Application of meta-analysis using an electronic spread sheet to exercise testing in patients after myocardial infarction.
Decision analysis is being applied to medical practice in order to achieve cost efficacy in health care delivery. Critical to this process is establishing the diagnostic and prognostic accuracy of medical tests and the effectiveness of interventions. Meta-analysis is an approach that applies statistical methods to groups of studies in order to extract consensus results. Electronic spreadsheets facilitate meta-analysis with their ability to store, sort, graph, and mathematically manipulate both the methodologic approaches and clinical findings of seemingly disparate studies. As an example, this application is demonstrated with an analysis of studies that were performed to evaluate the prognostic value of exercise testing in patients recovering from a myocardial infarction. The following conclusions were reached: (1) patients excluded from exercise testing have the highest mortality; (2) only subsets of patients have been tested resulting in highly selected patient samples that make findings difficult to generalize; (3) of the five exercise test responses, only an abnormal systolic blood pressure response and a poor exercise capacity predicted risk more frequently than by chance; (4) submaximal or predischarge testing has greater predictive power than postdischarge or maximal testing; and (5) exercise-induced ST segment depression only appears to be predictive of increased risk in patients with inferior-posterior myocardial infarctions. This approach to combining studies is important since even careful analysis of a single study cannot elucidate all of the complex interactions and selective biases that have occurred. However, comparison of many heterogeneous studies is at best an arduous and time-consuming task. This approach to using electronic spreadsheets to collate and analyze multiple studies facilitates recognition of the population characteristics, clinical factors, and methodologic considerations that affect outcome and allows the quick inclusion of additional studies for re-analysis and interpretation.
Froelicher
V F
VF
University of California, Long Beach, California.
Perdue
S
S
Pewen
W
W
Risch
M
M
D001794:Blood Pressure; D003625:Data Collection / Q000295:instrumentation*; D005080:Exercise Test; D005500:Follow-Up Studies; D006801:Humans; D009203:Myocardial Infarction / Q000401:mortality / Q000503:physiopathology; D011379:Prognosis; D012106:Research; D012307:Risk Factors