Amer J Perinatol 2018; 35(02): 134-139
DOI: 10.1055/s-0037-1606275
Original Article
Thieme Medical Publishers 333 Seventh Avenue, New York, NY 10001, USA.

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
  • 1Department of Obstetrics and Gynecology, Maternal and Child Health Research Program, University of Pennsylvania, Perelman School of Medicine, Philadelphia, Pennsylvania
Further Information

Publication History

02 June 2017

23 July 2017

Publication Date:
24 August 2017 (eFirst)


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.