OBJECTIVE: A multivariate logistic regression analysis model for predicting ectopic pregnancy in women with pregnancy of unknown location was designed and evaluated clinically. METHODS: Endometrial thickness, symmetry, resonance, pattern of echogenicity, helicine artery blood flow and blood flow resistance index (RI) in 129 patients with suspected early ectopic pregnancy were assessed by transvaginal power Doppler ultrasonography. Variables significant in univariate logistic regression analysis were included in a multivariate predictive logistic regression analysis model. RESULTS: The final predictive model included three factors: endometrial thickness≤9 mm; a multilayered endometrial echogenicity pattern with prominent outer and midline hyperechogenic lines and an inner hypoechogenic region; and visible endometrial arterial blood flow. The area under the receiver operating characteristic curve of the model was 0.980. When RI was >0.65 and the predictive probability>0.50, diagnostic accuracy was high. The model correctly diagnosed 52/55 (94.5%) clinically confirmed ectopic pregnancy cases. CONCLUSION: This multivariate predictive logistic regression analysis model has clinical value for the differential diagnosis of early ectopic pregnancy when the pregnancy location is unknown.
OBJECTIVE: A multivariate logistic regression analysis model for predicting ectopic pregnancy in women with pregnancy of unknown location was designed and evaluated clinically. METHODS: Endometrial thickness, symmetry, resonance, pattern of echogenicity, helicine artery blood flow and blood flow resistance index (RI) in 129 patients with suspected early ectopic pregnancy were assessed by transvaginal power Doppler ultrasonography. Variables significant in univariate logistic regression analysis were included in a multivariate predictive logistic regression analysis model. RESULTS: The final predictive model included three factors: endometrial thickness≤9 mm; a multilayered endometrial echogenicity pattern with prominent outer and midline hyperechogenic lines and an inner hypoechogenic region; and visible endometrial arterial blood flow. The area under the receiver operating characteristic curve of the model was 0.980. When RI was >0.65 and the predictive probability>0.50, diagnostic accuracy was high. The model correctly diagnosed 52/55 (94.5%) clinically confirmed ectopic pregnancy cases. CONCLUSION: This multivariate predictive logistic regression analysis model has clinical value for the differential diagnosis of early ectopic pregnancy when the pregnancy location is unknown.