The exclusion of women from clinical trials of thrombolytic therapy: implications for developing the thrombolytic predictive instrument database.
The thrombolytic predictive instrument (TPI) was developed to identify those patients most likely to benefit from thrombolytic therapy for acute myocardial infarction as well as to facilitate the earliest possible administration of this treatment. The TPI consists of predictive models derived from clinical data obtained from both clinical trials and data registries. These models are subject to potential bias due to combinations of primary data from different sources. The purpose of this investigation was to assess the influence of gender in developing the TPI database. In this database, there were 1,096 (22%) women and 3,826 (78%) men; only 38% of the women were enrolled in clinical trials, whereas 46% of the men were (p < 0.0001). Within clinical trials, there were few significant eligibility differences between women and men, as the vast majority of patients met eligibility standards for entry in these trials. However, within clinical registries, the women were older (p < 0.0001) and more often had elevated blood pressure on admission (p = 0.002). Multivariate logistic regression indicated that after adjustment for significant predictors of trial inclusion, women were 25% less likely to be included in clinical trials (odds ratio = 0.76, 95% confidence interval = 0.60, 0.96). In order to counter bias introduced by the exclusion of women from clinical trials, the TPI database included patients from non-trial settings. Carefully including patients from clinical registries or non-trial settings may be an important strategy in constructing generally applicable predictive instruments.
Maynard
C
C
Division of Clinical Care Research, New England Medical Center, Tufts University, Boston, MA.
Selker
H P
HP
Beshansky
J R
JR
Griffith
J L
JL
Schmid
C H
CH
Califf
R M
RM
D'Agostino
R B
RB
Laks
M M
MM
Lee
K L
KL
Wagner
G S
GS
D000368:Aged; D002986:Clinical Trials as Topic / Q000379:methods*; D016208:Databases, Factual; D003661:Decision Support Techniques; D005260:Female; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D015999:Multivariate Analysis; D009203:Myocardial Infarction / Q000188:drug therapy*; D018579:Patient Selection; D011237:Predictive Value of Tests; D012042:Registries; D012107:Research Design; D015983:Selection Bias; D012737:Sex Factors; D015912:Thrombolytic Therapy; D016387:Women's Health