Evidence map›Paper›PMID 39664986›Full record

ArticleClinical kidney journal2024

Diagnostic validity and solute-corrected prevalence for hyponatremia and hypernatremia among 1 813 356 admissions.

Akira Okada, Hayato Yamana, Hideaki Watanabe, Katsunori Manaka, Sachiko Ono, Kayo Ikeda Kurakawa, Masako Nishikawa, Makoto Kurano, Reiko Inoue, Hideo Yasunaga and 4 more

Abstract read
In one paragraph

Article in Clinical kidney journal, 2024. 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
–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

2 citing papers in PubMed.

  1. Article
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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

14 authors.

Akira OkadaDepartment of Prevention of Diabetes and Lifestyle-Related Diseases, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Hayato YamanaData Science Center, Jichi Medical University, Shimotsuke, Japan.
Hideaki WatanabeDepartment of Clinical Epidemiology and Health Economics, The University of Tokyo, Tokyo, Japan.ORCID https://orcid.org/0000-0002-3925-2665
Katsunori ManakaDivision of Nephrology and Endocrinology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Sachiko OnoDepartment of Eat-loss Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.ORCID https://orcid.org/0000-0003-4378-2578
Kayo Ikeda KurakawaDepartment of Prevention of Diabetes and Lifestyle-Related Diseases, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Masako NishikawaDepartment of Clinical Laboratory, University of Tokyo Hospital, Tokyo, Japan.
Makoto KuranoDepartment of Clinical Laboratory, University of Tokyo Hospital, Tokyo, Japan.ORCID https://orcid.org/0000-0002-2596-1145
Reiko InoueDepartment of Prevention of Diabetes and Lifestyle-Related Diseases, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Hideo YasunagaDepartment of Clinical Epidemiology and Health Economics, The University of Tokyo, Tokyo, Japan.
Toshimasa YamauchiDepartment of Diabetes and Metabolism, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Takashi KadowakiDepartment of Prevention of Diabetes and Lifestyle-Related Diseases, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Satoko YamaguchiDepartment of Prevention of Diabetes and Lifestyle-Related Diseases, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.ORCID https://orcid.org/0000-0003-2822-2281
Masaomi NangakuDivision of Nephrology and Endocrinology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.ORCID https://orcid.org/0000-0001-7401-2934

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background and hypothesis: We aimed to evaluate the diagnostic validity of the International Classification of Diseases, 10th Revision (ICD-10) codes for hyponatremia and hypernatremia, using a database containing laboratory data. We also aimed to clarify whether corrections for blood glucose, triglyceride, and total protein may affect the prevalence and the diagnostic validity. Methods: We retrospectively identified admissions with laboratory values using a Japanese hospital-based database. We calculated the sensitivity, specificity, and positive/negative predictive values of recorded ICD-10-based diagnoses of hyponatremia (E87.1) and hypernatremia (E87.2), using serum sodium measurements during hospitalization (<135 and >145 mmol/l, respectively) as the reference standard. We also performed analyses with corrections of sodium concentrations for blood glucose, triglyceride, and total protein. Results: We identified 1 813 356 hospitalizations, including 419 470 hyponatremic and 132 563 hypernatremic cases based on laboratory measurements, and 18 378 hyponatremic and 2950 hypernatremic cases based on ICD-10 codes. The sensitivity, specificity, positive predictive value, and negative predictive value of the ICD-10 codes were 4.1%, 99.9%, 92.5%, and 77.6%, respectively, for hyponatremia and 2.2%, >99.9%, 96.5%, and 92.8%, respectively, for hypernatremia. Corrections for blood glucose, triglyceride, and total protein did not largely alter diagnostic values, although prevalence changed especially after corrections for blood glucose and total protein. Conclusions: The ICD-10 diagnostic codes showed low sensitivity, high specificity, and high positive predictive value for identifying hyponatremia and hypernatremia. Corrections for glucose or total protein did not affect diagnostic values but would be necessary for accurate prevalence calculation.

Indexed as

clinical epidemiologydatabase studyelectrolytessolute correctionvalidation study

Identifiers

PMID39664986
PMCPMC11630772

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