Background There is high-quality evidence supporting induction of labour (IOL) for a number of maternal and fetal indications. However, one fifth of inductions fail to result in vaginal births, requiring cesarean deliveries. This has negative clinical, emotional and resource implications. The importance of predicting the success of labour induction to enable shared decision-making has been recognized, but existing models are limited in scope and generalizability. Our objective was to derive and internally validate a clinical prediction model that uses variables readily accessible through maternal demographic data, antenatal history, and cervical examination to predict the likelihood of vaginal birth following IOL. Methods Data was extracted from electronic medical records of consecutive pregnant women who were induced between April and December 2016, at Mount Sinai Hospital, Toronto, Canada. A multivariable logistic regression model was developed using 16 readily accessible variables identified through literature review and expert opinion, as predictors of vaginal birth after IOL. The final model was internally validated using 10-fold cross-validation. Results Of the 1123 cases of IOL, 290 (25.8%) resulted in a cesarean delivery. The multivariable logistic regression model found maternal age, parity, pre-pregnancy body mass index and weight, weight at delivery, and cervical dilation at time of induction as significant predictors of vaginal delivery following IOL. The prediction model was well calibrated (Hosmer-Lemeshow χ2 = 5.02, p = 0.76) and demonstrated good discriminatory ability (area under the receiver operating characteristic (AUROC) curve, 0.81 (95% CI 0.78 to 0.83)). Finally, the model showed good internal validity [AUROC 0.77 (95% CI 0.73 to 0.82)]. Conclusions We have derived and internally validated a well-performing clinical prediction model for IOL in a large and diverse population using variables readily accessible through maternal demographic data, antenatal history, and cervical examination. Once prospectively validated in diverse settings, and if shown to be acceptable to pregnant women and healthcare providers as well as clinically and cost-effective, this model has potential for widespread use in clinical practice and research for enhancing patient autonomy, improving induction outcomes, and optimizing allocation of resources. Electronic supplementary material The online version of this article (10.1186/s12884-019-2232-8) contains supplementary material, which is available to authorized users.
INTRODUCTION: There is high-quality evidence supporting induction of labour (IOL) for a number of maternal and fetal indications. However, one fifth of inductions fail, requiring emergency cesarean deliveries. This has negative clinical, emotional and financial implications. Our objective was to derive and internally validate a clinical prediction tool to determine the success of IOL. METHODS: Data was extracted from electronic medical records of consecutive pregnant women who were induced between April 26, 2016 and December 31, 2016, at Mount Sinai Hospital (Toronto, Canada). A multivariable logistic regression model was developed using variables identified as predictors of successful IOL by literature review and expert opinion. Repeated K-fold cross-validation was used to internally validate the model. RESULTS: Of the 916 cases of IOL, 249 (27%) failed. The multivariable logistic regression model found maternal age, parity, pre-pregnancy weight, pre-pregnancy body mass index, weight at delivery and cervical dilation at time of induction as significant predictors of successful IOL. The prediction tool was well calibrated (Hosmer-Lemeshow χ2=6.42, P=.60) and demonstrated good discriminatory ability (area under the receiver-operating characteristic curve [AUROC], 0.79 [95% CI 0.76–0.82]). Internal validation of the model showed a similar discriminatory ability (AUROC, 0.77 [95% CI 0.68–0.85). CONCLUSION: We have derived and internally validated a clinical prediction tool for IOL in a large and diverse population. Once prospectively validated in other settings, this has potential for widespread use in clinical practice and research, as well as for enhancing patient experience and allocation of healthcare resources.
INTRODUCTION: There is considerable overlap between symptoms related to iron deficiency anaemia (IDA) and a normally advancing gestation, posing challenges to the use of quality of life (QoL) tools to measure treatment effects. Our aim was to determine how best to use two existing QoL tools; short-form 36 (SF36) – a general questionnaire covering eight domains and the multidimensional fatigue symptom inventory: short form (MFSI-SF) – a fatigue-specific questionnaire to assess treatment effect in pregnant women with IDA. METHODS: We recruited consecutive women with IDA attending the Obstetric Day Unit at Mount Sinai Hospital, Toronto, for intravenous iron infusions over six months. Consenting participants completed SF36 and MFSI-SF questionnaires at each visit and upon completion of treatment. Scores for the entire questionnaire and individual domains were summed and plotted graphically over time. RESULTS: 29 women consented and completed at least one SF36 and MFSI-SF questionnaire, while 24 completed two, 12 completed three and four women completed four. The mean maternal age was 34 (24-46) years and the mean gestational age 29 (25-38) weeks. These women represented different ethnic groups and socio-economic strata. Although there was no consistent pattern in scores for 6/8 SF36 domains, there was a significant improvement between visits in the energy/fatigue (37.6 vs 42.8, p=0.044), and emotional wellness (62.7 vs 77.3, p<0.001) domains and in MFSI-SF scores (17.1 vs 12.2, p=0.025). CONCLUSION: When assessing treatment effects in pregnant women with IDA, consideration should be given to using QoL tools specifically designed to measure fatigue, such as MFSI-SF or the energy/fatigue domain in SF36.
INTRODUCTION: This study aims to generate gestational-age specific Health-Related Quality of Life (HRQoL) scores from a diverse population of low-risk pregnant women, to provide reference values for future research and clinical practice. METHODS: We conducted a prospective longitudinal study at Mount Sinai Hospital in Toronto, Canada. Over a period of three months, we recruited 333 women over the age of 18 with low-risk singleton pregnancies between 12 and 40 weeks of gestation. Participants completed a demographic survey at the first visit and two HRQoL questionnaires - the Short Form-36 (SF-36) and the Multidimensional Fatigue Symptom Inventory-Short Form (MFSI-SF) at each visit. The SF-36 was composed of eight domains: physical functioning, role limitations due to physical health, role limitations due to emotional health, energy/fatigue, emotional well-being, social functioning, pain and general health scores. The MFSI-SF produced domains consisting of the general score and the vigor score. Gestational-age-specific mean HRQoL scores and standard deviations were calculated to determine how they changed throughout the course of pregnancy in various domains. Approved by Mount Sinai Hospital Research Ethics Board. RESULTS: Although SF36 scores were constant for the general domain, those for the energy domain as well as MFSI-SF scores tended to rise from 12 weeks and peak at 20-27 weeks before dropping slightly again and plateauing between 34 and 40 weeks. CONCLUSION: HRQoL scores, especially those related to energy and vigor, fluctuate during the course of pregnancy and these fluctuations need to be considered when comparing the effect of interventions or the progression of disease conditions during pregnancy.
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