Pub Date : 2024-06-25DOI: 10.1101/2024.06.25.24309475
Romy Cappenberg, Jesus Garcia Garcia, Christine Happle, Anna Zychlinsky Scharff
Background Dysphoric Milk Ejection Reflex (DMER), which affects a significant proportion of lactating parents and may significantly impact feeding choices, is poorly understood.
背景 对很多哺乳期父母都会出现的排乳反射障碍（DMER）知之甚少，它可能会严重影响喂养选择。
{"title":"Dysphoric Milk Ejection Reflex: Risk, Prevalence, and Persistence","authors":"Romy Cappenberg, Jesus Garcia Garcia, Christine Happle, Anna Zychlinsky Scharff","doi":"10.1101/2024.06.25.24309475","DOIUrl":"https://doi.org/10.1101/2024.06.25.24309475","url":null,"abstract":"<strong>Background</strong> Dysphoric Milk Ejection Reflex (DMER), which affects a significant proportion of lactating parents and may significantly impact feeding choices, is poorly understood.","PeriodicalId":501409,"journal":{"name":"medRxiv - Obstetrics and Gynecology","volume":"206 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141512600","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-25DOI: 10.1101/2024.06.25.24309408
Elleke F. Bosma, Brynjulf Mortensen, Kevin DeLong, Mads Roepke, Helene Baek Juel, Randi Rich, Amalie Axelsen, Marouschka Scheeper, Rasmus Marvig, Thomas Gundelund Rasmussen, Colleen Acosta, Ulrich Binne, Anne Bloch Thomsen, Hans-Christian Ingerslev, Fareeha Zulfiqar, Tine Wroending, Paul Cotter, Marcus O'Brien, Shriram Patel, Sarita Dam, Julia Albert Nicholson, Henriette Svarre Nielsen, Timothy Dinan, Fergus McCarthy, Johan E.T. van Hylckama Vlieg, Laura M. Ensign
Here, we describe the first placebo-controlled trial of vaginal microbiota transplantation (VMT) in women with asymptomatic dysbiosis without the use of antibiotic pretreatment. Importantly, we also describe the implementation of a donor program and banking of donor cervicovaginal secretions (CVS) while retaining sample viability, which is crucial to allow for scale-up and confirmatory quality testing. By metagenome sequencing, we demonstrate that VMT provided a significant increase in combined Lactobacillus species in the active arm and strain-level genetic analysis confirmed Lactobacillus engraftment. Moreover, VMT was well tolerated and showed a good safety profile. Furthermore, a shift toward increased Lactobacillus was associated with a change in the expression profile of genes in the complement pathway to a more anti-inflammatory profile. Vaginal microbial and immune profile restoration using VMT may have a positive impact on a wide range of conditions in womens health.
{"title":"Antibiotic-free vaginal microbiota transplantation (VMT) changes vaginal microbiota and immune profile in women with asymptomatic dysbiosis: reporting of a randomized, placebo-controlled trial","authors":"Elleke F. Bosma, Brynjulf Mortensen, Kevin DeLong, Mads Roepke, Helene Baek Juel, Randi Rich, Amalie Axelsen, Marouschka Scheeper, Rasmus Marvig, Thomas Gundelund Rasmussen, Colleen Acosta, Ulrich Binne, Anne Bloch Thomsen, Hans-Christian Ingerslev, Fareeha Zulfiqar, Tine Wroending, Paul Cotter, Marcus O'Brien, Shriram Patel, Sarita Dam, Julia Albert Nicholson, Henriette Svarre Nielsen, Timothy Dinan, Fergus McCarthy, Johan E.T. van Hylckama Vlieg, Laura M. Ensign","doi":"10.1101/2024.06.25.24309408","DOIUrl":"https://doi.org/10.1101/2024.06.25.24309408","url":null,"abstract":"Here, we describe the first placebo-controlled trial of vaginal microbiota transplantation (VMT) in women with asymptomatic dysbiosis without the use of antibiotic pretreatment. Importantly, we also describe the implementation of a donor program and banking of donor cervicovaginal secretions (CVS) while retaining sample viability, which is crucial to allow for scale-up and confirmatory quality testing. By metagenome sequencing, we demonstrate that VMT provided a significant increase in combined Lactobacillus species in the active arm and strain-level genetic analysis confirmed Lactobacillus engraftment. Moreover, VMT was well tolerated and showed a good safety profile. Furthermore, a shift toward increased Lactobacillus was associated with a change in the expression profile of genes in the complement pathway to a more anti-inflammatory profile. Vaginal microbial and immune profile restoration using VMT may have a positive impact on a wide range of conditions in womens health.","PeriodicalId":501409,"journal":{"name":"medRxiv - Obstetrics and Gynecology","volume":"25 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141505957","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-21DOI: 10.1101/2024.06.20.24308970
