Mapping Metabolite and ICD-10 Associations.
Ontology highlight
ABSTRACT:
SUBMITTER: Taalberg E
PROVIDER: S-EPMC7281140 | biostudies-literature | 2020 May
REPOSITORIES: biostudies-literature
Publications
The search for novel metabolic biomarkers is intense but has had limited practical outcomes for medicine. Part of the problem is that we lack knowledge of how different comorbidities influence biomarkers' performance. In this study, 49 metabolites were measured by targeted LC/MS protocols in the serum of 1011 volunteers. Their performance as potential biomarkers was evaluated by the area under the curve of receiver operator characteristics (AUC-ROC) for 105 diagnosis codes or code groups from th ...[more]
Similar Datasets
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: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:BackgroundIn order to satisfy different needs, medical terminology systems must have richer structures. This study examines whether a Swedish primary health care version of the mono-hierarchical ICD-10 (KSH97-P) may obtain a richer structure using category and chapter mappings from KSH97-P to SNOMED CT and SNOMED CT's structure. Manually-built mappings from KSH97-P's categories and chapters to SNOMED CT's concepts are used as a starting point.ResultsThe mappings are manually evaluated using computer-produced information and a small number of mappings are updated. A new and poly-hierarchical chapter division of KSH97-P's categories has been created using the category and chapter mappings and SNOMED CT's generic structure. In the new chapter division, most categories are included in their original chapters. A considerable number of concepts are included in other chapters than their original chapters. Most of these inclusions can be explained by ICD-10's design. KSH97-P's categories are also extended with attributes using the category mappings and SNOMED CT's defining attribute relationships. About three-fourths of all concepts receive an attribute of type Finding site and about half of all concepts receive an attribute of type Associated morphology. Other types of attributes are less common.ConclusionsIt is possible to use mappings from KSH97-P to SNOMED CT and SNOMED CT's structure to enrich KSH97-P's mono-hierarchical structure with a poly-hierarchical chapter division and attributes of type Finding site and Associated morphology. The final mappings are available as additional files for this paper.
| S-EPMC2908062 | biostudies-literature
Project description:ObjectivesThe United States transitioned to the tenth version of the International Classification of Diseases (ICD) system (ICD-10) for mortality coding in 1999 and to the International Classification of Diseases, Clinical Modification and Procedure Coding System (ICD-10-CM/PCS) on October 1, 2015. The purpose of this study was to conduct a narrative literature review to better understand the impact of the implementation of ICD-10/ICD-10-CM/PCS.Materials and methodsWe searched English-language articles in PubMed, Web of Science, and Business Source Complete and reviewed websites of relevant professional associations, government agencies, research groups, and ICD-10 news aggregators to identify literature on the impact of the ICD-10/ICD-10-CM/PCS transition. We used Google to search for additional gray literature and used handsearching of the references of the most on-target articles to help ensure comprehensiveness.ResultsImpact areas reported in the literature include: productivity and staffing, costs, reimbursement, coding accuracy, mapping between ICD versions, morbidity and mortality surveillance, and patient care. With the exception of morbidity and mortality surveillance, quantitative studies describing the actual impact of the ICD-10/ICD-10-CM/PCS implementation were limited and much of the literature was based on the ICD-10-CM/PCS transition rather than the earlier conversion to ICD-10 for mortality coding.DiscussionThis study revealed several gaps in the literature that limit the ability to draw reliable conclusions about the overall impact, positive or negative, of moving to ICD-10/ICD-10-CM/PCS in the United States.ConclusionThese knowledge gaps present an opportunity for future research and knowledge sharing and will be important to consider when planning for ICD-11.
| S-EPMC7309233 | biostudies-literature
Project description:ContextIdentifying the seriously ill population is integral to improving the value of health care. Efforts to identify this population using existing data are anchored to a list of severe medical conditions (SMCs) using diagnostic codes. Published approaches have used International Classification of Diseases, Ninth Revision (ICD-9) codes, which has since been replaced by ICD-10.ObjectivesWe translated SMCs from ICD-9 to ICD-10 using a refined code list. We aimed to test the hypothesis that people identified by ICD-9 or ICD-10 codes would have similar Medicare costs, health care utilization, and mortality.MethodsUsing data from the National Health and Aging Trends Study linked to Medicare claims, we compared samples from periods using ICD-9 (2014) and ICD-10 (2016). We included participants with six-month fee-for-service Medicare data before their interview date who had an SMC identified within that period. We compared the groups' demographic, functional, and medical characteristics and followed up them for six months to compare outcomes.ResultsAmong subjects in the 2016 (ICD-10) sample, 19.9% were hospitalized, 24.6% used the emergency department, 7.2% died, and average Medicare spending totaled $9902.04 over six months of follow-up. We observed no significant differences between the 2014 and 2016 samples (P > 0.05); both samples represent 18% of Medicare fee-for-service beneficiaries.ConclusionIdentifying the seriously ill population using currently available data requires using ICD-10 to define SMCs. Routine measurement of function, quality of life, and caregiver strain will further enhance the identification process and efficiently target palliative care services and appropriate quality measures.
