Literature DB >> 28838005

Implementation of Quantification of Blood Loss Does Not Improve Prediction of Hemoglobin Drop in Deliveries with Average Blood Loss.

Rebecca F Hamm1, Eileen Wang1, April Romanos1, Kathleen O'Rourke1, Sindhu K Srinivas1.   

Abstract

OBJECTIVE: The National Partnership for Maternal Safety released a postpartum hemorrhage bundle in 2015 recommending quantification of blood loss (QBL) for all deliveries. We sought to determine whether QBL more accurately predicts hemoglobin (Hb) drop than visually estimated blood loss (EBL). STUDY
DESIGN: This is a prospective observational study. Preintervention data (PRE) were collected on all deliveries between October 15, 2013 and December 15, 2013. Deliveries were included if EBL, admission Hb, and 12-hour postpartum Hb (12hrCBC) were available. QBL was implemented in July 2015. Postintervention data (POST) were collected between October 20, 2015 and December 20, 2015. A total of 500 mL EBL was predicted to result in 1 g/dL Hb drop at 12hrCBC. Student's t-test was used to compare the means.
RESULTS: A total of 592 of 626 (95%) PRE and 583 of 613 (95%) POST deliveries were included. Overall, 278 (48%) POST deliveries had QBL recorded. In both PRE and POST, actual Hb drop differed from predicted by 0.6 g/dL in both groups of deliveries. When evaluating deliveries with EBL > 1,000 mL, QBL in POST was slightly better at predicting Hb drop versus EBL in PRE, although not statistically significant (0.2 vs. 0.5 g/dL, p = 0.17).
CONCLUSION: In all deliveries, QBL does not predict Hb drop more accurately than EBL. The decision to perform QBL needs to balance accuracy with a resource intense measurement process. Thieme Medical Publishers 333 Seventh Avenue, New York, NY 10001, USA.

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Year:  2017        PMID: 28838005     DOI: 10.1055/s-0037-1606275

Source DB:  PubMed          Journal:  Am J Perinatol        ISSN: 0735-1631            Impact factor:   1.862


  5 in total

1.  Effect of implementation of a colorimetric quantitative blood loss system for postpartum hemorrhage.

Authors:  Maryalice Wolfe; Jamil M Kazma; Ann B Burke; Homa K Ahmadzia
Journal:  Int J Gynaecol Obstet       Date:  2022-04-16       Impact factor: 4.447

2.  Machine Learning and Statistical Models to Predict Postpartum Hemorrhage.

Authors:  Kartik K Venkatesh; Robert A Strauss; Chad A Grotegut; R Philip Heine; Nancy C Chescheir; Jeffrey S A Stringer; David M Stamilio; Katherine M Menard; J Eric Jelovsek
Journal:  Obstet Gynecol       Date:  2020-04       Impact factor: 7.623

Review 3.  Implementation Science is Imperative to the Optimization of Obstetric Care.

Authors:  Rebecca F Hamm; Brian K Iriye; Sindhu K Srinivas
Journal:  Am J Perinatol       Date:  2020-12-15       Impact factor: 3.079

4.  Limitations of Gravimetric Quantitative Blood Loss during Cesarean Delivery.

Authors:  Robert L Thurer; Sahar Doctorvaladan; Brendan Carvalho; Andrea T Jelks
Journal:  AJP Rep       Date:  2022-02-04

Review 5.  Comparison of common perioperative blood loss estimation techniques: a systematic review and meta-analysis.

Authors:  Lara Gerdessen; Patrick Meybohm; Suma Choorapoikayil; Eva Herrmann; Isabel Taeuber; Vanessa Neef; Florian J Raimann; Kai Zacharowski; Florian Piekarski
Journal:  J Clin Monit Comput       Date:  2020-08-19       Impact factor: 2.502

  5 in total

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