Electronic medical record data to identify variables associated with a fibromyalgia diagnosis: importance of health care resource utilization

Early identification of persistent somatic symptoms in primary care: data-driven and theory-driven predictive modelling based on electronic medical records of Dutch general practices

OBJECTIVE

The present study aimed to early identify patients with persistent somatic symptoms (PSS) in primary care by exploring routine care data-based approaches.

DESIGN/SETTING

A cohort study based on routine primary care data from 76 general practices in the Netherlands was executed for predictive modelling.

PARTICIPANTS

Inclusion of 94 440 adult patients was based on: at least 7-year general practice enrolment, having more than one symptom/disease registration and >10 consultations.

METHODS

Cases were selected based on the first PSS registration in 2017-2018. Candidate predictors were selected 2-5 years prior to PSS and categorised into data-driven approaches: symptoms/diseases, medications, referrals, sequential patterns and changing lab results; and theory-driven approaches: constructed factors based on literature and terminology in free text. Of these, 12 candidate predictor categories were formed and used to develop prediction models by cross-validated least absolute shrinkage and selection operator regression on 80% of the dataset. Derived models were internally validated on the remaining 20% of the dataset.

RESULTS

All models had comparable predictive values (area under the receiver operating characteristic curves=0.70 to 0.72). Predictors are related to genital complaints, specific symptoms (eg, digestive, fatigue and mood), healthcare utilisation, and number of complaints. Most fruitful predictor categories are literature-based and medications. Predictors often had overlapping constructs, such as digestive symptoms (symptom/disease codes) and drugs for anti-constipation (medication codes), indicating that registration is inconsistent between general practitioners (GPs).

CONCLUSIONS

The findings indicate low to moderate diagnostic accuracy for early identification of PSS based on routine primary care data. Nonetheless, simple clinical decision rules based on structured symptom/disease or medication codes could possibly be an efficient way to support GPs in identifying patients at risk of PSS. A full data-based prediction currently appears to be hampered by inconsistent and missing registrations. Future research on predictive modelling of PSS using routine care data should focus on data enrichment or free-text mining to overcome inconsistent registrations and improve predictive accuracy.

Identifying persistent somatic symptoms in electronic health records: exploring multiple theory-driven methods of identification

OBJECTIVE

Persistent somatic symptoms (PSSs) are defined as symptoms not fully explained by well-established pathophysiological mechanisms and are prevalent in up to 10% of patients in primary care. The present study aimed to explore methods to identify patients with a recognisable risk of having PSS in routine primary care data.

DESIGN

A cross-sectional study to explore four identification methods that each cover part of the broad spectrum of PSS was performed. Cases were selected based on (1) PSS-related syndrome codes, (2) PSS-related symptom codes, (3) PSS-related terminology and (4) Four-Dimensional Symptom Questionnaire scores and all methods combined.

SETTING

Coded electronic health record data were extracted from 76 general practices in the Netherlands.

PARTICIPANTS

Patients who were registered for at least 1 year during 2014-2018, were included (n=169 138).

OUTCOME MEASURES

Identification methods were explored based on (1) PSS sample sizes and demographics, (2) presence of chronic conditions and (3) healthcare utilisation (HCU) variables. Overlap between methods and practice specific differences were examined.

RESULTS

The percentage of cases identified varied between 0.3% and 7.0% across the methods. Over 58.1% of cases had chronic physical condition(s) and over 33.8% had chronic mental condition(s). HCU was generally higher for cases selected by any method compared with the total cohort. HCU was higher for method B compared with the other methods. In 26.7% of cases, cases were selected by multiple methods. Overlap between methods was low.

CONCLUSIONS

Different methods yielded different patient samples which were general practice specific. Therefore, for the most comprehensive data-based selection of PSS cases, a combination of methods A, C and D would be recommended. Advanced (data-driven) methods are needed to create a more sensitive algorithm for identifying the full spectrum of PSS. For clinical purposes, method B could possibly support screening of patients who are currently missed in daily practice.

Obstructive sleep apnea co-morbidity in patients with fibromyalgia: a single-center retrospective analysis and literature review

