Dataset Information

Diagnostic Category Prevalence in 3 Classification Systems Across the Transition to the International Classification of Diseases, Tenth Revision, Clinical Modification.

ABSTRACT: Importance:On October 1, 2015, the US transitioned to the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) for recording diagnoses, symptoms, and procedures. It is unknown whether this transition was associated with changes in diagnostic category prevalence based on diagnosis classification systems commonly used for payment and quality reporting. Objective:To assess changes in diagnostic category prevalence associated with the ICD-10-CM transition. Design, Setting, and Participants:This interrupted time series analysis and cross-sectional study examined level and trend changes in diagnostic category prevalence associated with the ICD-10-CM transition and clinically reviewed a subset of diagnostic categories with changes of 20% or more. Data included insurance claim diagnoses from the IBM MarketScan Commercial Database from January 1, 2010, to December 31, 2017, for more than 18 million people aged 0 to 64 years with private insurance. Diagnoses were mapped using 3 common diagnostic classification systems: World Health Organization (WHO) disease chapters, Department of Health and Human Services Hierarchical Condition Categories (HHS-HCCs), and Agency for Healthcare Research and Quality Clinical Classification System (AHRQ-CCS). Data were analyzed from December 1, 2018, to January 21, 2020. Exposures:US implementation of ICD-10-CM. Main Outcomes and Measures:Monthly rates of individuals with at least 1 diagnosis in a diagnostic classification category per 10?000 eligible members. Results:The analytic sample contained information on 2.1 billion enrollee person-months with 3.4 billion clinically assigned diagnoses; the mean (range) monthly sample size was 22.1 (18.4 to 27.1 ) million individuals. While diagnostic category prevalence changed minimally for WHO disease chapters, the ICD-10-CM transition was associated with level changes of 20% or more among 20 of 127 HHS-HCCs (15.7%) and 46 of 282 AHRQ-CCS categories (16.3%) and with trend changes of 20% or more among 12 of 127 of HHS-HCCs (9.4%) and 27 of 282 of AHRQ-CCS categories (9.6%). For HHS-HCCs, monthly rates of individuals with any acute myocardial infarction diagnosis increased 131.5% (95% CI, 124.1% to 138.8%), primarily because HHS added non-ST-segment-elevation myocardial infarction diagnoses to this category. The HHS-HCC for diabetes with chronic complications increased by 92.4% (95% CI, 84.2% to 100.5%), primarily from including new diabetes-related hypoglycemia and hyperglycemia codes, and the rate for completed pregnancy with complications decreased by 54.5% (95% CI, -58.7% to -50.2%) partly due to removing vaginal birth after cesarean delivery as a complication. Conclusions and Relevance:These findings suggest that the ICD-10-CM transition was associated with large prevalence changes for many diagnostic categories. Diagnostic classification systems developed using ICD-9-CM may need to be refined using ICD-10-CM data to avoid unintended consequences for disease surveillance, performance assessment, and risk-adjusted payments.

SUBMITTER: Ellis RP

PROVIDER: S-EPMC7142382 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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Diagnostic Category Prevalence in 3 Classification Systems Across the Transition to the International Classification of Diseases, Tenth Revision, Clinical Modification.

Ellis Randall P RP Hsu Heather E HE Song Chenlu C Kuo Tzu-Chun TC Martins Bruno B Siracuse Jeffrey J JJ Liu Ying Y Ash Arlene S AS

JAMA network open 20200401 4

<h4>Importance</h4>On October 1, 2015, the US transitioned to the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) for recording diagnoses, symptoms, and procedures. It is unknown whether this transition was associated with changes in diagnostic category prevalence based on diagnosis classification systems commonly used for payment and quality reporting.<h4>Objective</h4>To assess changes in diagnostic category prevalence associated with the ICD-10-CM t ...[more]

PMID: 32267514

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Project description:BackgroundAdministrative data can be useful for stroke research but have historically lacked data on stroke severity. Hospitals increasingly report the National Institutes of Health Stroke Scale (NIHSS) score using an International Classification of Diseases, Tenth Revision (ICD-10) diagnosis code, but this code's validity remains unclear.MethodsWe examined the concordance of ICD-10 NIHSS scores versus NIHSS scores recorded in CAESAR (Cornell Acute Stroke Academic Registry). We included all patients with acute ischemic stroke from October 1, 2015, when US hospitals transitioned to ICD-10, through 2018, the latest year in our registry. The NIHSS score (range, 0-42) recorded in our registry served as the reference gold standard. ICD-10 NIHSS scores were derived from hospital discharge diagnosis code R29.7xx, with the latter 2 digits representing the NIHSS score. Multiple logistic regression was used to explore factors associated with availability of ICD-10 NIHSS scores. We used ANOVA to examine the proportion of variation (R2) in the true (registry) NIHSS score that was explained by the ICD-10 NIHSS score.ResultsAmong 1357 patients, 395 (29.1%) had an ICD-10 NIHSS score recorded. This proportion increased from 0% in 2015 to 46.5% in 2018. In a logistic regression model, only higher registry NIHSS score (odds ratio per point, 1.05 [95% CI, 1.03-1.07]) and cardioembolic stroke (odds ratio, 1.4 [95% CI, 1.0-2.0]) were associated with availability of the ICD-10 NIHSS score. In an ANOVA model, the ICD-10 NIHSS score explained almost all the variation in the registry NIHSS score (R2=0.88). Fewer than 10% of patients had a large discordance (≥4 points) between their ICD-10 and registry NIHSS scores.ConclusionsWhen present, ICD-10 codes representing NIHSS scores had excellent agreement with NIHSS scores recorded in our stroke registry. However, ICD-10 NIHSS scores were often missing, especially in less severe strokes, limiting the reliability of these codes for risk adjustment.

