The Journal of Maternal-Fetal & Neonatal Medicine, Год журнала: 2024, Номер 38(1)
Опубликована: Дек. 3, 2024
Objectives Intrahepatic cholestasis of pregnancy (ICP), a condition exclusive to pregnancy, necessitates prompt identification and intervention improve the perinatal outcomes. This study aims develop suitable machine-learning models for predicting ICP based on clinical laboratory indicators.
Язык: Английский
Journal of Inflammation Research, Год журнала: 2024, Номер Volume 17, С. 6083 - 6091
Опубликована: Сен. 1, 2024
Thrombophilia combined with pregnancy poses significant risks for adverse outcomes. Unfortunately, there are no indicators at high risk predicting This study investigates the predictive efficiency of serum immune-inflammatory markers on
Язык: Английский
Процитировано
1
American Journal of Reproductive Immunology, Год журнала: 2024, Номер 92(4)
Опубликована: Окт. 1, 2024
ABSTRACT Problem This study aims to evaluate the effectiveness of inflammation indexes (systemic immune‐inflammation index [SII], systemic response [SIRI], pan‐immune value [PIV], and neutrophil‐to‐lymphocyte ratio [NLR]) in diagnosis intrahepatic cholestasis pregnancy (ICP). Method Study A retrospective was conducted, reviewing medical records patients diagnosed with ICP who delivered between October 1, 2022, May 31, 2023, at Perinatology clinic Etlik City Hospital, Ankara. control group healthy pregnant women uncomplicated pregnancies also included. Demographic data, clinical characteristics, laboratory results, including indices liver enzyme levels, were collected analyzed. Results total 242 participants included, 121 controls. White blood cell count, neutrophil monocyte count showed significant differences two groups ( p = 0.011, 0.004, 0.039, respectively). Aspartate aminotransferase (AST) alanine (ALT) levels significantly elevated < 0.001 for both). SII NLR higher compared controls 0.032 0.010, Receiver operating characteristic (ROC) analysis revealed moderate predictive values (area under curve [AUC] 0.581, 0.030) (AUC 0.598, 0.009), no difference their power 0.502). Conclusions Systemic such as offer a cost‐effective rapid means diagnosing ICP, potentially complementing or surpassing traditional biomarkers like bile acid function tests (LFTs). These can be easily integrated into routine practice, providing timely intervention improve maternal fetal outcomes. Further research is warranted confirm these findings establish standardized protocols use management.
Язык: Английский
Процитировано
0
Saudi Medical Journal, Год журнала: 2024, Номер 45(11), С. 1217 - 1222
Опубликована: Ноя. 1, 2024
To investigate the role of systemic immune-inflammation index (SII) in diagnosis and severity intrahepatic cholestasis pregnancy (ICP).
Язык: Английский
Процитировано
0
American Journal of Reproductive Immunology, Год журнала: 2024, Номер 92(5)
Опубликована: Ноя. 1, 2024
ABSTRACT Aims To investigate whether systemic inflammatory indices and the last trimester APRI score change in PGDM to evaluate relationship between these alterations perinatal outcomes. Methods A total of 240 pregnant women, 120 whom were pregestational diabetic (40 with T1DM 80 T2DM), analyzed. In each trimester, WBC, NEU, LNF, PLT, NLR, dNLR, PLR, PNR, SII values, MON, PMR, SIRI, AST recorded compared control cohorts. Results The first LNF values higher PNR lower, second value was NLR third group. optimal cut‐off NEU for predicting LBW 6.965 × 10⁹/L (62.5% sensitivity 61.6% specificity), while preterm delivery 0.072 (61.9% specificity). NICU, found be 3.973 (70% 70% specificity) group, 2.395 (75% 71.1% 2.23 68.4% T2DM respectively. Conclusions routine clinical practice, may used as additional tools outcomes pregnancies affected by PGDM.
Язык: Английский
Процитировано
0
The Journal of Maternal-Fetal & Neonatal Medicine, Год журнала: 2024, Номер 38(1)
Опубликована: Дек. 3, 2024
Objectives Intrahepatic cholestasis of pregnancy (ICP), a condition exclusive to pregnancy, necessitates prompt identification and intervention improve the perinatal outcomes. This study aims develop suitable machine-learning models for predicting ICP based on clinical laboratory indicators.
Язык: Английский
Процитировано
0