Data-driven identification of co-morbidities associated with rheumatoid arthritis in a large US health plan claims database

Exploring disease comorbidities and temporal disease progression of psoriasis: an observational, retrospective, multi-database, cohort study

BACKGROUND

Comorbidities associated with psoriasis are well documented. However, few studies have explored the comorbidity trajectories that patients with psoriasis commonly experience over time. This study reports the 5-year comorbidity trajectories of patients with psoriasis.

OBJECTIVES

To determine the long-term comorbidity trajectories of patients with psoriasis in Denmark.

METHODS

This observational cohort study explored the Danish National Patient Registry (DNPR) between 1999 and 2013 to identify comorbidities diagnosed 5 years prior to or after a psoriasis diagnosis. Comorbidity occurrence in patients with psoriasis (psoriasis cohort) was compared with patients without psoriasis (the N group). Comparison groups, each the same size as the psoriasis cohort, were created by selecting random patients from the N group. If a comorbidity occurrence was higher in more than nine comparison groups than in the psoriasis cohort, it was not analysed and only comorbidities that occurred in ≥ 0·8% of the psoriasis cohort were analysed. The strength of association between a psoriasis diagnosis and a comorbidity diagnosis was measured using relative risk (RR). All psoriasis and comorbidity pairs that achieved RR > 1 (P < 0·001) (known as a Diagnosed Pair) were tested for directionality to identify the sequence of diagnoses using a binomial test. Diagnosed Pairs with a statistically significant direction (Bonferroni corrected P-value < 0·025) were then used to create comorbidity trajectory clusters 5 years before and after a psoriasis diagnosis.

RESULTS

A total of 17 683 patients with psoriasis were compared with 10 000 comparison groups. A total of 121 comorbidities met the minimum criteria that ≥ 0·8% of the psoriasis cohort were diagnosed with the comorbidity within 5 years (before or after) of their psoriasis diagnosis. Thirty-eight of these comorbidities achieved RR > 1 (P < 0·001) with psoriasis, of which 19 achieved a significant direction from psoriasis to a comorbidity (including psoriasis to hypothyroidism), and four achieved a significant direction from a comorbidity diagnosis to a psoriasis diagnosis (including Crohn disease to psoriasis); four of five comorbidity trajectories with three sequential diagnoses achieved an RR > 1 (P < 0·001) and a significant direction from psoriasis to the first comorbidity to the second comorbidity (including psoriasis to hypertension to atrial fibrillation and flutter).

CONCLUSIONS

Comorbidity trajectories may support clinicians in conducting disease risk analyses of patients with psoriasis and help plan optimal treatment to prevent future high-risk comorbidities.

Data-driven comorbidity analysis of 100 common disorders reveals patient subgroups with differing mortality risks and laboratory correlates

The populational heterogeneity of a disease, in part due to comorbidity, poses several complexities. Individual comorbidity profiles, on the other hand, contain useful information to refine phenotyping, prognostication, and risk assessment, and they provide clues to underlying biology. Nevertheless, the spectrum and the implications of the diagnosis profiles remain largely uncharted. Here we mapped comorbidity patterns in 100 common diseases using 4-year retrospective data from 526,779 patients and developed an online tool to visualize the results. Our analysis exposed disease-specific patient subgroups with distinctive diagnosis patterns, survival functions, and laboratory correlates. Computational modeling and real-world data shed light on the structure, variation, and relevance of populational comorbidity patterns, paving the way for improved diagnostics, risk assessment, and individualization of care. Variation in outcomes and biological correlates of a disease emphasizes the importance of evaluating the generalizability of current treatment strategies, as well as considering the limitations that selective inclusion criteria pose on clinical trials.

Using gradient boosting with stability selection on health insurance claims data to identify disease trajectories in chronic obstructive pulmonary disease

OBJECTIVE

We propose a data-driven method to detect temporal patterns of disease progression in high-dimensional claims data based on gradient boosting with stability selection.

MATERIALS AND METHODS

We identified patients with chronic obstructive pulmonary disease in a German health insurance claims database with 6.5 million individuals and divided them into a group of patients with the highest disease severity and a group of control patients with lower severity. We then used gradient boosting with stability selection to determine variables correlating with a chronic obstructive pulmonary disease diagnosis of highest severity and subsequently model the temporal progression of the disease using the selected variables.

RESULTS

We identified a network of 20 diagnoses (e.g. respiratory failure), medications (e.g. anticholinergic drugs) and procedures associated with a subsequent chronic obstructive pulmonary disease diagnosis of highest severity. Furthermore, the network successfully captured temporal patterns, such as disease progressions from lower to higher severity grades.

