Threatened preterm birth: Validation of a nomogram to predict the individual risk of very preterm delivery in a secondary care center

Analysis of cancer-specific survival in patients with metastatic colorectal cancer: A evidence-based medicine study

BACKGROUND

Metastatic colorectal cancer (mCRC) is a common malignancy whose treatment has been a clinical challenge. Cancer-specific survival (CSS) plays a crucial role in assessing patient prognosis and treatment outcomes. However, there is still limited research on the factors affecting CSS in mCRC patients and their correlation.

AIM

To predict CSS, we developed a new nomogram model and risk grading system to classify risk levels in patients with mCRC.

METHODS

Data were extracted from the United States Surveillance, Epidemiology, and End Results database from 2018 to 2023. All eligible patients were randomly divided into a training cohort and a validation cohort. The Cox proportional hazards model was used to investigate the independent risk factors for CSS. A new nomogram model was developed to predict CSS and was evaluated through internal and external validation.

RESULTS

A multivariate Cox proportional risk model was used to identify independent risk factors for CSS. Then, new CSS columns were developed based on these factors. The consistency index (C-index) of the histogram was 0.718 (95%CI: 0.712-0.725), and that of the validation cohort was 0.722 (95%CI: 0.711-0.732), indicating good discrimination ability and better performance than tumor-node-metastasis staging (C-index: 0.712-0.732). For the training set, 0.533, 95%CI: 0.525-0.540; for the verification set, 0.524, 95%CI: 0.513-0.535. The calibration map and clinical decision curve showed good agreement and good potential clinical validity. The risk grading system divided all patients into three groups, and the Kaplan-Meier curve showed good stratification and differentiation of CSS between different groups. The median CSS times in the low-risk, medium-risk, and high-risk groups were 36 months (95%CI: 34.987-37.013), 18 months (95%CI: 17.273-18.727), and 5 months (95%CI: 4.503-5.497), respectively.

CONCLUSION

Our study developed a new nomogram model to predict CSS in patients with synchronous mCRC. In addition, the risk-grading system helps to accurately assess patient prognosis and guide treatment.

Comparison of Four Tests for Preterm Birth Prediction in Symptomatic Singleton Women: A Prospective Cohort Study

Background and Purpose: It is challenging to predict preterm births accurately. We sought to compare the diagnostic performance of cervical length in predicting preterm birth to those of contraction recording in cardiotocography (CTG), cervical funneling, and Bishop score. Methods: A total of 371 women with singleton pregnancies and threatened preterm labor admitted to tertiary hospitals of northwestern Iran were included in this prospective cohort study and followed up until childbirth. Transvaginal ultrasound was utilized to assess the cervical length and funneling. Also, a CTG test and digital vaginal examination were performed. The data of 218 women were analyzed in the STATA software. The sensitivity and other diagnostic performances, and 95% confidence intervals, were reported. Results: The CTG uterine contraction recording had the highest sensitivity for predicting birth within the next 2 days, whereas a Bishop score ≥6 revealed the highest specificity, positive predictive value, and positive likelihood ratio. Inadequate cervical length for gestational age exhibited a high negative predictive value and the lowest negative likelihood ratio. Conclusion: The cervical length test has good diagnostic and prognostic performance among the four tests for preterm birth. Cervical funneling and CTG tests have poor predictive value, whereas the Bishop score has a superior diagnostic performance to the other tests.

Development of risk prediction models for preterm delivery in a rural setting in Ethiopia

Background

Preterm birth complications are the leading causes of death among children under five years. However, the inability to accurately identify pregnancies at high risk of preterm delivery is a key practical challenge, especially in resource-constrained settings with limited availability of biomarkers assessment.

Methods

We evaluated whether risk of preterm delivery can be predicted using available data from a pregnancy and birth cohort in Amhara region, Ethiopia. All participants were enrolled in the cohort between December 2018 and March 2020. The study outcome was preterm delivery, defined as any delivery occurring before week 37 of gestation regardless of vital status of the foetus or neonate. A range of sociodemographic, clinical, environmental, and pregnancy-related factors were considered as potential inputs. We used Cox and accelerated failure time models, alongside decision tree ensembles to predict risk of preterm delivery. We estimated model discrimination using the area-under-the-curve (AUC) and simulated the conditional distributions of cervical length (CL) and foetal fibronectin (FFN) to ascertain whether they could improve model performance.

Results

We included 2493 pregnancies; among them, 138 women were censored due to loss-to-follow-up before delivery. Overall, predictive performance of models was poor. The AUC was highest for the tree ensemble classifier (0.60, 95% confidence interval = 0.57-0.63). When models were calibrated so that 90% of women who experienced a preterm delivery were classified as high risk, at least 75% of those classified as high risk did not experience the outcome. The simulation of CL and FFN distributions did not significantly improve models' performance.

Conclusions

Prediction of preterm delivery remains a major challenge. In resource-limited settings, predicting high-risk deliveries would not only save lives, but also inform resource allocation. It may not be possible to accurately predict risk of preterm delivery without investing in novel technologies to identify genetic factors, immunological biomarkers, or the expression of specific proteins.

Validation of a Nomogram for Predicting Preterm Birth in Women With Threatened Preterm Labor: A Prospective Cohort Study in Iranian Tertiary Referral Hospitals

In this prospective cohort study, we aimed to investigate external validity of the Allouche's nomogram to predict preterm birth in symptomatic women in Iran. We employed six variables of cervical length, uterine contractions, rupture of membranes, vaginal bleeding, gestational age, and multiple pregnancy to draw the nomograms. These variables were examined in the first day of women's hospitalization and participants followed up until giving birth. The concordance index of area under the curve (AUC) was used for validation of the nomograms. Of the participants 10% gave birth within 48 hours and 29% before 34 weeks. The nomogram had sufficient accuracy in predicting birth within 48 hours (AUC 0.89 [95% CI 0.82-0.96]) and birth before 34 weeks (AUC 0.89 [95% CI 0.84-0.94]). The optimal risk threshold for nomogram predicting birth within 48 hours was 0.16. Use of these two nomograms, can improve the health of women and their neonates.