Evidence map›Paper›PMID 35330593›Full record

ArticleClinical epidemiology2022

Validation of Stroke Risk Factors in Patients with Acute Ischemic Stroke, Transient Ischemic Attack, or Intracerebral Hemorrhage on Taiwan's National Health Insurance Claims Data.

Meng-Tsang Hsieh, Cheng-Yang Hsieh, Tzu-Tung Tsai, Sheng-Feng Sung

Abstract read
In one paragraph

Article in Clinical epidemiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
17citing 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

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

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

  1. Pooled it
  2. Article
  3. Article
  4. Cardiovascular Outcomes Associated with Cholinesterase Inhibitor Use in Individuals at High Cardiovascular Risk.American journal of cardiovascular drugs : drugs, devices, and other interventions · 2025
    Article
  5. Article
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  7. Article
  8. Stroke Epidemiology in Asia.Cerebrovascular diseases extra · 2025
    Review
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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

4 authors.

Meng-Tsang HsiehStroke Center and Department of Neurology, E-Da Hospital, Kaohsiung, Taiwan.ORCID 0000-0002-2605-1813
Cheng-Yang HsiehDepartment of Neurology, Tainan Sin Lau Hospital, Tainan, Taiwan.ORCID 0000-0002-8772-4073
Tzu-Tung TsaiStroke Center and Department of Neurology, E-Da Hospital, Kaohsiung, Taiwan.ORCID 0000-0001-5211-0306
Sheng-Feng SungDivision of Neurology, Department of Internal Medicine, Ditmanson Medical Foundation Chia-Yi Christian Hospital, Chiayi City, Taiwan.ORCID 0000-0002-6253-8813

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: Taiwan has changed the coding system to the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) coding since 2016. This study aimed to determine the optimal algorithms for identifying stroke risk factors in Taiwan's National Health Insurance (NHI) claims data. Patients and Methods: We retrospectively enrolled 4538 patients hospitalized for acute ischemic stroke (AIS), transient ischemic attack (TIA), or intracerebral hemorrhage (ICH) from two hospitals' stroke registries, which were linked to NHI claims data. We developed several algorithms based on ICD-10-CM diagnosis codes and prescription claims data to identify hypertension, diabetes, hyperlipidemia, atrial fibrillation (AF), and ischemic heart disease (IHD) using registry data as the reference standard. The agreement of risk factor status between claims and registry data was quantified by calculating the kappa statistic. Results: According to the registry data, the prevalence of hypertension, diabetes, hyperlipidemia, AF, and IHD among all patients was 77.5%, 41.5%, 47.9%, 12.1%, and 7.1%, respectively. In general, including diagnosis codes from prior inpatient or outpatient claims to those from the stroke hospitalization claims improved the agreement. Incorporating prescription data could improve the agreement for hypertension, diabetes, hyperlipidemia, and AF, but not for IHD. The kappa values of the optimal algorithms were 0.552 (95% confidence interval 0.524-0.580) for hypertension, 0.802 (0.784-0.820) for diabetes, 0.514 (0.490-0.539) for hyperlipidemia, 0.765 (0.734-0.795) for AF, and 0.518 (0.473-0.564) for IHD. Conclusion: Algorithms using diagnosis codes alone are sufficient to identify hypertension, AF, and IHD whereas algorithms combining both diagnosis codes and prescription data are more suitable for identifying diabetes and hyperlipidemia. The study results may provide a reference for future studies using Taiwan's NHI claims data.

Indexed as

administrative claims datadiagnosisICD-10-CMrisk factorsstroke

Identifiers

PMID35330593
PMCPMC8938165

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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.