AB0103 THE ACCURACY OF ADMINISTRATIVE HEALTH DATA FOR IDENTIFYING PATIENTS WITH RHEUMATOID ARTHRITIS: A VALIDATION STUDY USING MEDICAL RECORDS IN WESTERN AUSTRALIA

Background:The use of large administrative health datasets is increasingly important in Rheumatology for disease trends and outcome research (1). We established the West Australian Rheumatic Disease Epidemiological Registry containing longitudinal health data for over 10000 patients with Rheumatoid Arthritis (RA) in Western Australia (WA). Accuracy of coding for RA is essential to validity of the datasets.Objectives:Investigate the diagnostic accuracy of International Classification of Diseases (ICD) based discharge codes for RA at WA’s largest tertiary hospital.Methods:Medical records for RA patients randomly selected from the hospital discharge database with ICD 10 codes (M05.00–M06.99) from 2008–2020 were retrospectively reviewed. Rheumatologist reported diagnosis and ACR/EULAR classification were used as gold standards to determine positive predictive value (PPV) with 95% Confidence Interval (CI) for RA primary diagnostic codes.Results:Medical chart review was completed for 87 patients (mean age 64.7 years, 67% female). Total of 80 (92%) patients had specialist confirmed RA diagnoses, while seven patients (8%) had alternate clinical diagnoses providing a PPV of 93.5% (95%CI: 89.9 to 95.86). Overall, 69 out 87 patients (79.3%) fulfilled ACR/EULAR classification criteria based on RA primary diagnostic codes with a PPV of 80.5% (95%CI: 76.81 to 83.7). A combination of a diagnostic RA code with biologic infusion codes in two or more codes increased the PPV to 97.9%.Conclusion:Hospital discharge diagnostic codes in WA identify RA patients with a high degree of accuracy. Combining a primary diagnostic code for RA with biological infusion codes can further increase the PPV.References:[1]Hanly et al. The use of administrative health care databases to identify patients with rheumatoid arthritis. Open Access Rheumatol 2015; 7: 69–75.Table 1.Accuracy measures of different algorithms for random sample of rheumatoid arthritis (RA) patients with one or more RA codes.Rheumatologist-reported diagnosisACR/EULAR classification criteriaAdministrative dataSNSPPPVNPVSNSPPPVNPVOne or more RA primary codes90%28.5%93.5%7.6%89.8%16.6%80.5%30%One or more RA biological infusion codes25%71.4%90.9%7.7%20.3%55.5%63.6%15.3%Two or more RA codes including biological codes60%85.7%97.9%15.8%56.5%44.4%79.6%21%RA=Rheumatoid Arthritis, SN=Sensitivity, SP=Specificity, PPV= Positive predictive value, NPV= Negative predictive value.Acknowledgements:Khalid Almutairi was supported by an Australian Government research training Program PhD Scholarship at the University of Western Australia.Disclosure of Interests:Khalid Almutairi: None declared, Johannes Nossent Speakers bureau: Janssen, David Preen: None declared, Helen Keen Speakers bureau: Pfizer Australia, Abbvie Australia, Katrina Roger: None declared, Charles Inderjeeth Speakers bureau: Eli Lilly

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The validity of heart failure diagnoses in the Finnish Hospital Discharge Register

Scandinavian Journal of Public Health ◽

10.1177/1403494819847051 ◽

2019 ◽

Vol 48 (1) ◽

pp. 20-28 ◽

Cited By ~ 3

Author(s):

Matti A. Vuori ◽

Jari A. Laukkanen ◽

Arto Pietilä ◽

Aki S. Havulinna ◽

Mika Kähönen ◽

...

Keyword(s):

