A comprehensive review of predictive and prognostic composite factors implicated in the heterogeneity of treatment response and outcome across disease areas.

AIM: To assess and present the current body of evidence regarding composite measures associated with differential treatment response or outcome as a result of patient heterogeneity and to evaluate their consistency across disease areas. METHODS: A comprehensive review of the literature from the last 10 years was performed using three databases (PubMed, Embase and Cochrane). All articles that met the inclusion/exclusion criteria were selected, abstracted and assessed using the NICE level-of-evidence criteria. RESULTS: Forty-nine studies were identified in the data abstraction. Approximately one-third focused on existing composite measures, and the rest investigated emerging composite factors. The majority of studies targeted patients with cancer, cardiovascular disease or psychological disorders. As a whole, the composite measures were found to be disease-specific, but some composite elements, including age, gender, comorbidities and health status, showed consistency across disease areas. To complement these findings, common individual factors found in five previous independent disease-specific literature assessments were also summarised, including age, gender, treatment adherence and satisfaction, healthcare resource utilisation and health status. CONCLUSIONS: Composite measures can play an important role in characterising heterogeneity of treatment response and outcome in patients suffering from various medical conditions. These measures can help clinicians to better distinguish between patients with high likelihood to respond well to treatment and patients with minimal chances of positive therapeutic outcomes. Herein, the individual factors identified can be used to develop novel predictive or prognostic composite measures that can be applicable across disease areas. Reflecting these cross-disease measures in clinical and public health decisions has the distinctive appeal to enable targeted treatment for patients suffering from multiple medical conditions, which may ultimately yield significant gains in individual outcomes, population health and cost-effective resource allocation.

A review of treatment response in type 2 diabetes: assessing the role of patient heterogeneity.

The response to treatment for type 2 diabetes typically varies among individuals within a study population. This variation is known as heterogeneity of treatment response. We conducted a comprehensive literature review to identify factors that account for heterogeneity of treatment response in patients treated for type 2 diabetes. Three databases (PubMed, EMBASE and Cochrane Library) were searched for articles published in the last 10 years describing investigations of factors associated with treatment response and outcomes among people with type 2 diabetes receiving pharmacological treatment. Of the 43 articles extracted and summarized, 35 (81%) discussed clinical factors, 31 (72%) described sociodemographic factors and 17 (40%) reported on comorbidity or behavioural factors. Clinical factors identified included baseline glycated hemoglobin A1c or fasting plasma glucose (FPG) levels, insulin response or sensitivity, C-peptide, body composition, adipose tissue proteins, lipid profile, plasma albumin levels and duration of disease or insulin treatment. Other factors identified included age, sex, race, socioeconomic status and comorbidities. This review identified the following research gaps: use of multiple definitions for response, few patient-reported measures and lack of evidence regarding whether factors were associated with treatment response for only specific medications or across pharmacological therapies. Furthermore, identification of factors associated with type 2 diabetes treatment response was generally a secondary objective in the research reviewed. Understanding which patient subgroups are more likely to respond to treatment and identifying factors associated with response may result in targeted treatment decisions and alter the interpretation of efficacy or effectiveness of results. In conclusion, accounting for these factors in clinical trials and when making clinical treatment decisions may improve therapy selection and individual patient outcomes.