Evidence map›Paper›PMID 33720372›Full record

ArticleJAMA network open2021

Assessment of Extractability and Accuracy of Electronic Health Record Data for Joint Implant Registries.

Nicholas J Giori, John Radin, Alison Callahan, Jason A Fries, Eni Halilaj, Christopher Ré, Scott L Delp, Nigam H Shah, Alex H S Harris

Abstract read
In one paragraph

Article in JAMA network open, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
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

9 authors.

Nicholas J GioriCenter for Innovation to Implementation, VA Palo Alto Health Care System, Palo Alto, California.
John RadinCenter for Innovation to Implementation, VA Palo Alto Health Care System, Palo Alto, California.
Alison CallahanCenter for Biomedical Informatics Research, Stanford University, Stanford, California.
Jason A FriesCenter for Biomedical Informatics Research, Stanford University, Stanford, California.
Eni HalilajDepartment of Bioengineering, Stanford University, Stanford, California.
Christopher RéDepartment of Computer Science, Stanford University, Stanford, California.
Scott L DelpDepartment of Bioengineering, Stanford University, Stanford, California.
Nigam H ShahCenter for Biomedical Informatics Research, Stanford University, Stanford, California.
Alex H S HarrisCenter for Innovation to Implementation, VA Palo Alto Health Care System, Palo Alto, California.

Funding

Training CoreU54EB020405 · NIBIB · STANFORD UNIVERSITY · PI DELP, SCOTT L · 2014 to 2018
$11.6M
TR&D Project 3: OpenSim for PredictionP41EB027060 · NIBIB · STANFORD UNIVERSITY · PI SCOTT L DELP · 2020 to 2026
$9.7M
NIBIB NIH HHS P41 EB027060NIBIB NIH HHS U54 EB020405
6 · The paper itself

Abstract

Importance: Implant registries provide valuable information on the performance of implants in a real-world setting, yet they have traditionally been expensive to establish and maintain. Electronic health records (EHRs) are widely used and may include the information needed to generate clinically meaningful reports similar to a formal implant registry. Objectives: To quantify the extractability and accuracy of registry-relevant data from the EHR and to assess the ability of these data to track trends in implant use and the durability of implants (hereafter referred to as implant survivorship), using data stored since 2000 in the EHR of the largest integrated health care system in the United States. Design, Setting, and Participants: Retrospective cohort study of a large EHR of veterans who had 45 351 total hip arthroplasty procedures in Veterans Health Administration hospitals from 2000 to 2017. Data analysis was performed from January 1, 2000, to December 31, 2017. Exposures: Total hip arthroplasty. Main Outcomes and Measures: Number of total hip arthroplasty procedures extracted from the EHR, trends in implant use, and relative survivorship of implants. Results: A total of 45 351 total hip arthroplasty procedures were identified from 2000 to 2017 with 192 805 implant parts. Data completeness improved over the time. After 2014, 85% of prosthetic heads, 91% of shells, 81% of stems, and 85% of liners used in the Veterans Health Administration health care system were identified by part number. Revision burden and trends in metal vs ceramic prosthetic femoral head use were found to reflect data from the American Joint Replacement Registry. Recalled implants were obvious negative outliers in implant survivorship using Kaplan-Meier curves. Conclusions and Relevance: Although loss to follow-up remains a challenge that requires additional attention to improve the quantitative nature of calculated implant survivorship, we conclude that data collected during routine clinical care and stored in the EHR of a large health system over 18 years were sufficient to provide clinically meaningful data on trends in implant use and to identify poor implants that were subsequently recalled. This automated approach was low cost and had no reporting burden. This low-cost, low-overhead method to assess implant use and performance within a large health care setting may be useful to internal quality assurance programs and, on a larger scale, to postmarket surveillance of implant performance.

Indexed as

AdultAgedAged, 80 and overArthroplasty, Replacement, HipCohort StudiesElectronic Health RecordsFemaleHumansMaleMiddle AgedRegistriesReproducibility of ResultsRetrospective StudiesYoung Adult

Identifiers

PMID33720372
PMCPMC7961313

What OpenQuestion holds

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LicenceCC BY
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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 OpenQuestion graph.