Heart Failure ◽

Ejection Fraction ◽

Positive Predictive Value ◽

Negative Predictive Value ◽

Hospital Discharge ◽

Predictive Value ◽

Medical Records ◽

Preserved Ejection Fraction ◽

Hospital Discharge Register

Background: Contemporary validation studies of register-based heart failure diagnoses based on current guidelines and complete clinical data are lacking in Finland and internationally. Our objective was to assess the positive and negative predictive values of heart failure diagnoses in a nationwide hospital discharge register. Methods: Using Finnish Hospital Discharge Register data from 2013–2015, we obtained the medical records for 120 patients with a register-based diagnosis for heart failure (cases) and for 120 patients with a predisposing condition for heart failure, but without a heart failure diagnosis (controls). The medical records of all patients were assessed by a physician who categorized each individual as having heart failure (with reduced or preserved ejection fraction) or no heart failure, based on the definition of current European Society of Cardiology heart failure guidelines. Unclear cases were assessed by a panel of three physicians. This classification was considered as the clinical gold standard, against which the registers were assessed. Results: Register-based heart failure diagnoses had a positive predictive value of 0.85 (95% CI 0.77–0.91) and a negative predictive value of 0.83 (95% CI 0.75–0.90). The positive predictive value decreased when we classified patients with transient heart failure (duration <6 months), dialysis/lung disease or heart failure with preserved ejection fraction as not having heart failure. Conclusions: Heart failure diagnoses of the Finnish Hospital Discharge Register have good positive predictive value and negative predictive value, even when patients with pre-existing heart conditions are used as healthy controls. Our results suggest that heart failure diagnoses based on register data can be reliably used for research purposes.

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The accuracy of administrative health data for identifying patients with rheumatoid arthritis: a retrospective validation study using medical records in Western Australia

Rheumatology International ◽

10.1007/s00296-021-04811-9 ◽

2021 ◽

Vol 41 (4) ◽

pp. 741-750

Author(s):

Khalid Almutairi ◽

Charles Inderjeeth ◽

David B. Preen ◽

Helen Keen ◽

Katrina Rogers ◽

...

Keyword(s):

Rheumatoid Arthritis ◽

Western Australia ◽

Validation Study ◽

Medical Records ◽

Health Data ◽

Administrative Health Data

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POS0706 PERFORMANCES OF DIFFERENT CLASSIFICATION CRITERIA FOR SYSTEMIC LUPUS ERYTHEMATOSUS IN A SINGLE CENTER COHORT FROM TURKEY

Annals of the Rheumatic Diseases ◽

10.1136/annrheumdis-2021-eular.366 ◽

2021 ◽

Vol 80 (Suppl 1) ◽

pp. 602.1-603

Author(s):

E. S. Torun ◽

E. Bektaş ◽

F. Kemik ◽

M. Bektaş ◽

C. Cetin ◽

...

Keyword(s):

Systemic Lupus Erythematosus ◽

Positive Predictive Value ◽

Negative Predictive Value ◽

Antiphospholipid Syndrome ◽

Lupus Erythematosus ◽

Predictive Value ◽

Sjogren's Syndrome ◽

Classification Criteria ◽

Systemic Lupus ◽

Acr Criteria

Background:Recently developed EULAR/ACR classification criteria for systemic lupus erythematosus (SLE) have important differences compared to the 2012 Systemic Lupus International Collaborating Clinics (SLICC) SLE classification criteria and the revised 1997 American College of Rheumatology (ACR) criteria: The obligatory entry criterion of antinuclear antibody (ANA) positivity is introduced and a “weighted” approach is used1. Sensitivity and specificity of these three criteria have been debated and may vary in different populations and clinical settings.Objectives:We aim to compare the performances of three criteria sets/rules in a large cohort of patients and relevant diseased controls from a reference center with dedicated clinics for SLE and other autoimmune/inflammatory connective tissue diseases from Turkey.Methods:We reviewed the medical records of SLE patients and diseased controls for clinical and laboratory features relevant to all sets of criteria. Criteria sets/rules were analysed based on sensitivity, positive predictive value, specificity and negative predictive value, using clinical diagnosis with at least 6 months of follow-up as the gold standard. A subgroup analysis was performed in ANA positive patients for both SLE patients and diseased controls. SLE patients that did not fulfil 2012 SLICC criteria and 2019 EULAR/ACR criteria and diseased controls that fulfilled these criteria were evaluated.Results:A total of 392 SLE patients and 294 non-SLE diseased controls (48 undifferentiated connective tissue disease, 51 Sjögren’s syndrome, 43 idiopathic inflammatory myopathy, 50 systemic sclerosis, 52 primary antiphospholipid syndrome, 15 rheumatoid arthritis, 15 psoriatic arthritis and 20 ANCA associated vasculitis) were included into the study. Hundred and fourteen patients (16.6%) were ANA negative.Sensitivity was more than 90% for 2012 SLICC criteria and 2019 EULAR/ACR criteria and positive predictive value was more than 90% for all three criteria (Table 1). Specificity was the highest for 1997 ACR criteria. Negative predictive value was 76.9% for ACR criteria, 88.4% for SLICC criteria and 91.7% for EULAR/ACR criteria.In only ANA positive patients, sensitivity was 79.6% for 1997 ACR criteria, 92.2% for 2012 SLICC criteria and 96.1% for 2019 EULAR/ACR criteria. Specificity was 92.6% for ACR criteria, 87.8% for SLICC criteria 85.2% for EULAR/ACR criteria.Eleven clinically diagnosed SLE patients had insufficient number of items for both 2012 SLICC and 2019 EULAR/ACR criteria. Both criteria were fulfilled by 16 diseased controls: 9 with Sjögren’s syndrome, 5 with antiphospholipid syndrome, one with dermatomyositis and one with systemic sclerosis.Table 1.Sensitivity, positive predictive value, specificity and negative predictive value of 1997 ACR, 2012 SLICC and 2019 EULAR/ACR classification criteriaSLE (+)SLE (-)Sensitivity (%)Positive Predictive Value (%)Specificity (%)Negative Predictive Value (%)1997 ACR(+) 308(-) 841527978.695.494.976.92012 SLICC(+) 357(-) 352626891.193.291.288.42019 EULAR/ACR(+) 368(-) 242826693.892.990.591.7Conclusion:In this cohort, although all three criteria have sufficient specificity, sensitivity and negative predictive value of 1997 ACR criteria are the lowest. Overall, 2019 EULAR/ACR and 2012 SLICC criteria have a comparable performance, but if only ANA positive cases and controls are analysed, the specificity of both criteria decrease to less than 90%. Some SLE patients with a clinical diagnosis lacked sufficient number of criteria. Mostly, patients with Sjögren’s syndrome or antiphospholipid syndrome are prone to misclassification by both recent criteria.References:[1]Aringer M, Costenbader K, Daikh D, et al. 2019 European League Against Rheumatism/American College of Rheumatology classification criteria for systemic lupus erythematosus. Ann Rheum Dis 2019;78:1151-1159.Disclosure of Interests:None declared

