Evidence map›Paper›PMID 38192568›Full record

ArticleFrontiers in public health2023

Discrepancy in diagnoses of diabetes and prediabetes using fasting plasma glucose and glycosylated hemoglobin and the underdiagnosis by ICD-10 coding: data from a tertiary hospital in Thailand.

Napalai Poorirerngpoom, Poranee Ganokroj, Arnond Vorayingyong, Thanapoom Rattananupong, Jennifer Pusavat, Thanan Supasiri

Open access · goldAbstract read
In one paragraph

Article in Frontiers in public health, 2023. 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
0.4field-weighted citation impact, top 31% 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

4 citing papers in PubMed, 2 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

6 authors at 2 institutions in 2 countries.

Napalai PoorirerngpoomDepartment of Preventive and Social Medicine, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand.
Poranee GanokrojDepartment of Laboratory Medicine, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand.
Arnond VorayingyongDepartment of Preventive and Social Medicine, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand.
Thanapoom RattananupongDepartment of Preventive and Social Medicine, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand.
Jennifer PusavatMichigan State University College of Osteopathic Medicine, East Lansing, MI, United States.
Thanan SupasiriDepartment of Preventive and Social Medicine, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand.
Chulalongkorn University · THMichigan State University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Early detection of prediabetes and diabetes better prevents long-term health complications. FPG and HbA1c levels are some common laboratory tests utilized as tools to diagnose diabetes and prediabetes, but the agreement rate between these two diagnostic tests varies, which could lead to underdiagnosis and thus undertreatment. This study aimed to analyze the agreement rate between FPG and HbA1c, as well as the physicians' accuracy of using these results to make a prediabetes or diabetes diagnosis through ICD-10 coding at a tertiary care hospital in Bangkok, Thailand. Methods: A cross-sectional descriptive study was conducted using secondary data collected in a tertiary hospital's check-up clinic from August 16, 2019 to June 30, 2022 to study the prevalence and diagnosis of diabetes and prediabetes, determined through FPG and HbA1c laboratory results. We analyzed the two laboratory tests' diagnosis agreement rate and the physicians' accuracy of diagnosing diabetes and prediabetes in ICD-10 coding using the FPG and HbA1c results. Results: Among 8,024 asymptomatic participants, the period prevalence diagnosed through laboratory results was 5.8% for diabetes and 19.8% for prediabetes. Diabetes and prediabetes prevalence based on laboratory data differs from that of ICD-10 coding data. Specifically, 79.6% of diabetes patients and 32.3% of prediabetes patients were coded using the ICD-10 coding system. 4,094 individuals had both FPG and HbA1c data. The agreement rate for diagnosing diabetes and prediabetes between the two laboratory results is 89.5%, with Kappa statistics of 0.58. Using only one of the two laboratory results would have missed a substantial number of patients. Conclusion: Our findings highlight screening test discrepancies and underdiagnosis issues that impede diagnostic accuracy enhancement and refined patient management strategies. Early diagnoses of prediabetes and diabetes, especially before symptoms arise, could increase health consciousness in individuals, thereby enabling the implementation of lifestyle modifications and prevention of serious health complications. We emphasize the importance of diagnosing these conditions using both FPG and HbA1c, along with subsequent accurate ICD-10 coding. Even though some hospitals lack certified HbA1c testing, we suggest enhancing the availability of HbA1c testing, which could benefit many people in Thailand.

Indexed as

Diabetes MellitusPrediabetic StateBlood GlucoseCross-Sectional StudiesFastingGlycated HemoglobinHumansInternational Classification of DiseasesTertiary Care CentersThailandBlood GlucoseGlycated Hemoglobindiabetes mellitusdiagnosisfasting plasma glucoseHbA1cICD-10prediabetes

Identifiers

PMID38192568
PMCPMC10773892
OpenAlexW4389684345

What Socratic holds

Textmetadata
LicenceCC BY
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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 Socratic graph.