Evidence map›Paper›PMID 30617499›Full record

ArticleDrug safety2019

Identifying Data Elements to Measure Frailty in a Dutch Nationwide Electronic Medical Record Database for Use in Postmarketing Safety Evaluation: An Exploratory Study.

Janet Sultana, Ingrid Leal, Marcel de Wilde, Maria de Ridder, Johan van der Lei, Miriam Sturkenboom, Gianluca Trifiro'

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Article in Drug safety, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.8field-weighted citation impact, top 30% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 6 citations in OpenAlex.

  1. Review
  2. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors at 3 institutions in 2 countries.

Janet SultanaDepartment of Biomedical and Dental Sciences and Morphofunctional Imaging, Policlinico Universitario, University of Messina, 1, Via Consolare Valeria, 98125, Messina, Italy. jaysultana@gmail.com.ORCID 0000-0001-9622-169X
Ingrid LealDepartment of Medical Informatics, Erasmus Medical Centre, s-Gravendijkwal 230, 3015 CE, Rotterdam, The Netherlands.
Marcel de WildeDepartment of Medical Informatics, Erasmus Medical Centre, s-Gravendijkwal 230, 3015 CE, Rotterdam, The Netherlands.
Maria de RidderDepartment of Medical Informatics, Erasmus Medical Centre, s-Gravendijkwal 230, 3015 CE, Rotterdam, The Netherlands.
Johan van der LeiDepartment of Medical Informatics, Erasmus Medical Centre, s-Gravendijkwal 230, 3015 CE, Rotterdam, The Netherlands.
Miriam SturkenboomJulius Centre for Global Health, Utrecht University Medical Centre, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands.
Gianluca Trifiro'Department of Biomedical and Dental Sciences and Morphofunctional Imaging, Policlinico Universitario, University of Messina, 1, Via Consolare Valeria, 98125, Messina, Italy.
Erasmus University Rotterdam · NLUniversity of Messina · ITUtrecht University · NL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionThe role of frailty in postmarketing drug safety is increasingly acknowledged. Few European electronic medical records (EMRs) have been used to explore frailty in observational drug safety research.

objectiveThe aim of this study was to identify data elements, beyond multimorbidity and polypharmacy, that could potentially contribute to measuring frailty among older adults in the Dutch nationwide Integrated Primary Care Information (IPCI) database.

methodsPersons aged between 65 and 90 years in the IPCI database were identified from 2008 to 2013. Clinical non-disease, non-drug measurements that could potentially contribute to measuring frailty were identified and selected if they were recorded in > 0.005% of patients and could be included in at least one of three definitions of frailty: the frailty phenotype model, the cumulative deficit model, and direct evaluations of frailty through standardized frailty scores. The frequency of these measures was calculated.

resultsOverall, 314,191 (17% of the source population) elderly persons were identified. Of these, 7948 (2.53%) had one or more of 12 clinical measurements identified that could potentially contribute to measuring frailty, such as clinical evaluations of cognition, mobility, and cachexia, as well as direct measures of frailty, such as the Groningen Frailty Index. Three of five measurements required for the frailty phenotype were identified in < 0.5% of the population: cachexia, reduced walking speed, and reduced physical activity; weakness and fatigue were not identified. The measurements outlined above may be appropriate for the cumulative deficit definition of frailty, provided that at least 30 deficits, including comorbidities and drug utilization, are evaluated in total. The most commonly recorded item identified that could potentially be used in a cumulative frailty model was the Mini-Mental State Examination score (N= 2850; 0.91%); the only recorded direct measurement of frailty was the Groningen Frailty Index (N = 2382; 0.76%).

conclusionNon-disease, non-drug clinical data that could potentially contribute to a frailty model was not commonly recorded in the IPCI; less than 3% of a cohort of elderly persons had these data recorded, suggesting that the use of these data in postmarketing drug safety evaluation may be limited.

Indexed as

AgedAged, 80 and overComorbidityDatabases, FactualElectronic Health RecordsEuropeFemaleFrail ElderlyFrailtyHumansMalePhenotypePolypharmacyPrimary Health CareProduct Surveillance, Postmarketing

Identifiers

PMID30617499
OpenAlexW2910603560

What Socratic holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.