Acute kidney injury comorbidity analysis based on international classification of diseases-10 codes
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SUBMITTER: Wang M
PROVIDER: S-EPMC10837944 | biostudies-literature | 2024 Jan
REPOSITORIES: biostudies-literature
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Project description:BackgroundIntentional and unintentional injuries are a leading cause of death and disability globally. International Classification of Diseases (ICD), Tenth Revision (ICD-10) codes are used to classify injuries in administrative health data and are widely used for health care planning and delivery, research, and policy. However, a systematic review of their overall validity and reliability has not yet been done.ObjectiveTo conduct a systematic review of the validity and reliability of external cause injury ICD-10 codes.MethodsMEDLINE, EMBASE, COCHRANE, and SCOPUS were searched (inception to April 2023) for validity and/or reliability studies of ICD-10 external cause injury codes in all countries for all ages. We examined all available data for external cause injuries and injuries related to specific body regions. Validity was defined by sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Reliability was defined by inter-rater reliability (IRR), measured by Krippendorff's alpha, Cohen's Kappa, and/or Fleiss' kappa.ResultsTwenty-seven published studies from 2006 to 2023 were included. Across all injuries, the mean outcome values and ranges were sensitivity: 61.6% (35.5%-96.0%), specificity: 91.6% (85.8%-100%), PPV: 74.9% (58.6%-96.5%), NPV: 80.2% (44.6%-94.4%), Cohen's kappa: 0.672 (0.480-0.928), Krippendorff's alpha: 0.453, and Fleiss' kappa: 0.630. Poisoning and hand and wrist injuries had higher mean sensitivity (84.4% and 96.0%, respectively), while self-harm and spinal cord injuries were lower (35.5% and 36.4%, respectively). Transport and pedestrian injuries and hand and wrist injuries had high PPVs (96.5% and 92.0%, respectively). Specificity and NPV were generally high, except for abuse (NPV 44.6%).Conclusions and significanceThe validity and reliability of ICD-10 external cause injury codes vary based on the injury types coded and the outcomes examined, and overall, they only perform moderately well. Future work, potentially utilizing artificial intelligence, may improve the validity and reliability of ICD codes used to document injuries.
| S-EPMC10903801 | biostudies-literature
Project description:[This corrects the article DOI: 10.1371/journal.pone.0298411.].
| S-EPMC11761078 | biostudies-literature
Project description:BackgroundAlthough healthcare administrative data are commonly used for traumatic brain injury research, there is currently no consensus or consistency on using the International Classification of Diseases version 10 codes to define traumatic brain injury among children and youth. This protocol is for a systematic review of the literature to explore the range of International Classification of Diseases version 10 codes that are used to define traumatic brain injury in this population.Methods/designThe databases MEDLINE, MEDLINE In-Process, Embase, PsychINFO, CINAHL, SPORTDiscus, and Cochrane Database of Systematic Reviews will be systematically searched. Grey literature will be searched using Grey Matters and Google. Reference lists of included articles will also be searched. Articles will be screened using predefined inclusion and exclusion criteria and all full-text articles that meet the predefined inclusion criteria will be included for analysis. The study selection process and reasons for exclusion at the full-text level will be presented using a PRISMA study flow diagram. Information on the data source of included studies, year and location of study, age of study population, range of incidence, and study purpose will be abstracted into a separate table and synthesized for analysis. All International Classification of Diseases version 10 codes will be listed in tables and the codes that are used to define concussion, acquired traumatic brain injury, head injury, or head trauma will be identified.DiscussionThe identification of the optimal International Classification of Diseases version 10 codes to define this population in administrative data is crucial, as it has implications for policy, resource allocation, planning of healthcare services, and prevention strategies. It also allows for comparisons across countries and studies. This protocol is for a review that identifies the range and most common diagnoses used to conduct surveillance for traumatic brain injury in children and youth. This is an important first step in reaching an appropriate definition using International Classification of Diseases version 10 codes and can inform future work on reaching consensus on the codes to define traumatic brain injury for this vulnerable population.
