The rise of endovascular repair for abdominal, thoracoabdominal, and thoracic aortic aneurysms.

BACKGROUND: Given changes in intervention guidelines and the growing popularity of endovascular treatment for aortic aneurysms, we examined the trends in admissions and repairs of abdominal aortic aneurysms (AAAs), thoracoabdominal aortic aneurysms (TAAAs), and thoracic aortic aneurysms (TAAs). METHODS: We identified all patients admitted with ruptured aortic aneurysms and intact aortic aneurysms repaired in the Nationwide Inpatient Sample between 2004 and 2019. We then examined the use of open, endovascular, and complex endovascular repair (OAR, EVAR, and cEVAR) for each aortic aneurysm location (AAA, TAAA, and TAA), alongside their resulting in-hospital mortality, over time. cEVAR included branched, fenestrated, and physician-modified endografts. RESULTS: 715,570 patients were identified with AAA (87% intact repairs and 13% rupture admissions). Both intact AAA repairs and ruptured AAA admissions decreased significantly between 2004 and 2019 (intact 41,060-34,215, P < .01; ruptured 7175-4625, P = .02). Of all AAA repairs performed in a given year, the use of EVAR increased (2004-2019: intact 45%-66%, P < .01; ruptured 10%-55%, P < .01) as well as cEVAR (2010-2019: intact 0%-23%, P < .01; ruptured 0%-14%, P < .01). Mortality after EVAR of intact AAAs decreased significantly by 29% (2004-2019, 0.73%-0.52%, P < .01), whereas mortality after OAR increased significantly by 16% (2004-2019, 4.4%-5.1%, P < .01). In the study, 27,443 patients were identified with TAAA (80% intact and 20% ruptured). In the same period, intact TAAA repairs trended upward (2004-2019, 1435-1640, P = .055), and cEVAR became the most common approach (2004-2019, 3.8%-72%, P = .055). A total of 141,651 patients were identified with ascending, arch, or descending TAAs (90% intact and 10% ruptured). Intact TAA repairs increased significantly (2004-2019, 4380-10,855, P < .01). From 2017 to 2019, the mortality after OAR of descending TAAs increased and mortality after thoracic endovascular aneurysm repair decreased (2017-2019, OAR 1.6%-3.1%; thoracic endovascular aneurysm repair 5.2%-3.8%). CONCLUSIONS: Both intact AAA repairs and ruptured AAA admissions significantly decreased between 2004 and 2019. The use of endovascular techniques for the repair of all aortic aneurysm locations, both intact and ruptured, increased over the past two decades. Most recently in 2019, 89% of intact AAA repairs, infrarenal through suprarenal, were endovascular (EVAR or cEVAR, respectively). cEVAR alone increased to 23% of intact AAA repairs in 2019, from 0% a decade earlier. In this period of innovation, with many new options to repair aortic aneurysms while maintaining arterial branches, endovascular repair is now used for the majority of all intact aortic aneurysm repairs. Long-term data are needed to evaluate the durability of these procedures.

Improving Accuracy of Administrative Data for Perforated Appendicitis Classification.

INTRODUCTION: Reliance on International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) diagnosis codes may misclassify perforated appendicitis with resultant research, fiscal, and public health implications. We aimed to improve the accuracy of administrative data for perforated appendicitis classification relying on ICD-10-CM codes from 2015 to 2018. METHODS: We conducted a retrospective study of randomly sampled patients aged ≤18 years diagnosed with acute appendicitis from eight children's hospitals. Patients were identified using the Pediatric Health Information System, and true perforation status was determined by medical record review. We developed two algorithms by leveraging Pediatric Health Information System data elements and data mining (DM) approaches. The two developed algorithm performance was compared against algorithms that exclusively relied on ICD-10-CM codes using area under the curve and other measures. RESULTS: Of 1051 clinically validated encounters that were included, 383 (36.4%) patients were identified to have perforated appendicitis. The two algorithms developed using DM approaches primarily leveraged ICD-10-CM codes and length of stay. DM-developed algorithms had a significantly higher accuracy than algorithms relying exclusively on ICD-10-CM (P value < 0.01): sensitivity and specificity for DM-developed algorithms were 0.86-0.88 and 0.95-0.97, respectively, which were overall higher than algorithms that relied on only ICD-10-CM. CONCLUSIONS: This study provides an algorithm that can improve the accuracy of perforated appendicitis classification using commonly available elements in administrative data. We recommend that this algorithm is used in future appendicitis classification to ensure valid reporting, hospital-level benchmarking, and fiscal or public health assessments.

