scholarly journals Impact of ICD10 and secular changes on electronic medical record rheumatoid arthritis algorithms

Rheumatology ◽  
2020 ◽  
Vol 59 (12) ◽  
pp. 3759-3766 ◽  
Author(s):  
Sicong Huang ◽  
Jie Huang ◽  
Tianrun Cai ◽  
Kumar P Dahal ◽  
Andrew Cagan ◽  
...  
Keyword(s):  
Language Processing ◽  
Gold Standard ◽  
International Classification Of Diseases ◽  
Data Mart ◽  
Classification Of Diseases ◽  
Icd Codes ◽  
Rule Based Approach ◽  
Citrullinated Peptide ◽  
Better Than

Abstract Objective The objective of this study was to compare the performance of an RA algorithm developed and trained in 2010 utilizing natural language processing and machine learning, using updated data containing ICD10, new RA treatments, and a new electronic medical records (EMR) system. Methods We extracted data from subjects with ≥1 RA International Classification of Diseases (ICD) codes from the EMR of two large academic centres to create a data mart. Gold standard RA cases were identified from reviewing a random 200 subjects from the data mart, and a random 100 subjects who only have RA ICD10 codes. We compared the performance of the following algorithms using the original 2010 data with updated data: (i) a published 2010 RA algorithm; (ii) updated algorithm, incorporating ICD10 RA codes and new DMARDs; and (iii) published algorithm using ICD codes only, ICD RA code ≥3. Results The gold standard RA cases had mean age 65.5 years, 78.7% female, 74.1% RF or antibodies to cyclic citrullinated peptide (anti-CCP) positive. The positive predictive value (PPV) for ≥3 RA ICD was 54%, compared with 56% in 2010. At a specificity of 95%, the PPV of the 2010 algorithm and the updated version were both 91%, compared with 94% (95% CI: 91, 96%) in 2010. In subjects with ICD10 data only, the PPV for the updated 2010 RA algorithm was 93%. Conclusion The 2010 RA algorithm validated with the updated data with similar performance characteristics as the 2010 data. While the 2010 algorithm continued to perform better than the rule-based approach, the PPV of the latter also remained stable over time.

scholarly journals Utility of ICD Codes for Stress Cardiomyopathy in Hospital Administrative Databases: What Do They Signify?

2019 ◽  
pp. 160-163
Author(s):  
Anusha G Bhat ◽  
Kevin White ◽  
Kyle Gobeil ◽  
Tara Lagu ◽  
Peter K Lindenauer ◽  
...  
Keyword(s):  
Chart Review ◽  
Clinical Manifestations ◽  
International Classification Of Diseases ◽  
Stress Cardiomyopathy ◽  
Administrative Databases ◽  
Classification Of Diseases ◽  
Icd Codes ◽  
Hospital Administrative ◽  
New Diagnosis

Prior studies of stress cardiomyopathy (SCM) have used International Classification of Diseases (ICD) codes to identify patients in administrative databases without evaluating the validity of these codes. Between 2010 and 2016, we identified 592 patients discharged with a first known principal or secondary ICD code for SCM in our medical system. On chart review, 580 charts had a diagnosis of SCM (positive predictive value 98%; 95% CI: 96.4-98.8), although 38 (6.4%) did not have active clinical manifestations of SCM during the hospitalization. Moreover, only 66.8% underwent cardiac catheterization and 91.5% underwent echocardiography. These findings suggest that, although all but a few hospitalized patients with an ICD code for SCM had a diagnosis of SCM, some of these were chronic cases, and numerous patients with a new diagnosis of SCM did not undergo a complete diagnostic workup. Researchers should be mindful of these limitations in future studies involving administrative databases.

scholarly journals High-throughput Multimodal Automated Phenotyping (MAP) with Application to PheWAS

2019 ◽  
Author(s):  
Katherine P. Liao ◽  
Jiehuan Sun ◽  
Tianrun A. Cai ◽  
Nicholas Link ◽  
Chuan Hong ◽  
...  
Keyword(s):  
Language Processing ◽  
High Throughput ◽  
Large Scale ◽  
International Classification Of Diseases ◽  
Mapping Method ◽  
Map Algorithm ◽  
Phenotype Definition ◽  
Classification Of Diseases ◽  
Icd Codes ◽  
Map Approach