Elizabeth T. Nguyen, Matthew G. Retzloff, Laura April Gago, John E. Nichols, John F. Payne, Barry A. Ripps, Michael Opsahl, Jeremy Groll, Ronald Beesley, Lorie Nowak, Gregory Neal, Jaye Adams, Trevor Swanson, Xiaocong Chen, Mylene W. M. Yao
Objective: To compare the performance of machine learning based, center-specific (MLCS) models and the US national registry-based, multicenter model (SART model) in predicting IVF live birth probabilities (LBPs) for 6 unrelated, geographically diverse US fertility centers. Design: Retrospective observational design. Subjects: Test sets comprised first IVF cycle data (2013-2022) extracted from a retrospective cohort of 4,645 patients at 6 fertility centers. Intervention or Exposure: The initial (MLCS1) and updated (MLCS2) models were compared against age control. MLSC2 and SART models were compared. Main Outcome Measures: Model validation metrics, reported in median and interquartile range (IQR), were compared using Wilcoxon signed-rank test: ROC AUC, posterior log-likelihood of odds ratio compared to age (PLORA), Precision-Recall (PR) AUC, F1 score and continuous net reclassification improvement (NRI). Results: MLCS1 and MLCS2 models showed improved AUC and PLORA compared to age control; MLCS1 models were validated using out-of-time test data. MLCS2 models showed improved PLORA 23.9 (IQR 10.2, 39.4) compared to 7.2 (IQR 3.6, 11.8) for MLCS1, p<0.05. MLCS2 showed higher median PR AUC at 0.75 (IQR 0.73, 0.77) compared to 0.69 (IQR 0.68, 0.71) for SART, p<0.05. In addition, the median F1 Score was higher for MLCS2 compared to SART model across predicted live birth probability (LBP) thresholds sampled at deciles at ≥40%, ≥50%, ≥60%, ≥70%. For example, at the 50% LBP threshold, MLCS2 had a median F1 score of 0.74 (IQR 0.72, 0.78) compared to 0.71 (IQR 0.68, 0.73) for SART. At these six centers, using the LBP threshold of ≥ 50%, MLCS2 models can identify ~84% of patients who would go on to have IVF live births, while the SART model can only identify ~75%. That means for every 100 patients who will have a first IVF cycle live birth, using LBR ≥ 50% as threshold, the MLCS2 model can identify 9 more such patients without overcalling or overestimating LBPs compared to the SART model. Conclusion: MLCS models accurately assign higher IVF LBPs to more patients compared to the SART model at 6 US fertility centers. We recommend testing a larger sample of fertility centers to evaluate generalizability of MLCS model benefits.
{"title":"Predicting IVF live birth probabilities using machine learning, center-specific and national registry-based models","authors":"Elizabeth T. Nguyen, Matthew G. Retzloff, Laura April Gago, John E. Nichols, John F. Payne, Barry A. Ripps, Michael Opsahl, Jeremy Groll, Ronald Beesley, Lorie Nowak, Gregory Neal, Jaye Adams, Trevor Swanson, Xiaocong Chen, Mylene W. M. Yao","doi":"10.1101/2024.06.20.24308970","DOIUrl":"https://doi.org/10.1101/2024.06.20.24308970","url":null,"abstract":"Objective:\u0000To compare the performance of machine learning based, center-specific (MLCS) models and the US national registry-based, multicenter model (SART model) in predicting IVF live birth probabilities (LBPs) for 6 unrelated, geographically diverse US fertility centers. Design:\u0000Retrospective observational design. Subjects:\u0000Test sets comprised first IVF cycle data (2013-2022) extracted from a retrospective cohort of 4,645 patients at 6 fertility centers. Intervention or Exposure:\u0000The initial (MLCS1) and updated (MLCS2) models were compared against age control. MLSC2 and SART models were compared. Main Outcome Measures:\u0000Model validation metrics, reported in median and interquartile range (IQR), were compared using Wilcoxon signed-rank test: ROC AUC, posterior log-likelihood of odds ratio compared to age (PLORA), Precision-Recall (PR) AUC, F1 score and continuous net reclassification