Cause of death coding in asthma.

BACKGROUND: While clinical coding is intended to be an objective and standardized practice, it is important to recognize that it is not entirely the case. The clinical and bureaucratic practices from event of death to a case being entered into a research dataset are important context for analysing and interpreting this data. Variation in practices can influence the accuracy of the final coded record in two different stages: the reporting of the death certificate, and the International Classification of Diseases (Version 10; ICD-10) coding of that certificate. METHODS: This study investigated 91,022 deaths recorded in the Scottish Asthma Learning Healthcare System dataset between 2000 and 2017. Asthma-related deaths were identified by the presence of any of ICD-10 codes J45 or J46, in any position. These codes were categorized either as relating to asthma attacks specifically (status asthmatic; J46) or generally to asthma diagnosis (J45). RESULTS: We found that one in every 200 deaths in this were coded as being asthma related. Less than 1% of asthma-related mortality records used both J45 and J46 ICD-10 codes as causes. Infection (predominantly pneumonia) was more commonly reported as a contributing cause of death when J45 was the primary coded cause, compared to J46, which specifically denotes asthma attacks. CONCLUSION: Further inspection of patient history can be essential to validate deaths recorded as caused by asthma, and to identify potentially mis-recorded non-asthma deaths, particularly in those with complex comorbidities.

Validity of ICD-10-based algorithms to identify patients with influenza in inpatient and outpatient settings.

PURPOSE: To evaluate the validity of ICD-10-CM code-based algorithms as proxies for influenza in inpatient and outpatient settings in the USA. METHODS: Administrative claims data (2015-2018) from the largest commercial insurer in New Jersey (NJ), USA, were probabilistically linked to outpatient and inpatient electronic health record (EHR) data containing influenza test results from a large NJ health system. The primary claims-based algorithms defined influenza as presence of an ICD-10-CM code for influenza, stratified by setting (inpatient/outpatient) and code position for inpatient encounters. Test characteristics and 95% confidence intervals (CIs) were calculated using test-positive influenza as a reference standard. Test characteristics of alternative outpatient algorithms incorporating CPT/HCPCS testing codes and anti-influenza medication pharmacy claims were also calculated. RESULTS: There were 430 documented influenza test results within the study period (295 inpatient, 135 outpatient). The claims-based influenza definition had a sensitivity of 84.9% (95% CI 72.9%-92.1%), specificity of 96.3% (95% CI 93.1%-98.0%), and PPV of 83.3% (95% CI 71.3%-91.0%) in the inpatient setting, and a sensitivity of 76.7% (95% CI 59.1%-88.2%), specificity of 96.2% (95% CI 90.6%-98.5%), PPV of 85.2% (95% CI 67.5%-94.1%) in the outpatient setting. Primary inpatient discharge diagnoses had a sensitivity of 54.7% (95% CI 41.5%-67.3%), specificity of 99.6% (95% CI 97.7%-99.9%), and PPV of 96.7% (95% CI 83.3%-99.4%). CPT/HCPCS codes and anti-influenza medication claims were present for few outpatient encounters (sensitivity 3%-10%). CONCLUSIONS: In a large US healthcare system, inpatient ICD-10-CM codes for influenza, particularly primary inpatient diagnoses, had high predictive value for test-positive influenza. Outpatient ICD-10-CM codes were moderately predictive of test-positive influenza.

Validation of alopecia coding in US claims data among women of childbearing age.

