1
|
Tang BH, Li QY, Liu HX, Zheng Y, Wu YE, van den Anker J, Hao GX, Zhao W. Machine Learning: A Potential Therapeutic Tool to Facilitate Neonatal Therapeutic Decision Making. Paediatr Drugs 2024; 26:355-363. [PMID: 38880837 DOI: 10.1007/s40272-024-00638-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 05/19/2024] [Indexed: 06/18/2024]
Abstract
Bacterial infection is one of the major causes of neonatal morbidity and mortality worldwide. Finding rapid and reliable methods for early recognition and diagnosis of bacterial infections and early individualization of antibacterial drug administration are essential to eradicate these infections and prevent serious complications. However, this is often difficult to perform due to non-specific clinical presentations, low accuracy of current diagnostic methods, and limited knowledge of neonatal pharmacokinetics. Although neonatal medicine has been relatively late to embrace the benefits of machine learning (ML), there have been some initial applications of ML for the early prediction of neonatal sepsis and individualization of antibiotics. This article provides a brief introduction to ML and discusses the current state of the art in diagnosing and treating neonatal bacterial infections, gaps, potential uses of ML, and future directions to address the limitations of current studies. Neonatal bacterial infections involve a combination of physiologic development, disease expression, and treatment response outcomes. To address this complex relationship, future models could consider appropriate ML algorithms to capture time series features while integrating influences from the host, microbes, and drugs to optimize antimicrobial drug use in neonates. All models require prospective clinical trials to validate their clinical utility before clinical use.
Collapse
Affiliation(s)
- Bo-Hao Tang
- Department of Pharmacy, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Qiu-Yue Li
- Department of Clinical Pharmacy, Institute of Clinical Pharmacology, Key Laboratory of Chemical Biology (Ministry of Education), NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Hui-Xin Liu
- Department of Clinical Pharmacy, Institute of Clinical Pharmacology, Key Laboratory of Chemical Biology (Ministry of Education), NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Yi Zheng
- Department of Clinical Pharmacy, Institute of Clinical Pharmacology, Key Laboratory of Chemical Biology (Ministry of Education), NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Yue-E Wu
- Department of Clinical Pharmacy, Institute of Clinical Pharmacology, Key Laboratory of Chemical Biology (Ministry of Education), NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - John van den Anker
- Division of Clinical Pharmacology, Children's National Hospital, Washington, DC, USA
- Department of Pediatrics, Pharmacology and Physiology, The George Washington University School of Medicine and Health Sciences, Washington, DC, USA
- Departments of Genomics and Precision Medicine, The George Washington University School of Medicine and Health Sciences, Washington, DC, USA
| | - Guo-Xiang Hao
- Department of Clinical Pharmacy, Institute of Clinical Pharmacology, Key Laboratory of Chemical Biology (Ministry of Education), NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China.
| | - Wei Zhao
- Department of Pharmacy, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.
- Department of Clinical Pharmacy, Institute of Clinical Pharmacology, Key Laboratory of Chemical Biology (Ministry of Education), NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China.