DISCUSSION

The temporal trajectories identified by our data-driven approach are compatible with existing knowledge about chronic obstructive pulmonary disease showing that the method can reliably select relevant variables in a high-dimensional context.

CONCLUSION

We provide a generalizable approach for the automatic detection of disease trajectories in claims data. This could help to diagnose diseases early, identify unknown risk factors and optimize treatment plans.

Twelve-year clinical trajectories of multimorbidity in a population of older adults

Multimorbidity-the co-occurrence of multiple diseases-is associated to poor prognosis, but the scarce knowledge of its development over time hampers the effectiveness of clinical interventions. Here we identify multimorbidity clusters, trace their evolution in older adults, and detect the clinical trajectories and mortality of single individuals as they move among clusters over 12 years. By means of a fuzzy c-means cluster algorithm, we group 2931 people ≥60 years in five clinically meaningful multimorbidity clusters (52%). The remaining 48% are part of an unspecific cluster (i.e. none of the diseases are overrepresented), which greatly fuels other clusters at follow-ups. Clusters contribute differentially to the longitudinal development of other clusters and to mortality. We report that multimorbidity clusters and their trajectories may help identifying homogeneous groups of people with similar needs and prognosis, and assisting clinicians and health care systems in the personalization of clinical interventions and preventive strategies.

Age density patterns in patients medical conditions: A clustering approach

This paper presents a data analysis framework to uncover relationships between health conditions, age and sex for a large population of patients. We study a massive heterogeneous sample of 1.7 million patients in Brazil, containing 47 million of health records with detailed medical conditions for visits to medical facilities for a period of 17 months. The findings suggest that medical conditions can be grouped into clusters that share very distinctive densities in the ages of the patients. For each cluster, we further present the ICD-10 chapters within it. Finally, we relate the findings to comorbidity networks, uncovering the relation of the discovered clusters of age densities to comorbidity networks literature.

Age and sex of a patient can be directly related to susceptibilities to certain medical conditions. We present a method to generate clusters of human phenotype, based on the age of the population. This method helps extract knowledge on age and sex from the data. The age and sex correlations with disease conditions can help in a task of predicting the susceptibility of incoming patients to conditions.

Effectiveness and safety of tofacitinib in rheumatoid arthritis: a cohort study

Background

Tofacitinib is the first oral Janus kinase inhibitor approved for the treatment of rheumatoid arthritis (RA). We compared the effectiveness and safety of tofacitinib, disease-modifying antirheumatic drugs (DMARDs), tumor necrosis factor inhibitors (TNFi), and non-TNF biologics in patients with RA previously treated with methotrexate.

Methods

We used MarketScan® databases (2011–2014) to study methotrexate-exposed patients with RA who were newly prescribed tofacitinib, DMARDs other than methotrexate, and biologics. The date of first prescription was defined as the cohort entry. The therapy was considered effective if all of the following criteria from a claims-based algorithm were achieved at the first year of follow-up: high adherence, no biologic or tofacitinib switch or addition, no DMARD switch or addition, no increase in dose or frequency of index drug, no more than one glucocorticoid joint injection, and no new/increased oral glucocorticoid dose. The safety outcome was serious infections requiring hospitalization. Non-TNF biologics comprised the reference group.

Results

We included 21,832 patients with RA, including 0.8% treated with tofacitinib, 24.7% treated with other DMARDs, 61.2% who had started therapy with TNFi, and 13.3% treated with non-TNF biologics. The rates of therapy effectiveness were 15.4% for tofacitinib, 11.1% for DMARDs, 18.6% for TNFi, and 19.8% for non-TNF biologics. In adjusted analyses, tofacitinib and non-TNF biologics appeared to have similar effectiveness rates, whereas DMARD initiators were less effective than non-TNF biologics. We could not clearly establish if tofacitinib was associated with a higher rate of serious infections.

Conclusions

In patients with RA previously treated with methotrexate, our comparisons of tofacitinib with non-TNF biologics, though not definitive, did not demonstrate differences with respect to hospitalized infections or effectiveness.

Electronic supplementary material

The online version of this article (10.1186/s13075-018-1539-6) contains supplementary material, which is available to authorized users.