improvement (NRI). Results:\u0000MLCS1 and MLCS2 models showed improved AUC and PLORA compared to age control; MLCS1 models were validated using out-of-time test data. MLCS2 models showed improved PLORA 23.9 (IQR 10.2, 39.4) compared to 7.2 (IQR 3.6, 11.8) for MLCS1, p<0.05. MLCS2 showed higher median PR AUC at 0.75 (IQR 0.73, 0.77) compared to 0.69 (IQR 0.68, 0.71) for SART, p<0.05. In addition, the median F1 Score was higher for MLCS2 compared to SART model across predicted live birth probability (LBP) thresholds sampled at deciles at ≥40%, ≥50%, ≥60%, ≥70%. For example, at the 50% LBP threshold, MLCS2 had a median F1 score of 0.74 (IQR 0.72, 0.78) compared to 0.71 (IQR 0.68, 0.73) for SART. At these six centers, using the LBP threshold of ≥ 50%, MLCS2 models can identify ~84% of patients who would go on to have IVF live births, while the SART model can only identify ~75%. That means for every 100 patients who will have a first IVF cycle live birth, using LBR ≥ 50% as threshold, the MLCS2 model can identify 9 more such patients without overcalling or overestimating LBPs compared to the SART model. Conclusion:\u0000MLCS models accurately assign higher IVF LBPs to more patients compared to the SART model at 6 US fertility centers. We recommend testing a larger sample of fertility centers to evaluate generalizability of MLCS model benefits.","PeriodicalId":501409,"journal":{"name":"medRxiv - Obstetrics and Gynecology","volume":"67 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141506016","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-21DOI: 10.1101/2024.06.19.24309129
McKenzie Maviso, Paula Z. Aines, Gracelyn P. Potjepat, Nancy Geregl, Glen D.L Mola, John W. Bolnga
Background Pregnancy termination or induced abortion is not decriminalized, and access to safe abortion services is largely unavailable in Papua New Guinea (PNG). However, the practice is common throughout the country. This study aimed to estimate the prevalence and determine factors associated with pregnancy termination among married women aged 15-49 in PNG. Methods Secondary data from the 2016-2018 PNG Demographic and Health Survey (PNGDHS) was used. A total weighted samples of 6,288 married women were included. Complex Sample Analysis method was used to account for the cluster design and sample weight of the study. Chi-square tests and multivariable logistic regression were used to assess factors associated with pregnancy termination. Adjusted odds ratios (aORs) with 95% Confidence Intervals (CIs) were reported. Results The prevalence of pregnancy termination was 5.3%. Nearly half (45.2%) of all pregnancy terminations occurred in the Highlands region. Women aged 35-44 years (aOR = 8.54; 95% CI: 1.61-45.26), not working (aOR = 6.17; 95% CI: 2.26-16.85), owned a mobile phone (aOR = 3.77; 95% CI: 1.60-8.84), and lived in urban areas (aOR = 5.66; 95% CI: 1.91-16.81) were more likely to terminate a pregnancy. Women who experienced intimate partner violence (IPV) were 2.27 times (aOR = 2.27; 95% CI: 1.17-4.41) more likely to terminate a pregnancy compared to those who did not experience IPV. Women with unplanned pregnancies were 6.23 times (aOR = 6.23; 95% CI: 2.61-14.87) more likely to terminate a pregnancy. Women who knew about modern contraceptive methods and made independent decisions for contraceptive use were 3.38 and 2.54 times (aOR = 3.38; 95% CI: 1.39-8.18 and aOR = 2.54; 95% CI: 1.18-5.45, respectively) more likely to terminate a pregnancy. Conclusion The findings highlight the role of sociodemographic and maternal factors in pregnancy termination among married women in PNG. Maternal age, occupation, mobile phone ownership, place of residence, IPV, unplanned pregnancy, knowledge of modern contraceptive methods, and decision-maker for contraceptive use were significantly associated with pregnancy termination. Efforts aimed at reducing unplanned pregnancies and terminations should focus on improving easy access to contraceptives and comprehensive sexual and reproductive health education for married couples. Post-abortion care should also be integrated into the country's legal framework and added as an important component of existing sexual and reproductive health services.