| |
Collapse
|
2
|
Javitt MC, Madrazo BL. Invited Commentary: Obstetric Hemorrhage: Imaging for Prevention of Maternal and Perinatal Mortality. Radiographics 2024; 44:e230237. [PMID: 38547032 DOI: 10.1148/rg.230237] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/02/2024]
Affiliation(s)
- Marcia C Javitt
- From the Department of Radiology, University of Miami Miller School of Medicine, 1611 NW 12th Ave, Miami, FL 33136
| | - Beatrice L Madrazo
- From the Department of Radiology, University of Miami Miller School of Medicine, 1611 NW 12th Ave, Miami, FL 33136
| |
Collapse
|
3
|
Kawakita T, Greenland P, Pemberton VL, Grobman WA, Silver RM, Bairey Merz CN, McNeil RB, Haas DM, Reddy UM, Simhan H, Saade GR. Prediction of metabolic syndrome following a first pregnancy. Am J Obstet Gynecol 2024:S0002-9378(24)00460-5. [PMID: 38527600 DOI: 10.1016/j.ajog.2024.03.031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Revised: 03/09/2024] [Accepted: 03/21/2024] [Indexed: 03/27/2024]
Abstract
BACKGROUND The prevalence of metabolic syndrome is rapidly increasing in the United States. We hypothesized that prediction models using data obtained during pregnancy can accurately predict the future development of metabolic syndrome. OBJECTIVE This study aimed to develop machine learning models to predict the development of metabolic syndrome using factors ascertained in nulliparous pregnant individuals. STUDY DESIGN This was a secondary analysis of a prospective cohort study (Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-to-Be Heart Health Study [nuMoM2b-HHS]). Data were collected from October 2010 to October 2020, and analyzed from July 2023 to October 2023. Participants had in-person visits 2 to 7 years after their first delivery. The primary outcome was metabolic syndrome, defined by the National Cholesterol Education Program Adult Treatment Panel III criteria, which was measured within 2 to 7 years after delivery. A total of 127 variables that were obtained during pregnancy were evaluated. The data set was randomly split into a training set (70%) and a test set (30%). We developed a random forest model and a lasso regression model using variables obtained during pregnancy. We compared the area under the receiver operating characteristic curve for both models. Using the model with the better area under the receiver operating characteristic curve, we developed models that included fewer variables based on SHAP (SHapley Additive exPlanations) values and compared them with the original model. The final model chosen would have fewer variables and noninferior areas under the receiver operating characteristic curve. RESULTS A total of 4225 individuals met the inclusion criteria; the mean (standard deviation) age was 27.0 (5.6) years. Of these, 754 (17.8%) developed metabolic syndrome. The area under the receiver operating characteristic curve of the random forest model was 0.878 (95% confidence interval, 0.846-0.909), which was higher than the 0.850 of the lasso model (95% confidence interval, 0.811-0.888; P<.001). Therefore, random forest models using fewer variables were developed. The random forest model with the top 3 variables (high-density lipoprotein, insulin, and high-sensitivity C-reactive protein) was chosen as the final model because it had the area under the receiver operating characteristic curve of 0.867 (95% confidence interval, 0.839-0.895), which was not inferior to the original model (P=.08). The area under the receiver operating characteristic curve of the final model in the test set was 0.847 (95% confidence interval, 0.821-0.873). An online application of the final model was developed (https://kawakita.shinyapps.io/metabolic/). CONCLUSION We developed a model that can accurately predict the development of metabolic syndrome in 2 to 7 years after delivery.
Collapse
Affiliation(s)
- Tetsuya Kawakita
- Department of Obstetrics and Gynecology, Eastern Virginia Medical School, Norfolk, VA.
| | - Philip Greenland
- Departments of Preventive Medicine and Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL
| | - Victoria L Pemberton
- Division of Cardiovascular Sciences, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD
| | - William A Grobman
