Establishing content validity for the migraine Global Impression Item (mGI-I) assessment: a modified single-item migraine symptom severity questionnaire.

OBJECTIVE: To establish content validity of a single-item, migraine-specific symptom severity questionnaire for completion by migraine patients, key family members (KFMs) of migraine patients, and Healthcare Professionals (HCPs) who treat migraine patients. BACKGROUND: Migraine is a common disabling primary headache disorder with high prevalence and significant socioeconomic burden and personal impacts. There is a need for a global assessment of migraine symptom severity to evaluate potential new therapies from multiple perspectives. METHODS: The migraine Global Impression Item (mGI-I) was drafted and tested in a non-interventional, qualitative study comprising telephone interviews with 15 migraine patients, 15 KFMs of migraine patients, and 15 migraine treating HCPs. The mGI-I was drafted with two different item stem options and two different response scale options to ask about the patient's migraine from the perspective of each respondent. Cognitive interviews were conducted to test comprehensiveness, clarity and ease of completion of the different versions of the mGI-I iteratively in three sequential waves of respondents. RESULTS: Revisions were made to the draft mGI-I after Wave 1 and Wave 2 of the interviews. Changes were made to simplify the item stem (removing unnecessary text), make language more patient-friendly (e.g. use of "migraine attack"), and add clarity to the item stem for consistent interpretation (include descriptive language of migraine attacks). Across both waves there was a preference for a 5-point response scale compared to a 7-point scale. In Wave 3, all respondents found the revised instructions, item stem, and 5-point response scale comprehensive, easy to understand and to answer. No further changes to the mGI-I were made after Wave 3. CONCLUSIONS: This qualitative study of 45 total respondents across 3 subpopulations, established the content validity and appropriateness of the mGI-I in migraine patients, KFMs, and migraine-treating HCPs. The study specifically confirmed that the mGI-I is comprehensive, easily understood and answered for each respondent population.

Experience of Daily Life with Generalized Myasthenia Gravis: A Qualitative Investigation and Assessment of Instrument Content Validity.

INTRODUCTION: Generalized myasthenia gravis (gMG) is a rare autoimmune disease. Symptoms of gMG are diverse, and understanding of their impact on patients is limited. This qualitative study aimed to provide an in-depth exploration of patients' daily experiences of gMG. METHODS: Published qualitative studies were reviewed to identify the most important signs, symptoms, and functional impacts related to the patient experience in gMG. Semi-structured hybrid concept elicitation interviews (allowing spontaneous generation of disease concepts) and cognitive debriefing interviews (assessing the validity of existing disease assessments) were conducted with clinicians and adult patients with gMG. Signs, symptoms, and impacts were reviewed to understand which were most salient (i.e., reported by at least 50% of patients, with disturbance rating 5 or higher [10-point numeric scale]); concept saturation was also assessed. The disease conceptual model was updated. Existing clinical outcomes assessments (COAs) that capture how patients feel, function, and survive were assessed. RESULTS: Interviews with patients (n = 24) identified seven new signs and symptoms and 37 new impacts compared with the literature. Concept saturation was reached. Signs and symptoms identified by patients as most important (salient) were shortness of breath, general fatigue, muscle weakness of arms, legs, and neck, dysphonia, dysarthria, trouble swallowing liquids, choking, and heat sensitivity. Patient-identified salient impacts were work life, depression, difficulty walking, grooming hair, showering, and brushing teeth, eating, personal relationships, family life, and participating in social activities. Clinicians considered ocular, respiratory, swallowing, speech/talking, and extremity function as key clinical manifestations of gMG. Patients and clinicians found clinical outcome assessments (COAs) to be conceptually relevant and comprehensive. CONCLUSION: This research provides a holistic understanding of gMG signs, symptoms, and impacts experienced by patients, as observed by patients and clinicians. The conceptual model of gMG highlights the range of signs, symptoms, and impacts that adult patients with gMG experience in their everyday lives, emphasizing the humanistic impact and unmet needs.

Combining Contrast Mining with Logistic Regression To Predict Healthcare Utilization in a Managed Care Population.

BACKGROUND: Because 5% of patients incur 50% of healthcare expenses, population health managers need to be able to focus preventive and longitudinal care on those patients who are at highest risk of increased utilization. Predictive analytics can be used to identify these patients and to better manage their care. Data mining permits the development of models that surpass the size restrictions of traditional statistical methods and take advantage of the rich data available in the electronic health record (EHR), without limiting predictions to specific chronic conditions. OBJECTIVE: The objective was to demonstrate the usefulness of unrestricted EHR data for predictive analytics in managed healthcare. METHODS: In a population of 9,568 Medicare and Medicaid beneficiaries, patients in the highest 5% of charges were compared to equal numbers of patients with the lowest charges. Contrast mining was used to discover the combinations of clinical attributes frequently associated with high utilization and infrequently associated with low utilization. The attributes found in these combinations were then tested by multiple logistic regression, and the discrimination of the model was evaluated by the c-statistic. RESULTS: Of 19,014 potential EHR patient attributes, 67 were found in combinations frequently associated with high utilization, but not with low utilization (support>20%). Eleven of these attributes were significantly associated with high utilization (p<0.05). A prediction model composed of these eleven attributes had a discrimination of 84%. CONCLUSIONS: EHR mining reduced an unusably high number of patient attributes to a manageable set of potential healthcare utilization predictors, without conjecturing on which attributes would be useful. Treating these results as hypotheses to be tested by conventional methods yielded a highly accurate predictive model. This novel, two-step methodology can assist population health managers to focus preventive and longitudinal care on those patients who are at highest risk for increased utilization.

Contrast Mining for Pattern Discovery and Descriptive Analytics to Tailor Sub-Groups of Patients Using Big Data Solutions.

The shift to electronic health records has created a plethora of information ready to be examined and acted upon by those in the medical and computational fields. While this allows for novel research on a scale unthinkable in the past, all discoveries still rely on some initial insight leading to a hypothesis. As the size and variety of data grows so do the number of potential findings, making it necessary to optimize hypothesis generation to increase the rate and importance of discoveries produced from the data. By using distributed Association Rule Mining and Contrast Mining in a big data ecosystem, it is possible to discover discrepancies within large, complex populations which are inaccessible using traditional methods. These discrepancies, when used as hypotheses, can help improve patient care through decision support, population health analytics, and other areas of healthcare.