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

Abstract

Background Spontaneous preterm birth is a global issue that contributed to perinatal morbidities and mortalities worldwide. The study aimed to describe the experience at UKM Medical Center in managing women at high risk for spontaneous preterm birth using the Arabin pessary. Methods This is a retrospective observational study involving 58 pregnancies from 1st January 2013 to 31st December 2019. Inclusion criteria were previous mid-trimester miscarriage and/or preterm birth, previous cervical surgery or short cervical length on routine sonogram. The demographic data, characteristics of each pregnancy and details of outcomes and management were described. Results The majority of women were Malay with mean age and body mass index of 32.9 ± 4.2 years and 27.1 ± 6.3 kg/m2 respectively. The most frequent indications for Arabin pessary insertion were previous mid-trimester miscarriage (46.4%) and early preterm birth (17.2%). A total of 73.4% of these women had the pessary inserted electively at a mean cervical length of 31.6 ± 9.1 mm at median gestation of 15.0 weeks. They were managed as outpatient (56.9%), inpatient (24.1%) or mixed (19.0%) with combination of progestogen (81.0%) and 53.4% received antenatal corticosteroids. Spontaneous preterm birth at or more than 34 weeks gestation occurred in 74.1% with birthweight at or more than 2000 g (82.4%). Despite cervical funneling in 12 women (20.7%), 66.7% delivered at or later than 34 weeks gestation and 2 (16.7%) resulted in miscarriage. Conclusions Insertion of the Arabin pessary is beneficial to prevent spontaneous preterm birth in pregnant women who are at high risk. In particular, early insertion and close monitoring allows the best possible outcomes. Trial registration This study was retrospectively registered with ClinicalTrials.gov (NCT04638023) on 20/11/2020.

Cervical Length and Perinatal Outcome

“…Specifically, prematurity was 14% in the vaginal pessary group versus 29% in the monitoring group (RR 0.49, 95% CI 0.24, 0.97), while poor perinatal outcome was 2% in the vaginal pessary group versus 29% in the monitoring group (RR 0.4, 95% CI 0.19, 0.83). The results were even more encouraging in the subanalysis of high-risk pregnancies for the occurrence of preterm birth [42][43][44][45][46][47][48][49][50].…”

“…In addition, there was no difference in the reduction of preterm birth before 37 weeks gestation when the combined treatment of cervical banding and progesterone administration was compared with cervical banding alone (RR 1.04, 95% CI 0.56, 1.93) or progesterone administration. (RR 0.82, 95% CI 0.57, 1.19) [48,[51][52][53][54].…”

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

“…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. Many studies have shown that implementing adequate programs to prevent preterm birth is desirable [ [9] , [10] , [11] , [12] ]. This prediction model is a promising approach for identifying risk factors and estimating the probability of preterm birth.…”

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

“…Prediction models for preterm birth are a promising approach to realize risk estimation and the implementation of adequate programs for preventing preterm birth is desirable. Studies show that progesterone and pessary insertion are beneficial to prevent preterm birth in women who are at high risk for preterm birth 18–21 . However, there is limited evidence on the reporting quality and risk of bias of prediction models using machine learning methods in preterm birth.…”

“…Studies show that progesterone and pessary insertion are beneficial to prevent preterm birth in women who are at high risk for preterm birth. 18 , 19 , 20 , 21 However, there is limited evidence on the reporting quality and risk of bias of prediction models using machine learning methods in preterm birth. Therefore, we conducted a systematic review to assess the reporting quality and risk of bias of studies on machine learning‐based prediction models in preterm birth.…”