- Department of Obstetrics and Gynecology, The Ohio State University, Columbus, OH
| | - Robert M Silver
- Department of Obstetrics and Gynecology, University of Utah, Salt Lake City, UT
| | - C Noel Bairey Merz
- Barbra Streisand Women's Heart Center, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA
| | | | - David M Haas
- Department of Obstetrics and Gynecology, Indiana University School of Medicine, Indianapolis, IN
| | - Uma M Reddy
- Department of Obstetrics and Gynecology, Columbia University, New York, NY
| | - Hyagriv Simhan
- Department of Obstetrics, Gynecology, and Reproductive Sciences, University of Pittsburgh, Pittsburgh, PA
| | - George R Saade
- Department of Obstetrics and Gynecology, Eastern Virginia Medical School, Norfolk, VA
| |
Collapse
|
4
|
Kolli R, Razzaghi T, Pierce S, Edwards RK, Maxted M, Parikh P. Predicting cesarean delivery among gravidas with morbid obesity-a machine learning approach. AJOG GLOBAL REPORTS 2023; 3:100276. [PMID: 38046532 PMCID: PMC10690625 DOI: 10.1016/j.xagr.2023.100276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/05/2023] Open
Abstract
BACKGROUND Women with obesity have higher rates of complications following cesarean delivery, such as wound infection and endometritis, with risks being the highest if a cesarean delivery is performed after labor. Previous efforts at predicting whether a patient's labor course would ultimately result in cesarean delivery have been intermediate with area under the curve in the 0.75 to 78 range. OBJECTIVE This study aimed to assess whether machine learning algorithms would outperform traditional modeling in developing a cesarean delivery prediction model among gravidas with morbid obesity (body mass index of ≥40 kg/m2) to determine whether a primary cesarean delivery may be beneficial. STUDY DESIGN This was a secondary analysis of a retrospective cohort of 1298 patients with morbid obesity presenting for vaginal delivery at ≥37 weeks of gestation between 2011 and 2016 at a single institution. Data available at the time of admission and delivery were modeled using logistic regression, decision tree, random forest, and support vector modeling with evaluation of area under the curve, accuracy, sensitivity, and specificity. RESULTS Logistic regression demonstrated an area under the curve of 0.816 (95% confidence interval, 0.810-0.817), which was superior to machine learning models when evaluating data at the time of delivery (demographic data, initial cervical examinations, comorbidities, and obstetrical interventions) (P<.001). However, there was no significant difference between most machine learning models and logistic regression area under the curve of 0.799 (95% confidence interval, 0.795-0.804) when evaluating parameters available at the time of admission (demographic data, initial cervical examinations, and comorbidities). Race was noted to be a significant predictor in both models (P<.001). CONCLUSION Machine learning and traditional modeling techniques are likely equivalent concerning cesarean delivery prediction in this population. The models developed showed good discrimination and may be used to guide clinical decision-making concerning the optimal mode of delivery.
Collapse
Affiliation(s)
- Rajasri Kolli
- Data Science and Analytics Institute, University of Oklahoma, Norman, OK (Ms Kolli and Dr Razzaghi)
| | - Talayeh Razzaghi
- Data Science and Analytics Institute, University of Oklahoma, Norman, OK (Ms Kolli and Dr Razzaghi)
- School of Industrial and Systems Engineering, University of Oklahoma, Norman, OK (Dr Razzaghi)
| | - Stephanie Pierce
- Department of Obstetrics and Gynecology, University of Oklahoma Health Sciences Center, Oklahoma City, OK (Drs Pierce, Edwards, Maxted, and Parikh)
| | - Rodney K. Edwards
- Department of Obstetrics and Gynecology, University of Oklahoma Health Sciences Center, Oklahoma City, OK (Drs Pierce, Edwards, Maxted, and Parikh)
| | - Marta Maxted
- Department of Obstetrics and Gynecology, University of Oklahoma Health Sciences Center, Oklahoma City, OK (Drs Pierce, Edwards, Maxted, and Parikh)
| | - Pavan Parikh
- Department of Obstetrics and Gynecology, University of Oklahoma Health Sciences Center, Oklahoma City, OK (Drs Pierce, Edwards, Maxted, and Parikh)
