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Xiao YH, Hu YL, Lv XY, Huang LJ, Geng LH, Liao P, Ding YB, Niu CC. The construction of machine learning-based predictive models for high-quality embryo formation in poor ovarian response patients with progestin-primed ovarian stimulation. Reprod Biol Endocrinol 2024; 22:78. [PMID: 38987797 DOI: 10.1186/s12958-024-01251-5] [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: 03/13/2024] [Accepted: 06/27/2024] [Indexed: 07/12/2024] Open
Abstract
OBJECTIVE To explore the optimal models for predicting the formation of high-quality embryos in Poor Ovarian Response (POR) Patients with Progestin-Primed Ovarian Stimulation (PPOS) using machine learning algorithms. METHODS A retrospective analysis was conducted on the clinical data of 4,216 POR cycles who underwent in vitro fertilization (IVF) / intracytoplasmic sperm injection (ICSI) at Sichuan Jinxin Xinan Women and Children's Hospital from January 2015 to December 2021. Based on the presence of high-quality cleavage embryos 72 h post-fertilization, the samples were divided into the high-quality cleavage embryo group (N = 1950) and the non-high-quality cleavage embryo group (N = 2266). Additionally, based on whether high-quality blastocysts were observed following full blastocyst culture, the samples were categorized into the high-quality blastocyst group (N = 124) and the non-high-quality blastocyst group (N = 1800). The factors influencing the formation of high-quality embryos were analyzed using logistic regression. The predictive models based on machine learning methods were constructed and evaluated accordingly. RESULTS Differential analysis revealed that there are statistically significant differences in 14 factors between high-quality and non-high-quality cleavage embryos. Logistic regression analysis identified 14 factors as influential in forming high-quality cleavage embryos. In models excluding three variables (retrieved oocytes, MII oocytes, and 2PN fertilized oocytes), the XGBoost model performed slightly better (AUC = 0.672, 95% CI = 0.636-0.708). Conversely, in models including these three variables, the Random Forest model exhibited the best performance (AUC = 0.788, 95% CI = 0.759-0.818). In the analysis of high-quality blastocysts, significant differences were found in 17 factors. Logistic regression analysis indicated that 13 factors influence the formation of high-quality blastocysts. Including these variables in the predictive model, the XGBoost model showed the highest performance (AUC = 0.813, 95% CI = 0.741-0.884). CONCLUSION We developed a predictive model for the formation of high-quality embryos using machine learning methods for patients with POR undergoing treatment with the PPOS protocol. This model can help infertility patients better understand the likelihood of forming high-quality embryos following treatment and help clinicians better understand and predict treatment outcomes, thus facilitating more targeted and effective interventions.
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Affiliation(s)
- Yu-Heng Xiao
- Chongqing Medical University, Chongqing, 400016, China
- Department of Laboratory, Chongqing General Hospital, Chongqing, 401121, China
| | - Yu-Lin Hu
- The Reproductive Center, Sichuan Jinxin Xinan Women and Children's Hospital, Chengdu, Sichuan, 610011, China
| | - Xing-Yu Lv
- The Reproductive Center, Sichuan Jinxin Xinan Women and Children's Hospital, Chengdu, Sichuan, 610011, China
| | - Li-Juan Huang
- Department of Obstetrics and Gynecology, Women and Children's Hospital of Chongqing Medical University, Chongqing, 401147, China
| | - Li-Hong Geng
- The Reproductive Center, Sichuan Jinxin Xinan Women and Children's Hospital, Chengdu, Sichuan, 610011, China
| | - Pu Liao
- Chongqing Medical University, Chongqing, 400016, China.
- Department of Laboratory, Chongqing General Hospital, Chongqing, 401121, China.
| | - Yu-Bin Ding
- Department of Obstetrics and Gynecology, Women and Children's Hospital of Chongqing Medical University, Chongqing, 401147, China.
- Department of Pharmacology, Academician Workstation, Changsha Medical University, Changsha, 410219, China.
| | - Chang-Chun Niu
- Chongqing Medical University, Chongqing, 400016, China.