Understanding Chinese Medicine Patterns of Rheumatoid Arthritis and Related Biomarkers

Background: A considerable number of Rheumatoid Arthritis (RA) patients only experience side effects from treatment, with little to no actual pain relief. The combination of disease diagnosis in biomedicine and multi-disciplinary integrative approaches such as Chinese Medicine (CM), can help to identify different functional diagnosis of RA in the context of biomarker discovery. We aimed to analyse CM patterns in RA and their biomarker profiles. Methods: Four electronic databases (web of science, CINAHL, Scopus and PubMed) were searched. The reference list of all identified reports and articles were searched for additional studies. All study designs were included and no date limits were set. Studies were considered if they were published in English and explored the possible biomarkers profiles in RA patients, classified according to the American College of Rheumatology and categorized in CM as either cold, heat/hot or deficiency patterns. Methodological quality of included studies was assessed using checklists adapted from the ©Critical Appraisal Skills Programme by two independent reviewers. A narrative synthesis was conducted, using thematic analysis. Results: A total of 10 articles were included. The studies examined 77 healthy volunteers and 1150 RA patients categorized as cold, heat/hot or deficiency pattern and related biomarkers were identified individually or concomitantly. Conclusions: CM pattern differentiation based on clinical signs and symptoms showed a diverse range of biomolecules, proteins and genes from RA patients correlated well with cold, heat/hot or deficiency phenotype-based CM patterns and could be used as diagnostic biomarkers for early detection, disease monitoring and therapeutic targets.

Chronic disease list conditions in patients with rheumatoid arthritis in the private healthcare sector of South Africa

INTRODUCTION

Little is known about the burden of rheumatoid arthritis (RA) in South Africa. The aim of this study was to establish the prevalence of RA and coexisting chronic disease list (CDL) conditions in the private health sector of South Africa.

METHODS

A retrospective, cross-sectional analysis was performed on medicine claims data from 1 January 2014 to 31 December 2014 to establish the prevalence of RA. The cohort of RA patients was then divided into those with and those without CDL conditions, to determine the number and type of CDL conditions per patient, stratified by age group and gender.

RESULTS

A total 4352 (0.5%) patients had RA, of whom 69.3% (3016) presented with CDL conditions. Patients had a median age of 61.31 years (3.38; 98.51), and 74.8% were female. Patients with CDL conditions were older than those patients without (p < 0.001; Cohen's d = 0.674). Gender had no influence on the presence of CDL conditions (p = 0.456). Men had relatively higher odds for hyperlipidemia (OR 1.83; CI 1.33-2.51; p < 0.001) and lower odds for asthma (OR 0.83; CI 0.48-1.42; p = 0.490) than women. In combination with hyperlipidemia, the odds for asthma were reversed and strongly increased (OR 6.74; CI 2.07-21.93; p = 0.002). The odds for men having concomitant hyperlipidemia, hypertension, type 2 diabetes mellitus and hypothyroidism were insignificant and low (OR 0.40; CI 0.16-1.02; p = 0.055); however, in the absence of hypothyroidism, the odds increased to 3.26 (CI 2.25-4.71; p < 0.001).

CONCLUSION

Hypothyroidism was an important discriminating factor for comorbidity in men with RA. This study may contribute to the body of evidence about the burden of RA and coexisting chronic conditions in South Africa.

Personal fable: optimistic bias in cigarette smokers

Background:

Several empirical studies have shown the attitude of smokers to formulate judgments based on distortion in the risk perception. This alteration is produced by the activation of the optimistic bias characterized by a set of the unrealistic beliefs compared to the outcomes of their behavior. This bias exposes individuals to adopt lifestyles potentially dangerous for their health, underestimate the risks and overestimate the immediate positive effects.

Objectives:

This study aimed to analyze the relationship between optimistic bias and smoking habits. In particular, it was hypothesized that smokers develop optimistic illusions, able to facilitate the adoption and the maintenance over time of the unhealthy lifestyles, such as cigarette smoking, and the former smokers could acquire a belief system centered on own responsibility.

Patients and Methods:

The samples (n = 633, female = 345, male = 288) composed of smokers (35.7%), ex-smokers (32.2%) and nonsmokers (32.1%). Each participant filled out two questionnaires including The Fagerström test and the motivational questionnaire as well as a set of items measured on a Likert scales to evaluate health beliefs.

Results:

The results confirmed the presence of the optimistic bias in comparative judgments, and the attitude to overestimate the effectiveness of their preventive behaviors in the smokers.

Conclusions:

Cognitive bias in risk perception may influence health behaviors in negative way and reinforce cigarette smoking over the time. Future research should be conducted to identify the better strategies to overtake this cognitive bias to improve the quitting rate.