Project description:PURPOSE:We previously developed and validated informatic algorithms that used International Classification of Diseases 9th revision (ICD9)-based diagnostic and procedure codes to detect the presence and timing of cancer recurrence (the RECUR Algorithms). In 2015, ICD10 replaced ICD9 as the worldwide coding standard. To understand the impact of this transition, we evaluated the performance of the RECUR Algorithms after incorporating ICD10 codes. METHODS:Using publicly available translation tables along with clinician and other expertise, we updated the algorithms to include ICD10 codes as additional input variables. We evaluated the performance of the algorithms using gold standard recurrence measures associated with a contemporary cohort of patients with stage I to III breast, colorectal, and lung (excluding IIIB) cancer and derived performance measures, including the area under the receiver operating curve, average absolute prediction error, and correct classification rate. These values were compared with the performance measures derived from the validation of the original algorithms. RESULTS:A total of 659 colorectal, 280 lung, and 2,053 breast cancer cases were identified. Area under the receiver operating curve derived from the updated algorithms was 89.0% (95% CI, 82.3% to 95.7%), 88.9% (95% CI, 79.3% to 98.2%), and 80.5% (95% CI, 72.8% to 88.2%) for the colorectal, lung, and breast cancer algorithms, respectively. Average absolute prediction errors for recurrence timing were 2.7 (SE, 11.3%), 2.4 (SE, 10.4%), and 5.6 months (SE, 21.8%), respectively, and timing estimates were within 6 months of actual recurrence for more than 80% of colorectal, more than 90% of lung, and more than 50% of breast cancer cases using the updated algorithm. CONCLUSION:Performance measures derived from the updated and original algorithms had overlapping confidence intervals, suggesting that the ICD9 to ICD10 transition did not affect the RECUR Algorithm performance.

Project description:ImportanceInternational Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) codes are used to characterize coronavirus disease 2019 (COVID-19)-related symptoms. Their accuracy is unknown, which could affect downstream analyses.ObjectiveTo compare the performance of fever-, cough-, and dyspnea-specific ICD-10 codes with medical record review among patients tested for COVID-19.Design, setting, and participantsThis cohort study included patients who underwent quantitative reverse transcriptase-polymerase chain reaction testing for severe acute respiratory syndrome coronavirus 2 at University of Utah Health from March 10 to April 6, 2020. Data analysis was performed in April 2020.Main outcomes and measuresThe sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of ICD-10 codes for fever (R50*), cough (R05*), and dyspnea (R06.0*) were compared with manual medical record review. Performance was calculated overall and stratified by COVID-19 test result, sex, age group (<50, 50-64, and >64 years), and inpatient status. Bootstrapping was used to generate 95% CIs, and Pearson χ2 tests were used to compare different subgroups.ResultsAmong 2201 patients tested for COVD-19, the mean (SD) age was 42 (17) years; 1201 (55%) were female, 1569 (71%) were White, and 282 (13%) were Hispanic or Latino. The prevalence of fever was 66% (1444 patients), that of cough was 88% (1930 patients), and that of dyspnea was 64% (1399 patients). For fever, the sensitivity of ICD-10 codes was 0.26 (95% CI, 0.24-0.29), specificity was 0.98 (95% CI, 0.96-0.99), PPV was 0.96 (95% CI, 0.93-0.97), and NPV was 0.41 (95% CI, 0.39-0.43). For cough, the sensitivity of ICD-10 codes was 0.44 (95% CI, 0.42-0.46), specificity was 0.88 (95% CI, 0.84-0.92), PPV was 0.96 (95% CI, 0.95-0.97), and NPV was 0.18 (95% CI, 0.16-0.20). For dyspnea, the sensitivity of ICD-10 codes was 0.24 (95% CI, 0.22-0.26), specificity was 0.97 (95% CI, 0.96-0.98), PPV was 0.93 (95% CI, 0.90-0.96), and NPV was 0.42 (95% CI, 0.40-0.44). ICD-10 code performance was better for inpatients than for outpatients for fever (χ2 = 41.30; P < .001) and dyspnea (χ2 = 14.25; P = .003) but not for cough (χ2 = 5.13; P = .16).Conclusions and relevanceThese findings suggest that ICD-10 codes lack sensitivity and have poor NPV for symptoms associated with COVID-19. This inaccuracy has implications for any downstream data model, scientific discovery, or surveillance that relies on these codes.

Dataset Information

Diagnostic Category Prevalence in 3 Classification Systems Across the Transition to the International Classification of Diseases, Tenth Revision, Clinical Modification.

Publications

Diagnostic Category Prevalence in 3 Classification Systems Across the Transition to the International Classification of Diseases, Tenth Revision, Clinical Modification.

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