- Department of Laboratory, Chongqing General Hospital, Chongqing, 401121, China.
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Kokori E, Olatunji G, Aderinto N, Muogbo I, Ogieuhi IJ, Isarinade D, Ukoaka B, Akinmeji A, Ajayi I, Chidiogo E, Samuel O, Nurudeen-Busari H, Muili AO, Olawade DB. The role of machine learning algorithms in detection of gestational diabetes; a narrative review of current evidence. Clin Diabetes Endocrinol 2024; 10:18. [PMID: 38915129 PMCID: PMC11197257 DOI: 10.1186/s40842-024-00176-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Accepted: 02/20/2024] [Indexed: 06/26/2024] Open
Abstract
Gestational Diabetes Mellitus (GDM) poses significant health risks to mothers and infants. Early prediction and effective management are crucial to improving outcomes. Machine learning techniques have emerged as powerful tools for GDM prediction. This review compiles and analyses the available studies to highlight key findings and trends in the application of machine learning for GDM prediction. A comprehensive search of relevant studies published between 2000 and September 2023 was conducted. Fourteen studies were selected based on their focus on machine learning for GDM prediction. These studies were subjected to rigorous analysis to identify common themes and trends. The review revealed several key themes. Models capable of predicting GDM risk during the early stages of pregnancy were identified from the studies reviewed. Several studies underscored the necessity of tailoring predictive models to specific populations and demographic groups. These findings highlighted the limitations of uniform guidelines for diverse populations. Moreover, studies emphasised the value of integrating clinical data into GDM prediction models. This integration improved the treatment and care delivery for individuals diagnosed with GDM. While different machine learning models showed promise, selecting and weighing variables remains complex. The reviewed studies offer valuable insights into the complexities and potential solutions in GDM prediction using machine learning. The pursuit of accurate, early prediction models, the consideration of diverse populations, clinical data, and emerging data sources underscore the commitment of researchers to improve healthcare outcomes for pregnant individuals at risk of GDM.
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Affiliation(s)
- Emmanuel Kokori
- Department of Medicine and Surgery, University of Ilorin, Ilorin, PMB 5000, Nigeria
| | - Gbolahan Olatunji
- Department of Medicine and Surgery, University of Ilorin, Ilorin, PMB 5000, Nigeria
| | - Nicholas Aderinto
- Department of Medicine, Ladoke Akintola University of Technology, Ogbomoso, Nigeria.
| | - Ifeanyichukwu Muogbo
- Department of Medicine, Ladoke Akintola University of Technology, Ogbomoso, Nigeria
| | | | - David Isarinade
- Department of Medicine and Surgery, University of Ilorin, Ilorin, PMB 5000, Nigeria
| | - Bonaventure Ukoaka
- Department of Internal Medicine, Asokoro District Hospital, Abuja, Nigeria
| | - Ayodeji Akinmeji
- Department of Medicine and Surgery, Olabisi Onabanjo University, Ogun, Nigeria
| | - Irene Ajayi
- Department of Medicine and Surgery, University of Ilorin, Ilorin, PMB 5000, Nigeria
| | - Ezenwoba Chidiogo
- Department of Medicine and Surgery, AfeBabalola University, Ado-Ekiti, Nigeria
| | - Owolabi Samuel
- Department of Medicine, Lagos State Health Service Commission, Lagos, Nigeria
| | | | | | - David B Olawade
- Department of Allied and Public Health, School of Health, Sport and Bioscience, University of East London, London, UK
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Zhang J, Huang H, Xu L, Wang S, Gao Y, Zhuo W, Wang Y, Zheng Y, Tang X, Jiang J, Lv H. Knowledge framework of intravenous immunoglobulin resistance in the field of Kawasaki disease: A bibliometric analysis (1997-2023). Immun Inflamm Dis 2024; 12:e1277. [PMID: 38775687 PMCID: PMC11110715 DOI: 10.1002/iid3.1277] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Revised: 04/23/2024] [Accepted: 05/06/2024] [Indexed: 05/24/2024] Open
Abstract
