The diagnostic value of a multivariate logistic regression analysis model with transvaginal power Doppler ultrasonography for the prediction of ectopic pregnancy

Predicting the Outcome of a Pregnancy of Unknown Location: What Can the Endometrial Stripe Thickness Reveal?

Objectives:

To investigate the role of the uterine endometrial thickness and sonographic pattern as potential predictors for a pregnancy with an unknown location (PUL) and its possible outcomes.

Materials and Methods:

A convenient sample of 330 symptomatic extra preposition female patients were enrolled in this study with a diagnosis of PUL. The clinical variables of endometrial stripe thickness and endometrial sonographic pattern were determined with transvaginal sonography. These sonographic examinations (it seems that examination in this context is a countable noun) were conducted during the first 24 hours of referral, and their predictive values for PUL outcomes (normal intrauterine pregnancy (IUP), ectopic pregnancy (EP), and pregnancy loss) were assessed after clinical follow-up sessions provided a definite outcome. The statistical significance was set a priori at a P value < .05.

Results:

The mean initial endometrial stripe thickness among participants, with a normal IUP, was more than those patients with an abnormal pregnancy outcome ( P < .05). The optimum cut-off value for predicting a an EP, compared to a normal IUP was 11 mm and had a sensitivity of 73.3% and specificity of 39% ( P < .001). These results also demonstrated no statistically significant relationship between the PUL outcome and the categories of endometrial sonographic pattern ( P = .15).

Conclusion:

In this large cohort of patients, the endometrial stripe thickness of more than 11 mm, among those who were symptomatic and deemed as PUL, had the potential to predict ectopic pregnancy as an unlikely diagnosis.

Assessment of risk based on variant pathways and establishment of an artificial neural network model of thyroid cancer

BACKGROUND

This study aimed to establish an artificial neural network (ANN) model based on variant pathways to predict the risk of thyroid cancer.

METHODS

The RNASeq data of 482 thyroid cancer samples were downloaded from the TCGA database. The samples were divided into low-risk and high-risk groups, followed by identification of differentially expressed genes (DEGs). Co-expression analysis and pathway enrichment analysis were then performed. The variant pathways were screened according to the functional deviation score of each pathway, and an ANN model was established. Finally, the efficiency of the ANN model for risk assessment was validated by survival analysis and analysis of an independent microarray dataset (GSE34289) for thyroid cancer.

RESULTS

In total, 190 DEGs (85 up-regulated and 105 down-regulated) were identified between the low-risk and high-risk groups. Ten risk-related variant pathways were identified between the low-risk and high-risk groups, which were related to inflammatory and immune responses. Based on these variant pathways, an ANN model was built, consisting of an input layer, two hidden layers, and an output layer, corresponding to 15, 8, 5, and 1 neuron, respectively. Survival analysis showed that this model could effectively distinguish the samples with different risks. Analysis of microarray dataset GSE34289 showed that the accuracy of this model for predicating low-risk and high-risk samples was 77.5 and 86.0%, respectively.

CONCLUSIONS

This study suggests that the ANN model based on variant pathways can be used for effectively evaluating the risk of thyroid cancer.

Angiopoietin-like Protein 2 is a Useful Biomarker for Pancreatic Cancer that is Associated with Type 2 Diabetes Mellitus and Inflammation

Pancreatic cancer is one of the tumors with the worst prognosis, with the 5-year survival rate reported to be 6%. The number of patients suffering from pancreatic cancer in recent years has continued to increase dramatically. Carbohydrate antigen 19-9 is an established biomarker of pancreatic cancer, but it does not have sufficient ability to detect pancreatic cancer at an early stage. We focused on angiopoietin-like protein 2 (ANGPTL2), which has been reported to be related to chronic inflammation and Type 2 diabetes mellitus. In this study, whether ANGPTL2 can detect early pancreatic cancer was evaluated. It was found that the concentration of serum ANGPTL2 was significantly higher in pancreatic cancer patients and tumor stage 0-I patients than in healthy individuals (5.84 ± 1.82 ng/mL vs 3.61 ± 0.64 ng/mL; P < 0.001) (5.68 ± 0.79 ng/mL vs 3.61 ± 0.64 ng/mL; P = 0.010). In addition, the diagnostic capability of serum ANGPTL2 levels for pancreatic cancer was evaluated using receiver operating characteristic (ROC) curve analysis. The area under the ROC curve (AUC) for ANGPTL2 was 0.906 (95% confidence interval (CI): 0.815-0.997; P < 0.001). To identify the risk factors for pancreatic cancer, multivariate regression models were used. Ten factors were included, and increasing age (odds ratio (OR), 1.318, 95% CI, 1.058-1.642; P = 0.014) and high ANGPTL2 levels (OR, 22.219, 95% CI, 1.962-251.659, P = 0.012) were found to be independent risk factors for pancreatic cancer, with ANGPTL2 having the strongest relationship. In addition, serum ANGPTL2 levels were strongly correlated with inflammatory markers, with blood sugar levels showing the strongest correlation with serum ANGPTL2 levels. In conclusion, this study suggested that an elevated serum ANGPTL2 level has the potential to be a biomarker capable of early detection of pancreatic cancer, and it was correlated with inflammation of the pancreas and the risk of developing diabetes mellitus.

