Catherine Lein

Primary Care Clinicians Treat Patients with Medically Unexplained Symptoms: A Randomized Controlled Trial

AbstractOBJECTIVE: There is no proven primary care treatment for patients with medically unexplained symptoms (MUS). We hypothesized that a long-term, multidimensional intervention by primary care providers would improve MUS patients’ mental health. DESIGN: Clinical trial. SETTING: HMO in Lansing, MI. PARTICIPANTS: Patients from 18 to 65 years old with 2 consecutive years of high utilization were identified as having MUS by a reliable chart rating procedure; 206 subjects were randomized and 200 completed the study. INTERVENTION: From May 2000 to January 2003, 4 primary care clinicians deployed a 12-month intervention consisting of cognitive-behavioral, pharmacological, and other treatment modalities. A behaviorally defined patient-centered method was used by clinicians to facilitate this treatment and the provider-patient relationship. MAIN OUTCOME MEASURE: The primary endpoint was an improvement from baseline to 12 months of 4 or more points on the Mental Component Summary of the SF-36. RESULTS: Two hundred patients averaged 13.6 visits for the year preceding study. The average age was 47.7 years and 79.1% were females. Using intent to treat, 48 treatment and 34 control patients improved (odds ratio [OR]=1.92, 95% confidence interval [CI]: 1.08 to 3.40; P=.02). The relative benefit (relative “risk” for improving) was 1.47 (CI: 1.05 to 2.07), and the number needed to treat was 6.4 (95% CI: 0.89 to 11.89). The following baseline measures predicted improvement: severe mental dysfunction (P<.001), severe body pain (P=.039), nonsevere physical dysfunction (P=.003), and at least 16 years of education (P=.022); c-statistic=0.75. CONCLUSION: The first multidimensional intervention by primary care clinicians led to clinically significant improvement in MUS patients.

Exploration of DSM-IV criteria in primary care patients with medically unexplained symptoms.

Objectives: Investigators and clinicians almost always rely on Diagnostic and Statistical Manual of Mental Disorder, 4th edition’s (DSM-IV) somatoform disorders (and its derivative diagnoses) to characterize and identify patients with medically unexplained symptoms (MUS). Our objective was to evaluate this use by determining the prevalence of DSM-IV somatoform and nonsomatoform disorders in patients with MUS proven by a gold standard chart review. Methods: In a community-based staff model HMO, we identified subjects for a clinical trial using a systematic and reliable chart rating procedure among high-utilizing MUS patients. Only baseline data are reported here. The World Health Organization Composite International Diagnostic Interview provided full and abridged DSM-IV diagnoses. Patients with full or abridged DSM-IV somatoform diagnoses were labeled “DSM somatoform-positive,” whereas those without them were labeled “DSM somatoform-negative.” Results: Two hundred six MUS patients averaged 13.6 visits in the year preceding study, 79.1% were females, and the average age was 47.7 years. We found that 124 patients (60.2%) had a nonsomatoform (“psychiatric”) DSM-IV diagnosis of any type; 36 (17.5%) had 2 full nonsomatoform diagnoses, and 41 (19.9%) had >2; 92 (44.7%) had some full anxiety diagnosis and 94 (45.6%) had either full depression or minor depression diagnoses. However, only 9 of 206 (4.4%) had any full DSM-IV somatoform diagnosis, and only 39 (18.9%) had abridged somatization disorder. Thus, 48 (23.3%) were “DSM somatoform-positive” and 158 (76.7%) were “DSM somatoform-negative.” The latter exhibited less anxiety, depression, mental dysfunction, and psychosomatic symptoms (all p <.001) and less physical dysfunction (p = .011). Correlates of this DSM somatoform-negative status were female gender (p = .007), less severe mental (p = .007), and physical dysfunction (p = .004), a decreased proportion of MUS (p <.10), and less psychiatric comorbidity (p <.10); c-statistic = 0.77. Conclusion: We concluded that depression and anxiety characterized MUS patients better than the somatoform disorders. Our data suggested radically revising the somatoform disorders for DSM-V by incorporating a new, very large group of now-overlooked DSM somatoform-negative patients who were typically women with less severe dysfunction. HMO = health maintenance organization DSM-IV = Diagnostic and Statistical Manual of Mental Disorders, 4th edition DSM-V = planned for approximately 2010, the Diagnostic and Statistical Manual of Mental Disorders, 5th edition MUS = medically unexplained symptoms ECA = epidemiologic catchment area SD = somatization disorder SF-36 = Short-Form 36 MCS = Mental Component Summary of the SF-36 PCS = Physical Component Summary of the SF-36 SSAS = Spielberger State Anxiety Scale CES-D = Center for Epidemiological Studies Depression inventory PSC = Psychosomatic Symptom Checklist WHO-CIDI = World Health Organization Composite International Diagnostic Interview.