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Identification of newborns at risk for autism using electronic medical records and machine learning

10.1101/19008367 ◽

2019 ◽

Author(s):

Rayees Rahman ◽

Arad Kodesh ◽

Stephen Z Levine ◽

Sven Sandin ◽

Abraham Reichenberg ◽

...

Keyword(s):

Machine Learning ◽

Autism Spectrum Disorder ◽

Positive Predictive Value ◽

Electronic Medical Records ◽

Predictive Value ◽

False Positive ◽

Medical Records ◽

False Positive Rate ◽

Autism Spectrum ◽

Positive Rate

AbstractImportanceCurrent approaches for early identification of individuals at high risk for autism spectrum disorder (ASD) in the general population are limited, where most ASD patients are not identified until after the age of 4. This is despite substantial evidence suggesting that early diagnosis and intervention improves developmental course and outcome.ObjectiveDevelop a machine learning (ML) method predicting the diagnosis of ASD in offspring in a general population sample, using parental electronic medical records (EMR) available before childbirthDesignPrognostic study of EMR data within a single Israeli health maintenance organization, for the parents of 1,397 ASD children (ICD-9/10), and 94,741 non-ASD children born between January 1st, 1997 through December 31st, 2008. The complete EMR record of the parents was used to develop various ML models to predict the risk of having a child with ASD.Main outcomes and measuresRoutinely available parental sociodemographic information, medical histories and prescribed medications data until offspring’s birth were used to generate features to train various machine learning algorithms, including multivariate logistic regression, artificial neural networks, and random forest. Prediction performance was evaluated with 10-fold cross validation, by computing C statistics, sensitivity, specificity, accuracy, false positive rate, and precision (positive predictive value, PPV).ResultsAll ML models tested had similar performance, achieving an average C statistics of 0.70, sensitivity of 28.63%, specificity of 98.62%, accuracy of 96.05%, false positive rate of 1.37%, and positive predictive value of 45.85% for predicting ASD in this dataset.Conclusion and relevanceML algorithms combined with EMR capture early life ASD risk. Such approaches may be able to enhance the ability for accurate and efficient early detection of ASD in large populations of children.Key pointsQuestionCan autism risk in children be predicted using the pre-birth electronic medical record (EMR) of the parents?FindingsIn this population-based study that included 1,397 children with autism spectrum disorder (ASD) and 94,741 non-ASD children, we developed a machine learning classifier for predicting the likelihood of childhood diagnosis of ASD with an average C statistic of 0.70, sensitivity of 28.63%, specificity of 98.62%, accuracy of 96.05%, false positive rate of 1.37%, and positive predictive value of 45.85%.MeaningThe results presented serve as a proof-of-principle of the potential utility of EMR for the identification of a large proportion of future children at a high-risk of ASD.