| S-EPMC3833640 | biostudies-literature
Project description:BackgroundInjury Severity Score (ISS) is a measurement of injury severity based on the Abbreviated Injury Scale. Because of the difficulty and expense of Abbreviated Injury Scale coding, there have been recent efforts in mapping ISS from administrative International Classification of Diseases ( ICD ) codes instead. Specifically, the open source and freely available International Classification of Diseases Programs for Injury Categorization (ICDPIC) in R (Foundation for Statistical Computing, Vienna, Austria) converts International Classification of Diseases, Ninth Revision, codes to ISS. This study aims to compare ICDPIC calculations versus manually derived Trauma Quality Improvement Program (TQIP) calculations for International Classification of Diseases, Tenth Revision ( ICD-10 ), codes. Moderate concordance was chosen as the hypothetical relationship because of previous work by both Fleischman et al. ( J Trauma Nurs. 2017;24(1):4-14) who found moderate to substantial concordance between ICDPIC and ISS and Di Bartolomeo et al. ( Scand J Trauma Resusc Emerg Med. 2010;18(1):17) who found none to slight concordance. Given these very different findings, we thought it reasonable to predict moderate concordance with the use of more detailed ICD-10 codes.MethodsThis was an observational cohort study of 1,040,728 encounters in the TQIP registry for the year 2018. International Classification of Diseases Programs for Injury Categorization in R was used to derive ISS from the ICD-10 codes in the registry. The resulting scores were compared with the manually derived ISS in TQIP.ResultsThe median difference between ISS calculated by ICDPIC-2021 using ICD-10, Clinical Modification (ISS-ICDPIC), and manually derived ISS was -3 (95% confidence interval, -5 to 0), while the mean difference was -2.09 (95% confidence interval, -2.10 to -2.07). There was substantial concordance between ISS-ICDPIC and manually derived ISS ( κ = 0.66). The ISS-ICDPIC was a better predictor of mortality (area under the curve, 0.853 vs. 0.836) but a worse predictor of intensive care unit admission (area under the curve, 0.741 vs. 0.757) and hospital stay ≥10 days (AUC, 0.701 vs. 0.743). The ICDPIC has substantial concordance with TQIP for the firearm ( κ = 0.69), motor vehicle trauma ( κ = 0.71), and pedestrian ( κ = 0.73) injury mechanisms.ConclusionWhen TQIP data are unavailable, ICDPIC remains a valid way to calculate ISS after transition to ICD-10 codes. The ISS-ICDPIC performs well in predicting a number of outcomes of interest but is best served as a predictor of mortality.Level of evidencePrognostic and Epidemiological; Level III.
| S-EPMC9708941 | biostudies-literature
Project description:International Classification of Diseases diagnostic codes are used to estimate acute gastroenteritis (AGE) disease burden. We validated AGE-related codes in pediatric and adult populations using 2 multiregional active surveillance platforms. The sensitivity of AGE codes was similar (54% and 58%) in both populations and increased with addition of vomiting-specific codes.
| S-EPMC7390357 | 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:BackgroundHealth administrative databases are essential to define patient populations, make socioeconomic predictions, and facilitate medical research and healthcare planning. The accuracy of this data is dependent on valid codes/coding algorithms.AimsThe aim of this study was to systematically identify and summarize the validity of International Classification of Diseases (ICD) codes for identifying patients with cirrhosis in administrative data.MethodsElectronic databases, MEDLINE (via Ovid), EMBASE (via Ovid), the Web of Science, and CINAHL (via EBSCOhost), were searched for validation studies which compared ICD codes related to cirrhosis to a clinical reference standard, and reported statistical measures of performance.ResultsFourteen studies were included in the review. There was a large variation in the algorithms used to validate ICD codes to diagnose cirrhosis. Despite the variation, the positive predictive value (PPV) was greater than 84% and the specificity was greater than 75% in the majority of the studies. The negative predictive value (NPV) was lower, but still was associated with values greater than 70% in the majority of studies. Sensitivity data varied significantly with values ranging from 0.27 to 99%.ConclusionsEvaluated ICD codes for cirrhosis, including codes for chronic liver disease, cirrhosis-specific codes, and cirrhosis-related complications, have demonstrated variable sensitivity and reasonable specificity for the identification of cirrhosis. Additional research is needed to maximize the identification of persons with cirrhosis to avoid underestimating the burden of disease.
| S-EPMC10291482 | biostudies-literature
Project description: Not available
| S-EPMC8008234 | biostudies-literature
Project description:ObjectivesInfective endocarditis (IE) secondary to injection drug use (IDU-IE) is a disease with high morbidity, cost, and rapid demographic evolution. Studies frequently utilize combinations of International Statistical Classification of Diseases (ICD) codes to identify IDU-IE cases in electronic medical records. This is a validation of this identification strategy in a US cohort.MethodsRecords from January 1, 2004 to September 31, 2015 for those aged ≥18yo with any ICD-coded IE encounter (inpatient or outpatient) were retrieved from the electronic medical record and then manually reviewed and classified as IDU-IE by strict and inclusive criteria. This registry was then used to assess the diagnostic accuracy of 10 identification algorithms that combined substance use, hepatitis C, and IE ICD codes.ResultsIE was present in 629 of the 2055 manually reviewed records; 109 reported IDU within 3 months of IE diagnosis and an additional 32 during their lifetime (141 cases). In contrast, no algorithm identified more than 46 (33%) of these cases. Algorithms assessing encounters with both an IE and substance use code had specificities >99% but sensitivities ≤11% with negative predictive values of 83% to 84% and positive predictive values ranging from 75% to 91%. Use of a hepatitis C OR substance use code with an IE-coded encounter resulted in higher sensitivities of 22% to 32% but more false positives and overall positive predictive value of <70%. This algorithm limited to age ≤45yo had the best, but still low, discrimination ability with an area under the receiver operating characteristic curve of 0.62.ConclusionSubstance use and hepatitis C codes have poor ability to accurately classify an IE-coded encounter as IDU-IE or routine IE.
| S-EPMC8815841 | biostudies-literature
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