Cross-validation of comorbidity items in two national databases in a sample of patients with end-stage kidney disease

BACKGROUND

The use of national medico-administrative databases for epidemiological studies has increased in the last decades. In France, the Healthcare Expenditures and Conditions Mapping (HECM) algorithm has been developed to analyse and monitor the morbidity and economic burden of 58 diseases. We aimed to assess the performance of the HECM in identifying different conditions in patients with end-stage kidney disease (ESKD) using data from the REIN registry (the French National Registry for patients with ESKD).

METHODS

We included all patients over 18 years of age who started renal replacement therapy in France in 2018. Five conditions with a similar definition in both databases were included (ESKD, diabetes, human immunodeficiency virus [HIV], coronary insufficiency, and cancer). The performance of each SNDS algorithm was assessed using sensitivity, specificity, positive predictive values (PPVs), negative predictive values (NPVs), and Cohen's kappa coefficient.

RESULTS

In total 5,971 patients were included. Among them, 81% were identified as having ESKD in both databases. Diabetes was the condition with the best performance, with a sensitivity, specificity, PPV, NPV, and Kappa coefficient all over 80%. Cancer had the lowest level of agreement with a Kappa coefficient of 51% and a high specificity and high NPV (94% and 95%). The conditions for which the definition in the HECM included disease-specific medications performed better in our study.

CONCLUSION

The HECM showed good to very good concordance with the REIN database information overall, with the exception of cancer. Further validation of the HECM tool in other populations should be performed.

Tracking clusters of patients over time enables extracting information from medico-administrative databases

CONTEXT

Identifying clusters (i.e., subgroups) of patients from the analysis of medico-administrative databases is particularly important to better understand disease heterogeneity. However, these databases contain different types of longitudinal variables which are measured over different follow-up periods, generating truncated data. It is therefore fundamental to develop clustering approaches that can handle this type of data.

OBJECTIVE

We propose here cluster-tracking approaches to identify clusters of patients from truncated longitudinal data contained in medico-administrative databases.

MATERIAL AND METHODS

We first cluster patients at each age. We then track the identified clusters over ages to construct cluster-trajectories. We compared our novel approaches with three classical longitudinal clustering approaches by calculating the silhouette score. As a use-case, we analyzed antithrombotic drugs used from 2008 to 2018 contained in the Échantillon Généraliste des Bénéficiaires (EGB), a French national cohort.

RESULTS

Our cluster-tracking approaches allow us to identify several cluster-trajectories with clinical significance without any imputation of data. The comparison of the silhouette scores obtained with the different approaches highlights the better performances of the cluster-tracking approaches.

CONCLUSION

The cluster-tracking approaches are a novel and efficient alternative to identify patient clusters from medico-administrative databases by taking into account their specificities.

A data-driven pipeline to extract potential adverse drug reactions through prescription, procedures and medical diagnoses analysis: application to a cohort study of 2,010 patients taking hydroxychloroquine with an 11-year follow-up

Context

Real-life data consist of exhaustive data which are not subject to selection bias. These data enable to study drug-safety profiles but are underused because of their temporality, necessitating complex models (i.e., safety depends on the dose, timing, and duration of treatment). We aimed to create a data-driven pipeline strategy that manages the complex temporality of real-life data to highlight the safety profile of a given drug.

Methods

We proposed to apply the weighted cumulative exposure (WCE) statistical model to all health events occurring after a drug introduction (in this paper HCQ) and performed bootstrap to select relevant diagnoses, drugs and interventions which could reflect an adverse drug reactions (ADRs). We applied this data-driven pipeline on a French national medico-administrative database to extract the safety profile of hydroxychloroquine (HCQ) from a cohort of 2,010 patients.

Results

The proposed method selected eight drugs (metopimazine, anethole trithione, tropicamide, alendronic acid & colecalciferol, hydrocortisone, chlormadinone, valsartan and tixocortol), twelve procedures (six ophthalmic procedures, two dental procedures, two skin lesions procedures and osteodensitometry procedure) and two medical diagnoses (systemic lupus erythematous, unspecified and discoid lupus erythematous) to be significantly associated with HCQ exposure.

Conclusion

We provide a method extracting the broad spectrum of diagnoses, drugs and interventions associated to any given drug, potentially highlighting ADRs. Applied to hydroxychloroquine, this method extracted among others already known ADRs.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12874-022-01628-3.

• The challenge of drug-safety signal detection methods is to handle four types of difficulties:

○ The data source, the study of long-term adverse drug reactions or effects not suspected by healthcare professionals, requires the use of a real-life data source.

○ The consideration of a broad spectrum of potential adverse drug reactions (ADRs), and not only candidate ADRs.

○ The temporal impact (meaning that safety depends on the dose, date and duration of treatment).

○ The difference between true ADRs and disease natural course.