- Department of Obstetrics and Gynecology, Oregon Health Science University, Portland, OR (Dr Parikh)
| |
Collapse
|
5
|
Hong CX, Zhang S, Eltahawi A, Borazjani A, Kalami H, San AN, Sham D, Ameri G, McDermott CD. Patient-Specific Pessaries for Pelvic Organ Prolapse Using Three-Dimensional Printing: A Pilot Study. UROGYNECOLOGY (PHILADELPHIA, PA.) 2023; 29:732-739. [PMID: 36946908 PMCID: PMC10476593 DOI: 10.1097/spv.0000000000001346] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/23/2023]
Abstract
IMPORTANCE Vaginal pessaries are an effective nonsurgical treatment for pelvic organ prolapse (POP) when properly fitted. However, pessary fitting and use are often unsuccessful or imperfect. OBJECTIVE The objective of this study was to assess the feasibility of using patient-specific pessaries fabricated from three-dimensional (3D)-printed molds to improve POP symptoms and increase overall satisfaction of pessary treatment in patients using standard vaginal pessaries. STUDY DESIGN Patients undergoing POP treatment with standard vaginal pessaries were enrolled in this pilot prospective study. Patient-specific pessaries were designed and fabricated for each patient using patient input, physician input, and anatomic measurements from clinical assessment. Pessary fabrication involved injection of biocompatible liquid silicone rubber into 3D-printed molds followed by a biocompatible silicone coating. Pelvic organ prolapse symptomatic distress and pessary treatment satisfaction were evaluated before and after a 3-week patient-specific pessary home trial using the validated Pelvic Organ Prolapse Distress Inventory-6 form and a visual analog scale, respectively. RESULTS Eight women were included in this study. Changing from standard pessary to patient-specific pessary treatment was associated with an improvement in prolapse symptoms on the Pelvic Organ Prolapse Distress Inventory-6 (median change, -3.5; interquartile range, -5 to -2.5; P = 0.02) and an increase in overall pessary satisfaction on a visual analog scale (median change, +2.0; interquartile range, +1.0 to +3.0; P = 0.02). All patients reported either an improvement or no change in pessary ease of use, comfort, and the feeling of support provided by the pessary. CONCLUSION Patient-specific vaginal pessaries are a promising alternative to standard pessaries for alleviating POP symptoms and improving patient satisfaction with pessary use.
Collapse
Affiliation(s)
- Christopher X. Hong
- From the Department of Obstetrics and Gynecology, University of Michigan, Ann Arbor, MI
| | | | | | - Ali Borazjani
- Department of Obstetrics and Gynecology, Northwestern University, Chicago, IL
| | | | | | | | | | - Colleen D. McDermott
- Division of Urogynecology, Department of Obstetrics and Gynecology, Mount Sinai Hospital, University of Toronto, Toronto, ON, Canada
| |
Collapse
|
6
|
Ramirez Zegarra R, Ghi T. Use of artificial intelligence and deep learning in fetal ultrasound imaging. ULTRASOUND IN OBSTETRICS & GYNECOLOGY : THE OFFICIAL JOURNAL OF THE INTERNATIONAL SOCIETY OF ULTRASOUND IN OBSTETRICS AND GYNECOLOGY 2023; 62:185-194. [PMID: 36436205 DOI: 10.1002/uog.26130] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Revised: 11/06/2022] [Accepted: 11/21/2022] [Indexed: 06/16/2023]
Abstract
Deep learning is considered the leading artificial intelligence tool in image analysis in general. Deep-learning algorithms excel at image recognition, which makes them valuable in medical imaging. Obstetric ultrasound has become the gold standard imaging modality for detection and diagnosis of fetal malformations. However, ultrasound relies heavily on the operator's experience, making it unreliable in inexperienced hands. Several studies have proposed the use of deep-learning models as a tool to support sonographers, in an attempt to overcome these problems inherent to ultrasound. Deep learning has many clinical applications in the field of fetal imaging, including identification of normal and abnormal fetal anatomy and measurement of fetal biometry. In this Review, we provide a comprehensive explanation of the fundamentals of deep learning in fetal imaging, with particular focus on its clinical applicability. © 2022 International Society of Ultrasound in Obstetrics and Gynecology.