{"title":"Prevalence of pregnancy termination and associated factors among married women in Papua New Guinea: a nationally representative cross-sectional survey","authors":"McKenzie Maviso, Paula Z. Aines, Gracelyn P. Potjepat, Nancy Geregl, Glen D.L Mola, John W. Bolnga","doi":"10.1101/2024.06.19.24309129","DOIUrl":"https://doi.org/10.1101/2024.06.19.24309129","url":null,"abstract":"<strong>Background</strong>\u0000Pregnancy termination or induced abortion is not decriminalized, and access to safe abortion services is largely unavailable in Papua New Guinea (PNG). However, the practice is common throughout the country. This study aimed to estimate the prevalence and determine factors associated with pregnancy termination among married women aged 15-49 in PNG.\u0000<strong>Methods</strong>\u0000Secondary data from the 2016-2018 PNG Demographic and Health Survey (PNGDHS) was used. A total weighted samples of 6,288 married women were included. Complex Sample Analysis method was used to account for the cluster design and sample weight of the study. Chi-square tests and multivariable logistic regression were used to assess factors associated with pregnancy termination. Adjusted odds ratios (aORs) with 95% Confidence Intervals (CIs) were reported.\u0000<strong>Results</strong>\u0000The prevalence of pregnancy termination was 5.3%. Nearly half (45.2%) of all pregnancy terminations occurred in the Highlands region. Women aged 35-44 years (aOR = 8.54; 95% CI: 1.61-45.26), not working (aOR = 6.17; 95% CI: 2.26-16.85), owned a mobile phone (aOR = 3.77; 95% CI: 1.60-8.84), and lived in urban areas (aOR = 5.66; 95% CI: 1.91-16.81) were more likely to terminate a pregnancy. Women who experienced intimate partner violence (IPV) were 2.27 times (aOR = 2.27; 95% CI: 1.17-4.41) more likely to terminate a pregnancy compared to those who did not experience IPV. Women with unplanned pregnancies were 6.23 times (aOR = 6.23; 95% CI: 2.61-14.87) more likely to terminate a pregnancy. Women who knew about modern contraceptive methods and made independent decisions for contraceptive use were 3.38 and 2.54 times (aOR = 3.38; 95% CI: 1.39-8.18 and aOR = 2.54; 95% CI: 1.18-5.45, respectively) more likely to terminate a pregnancy.\u0000<strong>Conclusion</strong>\u0000The findings highlight the role of sociodemographic and maternal factors in pregnancy termination among married women in PNG. Maternal age, occupation, mobile phone ownership, place of residence, IPV, unplanned pregnancy, knowledge of modern contraceptive methods, and decision-maker for contraceptive use were significantly associated with pregnancy termination. Efforts aimed at reducing unplanned pregnancies and terminations should focus on improving easy access to contraceptives and comprehensive sexual and reproductive health education for married couples. Post-abortion care should also be integrated into the country's legal framework and added as an important component of existing sexual and reproductive health services.","PeriodicalId":501409,"journal":{"name":"medRxiv - Obstetrics and Gynecology","volume":"21 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141506018","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-20DOI: 10.1101/2024.06.20.24309215
Adriaan Vanderstichele, Jolien Ceusters, Daniela Fischerova, Antonia Testa, Wouter Froyman, Chiara Landolfo, Ruben Heremans, Francesca Moro, Anne-Sophie Van Rompuy, Thaïs Baert, Els Van Nieuwenhuysen, Toon Van Gorp, Ignace Vergote, Pieter Busschaert, Tom Venken, Diether Lambrechts, Joris Robert Vermeesch, Tom Bourne, An Coosemans, Ben Van Calster, Dirk Timmerman
Background: We previously proposed two cfDNA-based scores (genome-wide z-score and nucleosome score) as candidate non-invasive biomarkers to further improve pre-surgical diagnosis of ovarian malignancy. We aimed to investigate the added value of these cfDNA-based scores to the predictors of the ADNEX model (Assessment of Different NEoplasias in the adnexa) to estimate the risk of ovarian malignancy. Methods: 526 patients with an adnexal mass scheduled for surgery were consecutively recruited in three oncology referral centers. cfDNA-based scores were calculated in pre-operative plasma samples. Logistic regression models were fitted for ADNEX predictors alone and after adding cfDNA scores. We reported likelihood ratio tests, the area under the Receiver Operating Characteristic curve (AUC), sensitivity, specificity, and Net Benefit for thresholds between 5% and 40%. Results: The study included 272 benign, 86 borderline, 36 stage I invasive, 113 stage II-IV invasive, and 19 secondary metastatic tumors. The likelihood ratio tests for adding the cfDNA variables to the ADNEX model were statistically significant (p<0.001 for ADNEX without CA125, p=0.001 for ADNEX with CA125). The accompanying increases in AUC were 0.013 and 0.003. Net Benefit, sensitivity and specificity were similar for all models. The increase in Net Benefit at the recommended 10% threshold estimated risk of malignancy was 0.0017 and 0.0020, respectively. According to these results, adding cfDNA markers required at least 453 patients per additional true positive. Conclusion: Although statistically significant, the addition of the cfDNA scores to the ADNEX model do not improve the ADNEX model in a clinically meaningful way.