• We aimed to create a data-driven pipeline strategy, without any assumption of any ADRs, which take into account the complex temporality of real-life data to provide the safety profile of a given drug.

• Our pipeline used three sources of real-life data to establish a safety profile of a given drug: drug prescriptions, procedures and medical diagnoses.

• We successfully applied our data-driven pipeline strategy to hydroxychloroquine (HCQ). Our pipeline enabled us to find diagnoses, drugs and interventions related to HCQ and which could reflect an ADR due to HCQ or the disease itself.

• This data-driven pipeline strategy may be of interest to other experts involved in the pharmacovigilance discipline.

Antiherpetic drugs: a potential way to prevent Alzheimer's disease?

Background

Considering the growing body of evidence suggesting a potential implication of herpesviruses in the development of dementia, several authors have questioned a protective effect of antiherpetic drugs (AHDs) which may represent a new means of prevention, well tolerated and easily accessible. Subsequently, several epidemiological studies have shown a reduction in the risk of dementia in subjects treated with AHDs, but the biological plausibility of this association and the impact of potential methodological biases need to be discussed in more depth.

Methods

Using a French medico-administrative database, we assessed the association between the intake of systemic AHDs and the incidence of (i) dementia, (ii) Alzheimer’s disease (AD), and (iii) vascular dementia in 68,291 subjects over 65 who were followed between 2009 and 2017. Regarding potential methodological biases, Cox models were adjusted for numerous potential confounding factors (including proxies of sociodemographic status, comorbidities, and use of healthcare) and sensitivity analyses were performed in an attempt to limit the risk of indication and reverse causality biases.

Results

9.7% of subjects (n=6642) had at least one intake of systemic AHD, and 8883 incident cases of dementia were identified. Intake of at least one systemic AHD during follow-up was significantly associated with a decreased risk of AD (aHR 0.85 95% confidence interval [0.75–0.96], p=0.009) and, to a lesser extent with respect to p values, to both dementia from any cause and vascular dementia. The association with AD remained significant in sensitivity analyses. The number of subjects with a regular intake was low and prevented us from studying its association with dementia.

Conclusions

Taking at least one systemic AHD during follow-up was significantly associated with a 15% reduced risk of developing AD, even after taking into account several potential methodological biases. Nevertheless, the low frequency of subjects with a regular intake questions the biological plausibility of this association and highlights the limits of epidemiological data to evaluate a potential protective effect of a regular treatment by systemic AHDs on the incidence of dementia

Supplementary Information

The online version contains supplementary material available at 10.1186/s13195-021-00950-0.

Enhancing Nationwide Medico-Administrative Databases Analysis with SAF4SUHAD: A Statistical Analysis Framework for Secondary Use of Healthcare Administrative Databases

Many epidemiological studies now rely on the reuse of large healthcare administrative databases. In those studies, most of the time is consumed in managing data and performing basic statistical analyses and is not available anymore for complex statistical and medical analysis, therefore the potential of such databases is sometimes underexploited. The objective of this work is to build SAF4SUHAD, a statistical analysis framework for secondary use of healthcare administrative databases, using literature-based specifications. A literature review was performed on PubMed in four different medical domains: caesarian deliveries, cholecystectomies, hip replacement surgeries and bariatric surgeries. We identified 22 papers relating analyses of large databases. They reported epidemiological indicators (e.g. mean age), that were abstracted to features (e.g. univariate description of a quantitative variable), and then were implemented through 32 functions available for the user in R programming language. For instance, a function will draw a histogram, compute the mean with confidence interval, quantiles, etc. Those functions comprehend 4 functions for data management, 9 for univariate analysis, 8 for bivariate analysis, 11 for multivariate analysis, and many other intermediate functions. Those functions were successfully used to analyze a French database of 250 million discharge summaries. The set of R ready-to-use functions defined in this work could enable to secure repetitive tasks, and to refocus efforts on expert analysis.

French health insurance databases: What interest for medical research?

French health insurance databases are organized since 2003 into a huge digital data warehouse, the Système national d'information inter-régime de l'assurance maladie (SNIIR-AM). It covers the entire French population (65 million inhabitants). In order to facilitate studies on more frequent conditions, a random sample of 1/97th of national health system beneficiaries has been built since 2005, called the échantillon généraliste des bénéficiaires (EGB). The aim of this article is to describe the main characteristics of the SNIIR-AM and the EGB, to detail their accessibility according to French law, and to present their strengths and limits. It is illustrated with the most recent studies conducted in these databases. These databases include demographic, out-hospital reimbursement (including drug dispensing), medical (costly long-term diseases, occupational diseases, sick-leaves…), and in-hospital data. All these data are prospectively recorded, individualized, made anonymous and linkable. Consequently, the SNIIR-AM is a very useful data source for epidemiological, pharmacoepidemiological and health economics studies, particularly for rare diseases. The EGB is appropriate for long-term research on more frequent diseases.