Treating Patients with Medically Unexplained Symptoms in Primary Care

BACKGROUND: There are no proven, comprehensive treatments in primary care for patients with medically unexplained symptoms (MUS) even though these patients have high levels of psychosocial distress, medical disability, costs, and utilization. Despite extensive care, these common patients often become worse.OBJECTIVE: We sought to identify an effective, research-based treatment that can be conducted by primary care personnel.DESIGN: We used our own experiences and files, consulted with experts, and conducted an extensive review of the literature to identify two things: 1) effective treatments from randomized controlled trials for MUS patients in primary care and in specialty settings; and 2) any type of treatment study in a related area that might inform primary care treatment, for example, depression, provider-patient relationship.MAIN RESULTS: We developed a multidimensional treatment plan by integrating several areas of the literature: collaborative/stepped care, cognitive-behavioral treatment, and the provider-patient relationship. The treatment is designed for primary care personnel (physicians, physician assistants, nurse practitioners) and deployed intensively at the outset; visit intervals are progressively increased as stability and improvement occur.CONCLUSION: Providing a comprehensive treatment plan for chronic, high-utilizing MUS patients removes one barrier to treating this common problem effectively in primary care by primary care personnel.

Using nurse practitioners to implement an intervention in primary care for high-utilizing patients with medically unexplained symptoms

Patients with medically unexplained symptoms (MUS) often are a source of frustration for clinicians, and despite high quality biomedical attention and frequent diagnostic tests, they have poor health outcomes. Following upon progress in depression treatment approaches, we developed a multidimensional treatment protocol for deployment by primary care personnel. This multi-faceted intervention for MUS patients emphasized cognitive-behavioral principles, the provider-patient relationship, pharmacological management, and treating comorbid medical diseases. We deployed it in an HMO using nurse practitioners (NP) to deliver the intervention to 101 patients, while 102 controls continued to receive medical care from their usual primary care physician. Successful deployment of the intervention required training the NPs, continuing support for the NPs in their management of this difficult population, and establishing strong communication links with the HMO. This paper addresses the practical considerations of using primary care personnel to implement a complex intervention in primary care, and it includes a discussion of special challenges encountered as well as solutions developed to overcome them.

A Method for Rating Charts to Identify and Classify Patients with Medically Unexplained Symptoms

Background: As part of conducting a randomized control trial (RCT) to treat chronically high utilizing patients with medically unexplained symptoms (MUS), we developed the chart rating method reported here to identify and classify MUS subjects. Method: Intended at this point only as a research tool, the method is comprehensive, uses explicit guidelines, and requires clinician raters. It distinguishes primary organic disease patients from those with primary MUS, quantifies medical comorbidities in primary MUS patients, and also distinguishes subgroups among MUS patients that we call somatization (resembles DSM-IV somatoform disorders) and minor acute illness (MAI) which differs from DSM-IV somatoform definitions. Scoring rules are used to generate the diagnoses above. The rules may be set according to the investigator’s needs, from highly sensitive to highly specific. Results: We found high levels of agreement with the gold standard for MUS vs. organic disease (97.6%) and among raters for the key individual chart elements rated (92–96%). The method identified 206 MUS subjects and the extent of their medical comorbidities for entry into a RCT. It also identified somatization and MAI; the latter supports the validity of this newly reported MAI syndrome. Conclusion: We concluded that this method offered research potential for identifying MUS patients, for quantifying their medical comorbidities, and for classifying MUS subgroups.