A systematic review of validated methods for identifying patients with rheumatoid arthritis using administrative or claims data

PURPOSE

To review the evidence supporting the validity of billing, procedural, or diagnosis code, or pharmacy claim-based algorithms used to identify patients with rheumatoid arthritis (RA) in administrative and claim databases.

METHODS

We searched the MEDLINE database from 1991 to September 2012 using controlled vocabulary and key terms related to RA and reference lists of included studies were searched. Two investigators independently assessed the full text of studies against pre-determined inclusion criteria and extracted the data. Data collected included participant and algorithm characteristics.

RESULTS

Nine studies reported validation of computer algorithms based on International Classification of Diseases (ICD) codes with or without free-text, medication use, laboratory data and the need for a diagnosis by a rheumatologist. These studies yielded positive predictive values (PPV) ranging from 34 to 97% to identify patients with RA. Higher PPVs were obtained with the use of at least two ICD and/or procedure codes (ICD-9 code 714 and others), the requirement of a prescription of a medication used to treat RA, or requirement of participation of a rheumatologist in patient care. For example, the PPV increased from 66 to 97% when the use of disease-modifying antirheumatic drugs and the presence of a positive rheumatoid factor were required.

CONCLUSIONS

There have been substantial efforts to propose and validate algorithms to identify patients with RA in automated databases. Algorithms that include more than one code and incorporate medications or laboratory data and/or required a diagnosis by a rheumatologist may increase the PPV.

Temporal disease trajectories condensed from population-wide registry data covering 6.2 million patients

A key prerequisite for precision medicine is the estimation of disease progression from the current patient state. Disease correlations and temporal disease progression (trajectories) have mainly been analysed with focus on a small number of diseases or using large-scale approaches without time consideration, exceeding a few years. So far, no large-scale studies have focused on defining a comprehensive set of disease trajectories. Here we present a discovery-driven analysis of temporal disease progression patterns using data from an electronic health registry covering the whole population of Denmark. We use the entire spectrum of diseases and convert 14.9 years of registry data on 6.2 million patients into 1,171 significant trajectories. We group these into patterns centred on a small number of key diagnoses such as chronic obstructive pulmonary disease (COPD) and gout, which are central to disease progression and hence important to diagnose early to mitigate the risk of adverse outcomes. We suggest such trajectory analyses may be useful for predicting and preventing future diseases of individual patients.

Identifying phenotypic signatures of neuropsychiatric disorders from electronic medical records

OBJECTIVE

Mental illness is the leading cause of disability in the USA, but boundaries between different mental illnesses are notoriously difficult to define. Electronic medical records (EMRs) have recently emerged as a powerful new source of information for defining the phenotypic signatures of specific diseases. We investigated how EMR-based text mining and statistical analysis could elucidate the phenotypic boundaries of three important neuropsychiatric illnesses-autism, bipolar disorder, and schizophrenia.

METHODS

We analyzed the medical records of over 7000 patients at two facilities using an automated text-processing pipeline to annotate the clinical notes with Unified Medical Language System codes and then searching for enriched codes, and associations among codes, that were representative of the three disorders. We used dimensionality-reduction techniques on individual patient records to understand individual-level phenotypic variation within each disorder, as well as the degree of overlap among disorders.

RESULTS

We demonstrate that automated EMR mining can be used to extract relevant drugs and phenotypes associated with neuropsychiatric disorders and characteristic patterns of associations among them. Patient-level analyses suggest a clear separation between autism and the other disorders, while revealing significant overlap between schizophrenia and bipolar disorder. They also enable localization of individual patients within the phenotypic 'landscape' of each disorder.

CONCLUSIONS

Because EMRs reflect the realities of patient care rather than idealized conceptualizations of disease states, we argue that automated EMR mining can help define the boundaries between different mental illnesses, facilitate cohort building for clinical and genomic studies, and reveal how clear expert-defined disease boundaries are in practice.

Factors associated with gastrointestinal perforation in a cohort of patients with rheumatoid arthritis

OBJECTIVE

To estimate the incidence and risk factors for gastrointestinal (GI) perforation among patients with rheumatoid arthritis (RA).

METHODS

Claims from employer health insurance plans were used to identify RA patients and those hospitalized for upper or lower GI perforation. GI perforation cases were identified using both a sensitive and a specific definition. A Cox model using fixed and time-varying covariates was used to evaluate the risk of GI perforation.

