Use of the Arabin pessary in women at high risk for preterm birth: long-term experience at a single tertiary center in Malaysia

A systematic review of prediction models for spontaneous preterm birth in singleton asymptomatic pregnant women with risk factors

Backgrounds

Spontaneous preterm birth (SPB) is a global problem. Early screening, identification, and prevention in asymptomatic pregnant women with risk factors for preterm birth can help reduce the incidence and mortality of preterm births. Therefore, this study systematically reviewed prediction models for spontaneous preterm birth, summarised the model characteristics, and appraised their quality to identify the best-performing prediction model for clinical decision-making.

Methods

PubMed, Embase, Cochrane Library, China National Knowledge Infrastructure, China Biology Medicine disc, VIP Database, and Wanfang Data were searched up to September 27, 2021. Prediction models for spontaneous preterm births in singleton asymptomatic pregnant women with risk factors were eligible for inclusion. Six independent reviewers selected the eligible studies and extracted data from the prediction models. The findings were summarised using descriptive statistics and visual plots.

Results

Twelve studies with twelve developmental models were included. Discriminative performance was reported in 11 studies, with an Area Under the Curve (AUC) ranging from 0.75 to 0.95. The AUCs of the seven models were greater than 0.85. Cervical length (CL) is the most commonly used predictor of spontaneous preterm birth. A total of 91.7% of the studies had a high risk of bias in the analysis domain, mainly because of the small sample size and lack of adjustment for overfitting.

Conclusion

The accuracy of the models for spontaneous preterm births in singleton asymptomatic women with risk factors was good. However, these models are not widely used in clinical practice because they lack replicability and transparency. Future studies should transparently report methodological details and consider more meaningful predictors with new progress in research on preterm birth.

Reporting and risk of bias of prediction models based on machine learning methods in preterm birth: A systematic review

INTRODUCTION

There was limited evidence on the quality of reporting and methodological quality of prediction models using machine learning methods in preterm birth. This systematic review aimed to assess the reporting quality and risk of bias of a machine learning-based prediction model in preterm birth.

MATERIAL AND METHODS

We conducted a systematic review, searching the PubMed, Embase, the Cochrane Library, China National Knowledge Infrastructure, China Biology Medicine disk, VIP Database, and WanFang Data from inception to September 27, 2021. Studies that developed (validated) a prediction model using machine learning methods in preterm birth were included. We used the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement and Prediction model Risk of Bias Assessment Tool (PROBAST) to evaluate the reporting quality and the risk of bias of included studies, respectively. Findings were summarized using descriptive statistics and visual plots. The protocol was registered in PROSPERO (no. CRD 42022301623).

RESULTS

Twenty-nine studies met the inclusion criteria, with 24 development-only studies and 5 development-with-validation studies. Overall, TRIPOD adherence per study ranged from 17% to 79%, with a median adherence of 49%. The reporting of title, abstract, blinding of predictors, sample size justification, explanation of model, and model performance were mostly poor, with TRIPOD adherence ranging from 4% to 17%. For all included studies, 79% had a high overall risk of bias, and 21% had an unclear overall risk of bias. The analysis domain was most commonly rated as high risk of bias in included studies, mainly as a result of small effective sample size, selection of predictors based on univariable analysis, and lack of calibration evaluation.

CONCLUSIONS

Reporting and methodological quality of machine learning-based prediction models in preterm birth were poor. It is urgent to improve the design, conduct, and reporting of such studies to boost the application of machine learning-based prediction models in preterm birth in clinical practice.

Managing pneumonia through facility-based integrated management of childhood management (IMCI) services: an analysis of the service availability and readiness among public health facilities in Bangladesh

BACKGROUND

With an estimated 24,000 deaths per year, pneumonia is the single largest cause of death among young children in Bangladesh, accounting for 18% of all under-5 deaths. The Government of Bangladesh adopted the WHO recommended Integrated Management of Childhood Illness (IMCI)-strategy in 1998 for outpatient management of pneumonia, which was scaled-up nationally by 2014. This paper reports the service availability and readiness related to IMCI-based pneumonia management in Bangladesh. We conducted a secondary analysis of the Bangladesh Health Facility Survey-2017, which was conducted with a nationally representative sample including all administrative divisions and types of health facilities. We limited our analysis to District Hospitals (DHs), Maternal and Child Welfare Centres (MCWCs), Upazila (sub-district) Health Complexes (UHCs), and Union Health and Family Welfare Centres (UH&FWCs), which are mandated to provide IMCI services. Readiness was reported based on 10 items identified by national experts as 'essential' for pneumonia management.

RESULTS

More than 90% of DHs and UHCs, and three-fourths of UH&FWCs and MCWCs provide IMCI-based pneumonia management services. Less than two-third of the staff had ever received IMCI-based pneumonia training. Only one-third of the facilities had a functional ARI timer or a watch able to record seconds on the day of the visit. Pulse oximetry was available in 27% of the district hospitals, 18% of the UHCs and none of the UH&FWCs. Although more than 80% of the facilities had amoxicillin syrup or dispersible tablets, only 16% had injectable gentamicin. IMCI service registers were not available in nearly one-third of the facilities and monthly reporting forms were not available in around 10% of the facilities. Only 18% of facilities had a high-readiness (score 8-10), whereas 20% had a low-readiness (score 0-4). The readiness was significantly poorer among rural and lower level facilities (p < 0.001). Seventy-two percent of the UHCs had availability of one of any of the four oxygen sources (oxygen concentrators, filled oxygen cylinder with flowmeter, filled oxygen cylinder without flowmeter, and oxygen distribution system) followed by DHs (66%) and MCWCs (59%).

CONCLUSION

There are substantial gaps in the readiness related to IMCI-based pneumonia management in public health facilities in Bangladesh. Since pneumonia remains a major cause of child death nationally, Bangladesh should make a substantial effort in programme planning, implementation and monitoring to address these critical gaps to ensure better provision of essential care for children suffering from pneumonia.