Factors associated with discontinuation in fertility treatment: a systematic scoping review.

PURPOSE: The discontinuation of fertility treatment could decrease the chances of achieving parenthood for infertile patients and often leads to economic loss and medical resource waste. However, the evidence on the factors associated with discontinuation is unclear and inconsistent in the context of fertility treatment. This scoping review aimed to summarize the evidence on factors associated with discontinuation in fertility treatment, identify the current knowledge gap, and generate recommendations for future research. METHODS: We searched PubMed, Embase, The Cochrane Library, Web of Science, CINAHL, American Psychological Association, and http://clinicaltrials.gov from inception to June 2023 without language or time restrictions. We also searched the grey literature in Open Grey and Google Scholar and hand-searched the reference lists of relevant studies to identify potentially eligible studies. Publications that studied factors associated with discontinuation in fertility treatment were included. The identified factors were mapped to the World Health Organization's treatment adherence model. RESULTS: Thirty-seven articles involving 41,973 infertile patients from 13 countries were included in this scoping review. All studies identified the factors from the perspective of patients, except for one that described the factors from the healthcare providers' perspective. A total of 42 factors were identified, with most of them belonging to the patient-related dimension, followed by socio-economic-related, treatment-related, condition-related, and healthcare system-related dimensions. Female education level, social support, and insurance coverage decreased the likelihood of treatment discontinuation, whereas multiparous women, male infertility, depression, higher infertility duration, and treatment duration increased the likelihood of treatment discontinuation. Age, education level, and ethnicity are the commonly nonmodifiable factors for treatment discontinuation, while insurance coverage, depression, and anxiety symptoms are among some of the more commonly reported modifiable factors. CONCLUSION: This is the first scoping review examining and synthesizing evidence on the factors influencing of discontinuation in fertility treatment. This review could inform researchers, clinicians, and policymakers to address modifiable barriers and facilitators to develop personalized and multicomponent interventions that could improve the discontinuation in fertility treatment.

A nomogram model based on clinical markers for predicting malignancy of ovarian tumors.

Objective: The aim of this study was to build a nomogram based on clinical markers for predicting the malignancy of ovarian tumors (OTs). Method: A total of 1,268 patients diagnosed with OTs that were surgically removed between October 2017 and May 2019 were enrolled. Clinical markers such as post-menopausal status, body mass index (BMI), serum human epididymis protein 4 (HE4) value, cancer antigen 125 (CA125) value, Risk of Ovarian Malignancy Algorithm (ROMA) index, course of disease, patient-generated subjective global assessment (PG-SGA) score, ascites, and locations and features of masses were recorded and analyzed (p 0.05). Significant variables were further selected using multivariate logistic regression analysis and were included in the decision curve analysis (DCA) used to assess the value of the nomogram model for predicting OT malignancy. Result: The significant variables included post-menopausal status, BMI, HE4 value, CA125 value, ROMA index, course of disease, PG-SGA score, ascites, and features and locations of masses (p 0.05). The ROMA index, BMI (≥ 26), unclear/blurred mass boundary (on magnetic resonance imaging [MRI]/computed tomography [CT]), mass detection (on MRI/CT), and mass size and features (on type B ultrasound [BUS]) were screened out for multivariate logistic regression analysis to assess the value of the nomogram model for predicting OT malignant risk (p 0.05). The DCA revealed that the net benefit of the nomogram's calculation model was superior to that of the CA125 value, HE4 value, and ROMA index for predicting OT malignancy. Conclusion: We successfully tailored a nomogram model based on selected clinical markers which showed superior prognostic predictive accuracy compared with the use of the CA125, HE4, or ROMA index (that combines both HE and CA125 values) for predicting the malignancy of OT patients.