Collapse
Affiliation(s)
- R Ramirez Zegarra
- Department of Medicine and Surgery, Obstetrics and Gynecology Unit, University of Parma, Parma, Italy
| | - T Ghi
- Department of Medicine and Surgery, Obstetrics and Gynecology Unit, University of Parma, Parma, Italy
| |
Collapse
|
7
|
Elammary MN, Zohiry M, Sayed A, Atef F, Ali N, Hussein I, Mahran MA, Said AE, Elassall GM, Radwan AA, Shazly SA. Middle eastern college of obstetricians and gynecologists (MCOG) practice guidelines: Role of prediction models in management of trial of labor after cesarean section. Practice guideline no. 05-O-22 ✰,✰✰,★,★★. J Gynecol Obstet Hum Reprod 2023; 52:102598. [PMID: 37087045 DOI: 10.1016/j.jogoh.2023.102598] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 04/11/2023] [Accepted: 04/19/2023] [Indexed: 04/24/2023]
Abstract
Cesarean delivery rates have been steadily rising since the beginning of the 21st century. The growing incidence is even more prominent in developing countries owing to lack of evidence-based guidance and audit, and the expansion of private practice. The uprise in Cesarean delivery rate has been associated with considerable financial burden and has increased the risk otherwise uncommon serious complications such as placenta accreta disorders and uterine rupture. In addition to primary prevention of Cesarean delivery, trial of labor after cesarean section is one of the most successful strategies to reduce Cesarean deliveries and minimize risks associated with higher order Cesarean deliveries. This guideline appraises patient selection strategies and use of prediction model to promote counseling and enhance safety in women considering vaginal birth after Cesarean.
Collapse
Affiliation(s)
| | - Mariam Zohiry
- Middle Eastern College of Obstetricians and Gynecologists (MCOG) Practice Office. Leeds, United Kingdom
| | - Asmaa Sayed
- Middle Eastern College of Obstetricians and Gynecologists (MCOG) Practice Office. Leeds, United Kingdom
| | - Fatma Atef
- Middle Eastern College of Obstetricians and Gynecologists (MCOG) Practice Office. Leeds, United Kingdom
| | - Nada Ali
- Middle Eastern College of Obstetricians and Gynecologists (MCOG) Practice Office. Leeds, United Kingdom
| | - Islam Hussein
- Middle Eastern College of Obstetricians and Gynecologists (MCOG) Practice Office. Leeds, United Kingdom
| | - Manar A Mahran
- Middle Eastern College of Obstetricians and Gynecologists (MCOG) Practice Office. Leeds, United Kingdom
| | - Aliaa E Said
- Middle Eastern College of Obstetricians and Gynecologists (MCOG) Practice Office. Leeds, United Kingdom
| | - Gena M Elassall
- Middle Eastern College of Obstetricians and Gynecologists (MCOG) Practice Office. Leeds, United Kingdom
| | - Ahmad A Radwan
- Middle Eastern College of Obstetricians and Gynecologists (MCOG) Practice Office. Leeds, United Kingdom
| | - Sherif A Shazly
- Middle Eastern College of Obstetricians and Gynecologists (MCOG) Practice Office. Leeds, United Kingdom; Department of Obstetrics and Gynecology, Leeds Teaching Hospitals, Leeds, United Kingdom.
| |
Collapse
|
8
|
Shazly SA, Coronado PJ, Yılmaz E, Melekoglu R, Sahin H, Giannella L, Ciavattini A, Carpini GD, Di Giuseppe J, Yordanov A, Karakadieva K, Nedelcheva NM, Vasileva-Slaveva M, Alcazar JL, Chacon E, Manzour N, Vara J, Karaman E, Karaaslan O, Hacıoğlu L, Korkmaz D, Onal C, Knez J, Ferrari F, Hosni EM, Mahmoud ME, Elassall GM, Abdo MS, Mohamed YI, Abdelbadie AS. Endometrial Cancer Individualized Scoring System (ECISS): A machine learning-based prediction model of endometrial cancer prognosis. Int J Gynaecol Obstet 2022; 161:760-768. [PMID: 36572053 DOI: 10.1002/ijgo.14639] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 12/02/2022] [Accepted: 12/16/2022] [Indexed: 12/28/2022]
Abstract
OBJECTIVE To establish a prognostic model for endometrial cancer (EC) that individualizes a risk and management plan per patient and disease characteristics. METHODS A multicenter retrospective study conducted in nine European gynecologic cancer centers. Women with confirmed EC between January 2008 to December 2015 were included. Demographics, disease characteristics, management, and follow-up information were collected. Cancer-specific survival (CSS) and disease-free survival (DFS) at 3 and 5 years comprise the primary outcomes of the study. Machine learning algorithms were applied to patient and disease characteristics. Model I: pretreatment model. Calculated probability was added to management variables (model II: treatment model), and the second calculated probability was added to perioperative and postoperative variables (model III). RESULTS Of 1150 women, 1144 were eligible for 3-year survival analysis and 860 for 5-year survival analysis. Model I, II, and III accuracies of prediction of 5-year CSS were 84.88%/85.47% (in train and test sets), 85.47%/84.88%, and 87.35%/86.05%, respectively. Model I predicted 3-year CSS at an accuracy of 91.34%/87.02%. Accuracies of models I, II, and III in predicting 5-year DFS were 74.63%/76.72%, 77.03%/76.72%, and 80.61%/77.78%, respectively. CONCLUSION The Endometrial Cancer Individualized Scoring System (ECISS) is a novel machine learning tool assessing patient-specific survival probability with high accuracy.
