Validating the optimal classification approach using International Classification of Diseases, 10th Revision codes to identify dermatology patients with acne.
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ABSTRACT:
SUBMITTER: Barbieri JS
PROVIDER: S-EPMC8008234 | biostudies-literature | 2021 May
REPOSITORIES: biostudies-literature
Publications
Validating the optimal classification approach using International Classification of Diseases, 10th Revision codes to identify dermatology patients with acne.
Barbieri John S JS Weiner David M DM Kakpovbia Efe E Nagler Arielle R AR
Journal of the American Academy of Dermatology 20200722 5
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Project description:BackgroundClinical research requires that diagnostic codes captured from routinely collected health administrative data accurately identify individuals with a disease.ObjectiveIn this study, we validated the International Classification of Disease 10th Revision (ICD-10) definition for kidney transplant rejection (T86.100) and for kidney transplant failure (T86.101).DesignRetrospective cohort study.SettingA large, regional transplantation center in Ontario, Canada.PatientsAll adult kidney transplant recipients from 2002 to 2018.MeasurementsChart review was undertaken to identify the first occurrence of biopsy-confirmed rejection and graft loss for all participants. For each observation, we determined the first date a single ICD-10 code T86.100 or T86.101 was recorded as a hospital encounter discharge diagnosis.MethodsUsing chart review as the gold standard, we determined the sensitivity, specificity, and positive predictive value (PPV) for the ICD-10 codes T86.100 and T86.101.ResultsOur study population comprised of 1,258 kidney transplant recipients. The prevalence of rejection and death-censored graft loss were 15.6 and 9.1%, respectively. For the ICD-10 rejection code (T86.100), sensitivity was 72.9% (95% confidence interval [CI], 66.6-79.2), specificity 97.5% (96.5-98.4), and PPV 83.8% (78.3-89.4). For the ICD-10 graft loss code (T86.101), sensitivity was 21.2% (95% CI, 13.2-29.3), specificity 86.3% (84.3-88.3), and PPV 11.7% (7.0-16.4).LimitationsSingle-center study which may limit generalizability of our findings.ConclusionsA single ICD-10 code for kidney transplant rejection (T86.100) was present in 84% of true kidney transplant rejections and is an accurate way of identifying kidney transplant recipients with rejection using administrative health data. The ICD-10 code for graft failure (T86.101) performed poorly and should not be used for administrative health research.
| S-EPMC7747098 | biostudies-literature
Project description:ObjectiveRoutinely collected health administrative data can be used to efficiently assess disease burden in large populations, but it is important to evaluate the validity of these data. The objective of this study was to develop and validate International Classification of Disease 10th revision (ICD -10) algorithms that identify laboratory-confirmed influenza or laboratory-confirmed respiratory syncytial virus (RSV) hospitalizations using population-based health administrative data from Ontario, Canada.Study design and settingInfluenza and RSV laboratory data from the 2014-15, 2015-16, 2016-17 and 2017-18 respiratory virus seasons were obtained from the Ontario Laboratories Information System (OLIS) and were linked to hospital discharge abstract data to generate influenza and RSV reference cohorts. These reference cohorts were used to assess the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of the ICD-10 algorithms. To minimize misclassification in future studies, we prioritized specificity and PPV in selecting top-performing algorithms.Results83,638 and 61,117 hospitalized patients were included in the influenza and RSV reference cohorts, respectively. The best influenza algorithm had a sensitivity of 73% (95% CI 72% to 74%), specificity of 99% (95% CI 99% to 99%), PPV of 94% (95% CI 94% to 95%), and NPV of 94% (95% CI 94% to 95%). The best RSV algorithm had a sensitivity of 69% (95% CI 68% to 70%), specificity of 99% (95% CI 99% to 99%), PPV of 91% (95% CI 90% to 91%) and NPV of 97% (95% CI 97% to 97%).ConclusionWe identified two highly specific algorithms that best ascertain patients hospitalized with influenza or RSV. These algorithms may be applied to hospitalized patients if data on laboratory tests are not available, and will thereby improve the power of future epidemiologic studies of influenza, RSV, and potentially other severe acute respiratory infections.