Artificial neural network approach to predict surgical site infection after free-flap reconstruction in patients receiving surgery for head and neck cancer

Background

The aim of this study was to develop an effective surgical site infection (SSI) prediction model in patients receiving free-flap reconstruction after surgery for head and neck cancer using artificial neural network (ANN), and to compare its predictive power with that of conventional logistic regression (LR).

Materials and methods

There were 1,836 patients with 1,854 free-flap reconstructions and 438 postoperative SSIs in the dataset for analysis. They were randomly assigned tin ratio of 7:3 into a training set and a test set. Based on comprehensive characteristics of patients and diseases in the absence or presence of operative data, prediction of SSI was performed at two time points (pre-operatively and post-operatively) with a feed-forward ANN and the LR models. In addition to the calculated accuracy, sensitivity, and specificity, the predictive performance of ANN and LR were assessed based on area under the curve (AUC) measures of receiver operator characteristic curves and Brier score.

Results

ANN had a significantly higher AUC (0.892) of post-operative prediction and AUC (0.808) of pre-operative prediction than LR (both P<0.0001). In addition, there was significant higher AUC of post-operative prediction than pre-operative prediction by ANN (p<0.0001). With the highest AUC and the lowest Brier score (0.090), the post-operative prediction by ANN had the highest overall predictive performance.

Conclusion

The post-operative prediction by ANN had the highest overall performance in predicting SSI after free-flap reconstruction in patients receiving surgery for head and neck cancer.

Managing pregnancy of unknown location based on initial serum progesterone and serial serum hCG levels: development and validation of a two-step triage protocol

OBJECTIVES

A uniform rationalized management protocol for pregnancies of unknown location (PUL) is lacking. We developed a two-step triage protocol to select PUL at high risk of ectopic pregnancy (EP), based on serum progesterone level at presentation (step 1) and the serum human chorionic gonadotropin (hCG) ratio, defined as the ratio of hCG at 48 h to hCG at presentation (step 2).

METHODS

This was a cohort study of 2753 PUL (301 EP), involving a secondary analysis of prospectively and consecutively collected PUL data from two London-based university teaching hospitals. Using a chronological split we used 1449 PUL for development and 1304 for validation. We aimed to assign PUL as low risk with high confidence (high negative predictive value (NPV)) while classifying most EP as high risk (high sensitivity). The first triage step assigned PUL as low risk using a threshold of serum progesterone at presentation. The remaining PUL were triaged using a novel logistic regression risk model based on hCG ratio and initial serum progesterone (second step), defining low risk as an estimated EP risk of < 5%.

RESULTS

On validation, initial serum progesterone ≤ 2 nmol/L (step 1) classified 16.1% PUL as low risk. Second-step classification with the risk model selected an additional 46.0% of all PUL as low risk. Overall, the two-step protocol classified 62.1% of PUL as low risk, with an NPV of 98.6% and a sensitivity of 92.0%. When the risk model was used in isolation (i.e. without the first step), 60.5% of PUL were classified as low risk with 99.1% NPV and 94.9% sensitivity.

CONCLUSION

PUL can be classified efficiently into being either high or low risk for complications using a two-step protocol involving initial progesterone and hCG levels and the hCG ratio. Copyright © 2016 ISUOG. Published by John Wiley & Sons Ltd.

Clinical risk model assessment for cardiovascular autonomic dysfunction in the general Chinese population

BACKGROUND

The aim of this study was to evaluate the prevalence of cardiovascular autonomic (CA) dysfunction in the general Chinese population (instead of focusing on only patients with diabetes) and to develop a clinical risk model for the disease.

METHODS AND MATERIALS

We evaluated CA dysfunction prevalence in a dataset based on a population sample consisting of 2,092 individuals. Clinical risk models were derived from exploratory sets using multiple logistic regression analysis. The performance of the clinical risk models was tested in the validation sets.

RESULTS

CA dysfunction prevalence was 18.50% in the general Chinese population, while the prevalence was 24.14% in individuals aged ≥60 years. Its prevalence was 31.17, 24.69, and 21.26% in patients with diabetes, and hypertensive, and metabolic syndrome populations, respectively. Finally, we developed clinical risk models involving seven risk factors. The mean area under the receiver-operating curve was 0.758 (95% CI 0.724-0.793) for these models. The mean sensitivity and specificity of the clinical risk models was 75.0 and 66.2%, respectively.