Collapse
Affiliation(s)
- Sherif A Shazly
- Women's Service, Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - Pluvio J Coronado
- Department of Obstetrics and Gynecology, Hospital Clinico San Carlos, Madrid, Spain
| | - Ercan Yılmaz
- Department of Obstetrics and Gynecology, Inonu University, Malatya, Turkey
| | - Rauf Melekoglu
- Department of Obstetrics and Gynecology, Inonu University, Malatya, Turkey
| | - Hanifi Sahin
- Department of Obstetrics and Gynecology, Inonu University, Malatya, Turkey
| | - Luca Giannella
- Woman's Health Sciences Department, Gynecologic Section, Polytechnic University of Marche, Ancona, Italy
| | - Andrea Ciavattini
- Woman's Health Sciences Department, Gynecologic Section, Polytechnic University of Marche, Ancona, Italy
| | - Giovanni Delli Carpini
- Woman's Health Sciences Department, Gynecologic Section, Polytechnic University of Marche, Ancona, Italy
| | - Jacopo Di Giuseppe
- Woman's Health Sciences Department, Gynecologic Section, Polytechnic University of Marche, Ancona, Italy
| | - Angel Yordanov
- Department of Gynecologic Oncology, Medical University of Pleven, Pleven, Bulgaria
| | | | | | | | - Juan Luis Alcazar
- Gynecologic Oncologic Division, Clinica Universidad de Navarra, Navarra, Spain
| | - Enrique Chacon
- Gynecologic Oncologic Division, Clinica Universidad de Navarra, Navarra, Spain
| | - Nabil Manzour
- Gynecologic Oncologic Division, Clinica Universidad de Navarra, Navarra, Spain
| | - Julio Vara
- Gynecologic Oncologic Division, Clinica Universidad de Navarra, Navarra, Spain
| | - Erbil Karaman
- Department of Obstetrics and Gynecology, Division of Gynecologic Oncology, Van Yuzuncu Yıl University, Van, Turkey
| | - Onur Karaaslan
- Department of Obstetrics and Gynecology, Division of Gynecologic Oncology, Van Yuzuncu Yıl University, Van, Turkey
| | - Latif Hacıoğlu
- Department of Obstetrics and Gynecology, Division of Gynecologic Oncology, Van Yuzuncu Yıl University, Van, Turkey
| | - Duygu Korkmaz
- Department of Obstetrics and Gynecology, Division of Gynecologic Oncology, Van Yuzuncu Yıl University, Van, Turkey
| | - Cem Onal
- Department of Radiation Oncology, Baskent University Faculty of Medicine, Adana, Turkey
| | - Jure Knez
- University Medical Center Maribor, Maribor, Slovenia
| | - Federico Ferrari
- Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
| | - Esraa M Hosni
- Middle-Eastern College of Obstetricians and Gynaecologists, Leeds, UK
| | - Mohamed E Mahmoud
- Middle-Eastern College of Obstetricians and Gynaecologists, Leeds, UK
| | - Gena M Elassall
- Middle-Eastern College of Obstetricians and Gynaecologists, Leeds, UK
| | - Mohamed S Abdo
- Middle-Eastern College of Obstetricians and Gynaecologists, Leeds, UK
| | - Yasmin I Mohamed
- Middle-Eastern College of Obstetricians and Gynaecologists, Leeds, UK
| | - Amr S Abdelbadie
- Department of Obstetrics and Gynecology, Aswan University, Aswan, Egypt
| | | |
Collapse
|
9
|
Preis H, Djurić PM, Ajirak M, Chen T, Mane V, Garry DJ, Heiselman C, Chappelle J, Lobel M. Applying machine learning methods to psychosocial screening data to improve identification of prenatal depression: Implications for clinical practice and research. Arch Womens Ment Health 2022; 25:965-973. [PMID: 35986793 PMCID: PMC9709634 DOI: 10.1007/s00737-022-01259-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/08/2022] [Accepted: 08/07/2022] [Indexed: 11/30/2022]