BACKGROUND: Accurately identifying alopecia in claims data is important to study this rare medication side effect. OBJECTIVES: To develop and validate a claims-based algorithm to identify alopecia in women of childbearing age. METHODS: We linked electronic health records from a large healthcare system in Massachusetts (Mass General Brigham) with Medicaid claims data from 2016 through 2018 to identify all women aged 18 to 50 years with an ICD-10 code for alopecia, including alopecia areata, androgenic alopecia, non-scarring alopecia, or cicatricial alopecia, from a visit to the MGB system. Using eight predefined algorithms to identify alopecia in Medicaid claims data, we randomly selected 300 women for whom we reviewed their charts to validate the alopecia diagnosis. Positive predictive values (PPVs) were computed for the primary algorithm and seven algorithm variations, stratified by race. RESULTS: Out of 300 patients with at least 1 ICD-10 code for alopecia in the Medicaid claims, 286 had chart-confirmed alopecia (PPV = 95.3%). The algorithm requiring two diagnosis codes plus one prescription claim for alopecia treatment identified 55 patients (PPV = 100%). The algorithm requiring 1 diagnosis code for alopecia plus 1 procedure claim for intralesional triamcinolone injection identified 35 patients (PPV = 100%). Across all 8 algorithms tested, the PPV varied between 95.3% and 100%. The PPV for alopecia ranged from 94% to 100% in White and 96%-100% in 48 non-White women. The exact date of alopecia onset was difficult to determine in charts. CONCLUSION: At least one recorded ICD-10 code for alopecia in claims data identified alopecia in women of childbearing age with high accuracy.

Is complementary medicine and diagnosis associated among spinal patients in the secondary sector: a cross-sectional study.

PURPOSE: The services defined as complementary and alternative medicine/healthcare (CAM) are used to varying degrees according to the nature of the health problem, and musculoskeletal disorders, in particular, often lead to the use of CAM. Chronic pain is often cited as a reason for using CAM, and it is also the cardinal symptom of patients with back pain referred for specialist care. However, previous studies do not consider the heterogeneity of back pain when examining the use of CAM. Thus, this study aimed to explore the associations between CAM use and clinical findings incl. ICD-10 diagnostic codes in such a context. METHODS: In a cross-sectional study, a logistic regression analysis examined associations between CAM use and clinical findings at a public outpatient spine department. Chi-squared test examined the association between self-reported reasons for CAM use and the diagnostic groups. RESULTS: Of the 432 patients in the study population, 23.8% reported using CAM within 12 months prior to clinical assessment. CAM use was associated with being female and of younger age. Seeking CAM was not associated with clinical findings nor diagnosis, and no statistically significant association between the reasons for seeking CAM and the diagnostic groups was described. CONCLUSIONS: Among patients referred to specialist care for back pain, this study provides no evidence that the spinal condition should be expected to lead to the use of CAM. Only the individual demographic findings, specifically age and gender, were associated with CAM use.

Deaths Among Ambulance Patients Released from the Emergency Department Within the First 24 Hours With Nonspecific Diagnoses - Expected or Not?

BACKGROUND: Emergency patients are frequently assigned nonspecific diagnoses. Nonspecific diagnoses describe observations or symptoms and are found in chapters R and Z of the International Classification of Diseases, 10th edition (ICD-10). Patients with such diagnoses have relatively low mortality, but due to patient volume, the absolute number of deaths is substantial. However, information on cause of short-term mortality is limited. OBJECTIVES: To investigate whether death could be expected for ambulance patients brought to the emergency department (ED) after a 1-1-2 call, released with a nonspecific ICD-10 diagnosis within 24 h, and who subsequently died within 30 days. METHODS: Retrospective medical record review of adult 1-1-2 emergency ambulance patients brought to an ED in the North Denmark Region during 2017-2021. Patients were divided into three categories: unexpected death, expected death (terminal illness), and miscellaneous. Charlson Comorbidity Index (CCI) was assessed. RESULTS: We included 492 patients. Mortality was distributed as follows: Unexpected death 59.2% (n = 291), expected death (terminal illness) 25.8% (n = 127), and miscellaneous 15.0% (n = 74). Patients who died unexpectedly were old (median age of 82 years) and had CCI 1-2 (58.1%); 43.0% used at least five daily prescription drugs, and they were severely acutely ill upon arrival (24.7% with red triage, 60.1% died within 24 h). CONCLUSIONS: More than half of ambulance patients released within 24 h from the ED with nonspecific diagnoses, and who subsequently died within 30 days, died unexpectedly. One-fourth died from a pre-existing terminal illness. Patients dying unexpectedly were old, treated with polypharmacy, and often life-threateningly sick at arrival.

