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Article ; Online: What Is New in Hysterectomy for Benign Gynecologic Disorders?: Best Articles From the Past Year.

Famuyide, Abimbola O

2019 Volume 133, Issue 6, Page(s) 1278–1280

Abstract: This month we focus on current research in hysterectomy for benign gynecologic disorders. Dr. Famuyide discusses four recent publications, which are concluded with a "bottom-line" that is the take-home message. A complete reference for each can be found ... ...

Abstract	This month we focus on current research in hysterectomy for benign gynecologic disorders. Dr. Famuyide discusses four recent publications, which are concluded with a "bottom-line" that is the take-home message. A complete reference for each can be found on on this page along with direct links to abstracts.
MeSH term(s)	Female ; Gynecology/trends ; Humans ; Hysterectomy/trends
Language	English
Publishing date	2019-06-24
Publishing country	United States
Document type	Journal Article
ZDB-ID	207330-4
ISSN	1873-233X ; 0029-7844
ISSN (online)	1873-233X
ISSN	0029-7844
DOI	10.1097/AOG.0000000000003294
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Article ; Online: Introduction to Machine Learning in Obstetrics and Gynecology.

Shazly, Sherif A / Trabuco, Emanuel C / Ngufor, Che G / Famuyide, Abimbola O

Obstetrics and gynecology

2022 Volume 139, Issue 4, Page(s) 669–679

Abstract: In the digital age of the 21st century, we have witnessed an explosion in data matched by remarkable progress in the field of computer science and engineering, with the development of powerful and portable artificial intelligence-powered technologies. At ...

Abstract	In the digital age of the 21st century, we have witnessed an explosion in data matched by remarkable progress in the field of computer science and engineering, with the development of powerful and portable artificial intelligence-powered technologies. At the same time, global connectivity powered by mobile technology has led to an increasing number of connected users and connected devices. In just the past 5 years, the convergence of these technologies in obstetrics and gynecology has resulted in the development of innovative artificial intelligence-powered digital health devices that allow easy and accurate patient risk stratification for an array of conditions spanning early pregnancy, labor and delivery, and care of the newborn. Yet, breakthroughs in artificial intelligence and other new and emerging technologies currently have a slow adoption rate in medicine, despite the availability of large data sets that include individual electronic health records spanning years of care, genomics, and the microbiome. As a result, patient interactions with health care remain burdened by antiquated processes that are inefficient and inconvenient. A few health care institutions have recognized these gaps and, with an influx of venture capital investments, are now making in-roads in medical practice with digital products driven by artificial intelligence algorithms. In this article, we trace the history, applications, and ethical challenges of the artificial intelligence that will be at the forefront of digitally transforming obstetrics and gynecology and medical practice in general.
MeSH term(s)	Algorithms ; Artificial Intelligence ; Female ; Gynecology ; Humans ; Infant, Newborn ; Machine Learning ; Obstetrics ; Pregnancy
Language	English
Publishing date	2022-03-10
Publishing country	United States
Document type	Journal Article
ZDB-ID	207330-4
ISSN	1873-233X ; 0029-7844
ISSN (online)	1873-233X
ISSN	0029-7844
DOI	10.1097/AOG.0000000000004706
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Article ; Online: Medicaid Cost and Reimbursement for Low-Risk Prenatal Care in the United States.

Baker, Mary V / Butler-Tobah, Yvonne S / Famuyide, Abimbola O / Theiler, Regan N

Journal of midwifery & women's health

2021 Volume 66, Issue 5, Page(s) 589–596

Abstract: Introduction: We calculate the financial margins for delivery of routine antenatal care as reimbursed by Medicaid. Prenatal care cost varies with overhead, health care provider type, and number of office visits. Antenatal care is only one component of ... ...