Using patient-centered interviewing skills to manage complex patient encounters in primary care

Purpose: To describe effective and efficient patient‐centered interviewing strategies to enhance the management of complex primary care patient encounters. Data sources: Research literature and applied case study analysis. Conclusions: Patient‐centered interviewing can enhance effectiveness of care in complex patient encounters. A relatively small investment of time and energy has positive yields in regard to improvements in longer term physiological status, treatment adherence, quality of life, patient–provider working relationship, and patient and nurse practitioner satisfaction. Implications for practice: Use of patient‐centered interviewing strategies can enhance effectiveness of patient care processes and outcomes while retaining efficiency of patient management.

Screening for high utilizing somatizing patients using a prediction rule derived from the management information system of an HMO: a preliminary study.

Background.Somatization is a common, costly problem with great morbidity, but there has been no effective screening method to identify these patients and target them for treatment. Objectives.We tested a hypothesis that we could identify high utilizing somatizing patients from a management information system (MIS) by total number of visits and what we termed “somatization potential,” the percentage of visits for which ICD-9 primary diagnosis codes represented disorders in the musculoskeletal, nervous, or gastrointestinal systems or ill-defined complaints. Methods.We identified 883 high users from the MIS of a large staff model HMO as those having six or more visits during the year studied (65th percentile). A physician rater, without knowledge of hypotheses and predictors, then reviewed the medical records of these patients and identified somatizing patients (n = 122) and nonsomatizing patients (n = 761). In two-thirds of the population (the derivation set), we used logistic regression to refine our hypothesis and identify predictors of somatization available from the MIS: demographic data, all medical encounters, and primary diagnoses made by usual care physicians (ICD-9 codes). We then tested our prediction model in the remaining one-third of the population (the validation set) to validate its usefulness. Results.The derivation set contained the following significant correlates of somatization: gender, total number of visits, and percent of visits with somatization potential. The c-statistic, equivalent to the area under the ROC curve, was 0.90. In the validation set, the explanatory power was less with a still impressive c-statistic of 0.78. A predicted probability of 0.04 identified almost all somatizers, whereas a predicted probability of 0.40 identified about half of all somatizers but produced few false positives. Conclusions.We have developed and validated a prediction model from the MIS that helps to distinguish chronic somatizing patients from other high utilizing patients. Our method requires corroboration but carries the promise of providing clinicians and health plan directors with an inexpensive, simple approach for identifying the common somatizing patient and, in turn, targeting them for treatment. The screener does not require clinicians’ time.

Contemporary Adult Diabetes Mellitus Management Perceptions

AbstractBackground: Over 180 million people have been diagnosed with diabetes mellitus worldwide, with this number expected to more than double by 2030. Due to the increasing mortality and morbidity associated with this epidemic, the improved primary-care management of diabetes during routine office visits remains an emerging international challenge. Objective: To report the results of a series of exploratory semi-structured group interview sessions with a sample of 44 American adults with type 2 diabetes, concerning their diabetes management perceptions and office-based diabetes care processes. Methods: A total of 44 adults from a Midwest Internal Medicine Clinic were interviewed during 2004 and 2005 before starting a larger, quantitative, shared decision-making intervention study. During group interviews, participants offered their perceptions of their self-management practices, interactions with office clinicians, and diabetes-related health outcomes to date. A total of 178 audio-taped interview comments (across 44 participants) were transcribed and analyzed for core themes and sub-themes. Results: Many participants reported frustrating experiences regarding the relationship between their personal diabetes self-management practices and typical office visit interactions with clinicians. Most participants perceived these diabetes management processes as inherently different from each other. Many participants were intrigued with the proposed shared decision-making management approach of the larger intervention study. Conclusions: Primary-care clinicians should assess how patients may perceive their self-management strategies relate to office-based diabetes care processes. Patients’ self-management beliefs and practices should be routinely evaluated since they frequently affect the nature of key diabetes care office visit decisions. These qualitative results suggest that clinicians should convey the increasing interdependence between their patients’ daily diabetes self-management practices and contemporary office visit decision-making discussions.