{"title":"Added value of cell-free DNA over clinical and ultrasound information for diagnosing ovarian cancer","authors":"Adriaan Vanderstichele, Jolien Ceusters, Daniela Fischerova, Antonia Testa, Wouter Froyman, Chiara Landolfo, Ruben Heremans, Francesca Moro, Anne-Sophie Van Rompuy, Thaïs Baert, Els Van Nieuwenhuysen, Toon Van Gorp, Ignace Vergote, Pieter Busschaert, Tom Venken, Diether Lambrechts, Joris Robert Vermeesch, Tom Bourne, An Coosemans, Ben Van Calster, Dirk Timmerman","doi":"10.1101/2024.06.20.24309215","DOIUrl":"https://doi.org/10.1101/2024.06.20.24309215","url":null,"abstract":"Background: We previously proposed two cfDNA-based scores (genome-wide z-score and nucleosome score) as candidate non-invasive biomarkers to further improve pre-surgical diagnosis of ovarian malignancy. We aimed to investigate the added value of these cfDNA-based scores to the predictors of the ADNEX model (Assessment of Different NEoplasias in the adnexa) to estimate the risk of ovarian malignancy. Methods: 526 patients with an adnexal mass scheduled for surgery were consecutively recruited in three oncology referral centers. cfDNA-based scores were calculated in pre-operative plasma samples. Logistic regression models were fitted for ADNEX predictors alone and after adding cfDNA scores. We reported likelihood ratio tests, the area under the Receiver Operating Characteristic curve (AUC), sensitivity, specificity, and Net Benefit for thresholds between 5% and 40%. Results: The study included 272 benign, 86 borderline, 36 stage I invasive, 113 stage II-IV invasive, and 19 secondary metastatic tumors. The likelihood ratio tests for adding the cfDNA variables to the ADNEX model were statistically significant (p<0.001 for ADNEX without CA125, p=0.001 for ADNEX with CA125). The accompanying increases in AUC were 0.013 and 0.003. Net Benefit, sensitivity and specificity were similar for all models. The increase in Net Benefit at the recommended 10% threshold estimated risk of malignancy was 0.0017 and 0.0020, respectively. According to these results, adding cfDNA markers required at least 453 patients per additional true positive. Conclusion: Although statistically significant, the addition of the cfDNA scores to the ADNEX model do not improve the ADNEX model in a clinically meaningful way.","PeriodicalId":501409,"journal":{"name":"medRxiv - Obstetrics and Gynecology","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141512534","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-20DOI: 10.1101/2024.06.19.24309209
John Baptist Asiimwe, Angella Namulema, Quraish Sserwanja, Joseph Kawuki, Mathius Amperiize, Earnest Amwiine, Lilian Nuwabaine
Introduction: One of the most important strategies to lower mother and newborn fatalities worldwide is providing quality Antenatal care (ANC). The utilization of quality ANC by women of reproductive age and associated factors remains unclear in many developing countries. Therefore, the purpose of this study was to determine the factors associated with the utilization of quality ANC in Kenya. Methods: We analyzed Secondary data from the Kenya Demographic Health Survey (KDHS) 2022, which included 11,863 women. Participants were selected using a two-stage stratified sampling design. Using SPSS, version 20, univariate and multivariable logistic regression was used to analyze the data. Results: Of the 11,863 women, 61.2% (95% CI: 59.7-62.6) received quality ANC. Older mothers (aged 20–34) had a 1.82 (95%CI: 1.15-2.87) times higher likelihood of receiving quality ANC when compared with younger mothers (15–19 years old). Participating mothers who had attended 4 or more ANC visits were 1.42 (95%CI: 1.14-1.79) times more likely to receive quality ANC than those who attended 3 or fewer visits. Comparing participants with and without media access, those with media access were 1.47 (95%CI: 1.06-2.03) times more likely to receive quality ANC. Furthermore, the likelihood of receiving quality ANC was 1.93 (95%CI: 1.21-3.08) and 1.44 (95%CI: 1.01-2.06) times higher for participants in the richest and richer quintiles, respectively, than for those in the poorest quintile. On the contrary, the odds of receiving quality ANC were 0.25 (95%CI: 0.15-0.31) to 0.64 (95%CI: 0.44-0.92) times lower for participating mothers from all other Kenyan regions than for those from the coastal region. Participants whose husbands or partners made decisions for them to seek healthcare, compared with those who made decisions independently were 0.74 (95%CI: 0.58-0.95) times less likely to receive quality antenatal care. Conclusion: The study revealed that about 60% of mothers received quality ANC. Several factors associated with quality ANC were identified: age, region, maternal education, health-seeking decision-making, access to media (TV), time to the health facility, ANC visits, and ANC providers (doctor and nurse/midwife/clinical officer). Maternal health improvement programs should prioritize promoting access to education for girls. Additionally, interventions should focus on promoting shared decision-making and autonomy in healthcare-seeking behaviors among pregnant women and their partners, increasing access to care provided by skilled health workers, and addressing regional disparities in healthcare delivery.
