Dataset Information

Accuracy of surgical complication rate estimation using ICD-10 codes.

ABSTRACT: BACKGROUND:The 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. METHODS:This 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. RESULTS:A 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. CONCLUSION:Verified 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.

SUBMITTER: Storesund A

PROVIDER: S-EPMC6519147 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature

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Accuracy of surgical complication rate estimation using ICD-10 codes.

Storesund A A Haugen A S AS Hjortås M M Nortvedt M W MW Flaatten H H Eide G E GE Boermeester M A MA Sevdalis N N Søfteland E E

The British journal of surgery 20180918 3

<h4>Background</h4>The 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.<h4>Methods</h4>This was a pro ...[more]

PMID: 30229870

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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.

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.

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.

Project description:The study aims to identify 'missing' diagnoses amongst paediatric admissions during the UK's first national lockdown, compared with the previous 5 years. A retrospective observational cohort study of all children (0-15 years) attending for urgent care across Oxfordshire, during the first UK lockdown in 2020, compared to matched dates in 2015-2019, across two paediatric hospitals providing secondary care, including one with tertiary services. Our outcome measures were changes in numbers of patients attending and inpatient diagnoses (using ICD-10 classification) during the first 2020 lockdown, compared with the previous 5 years, were used. We found that total Emergency Department (ED) attendances (n = 4030) and hospital admissions (n = 1416) during the first UK lockdown were reduced by 56.8% and 59.4%, respectively, compared to 2015-2019 (5-year means n = 7446.8 and n = 2491.6, respectively). Proportions of patients admitted from ED and length of stay were similar across 2015-2020. ICD-10 diagnoses in lockdown of 2020 (n = 2843) versus matched 2015-2019 dates (n = 19,946) demonstrated significantly greater neoplasm diagnoses (p = 0.0123). Of diagnoses 'missing' in lockdown, 80% were categorised as infectious diseases or their sequelae and 20% were non-specific pains/aches/malaise and accidental injury/poisonings.Conclusions: Pandemic public health measures significantly altered paediatric presentations. Oxfordshire hospitals had a 58% reduction in ED attendances/inpatient admissions, with 'missing' diagnoses predominantly infection-related illnesses. These are likely driven by a combination of the following: (1) public health infection control measures successfully reducing disease transmission, (2) parents/carers keeping mild/self-limiting disease at home, and (3) pandemic-related healthcare anxieties. Prospective studies are needed to ensure referral pathways identify vulnerable children, those with social concerns, and avoid delayed presentation. What is Known: • Significant reductions of paediatric ED attendances and inpatient admissions are reported globally, throughout national and regional lockdowns for COVID-19. • Previous studies (supplemental table 5) examined only ED diagnoses or specific inpatient diagnoses during lockdown periods, demonstrating reductions of infectious diseases, accidents/injuries, and safeguarding referrals. What is New: • Using ICD-10 coding, robustly controlling for five historical years and adopting a hypothesis-independent analysis, demonstrating 80% of 'missing' inpatient diagnoses during national COVID-19 lockdown were infectious diseases or its sequelae, the remainder being non-specific aches/pains/malaise and accidental injuries/poisonings. • Greater numbers of neoplasms and other specific diagnoses were detected during lockdown, including greater documentation of co-morbidities and incidental findings.

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Accuracy of surgical complication rate estimation using ICD-10 codes.

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Accuracy of surgical complication rate estimation using ICD-10 codes.

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