Clinical data representation in multidimensional space.

Abstract A number of measured and binary categorical variables were observed for several days in a group of patients hospitalized for suspected myocardial infarction: temperature, systolic and diastolic blood pressure, pulse, respiratory rate, P-R interval (electrocardiogram), white blood count, serum enzyme levels (LDH, SGOT, SGPT), along with the presence or absence of chest pain, abnormal rhythm, ventricular gallop, rales, cardiac arrest, external cardiac pacing, assisted respiration, and the administration of digitalis, diuretics, antiarrhythmic agents, and vasopressors. Volumes of data of this magnitude are often not comprehensible in graphical or numerical form. In order to aid in the compression and interpretation of the data, each patient on a given day was represented as a single point in a multidimensional space. Computed distances between points are measures of clinical dissimilarity. Trajectories in the space are indicative of the clinical course of the patient's illness. A computer program was used to connect points to their nearest neighbors so as to yield a “minimum coverage tree,” of which the connections, branches, etc., provide information concerning relationships between points. A two-dimensional graphical representation of the points was generated, locating the points by minimizing the sum of the squared differences between n-dimensional and two-dimensional squared distances. The presence of an important nonobserved variable may be signaled by a long series of minimum coverage connections between two apparent neighbors. These multidimensional spatial techniques offer promise of usefulness in a variety of other types of clinical research studies.