Visualisation and optimisation of alcohol-related hospital admissions ICD-10 codes in Welsh e-cohort data.
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ABSTRACT: Introduction
The excessive consumption of alcohol is detrimental to long term health and increases the likelihood of hospital admission. However, definitions of alcohol-related hospital admission vary, giving rise to uncertainty in the effect of alcohol on alcohol-related health care utilization.Objectives
To compare diagnostic codes on hospital admission and discharge and to determine the ideal combination of codes necessary for an accurate determination of alcohol-related hospital admission.Methods
Routine population-linked e-cohort data were extracted from the Secure Anonymised Information Linkage (SAIL) Databank containing all alcohol-related hospital admissions (n,= 92,553) from 2006 to 2011 in Wales, United Kingdom. The distributions of the diagnostic codes recorded at admission and discharge were compared. By calculating a misclassification rate (sensitivity-like measure) the appropriate number of coding fields to examine for alcohol-codes was established.Results
There was agreement between admission and discharge codes. When more than ten coding fields were used the misclassification rate was less than 1%.Conclusion
With the data at present and alcohol-related codes used, codes recorded at admission and discharge can be used equivalently to identify alcohol-related admissions. The appropriate number of coding fields to examine was established: fewer than ten is likely to lead to under-reporting of alcohol-related admissions. The methods developed here can be applied to other medical conditions that can be described using a certain set of diagnostic codes, each of which can be a known sole cause of the condition and recorded in multiple positions in e-cohort data.
SUBMITTER: Trefan L
PROVIDER: S-EPMC8103565 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:ObjectiveTo evaluate the validity of COVID-19 International Classification of Diseases, 10th Revision (ICD-10) codes and their combinations.DesignRetrospective cohort study.SettingAcute care hospitals and emergency departments (EDs) in Alberta, Canada.ParticipantsPatients who were admitted to hospital or presented to an ED in Alberta, as captured by local administrative databases between 1 March 2020 and 28 February 2021, who had a positive COVID-19 test and/or a COVID-19-related ICD-10 code.Main outcome measuresThe sensitivity, positive predictive value (PPV) and 95% CIs for ICD-10 codes were computed. Stratified analysis on age group, sex, symptomatic status, mechanical ventilation, hospital type, patient intensive care unit (ICU) admission, discharge status and season of pandemic were conducted.ResultsTwo overlapping subsets of the study population were considered: those who had a positive COVID-19 test (cohort A, for estimating sensitivity) and those who had a COVID-19-related ICD-10 code (cohort B, for estimating PPV). Cohort A included 17 979 ED patients and 6477 inpatients while cohort B included 33 675 ED patients and 18 746 inpatients. Of inpatients, 9.5% in cohort A and 8.1% in cohort B received mechanical ventilation. Over 13% of inpatients were admitted to ICU. The length of hospital stay was 6 days (IQR: 3-14) for cohort A and 8 days (IQR: 3-19) for cohort B. In-hospital mortality was 15.9% and 38.8% for cohort A and B, respectively. The sensitivity for ICD-10 code U07.1 (COVID-19, virus identified) was 82.5% (81.8%-83.2%) with a PPV of 93.1% (92.6%-93.6%). The combination of U07.1 and U07.3 (multisystem inflammatory syndrome associated with COVID-19) had a sensitivity of 82.5% (81.9%-83.2%) and PPV of 92.9% (92.4%-93.4%).ConclusionsIn Alberta, ICD-10 COVID-19 codes (U07.1 and U07.3) were coded well with high validity. This indicates administrative data can be used for COVID-19 research and pandemic management purposes.
