Evidence map›Paper›PMID 35223412›Full record

ArticleBioMedicine2021

Electronic medical record-based deep data cleaning and phenotyping improve the diagnostic validity and mortality assessment of infective endocarditis: medical big data initiative of CMUH.

Hsiu-Yin Chiang, Li-Ying Liang, Che-Chen Lin, Yi-Jin Chen, Min-Yen Wu, Sheng-Hsuan Chen, Pin-Hua Wu, Chin-Chi Kuo, Chih-Yu Chi

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In one paragraph

Article in BioMedicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed, 1 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

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3 · Its place in the literature

Who cites it

26 citing papers in PubMed, 1 synthesis or guideline pooled it.

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  8. SepsisJournal of medical microbiology · 2025
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4 · The record

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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.

Hsiu-Yin ChiangBig Data Center, China Medical University Hospital, Taichung, Taiwan.
Li-Ying LiangDivision of Infectious Diseases, Department of Internal Medicine, China Medical University Hospital, Taichung, Taiwan.
Che-Chen LinBig Data Center, China Medical University Hospital, Taichung, Taiwan.
Yi-Jin ChenDepartment of Medical Research, China Medical University Hospital, Taichung, Taiwan.
Min-Yen WuBig Data Center, China Medical University Hospital, Taichung, Taiwan.
Sheng-Hsuan ChenBig Data Center, China Medical University Hospital, Taichung, Taiwan.
Pin-Hua WuDepartment of Computer Science, National Tsing-Hua University, Hsinchu, Taiwan.
Chin-Chi KuoBig Data Center, China Medical University Hospital, Taichung, Taiwan.
Chih-Yu ChiDivision of Infectious Diseases, Department of Internal Medicine, China Medical University Hospital, Taichung, Taiwan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundInternational Classification of Diseases (ICD) code-based claims databases are often used to study infective endocarditis (IE). However, the quality of ICD coding can influence the reliability of IE research. The impact of complementing the ICD-only approach with data extracted from electronic medical records (EMRs) has yet to be explored.

methodsWe selected the information of adult patients with discharge ICD codes for IE (ICD-9: 421, 112.81, 036.42, 098.84, 115.04, 115.14, 115.94, 424.9; ICD-10: I33, I38, I39) during 2005-2016 in China Medical University Hospital. Data extraction was conducted on the basis of the modified Duke criteria to establish a reference group comprising patients with definite or possible IE. Clinical characteristics and in-hospital mortality were compared between ICD-identified and Duke-confirmed cases. The positive predictive value (PPV) was used to quantify the IE identification performance of various phenotyping algorithms.

resultsA total of 593 patients with discharge ICD codes for IE were identified, only 56.7% met the modified Duke criteria. The crude in-hospital mortality for Duke-confirmed and Duke-rejected IE were 24.4% and 8.2%, respectively. The adjusted in-hospital mortality for ICD-identified IE was lower than that for Duke-confirmed IE by a difference of 5.1%. The best PPV was achieved (0.90, 95% CI 0.86-0.93) when major components of the Duke criteria (positive blood culture and vegetation) were integrated with ICD codes.

conclusionIntegrating EMR data can considerably improve the accuracy of ICD-only approaches in phenotyping IE, which can improve the validity of EMR-based studies and their applications, including real-time surveillance and clinical decision support.

Indexed as

Disease phenotypingElectronic medical recordInfective endocarditisInternational Classification of DiseasesPositive predictive value

Identifiers

PMID35223412
PMCPMC8823496

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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.