BACKGROUND Kawasaki disease (KD) is an autoimmune disease with cardiovascular disease as its main complication, mainly affecting children under 5 years old. KD treatment has made tremendous progress in recent years, but intravenous immunoglobulin (IVIG) resistance remains a major dilemma. Bibliometric analysis had not been used previously to summarize and analyze publications related to IVIG resistance in KD. This study aimed to provide an overview of the knowledge framework and research hotspots in this field through bibliometrics, and provide references for future basic and clinical research. METHODS Through bibliometric analysis of relevant literature published on the Web of Science Core Collection (WoSCC) database between 1997 and 2023, we investigated the cooccurrence and collaboration relationships among countries, institutions, journals, and authors and summarized key research topics and hotspots. RESULTS Following screening, a total of 364 publications were downloaded, comprising 328 articles and 36 reviews. The number of articles on IVIG resistance increased year on year and the top three most productive countries were China, Japan, and the United States. Frontiers in Pediatrics had the most published articles, and the Journal of Pediatrics had the most citations. IVIG resistance had been studied by 1889 authors, of whom Kuo Ho Chang had published the most papers. CONCLUSION Research in the field was focused on risk factors, therapy (atorvastatin, tumor necrosis factor-alpha inhibitors), pathogenesis (gene expression), and similar diseases (multisystem inflammatory syndrome in children, MIS-C). "Treatment," "risk factor," and "prediction" were important keywords, providing a valuable reference for scholars studying this field. We suggest that, in the future, more active international collaborations are carried out to study the pathogenesis of IVIG insensitivity, using high-throughput sequencing technology. We also recommend that machine learning techniques are applied to explore the predictive variables of IVIG resistance.
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Affiliation(s)
- Jiaying Zhang
- Institute of Pediatric ResearchChildren's Hospital of Soochow UniversitySuzhouJiangsuChina
| | - Hongbiao Huang
- Institute of Pediatric ResearchChildren's Hospital of Soochow UniversitySuzhouJiangsuChina
- Department of PediatricsFujian Province HospitalFuzhouFujianChina
| | - Lei Xu
- Institute of Pediatric ResearchChildren's Hospital of Soochow UniversitySuzhouJiangsuChina
| | - Shuhui Wang
- Institute of Pediatric ResearchChildren's Hospital of Soochow UniversitySuzhouJiangsuChina
| | - Yang Gao
- Institute of Pediatric ResearchChildren's Hospital of Soochow UniversitySuzhouJiangsuChina
| | - Wenyu Zhuo
- Institute of Pediatric ResearchChildren's Hospital of Soochow UniversitySuzhouJiangsuChina
| | - Yan Wang
- Institute of Pediatric ResearchChildren's Hospital of Soochow UniversitySuzhouJiangsuChina
| | - Yiming Zheng
- Institute of Pediatric ResearchChildren's Hospital of Soochow UniversitySuzhouJiangsuChina
| | - Xuan Tang
- Institute of Pediatric ResearchChildren's Hospital of Soochow UniversitySuzhouJiangsuChina
| | - Jiaqi Jiang
- Department of Pediatrics, No.2 Affiliated HospitalAir Force Medical UniversityXianShanxiChina
| | - Haitao Lv
- Institute of Pediatric ResearchChildren's Hospital of Soochow UniversitySuzhouJiangsuChina
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Wang M, Li W, Wang H, Song P. Development and validation of machine learning-based models for predicting healthcare-associated bacterial/fungal infections among COVID-19 inpatients: a retrospective cohort study. Antimicrob Resist Infect Control 2024; 13:42. [PMID: 38616284 PMCID: PMC11017584 DOI: 10.1186/s13756-024-01392-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Accepted: 03/30/2024] [Indexed: 04/16/2024] Open
Abstract
BACKGROUND COVID-19 and bacterial/fungal coinfections have posed significant challenges to human health. However, there is a lack of good tools for predicting coinfection risk to aid clinical work. OBJECTIVE We aimed to investigate the risk factors for bacterial/fungal coinfection among COVID-19 patients and to develop machine learning models to estimate the risk of coinfection. METHODS In this retrospective cohort study, we enrolled adult inpatients confirmed with COVID-19 in a tertiary hospital between January 1 and July 31, 2023, in China and collected baseline information at admission. All the data were randomly divided into a training set and a testing set at a ratio of 7:3. We developed the generalized linear and random forest models for coinfections in the training set and