| S-EPMC8787827 | biostudies-literature
Project description:BACKGROUND:The phecode system was built upon the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) for phenome-wide association studies (PheWAS) using the electronic health record (EHR). OBJECTIVE:The goal of this paper was to develop and perform an initial evaluation of maps from the International Classification of Diseases, 10th Revision (ICD-10) and the International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) codes to phecodes. METHODS:We mapped ICD-10 and ICD-10-CM codes to phecodes using a number of methods and resources, such as concept relationships and explicit mappings from the Centers for Medicare & Medicaid Services, the Unified Medical Language System, Observational Health Data Sciences and Informatics, Systematized Nomenclature of Medicine-Clinical Terms, and the National Library of Medicine. We assessed the coverage of the maps in two databases: Vanderbilt University Medical Center (VUMC) using ICD-10-CM and the UK Biobank (UKBB) using ICD-10. We assessed the fidelity of the ICD-10-CM map in comparison to the gold-standard ICD-9-CM phecode map by investigating phenotype reproducibility and conducting a PheWAS. RESULTS:We mapped >75% of ICD-10 and ICD-10-CM codes to phecodes. Of the unique codes observed in the UKBB (ICD-10) and VUMC (ICD-10-CM) cohorts, >90% were mapped to phecodes. We observed 70-75% reproducibility for chronic diseases and <10% for an acute disease for phenotypes sourced from the ICD-10-CM phecode map. Using the ICD-9-CM and ICD-10-CM maps, we conducted a PheWAS with a Lipoprotein(a) genetic variant, rs10455872, which replicated two known genotype-phenotype associations with similar effect sizes: coronary atherosclerosis (ICD-9-CM: P<.001; odds ratio (OR) 1.60 [95% CI 1.43-1.80] vs ICD-10-CM: P<.001; OR 1.60 [95% CI 1.43-1.80]) and chronic ischemic heart disease (ICD-9-CM: P<.001; OR 1.56 [95% CI 1.35-1.79] vs ICD-10-CM: P<.001; OR 1.47 [95% CI 1.22-1.77]). CONCLUSIONS:This study introduces the beta versions of ICD-10 and ICD-10-CM to phecode maps that enable researchers to leverage accumulated ICD-10 and ICD-10-CM data for PheWAS in the EHR.
| S-EPMC6911227 | biostudies-literature
Project description:BackgroundThe ICD-10 codes are used globally for comparison of diagnoses and complications, and are an important tool for the development of patient safety, healthcare policies and the health economy. The aim of this study was to investigate the accuracy of verified complication rates in surgical admissions identified by ICD-10 codes and to validate these estimates against complications identified using the established Global Trigger Tool (GTT) methodology.MethodsThis was a prospective observational study of a sample of surgical admissions in two Norwegian hospitals. Complications were identified and classified by two expert GTT teams who reviewed patients' medical records. Three trained reviewers verified ICD-10 codes indicating a complication present on admission or emerging in hospital.ResultsA total of 700 admissions were drawn randomly from 12 966 procedures. Some 519 possible complications were identified in 332 of 700 admissions (47·4 per cent) from ICD-10 codes. Verification of the ICD-10 codes against information from patients' medical records confirmed 298 as in-hospital complications in 141 of 700 admissions (20·1 per cent). Using GTT methodology, 331 complications were found in 212 of 700 admissions (30·3 per cent). Agreement between the two methods reached 83·3 per cent after verification of ICD-10 codes. The odds ratio for identifying complications using the GTT increased from 5·85 (95 per cent c.i. 4·06 to 8·44) to 25·38 (15·41 to 41·79) when ICD-10 complication codes were verified against patients' medical records.ConclusionVerified ICD-10 codes strengthen the accuracy of complication rates. Use of non-verified complication codes from administrative systems significantly overestimates in-hospital surgical complication rates.
| S-EPMC6519147 | biostudies-literature
Project description:AimTo validate a case definition for foreign body airway obstructions (FBAO) using International Classification of Diseases version 10 (ICD-10) codes to accurately identify patients in administrative health databases and improve reporting on this injury.MethodsWe identified prehospital patient encounters in Alberta, Canada between Jan 1, 2018 and Dec 31, 2021 by querying the provincial emergency medical services' (EMS) patient care records for FBAO-related presentations, EMS protocols, or treatments. We deterministically linked EMS patient encounters to data on emergency department visits and hospital admissions, which included ICD-10 codes. Two physicians independently reviewed encounters to determine true FBAO cases. We then calculated diagnostic accuracy measures (sensitivity, specificity, likelihood ratios) of various algorithms.ResultsWe identified 3677 EMS patient encounters, 2121 were linked to hospital administrative databases. Of these encounters, 825 (38.9%) were true FBAO. The combination of two ICD-10 codes (T17 = foreign body in the respiratory tract or T18.0 = foreign body in the mouth) was the most specific algorithm (96.9% [95%CI 95.8-97.8%]), while the combination of all FBAO-related ICD-10 codes and R06.8 (other breathing abnormalities) was the most sensitive (75.0% [95%CI 71.9-78.0]). We identified an additional 453 (35.4%) FBAO cases not transported by EMS (due to death or transport refusal), and therefore not linked to the hospital administrative databases. Of these unlinked encounters, 44 (9.7%) cases resulted in the patient's death.ConclusionsFBAO can be identified with reasonable accuracy using health administrative data and ICD-10 codes. All algorithms had a trade-off between sensitivity and specificity, and failed to identify a third of FBAO cases, of which 10% resulted in death.