| S-EPMC11200201 | biostudies-literature
Project description:BackgroundThe 10th revision of the International Classification of Diseases (ICD) represents a major change in the ICD system. This paper investigates the impact on relative risk estimates of inconsistencies in outcome classification between ICD-9 and ICD-10, including scenarios in which occupational exposure levels are correlated with year of death (and therefore with the ICD revision in effect at death). The setting of interest is a cohort mortality study in which follow up spans the periods during which ICD-9 and ICD-10 were in effect. The relative risk estimate obtained when death certificates are coded to the ICD revision in effect at time of death is compared to the relative risk estimate that would be obtained if all death certificates were coded to a consistent ICD revision (that is, ICD-10). The ratio of these relative risks is referred to as the coefficient of bias.MethodsSimple equations relate the coefficient of bias to the sensitivity and specificity of the classification of decedents into categories of cause of death via ICD-9 (treating classifications based upon ICD-10 as the standard). Bridge coded mortality data for 2,296,922 decedents (that is, death certificates coded to ICD-9 and ICD-10) are used to derive estimates of sensitivity and specificity by category of cause of death. Numerical examples illustrate the application of these equations.ResultsEstimates of the sensitivity of classification of decedents into categories of death defined by ICD-9 ranged from 0.26-1.00. Specificity was above 0.98 for all categories of cause of death. Numerical examples illustrate that inconsistencies in outcome classification between ICD-9 and ICD-10 may have substantial impact on relative risk estimates if there is a strong relation between exposure status and the proportion of deaths coded to a given ICD revision.ConclusionsFor analyses of mortality outcomes that exhibit poor comparability between ICD-9 and -10, it may be prudent to recode cause of death information to a standard ICD revision in order to avoid bias that can occur when exposures are correlated with the proportion of deaths coded to a given ICD revision.
| S-EPMC2077995 | biostudies-literature
Project description:BackgroundThe original Charlson Comorbidity Index (CCI) encompassed 19 categories of medical conditions that were identifiable in medical records. Subsequent publications provided scoring algorithms based on International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes. The recent adoption of International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) codes in the United States created a need for a new scoring scheme. In addition, a review of existing claims-based scoring systems suggested 3 areas for improvement: the lack of explicit identification of secondary diabetes, the lack of differentiation between HIV infection and AIDS, and insufficient guidance on scoring hierarchy. In addition, addressing the third need raised the issue of disease severity in renal disease.ObjectivesThis initiative aimed to create an expanded and refined ICD-9 scoring system for CCI, addressing the classification of issues noted above, create a corresponding ICD-10 system, assess the comparability of ICD-9- and ICD-10-based scores, and validate the new scoring scheme.MethodsWe created ICD-9 and ICD-10 code tables for 19 CCI medical conditions. The new scoring scheme was labeled CDMF CCI and was tested using claims-based data for individuals aged ≥65 years who participated in a Humana Medicare Advantage plan during at least 1 of 3 consecutive 12-month periods. Two 12-month periods were during the ICD-9 era and the third 12-month period was during the ICD-10 era. Because many individuals were counted in more than one 12-month period, we described the study population as comprising 3 panels. We used regression models to analyze the association between the CCI score and same-year inpatient admissions and near-term (90-day) mortality. Additional testing was done by comparing the mean CCI score or disease prevalence in the 3 subpopulations of people with HIV/AIDS, renal disease, or diabetes. Finally, we calculated area under the receiver operating characteristics (AUC-ROC) curve values by applying the Deyo system and our ICD-9 and ICD-10 scoring systems.ResultsThe CDMF ICD-9 and ICD-10 scoring scheme yielded comparable scores across the 3 panels, and inpatient admissions and mortality rates consistently increased in each panel as the CCI score increased. Comparisons of the performance of the Deyo system and our proposed CDMF ICD-9 system in the 3 key subpopulations showed that the CDMF ICD-9 system produced a lower CCI score in the presence of HIV infection without AIDS, achieved similar detection ability of diabetes, and allowed good differentiation between mild-to-moderate and severe renal disease. AUC-ROC values were similar between the CDMF ICD-9 coding system and the Deyo system.ConclusionOur results support the implementation of the CDMF CCI scoring instrument to triage individual patients for disease- and care-management programs. In addition, the CDMF scheme allows for a more precise understanding of chronic disease at a population level, thus allowing health systems and plans to design services and benefits to meet multifactorial clinical needs. Preliminary validation sets the stage for further testing using long-term follow-up data and for the adaptation of this coding scheme to a chart review instrument.