CONCLUSION

CA dysfunction prevalence was high in the general Chinese population, and its prevalence was more frequent in individuals with diabetes, and hypertensive, and metabolic syndrome. Clinical risk models with a high value for predicting CA dysfunction were developed. CA dysfunction has become a major public health problem in China that requires strategies aimed at the prevention and treatment of the disease.

Ectopic pregnancy secondary to in vitro fertilisation-embryo transfer: pathogenic mechanisms and management strategies

BACKGROUND

Ectopic pregnancy (EP) is the leading cause of maternal morbidity and mortality during the first trimester and the incidence increases dramatically with in vitro fertilisation and embryo transfer (IVF-ET). The co-existence of an EP with a viable intrauterine pregnancy (IUP) is known as heterotopic pregnancy (HP) affecting about 1% of patients during assisted conception. EP/HP can cause significant morbidity and occasional mortality and represent diagnostic and therapeutic challenges, particularly during fertility treatment. Many risk factors related to IVF-ET techniques and the cause of infertility have been documented. The combination of transvaginal ultrasound (TVS) and serum human chorionic gonadotrophin (hCG) is the most reliable diagnostic tool, with early diagnosis of EP/HP permitting conservative management. This review describes the risk factors, diagnostic modalities and treatment approaches of EP/HP during IVF-ET and also their impact on subsequent fertility treatment.

METHODS

The scientific literature was searched for studies investigating EP/HP during IVF-ET. Publications in English and within the past 6 years were mostly selected.

RESULTS

A history of tubal infertility, pelvic inflammatory disease and specific aspects of embryo transfer technique are the most significant risk factors for later EP. Early measurement of serum hCG and performance of TVS by an expert operator as early as gestational week 5 can identify cases of possible EP. These women should be closely monitored with repeated ultrasound and hCG measurement until a diagnosis is reached. Treatment must be customised to the clinical condition and future fertility requirements of the patient. In cases of HP, the viable IUP can be preserved in the majority of cases but requires early detection of HP. No apparent negative impact of the different treatment approaches for EP/HP on subsequent IVF-ET, except for risk of recurrence.

CONCLUSIONS

EP/HP are tragic events in a couple's reproductive life, and the earlier the diagnosis the better the prognosis. Due to the increase incidence following IVF-ET, there is a compelling need to develop a diagnostic biomarker/algorithm that can predict pregnancy outcome with high sensitivity and specificity before IVF-ET to prevent and/or properly manage those who are at higher risk of EP/HP.

Comparison of prediction model for cardiovascular autonomic dysfunction using artificial neural network and logistic regression analysis

Background

This study aimed to develop the artificial neural network (ANN) and multivariable logistic regression (LR) analyses for prediction modeling of cardiovascular autonomic (CA) dysfunction in the general population, and compare the prediction models using the two approaches.

Methods and Materials

We analyzed a previous dataset based on a Chinese population sample consisting of 2,092 individuals aged 30–80 years. The prediction models were derived from an exploratory set using ANN and LR analysis, and were tested in the validation set. Performances of these prediction models were then compared.

Results

Univariate analysis indicated that 14 risk factors showed statistically significant association with the prevalence of CA dysfunction (P<0.05). The mean area under the receiver-operating curve was 0.758 (95% CI 0.724–0.793) for LR and 0.762 (95% CI 0.732–0.793) for ANN analysis, but noninferiority result was found (P<0.001). The similar results were found in comparisons of sensitivity, specificity, and predictive values in the prediction models between the LR and ANN analyses.

Conclusion

The prediction models for CA dysfunction were developed using ANN and LR. ANN and LR are two effective tools for developing prediction models based on our dataset.

Artificial neural network models for prediction of cardiovascular autonomic dysfunction in general Chinese population

BACKGROUND

The present study aimed to develop an artificial neural network (ANN) based prediction model for cardiovascular autonomic (CA) dysfunction in the general population.

METHODS

We analyzed a previous dataset based on a population sample consisted of 2,092 individuals aged 30-80 years. The prediction models were derived from an exploratory set using ANN analysis. Performances of these prediction models were evaluated in the validation set.

RESULTS

Univariate analysis indicated that 14 risk factors showed statistically significant association with CA dysfunction (P < 0.05). The mean area under the receiver-operating curve was 0.762 (95% CI 0.732-0.793) for prediction model developed using ANN analysis. The mean sensitivity, specificity, positive and negative predictive values were similar in the prediction models was 0.751, 0.665, 0.330 and 0.924, respectively. All HL statistics were less than 15.0.

CONCLUSION

ANN is an effective tool for developing prediction models with high value for predicting CA dysfunction among the general population.