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Positive predictive value of infective endocarditis in the Danish National Patient Registry: a validation study

Epidemiology and Infection ◽

10.1017/s0950268818001401 ◽

2018 ◽

Vol 146 (15) ◽

pp. 1965-1967 ◽

Cited By ~ 9

Author(s):

Lauge Østergaard ◽

Kasper Adelborg ◽

Jens Sundbøll ◽

Lars Pedersen ◽

Emil Loldrup Fosbøl ◽

...

Keyword(s):

Infective Endocarditis ◽

Positive Predictive Value ◽

Predictive Value ◽

Medical Records ◽

Electronic Device ◽

Patient Registry ◽

Cardiac Implantable Electronic Device ◽

National Patient Registry ◽

Danish National Patient Registry ◽

National Patient

AbstractThe positive predictive value of an infective endocarditis diagnosis is approximately 80% in the Danish National Patient Registry. However, since infective endocarditis is a heterogeneous disease implying long-term intravenous treatment, we hypothesiszed that the positive predictive value varies by length of hospital stay. A total of 100 patients with first-time infective endocarditis in the Danish National Patient Registry were identified from January 2010 – December 2012 at the University hospital of Aarhus and regional hospitals of Herning and Randers. Medical records were reviewed. We calculated the positive predictive value according to admission length, and separately for patients with a cardiac implantable electronic device and a prosthetic heart valve using the Wilson score method. Among the 92 medical records available for review, the majority of the patients had admission length ⩾2 weeks. The positive predictive value increased with length of admission. In patients with admission length <2 weeks the positive predictive value was 65% while it was 90% for admission length ⩾2 weeks. The positive predictive value was 81% for patients with a cardiac implantable electronic device and 87% for patients with a prosthetic valve. The positive predictive value of the infective endocarditis diagnosis in the Danish National Patient Registry is high for patients with admission length ⩾2 weeks. Using this algorithm, the Danish National Patient Registry provides a valid source for identifying infective endocarditis for research.

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Positive predictive value of acute and chronic pancreatitis diagnoses in the Danish National Patient Registry: A validation study

Scandinavian Journal of Public Health ◽

10.1177/1403494818773535 ◽

2018 ◽

Vol 48 (1) ◽

pp. 14-19 ◽

Cited By ~ 6

Author(s):

Jakob Kirkegård ◽

Marie R. Mortensen ◽

Ida R. Johannsen ◽

Frank V. Mortensen ◽

Deirdre Cronin-Fenton

Keyword(s):

Acute Pancreatitis ◽

Chronic Pancreatitis ◽

Positive Predictive Value ◽

Predictive Value ◽

Medical Records ◽

Patient Registry ◽

National Patient Registry ◽

Danish National Patient Registry ◽

National Patient ◽

Acute And Chronic Pancreatitis

Aims: To examine the validity of the diagnoses of acute and chronic pancreatitis registered in the Danish National Patient Registry. Methods: We identified all patients in the Danish National Patient Registry admitted to two Danish hospitals with acute or chronic pancreatitis from 1996 to 2013. From this population, we randomly sampled 100 patients with acute pancreatitis and 100 patients with chronic pancreatitis. For each cohort, we computed the positive predictive values and associated 95% confidence intervals (CIs) for the discharge diagnosis of acute or chronic pancreatitis using medical records as the gold standard. Results: We identified 2617 patients with acute pancreatitis and 1284 patients with chronic pancreatitis discharged from either of the two hospitals during the study period. Of these, 776 (19.9%) had a diagnosis of both acute and chronic pancreatitis and are thus present in both cohorts. From the 200 sampled patients, a total of 138 (69.0%) medical records were available for review. The positive predictive value for a diagnosis of acute pancreatitis in the Danish National Patient Registry was 97.3% (95% CI 90.5–99.2%) and for chronic pancreatitis 83.1% (95% CI 72.2–90.3%). Conclusions: The validity of diagnoses of acute and chronic pancreatitis registered in the Danish National Patient Registry since 1996 is generally high.

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Association between HACEK bacteraemia and endocarditis

Journal of Medical Microbiology ◽

10.1099/jmm.0.070060-0 ◽

2014 ◽

Vol 63 (6) ◽

pp. 892-895 ◽

Cited By ~ 18

Author(s):

Haur Sen Yew ◽

Stephen T. Chambers ◽

Sally A. Roberts ◽

David J. Holland ◽

Kylie A. Julian ◽

...

