advisable to take the first observation as the time origin, either in accelerated failure time or proportional hazards models. Instead, we advocate using birth as the time origin. We use a two-stage process to account for the fact that baseline observations may be made at different ages in different subjects. First, we marginally regress any potentially age-varying covariates against age, retaining the residuals. These residuals are then used as covariates in fitting an accelerated failure time or proportional hazards model - we call the procedures residual accelerated failure time regression and residual proportional hazards regression, respectively. We compare residual accelerated failure time regression with the standard approach, demonstrating superior predictive ability of the residual method in realistic examples and potentially higher power of the residual method. This highlights flaws in current approaches to communicating risks from epidemiological evidence to support clinical and health policy decisions., (Copyright © 2012 John Wiley & Sons, Ltd.)