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Serum Lactate Level as a Predictor for Blood Transfusion in Postpartum Hemorrhage

Agrawal, Surbhi; Smith, Maria; Berg, Robert; Hoskins, Iffath A
OBJECTIVE: Postpartum hemorrhage (PPH) is the leading cause of maternal morbidity and mortality. At present, there are no reliable clinical or laboratory indicators to identify which patients might require blood transfusions during a PPH. Serum lactate has long been used as an early biomarker of tissue hypoperfusion in trauma settings. The aim of this study is to understand serum lactate's role in the management of obstetric hemorrhage. STUDY DESIGN/METHODS: A retrospective chart review was performed of women who delivered between 2016 and 2019 at our institution and experienced a PPH. The patients were divided into two groups: those with a normal serum lactate level, defined as ≤2 mmol/L, and those with an abnormal serum lactate level, defined as >2 mmol/L. Need for packed red blood cell transfusion, as part of the resuscitation, was assessed for both groups. RESULTS: < 0.001). CONCLUSION/CONCLUSIONS: Women with elevated serum lactate levels were more likely to require blood transfusions during a PPH versus those with a normal serum lactate level. Thus, serum lactate levels are useful as an early indicator of requirement for blood transfusion in the management of obstetric hemorrhage. KEY POINTS/CONCLUSIONS:· Lactate is a biomarker for blood transfusion in trauma.. · Lactate's role in PPH is unknown.. · Elevated lactate predicts receiving more blood transfusions..
PMID: 34808683
ISSN: 1098-8785
CID: 5063382

Patient Experience with Telehealth Medicine During the Ongoing COVID-19 Pandemic in New York City

Aquino, Jennifer; Cyr, Micaela E; Hoskins, Iffath A; Conroy, Erin M; Gossett, Dana R
PMID: 36607815
ISSN: 1556-3669
CID: 5433522

Prediction of Shoulder Dystocia Utilizing Machine Learning [Meeting Abstract]

Griffin, M; Liu, W; Hoskins, I A; Fenyo, D; Roman, A S
Objective: To determine whether a machine learning model can predict patients at risk for shoulder dystocia (SD) better than estimations of birthweight (BW) alone.
Study Design: This was a retrospective analysis of 17,731 pregnant individuals from 7/2013 to 10/2018. Utilizing a machine learning model, a total of 122 binary and continuous variables were included. Baseline models were built with different sets of variables during 3 time periods: 57 in antepartum period, 96 in stage 1 and 119 in stage 2 of labor. BW was used as a proxy for estimated fetal weight (EFW) because documented assessment of EFW was not available in all cases. Two decision tree-based models, Random Forest and XGBoost, were used as predictive models and performance was evaluated with 5-fold cross validation. Area under the receiver operating characteristic curve (AUROC) and area under precision/recall curve (AUPR) were used as metrics for evaluating model performance. Mean and standard error of performance metrics were calculated.
Result(s): The cohort included 21,232 vaginal deliveries. There were 415 (1.95%) vaginal deliveries complicated by SD that occurred in 406 patients. An AUROC of 0.73 +/- 0.01 (mean +/- standard error) was achieved for RandomForest model and 0.78 +/- 0.01 for XGBoost model for BW. BW was then added as a variable to variable sets from the antepartum period and each labor stage to assess performance change. The RandomForest model predicted patients at risk for SD better than BW alone, but only the AUROC was statistically significant at all stages (p < 0.05). (Figure 1) This finding was not demonstrated in the XGBoost model.
Conclusion(s): Our machine learning model performed better in predicting SD than EFW alone (using BW as a proxy) at each time period evaluated - the antepartum period, 1st stage and 2nd stage of labor. While our results indicate that our model may enhance the prediction of SD, these findings should be validated using a more robust data set that includes documented EFW to account for the margin of error between BW and EFW. [Formula presented] [Formula presented]
Copyright
EMBASE:2022102363
ISSN: 1097-6868
CID: 5512832

Thromboelastography versus standard coagulation assays in patients with postpartum hemorrhage