Keyword(s):

Positive Predictive Value ◽

Predictive Value ◽

Medical Records ◽

Aggregatibacter Actinomycetemcomitans ◽

Eikenella Corrodens ◽

Aggregatibacter Aphrophilus ◽

Haemophilus Parainfluenzae ◽

Duke Criteria

We retrospectively examined medical records of 87 patients with bacteraemia caused by members of the HACEK group (Haemophilus parainfluenzae, Aggregatibacter actinomycetemcomitans, Aggregatibacter aphrophilus, Aggregatibacter paraphrophilus, Cardiobacterium spp., Eikenella corrodens and Kingella spp.) to determine whether endocarditis was present, as defined by the Duke criteria. The overall positive predictive value (PPV) of HACEK bacteraemia for endocarditis was 60 %. The PPV varied with different HACEK species from 0 % (E. corrodens) to 100 % (A. actinomycetemcomitans).

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Investigating the impact of disease and health record duration on the eMERGE algorithm for rheumatoid arthritis

Journal of the American Medical Informatics Association ◽

10.1093/jamia/ocaa014 ◽

2020 ◽

Vol 27 (4) ◽

pp. 601-605

Author(s):

Vanessa L Kronzer ◽

Liwei Wang ◽

Hongfang Liu ◽

John M Davis ◽

Jeffrey A Sparks ◽

...

Keyword(s):

Rheumatoid Arthritis ◽

Positive Predictive Value ◽

Negative Predictive Value ◽

Predictive Value ◽

Index Date ◽

Model Performance ◽

Area Under The Curve ◽

Population Based ◽

Health Record ◽

The Impact

Abstract Objective The study sought to determine the dependence of the Electronic Medical Records and Genomics (eMERGE) rheumatoid arthritis (RA) algorithm on both RA and electronic health record (EHR) duration. Materials and Methods Using a population-based cohort from the Mayo Clinic Biobank, we identified 497 patients with at least 1 RA diagnosis code. RA case status was manually determined using validated criteria for RA. RA duration was defined as time from first RA code to the index date of biobank enrollment. To simulate EHR duration, various years of EHR lookback were applied, starting at the index date and going backward. Model performance was determined by sensitivity, specificity, positive predictive value, negative predictive value, and area under the curve (AUC). Results The eMERGE algorithm performed well in this cohort, with overall sensitivity 53%, specificity 99%, positive predictive value 97%, negative predictive value 74%, and AUC 76%. Among patients with RA duration <2 years, sensitivity and AUC were only 9% and 54%, respectively, but increased to 71% and 85% among patients with RA duration >10 years. Longer EHR lookback also improved model performance up to a threshold of 10 years, in which sensitivity reached 52% and AUC 75%. However, optimal EHR lookback varied by RA duration; an EHR lookback of 3 years was best able to identify recently diagnosed RA cases. Conclusions eMERGE algorithm performance improves with longer RA duration as well as EHR duration up to 10 years, though shorter EHR lookback can improve identification of recently diagnosed RA cases.

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Interleukin 21 as a diagnostic marker of rheumatoid arthritis.

The Professional Medical Journal ◽

10.29309/tpmj/2019.26.08.1897 ◽

2019 ◽

Vol 26 (08) ◽

pp. 1229-1232

Author(s):

Khadija Kiran ◽

Amtul Huda ◽

Zuhair Bhatti

Keyword(s):

Rheumatoid Arthritis ◽

Positive Predictive Value ◽

Negative Predictive Value ◽

Predictive Value ◽

Diagnostic Marker ◽

Cross Sectional Study ◽

P Value ◽

Cross Sectional ◽

Medical College ◽

Sensitivity Specificity

To investigate the role of IL-21 as diagnostic marker in diagnosis of rheumatoid arthritis. Study Design: Cross sectional study. Setting: Department of Physiology and Orthopedic Gujranwala Medical College, Gujranwala. Period: October 2017 to October 2018 in one year duration. Materials and Methods: A total of 150 patients were included in the study, main variables assessed in this study were positive predictive value negative predictive value, sensitivity, specificity and accuracy of IL-21 in diagnosis of rheumatoid arthritis. SPSS version 23 was used to analyze the data. P value less than or equal to 0.05 was taken as significant. Study was started after permission from hospital ethical committee and patients were informed in detail about disease and procedure to be done. Non probability consecutive sampling was used. Results: The estimated sensitivity was 93.6%. The estimated specificity was 50%. Positive predictive value was 96.3% and negative predictive value was 35.7%. The overall accuracy was 90.6% for diagnosing rheumatoid arthritis. Conclusion: IL-21 induces MMP3 in rheumatoid arthritis patients, identification of IL-21 from synovium of patients indicates the presence of rheumatoid arthritis. We observed 90.6% diagnostic accuracy of IL-21 for rheumatoid patients taking RA factor as gold standard of diagnostic tool.

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