{"title":"Determinants of quality Antenatal Care utilization in Kenya: insights from the 2022 Kenya Demographic and Health Survey","authors":"John Baptist Asiimwe, Angella Namulema, Quraish Sserwanja, Joseph Kawuki, Mathius Amperiize, Earnest Amwiine, Lilian Nuwabaine","doi":"10.1101/2024.06.19.24309209","DOIUrl":"https://doi.org/10.1101/2024.06.19.24309209","url":null,"abstract":"Introduction: One of the most important strategies to lower mother and newborn fatalities worldwide is providing quality Antenatal care (ANC). The utilization of quality ANC by women of reproductive age and associated factors remains unclear in many developing countries. Therefore, the purpose of this study was to determine the factors associated with the utilization of quality ANC in Kenya. Methods: We analyzed Secondary data from the Kenya Demographic Health Survey (KDHS) 2022, which included 11,863 women. Participants were selected using a two-stage stratified sampling design. Using SPSS, version 20, univariate and multivariable logistic regression was used to analyze the data.\u0000Results: Of the 11,863 women, 61.2% (95% CI: 59.7-62.6) received quality ANC. Older mothers (aged 20–34) had a 1.82 (95%CI: 1.15-2.87) times higher likelihood of receiving quality ANC when compared with younger mothers (15–19 years old). Participating mothers who had attended 4 or more ANC visits were 1.42 (95%CI: 1.14-1.79) times more likely to receive quality ANC than those who attended 3 or fewer visits. Comparing participants with and without media access, those with media access were 1.47 (95%CI: 1.06-2.03) times more likely to receive quality ANC. Furthermore, the likelihood of receiving quality ANC was 1.93 (95%CI: 1.21-3.08) and 1.44 (95%CI: 1.01-2.06) times higher for participants in the richest and richer quintiles, respectively, than for those in the poorest quintile. On the contrary, the odds of receiving quality ANC were 0.25 (95%CI: 0.15-0.31) to 0.64 (95%CI: 0.44-0.92) times lower for participating mothers from all other Kenyan regions than for those from the coastal region. Participants whose husbands or partners made decisions for them to seek healthcare, compared with those who made decisions independently were 0.74 (95%CI: 0.58-0.95) times less likely to receive quality antenatal care. Conclusion: The study revealed that about 60% of mothers received quality ANC. Several factors associated with quality ANC were identified: age, region, maternal education, health-seeking decision-making, access to media (TV), time to the health facility, ANC visits, and ANC providers (doctor and nurse/midwife/clinical officer). Maternal health improvement programs should prioritize promoting access to education for girls. Additionally, interventions should focus on promoting shared decision-making and autonomy in healthcare-seeking behaviors among pregnant women and their partners, increasing access to care provided by skilled health workers, and addressing regional disparities in healthcare delivery.","PeriodicalId":501409,"journal":{"name":"medRxiv - Obstetrics and Gynecology","volume":"27 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141512601","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-29DOI: 10.1101/2024.05.29.24308088
Lovepreet K. Mann, Shreya Pandiri, Neha Agarwal, Hope Northrup, Kit Sing Au, Elin Grundberg, Eric P. Bergh, Mary T. Austin, Rajan Patel, Brandon Miller, Sen Zhu, Jonathan S. Feinberg, Dejian Lai, KuoJen Tsao, Stephen A. Fletcher, Ramesha Papanna
Importance The binary classification of spina bifida lesions as myelomeningocele (with sac) or myeloschisis (without sac) belies a spectrum of morphologies, which have not been correlated to clinical characteristics and outcomes.
{"title":"Morphometric Analysis of Spina Bifida after Fetal Repair Shows New Subtypes with Associated Outcomes","authors":"Lovepreet K. Mann, Shreya Pandiri, Neha Agarwal, Hope Northrup, Kit Sing Au, Elin Grundberg, Eric P. Bergh, Mary T. Austin, Rajan Patel, Brandon Miller, Sen Zhu, Jonathan S. Feinberg, Dejian Lai, KuoJen Tsao, Stephen A. Fletcher, Ramesha Papanna","doi":"10.1101/2024.05.29.24308088","DOIUrl":"https://doi.org/10.1101/2024.05.29.24308088","url":null,"abstract":"<strong>Importance</strong> The binary classification of spina bifida lesions as myelomeningocele (with sac) or myeloschisis (without sac) belies a spectrum of morphologies, which have not been correlated to clinical characteristics and outcomes.","PeriodicalId":501409,"journal":{"name":"medRxiv - Obstetrics and Gynecology","volume":"106 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141194397","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-14DOI: 10.1101/2024.05.13.24305466
Divyanshu Mishra, Varun Chandramohan, Nikhil Sharma, Mudita Gosain, Nitya Wadhwa, Uma Chandra Mouli Natchu, GARBH-Ini study group, Ashok Khurana, J. Alison Noble, Ramachandran Thiruvengadam, Bapu Koundinya Desiraju, Shinjini Bhatnagar
Accurate estimation of gestational age (GA) is essential to plan appropriate antenatal care. Current GA estimation models rely on fetal biometry measurements, which are susceptible to ethnic and pathological variations in fetal growth, especially in the second and third trimesters of pregnancy. In this study, we challenge the current paradigm of estimating GA using fetal biometry, by using ultrasound (US) images and deep learning models which can automatically learn image features associated with GA. We developed deep learning models for GA estimation using US images taken at 18-32 weeks of pregnancy from 2207 participants of Garbh-Ini - a hospital-based prospective cohort of pregnant women in North India. Further, we designed a novel conformal prediction (CP) algorithm to detect and reject images when there is a data distribution shift, preventing erroneous predictions. Our best model, GArbh-Ini Ultrasound image-based Gestational age Estimator (GAUGE), which was trained on US images of the fetal head (9647 images from 2207 participants), had a mean absolute error (MAE) of 2.8 days when evaluated on an internal test dataset (N = 204). GAUGE is 44% and 35% more accurate than the widely used Hadlock and INTERGROWTH-21st biometry-based GA models, respectively on the internal test dataset. For an external test dataset (N = 311), collected retrospectively from The Ultrasound Lab, New Delhi, the same model achieved a MAE of 5.9 days. In addition, we show that GAUGE relies on the finer details in the image instead of the fetal biometry and that this leads to a similar performance across small for gestational age (SGA) and appropriate for gestational age (AGA) groups. The ability of GAUGE to consider image features beyond derived biometry suggests that GAUGE offers a better choice for populations with a high prevalence of fetal growth restriction.