Abstract	Introduction: We calculate the financial margins for delivery of routine antenatal care as reimbursed by Medicaid. Prenatal care cost varies with overhead, health care provider type, and number of office visits. Antenatal care is only one component of the global maternity bundle, which also includes intrapartum and postpartum care. Methods: Time for provision of low-risk antenatal care was determined prospectively from a study of 133 low-risk pregnant patients. Health care provider time cost was estimated using mean wages for obstetricians and midwives. Margins were estimated by subtracting cost of provider services and overhead for the antenatal component of maternity care from total Medicaid reimbursement for the pregnancy global package (CPT 59400) using 2015 dollars. The maternity bundle elements of routine prenatal laboratory tests, ultrasounds, intrapartum care, and postpartum care were not included in our analysis of cost components. Results: Patients received an average of 215 minutes of direct provider time per pregnancy. At the 50th percentile for physician payment and assuming overhead is 53.4% of revenue, practice margins varied by state from -$1067 to +$675, with a median of -$357. Median margins for midwifery care were +$15, with a range of -$579 to +$885. Margins were negative if overhead costs exceeded 33% of revenue for physician care and 55% of revenue for midwifery care. Discussion: In many states, Medicaid reimbursement for the global maternity package is less than the actual cost of antenatal care alone. Improving reimbursement or decreasing costs is necessary to make maternity care more cost-effective.
MeSH term(s)	Delivery of Health Care ; Female ; Health Care Costs ; Humans ; Maternal Health Services ; Medicaid ; Pregnancy ; Prenatal Care ; United States
Language	English
Publishing date	2021-10-01
Publishing country	United States
Document type	Journal Article
ZDB-ID	2008180-7
ISSN	1542-2011 ; 1526-9523
ISSN (online)	1542-2011
ISSN	1526-9523
DOI	10.1111/jmwh.13271
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Article ; Online: Quality indicators in gynecologic oncology.

Dowdy, Sean C / Cliby, William A / Famuyide, Abimbola O

Gynecologic oncology

2018 Volume 151, Issue 2, Page(s) 366–373

MeSH term(s)	Female ; Genital Neoplasms, Female/therapy ; Gynecology/methods ; Gynecology/standards ; Humans ; Medical Oncology/methods ; Medical Oncology/standards ; Quality Assurance, Health Care
Language	English
Publishing date	2018-09-13
Publishing country	United States
Document type	Journal Article ; Review
ZDB-ID	801461-9
ISSN	1095-6859 ; 0090-8258
ISSN (online)	1095-6859
ISSN	0090-8258
DOI	10.1016/j.ygyno.2018.09.002
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Article ; Online: Impact of labor characteristics on maternal and neonatal outcomes of labor: A machine-learning model.

Shazly, Sherif A / Borah, Bijan J / Ngufor, Che G / Torbenson, Vanessa E / Theiler, Regan N / Famuyide, Abimbola O

PloS one

2022 Volume 17, Issue 8, Page(s) e0273178

Abstract: Introduction: Since Friedman's seminal publication on laboring women, numerous publications have sought to define normal labor progress. However, there is paucity of data on contemporary labor cervicometry incorporating both maternal and neonatal ... ...

Abstract	Introduction: Since Friedman's seminal publication on laboring women, numerous publications have sought to define normal labor progress. However, there is paucity of data on contemporary labor cervicometry incorporating both maternal and neonatal outcomes. The objective of this study is to establish intrapartum prediction models of unfavorable labor outcomes using machine-learning algorithms. Materials and methods: Consortium on Safe Labor is a large database consisting of pregnancy and labor characteristics from 12 medical centers in the United States. Outcomes, including maternal and neonatal outcomes, were retrospectively collected. We defined primary outcome as the composite of following unfavorable outcomes: cesarean delivery in active labor, postpartum hemorrhage, intra-amniotic infection, shoulder dystocia, neonatal morbidity, and mortality. Clinical and obstetric parameters at admission and during labor progression were used to build machine-learning risk-prediction models based on the gradient boosting algorithm. Results: Of 228,438 delivery episodes, 66,586 were eligible for this study. Mean maternal age was 26.95 ± 6.48 years, mean parity was 0.92 ± 1.23, and mean gestational age was 39.35 ± 1.13 weeks. Unfavorable labor outcome was reported in 14,439 (21.68%) deliveries. Starting at a cervical dilation of 4 cm, the area under receiver operating characteristics curve (AUC) of prediction models increased from 0.75 (95% confidence interval, 0.75-0.75) to 0.89 (95% confidence interval, 0.89-0.90) at a dilation of 10 cm. Baseline labor risk score was above 35% in patients with unfavorable outcomes compared to women with favorable outcomes, whose score was below 25%. Conclusion: Labor risk score is a machine-learning-based score that provides individualized and dynamic alternatives to conventional labor charts. It predicts composite of adverse birth, maternal, and neonatal outcomes as labor progresses. Therefore, it can be deployed in clinical practice to monitor labor progress in real time and support clinical decisions.
MeSH term(s)	Adult ; Cesarean Section ; Female ; Humans ; Infant ; Infant, Newborn ; Labor Stage, First ; Labor, Obstetric ; Machine Learning ; Pregnancy ; Retrospective Studies ; Young Adult
Language	English
Publishing date	2022-08-22
Publishing country	United States
Document type	Journal Article
ZDB-ID	2267670-3
ISSN	1932-6203 ; 1932-6203
ISSN (online)	1932-6203
ISSN	1932-6203
DOI	10.1371/journal.pone.0273178
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Article: Prognostic stratification of endometrial cancers with high microsatellite instability or no specific molecular profile.

