Observational studyPloS one2023
Validation study of case-identifying algorithms for severe hypoglycemia using hospital administrative data in Japan.
Observational study in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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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.
The trial behind it
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Who cites it
1 citing paper in PubMed, 1 citations in OpenAlex.
- Investigation of Severe Hypoglycemia Risk Among Patients with Diabetes Treated with Ultra-Rapid Lispro in Japan.Advances in therapy · 2025Observational
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThe purpose of this study was to evaluate the performance of algorithms for identifying cases of severe hypoglycemia in Japanese hospital administrative data.
methodsThis was a multicenter, retrospective, observational study conducted at 3 acute-care hospitals in Japan. The study population included patients aged ≥18 years with diabetes who had an outpatient visit or hospital admission for possible hypoglycemia. Possible cases of severe hypoglycemia were identified using health insurance claims data and Diagnosis Procedure Combination data. Sixty-one algorithms using combinations of diagnostic codes and prescription of high concentration (≥20% mass/volume) injectable glucose were used to define severe hypoglycemia. Independent manual chart reviews by 2 physicians at each hospital were used as the reference standard. Algorithm validity was evaluated using standard performance metrics.
resultsIn total, 336 possible cases of severe hypoglycemia were identified, and 260 were consecutively sampled for validation. The best performing algorithms included 6 algorithms that had sensitivity ≥0.75, and 6 algorithms that had positive predictive values ≥0.75 with sensitivity ≥0.30. The best-performing algorithm with sensitivity ≥0.75 included any diagnoses for possible hypoglycemia or prescription of high-concentration glucose but excluded suspected diagnoses (sensitivity: 0.986 [95% confidence interval 0.959-1.013]; positive predictive value: 0.345 [0.280-0.410]). Restricting the algorithm definition to those with both a diagnosis of possible hypoglycemia and a prescription of high-concentration glucose improved the performance of the algorithm to correctly classify cases as severe hypoglycemia but lowered sensitivity (sensitivity: 0.375 [0.263-0.487]; positive predictive value: 0.771 [0.632-0.911]).
conclusionThe case-identifying algorithms in this study showed moderate positive predictive value and sensitivity for identification of severe hypoglycemia in Japanese healthcare data and can be employed by future pharmacoepidemiological studies using Japanese hospital administrative databases.
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