RESULTS

Among 143,433 RA patients, and using a maximally sensitive GI perforation definition, 696 hospitalizations with perforation were identified. The rate of perforation was 1.70 per 1,000 person years (PYs; 95% confidence interval [95% CI] 1.58-1.83), and most perforations (83%) occurred in the lower GI tract. The rate of perforation was lower when a more specific GI perforation definition was used (0.87; 95% CI 0.78-0.96 per 1,000 PYs). Age and diverticulitis were among the strongest risk factors for perforation (diverticulitis hazard ratio [HR] 14.5 [95% CI 11.8-17.7] for the more sensitive definition, HR 3.9 [95% CI 2.5-5.9] for the more specific definition). Among various RA medication groups and compared to methotrexate, the risk of GI perforation was highest among patients with exposure to nonsteroidal antiinflammatory drugs (NSAIDs), concomitant nonbiologic disease-modifying antirheumatic drugs, and glucocorticoids. Biologic agents without glucocorticoid exposure were not a risk factor for perforation.

CONCLUSION

GI perforation is a rare but serious condition that affects patients with RA, most frequently in the lower GI tract. Clinicians should be aware of risk factors for GI perforation when managing RA patients, including age, history of diverticulitis, and use of glucocorticoids or NSAIDs.

Is polycystic ovary syndrome another risk factor for venous thromboembolism? United States, 2003-2008

OBJECTIVE

We sought to determine prevalence and likelihood of venous thromboembolism (VTE) among women with and without polycystic ovary syndrome (PCOS).

STUDY DESIGN

We performed a cross-sectional analysis using Thomson Reuters MarketScan Commercial databases for the years 2003 through 2008. The association between VTE and PCOS among women aged 18-45 years was assessed using age-stratified multivariable logistic regression models.

RESULTS

Prevalence of VTE per 100,000 was 374.2 for PCOS women and 193.8 for women without PCOS. Compared with women without PCOS, those with PCOS were more likely to have VTE (adjusted odds ratio [aOR] 18-24 years, 3.26; 95% confidence interval [CI], 2.61-4.08; aOR 25-34 years, 2.39; 95% CI, 2.12-2.70; aOR 35-45 years, 2.05; 95% CI, 1.84-2.38). A protective association (odds ratio, 0.8; 95% CI, 0.73-0.98) with oral contraceptive use was noted for PCOS women.

CONCLUSION

PCOS might be a predisposing condition for VTE, particularly among women aged 18-24 years. Oral contraceptive use might be protective.

Prevalence of polycystic ovary syndrome among the privately insured, United States, 2003-2008

OBJECTIVE

The purpose of this study was to estimate the prevalence of polycystic ovary syndrome (PCOS) and its phenotypes as defined by the National Institutes of Health, Rotterdam criteria, and Androgen Society.

STUDY DESIGN

Thomson Reuters MarketScan Commercial databases (Thomson Reuters Healthcare Inc, New York, NY) for 2003-2008 were used to calculate the prevalence of PCOS and to assess differences in demographic characteristics and comorbid conditions among women who were 18-45 years old with and without PCOS.

RESULTS

The prevalence of PCOS was 1585.1 per 100,000; women with phenotype A or classic PCOS were most prevalent at 1031.5 per 100,000. Women with PCOS were more likely than those without PCOS to be 25-34 years old, be from the South, be infertile, have metabolic syndrome, have been seen by an endocrinologist, and have taken oral contraceptives.

CONCLUSION

This is the first study to use all available criteria to estimate the prevalence of PCOS. Providers should evaluate women with menstrual dysfunction for the presence of PCOS.

Enabling enrichment analysis with the Human Disease Ontology

Advanced statistical methods used to analyze high-throughput data such as gene-expression assays result in long lists of "significant genes." One way to gain insight into the significance of altered expression levels is to determine whether Gene Ontology (GO) terms associated with a particular biological process, molecular function, or cellular component are over- or under-represented in the set of genes deemed significant. This process, referred to as enrichment analysis, profiles a gene set, and is widely used to make sense of the results of high-throughput experiments. Our goal is to develop and apply general enrichment analysis methods to profile other sets of interest, such as patient cohorts from the electronic medical record, using a variety of ontologies including SNOMED CT, MedDRA, RxNorm, and others. Although it is possible to perform enrichment analysis using ontologies other than the GO, a key pre-requisite is the availability of a background set of annotations to enable the enrichment calculation. In the case of the GO, this background set is provided by the Gene Ontology Annotations. In the current work, we describe: (i) a general method that uses hand-curated GO annotations as a starting point for creating background datasets for enrichment analysis using other ontologies; and (ii) a gene-disease background annotation set - that enables disease-based enrichment - to demonstrate feasibility of our method.