Virtual Visit Trends in Nursing Homes During the COVID-19 Pandemic.

Background: Physical distancing and visiting restrictions during the COVID-19 pandemic particullarly posed significant challenges in providing timely medical care in nursing homes. Virtual visits were pivotal in providing both scheduled and urgent patient care in addition to in-person care when possible. These virtual visits could have been used to provide urgent medical care than the regular scheduled visits. We hypothesize that regular non-COVID-19-related virtual visits in the evaluated nursing home facilities were lower than COVID-19-related virtual visits during this period. The objective was to compare the frequency of COVID-19-related virtual visits with non-COVID-19-related virtual visits performed in three suburban nursing homes in Michigan from December 2020 through February 2022. Methods: Retrospective chart review of 283 patients, comprising 563 virtual visits. The most frequent ICD-10 diagnostic codes used by providers for primary encounters were analyzed for three participating facilities Results: Only 13.8% (78/563) of all virtual visits during the study period were for COVID-19 as a primary encounter. In total 86.1% (485/563) of visits were non-COVID-19 related, which comprised most frequently M6281 muscle weakness (45), E119 diabetes type with complications (20), NI86 end-stage renal disease (11), N390 urinary tract infection (10), G309 Alzheimer's disease (10), and I4891 unspecified atrial fibrillation (10). Conclusion: We found that the primary ICD-10 diagnosis codes for virtual COVID-19-related visits were significantly lower than non-COVID-19-related virtual visits in our study population. This trend could be associated with multiple system-based factors. Further studies to examine factors responsible for these findings are warranted.

Validation of Vascular Location Subcodes for Acute Ischemic Stroke by the International Classification of Diseases-10.

BACKGROUND: Vascular region of infarct is part of the International Classification of Diseases-10 (ICD-10) coding scheme for ischemic stroke. These data could potentially be used for studies about vascular location, such as comparisons of anterior versus posterior circulation stroke. The objective of this study was to evaluate the validity of these subcodes. METHODS: We selected a random sample of 100 hospitalizations specifying 50 with anterior circulation ICD-10 ischemic stroke (carotid, anterior cerebral artery [CA], middle CA) and 50 with posterior circulation stroke (vertebral, basilar, cerebellar, posterior CA). The gold standard primary vascular distribution was scored using imaging studies and reports, blinded to the subcode. We compared gold-standard distribution to coded distribution and calculated the operating characteristics of ICD-10 posterior circulation versus anterior circulation codes with the gold standard. We also calculated the kappa statistic for agreement across all 7 vascular regions. RESULTS: In our population of 100 strokes, mean NIHSS was 8 (SD, 8). Head CT was performed in 95 % (95/100) and MRI in 77 % (77/100). The gold standard classified 55 primary posterior circulation strokes (26 PCA, 16 cerebellar, 8 basilar, 5 vertebral), 44 primary anterior circulation strokes (35 MCA, 6 carotid, 3 ACA), and 1 stroke with no infarct on imaging. The accuracy of the ICD-10 classification for primary posterior circulation stroke versus anterior circulation/no infarct was: sensitivity 89 % (49/55); specificity 98 % (44/45); positive predictive value 98 % (49/50); negative predictive value 88 % (44/50). The reliability of the 7-region classification was excellent (kappa 0.85). CONCLUSIONS: We found that ICD-10 classification of vascular location in routine practice correlates strongly with gold-standard localization for hospitalized ischemic stroke and supports validity in differentiating posterior versus anterior circulation. At a more granular vascular level, the location reliability was excellent, although limited data were available for some subcodes.

