FP414THE VALIDITY OF DUTCH HEALTH CLAIMS DATA IN IDENTIFYING PATIENTS WITH CHRONIC KIDNEY DISEASE

2019 ◽  
Vol 34 (Supplement_1) ◽  
Author(s):  
Manon van Oosten ◽  
Richard Brohet ◽  
Susan Logtenberg ◽  
Anneke Kramer ◽  
Marc Hemmelder ◽  
...  
Keyword(s):  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Claims Data ◽  
Health Claims ◽  
Health Claims Data ◽  
Dutch Health

scholarly journals The validity of Dutch health claims data for identifying patients with chronic kidney disease: a hospital-based study in the Netherlands

2020 ◽  
Author(s):  
Manon J M van Oosten ◽  
Richard M Brohet ◽  
Susan J J Logtenberg ◽  
Anneke Kramer ◽  
Lambert D Dikkeschei ◽  
...  
Keyword(s):  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Claims Data ◽  
Reference Group ◽  
Health Claims ◽  
Predictive Values ◽  
Study Results ◽  
Health Claims Data ◽  
Dutch Health ◽  
Creatinine Measurement

Abstract Background Health claims data may be an efficient and easily accessible source to study chronic kidney disease (CKD) prevalence in a nationwide population. Our aim was to study Dutch claims data for their ability to identify CKD patients in different subgroups. Methods From a laboratory database, we selected 24 895 adults with at least one creatinine measurement in 2014 ordered at an outpatient clinic. Of these, 15 805 had ≥2 creatinine measurements at least 3 months apart and could be assessed for the chronicity criterion. We estimated the validity of a claim-based diagnosis of CKD and advanced CKD. The estimated glomerular filtration rate (eGFR)-based definitions for CKD (eGFR < 60 mL/min/1.73 m2) and advanced CKD (eGFR < 30 mL/min/1.73 m2) satisfying and not satisfying the chronicity criterion served as reference group. Analyses were stratified by age and sex. Results In general, sensitivity of claims data was highest in the population with the chronicity criterion as reference group. Sensitivity was higher in advanced CKD patients than in CKD patients {51% [95% confidence interval (CI) 47–56%] versus 27% [95% CI 25–28%]}. Furthermore, sensitivity was higher in young versus elderly patients. In patients with advanced CKD, sensitivity was 72% (95% CI 62–83%) for patients aged 20–59 years and 43% (95% CI 38–49%) in patients ≥75 years. The specificity of CKD and advanced CKD was ≥99%. Positive predictive values ranged from 72% to 99% and negative predictive values ranged from 40% to 100%. Conclusion When using health claims data for the estimation of CKD prevalence, it is important to take into account the characteristics of the population at hand. The younger the subjects and the more advanced the stage of CKD the higher the sensitivity of such data. Understanding which patients are selected using health claims data is crucial for a correct interpretation of study results.

scholarly journals Health claims databases used for kidney research around the world

2020 ◽  
Author(s):  
Manon J M van Oosten ◽  
Susan J J Logtenberg ◽  
Mireille A Edens ◽  
Marc H Hemmelder ◽  
Kitty J Jager ◽  
...  
Keyword(s):  
Kidney Disease ◽  
International Comparisons ◽  
International Health ◽  
Epidemiological Data ◽  
Health Claims ◽  
Claims Database ◽  
Diagnosis Codes ◽  
Health Claims Data ◽  
Dutch Health ◽  
Large Populations

Abstract Health claims databases offer opportunities for studies on large populations of patients with kidney disease and health outcomes in a non-experimental setting. Among others, their unique features enable studies on healthcare costs or on longitudinal, epidemiological data with nationwide coverage. However, health claims databases also have several limitations. Because clinical data and information on renal function are often lacking, the identification of patients with kidney disease depends on the actual presence of diagnosis codes only. Investigating the validity of these data is therefore crucial to assess whether outcomes derived from health claims data are truly meaningful. Also, one should take into account the coverage and content of a health claims database, especially when making international comparisons. In this article, an overview is provided of international health claims databases and their main publications in the area of nephrology. The structure and contents of the Dutch health claims database will be described, as well as an initiative to use the outcomes for research and the development of the Dutch Kidney Atlas. Finally, we will discuss to what extent one might be able to identify patients with kidney disease using health claims databases, as well as their strengths and limitations.

scholarly journals Predictive modeling of nontuberculous mycobacterial pulmonary disease epidemiology using German health claims data

2021 ◽  
Vol 104 ◽  
pp. 398-406
Author(s):  
Felix C. Ringshausen ◽  
Raphael Ewen ◽  
Jan Multmeier ◽  
Bondo Monga ◽  
Marko Obradovic ◽  
...  
Keyword(s):  
Pulmonary Disease ◽  
Predictive Modeling ◽  
Claims Data ◽  
Health Claims ◽  
Disease Epidemiology ◽  
German Health ◽  
Health Claims Data

scholarly journals An observational claims data analysis on the risk of maternal chronic kidney disease after preterm delivery and preeclampsia

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Maren Goetz ◽  
Mitho Müller ◽  
Raphael Gutsfeld ◽  
Tjeerd Dijkstra ◽  
Kathrin Hassdenteufel ◽  
...  
Keyword(s):  
Chronic Kidney Disease ◽  
Preterm Birth ◽  
Kidney Disease ◽  
Preterm Delivery ◽  
Claims Data ◽  
Maternal Risk ◽  
Live Births ◽  
Increased Risk ◽  
History Of