assessed the performance of the models in the testing set. Decision curve analysis was performed to evaluate the clinical applicability. RESULTS A total of 1244 patients were included in the training cohort with 62 healthcare-associated bacterial/fungal infections, while 534 were included in the testing cohort with 22 infections. We found that patients with comorbidities (diabetes, neurological disease) were at greater risk for coinfections than were those without comorbidities (OR = 2.78, 95%CI = 1.61-4.86; OR = 1.93, 95%CI = 1.11-3.35). An indwelling central venous catheter or urinary catheter was also associated with an increased risk (OR = 2.53, 95%CI = 1.39-4.64; OR = 2.28, 95%CI = 1.24-4.27) of coinfections. Patients with PCT > 0.5 ng/ml were 2.03 times (95%CI = 1.41-3.82) more likely to be infected. Interestingly, the risk of coinfection was also greater in patients with an IL-6 concentration < 10 pg/ml (OR = 1.69, 95%CI = 0.97-2.94). Patients with low baseline creatinine levels had a decreased risk of bacterial/fungal coinfections(OR = 0.40, 95%CI = 0.22-0.71). The generalized linear and random forest models demonstrated favorable receiver operating characteristic curves (ROC = 0.87, 95%CI = 0.80-0.94; ROC = 0.88, 95%CI = 0.82-0.93) with high accuracy, sensitivity and specificity of 0.86vs0.75, 0.82vs0.86, 0.87vs0.74, respectively. The corresponding calibration evaluation P statistics were 0.883 and 0.769. CONCLUSIONS Our machine learning models achieved strong predictive ability and may be effective clinical decision-support tools for identifying COVID-19 patients at risk for bacterial/fungal coinfection and guiding antibiotic administration. The levels of cytokines, such as IL-6, may affect the status of bacterial/fungal coinfection.
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Affiliation(s)
- Min Wang
- Department of Infection Management, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School,Nanjing University, 321 Zhongshan Road, Nanjing, Jiangsu Province, 210009, China
| | - Wenjuan Li
- Department of Medical Big Data, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, 321 Zhongshan Road, Nanjing, Jiangsu Province, 210009, China
| | - Hui Wang
- Department of Infection Management, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School,Nanjing University, 321 Zhongshan Road, Nanjing, Jiangsu Province, 210009, China
| | - Peixin Song
- Department of Infection Management, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School,Nanjing University, 321 Zhongshan Road, Nanjing, Jiangsu Province, 210009, China.
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Sarker MR, Ramos GA. Routine screening for gestational diabetes: a review. Curr Opin Obstet Gynecol 2024; 36:97-103. [PMID: 38259247 DOI: 10.1097/gco.0000000000000940] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
PURPOSE OF REVIEW Rates of gestational diabetes mellitus (GDM) throughout the world continue to increase associated with the increasing rates of obesity. Given this epidemiologic burden, the importance of proper screening, diagnosis, and management cannot be understated. This review focuses on the current screening guidelines utilized throughout the world and new data recently published regarding the most optimal screening techniques and future directions for research. RECENT FINDINGS Despite unanimous opinion that GDM warrants screening, the optimal screening regimen remains controversial. Notably, in the United States per the consensus recommendation by the American College of Obstetrics and Gynecology and the Society for Maternal-Fetal Medicine, a 2-step screening approach is often used. Recently, there have been multiple studies published that have compared the 1-step and 2-step screening process with respect to GDM incidence and perinatal outcomes. These new findings are summarized below. SUMMARY Utilization of the 1-step screening as opposed to the 2-step screening results in an increased diagnosis of GDM without significant population level benefit in outcomes. However, these studies remain underpowered to allow for meaningful comparison of outcomes in those diagnosed with GDM.
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Affiliation(s)
- Minhazur R Sarker
- Division of Maternal-Fetal Medicine, University of California, San Diego, San Diego, California, USA
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