Validation Study of Cardiovascular International Statistical Classification of Diseases and Related Health Problems, Tenth Edition, Australian Modification (ICD-10-AM) Codes in Administrative Healthcare Databases (ANZACS-QI 77).

BACKGROUND: Administrative healthcare databases can be utilised for research. The accuracy of the International Statistical Classification of Diseases and Related Health Problems, Tenth Edition, Australian Modification (ICD-10-AM) coding of cardiovascular conditions in New Zealand is not known and requires validation. METHOD: International Statistical Classification of Diseases and Related Health Problems, Tenth Edition, Australian Modification coded discharges for acute coronary syndrome (ACS), heart failure (HF) and atrial fibrillation (AF), in both primary and secondary diagnostic positions, were identified from four district health boards between 1 January 2019 and 31 June 2019. A sample was randomly selected for retrospective clinician review for evidence of the coded diagnosis according to contemporary diagnostic criteria. Positive predictive values (PPVs) for ICD-10-AM coding vs clinician review were calculated. This study is also known as All of New Zealand, Acute Coronary Syndrome-Quality Improvement (ANZACS-QI) 77. RESULTS: A total of 600 cases (200 for each diagnosis, 5.0% of total identified cases) were reviewed. The PPV of ACS was 93% (95% confidence interval [CI] 89%-96%), HF was 93% (95% CI 89%-96%) and AF was 96% (95% CI 92%-98%). There were no differences in PPV between district health boards. PPV for ACS were lower in Maori vs non-Maori (72% vs 96%; p=0.004), discharge from non-Cardiology vs Cardiology services (89% vs 96%; p=0.048) and ICD-10-AM coding for unstable angina vs myocardial infarction (81% vs 95%; p=0.011). PPV for HF were higher in the primary vs secondary diagnostic position (100% vs 89%; p=0.001). CONCLUSIONS: The PPVs of ICD-10-AM coding for ACS, HF, and AF were high in this validation study. ICD-10-AM coding can be used to identify these diagnoses in administrative databases for the purposes of healthcare evaluation and research.

[Optimal search conditions for identification of patients with chronic myeloid leukemia from Japanese National Health Insurance data].

This study investigated which conditions could be used to identify patients with chronic myeloid leukemia (CML) from a National Health Insurance claims dataset. During April 2012 and September 2018, 1,789,462 employees were enrolled in the dataset for Shizuoka Prefecture residents. The number of patients with the ICD-10 code for CML was 761. Among them, 246 who had been prescribed a tyrosine kinase inhibitor were considered as having true CML. The positive predictive value was calculated as 32.3% when CML was identified by ICD-10 code alone. Combination of ICD-10 code with prescribed drugs was required to accurately identify patients with CML from the insurance database.

A compressed large language model embedding dataset of ICD 10 CM descriptions.

This paper presents novel datasets providing numerical representations of ICD-10-CM codes by generating description embeddings using a large language model followed by a dimension reduction via autoencoder. The embeddings serve as informative input features for machine learning models by capturing relationships among categories and preserving inherent context information. The model generating the data was validated in two ways. First, the dimension reduction was validated using an autoencoder, and secondly, a supervised model was created to estimate the ICD-10-CM hierarchical categories. Results show that the dimension of the data can be reduced to as few as 10 dimensions while maintaining the ability to reproduce the original embeddings, with the fidelity decreasing as the reduced-dimension representation decreases. Multiple compression levels are provided, allowing users to choose as per their requirements, download and use without any other setup. The readily available datasets of ICD-10-CM codes are anticipated to be highly valuable for researchers in biomedical informatics, enabling more advanced analyses in the field. This approach has the potential to significantly improve the utility of ICD-10-CM codes in the biomedical domain.

Comparison of clinician diagnosis of COVID-19 with real time polymerase chain reaction in an adult-representative population in Sweden.