Gonzalez-Bosquet, Jesus / Weroha, S John / Bakkum-Gamez, Jamie N / Weaver, Amy L / McGree, Michaela E / Dowdy, Sean C / Famuyide, Abimbola O / Kipp, Benjamin R / Halling, Kevin C / Yadav, Siddhartha / Couch, Fergus J / Podratz, Karl C

Frontiers in oncology

2023 Volume 13, Page(s) 1105504

Abstract: Objective: To identify high-risk disease in clinicopathologic low-risk endometrial cancer (EC) with high microsatellite instability (MSI-H) or no specific molecular profile (NSMP) and therapeutic insensitivity in clinicopathologic high-risk MSI-H/NSMP ... ...

Abstract	Objective: To identify high-risk disease in clinicopathologic low-risk endometrial cancer (EC) with high microsatellite instability (MSI-H) or no specific molecular profile (NSMP) and therapeutic insensitivity in clinicopathologic high-risk MSI-H/NSMP EC. Methods: We searched The Cancer Genome Atlas for DNA sequencing, RNA expression, and surveillance data regarding MSI-H/NSMP EC. We used a molecular classification system of Results: Data were available for 239 patients with EC, which included 58 MSI-H and 89 NSMP cases. ECPPF effectively stratified MSI-H/NSMP EC into distinct molecular groups with prognostic implications: molecular low risk (MLR), with low Conclusion: ECPPF may resolve prognostic challenges for MSI-H/NSMP EC by identifying occult high-risk disease in EC with clinicopathologic low-risk indicators and therapeutic insensitivity in EC with clinicopathologic high-risk indicators.
Language	English
Publishing date	2023-05-23
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2649216-7
ISSN	2234-943X
ISSN	2234-943X
DOI	10.3389/fonc.2023.1105504
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Article ; Online: Corrigendum to "Host immunity and KLF 11 deficiency together promote fibrosis in a mouse model of endometriosis" [BBA - Mol. Basis of Dis. 1869 (2023) 166784].

Grande, Joseph / Jones, Tiffanny L / Sun, Zhifu / Chanana, Pritha / Jaiswal, Indu / Leontovich, Alexey / Carapanceanu, Nicoletta / Carapanceanu, Valentin / Saadalla, Abdulrahman / Osman, Abu / Famuyide, Abimbola O / Daftary, Gaurang S / Khan, Zaraq / Khazaie, Khashayarsha

Biochimica et biophysica acta. Molecular basis of disease

2023 Volume 1870, Issue 3, Page(s) 166923

Language	English
Publishing date	2023-11-08
Publishing country	Netherlands
Document type	Published Erratum
ZDB-ID	60-7
ISSN	1879-260X ; 1879-2596 ; 1872-8006 ; 1879-2642 ; 1879-2618 ; 1879-2650 ; 0006-3002 ; 0005-2728 ; 0005-2736 ; 0304-4165 ; 0167-4838 ; 1388-1981 ; 0167-4889 ; 0167-4781 ; 0304-419X ; 1570-9639 ; 0925-4439 ; 1874-9399
ISSN (online)	1879-260X ; 1879-2596 ; 1872-8006 ; 1879-2642 ; 1879-2618 ; 1879-2650
ISSN	0006-3002 ; 0005-2728 ; 0005-2736 ; 0304-4165 ; 0167-4838 ; 1388-1981 ; 0167-4889 ; 0167-4781 ; 0304-419X ; 1570-9639 ; 0925-4439 ; 1874-9399
DOI	10.1016/j.bbadis.2023.166923
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Article ; Online: Host immunity and KLF 11 deficiency together promote fibrosis in a mouse model of endometriosis.

