Evidence map›Paper›PMID 31916081›Full record

ArticleDrug safety2020

Utility of a Computerized ICD-10 Algorithm to Identify Idiosyncratic Drug-Induced Liver Injury Cases in the Electronic Medical Record.

Amoah Yeboah-Korang, Jeremy Louissaint, Irene Tsung, Sharmila Prabhu, Robert J Fontana

Open access · greenAbstract read
In one paragraph

Article in Drug safety, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
1.9field-weighted citation impact, top 15% 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

6 citing papers in PubMed, 13 citations in OpenAlex.

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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

5 authors at 1 institution in 1 country.

Amoah Yeboah-KorangDivision of Gastroenterology and Hepatology, University of Michigan Medical School, Ann Arbor, MI, 48109, USA.
Jeremy LouissaintDivision of Gastroenterology and Hepatology, University of Michigan Medical School, Ann Arbor, MI, 48109, USA.
Irene TsungDivision of Gastroenterology and Hepatology, University of Michigan Medical School, Ann Arbor, MI, 48109, USA.
Sharmila PrabhuDivision of Gastroenterology and Hepatology, University of Michigan Medical School, Ann Arbor, MI, 48109, USA.
Robert J FontanaDivision of Gastroenterology and Hepatology, University of Michigan Medical School, Ann Arbor, MI, 48109, USA. rfontana@med.umich.edu.
University of Michigan–Ann Arbor · US

Funding

Training Program in Gastrointestinal EpidemiologyT32DK062708 · NIDDK · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Peter D.R. Higgins, Elliot Tapper · 2003 to 2026
$3.9M
NIDDK NIH HHS T32 DK062708
6 · The paper itself

Abstract

introductionIdiosyncratic drug-induced liver injury (DILI) is an important cause of liver injury that is difficult to diagnose and identify in the electronic medical record (EMR).

objectiveOur objective was to develop a computerized algorithm that can reliably identify DILI cases from the EMR.

methodsThe EMR was searched for all encounters with an International Classification of Diseases, Tenth Revision (ICD-10) T code for drug toxicity and a K-71 code for toxic liver injury between 1 October 2015 and 30 September 2018. Clinically significant liver injury was defined using predetermined laboratory values. An expert opinion causality score (1-3 = probable DILI, 4/5 = non-DILI), Roussel Uclaf Causality Assessment Method (RUCAM) score, and severity score was assigned to each case.

resultsAmong the 1,211,787 encounters searched, 517 had both an ICD-10 T code and a K-71 code, with 257 patients meeting the laboratory criteria. After excluding 75 cases of acetaminophen hepatotoxicity, the final study sample included 182 cases of potential DILI, with antineoplastics and antibiotics being the most frequently implicated agents. Causality assessment identified probable DILI in 121 patients (66.5%), whereas 61 (33.5%) had an alternative cause of liver injury. Although age, sex, race, and suspect drugs were similar, the probable DILI cases were more likely to present with a hepatocellular injury profile and have more severe liver injury than the non-DILI cases (p < 0.05).

conclusionA computerized algorithm based on a combination of ICD-10 codes identified 182 potential DILI cases with 121 true positives, 61 false positives, and a positive predictive value of 66.5%. Future studies incorporating natural language processing may further improve the utility of this algorithm in identifying high-causality idiosyncratic DILI cases.

Indexed as

AlgorithmsChemical and Drug Induced Liver InjuryElectronic Health RecordsInternational Classification of DiseasesDatabases, FactualHospitalizationHumansLiver Function TestsSeverity of Illness IndexUnited States

Identifiers

PMID31916081
PMCPMC8487619
OpenAlexW2999821767

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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.