| S-EPMC6684052 | biostudies-literature
Project description:BackgroundWhile potentially timesaving, there is no program to automatically transform diagnosis codes of the ICD-10 German modification (ICD-10-GM) into the injury severity score (ISS).ObjectiveTo develop a mapping method from ICD-10-GM into ICD-10 clinical modification (ICD-10-CM) to calculate the abbreviated injury scale (AIS) and ISS of each patient using the ICDPIC-R and to compare the manually and automatically calculated scores.MethodsBetween January 2019 and June 2021, the most severe AIS of each body region and the ISS were manually calculated using medical documentation and radiology reports of all major trauma patients of a German level I trauma centre. The ICD-10-GM codes of these patients were exported from the electronic medical data system SAP, and a Java program was written to transform these into ICD-10-CM codes. Afterwards, the ICDPIC-R was used to automatically generate the most severe AIS of each body region and the ISS. The automatically and manually determined ISS and AIS scores were then tested for equivalence.ResultsStatistical analysis revealed that the manually and automatically calculated ISS were significantly equivalent over the entire patient cohort. Further sub-group analysis, however, showed that equivalence could only be demonstrated for patients with an ISS between 16 and 24. Likewise, the highest AIS scores of each body region were not equal in the manually and automatically calculated group.ConclusionThough achieving mapping results highly comparable to previous mapping methods of ICD-10-CM diagnosis codes, it is not unrestrictedly possible to automatically calculate the AIS and ISS using ICD-10-GM codes.
| S-EPMC8432850 | biostudies-literature
Project description:The objective of this study was to examine the impact of the transition from International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM), to Interactional Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM), on family medicine and to identify areas where additional training might be required.Family medicine ICD-9-CM codes were obtained from an Illinois Medicaid data set (113,000 patient visits and $5.5 million in claims). Using the science of networks, we evaluated each ICD-9-CM code used by family medicine physicians to determine whether the transition was simple or convoluted. A simple transition is defined as 1 ICD-9-CM code mapping to 1 ICD-10-CM code, or 1 ICD-9-CM code mapping to multiple ICD-10-CM codes. A convoluted transition is where the transitions between coding systems is nonreciprocal and complex, with multiple codes for which definitions become intertwined. Three family medicine physicians evaluated the most frequently encountered complex mappings for clinical accuracy.Of the 1635 diagnosis codes used by family medicine physicians, 70% of the codes were categorized as simple, 27% of codes were convoluted, and 3% had no mapping. For the visits, 75%, 24%, and 1% corresponded with simple, convoluted, and no mapping, respectively. Payment for submitted claims was similarly aligned. Of the frequently encountered convoluted codes, 3 diagnosis codes were clinically incorrect, but they represent only <0.1% of the overall diagnosis codes.The transition to ICD-10-CM is simple for 70% or more of diagnosis codes, visits, and reimbursement for a family medicine physician. However, some frequently used codes for disease management are convoluted and incorrect, and for which additional resources need to be invested to ensure a successful transition to ICD-10-CM.
| S-EPMC5553540 | biostudies-other
Project description:Management of patients with chronic conditions relies on accurate measurement. It is unknown how transition to the ICD-10 coding system affected reporting of chronic condition rates over time. We measured chronic condition rates 2 years before and 1 year after the transition to ICD-10 to examine changes in prevalence rates and potential measurement issues in the Veterans Affairs (VA) health care system.We developed definitions for 34 chronic conditions using ICD-9 and ICD-10 codes and compared the prevalence rates of these conditions from FY2014 to 2016 in a 20% random sample (1.0 million) of all VA patients. In each year we estimated the total number of patients diagnosed with the conditions. We regressed each condition on an indicator of ICD-10 (versus ICD-9) measurement to obtain the odds ratio associated with ICD-10.Condition prevalence estimates were similar for most conditions before and after ICD-10 transition. We found significant changes in a few exceptions. Alzheimer's disease and spinal cord injury had more than twice the odds of being measured with ICD-10 compared to ICD-9. HIV/AIDS had one-third the odds, and arthritis had half the odds of being measured with ICD-10. Alcohol dependence and tobacco/nicotine dependence had half the odds of being measured in ICD-10.Many chronic condition rates were consistent from FY14-16, and there did not appear to be widespread undercoding of conditions after ICD-10 transition. It is unknown whether increased sensitivity or undercoding led to decreases in mental health conditions.
| S-EPMC5561575 | biostudies-other