Perelman, Allison; Limaye, Meghana; Blakemore, Jennifer; Hoskins, Iffath Abbasi
KEY WORDS/BACKGROUND:postpartum hemorrhage, thromboelastography, coagulopathy, fibrinogen, massive transfusion. OBJECTIVE:Thromboelastography, a point-of-care test that measures blood's dynamic viscoelastic properties, is routinely used to guide resuscitation in surgical specialties with high hemorrhage risk. Patients with ongoing postpartum hemorrhage often develop coagulopathy and hypofibrinogenemia. Timely assessment of fibrinogen is crucial because cryoprecipitate for repletion requires thawing prior to administration. Thromboelastography may provide rapid assessment of coagulopathy in ongoing hemorrhage but this has not been thoroughly studied. Our objective was to determine if thromboelastography accurately reflects coagulopathy in ongoing postpartum hemorrhage when compared to standard assays. STUDY DESIGN/METHODS:This was a retrospective cohort study of people with ongoing postpartum hemorrhage (quantified blood loss >1000 mL), from 1/1/16-12/31/19. Thromboelastography variables and standard coagulation parameters were compared in patients who had both assays drawn simultaneously. As a secondary analysis, patients who had thromboelastography were compared to those who did not. Mann-Whitney, Fisher's Exact, Kruskal-Wallis, Spearman's Rho, and logistic regression tests were used for analysis. Significance was set at p < 0.05. RESULTS:A total of 680 patients were included. 69 had thromboelastography and coagulation parameters drawn simultaneously and were included in the primary analysis. The remainder were included in the secondary analysis. Thromboelastography variables and coagulation assays correlated significantly - prolonged R with increased PTT (rho 0.25, p=0.04), prolonged K and decreased alpha angle with decreased fibrinogen (rho -0.61, p<0.001; rho 0.24, p<0.001), and decreased maximum amplitude with decreased platelets (rho 0.62, p<0.001). Those who had thromboelastographic assays had higher blood loss and need for interventions to manage hemorrhage than those who did not. CONCLUSION/CONCLUSIONS:Thromboelastography correlated significantly with standard laboratory assays in ongoing postpartum hemorrhage, including for patients with hypofibrinogenemia Given the point-of-care nature and rapid turnaround time, thromboelastography should be considered for timely hemorrhage evaluation and directed resuscitation of coagulopathy.
PMID: 36347507
ISSN: 1098-8785
CID: 5357242

In Reply

Bayefsky, Michelle J; Caplan, Arthur L; Hoskins, Iffath A
PMID: 36441934
ISSN: 1873-233x
CID: 5373882

Postpartum Readmissions for Hypertensive Disorders in Pregnancy During the COVID-19 Pandemic

Griffin, Myah M; Black, Mara; Deeb, Jessica; Penfield, Christina A; Hoskins, Iffath A
Background/UNASSIGNED:Hypertensive disorders in pregnancy are one of the most common causes of readmission postpartum. Due to the COVID-19 pandemic, early hospital discharge was encouraged for patients who were medically stable as hospitalization rates among COVID-infected patients steadily increased in 2020. The impact of an early discharge policy on postpartum readmission rates among patients with hypertensive disorders in pregnancy is unknown. Objective/UNASSIGNED:To compare the postpartum readmission rates of patients with hypertensive disorders in pregnancy before and after implementation of an early discharge policy due to the COVID-19 pandemic. Study Design/UNASSIGNED:This is a quality improvement, retrospective cohort study of postpartum patients with antenatal hypertensive disorders in pregnancy who delivered and were readmitted due to hypertensive disorders in pregnancy at NYU Langone Health on 3/1/2019-2/29/2020 (control cohort) to 4/1/2020-3/31/2021 (COVID cohort). During the pandemic, our institution introduced an early discharge policy for all postpartum patients to be discharged no later than 2 days postpartum during the delivery admission if deemed medically appropriate. The reduction of postpartum length of stay was accompanied by the continuation of patient education, home blood pressure monitoring, and outpatient follow-up. The primary outcome was the comparison of the postpartum hypertensive disorders in pregnancy readmission rates. Data were analyzed using Fisher's Exact test, chi-square test, and Wilcoxon rank-sum test with significance defined as p<0.05. Results/UNASSIGNED:There was no statistical difference in readmission rates for postpartum hypertensive disorders in pregnancy before versus after implementation of an early discharge policy (1.08% for control cohort vs 0.59% for COVID cohort). Demographics in each group were similar, as were median times to readmission (5.0 days, IQR 4.0-6.0 days vs 6.0 days, IQR 5.0-6.0 days, p=0.13) and median readmission lengths of stay (3.0 days, IQR 2.0-4.0 days vs 3.0 days, IQR 2.0-4.0 days, p=0.45). There was one ICU readmission in the COVID cohort and none in the control cohort (p=0.35). There were no severe maternal morbidities or maternal deaths. Conclusion/UNASSIGNED:These findings suggest policies reducing postpartum length of stay, which includes patients with hypertensive disorders in pregnancy, can be implemented without impacting the hospital readmission rate for patients with hypertensive disorders in pregnancy. Continuation of patient education and outpatient surveillance during the pandemic was instrumental in the outpatient postpartum management of the study cohort. Further investigation into best practices to support early discharges is warranted.
PMCID:9493139
PMID: 36164558
ISSN: 2666-5778
CID: 5334132

Prediction of Maternal Hemorrhage Using Machine Learning: Retrospective Cohort Study