准确估计胎龄（GA）对于制定适当的产前保健计划至关重要。目前的胎龄估计模型依赖于胎儿生物测量，而胎儿生物测量容易受到胎儿生长的种族和病理变化的影响，尤其是在怀孕的第二和第三个三个月。在这项研究中，我们利用超声波（US）图像和可自动学习与 GA 相关的图像特征的深度学习模型，对目前使用胎儿生物测量来估计 GA 的模式提出了挑战。我们开发了深度学习模型，利用来自印度北部医院前瞻性孕妇队列 Garbh-Ini 的 2207 名参与者在怀孕 18-32 周时拍摄的 US 图像来估计 GA。此外，我们还设计了一种新颖的保形预测（CP）算法，用于在数据分布发生变化时检测和剔除图像，从而避免错误预测。我们的最佳模型--GArbh-Ini 基于超声图像的妊娠年龄估计器（GAUGE）是在胎儿头部的 US 图像（来自 2207 名参与者的 9647 张图像）上训练出来的，在内部测试数据集（N = 204）上进行评估时，其平均绝对误差（MAE）为 2.8 天。在内部测试数据集上，GAUGE 比广泛使用的 Hadlock 和 INTERGROWTH-21st 基于生物测量的 GA 模型的准确率分别高出 44% 和 35%。对于外部测试数据集（N = 311）（由新德里超声实验室回顾性收集），同一模型的 MAE 为 5.9 天。此外，我们还展示了 GAUGE 依靠图像中更精细的细节而不是胎儿生物测量，这使得小胎龄（SGA）和适龄胎龄（AGA）组的性能相似。高分辨胎儿生长受限的发生率较高，而高分辨胎儿生长受限能够考虑衍生生物测量以外的图像特征，这表明高分辨胎儿生长受限是一种更好的选择。
{"title":"Development and external validation of an ultrasound image-based deep learning model to estimate gestational age in the second and third trimesters of pregnancy using data from Garbh-Ini cohort: a prospective cohort study in North Indian population","authors":"Divyanshu Mishra, Varun Chandramohan, Nikhil Sharma, Mudita Gosain, Nitya Wadhwa, Uma Chandra Mouli Natchu, GARBH-Ini study group, Ashok Khurana, J. Alison Noble, Ramachandran Thiruvengadam, Bapu Koundinya Desiraju, Shinjini Bhatnagar","doi":"10.1101/2024.05.13.24305466","DOIUrl":"https://doi.org/10.1101/2024.05.13.24305466","url":null,"abstract":"Accurate estimation of gestational age (GA) is essential to plan appropriate antenatal care. Current GA estimation models rely on fetal biometry measurements, which are susceptible to ethnic and pathological variations in fetal growth, especially in the second and third trimesters of pregnancy. In this study, we challenge the current paradigm of estimating GA using fetal biometry, by using ultrasound (US) images and deep learning models which can automatically learn image features associated with GA. We developed deep learning models for GA estimation using US images taken at 18-32 weeks of pregnancy from 2207 participants of Garbh-Ini - a hospital-based prospective cohort of pregnant women in North India. Further, we designed a novel conformal prediction (CP) algorithm to detect and reject images when there is a data distribution shift, preventing erroneous predictions. Our best model, GArbh-Ini Ultrasound image-based Gestational age Estimator (GAUGE), which was trained on US images of the fetal head (9647 images from 2207 participants), had a mean absolute error (MAE) of 2.8 days when evaluated on an internal test dataset (N = 204). GAUGE is 44% and 35% more accurate than the widely used Hadlock and INTERGROWTH-21st biometry-based GA models, respectively on the internal test dataset. For an external test dataset (N = 311), collected retrospectively from The Ultrasound Lab, New Delhi, the same model achieved a MAE of 5.9 days. In addition, we show that GAUGE relies on the finer details in the image instead of the fetal biometry and that this leads to a similar performance across small for gestational age (SGA) and appropriate for gestational age (AGA) groups. The ability of GAUGE to consider image features beyond derived biometry suggests that GAUGE offers a better choice for populations with a high prevalence of fetal growth restriction.","PeriodicalId":501409,"journal":{"name":"medRxiv - Obstetrics and Gynecology","volume":"48 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141060881","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-09DOI: 10.1101/2024.05.08.24307084
Jung Yun Lee, Kidong Kim, Yong Beom Kim, Jae Hong No, Dong Hoon Suh
Objective We aimed to investigate the circadian rhythm of patients undergoing gynaecological surgery using activity log data.
