Objective: Ectopic pregnancy (EP) is a serious condition. Delayed diagnosis could lead to life-threatening outcomes. The study aimed to develop a diagnostic predictive model for EP to approach suspected cases with prompt intervention before the rupture occurred.Methods: A retrospective cross-sectional study enrolled 347 pregnant women presenting first-trimester complications (abdominal pain or vaginal bleeding) with diagnosis suspected of pregnancy of unknown location, who were eligible and underwent chart review. The data including clinical risk factors, signs and symptoms, serum human chorionic gonadotropin (hCG), and ultrasound findings were analyzed. The statistical predictive score was developed by performing logistic regression analysis. The testing data of 30 patients were performed to test the validation of predictive scoring.Results: From a total of 22 factors, logistic regression method–derived scoring model was based on five potent factors (history of pelvic inflammatory disease, current use of emergency pills, cervical motion tenderness, serum hCG ≥1,000 mIU/ml, and ultrasound finding of adnexal mass) using a cutoff score ≥3. This predictive index score was able to determine ectopic pregnancy with an accuracy of 77.8% [95% confidence interval (CI) = 73.1–82.1], specificity of 91.0% (95% CI = 62.1–72.0), sensitivity of 67.0% (95% CI = 88.0–94.0), and area under the curve of 0.906 (95% CI = 0.875–0.937). In the validation group, no patient with negative result of this score had an EP.Conclusion: Statistical predictive score was derived with high accuracy and applicable performance for EP diagnosis. This score could be used to support clinical decision making in routine practice for management of EP.
ObjectiveEctopic pregnancy (EP) is well known for its critical maternal outcome. Early detection could make the difference between life and death in pregnancy. Our aim was to make a prompt diagnosis before the rupture occur. Thus, the predictive analytical models using both conventional statistics and machine learning (ML) methods were studied.Materials and methodsA retrospective cohort study was conducted on 407 pregnancies with unknown location (PULs): 306 PULs for internal validation and 101 PULs for external validation, randomized with a nested cross-validation technique. Using a set of 22 study features based on clinical factors, serum marker and ultrasound findings from electronic medical records, analyzing with neural networks (NNs), decision tree (DT), support vector machines (SVMs), and a statistical logistic regression (LR). Diagnostic performances were compared with the area under the curve (ROC-AUC), including sensitivity and specificity for decisional use.ResultsComparing model performance (internal validation) to predict EP, LR ranked first, with a mean ROC-AUC ± SD of 0.879 ± 0.010. In testing data (external validation), NNs ranked first, followed closely by LR, SVMs, and DT with average ROC-AUC ± SD of 0.898 ± 0.027, 0.896 ± 0.034, 0.882 ± 0.029, and 0.856 ± 0.033, respectively. For clinical aid, we report sensitivity of mean ± SD in LR: 90.20% ± 3.49%; SVM: 89.79% ± 3.66%; DT: 89.22% ± 4.53%; and NNs: 86.92% ± 3.24%, consecutively. However, specificity ± SD was ranked by NNs, followed by SVMs, LR, and DT, which were 82.02 ± 8.34%, 80.37 ± 5.15%, 79.65% ± 6.01%, and 78.97% ± 4.07%, respectively.ConclusionBoth statistics and the ML model could achieve satisfactory predictions for EP. In model learning, the highest ranked model was LR, showing that EP prediction might possess linear or causal data pattern. However, in new testing data, NNs could overcome statistics. This highlights the potency of ML in solving complicated problems with various patterns, while overcoming generalization error of data.
Background: Ectopic pregnancy is well-known for its serious outcome. Early detection could make the difference between life and death in pregnancy. Although, prompt diagnosis was challenged and numbers of predictive models had been developed, there’s still lack of gold standard tool. Our research introduces predictive analytical models using both conventional statistics and machine learning methods based on all three domains of features (clinical factors, serum human chorionic gonadotropin and ultrasound findings). Methods: Retrospective cohort study on 377 pregnancy of unknown location women, training and validating for model for prediction of ectopic pregnancy outcome, using set of 22 features. Analysis performed by three difference machine learning models neural networks (NNs), decision tree (DT) and support vector machines (SVMs) using cross validation technique. In addition, with traditional statistical model, logistic regression (LR). In which we compare the model performance with ROC-AUC and accuracy, PPV and NPV. Finally, new 30 PULs were tested for external validation. Result Comparing of model performance (validation) to predict EP, LR Ranked first, followed by NNs, DT and SVMs with mean ROC-AUC ± SD of 0.904, 0.895, 0.871 and 0.862 respectively. In terms of clinical parameters, we found sensitivity ± SD performed in the same sequence as AUC (LR; 87.97% ± 7.86%, NNs; 85.97% ± 9.34%, SVM; 87.47% ± 8.23% and DT; 85.47% ± 10.37%, orderly. While specificity (± SD) was ranked by DT, then followed by SVMs, LG and NNs, which were 84.08 ± 8.56%, 83.53 ± 9.18%, 82.94 ± 9.84% and 81.76 ± 9.84%, respectively. In testing data, NNs and SVM performed equally best, then DTs and LR at ROC-AUC of 0.784, 0.778 and 0.739, respectively. Conclusion The result demonstrates that both statistics and ML model could be utilized to achieve satisfied predictions for EP. Surprisingly, the highest ranked model was LR in validating test, but in new testing data machine learning, NNs and SVMs could overcome statistics. This could shade a new light for further research of unsolved medical problem with more complexity and bigger database.
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