BACKGROUND: Due to the high transmissibility of SARS-CoV-2, accurate diagnosis is essential for effective infection control, but the gold standard, real-time reverse transcriptase-polymerase chain reaction (RT-PCR), is costly, slow, and test capacity has at times been insufficient. We compared the accuracy of clinician diagnosis of COVID-19 against RT-PCR in a general adult population. METHODS: COVID-19 diagnosis data by 30th September 2021 for participants in an ongoing population-based cohort study of adults in Western Sweden were retrieved from registers, based on positive RT-PCR and clinician diagnosis using recommended ICD-10 codes. We calculated accuracy measures of clinician diagnosis using RT-PCR as reference for all subjects and stratified by age, gender, BMI, and comorbidity collected pre-COVID-19. RESULTS: Of 42,621 subjects, 3,936 (9.2%) and 5705 (13.4%) had had COVID-19 identified by RT-PCR and clinician diagnosis, respectively. Sensitivity and specificity of clinician diagnosis against RT-PCR were 78% (95%CI 77-80%) and 93% (95%CI 93-93%), respectively. Positive predictive value (PPV) was 54% (95%CI 53-55%), while negative predictive value (NPV) was 98% (95%CI 98-98%) and Youden's index 71% (95%CI 70-72%). These estimates were similar between men and women, across age groups, BMI categories, and between patients with and without asthma. However, while specificity, NPV, and Youden's index were similar between patients with and without chronic obstructive pulmonary disease (COPD), sensitivity was slightly higher in patients with (84% [95%CI 74-90%]) than those without (78% [95%CI 77-79%]) COPD. CONCLUSIONS: The accuracy of clinician diagnosis for COVID-19 is adequate, regardless of gender, age, BMI, and asthma, and thus can be used for screening purposes to supplement RT-PCR.

Hospitalizations due to respiratory syncytial virus (RSV) infections in Germany: a nationwide clinical and direct cost data analysis (2010-2019).

PURPOSE: Clinical and direct medical cost data on RSV-related hospitalizations are relevant for public health decision-making. We analyzed nationwide data on RSV-coded hospitalizations from Germany in different age and risk groups. METHODS: Assessment of RSV-coded hospitalizations (ICD-10-GM RSV-code J12.1/J20.5/J21.0 as primary discharge diagnosis) from 01/2010 to 12/2019, using remote data retrieval from the Hospital Statistics Database of the German Federal Statistical Office. RESULTS: Overall, 205,352 RSV-coded hospitalizations (198,139 children < 18 years, 1,313 adults, 5,900 seniors > 59 years) were reported (median age < 1 year, IQR 0; 1; 56% males, 32% with RSV pneumonia). Annual median RSV-coded hospitalization incidence was 24.8/100,000 persons (IQR 21.3; 27.5); children reported a median incidence of 145.8 (IQR 130.9; 168.3). Between 2010 and 2019, hospitalization incidence increased 1.7-fold/15.1-fold/103-fold in children/adults/seniors. Adults and seniors reported higher rates of underlying chronic conditions, complications, and intensive care treatment than children; of 612 in-hospital fatalities, 103/51/458 occurred in children/adults/seniors. Per-patient mean costs varied between 3286€ ± 4594 in 1-4-year-olds and 7215€ ± 13,564 among adults. Increased costs were associated with immune disorders (2.55-fold increase compared to those without), nervous system disorders (2.66-fold), sepsis (7.27-fold), ARDS (12.85-fold), intensive care (4.60-fold) and ECMO treatment (16.88-fold). CONCLUSION: The economic burden of RSV-related hospitalizations in Germany is substantial, even when only considering cases with RSV-coded as the primary discharge diagnosis. Children represented the vast majority of RSV-coded hospitalizations. However, adults and seniors hospitalized for RSV were at a higher risk of severe complications, required more costly treatments, and had higher fatality rates; although their RSV-coded hospitalization incidence showed a clear upward trend since 2017, their true hospitalization incidence is still likely to be underestimated due to lack of routine RSV testing in these age groups. Hence, new treatments and vaccines for RSV ideally should also target adults and seniors in addition to children.

Administrative coding for non-alcoholic fatty liver disease is accurate in Swedish patients.