AbstractObjectiveElectronic health records (EHR) linked with biorepositories are a powerful platform for translational studies. A major bottleneck exists in the ability to phenotype patients accurately and efficiently. The objective of this study was to develop an automated high-throughput phenotyping method integrating International Classification of Diseases (ICD) codes and narrative data extracted using natural language processing (NLP).MethodWe developed a mapping method for automatically identifying relevant ICD and NLP concepts for a specific phenotype leveraging the UMLS. Aggregated ICD and NLP counts along with healthcare utilization were jointly analyzed by fitting an ensemble of latent mixture models. The MAP algorithm yields a predicted probability of phenotype for each patient and a threshold for classifying subjects with phenotype yes/no. The algorithm was validated using labeled data for 16 phenotypes from a biorepository and further tested in an independent cohort PheWAS for two SNPs with known associations.ResultsThe MAP algorithm achieved higher or similar AUC and F-scores compared to the ICD code across all 16 phenotypes. The features assembled via the automated approach had comparable accuracy to those assembled via manual curation (AUCMAP 0.943, AUCmanual 0.941). The PheWAS results suggest that the MAP approach detected previously validated associations with higher power when compared to the standard PheWAS method based on ICD codes.ConclusionThe MAP approach increased the accuracy of phenotype definition while maintaining scalability, facilitating use in studies requiring large scale phenotyping, such as PheWAS.

scholarly journals Characterizing Sleep Disorders in an Autism-Specific Collection of Electronic Health Records

2021 ◽  
Author(s):  
Emily Singer ◽  
Beth Malow ◽  
Maria Niarchou ◽  
Lea Davis ◽  
Rebecca Johnston ◽  
...  
Keyword(s):  
Sleep Problems ◽  
International Classification Of Diseases ◽  
Autism Spectrum ◽  
Health Records ◽  
Classification Of Diseases ◽  
Icd Codes ◽  
Treatment Responses ◽  
Electronic Health ◽  
Future Work

Background: Sleep problems are common in individuals with autism spectrum disorder (ASD). This study reviews one detailed approach to querying the electronic health record (EHR). Methods: We developed methods for identifying individuals with ASD and defined their sleep problems using International Classification of Diseases (ICD) codes or key words. We examined treatment responses to melatonin. Results: Sleep problems were documented in 86% of patients and using specific keywords yielded more sleep diagnoses than ICD codes alone. About two-thirds of patients benefitted from melatonin. Conclusions: Our study provides a framework for using deidentified medical records to characterize sleep, a common co-occurring condition, in ASD. Using specific keywords could be helpful in future work that queries the EHR.

scholarly journals Risk of Severe COVID-19 Outcomes Among Patients with Rheumatoid Arthritis in the United States

2021 ◽  
Author(s):  
Ching-Yi Chuo ◽  
Vince Yau ◽  
Sriraman Madhavan ◽  
Larry Tsai ◽  
Jenny Chia
Keyword(s):  
Rheumatoid Arthritis ◽  
International Classification Of Diseases ◽  
The United States ◽  
Inpatient Mortality ◽  
Laboratory Results ◽  
Classification Of Diseases ◽  
Icd Codes ◽  
Procedure Codes ◽  
The U.S

Introduction Coronavirus disease 2019 (COVID-19) has infected over 22 million individuals worldwide. It remains unclear whether patients with COVID-19 and Rheumatoid Arthritis (RA) experience worse clinical outcomes compared to similar patients with COVID-19 without RA. Objective The aim of this study is to provide insights on how COVID-19 impacted patients with RA given the nature of the disease and medication used. Methods RA cases were identified via International Classification of Diseases (ICD) codes and COVID-19 cases by laboratory results in the U.S. based TriNetX network. Patients with COVID-19 and RA were propensity-score matched based on demographics with patients with COVID-19 without RA at a 1:3 ratio. A hospitalized sub-population was defined by procedure codes. Results We identified 1,014 COVID-19 patients with RA and 3,042 non-RA matches selected from 137,757 patients. The odds of hospitalization (non-RA:23%, RA:24.6%, OR:1.08, 95% CI: 0.88 to 1.33) or mortality (non-RA:5.4%, RA:6%, OR:0.93, 95% CI: 0.65 to 1.34) were not significantly different. The hospitalized sub-population included 249 patients with COVID-19 and RA and 745 non-RA matches selected from 21,435 patients. The risk of intensive care unit (ICU) admission (non-RA:18.8%, RA:18.1%, OR:0.94, 95% CI: 0.60 to 1.45), and inpatient mortality (non-RA:14.4%, RA:14.5%, OR:0.86, 95% CI: 0.53 to 1.40) were not significantly different. Conclusion We did not find evidence suggesting patients with COVID-19 and RA are more likely to have severe outcomes than patients with COVID-19 without RA.

scholarly journals Validating Coding for the Identification of Pediatric Treatment Resistant Epilepsy Patients

2021 ◽  
Vol 8 ◽  
pp. 2329048X2110378
Author(s):  
Daniel A. Freedman ◽  
Zachary Grinspan ◽  
Peter Glynn ◽  
Jackson Mittlesteadt ◽  
Alex Dawes ◽  
...  
Keyword(s):  
Refractory Epilepsy ◽  
International Classification Of Diseases ◽  
Cross Sectional Study ◽  
Absence Epilepsy ◽  
Treatment Resistant ◽  
Cross Sectional ◽  
Classification Of Diseases ◽  
Icd Codes ◽  
Pediatric Treatment