| S-EPMC10568271 | biostudies-literature
Project description:Periodic revisions of the international classification of diseases (ICD) ensure that the classification reflects new practices and knowledge; however, this complicates retrospective research as diagnoses are coded in different versions. For longitudinal disease trajectory studies, a crosswalk is an essential tool and a comprehensive mapping between ICD-8 and ICD-10 has until now been lacking. In this study, we map all ICD-8 morbidity codes to ICD-10 in the expanded Danish ICD version. We mapped ICD-8 codes to ICD-10, using a many-to-one system inspired by general equivalence mappings such that each ICD-8 code maps to a single ICD-10 code. Each ICD-8 code was manually and unidirectionally mapped to a single ICD-10 code based on medical setting and context. Each match was assigned a score (1 of 4 levels) reflecting the quality of the match and, if applicable, a "flag" signalling choices made in the mapping. We provide the first complete mapping of the 8596 ICD-8 morbidity codes to ICD-10 codes. All Danish ICD-8 codes representing diseases were mapped and 5106 (59.4%) achieved the highest consistency score. Only 334 (3.9%) of the ICD-8 codes received the lowest mapping consistency score. The mapping provides a scaffold for translation of ICD-8 to ICD-10, which enable longitudinal disease studies back to and 1969 in Denmark and to 1965 internationally with further adaption.
| S-EPMC10570238 | biostudies-literature
Project description:Background On October 1, 2015, the Center for Medicare and Medicaid Services transitioned from the International Classification of Diseases, Ninth Revision ( ICD-9) to the ICD, Tenth Revision ( ICD-10) compendium of codes for diagnosis and billing in health care, but translation between the two is often inexact. Here we describe a validated crosswalk to translate ICD-9 codes into ICD-10 codes, with a focus on complications after carotid revascularization and endovascular aortic aneurysm repair. Methods and Results We devised an 8-step process to derive and validate ICD-10 codes from existing ICD-9 codes. We used publicly available sources, including the General Equivalence Mapping database, to translate ICD-9 codes used in prior work to ICD-10 codes. We defined ICD-10 codes as validated if they were concordant with the initial ICD-9 codes after manual comparison by two physicians. Our primary validation measure was the percent of valid ICD-10 codes out of the total ICD-10 codes obtained during translation. We began with 126 ICD-9 diagnosis codes used for complication identification after carotid revascularization procedures, and 97 ICD-9 codes for complications after endovascular aortic aneurysm procedures. Translation generated 143 ICD-10 codes for carotid revascularization, a 14% increase from the initial 126 codes. Manual comparison demonstrated 98% concordance, with 99% agreement between the reviewers. Similarly, we identified 108 ICD-10 codes for endovascular aortic aneurysm repair, an 11% increase from the initial 97 ICD-9 codes. We again noted excellent concordance and agreement (98% and 100%, respectively). Manual review identified 4 ICD-10 codes incorrectly translated from ICD-9 codes for carotid revascularization, and 3 codes incorrectly translated for endovascular aortic aneurysm repair. Conclusions Algorithms to crosswalk lists of ICD-9 codes to ICD-10 codes can leverage electronic resources to minimize the burden of code translation. However, manual review for code validation may be necessary, with collaboration across institutions for researchers to share their efforts.