Biochimica et biophysica acta. Molecular basis of disease

2023 Volume 1869, Issue 7, Page(s) 166784

Abstract: Background: Endometriosis is a debilitating disease typically characterized by prolific fibrotic scarring. Earlier we reported downregulation of two transcription factors belonging TGF-βR signaling pathway Sp/Krüppel-like factor 11 (KLF11) and 10 (KLF10) ...

Abstract	Background: Endometriosis is a debilitating disease typically characterized by prolific fibrotic scarring. Earlier we reported downregulation of two transcription factors belonging TGF-βR signaling pathway Sp/Krüppel-like factor 11 (KLF11) and 10 (KLF10) in human endometriosis lesions. Here we investigated the role of these nuclear factors and immunity in the scaring fibrosis associated with endometriosis. Methods: We used a well characterized experimental mouse model of endometriosis. WT, KLF10 or KLF11 deficient mice were compared. The lesions were evaluated histologically, fibrosis was quantified with Masons' Trichome staining, immune-infiltrates were quantified by immunohistochemistry, peritoneal adhesions were score, gene expression was evaluated by bulk RNA sequencing. Results: Intense fibrotic reactions and large changes in gene expression were detected in KLF11 deficient implants associated with squamous metaplasia of the ectopic endometrium, as compared to KLF10 deficient or WT implants. Fibrosis was mitigated with pharmacologic agents that blocked histone acetylation or TGF-βR signaling or with genetic deficiency for SMAD3. The lesions were richly infiltrated with T-cells, regulatory T-cells, and innate immune cells. Fibrosis was exacerbated when implants expressed ectopic genes implicating autoimmunity as a major factor contributing to the scaring fibrosis. Conclusions: Our findings identify KLF11 and TGF-βR signaling as cell intrinsic mechanisms and autoimmune responses as cell extrinsic mechanisms of scaring fibrosis in ectopic endometrium lesions. General significance: Immunological factors associated with inflammation and tissue repair drive scaring fibrosis in experimental endometriosis, providing the rationale for immune therapy of endometriosis.
MeSH term(s)	Animals ; Female ; Humans ; Mice ; Endometriosis/metabolism ; Fibrosis ; Transcription Factors/metabolism
Chemical Substances	Transcription Factors ; KLF11 protein, mouse
Language	English
Publishing date	2023-06-13
Publishing country	Netherlands
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	60-7
ISSN	1879-260X ; 1879-2596 ; 1872-8006 ; 1879-2642 ; 1879-2618 ; 1879-2650 ; 0006-3002 ; 0005-2728 ; 0005-2736 ; 0304-4165 ; 0167-4838 ; 1388-1981 ; 0167-4889 ; 0167-4781 ; 0304-419X ; 1570-9639 ; 0925-4439 ; 1874-9399
ISSN (online)	1879-260X ; 1879-2596 ; 1872-8006 ; 1879-2642 ; 1879-2618 ; 1879-2650
ISSN	0006-3002 ; 0005-2728 ; 0005-2736 ; 0304-4165 ; 0167-4838 ; 1388-1981 ; 0167-4889 ; 0167-4781 ; 0304-419X ; 1570-9639 ; 0925-4439 ; 1874-9399
DOI	10.1016/j.bbadis.2023.166784
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Article ; Online: ECPPF (E2F1, CCNA2, POLE, PPP2R1A, FBXW7) stratification: Profiling high-risk subtypes of histomorphologically low-risk and treatment-insensitive endometrioid endometrial cancer.

PloS one

2022 Volume 17, Issue 12, Page(s) e0278408

Abstract: In endometrial cancer, occult high-risk subtypes (rooted in histomorphologically low-risk disease) with insensitivity to adjuvant therapies impede improvements in therapeutic efficacy. Therefore, we aimed to assess the ability of molecular high-risk (MHR) ...