Westcott, Jill M; Hughes, Francine; Liu, Wenke; Grivainis, Mark; Hoskins, Iffath; Fenyo, David
BACKGROUND:Postpartum hemorrhage remains one of the largest causes of maternal morbidity and mortality in the United States. OBJECTIVE:The aim of this paper is to use machine learning techniques to identify patients at risk for postpartum hemorrhage at obstetric delivery. METHODS:Women aged 18 to 55 years delivering at a major academic center from July 2013 to October 2018 were included for analysis (N=30,867). A total of 497 variables were collected from the electronic medical record including the following: demographic information; obstetric, medical, surgical, and family history; vital signs; laboratory results; labor medication exposures; and delivery outcomes. Postpartum hemorrhage was defined as a blood loss of ≥1000 mL at the time of delivery, regardless of delivery method, with 2179 (7.1%) positive cases observed. Supervised learning with regression-, tree-, and kernel-based machine learning methods was used to create classification models based upon training (21,606/30,867, 70%) and validation (4630/30,867, 15%) cohorts. Models were tuned using feature selection algorithms and domain knowledge. An independent test cohort (4631/30,867, 15%) determined final performance by assessing for accuracy, area under the receiver operating curve (AUROC), and sensitivity for proper classification of postpartum hemorrhage. Separate models were created using all collected data versus models limited to data available prior to the second stage of labor or at the time of decision to proceed with cesarean delivery. Additional models examined patients by mode of delivery. RESULTS:Gradient boosted decision trees achieved the best discrimination in the overall model. The model including all data mildly outperformed the second stage model (AUROC 0.979, 95% CI 0.971-0.986 vs AUROC 0.955, 95% CI 0.939-0.970). Optimal model accuracy was 98.1% with a sensitivity of 0.763 for positive prediction of postpartum hemorrhage. The second stage model achieved an accuracy of 98.0% with a sensitivity of 0.737. Other selected algorithms returned models that performed with decreased discrimination. Models stratified by mode of delivery achieved good to excellent discrimination but lacked the sensitivity necessary for clinical applicability. CONCLUSIONS:Machine learning methods can be used to identify women at risk for postpartum hemorrhage who may benefit from individualized preventative measures. Models limited to data available prior to delivery perform nearly as well as those with more complete data sets, supporting their potential utility in the clinical setting. Further work is necessary to create successful models based upon mode of delivery and to validate the findings of this study. An unbiased approach to hemorrhage risk prediction may be superior to human risk assessment and represents an area for future research.
PMID: 35849436
ISSN: 1438-8871
CID: 5278582

Evaluating Expanded Noninvasive Prenatal Screening

Bayefsky, Michelle J; Caplan, Arthur L; Hoskins, Iffath A
Public attention has been drawn recently to the commercial availability of noninvasive prenatal screening for rare genetic conditions, despite estimates that the positive predictive value of such tests is low. Many have focused on the importance of educating patients on the difference between screening and diagnostic testing so that they are adequately prepared for the possibility of a false-positive result. Even so, we question whether expanded noninvasive prenatal screening is ready to be used a screening tool. We argue that established public health criteria for evaluating the risks and benefits of a screening tool should be applied to expanded noninvasive prenatal screening. If a test fails to meet accepted standards, governmental agencies should consider regulating the accuracy and promotional claims of commercially available tests.
PMID: 35675597
ISSN: 1873-233x
CID: 5244822

Postpartum Readmission Rates for Hypertensive Disorders of Pregnancy During the COVID-19 Pandemic [Meeting Abstract]

Griffin, M; Black, M; Deeb, J; Hoskins, I A
INTRODUCTION: To compare the postpartum (PP) readmission rates of patients with hypertensive disorders of pregnancy (HDP) before and after implementation of an early discharge policy due to the COVID-19 pandemic.
METHOD(S): This is a retrospective, quality improvement study of patients with HDP who delivered and were readmitted at NYU Langone Health from March 1, 2019, to February 29, 2020 (Pre) and from April 1, 2020, to March 31, 2021 (Post). The primary outcome was comparison of PP readmission rates between groups. Data were analyzed using ANOVA, Fisher's exact test, and chi-square test, with significance defined as P<.05.
RESULT(S): There was no statistical difference in readmission rates for PP HDP after implementation of an early discharge policy (19 readmissions of 1,851 deliveries with HDP [1.03%] versus 23 readmissions of 1,847 deliveries with HDP [1.25%]; P=.53). Demographics in each group were similar, as were mean times to readmission (6.4+/-5.4 days versus 6.4+/-5.4 days; P=.88) and mean readmission lengths of stay (3.0+/-1.04 days versus 3.3+/-1.2 days; P=.45). There was one ICU readmission in the post-group versus none in the pregroup (P=.45). There were no maternal deaths.
CONCLUSION(S): These findings support reducing postpartum lengths of stay without increasing readmissions for HDP. The reduction of postpartum length of stay was accompanied by the continuation of a home blood pressure monitoring and introduction of outpatient HDP management guidelines. Further investigation into best practices to support early discharge is warranted
EMBASE:638410924
ISSN: 1873-233x
CID: 5291312

SMFM Special Statement: Updated checklists for management of monochorionic twin pregnancy

Hoskins, Iffath Abbasi; Combs, C Andrew
PMID: 32861686
ISSN: 1097-6868
CID: 4582682