Accuracy of a composite event definition for hypoglycemia

Min, Jea Young; Presley, Caroline A.; Wharton, Jennifer; Griffin, Marie R.; Greevy, Robert A.; Hung, Adriana M.; Chipman, Jonathan; Grijalva, Carlos G.; Hackstadt, Amber J.; Roumie, Christianne L.

doi:10.1002/pds.4712

Cited by 3 publications

(14 citation statements)

References 19 publications

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“…Previous studies validating the accuracy of hypoglycemia diagnosis codes have focused on either ICD-9-CM codes or non-US data. 17,18,29 Our study calculates PPV, NPV, sensitivity and specificity of ICD-10-CM hypoglycemia codes using US Medicare claims and medical record review of care encounters from 4 study hospitals. The 4 hospitals, size ranging from ~400-1300 beds, were selected from different geographic regions (California, Florida, Ohio, and Pennsylvania) and different health care systems.…”

Section: Discussionmentioning

confidence: 99%

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Assessment of International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) Code Assignment Validity for Case Finding of Medication-related Hypoglycemia Acute Care Visits Among Medicare Beneficiaries

Yang¹,

Ziemba²,

Shehab

et al. 2022

Medical Care

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Objective: Administrative claims are commonly relied upon to identify hypoglycemia. We assessed validity of 14 International Classification of Diseases, Tenth Revision, Clinical Modification diagnosis code assignments to identify medication-related hypoglycemia leading to acute care encounters.Research Design and Methods: A multisite, retrospective medical record review study was conducted in a sample of Medicare beneficiaries prescribed outpatient diabetes medications and who received hospital care between January 1, 2016 and September 30, 2017. Diagnosis codes were validated with structured medical record review using prespecified criteria (clinical presentation, blood glucose values, and treatments for hypoglycemia). Sensitivity, specificity, and positive and negative predictive value (PPV, NPV) were calculated and adjusted using sampling weights to correct for partial verification bias.Results: Among 990 encounters (496 cases, 494 controls), hypoglycemia codes demonstrated moderate PPV (69.2%; 95% confidence interval: 65.0-73.0) and moderate sensitivity (83.9%; 95% confidence interval: 70.0-95.5). Codes performed better at identifying hypoglycemic events among emergency department/ob-servation encounters compared with hospitalizations (PPV 92.9%, sensitivity 100.0% vs. PPV 53.7%, sensitivity 71.0%). Accuracy varied by diagnosis position, especially for hospitalizations, with PPV of 95.6% versus 46.5% with hypoglycemia in primary versus secondary positions. Use of adverse event/poisoning codes did not improve accuracy; reliance on these codes alone would have missed 97% of true hypoglycemic events.Conclusions: Accuracy of International Classification of Diseases, Tenth Revision codes in administrative claims to identify medicationrelated hypoglycemia varied substantially by encounter type and diagnosis position. Consideration should be given to the trade-off between PPV and sensitivity when selecting codes, encounter types, and diagnosis positions to identify hypoglycemia.

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Section: Discussionmentioning

confidence: 99%

“…Previous studies validating the accuracy of hypoglycemia diagnosis codes have focused on either ICD-9-CM codes or non-US data 17,18,29. Our study calculates PPV, NPV, sensitivity and specificity of ICD-10-CM hypoglycemia codes using US Medicare claims and medical record review of care encounters from 4 study hospitals.…”

Section: Discussionmentioning

confidence: 99%

Assessment of International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) Code Assignment Validity for Case Finding of Medication-related Hypoglycemia Acute Care Visits Among Medicare Beneficiaries

Yang¹,

Ziemba²,

Shehab

et al. 2022

Medical Care

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“…This study validated an ICD-10-CM hypoglycaemia algorithm derived from a previously validated ICD-9-CM hypoglycaemia algorithm with electronic health records (EHRs), conducted as a part of a multicentre comparative safety study of oral antidiabetic drugs (metformin, sulfonylureas, dipeptidyl peptidase-4 inhibitors, glucagon-like peptide-1 receptor agonists and sodium-glucose cotransporter-2 inhibitors). 4 We assembled a cohort of Medicare patients with linked data to the UNC Health System between October 1, 2015 and December 31, 2017. The UNC Health System is a large, academic, integrated health system owned by the state of North Carolina with 11 hospitals, 13 hospital campuses and hundreds of clinics that provide health care to patients across the state.…”

