Derivation of a clinical prediction model for the emergency department diagnosis of ectopic pregnancy

Objective: To derive a clinical prediction model for estimating the pretest probability of ectopic pregnancy in ED patients with first-trimester abdominal pain or vaginal bleeding. Methods: All hemodynamically stable first-trimester patients presenting to the ED of a tertiary care military teaching hospital over a 14-month period with a chief complaint of abdominal pain and/or vaginal bleeding had clinical data coded prior to determining outcome. They were then followed longitudinally until a criterion standard pregnancy outcome was established. Results: Of the 486 patients enrolled, 280 (58%) had viable intrauterine pregnancies, 167 (34%) had nonviable intrauterine pregnancies, and 39 (8%) had ectopic pregnancies. Using a recursive partitioning model, a high-risk group was derived (that was separated from intermediate and low-risk groups), consisting of patients with abdominal peritoneal signs or definite cervical motion tenderness, with a sensitivity of 31% (95% CI: 17-48%), a specificity of 93% (95% CI: 90-95%), a positive likelihood ratio of 4.3, and a negative likelihood ratio of 0.74. A low-risk group, consisting of patients with either fetal heart tones or tissue at the cervical os, or the absence of pain other than midline menstrual-like cramping and lacking any pelvic tenderness, was differentiated from an intermediate-risk group, with a sensitivity of 96% (95% CI: 81-100%), a specificity of 22% (95% CI: 18-26%), a positive likelihood ratio of 1.2, and a negative likelihood ratio of 0.17. Conclusion: A clinical prediction model for estimating the probability of ectopic pregnancy in ED patients has been derived. It may prove to have practical clinical application for estimating pretest probability of ectopic pregnancy as well as assisting in medical decision making when laboratory and ultrasonographic findings are nondiagnostic. Clinical application should await prospective validation in an independent sample.