Abstract	In endometrial cancer, occult high-risk subtypes (rooted in histomorphologically low-risk disease) with insensitivity to adjuvant therapies impede improvements in therapeutic efficacy. Therefore, we aimed to assess the ability of molecular high-risk (MHR) and low-risk (MLR) ECPPF (E2F1, CCNA2, POLE, PPP2R1A, FBXW7) stratification to profile recurrence in early, low-risk endometrioid endometrial cancer (EEC) and insensitivity to platinum-based chemotherapy or radiotherapy (or both) in high-risk EEC. Using The Cancer Genome Atlas endometrial cancer database, we identified 192 EEC cases with available DNA sequencing and RNA expression data. Molecular parameters were integrated with clinicopathologic risk factors and adverse surveillance events. MHR was defined as high (-H) CCNA2 or E2F1 log2 expression (≥2.75), PPP2R1A mutations (-mu), or FBXW7mu; MLR was defined as low (-L) CCNA2 and E2F1 log2 expression (<2.75). We assessed 164 cases, plus another 28 with POLEmu for favorable-outcomes comparisons. MHR and MLR had significantly different progression-free survival (PFS) rates (P < .001), independent of traditional risk factors (eg, TP53mu), except for stage IV disease. PFS of CCNA2-L/E2F1-L paralleled that of POLEmu. ECPPF status stratified responses to adjuvant therapy in stage III-IV EEC (P < .01) and profiled stage I, grade 1-2 cases with risk of recurrence (P < .001). MHR was associated with CTNNB1mu-linked treatment failures (P < .001). Expression of homologous recombination repair (HR) and cell cycle genes was significantly elevated in CCNA2-H/E2F1-H compared with CCNA2-L/E2F1-L (P<1.0E-10), suggesting that HR deficiencies may underlie the favorable PFS in MLR. HRmu were detected in 20.7%. No treatment failures were observed in high-grade or advanced EEC with HRmu (P = .02). Favorable PFS in clinically high-risk EEC was associated with HRmu and MLR ECPPF (P < .001). In summary, MLR ECPPF and HRmu were associated with therapeutic efficacy in EEC. MHR ECPPF was associated with low-risk, early-stage recurrences and insensitivity to adjuvant therapies.
MeSH term(s)	Female ; Humans ; Genes, cdc ; F-Box-WD Repeat-Containing Protein 7/genetics ; Genes, Regulator ; Carcinoma, Endometrioid ; Transcription Factors ; Endometrial Neoplasms/genetics ; Endometrial Neoplasms/therapy ; E2F1 Transcription Factor ; Cyclin A2 ; Protein Phosphatase 2/genetics
Chemical Substances	F-Box-WD Repeat-Containing Protein 7 ; Transcription Factors ; FBXW7 protein, human ; E2F1 protein, human ; E2F1 Transcription Factor ; CCNA2 protein, human ; Cyclin A2 ; PPP2R1A protein, human ; Protein Phosphatase 2 (EC 3.1.3.16)
Language	English
Publishing date	2022-12-01
Publishing country	United States
Document type	Journal Article
ZDB-ID	2267670-3
ISSN	1932-6203 ; 1932-6203
ISSN (online)	1932-6203
ISSN	1932-6203
DOI	10.1371/journal.pone.0278408
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Article ; Online: Reply: To PMID 24632398.

Famuyide, Abimbola O / Laughlin-Tommaso, Shannon K

Journal of minimally invasive gynecology

2015 Volume 22, Issue 4, Page(s) 695

MeSH term(s)	Endometrium/pathology ; Female ; Humans ; Hysteroscopy ; Leiomyoma/diagnosis ; Polyps/pathology ; Pregnancy ; Uterine Hemorrhage/etiology ; Uterine Neoplasms/diagnosis
Language	English
Publishing date	2015-05
Publishing country	United States
Document type	Comment ; Letter
ZDB-ID	2186934-0
ISSN	1553-4669 ; 1553-4650
ISSN (online)	1553-4669
ISSN	1553-4650
DOI	10.1016/j.jmig.2015.01.001
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