Background: Fibromyalgia (FM) is a chronic medical condition characterized by widespread pain, sleep disturbance, and cognitive dysfunction. Sleep disorders are thought to play a prominent role in the etiology and symptomatic management of FM, specifically obstructive sleep apnea (OSA). In order to provide collaborative care, we need a better understanding of any overlapping presentation of FM and OSA. We conducted a site-wide review of patients from 2012-2016 to identify FM patients diagnosed with OSA. Methods: Charts were reviewed in patients aged 18 and above from 2012-2016 using ICD codes from a clinical data repository. Intersection of patients with a diagnosis of FM and OSA in clinics of psychiatry, sleep, rheumatology, and other outpatient clinics was compared. Polysomnography order patterns for FM patients were investigated. Results: Co-morbidity was highest in the sleep clinic (85.8%) compared to psychiatry (42.0%), rheumatology (18.7%), and other outpatient clinics (3.6%) (p<0.001). In the rheumatology and other outpatient clinics, 93.5% and 96% of patients respectively, had no polysomnography ordered. Pairwise comparison of co-morbidity in clinics: sleep vs psychiatry, sleep vs rheumatology, sleep vs other clinics, psychiatry vs rheumatology, psychiatry vs other clinics, and rheumatology vs other clinics were statistically significant after applying a Sidak adjustment to the p-values (all p<0.001). Conclusion: Our analysis suggests that there could be a correlation between FM and OSA, and referral to sleep studies is recommended in the management of patients with FM. The varying prevalence of FM patients with co-morbid OSA in sleep clinics when compared to other outpatient clinics suggests a discrepancy in the identification of FM patients with OSA. When properly screened, OSA co-morbidity has the potential to be higher in other outpatient clinics.

Potential Mediators between Fibromyalgia and C-Reactive protein: Results from a Large U.S. Community Survey

BACKGROUND

Fibromyalgia, a potentially debilitating chronic pain syndrome of unknown etiology, may be characterized by inflammation. In this study, we investigated the relation of FMS to serum C-reactive protein (CRP) in a large population of adults (18+) and investigated the influence of other factors on this relationship, including BMI, comorbidities, as well as mood and sleep disturbance.

METHODS

Participants were 52,535 Ohio Valley residents (Fibromyalgia n = 1125). All participants completed a comprehensive health survey (2005-2006) part of the C8 Health Project; serum levels of CRP were obtained, as was history of Fibromyalgia physician diagnosis. Logistic and linear regressions were used for this cross-sectional analysis.

RESULTS

Mean CRP was higher among participants reporting Fibromyalgia than those without (5.54 ± 9.8 vs.3.75 ± 7.2 mg/L, p < .0001)). CRP level showed a strong, positive association with FMS (unadjusted odds ratio (OR) for highest vs. lowest quartile = 2.5 (CI 2.1,3.0;p for trend < .0001)); adjustment for demographic and lifestyle factors attenuated but did not eliminate this association (AOR for highest vs. lowest quartile = 1.4 (CI 1.1,1.6;p for trend < .0001)). Further addition of body mass index (BMI) and comorbidities to the model markedly weakened this relationship (AORs, respectively, for highest vs lowest CRP quartile = 1.2 (CI 1.0,1.4) and 1.1 (CI 0.9,1.3). In contrast, inclusion of mood and sleep impairment only modestly reduced the adjusted risk estimate (AORs for highest vs. lowest quartile = 1.3 (CI 1.1,1.5) for each)).

CONCLUSIONS

Findings from this large cross-sectional study indicate a significant positive cross-sectional association of Fibromyalgia to serum C-reactive protein may be explained, in part, by BMI and comorbidity. Prospective research is needed to confirm this, and clarify the potential mediating influence of obesity and comorbid conditions on this relationship.

Evaluating increased resource use in fibromyalgia using electronic health records

Objective

The management of fibromyalgia (FM), a chronic musculoskeletal disease, remains challenging, and patients with FM are often characterized by high health care resource utilization. This study sought to explore potential drivers of all-cause health care resource utilization and other factors associated with high resource use, using a large electronic health records (EHR) database to explore data from patients diagnosed with FM.

Methods

This was a retrospective analysis of de-identified EHR data from the Humedica database. Adults (≥18 years) with FM were identified based on ≥2 International Classification of Diseases, Ninth Revision codes for FM (729.1) ≥30 days apart between January 1, 2008 and December 31, 2012 and were required to have evidence of ≥12 months continuous care pre- and post-index; first FM diagnosis was the index event; 12-month pre- and post-index reporting periods. Multivariable analysis evaluated relationships between variables and resource utilization.

Results

Patients were predominantly female (81.4%), Caucasian (87.7%), with a mean (standard deviation) age of 54.4 (14.8) years. The highest health care resource utilization was observed for the categories of “medication orders” and “physician office visits,” with 12-month post-index means of 21.2 (21.5) drug orders/patient and 15.1 (18.1) office visits/patient; the latter accounted for 73.3% of all health care visits. Opioids were the most common prescription medication, 44.3% of all patients. The chance of high resource use was significantly increased (P<0.001) 26% among African-Americans vs Caucasians and for patients with specific comorbid conditions ranging from 6% (musculoskeletal pain or depression/bipolar disorder) to 21% (congestive heart failure). Factors significantly associated with increased medications ordered included being female (P<0.001) and specific comorbid conditions (P<0.05).

Conclusion

Physician office visits and pharmacotherapy orders were key drivers of all-cause health care utilization, with demographic factors, opioid use, and specific comorbidities associated with resource intensity. Health systems and providers may find their EHRs to be a useful tool for identifying and managing resource-intensive FM patients.