Background and Aims: Epidemiological studies of non-alcoholic fatty liver disease (NAFLD) frequently use the International Classification of Disease (ICD) codes to identify patients. The validity of such ICD codes in a Swedish setting is unknown. Here, we aimed to validate the administrative code for NAFLD in Sweden.Methods: In total, 150 patients with an ICD-10 code for NAFLD (K76.0) from the Karolinska University Hospital between 1 January 2015 and 3 November 2021 were randomly selected. Patients were classified as true or false positives for NAFLD by medical chart review and the positive predictive value (PPV) for the ICD-10 code corresponding to NAFLD was calculated.Results: The PPV of the ICD-10 code for NAFLD was 0.82 (95% confidence interval [CI] = 0.76-0.89). After exclusion of patients with diagnostic coding for other liver diseases or alcohol abuse disorder (n = 14), the PPV was improved to 0.91 (95% CI 0.87-0.96). The PPV was higher in patients with coding for NAFLD in combination with obesity (0.95, 95%CI = 0.87-1.00) or type 2 diabetes (0.96, 95%CI = 0.89-1.00). However, in false-positive cases, a high alcohol consumption was common and such patients had somewhat higher Fibrosis-4 scores than true-positive patients (1.9 vs 1.3, p = 0.16)Conclusions: The ICD-10 code for NAFLD had a high PPV, that was further improved after exclusion of patients with coding for other liver diseases than NAFLD. This approach should be preferred when performing register-based studies to identify patients with NAFLD in Sweden. Still, residual alcohol-related liver disease might risk confound some findings seen in epidemiological studies which needs to be considered.

A unidirectional mapping of ICD-8 to ICD-10 codes, for harmonized longitudinal analysis of diseases.

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.

Defining the distance between diseases using SNOMED CT embeddings.

Characterizing disease relationships is essential to biomedical research to understand disease etiology and improve clinical decision-making. Measurements of distance between disease pairs enable valuable research tasks, such as subgrouping patients and identifying common time courses of disease onset. Distance metrics developed in prior work focused on smaller, targeted disease sets. Distance metrics covering all diseases have not yet been defined, which limits the applications to a broader disease spectrum. Our current study defines disease distances for all disease pairs within the International Classification of Diseases, version 10 (ICD-10), the diagnostic classification system universally used in electronic health records. Our proposed distance is computed based on a biomedical ontology, SNOMED CT (Systemized Nomenclature of Medicine, Clinical Terms), which can also be viewed as a structured knowledge graph. We compared the knowledge graph-based metric to three other distance metrics based on the hierarchical structure of ICD, clinical comorbidity, and genetic correlation, to evaluate how each may capture similar or unique aspects of disease relationships. We show that our knowledge graph-based distance metric captures known phenotypic, clinical, and molecular characteristics at a finer granularity than the other three. With the continued growth of using electronic health records data for research, we believe that our distance metric will play an important role in subgrouping patients for precision health, and enabling individualized disease prevention and treatments.

Validation of serostatus of rheumatoid arthritis using ICD-10 codes in administrative claims data.

PURPOSE: To determine the accuracy of International Classification of Diseases- Tenth Revision (ICD-10) diagnosis codes for rheumatoid arthritis (RA) serostatus using a U.S. claims database (Optum Clinformatics Data Mart, Optum) and to compare the results to a previous validation study performed in IBM Marketscan Research Database (sensitivity 73%, positive predictive value, PPV, 84%). METHODS: In Optum (01/01/2016-03/31/2020) linked with laboratory results, we selected RA patients based on ≥2 ICD-10 diagnosis codes for RA (M05 or M06) and at least one dispensing of RA treatments. We included individuals with at least one laboratory result for rheumatoid factor (RF) or anti-cyclic citrullinated peptide (CCP) performed 365 days prior to and including the cohort entry date. An individual was "seropositive" if at least one of the 2 diagnosis codes used to define RA status was M05. "Seronegative" patients were required to have only M06. Secondary analyses were performed using subsets of M05 and M06 diagnosis codes. We calculated the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and kappa of M05 and M06 against the prespecified reference standard laboratory data. RESULTS: We identified 14 490 adult RA patients who had at least 1 RF or anti-CCP result. The number of patients identified for each reference standard definition ranged from 3315 (reference standard definition: high + anti-CCP) to 13 636 (any + RF). PPV for seropositive RA, M05, was 77.1%. The PPV of M06 for seronegative RA was 61.6%. When we applied more restricted definitions of M05 and M06, the PPV for seropositive RA increased to 79.2%. The PPV for seronegative RA also notably increased to 89.5%. CONCLUSION: ICD-10 codes (M05 and M06) can help identify RA serostatus in claims data, but their limitations should be acknowledged. The PPVs for seropositive and seronegative RA found in the Optum database were lower than those found in MarketScan, perhaps related to database variability or differing patient characteristics and clinical practice. When more restricted definitions of M05 and M06 were used, the PPVs for seropositive and seronegative RA improved to 79.2% and 89.5%, respectively.

