foley catheter
Recently Published Documents


TOTAL DOCUMENTS

1120
(FIVE YEARS 279)

H-INDEX

31
(FIVE YEARS 3)

Author(s):  
Narayanan Vallikkannu ◽  
Naumi Laboh ◽  
Peng Chiong Tan ◽  
Jesrine Gek Shan Hong ◽  
Mukhri Hamdan ◽  
...  

2022 ◽  
Vol 226 (1) ◽  
pp. S35
Author(s):  
Helen B. Gomez Slagle ◽  
Yaneve N. Fonge ◽  
Richard Caplan ◽  
Anthony Sciscione ◽  
Matthew Hoffman

2022 ◽  
Vol 226 (1) ◽  
pp. S772-S773
Author(s):  
Rebecca Pierce-Williams ◽  
Henry Lesser ◽  
Isabelle Cohen ◽  
Monica Bauer ◽  
Vincenzo Berghella ◽  
...  

Author(s):  
Nnabugwu Alfred Adiele ◽  
Christian C. Mgbafulu ◽  
Arinze Chidiebere Ikeotuonye ◽  
Christian Chidebe Anikwe ◽  
Joshua Adeniyi Adebayo ◽  
...  

Background: The ripeness of the cervix is an important prerequisite to a successful labour induction. Use of extra-amniotic Foley catheter is a mechanical method of cervical ripening with proven efficacy. This study compared the difference in efficacy between 30 ml and 60 ml of water for inflation of Foley catheter balloon when used for cervical ripening during induction of labour.Methods: A single-blind randomized controlled study where 260 term pregnant women with intact membranes and unfavourable cervix were selected for induction of labour and randomized into two equal groups (30 ml- and 60 ml- groups) from October, 2019 to July 2020. Each participant had cervical ripening with the catheter bulb inflated with either 30 ml or 60 ml of sterile water (as assigned to the individual). After achieving favourable cervix (BS ≥6), oxytocin titration was commenced and the labour monitored with the outcomes well documented and statistically analysed.Results: Mean duration to favourable Bishop Score significantly reduced in the 60 ml group (10.8 hours±2.99) as against 12.7 hours±10.0 in 30ml group (p=0.038). Mean duration of active phase of labour was significantly reduced in 60 ml group (5.6 hours±2.4) as against 8.4 hours±3.2 in 30 ml group (p=0.010). Caesarean delivery rate was significantly reduced in the 60 ml groups (p=0.027).Conclusions: The use of 60 ml inflated Foley’s balloon catheter when compared with 30mls to ripen the cervix effectively reduced the duration to favourable Bishop Score, duration of the active phase of labour and the rate of Caesarean sections.  


2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Mira Prabhakar ◽  
Troy Markel

Background: Necrotizing enterocolitis (NEC) is a devastating clinical problem that often necessitates resection of necrotic intestine, leaving infants with a suboptimal length of intestine to absorb nutrition. There are no adequate tests or biomarkers to predict NEC onset. We hypothesized that assessment of biological samples with a novel electronic nose can be used with machine learning algorithms to detect aberrancies in stool and urine volatile organic compounds to predict NEC risk.  Methods: 18 infants of gestational age ≤34 weeks in the Riley Hospital NICU were enrolled in the study and underwent stool (38 control, 3 NEC) and urine (42 control, 5 NEC) sample collection. Stool was collected by the bedside nurse via the infant’s diaper or ostomy bag. Urine was collected using a bag around the infant’s perineum or via foley catheter if already present. Stool samples and 250uL urine samples were aliquoted into Eppendorf tubes and covered with Parafilm. Samples were heated to 40°C, 30 minutes for stool, and 10 minutes for urine. Sample headspace was analyzed using the Cyranose 320 electronic nose, creating a “smellprint” comprised of readings from 32 unique sensors. Individual sensors were compared using Mann-Whitney U test.  P<0.05 was significant.  Results: There was no significant difference in urine or stool signals among the 32 sensors between NEC and control groups.   Potential Impact: While no significant difference was found, this study is greatly limited by the number of patients enrolled, with only 2 of the 18 being diagnosed with NEC. Past retrospective studies with this device have found differences between NEC and non-NEC stool. The future goals of this study are to continue enrolling patients to have a more robust data set. Using machine learning, we aim to create a model to predict NEC before its clinical manifestation so that beneficial treatment can be initiated earlier.    Acknowledgment: This project was funded, in part, with support from the NIH NHLBI Short-Term Training Program in Biomedical Sciences Grant funded, in part by T35HL110854 from the National Institutes of Health. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. 


Cureus ◽  
2021 ◽  
Author(s):  
Terry Lefcourt ◽  
Andrew Ku ◽  
Leo Issagholian ◽  
Arianna S Neeki ◽  
Milton Retamozo ◽  
...  

Author(s):  
Nicolas Beysard ◽  
Mathieu Pasquier ◽  
Tobias Zingg ◽  
Pierre-Nicolas Carron ◽  
Vincent Darioli

Sign in / Sign up

Export Citation Format

Share Document