| S-EPMC6501611 | biostudies-literature
Project description:Background:The International Classification of Diseases, Ninth Edition, Clinical Modification (ICD-9-CM) Injury Severity Score (ICISS) is a risk adjustment model when injuries are recorded using ICD-9-CM coding. The trauma mortality prediction model (TMPM-ICD9) provides better calibration and discrimination compared with ICISS and injury severity score (ISS). Though TMPM-ICD9 is statistically rigorous, it is not precise enough mathematically and has the tendency to overestimate injury severity. The purpose of this study is to develop a new ICD-10-CM injury model which estimates injury severities for every injury in the ICD-10-CM lexicon by a combination of rigorous statistical probit models and mathematical properties and improves the prediction accuracy. Methods:We developed an injury mortality prediction (IMP-ICDX) using data of 794,098 patients admitted to 738 hospitals in the National Trauma Data Bank from 2015 to 2016. Empiric measures of severity for each of the trauma ICD-10-CM codes were estimated using a weighted median death probability (WMDP) measurement and then used as the basis for IMP-ICDX. ISS (version 2005) and the single worst injury (SWI) model were re-estimated. The performance of each of these models was compared by using the area under the receiver operating characteristic (AUC), the Hosmer-Lemeshow (HL) statistic, and the Akaike information criterion statistic. Results:IMP-ICDX exhibits significantly better discrimination (AUCIMP-ICDX, 0.893, and 95% confidence interval (CI), 0.887 to 0.898; AUCISS, 0.853, and 95% CI, 0.846 to 0.860; and AUCSWI, 0.886, and 95% CI, 0.881 to 0.892) and calibration (HLIMP-ICDX, 68, and 95% CI, 36 to 98; HLISS, 252, and 95% CI, 191 to 310; and HLSWI, 92, and 95% CI, 53 to 128) compared with ISS and SWI. All models were improved after the extension of age, gender, and injury mechanism, but the augmented IMP-ICDX still dominated ISS and SWI by every performance. Conclusions:The IMP-ICDX has a better discrimination and calibration compared to ISS. Therefore, we believe that IMP-ICDX could be a new viable trauma research assessment method.
| S-EPMC6787998 | biostudies-literature
Project description:ObjectivesTo assess the validity of an International Classification of Diseases (ICD) code based definition of non-fatal head trauma caused by child abuse (abusive head trauma) for population surveillance in New Zealand.DesignA retrospective cohort study of hospital inpatient records.SettingA tertiary children's hospital in Auckland, New Zealand.Participants1731 children less than 5 years of age who were discharged after a non-fatal head trauma event over a 10-year period from 1 January 2010 to 31 December 2019.Outcome measuresThe outcome of assessment by the hospital's multidisciplinary child protection team (CPT) was compared with the outcome of ICD, Tenth Revision (ICD-10) discharge coding for non-fatal abusive head trauma (AHT). The ICD-10 code definition of AHT was derived from an ICD, Ninth Revision, Clinical Modification definition developed by the Centers for Disease Control, Atlanta, Georgia, which requires both a clinical diagnosis code and a cause-of-injury code.ResultsThere were 1755 head trauma events with 117 determined as AHT by the CPT. The ICD-10 code definition had a sensitivity of 66.7% (95% CI 57.4 to 75.1) and specificity of 99.8% (95% CI 99.5 to 100). There were only three false positives but 39 false negatives, with 18 of the false negatives coded with X59 (exposure to unspecified factor).ConclusionsThe ICD-10 code broad definition of AHT is a reasonable epidemiological tool for passive surveillance of AHT in New Zealand but it underestimates the incidence. Its performance could be improved by clear documentation of child protection conclusions in clinical notes, clarifying coding practice and removing the exclusion criteria from the definition.
| S-EPMC10254934 | biostudies-literature
Project description:Objective: Using inpatient data from a 1,160-bed health system, we assessed the positive predictive value (PPV) of ICD-10-CM (International Classification of Diseases, Tenth Revision, Clinical Modification) codes included in a traumatic brain injury (TBI) surveillance definition proposed by the Centers for Disease Control and Prevention (CDC) in 2016. Methods: A random sample of 196 records with ICD-10-CM TBI codes was reviewed. The PPVs for the ICD-10-CM codes' ability to capture true TBI cases were calculated as the percentage of records with confirmed clinical provider-documented TBI and reported with 95% confidence intervals [95%CIs]. Results: The estimated overall PPV was 74% [67.9%, 80.1%] when the codes were listed in any diagnostic field, but 91.5% [86.2%, 96.8%] when listed as the principal diagnosis. S06 codes (intracranial injury) had an overall PPV of 80.2% [74.3%, 86.1%] and 96.9% [93.3%, 100%] when listed as the principal diagnosis. S02.0-.1 codes (vault/base skull fractures) in any position without co-existing S06 codes had a PPV of 15.8% [0%, 33.2%]. Conclusions: Intracranial injury codes (S06) in any diagnostic position had a very high estimated PPV. Further research is needed to determine the utility of other codes included in the CDC proposed definition for TBI surveillance.
| S-EPMC7808251 | biostudies-literature
Project description:Adverse 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.The 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.Of 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.ICD 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