| S-EPMC7790248 | biostudies-literature
Project description:BackgroundAdministrative billing data are critical to many initiatives in congenital heart surgery. Mapping algorithms for International Classification of Disease, 10th Revision diagnosis and procedure codes to clinical registry procedure definitions will allow identification of surgical cases and account for patient and procedural factors within administrative data. Our objectives were to develop mapping logic to crosswalk International Classification of Disease, 10th Revision procedure codes to 10 Society of Thoracic Surgeons Congenital Heart Surgery Database benchmark and beta-test the algorithm.MethodsPatients undergoing Society of Thoracic Surgeons Congenital Heart Surgery Database benchmark procedures from 2015 to 2019 were identified and served as the gold standard. Cases were linked on direct identifiers to cases from the Pediatric Health Information System Database. Two independent teams developed International Classification of Disease, 10th Revision-based algorithms for cases capture. Algorithms were compared and iteratively refined to optimize sensitivity and specificity. Operative mortalities for cases identified in the administrative versus registry data were compared.ResultsOverall sensitivity was 91% and specificity was 99% for capture of benchmark operations using International Classification of Diseases 10th Revision codes. Sensitivity was more than 90% in identifying 6 of the 10 individual benchmark procedures and more than 98% sensitive in identifying Fontan, Glenn, and arterial switch with ventricular septal defect procedures. Specificity was more than 98% for all benchmark operations. There were no statistical differences in operative mortality between cases identified in the administrative versus the registry data.ConclusionsNovel mapping algorithm for International Classification of Disease, 10th Revision procedure codes enables identification of congenital heart benchmark procedures within administrative billing data. This crosswalk facilitates population-based congenital heart surgical research and quality assessment.
| S-EPMC9021320 | biostudies-literature
Project description:Introduction:Administrative health data from emergency departments play important roles in understanding health needs of the public and reasons for health care resource use. International Classification of Disease (ICD) diagnostic codes have been widely used to code reasons of clinical encounters for administrative purposes in emergency departments. Objective:The purpose of the study is to examine the coding agreement and reliability of ICD diagnosis codes in emergency department records through auditing the routinely collected data. Methods:We randomly sampled 1 percent of records (n=1636) between October and December 2013 from 11 emergency departments in Alberta, Canada. Auditors were employed to review the same chart and independently assign main diagnosis codes. We assessed coding agreement and reliability through comparison of codes assigned by auditors and hospital coders using proportion of agreement and Cohen's kappa. Error analysis was conducted to review diagnosis codes with disagreement and categorized them into six groups. Results:Overall, the agreement was 86.5% and 82.2% at 3 and 4 digits levels respectively, and reliability was 0.86 and 0.82 respectively. Variations of agreement and reliability were identified across different emergency departments. The major two categories of coding discrepancy were the use of different codes for same condition (23.6%) and the use of codes at different levels of specificity (20.9%). Conclusions:Diagnosis codes in emergency departments show high agreement and reliability, although there are variations of coding quality across different hospitals. Stricter coding guidelines regarding the use of unspecified codes are needed to enhance coding consistency.
| S-EPMC7299481 | biostudies-literature
Project description:Administrative databases are increasingly used in research studies to capture clinical outcomes such as sepsis. This systematic review and meta-analysis examines the accuracy of International Classification of Diseases, 10th revision (ICD-10), codes for identifying sepsis in adult and pediatric patients.Data sourcesWe searched MEDLINE, EMBASE, Web of Science, CENTRAL, Epistemonikos, and McMaster Superfilters from inception to September 7, 2021.Study selectionWe included studies that validated the accuracy of sepsis ICD-10 codes against any reference standard.Data extractionThree authors, working in duplicate, independently extracted data. We conducted meta-analysis using a random effects model to pool sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). We evaluated individual study risk of bias using the Quality Assessment of Diagnostic Accuracy Studies tool and assessed certainty in pooled diagnostic effect measures using the Grading of Recommendations Assessment, Development, and Evaluation framework.Data synthesisThirteen eligible studies were included in the qualitative synthesis and the meta-analysis. Eleven studies used manual chart review as the reference standard, and four studies used registry databases. Only one study evaluated pediatric patients exclusively. Compared with the reference standard of detailed chart review and/or registry databases, the pooled sensitivity for sepsis ICD-10 codes was 35% (95% CI, 22-48, low certainty), whereas the pooled specificity was 98% (95% CI: 98-99, low certainty). The PPV for ICD-10 codes ranged from 9.8% to 100% (median, 72.0%; interquartile range [IQR], 50.0-84.7%). NPV ranged from 54.7% to 99.1% (median, 95.9%; interquartile range, 85.5-98.3%).ConclusionsSepsis is undercoded in administrative databases. Future research is needed to explore if greater consistency in ICD-10 code definitions and enhanced quality measures for ICD-10 coders can improve the coding accuracy of sepsis in large databases.
