OBJECTIVE: To show cluster analysis as a potentially useful tool in defining common outcomes empirically and in facilitating the assessment of preferences for health states. DATA SOURCES: A survey of 224 patients with ventricular arrhythmias treated at Kaiser Permanente of Northern California. STUDY DESIGN/ METHODS: Physical functioning was measured using the Duke Activity Status Index (DASI), and mental status and vitality using the Medical Outcomes Study Short Form-36 items (SF-36). A "k-means" clustering algorithm was used to identify prototypical health states, in which patients in the same cluster shared similar responses to items in the survey. PRINCIPAL FINDINGS: The clustering algorithm yielded four prototypical health states. Cluster 1 (21 percent of patients) was characterized by high scores on physical functioning, vitality, and mental health. Cluster 2 (33 percent of patients) had low physical function but high scores on vitality and mental health. Cluster 3 (29 percent of patients) had low physical function and low vitality but preserved mental health. Cluster 4 (17 percent of patients) had low scores on all scales. These clusters served as the basis of written descriptions of the health states. CONCLUSIONS: Employing a clustering algorithm to analyze health status survey data enables researchers to gain a data-driven, concise summary of the experiences of patients.
OBJECTIVE: To show cluster analysis as a potentially useful tool in defining common outcomes empirically and in facilitating the assessment of preferences for health states. DATA SOURCES: A survey of 224 patients with ventricular arrhythmias treated at Kaiser Permanente of Northern California. STUDY DESIGN/ METHODS: Physical functioning was measured using the Duke Activity Status Index (DASI), and mental status and vitality using the Medical Outcomes Study Short Form-36 items (SF-36). A "k-means" clustering algorithm was used to identify prototypical health states, in which patients in the same cluster shared similar responses to items in the survey. PRINCIPAL FINDINGS: The clustering algorithm yielded four prototypical health states. Cluster 1 (21 percent of patients) was characterized by high scores on physical functioning, vitality, and mental health. Cluster 2 (33 percent of patients) had low physical function but high scores on vitality and mental health. Cluster 3 (29 percent of patients) had low physical function and low vitality but preserved mental health. Cluster 4 (17 percent of patients) had low scores on all scales. These clusters served as the basis of written descriptions of the health states. CONCLUSIONS: Employing a clustering algorithm to analyze health status survey data enables researchers to gain a data-driven, concise summary of the experiences of patients.
Authors: M A Hlatky; R E Boineau; M B Higginbotham; K L Lee; D B Mark; R M Califf; F R Cobb; D B Pryor Journal: Am J Cardiol Date: 1989-09-15 Impact factor: 2.778
Authors: Rajendrakumar S V Chadalavada; Jane Houldsworth; Adam B Olshen; George J Bosl; Lorenz Studer; R S K Chaganti Journal: Funct Integr Genomics Date: 2005-02-03 Impact factor: 3.410
Authors: Jing Huang; Alfred Lin; Balasubramanian Narasimhan; Thomas Quertermous; C Agnes Hsiung; Low-Tone Ho; John S Grove; Michael Olivier; Koustubh Ranade; Neil J Risch; Richard A Olshen Journal: Proc Natl Acad Sci U S A Date: 2004-07-12 Impact factor: 11.205