Evidence map›Paper›PMID 37026443›Full record

ArticleClinical pharmacology and therapeutics2023

The Impact of Longitudinal Data-Completeness of Electronic Health Record Data on the Prediction Performance of Clinical Risk Scores.

Yinzhu Jin, Janick G Weberpals, Shirley V Wang, Rishi J Desai, David Merola, Kueiyu Joshua Lin

Open access · bronzeAbstract read
In one paragraph

Article in Clinical pharmacology and therapeutics, 2023. 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
0.5field-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, 3 citations in OpenAlex.

  1. Article
  2. 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

6 authors at 2 institutions in 1 country.

Yinzhu JinDivision of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA.ORCID 0000-0002-1397-3106
Janick G WeberpalsDivision of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA.ORCID 0000-0003-0404-7394
Shirley V WangDivision of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA.ORCID 0000-0001-7761-7090
Rishi J DesaiDivision of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA.ORCID 0000-0003-0299-7273
David MerolaAetion, Inc., Boston, Massachusetts, USA.ORCID 0000-0003-0528-0956
Kueiyu Joshua LinDivision of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA.
Brigham and Women's Hospital · USAetion (United States) · US

Funding

Developing scalable algorithms to incorporate unstructured electronic health records for causal inference based on real-world dataR01LM013204 · NLM · BRIGHAM AND WOMEN'S HOSPITAL · PI LIN, JOSHUA K · 2020 to 2024
$2.7M
Improving comparative effectiveness research through electronic health records continuity cohortsR01LM012594 · NLM · BRIGHAM AND WOMEN'S HOSPITAL · PI LIN, JOSHUA K · 2017 to 2020
$1.5M
NLM NIH HHS R01 LM012594NLM NIH HHS R01 LM013204
6 · The paper itself

Abstract

The impact of electronic health record (EHR) discontinuity (i.e., receiving care outside of a given EHR system) on EHR-based risk prediction is unknown. We aimed to assess the impact of EHR-continuity on the performance of clinical risk scores. The study cohort consisted of patients aged ≥ 65 years with ≥ 1 EHR encounter in the 2 networks in Massachusetts (MA; 2007/1/1-2017/12/31, internal training and validation dataset), and one network in North Carolina (NC; 2007/1/1-2016/12/31, external validation dataset) that were linked with Medicare claims data. Risk scores were calculated using EHR data alone vs. linked EHR-claims data (not subject to misclassification due to EHR-discontinuity): (i) combined comorbidity score (CCS), (ii) claim-based frailty score (CFI), (iii) CHAD

Indexed as

Atrial FibrillationStrokeAgedElectronic Health RecordsHemorrhageHumansMedicareRisk AssessmentRisk FactorsUnited States

Identifiers

PMID37026443
PMCPMC10924806
OpenAlexW4362692706

What OpenQuestion holds

Textmetadata
LicenceTDM
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.