| S-EPMC9649267 | biostudies-literature
Project description:ObjectiveAdverse drug events (ADEs) during hospital stays are a significant problem of healthcare systems. Established monitoring systems lack completeness or are cost intensive. Routinely assigned International Statistical Classification of Diseases and Related Health Problems (ICD) codes could complement existing systems for ADE identification. To analyze the potential of using routine data for ADE detection, the validity of a set of ICD codes was determined focusing on hospital-acquired events.Material and methodsThe study utilized routine data from four German hospitals covering the years 2014 and 2015. A set of ICD, 10th Revision, German Modification (ICD-10-GM) diagnoses coded most frequently in the routine data and identified as codes indicating ADEs was analyzed. Data from psychiatric and psychotherapeutic departments were excluded. Retrospective chart review was performed to calculate positive predictive values (PPV) and sensitivity.ResultsOf 807 reviewed ADE codes, 91.2% (95%-confidence interval: 89.0, 93.1) were identified as disease in the medical records and 65.1% (61.7, 68.3) were confirmed as ADE. For code groups being predominantly hospital-acquired, 78.5% (73.7, 82.9) were confirmed as ADE, ranging from 68.5% to 94.4% dependent on the ICD code. However, sensitivity of inpatient ADEs was relatively low. 49.7% (45.2, 54.2) of 495 identified hospital-acquired ADEs were coded as disease in the routine data, from which a subgroup of 12.1% (9.4, 15.3) was coded as drug-associated disease.ConclusionsICD codes from routine data can provide an important contribution to the development and improvement of ADE monitoring systems. Documentation quality is crucial to further increase the PPV, and actions against under-reporting of ADEs in routine data need to be taken.
| S-EPMC5667751 | biostudies-literature
Project description:ObjectiveTo examine the validity of the International Classification of Diseases, 10th revision (ICD-10) codes for hyponatraemia in the nationwide population-based Danish National Registry of Patients (DNRP) among inpatients of all ages.DesignPopulation-based validation study.SettingAll somatic hospitals in the North and Central Denmark Regions from 2006 through 2011.ParticipantsPatients of all ages admitted to hospital (n=819 701 individual patients) during the study period. The patient could be included in the study more than once, and our study did not restrict to patients with serum sodium measurements (total of n=2 186 642 hospitalisations).Main outcome measureWe validated ICD-10 discharge diagnoses of hyponatraemia recorded in the DNRP, using serum sodium measurements obtained from the laboratory information systems (LABKA) research database as the gold standard. One sodium value <135 mmol/L measured at any time during hospitalisation confirmed the diagnosis. We estimated sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) for ICD-10 codes for hyponatraemia overall and for cut-off points for increasing hyponatraemia severity.ResultAn ICD-10 code for hyponatraemia was recorded in the DNRP in 5850 of the 2 186 642 hospitalisations identified. According to laboratory measurements, however, hyponatraemia was present in 306 418 (14%) hospitalisations. Sensitivity of hyponatraemia diagnoses was 1.8% (95% CI 1.7% to 1.8%). For sodium values <115 mmol/L, sensitivity was 34.3% (95% CI 32.6% to 35.9%). The overall PPV was 92.5% (95% CI 91.8% to 93.1%) and decreased with increasing hyponatraemia severity. Specificity and NPV were high for all cut-off points (≥99.8% and ≥86.2%, respectively). Patients with hyponatraemia without a corresponding ICD-10 discharge diagnosis were younger and had higher Charlson Comorbidity Index scores than patients with hyponatraemia with a hyponatraemia code in the DNRP.ConclusionsICD-10 codes for hyponatraemia in the DNRP have high specificity but very low sensitivity. Laboratory test results, not discharge diagnoses, should be used to ascertain hyponatraemia.
| S-EPMC4010845 | biostudies-literature
Project description:Data presented in this article relates to the research article entitled "Exploration of association rule mining for coding consistency and completeness assessment in inpatient administrative health data" (Peng et al. [1]) in preparation). We provided a set of ICD-10 coding association rules in the age group of 55 to 65. The rules were extracted from an inpatient administrative health data at five acute care hospitals in Alberta, Canada, using association rule mining. Thresholds of support and confidence for the association rules mining process were set at 0.19% and 50% respectively. The data set contains 426 rules, in which 86 rules are not nested. Data are provided in the supplementary material. The presented coding association rules provide a reference for future researches on the use of association rule mining for data quality assessment.
| S-EPMC5995749 | biostudies-literature
Project description: Not available
| S-EPMC8202141 | biostudies-literature
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.
| S-EPMC6706070 | biostudies-literature