Abstract
We utilized machine learning (ML) methods on data from the PROMOTE, a novel psychosocial screening tool, to quantify risk for prenatal depression for individual patients and identify contributing factors that impart greater risk for depression. Random forest algorithms were used to predict likelihood for being at high risk for prenatal depression (Edinburgh Postnatal Depression Scale; EPDS ≥ 13 and/or positive self-injury item) using data from 1715 patients who completed the PROMOTE. Performance matrices were calculated to assess the ability of the PROMOTE to accurately classify patients. Probability for depression was calculated for individual patients. Finally, recursive feature elimination was used to evaluate the importance of each PROMOTE item in the classification of depression risk. PROMOTE data were successfully used to predict depression with acceptable performance matrices (accuracy = 0.80; sensitivity = 0.75; specificity = 0.81; positive predictive value = 0.79; negative predictive value = 0.97). Perceived stress, emotional problems, family support, age, major life events, partner support, unplanned pregnancy, current employment, lifetime abuse, and financial state were the most important PROMOTE items in the classification of depression risk. Results affirm the value of the PROMOTE as a psychosocial screening tool for prenatal depression and the benefit of using it in conjunction with ML methods. Using such methods can help detect underreported outcomes and identify what in patients' lives makes them more vulnerable, thus paving the way for effective individually tailored precision medicine.
Collapse
Affiliation(s)
- Heidi Preis
- Department of Psychology, Stony Brook University, Stony Brook, NY, 11794, USA.
- Department of Obstetrics, Gynecology and Reproductive Medicine, Renaissance School of Medicine, Stony Brook University, Stony Brook, NY, 11794, USA.
| | - Petar M Djurić
- Department of Electrical and Computer Engineering, Stony Brook University, Stony Brook, NY, 11794, USA
| | - Marzieh Ajirak
- Department of Electrical and Computer Engineering, Stony Brook University, Stony Brook, NY, 11794, USA
| | - Tong Chen
- Department of Electrical and Computer Engineering, Stony Brook University, Stony Brook, NY, 11794, USA
| | - Vibha Mane
- Department of Electrical and Computer Engineering, Stony Brook University, Stony Brook, NY, 11794, USA
| | - David J Garry
- Department of Obstetrics, Gynecology and Reproductive Medicine, Renaissance School of Medicine, Stony Brook University, Stony Brook, NY, 11794, USA
| | - Cassandra Heiselman
- Department of Obstetrics, Gynecology and Reproductive Medicine, Renaissance School of Medicine, Stony Brook University, Stony Brook, NY, 11794, USA
| | - Joseph Chappelle
- Department of Obstetrics, Gynecology and Reproductive Medicine, Renaissance School of Medicine, Stony Brook University, Stony Brook, NY, 11794, USA
| | - Marci Lobel
- Department of Psychology, Stony Brook University, Stony Brook, NY, 11794, USA
- Department of Obstetrics, Gynecology and Reproductive Medicine, Renaissance School of Medicine, Stony Brook University, Stony Brook, NY, 11794, USA
| |
Collapse
|