Prediction models in gynaecology: Transparent reporting needed for clinical application

The effect of opportunistic salpingectomy for primary prevention of ovarian cancer on ovarian reserve: a systematic review and meta-analysis

BACKGROUND

Opportunistic salpingectomy (OS) is an attractive method for primary prevention of ovarian cancer. Although OS has not been associated with a higher complication rate, it may be associated with earlier onset of menopause.

OBJECTIVE

To provide a systematic review and meta-analysis of the effect of OS on both age at menopause and ovarian reserve.

METHODS

A search was conducted in the Cochrane Library, Embase and MEDLINE databases from inception until March 2022. We included randomized clinical trials and cohort studies investigating the effect of OS on onset of menopause and/or ovarian reserve through change in anti-Müllerian hormone (AMH), antral follicle count (AFC), estradiol (E2), follicle stimulating hormone (FSH) and luteinizing hormone (LH). Data was extracted independently by two researchers. Random-effects meta-analyses were conducted to estimate the pooled effect of OS on ovarian reserve.

RESULTS

The initial search yielded 1047 studies. No studies were found investigating the effect of OS on age of menopause. Fifteen studies were included in the meta-analysis on ovarian reserve. Meta-analyses did not result in statistically significant differences in mean change in AMH (MD -0.07 ng/ml, 95%CI -0.18;0.05), AFC (MD 0.20 n, 95 % CI -4.91;5.30), E2 (MD 3.97 pg/ml, 95%CI -0.92;8.86), FSH (MD 0.33mIU/ml, 95%CI -0.15;0.81) and LH (MD 0.03mIU/ml; 95%CI -0.47;0.53).

CONCLUSION

Our study shows that OS does not result in a significant reduction of ovarian reserve in the short term. Further research is essential to confirm the absence of major effects of OS on menopausal onset since clear evidence on this subject is lacking. Registration number PROSPERO CRD42021260966.

Multivariable prediction models for health care spending using machine learning: a protocol of a systematic review

BACKGROUND

With rising cost pressures on health care systems, machine-learning (ML)-based algorithms are increasingly used to predict health care costs. Despite their potential advantages, the successful implementation of these methods could be undermined by biases introduced in the design, conduct, or analysis of studies seeking to develop and/or validate ML models. The utility of such models may also be negatively affected by poor reporting of these studies. In this systematic review, we aim to evaluate the reporting quality, methodological characteristics, and risk of bias of ML-based prediction models for individual-level health care spending.

METHODS

We will systematically search PubMed and Embase to identify studies developing, updating, or validating ML-based models to predict an individual's health care spending for any medical condition, over any time period, and in any setting. We will exclude prediction models of aggregate-level health care spending, models used to infer causality, models using radiomics or speech parameters, models of non-clinically validated predictors (e.g., genomics), and cost-effectiveness analyses without predicting individual-level health care spending. We will extract data based on the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies (CHARMS), previously published research, and relevant recommendations. We will assess the adherence of ML-based studies to the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement and examine the inclusion of transparency and reproducibility indicators (e.g. statements on data sharing). To assess the risk of bias, we will apply the Prediction model Risk Of Bias Assessment Tool (PROBAST). Findings will be stratified by study design, ML methods used, population characteristics, and medical field.

DISCUSSION

Our systematic review will appraise the quality, reporting, and risk of bias of ML-based models for individualized health care cost prediction. This review will provide an overview of the available models and give insights into the strengths and limitations of using ML methods for the prediction of health spending.