Development and validation of sex-specific hip fracture prediction models using electronic health records: a retrospective, population-based cohort study

Background: Hip fracture is associated with immobility, morbidity, mortality, and high medical cost. Due to limited availability of dual-energy X-ray absorptiometry (DXA), hip fracture prediction models without using bone mineral density (BMD) data are essential. We aimed to develop and validate 10-year sex-specific hip fracture prediction models using electronic health records (EHR) without BMD. Methods: In this retrospective, population-based cohort study, anonymized medical records were retrieved from the Clinical Data Analysis and Reporting System for public healthcare service users in Hong Kong aged ≥60 years as of 31 December 2005. A total of 161,051 individuals (91,926 female; 69,125 male) with complete follow-up from 1 January 2006 till the study end date on 31 December 2015 were included in the derivation cohort. The sex-stratified derivation cohort was randomly divided into 80% training and 20% internal testing datasets. An independent validation cohort comprised 3046 community-dwelling participants aged ≥60 years as of 31 December 2005 from the Hong Kong Osteoporosis Study, a prospective cohort which recruited participants between 1995 and 2010. With 395 potential predictors (age, diagnosis, and drug prescription records from EHR), 10-year sex-specific hip fracture prediction models were developed using stepwise selection by logistic regression (LR) and four machine learning (ML) algorithms (gradient boosting machine, random forest, eXtreme gradient boosting, and single-layer neural networks) in the training cohort. Model performance was evaluated in both internal and independent validation cohorts. Findings: In female, the LR model had the highest AUC (0.815; 95% Confidence Interval [CI]: 0.805–0.825) and adequate calibration in internal validation. Reclassification metrics showed the LR model had better discrimination and classification performance than the ML algorithms. Similar performance was attained by the LR model in independent validation, with high AUC (0.841; 95% CI: 0.807–0.87) comparable to other ML algorithms. In internal validation for male, LR model had high AUC (0.818; 95% CI: 0.801–0.834) and it outperformed all ML models as indicated by reclassification metrics, with adequate calibration. In independent validation, the LR model had high AUC (0.898; 95% CI: 0.857–0.939) comparable to ML algorithms. Reclassification metrics demonstrated that LR model had the best discrimination performance. Interpretation: Even without using BMD data, the 10-year hip fracture prediction models developed by conventional LR had better discrimination performance than the models developed by ML algorithms. Upon further validation in independent cohorts, the LR models could be integrated into the routine clinical workflow, aiding the identification of people at high risk for DXA scan. Funding: Health and Medical Research Fund, Health Bureau, Hong Kong SAR Government (reference: 17181381)

Systematic Review and Meta-Analysis of the Safety of Perampanel in the Treatment of Partial-onset Epilepsy

Topic: Drug-induced adverse reactions in specific organs and systemsConference Theme: The Renaissance of PharmacovigilancePoster Session A: abstract no. ISP3582-4

Chinese medicine students’ view on electronic prescribing: A survey in Hong Kong

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Systematic Review and Meta-Analysis of the Safety of Perampanel in the Treatment of Partial-onset Epilepsy

Jointly organize by The International Society for Pharmacoepidemiology (ISPE), The University of Hong Kong (HKU), The Pharmaceutical Society of Hong Kong (PSHK) and Chinese Pharmaceutical Association (CPA

Systematic Review and Meta-Analysis of the Efficacy and Safety of Perampanel in the Treatment of Partial-Onset Epilepsy.

INTRODUCTION: Perampanel is a first-in-class antiepileptic drug approved for adjunctive treatment of partial-onset seizure in patients aged 12 years or older. Published randomised controlled trials (RCTs) had small sample sizes, and meta-analyses have included too few studies to draw conclusive results for the assessment of tolerability, efficacy and safety of perampanel. There is a need to conduct a meta-analysis with a larger dataset and an appropriate study design. OBJECTIVE: The aim of this study was to systematically review the efficacy and safety of perampanel in the treatment of partial-onset epilepsy. METHODS: Electronic and clinical trials databases were searched for RCTs of perampanel published up to March 2013. Outcomes of interest were 50 % responder rates, seizure freedom, treatment-emergent adverse events (TEAEs) and incidence of withdrawal. Meta-analysis was performed to investigate the outcomes of interest. RESULTS: Five RCTs with a total of 1,678 subjects were included. The 50 % responder rates were significantly greater in patients receiving 4, 8 and 12 mg perampanel versus placebo, with risk ratios of 1.54 (95 % CI 1.11–2.13), 1.80 (95 % CI 1.38–2.35) and 1.72 (95 % CI 1.17–2.52), respectively. There was no statistical evidence of a difference in seizure freedom between 8 or 12 mg perampanel and placebo. Of the five commonly reported TEAEs included, both dizziness and somnolence were statistically associated with 8 mg perampanel, whilst dizziness was statistically associated with 12 mg perampanel. Incidences of withdrawal due to adverse events were significantly higher in the 8 mg and 12 mg perampanel groups versus placebo. CONCLUSION: The use of perampanel resulted in a statistically significant reduction of seizure frequency with respect to the 50 % responder rate in patients with partial-onset epilepsy. Perampanel is well tolerated at 4 mg and reasonably tolerated at 8 and 12 mg. Further clinical and pharmacovigilance studies are required to investigate the long-term efficacy and safety of perampanel in the management of other types of epilepsy. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s40263-013-0091-9) contains supplementary material, which is available to authorized users

Development and validation of sex-specific hip fracture prediction models using electronic health records : a retrospective, population-based cohort study

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