Section: Study Design Data Sources and Populationmentioning

confidence: 99%

“…However, the effective management of diabetes with antidiabetic drugs is often limited by hypoglycaemia. 4 Patients taking antidiabetic drugs will experience between 0.3 and 2 mild (self-treated) hypoglycaemia episodes each week and 0-2 severe (requiring intervention from another person) hypoglycaemia episodes per person-year. [5][6][7] According to the CDC, hypoglycaemia was responsible for 51 000 hospitalizations (8.3 per 1000) and 202 000 emergency department (ED) visits (2.2 per 1000) annually, where antidiabetic drugs are the root cause of these health care encounters.…”

Section: Introductionmentioning

confidence: 99%

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Validation of an international classification of disease, tenth revision, clinical modification (ICD‐10‐CM) algorithm in identifying severe hypoglycaemia events for real‐world studies

Her,

Dejene,

Ismail

et al. 2024

Diabetes Obesity Metabolism

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AimThe transition to the ICD‐10‐CM coding system has reduced the utility of hypoglycaemia algorithms based on ICD‐9‐CM diagnosis codes in real‐world studies of antidiabetic drugs. We mapped a validated ICD‐9‐CM hypoglycaemia algorithm to ICD‐10‐CM codes to create an ICD‐10‐CM hypoglycaemia algorithm and assessed its performance in identifying severe hypoglycaemia.Materials and MethodsWe assembled a cohort of Medicare patients with DM and linked electronic health record (EHR) data to the University of North Carolina Health System and identified candidate severe hypoglycaemia events from their Medicare claims using the ICD‐10‐CM hypoglycaemia algorithm. We confirmed severe hypoglycaemia by EHR review and computed a positive predictive value (PPV) of the algorithm to assess its performance. We refined the algorithm by removing poor performing codes (PPV ≤0.5) and computed a Cohen's statistic to evaluate the agreement of the EHR reviews.ResultsThe algorithm identified 642 candidate severe hypoglycaemia events, and we confirmed 455 as true severe hypoglycaemia events, PPV of 0.709 (95% confidence interval: 0.672, 0.744). When we refined the algorithm, the PPV increased to 0.893 (0.862, 0.918) and missed <2.42% (<11) true severe hypoglycaemia events. Agreement between reviewers was high, = 0.93 (0.89, 0.97).ConclusionsWe translated an ICD‐9‐CM hypoglycaemia algorithm to an ICD‐10‐CM version and found its performance was modest. The performance of the algorithm improved by removing poor performing codes at the trade‐off of missing very few severe hypoglycaemia events. The algorithm has the potential to be used to identify severe hypoglycaemia in real‐world studies of antidiabetic drugs.

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Validation study of case-identifying algorithms for severe hypoglycemia using hospital administrative data in Japan

Osaga,

Kimura,

Okumura

et al. 2023

PLoS ONE

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Objective The purpose of this study was to evaluate the performance of algorithms for identifying cases of severe hypoglycemia in Japanese hospital administrative data. Methods This 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. Results In 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]). Conclusion The 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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Accuracy of a composite event definition for hypoglycemia

Cited by 3 publications

References 19 publications

Assessment of International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) Code Assignment Validity for Case Finding of Medication-related Hypoglycemia Acute Care Visits Among Medicare Beneficiaries

Assessment of International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) Code Assignment Validity for Case Finding of Medication-related Hypoglycemia Acute Care Visits Among Medicare Beneficiaries

Validation of an international classification of disease, tenth revision, clinical modification (ICD‐10‐CM) algorithm in identifying severe hypoglycaemia events for real‐world studies

Validation study of case-identifying algorithms for severe hypoglycemia using hospital administrative data in Japan

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