目的 我们旨在利用活动记录数据研究妇科手术患者的昼夜节律。
{"title":"Circadian rhythm measured by step count in patients undergoing midline laparotomy for gynaecological conditions","authors":"Jung Yun Lee, Kidong Kim, Yong Beom Kim, Jae Hong No, Dong Hoon Suh","doi":"10.1101/2024.05.08.24307084","DOIUrl":"https://doi.org/10.1101/2024.05.08.24307084","url":null,"abstract":"<strong>Objective</strong> We aimed to investigate the circadian rhythm of patients undergoing gynaecological surgery using activity log data.","PeriodicalId":501409,"journal":{"name":"medRxiv - Obstetrics and Gynecology","volume":"14 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140928707","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-06DOI: 10.1101/2024.05.06.24306766
Petra C. Schwalie, Cemsel Bafligil, Julie Russeil, Magda Zachara, Marjan Biocanin, Daniel Alpern, Evelin Aasna, Bart Deplancke, Geraldine Canny, Angela Goncalves
Progress in detecting and understanding endometrial conditions in women of fertile age, such as endometriosis, has been hampered by the invasiveness of the sample collection procedure. Menstrual fluid (MF) can be sampled non-invasively and could provide a unique opportunity to study the physiological state of tissues in the reproductive system. Despite this potential, the use of MF for diagnostics and research has been limited. Here we establish protocols and assess the feasibility of collecting and processing MF in an outpatient setting. We characterize the cellular contents of MF from 15 healthy women using flow cytometry and single-cell RNA-sequencing, and demonstrate the ability to recover millions of live cells from the different cellular fractions of interest (epithelial, stromal, endothelial, perivascular and blood). Through computational integration of MF with endometrial samples we show that MF sampling is a good surrogate for endometrial biopsy. In a proof-of-principle case-control study, we collect MF from a further 7 women with a diagnosis of endometriosis and 11 healthy controls. Through RNA sequencing of 93 MF samples from these women we highlight important differences between ex vivo and cultured cells, identify impaired decidualisation, low apoptosis, high proliferation, and both higher and lower inflammatory activity in different subsets of immune cells as distinguishing features of endometriosis patients. Finally, we identify potential novel pan-cell-type biomarkers for this neglected condition.
{"title":"Single-cell characterization of menstrual fluid at homeostasis and in endometriosis","authors":"Petra C. Schwalie, Cemsel Bafligil, Julie Russeil, Magda Zachara, Marjan Biocanin, Daniel Alpern, Evelin Aasna, Bart Deplancke, Geraldine Canny, Angela Goncalves","doi":"10.1101/2024.05.06.24306766","DOIUrl":"https://doi.org/10.1101/2024.05.06.24306766","url":null,"abstract":"Progress in detecting and understanding endometrial conditions in women of fertile age, such as endometriosis, has been hampered by the invasiveness of the sample collection procedure. Menstrual fluid (MF) can be sampled non-invasively and could provide a unique opportunity to study the physiological state of tissues in the reproductive system. Despite this potential, the use of MF for diagnostics and research has been limited. Here we establish protocols and assess the feasibility of collecting and processing MF in an outpatient setting. We characterize the cellular contents of MF from 15 healthy women using flow cytometry and single-cell RNA-sequencing, and demonstrate the ability to recover millions of live cells from the different cellular fractions of interest (epithelial, stromal, endothelial, perivascular and blood). Through computational integration of MF with endometrial samples we show that MF sampling is a good surrogate for endometrial biopsy. In a proof-of-principle case-control study, we collect MF from a further 7 women with a diagnosis of endometriosis and 11 healthy controls. Through RNA sequencing of 93 MF samples from these women we highlight important differences between <em>ex vivo</em> and cultured cells, identify impaired decidualisation, low apoptosis, high proliferation, and both higher and lower inflammatory activity in different subsets of immune cells as distinguishing features of endometriosis patients. Finally, we identify potential novel pan-cell-type biomarkers for this neglected condition.","PeriodicalId":501409,"journal":{"name":"medRxiv - Obstetrics and Gynecology","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140929067","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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