Evidence map›Paper›PMID 27189013›Full record

SynthesisJournal of the American Medical Informatics Association : JAMIA2017

Opportunities and challenges in developing risk prediction models with electronic health records data: a systematic review.

Benjamin A Goldstein, Ann Marie Navar, Michael J Pencina, John P A Ioannidis

Abstract readSystematic Review
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in Journal of the American Medical Informatics Association : JAMIA, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 450 papers, 8 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
450citing papers in PubMed, 8 pooled it
–field-weighted citation impact
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

450 citing papers in PubMed, 8 syntheses or guidelines pooled it.

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390 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Benjamin A GoldsteinDepartment of Biostatistics & Bioinformatics, Duke University, Durham, NC 27710, USA ben.goldstein@duke.edu.
Ann Marie NavarCenter for Predictive Medicine, Duke Clinical Research Institute, Duke University, Durham, NC 27710, USA.
Michael J PencinaDepartment of Biostatistics & Bioinformatics, Duke University, Durham, NC 27710, USA.
John P A IoannidisDepartment of Medicine, Stanford University, Palo Alto, CA 94305, USA.

Funding

Understanding and predicting cardiac events in HD using real-time EHRsK25DK097279 · NIDDK · STANFORD UNIVERSITY · PI GOLDSTEIN, BENJAMIN ALAN · 2013 to 2017
$797k
NIDDK NIH HHS K25 DK097279
6 · The paper itself

Abstract

objectiveElectronic health records (EHRs) are an increasingly common data source for clinical risk prediction, presenting both unique analytic opportunities and challenges. We sought to evaluate the current state of EHR based risk prediction modeling through a systematic review of clinical prediction studies using EHR data.

methodsWe searched PubMed for articles that reported on the use of an EHR to develop a risk prediction model from 2009 to 2014. Articles were extracted by two reviewers, and we abstracted information on study design, use of EHR data, model building, and performance from each publication and supplementary documentation.

resultsWe identified 107 articles from 15 different countries. Studies were generally very large (median sample size = 26 100) and utilized a diverse array of predictors. Most used validation techniques (n = 94 of 107) and reported model coefficients for reproducibility (n = 83). However, studies did not fully leverage the breadth of EHR data, as they uncommonly used longitudinal information (n = 37) and employed relatively few predictor variables (median = 27 variables). Less than half of the studies were multicenter (n = 50) and only 26 performed validation across sites. Many studies did not fully address biases of EHR data such as missing data or loss to follow-up. Average c-statistics for different outcomes were: mortality (0.84), clinical prediction (0.83), hospitalization (0.71), and service utilization (0.71).

conclusionsEHR data present both opportunities and challenges for clinical risk prediction. There is room for improvement in designing such studies.

Indexed as

Electronic Health RecordsModels, StatisticalHumansRisk AssessmentElectronic Medical RecordReviewRisk Assessment

Identifiers

PMID27189013
PMCPMC5201180

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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 OpenQuestion graph.