The International Classification of Diseases (ICD) system includes sub codes to indicate that an individual with epilepsy is treatment resistant. These codes would be a valuable tool to identify individuals for quality improvement and population health, as well as for recruitment into clinical trials. However, the accuracy of these codes is unclear. We performed a single center cross sectional study to understand the accuracy of ICD codes for treatment resistant epilepsy. We identified 344 individuals, roughly half with treatment resistant epilepsy The ICD code had a sensitivity of 90% (147 of 164) and specificity of 86% (155 of 180). The miscoding of children with refractory epilepsy was attributed to the following reasons: 5 patients had epilepsy surgery, 4 had absence epilepsy, 4 patients were seen by different providers, and 1 patient was most recently seen in movement disorders clinic. ICD codes accurately identify children with treatment resistant epilepsy.

Development and Use of Natural Language Processing for Identification of Distant Cancer Recurrence and Sites of Distant Recurrence Using Unstructured Electronic Health Record Data

2021 ◽  
pp. 469-478
Author(s):  
Yasmin H. Karimi ◽  
Douglas W. Blayney ◽  
Allison W. Kurian ◽  
Jeanne Shen ◽  
Rikiya Yamashita ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Language Processing ◽  
Large Scale ◽  
Cancer Recurrence ◽  
International Classification Of Diseases ◽  
Distant Recurrence ◽  
Electronic Health Record Data ◽  
Record Data ◽  
Classification Of Diseases

PURPOSE Large-scale analysis of real-world evidence is often limited to structured data fields that do not contain reliable information on recurrence status and disease sites. In this report, we describe a natural language processing (NLP) framework that uses data from free-text, unstructured reports to classify recurrence status and sites of recurrence for patients with breast and hepatocellular carcinomas (HCC). METHODS Using two cohorts of breast cancer and HCC cases, we validated the ability of a previously developed NLP model to distinguish between no recurrence, local recurrence, and distant recurrence, based on clinician notes, radiology reports, and pathology reports compared with manual curation. A second NLP model was trained and validated to identify sites of recurrence. We compared the ability of each NLP model to identify the presence, timing, and site of recurrence, when compared against manual chart review and International Classification of Diseases coding. RESULTS A total of 1,273 patients were included in the development and validation of the two models. The NLP model for recurrence detects distant recurrence with an area under the curve of 0.98 (95% CI, 0.96 to 0.99) and 0.95 (95% CI, 0.88 to 0.98) in breast and HCC cohorts, respectively. The mean accuracy of the NLP model for detecting any site of distant recurrence was 0.9 for breast cancer and 0.83 for HCC. The NLP model for recurrence identified a larger proportion of patients with distant recurrence in a breast cancer database (11.1%) compared with International Classification of Diseases coding (2.31%). CONCLUSION We developed two NLP models to identify distant cancer recurrence, timing of recurrence, and sites of recurrence based on unstructured electronic health record data. These models can be used to perform large-scale retrospective studies in oncology.

The course and outcome of cycloid psychosis

1982 ◽  
Vol 12 (1) ◽  
pp. 97-105 ◽  
Author(s):  
I. F. Brockington ◽  
C. Perris ◽  
R. E. Kendell ◽  
V. E. Hillier ◽  
S. Wainwright
Keyword(s):  
Final Diagnosis ◽  
International Classification Of Diseases ◽  
Acute Onset ◽  
Cycloid Psychosis ◽  
Manic Symptoms ◽  
Manic Depressive ◽  
Classification Of Diseases ◽  
Anglo American ◽  
Better Than

SynopsisThirty patients with cycloid psychosis were found among 244 general psychotic and schizo-affective patients studied in London. The main clues to the diagnosis were the presence of ‘confusion’, a pleomorphic clinical picture or an acute onset. Most of the patients were classified as schizophrenic by British psychiatrists and the Catego system, and as schizo-affective or mood-incongruent affective psychotics by the American Research Diagnostic Criteria and DSM-III. There was little overlap between the cycloids and any Anglo-American category, and cycloid psychosis is not synonymous with schizo-affective psychosis. The outcome of the cycloids was better than that of psychotic patients as a whole, and much better than schizophrenia as defined by Catego, Schneider's, Langfeldt's or Carpenter's rules, or by the guidelines set by the International Classification of Diseases. Compared with manic-depressive patients (defined by the presence of mania at some stage), cycloids had more schizophrenic and fewer depressive and manic symptoms. There was a negligible concordance between the diagnosis of cycloid psychosis and the final diagnosis of manic-depressive disease. It is concluded that these patients should not be diagnosed as schizophrenic, but that the relation of cycloid psychosis to manic-depressive disease is not yet resolved.

Sign in / Sign up

Export Citation Format

Share Document