AbstractWomen with complications of pregnancy such as preeclampsia and preterm birth are at risk for adverse long-term outcomes, including an increased future risk of chronic kidney disease (CKD) and end-stage kidney disease (ESKD). This observational cohort study aimed to examine the risk of CKD after preterm delivery and preeclampsia in a large obstetric cohort in Germany, taking into account preexisting comorbidities, potential confounders, and the severity of CKD. Statutory claims data of the AOK Baden-Wuerttemberg were used to identify women with singleton live births between 2010 and 2017. Women with preexisting conditions including CKD, ESKD, and kidney replacement therapy (KRT) were excluded. Preterm delivery (< 37 gestational weeks) was the main exposure of interest; preeclampsia was investigated as secondary exposure. The main outcome was a newly recorded diagnosis of CKD in the claims database. Data were analyzed using Cox proportional hazard regression models. The time-dependent occurrence of CKD was analyzed for four strata, i.e., births with (i) neither an exposure of preterm delivery nor an exposure of preeclampsia, (ii) no exposure of preterm delivery but exposure of at least one preeclampsia, (iii) an exposure of at least one preterm delivery but no exposure of preeclampsia, or (iv) joint exposure of preterm delivery and preeclampsia. Risk stratification also included different CKD stages. Adjustments were made for confounding factors, such as maternal age, diabetes, obesity, and dyslipidemia. The cohort consisted of 193,152 women with 257,481 singleton live births. Mean observation time was 5.44 years. In total, there were 16,948 preterm deliveries (6.58%) and 14,448 births with at least one prior diagnosis of preeclampsia (5.61%). With a mean age of 30.51 years, 1,821 women developed any form of CKD. Compared to women with no risk exposure, women with a history of at least one preterm delivery (HR = 1.789) and women with a history of at least one preeclampsia (HR = 1.784) had an increased risk for any subsequent CKD. The highest risk for CKD was found for women with a joint exposure of preterm delivery and preeclampsia (HR = 5.227). These effects were the same in magnitude only for the outcome of mild to moderate CKD, but strongly increased for the outcome of severe CKD (HR = 11.90). Preterm delivery and preeclampsia were identified as independent risk factors for all CKD stages. A joint exposure or preterm birth and preeclampsia was associated with an excessive maternal risk burden for CKD in the first decade after pregnancy. Since consequent follow-up policies have not been defined yet, these results will help guide long-term surveillance for early detection and prevention of kidney disease, especially for women affected by both conditions.

scholarly journals JADE: A tool for medical researchers to explore adverse drug events using health claims data

2014 ◽  
Vol 05 (03) ◽  
pp. 621-629 ◽  
Author(s):  
S.K. Sauter ◽  
C. Rinner ◽  
L.M. Neuhofer ◽  
M. Wolzt ◽  
W. Grossmann ◽  
...  
Keyword(s):  
Adverse Drug Events ◽  
Claims Data ◽  
Age Groups ◽  
Rule Learning ◽  
Health Claims ◽  
Drug Event ◽  
Study Cohort ◽  
Defined Daily Doses ◽  
Health Claims Data ◽  
Medical Researchers

SummaryObjective: The objective of our project was to create a tool for physicians to explore health claims data with regard to adverse drug reactions. The Java Adverse Drug Event (JADE) tool should enable the analysis of prescribed drugs in connection with diagnoses from hospital stays.Methods: We calculated the number of days drugs were taken by using the defined daily doses and estimated possible interactions between dispensed drugs using the Austria Codex, a database including drug-drug interactions. The JADE tool was implemented using Java, R and a PostgreSQL database.Results: Beside an overview of the study cohort which includes selection of gender and age groups, selected statistical methods like association rule learning, logistic regression model and the number needed to harm have been implemented.Conclusion: The JADE tool can support physicians during their planning of clinical trials by showing the occurrences of adverse drug events with population based information.Citation: Edlinger D, Sauter SK, Rinner C, Neuhofer LM, Wolzt M, Grossmann W, Endel G, Gall W. JADE: A tool for medical researchers to explore adverse drug events using health claims data. Appl Clin Inf 2014; 5: 621–629http://dx.doi.org/10.4338/ACI-2014-04-RA-0036

scholarly journals An Explainable Multi-Modal Neural Network Architecture for Predicting Epilepsy Comorbidities Based on Administrative Claims Data

2020 ◽  
Author(s):  
Thomas Linden ◽  
Johann de Jong ◽  
Chao Lu ◽  
Victor Kiri ◽  
Kathrin Haeffs ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Claims Data ◽  
Health Claims ◽  
Superior Performance ◽  
Patient Specific ◽  
Administrative Claims ◽  
Theoretic Approach ◽  
Neural Network Architecture ◽  
Health Claims Data

1AbstractEpilepsy is a complex brain disorder characterized by repetitive seizure events. Epilepsy patients often suffer from various and severe physical and psychological co-morbidities (e.g. anxiety, migraine, stroke, etc.). While general comorbidity prevalences and incidences can be estimated from epidemiological data, such an approach does not take into account that actual patient specific risks can depend on various individual factors, including medication. This motivates to develop a machine learning approach for predicting risks of future comorbidities for the individual epilepsy patient.In this work we use inpatient and outpatient administrative health claims data of around 19,500 US epilepsy patients. We suggest a dedicated multi-modal neural network architecture (Deep personalized LOngitudinal convolutional RIsk model - DeepLORI) to predict the time dependent risk of six common comorbidities of epilepsy patients. We demonstrate superior performance of DeepLORI in a comparison with several existing methods Moreover, we show that DeepLORI based predictions can be interpreted on the level of individual patients. Using a game theoretic approach, we identify relevant features in DeepLORI models and demonstrate that model predictions are explainable in the light of existing knowledge about the disease. Finally, we validate the model on independent data from around 97,000 patients, showing good generalization and stable prediction performance over time.

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