Identification of rheumatoid arthritis in German claims data using different algorithms: Validation by cross-sectional patient-reported survey data.

OBJECTIVE: To evaluate different algorithms for the identification of rheumatoid arthritis (RA) in claims data using patient-reported diagnosis as reference. METHODS: Within longitudinal data from a large German statutory health insurance, we selected a random sample of persons with ICD-10 code for RA (M05/M06) in ≥2 quarters in 2013. The sample was stratified for age, sex, and M05/M06. Persons were asked to confirm RA diagnosis (gold standard), which was linked to claims data given consent. Analyses were weighted to represent the total RA population of the database. Positive predictive values (PPVs) and discriminative properties were calculated for different algorithms: ICD-10 code only, additional examination of inflammatory markers, prescription of specific medication, rheumatologist appointment, or combination of these. RESULTS: Of 6193 persons with a claims diagnosis of RA, 3184 responded (51%). Overall, PPV for the ICD-10 code was 81% (95% confidence interval 79%-83%) with 94% (92%-95%) for M05 and 76% (73%-79%) for M06. PPVs increased (with loss of case numbers) if inflammatory markers (82% [80%-84%]), rheumatology visits (85% [82%-87%]) or specific medication (89% [87%-91%]) had been used in addition. Specific medication had the best discriminative properties (diagnostic odds ratio of 3.0) among persons with RA diagnosis. CONCLUSIONS: The ICD-10 codes M05 and (less optimal) M06 have high PPVs and are valuable to identify RA in German claims data. Depending on the respective research question, researchers should use different criteria for identification of RA.

Accuracy of identifying pediatric acute bacterial sinusitis diagnoses in outpatient claims data.

PURPOSE: Acute bacterial sinusitis is among the most frequent outpatient infections in children and adolescents and is well suited to study in large healthcare utilization databases, but the validity of International Classification of Diseases, 10th Revision (ICD-10) codes together with antibiotic prescriptions to identify cases of acute bacterial sinusitis has not been established. We aimed to evaluate the validity of ICD-10 codes combined with antibiotic prescriptions to identify new diagnoses of acute bacterial sinusitis among pediatric patients evaluated in the outpatient setting. METHODS: Children and adolescents aged 17 years and younger with an outpatient diagnosis of acute sinusitis along with an antibiotic prescription from an ambulatory facility affiliated with the Mass General Brigham health system were identified via a clinical data warehouse. Patients were stratified by age (0-5 years, 6-11 years, and 12-17 years), and 50 cases per age group were randomly sampled. Medical records were independently reviewed by two pediatric infectious diseases physicians to assess for the documentation of a clinician-defined diagnosis of acute bacterial sinusitis. Positive predictive values (PPVs) and 95% confidence intervals (CIs) were calculated. RESULTS: A total of 150 patients were included in the final cohort. Frontal, maxillary, and "unspecified" sinuses accounted for 88% of the diagnoses. The positive predictive value of the algorithm to identify clinician-defined diagnoses of acute bacterial sinusitis was 92% (95% CI 87%, 95%). The PPVs were consistent across age strata. CONCLUSIONS: ICD-10 codes for acute sinusitis, when paired with a same-day antibiotic prescription, have a high positive predictive value among a cohort of pediatric patients, suggesting that they can be used to study new acute bacterial sinusitis diagnoses with claims.