Fibromyalgia: management strategies for primary care providers

AIMS

Fibromyalgia (FM), a chronic disorder defined by widespread pain, often accompanied by fatigue and sleep disturbance, affects up to one in 20 patients in primary care. Although most patients with FM are managed in primary care, diagnosis and treatment continue to present a challenge, and patients are often referred to specialists. Furthermore, the lack of a clear patient pathway often results in patients being passed from specialist to specialist, exhaustive investigations, prescription of multiple drugs to treat different symptoms, delays in diagnosis, increased disability and increased healthcare resource utilisation. We will discuss the current and evolving understanding of FM, and recommend improvements in the management and treatment of FM, highlighting the role of the primary care physician, and the place of the medical home in FM management.

METHODS

We reviewed the epidemiology, pathophysiology and management of FM by searching PubMed and references from relevant articles, and selected articles on the basis of quality, relevance to the illness and importance in illustrating current management pathways and the potential for future improvements.

RESULTS

The implementation of a framework for chronic pain management in primary care would limit unnecessary, time-consuming, and costly tests, reduce diagnostic delay and improve patient outcomes.

DISCUSSION

The patient-centred medical home (PCMH), a management framework that has been successfully implemented in other chronic diseases, might improve the care of patients with FM in primary care, by bringing together a team of professionals with a range of skills and training.

CONCLUSION

Although there remain several barriers to overcome, implementation of a PCMH would allow patients with FM, like those with other chronic conditions, to be successfully managed in the primary care setting.

Identification of a potential fibromyalgia diagnosis using random forest modeling applied to electronic medical records

Background

Diagnosis of fibromyalgia (FM), a chronic musculoskeletal condition characterized by widespread pain and a constellation of symptoms, remains challenging and is often delayed.

Methods

Random forest modeling of electronic medical records was used to identify variables that may facilitate earlier FM identification and diagnosis. Subjects aged ≥18 years with two or more listings of the International Classification of Diseases, Ninth Revision, (ICD-9) code for FM (ICD-9 729.1) ≥30 days apart during the 2012 calendar year were defined as cases among subjects associated with an integrated delivery network and who had one or more health care provider encounter in the Humedica database in calendar years 2011 and 2012. Controls were without the FM ICD-9 codes. Seventy-two demographic, clinical, and health care resource utilization variables were entered into a random forest model with downsampling to account for cohort imbalances (<1% subjects had FM). Importance of the top ten variables was ranked based on normalization to 100% for the variable with the largest loss in predicting performance by its omission from the model. Since random forest is a complex prediction method, a set of simple rules was derived to help understand what factors drive individual predictions.

Results

The ten variables identified by the model were: number of visits where laboratory/non-imaging diagnostic tests were ordered; number of outpatient visits excluding office visits; age; number of office visits; number of opioid prescriptions; number of medications prescribed; number of pain medications excluding opioids; number of medications administered/ordered; number of emergency room visits; and number of musculoskeletal conditions. A receiver operating characteristic curve confirmed the model’s predictive accuracy using an independent test set (area under the curve, 0.810). To enhance interpretability, nine rules were developed that could be used with good predictive probability of an FM diagnosis and to identify no-FM subjects.

Conclusion

Random forest modeling may help to quantify the predictive probability of an FM diagnosis. Rules can be developed to simplify interpretability. Further validation of these models may facilitate earlier diagnosis and enhance management.

Fibromyalgia and the Risk of a Subsequent Motor Vehicle Crash

OBJECTIVE

Motor vehicle crashes are a widespread contributor to mortality and morbidity, sometimes related to medically unfit motorists. We tested whether patients diagnosed with fibromyalgia (FM) have an increased risk of a subsequent serious motor vehicle crash.

METHODS

We conducted a population-based self-matched longitudinal cohort analysis to estimate the incidence rate ratio of crashes among patients diagnosed with FM relative to the population norm in Ontario, Canada. We included adults diagnosed from April 1, 2006, to March 31, 2012, excluding individuals younger than 18 years, living outside Ontario, lacking valid identifiers, or having only a single visit for the diagnosis. The primary outcome was an emergency department visit as a driver involved in a motor vehicle crash.

RESULTS

The patients (n = 137,631) accounted for 738 crashes during the first year of followup after diagnosis, equal to an incidence rate ratio of 2.44 compared with the population norm (95% CI 2.27-2.63, p < 0.001). The crash rate was more than twice the population norm for those with a new or a persistent diagnosis. The increased risk included patients with diverse characteristics, approached the rate observed among other patients diagnosed with alcoholism, and was mitigated among those who received dedicated FM care or a physician warning for driving safety.

CONCLUSION

A diagnosis of FM is associated with an increased risk of a subsequent motor vehicle crash that might justify medical interventions for traffic safety.