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.
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.
Authors: Richard J Willke; Zhiyuan Zheng; Prasun Subedi; Rikard Althin; C Daniel Mullins Journal: BMC Med Res Methodol Date: 2012-12-13 Impact factor: 4.615
Authors: Gebra Cuyún Carter; Amy M Barrett; James A Kaye; Astra M Liepa; Katherine B Winfree; William J John Journal: Cancer Manag Res Date: 2014-10-23 Impact factor: 3.989