RSS-Feed abonnieren
DOI: 10.1055/s-0041-1739432
The Predictive Value of Vital Signs for Morbidity in Pregnancy: Evaluating and Optimizing Maternal Early Warning Systems
![](https://www.thieme-connect.de/media/ajp/202314/lookinside/thumbnails/10-1055-s-0041-1739432_210014-1.jpg)
Abstract
Objective Vital sign scoring systems that alert providers of clinical deterioration prior to critical illness have been proposed as a means of reducing maternal risk. This study examined the predictive ability of established maternal early warning systems (MEWS)—as well as their component vital sign thresholds—for different types of maternal morbidity, to discern an optimal early warning system.
Study Design This retrospective cohort study analyzed all patients admitted to the obstetric services of a four-hospital urban academic system in 2018. Three sets of published MEWS criteria were evaluated. Maternal morbidity was defined as a composite of hemorrhage, infection, acute cardiac disease, and acute respiratory disease ascertained from the electronic medical record data warehouse and administrative data. The test characteristics of each MEWS, as well as for heart rate, blood pressure, and oxygen saturation were compared.
Results Of 14,597 obstetric admissions, 2,451 patients experienced the composite morbidity outcome (16.8%) including 980 cases of hemorrhage (6.7%), 1,337 of infection (9.2%), 362 of acute cardiac disease (2.5%), and 275 of acute respiratory disease (1.9%) (some patients had multiple types of morbidity). The sensitivities (15.3–64.8%), specificities (56.8–96.1%), and positive predictive values (22.3–44.5%) of the three MEWS criteria ranged widely for overall morbidity, as well as for each morbidity subcategory. Of patients with any morbidity, 28% met criteria for the most liberal vital sign combination, while only 2% met criteria for the most restrictive parameters, compared with 14 and 1% of patients without morbidity, respectively. Sensitivity for all combinations was low (maximum 28.2%), while specificity for all combinations was high, ranging from 86.1 to 99.3%.
Conclusion Though all MEWS criteria demonstrated poor sensitivity for maternal morbidity, permutations of the most abnormal vital signs have high specificity, suggesting that MEWS may be better implemented as a trigger tool for morbidity reduction strategies in the highest risk patients, rather than a general screen.
Key Points
-
MEWS have poor sensitivity for maternal morbidity.
-
MEWS can be optimized for high specificity using modified criteria.
-
MEWS could be better used as a trigger tool.
Presentation
The study was presented at the Society for Maternal-Fetal Medicine 40th Annual Pregnancy Meeting. Grapevine, TX. February 3 to 8, 2020. Abstracts 373, 775, 1058, 1059.
Publikationsverlauf
Eingereicht: 08. Januar 2021
Angenommen: 04. Oktober 2021
Artikel online veröffentlicht:
27. Mai 2022
© 2022. Thieme. All rights reserved.
Thieme Medical Publishers, Inc.
333 Seventh Avenue, 18th Floor, New York, NY 10001, USA
-
References
- 1 Callaghan WM, Creanga AA, Kuklina EV. Severe maternal morbidity among delivery and postpartum hospitalizations in the United States. Obstet Gynecol 2012; 120 (05) 1029-1036
- 2 Joint Commission on Accreditation of Healthcare Organizations, USA. Preventing maternal death. Sentinel Event Alert 2010; (44) 1-4
- 3 Petersen EE, Davis NL, Goodman D. et al. Vital signs: pregnancy-related deaths, United States, 2011-2015, and strategies for prevention, 13 states, 2013-2017. MMWR Morb Mortal Wkly Rep 2019; 68 (18) 423-429
- 4 Clark SL. Strategies for reducing maternal mortality. Semin Perinatol 2012; 36 (01) 42-47
- 5 Ludikhuize J, Smorenburg SM, de Rooij SE, de Jonge E. Identification of deteriorating patients on general wards; measurement of vital parameters and potential effectiveness of the Modified Early Warning Score. J Crit Care 2012; 27 (04) 424.e7-424.e13
- 6 Olsson T, Terent A, Lind L. Rapid Emergency Medicine Score: a new prognostic tool for in-hospital mortality in nonsurgical emergency department patients. J Intern Med 2004; 255 (05) 579-587
- 7 Subbe CP, Kruger M, Rutherford P, Gemmel L. Validation of a modified Early Warning Score in medical admissions. QJM 2001; 94 (10) 521-526
- 8 Ye C, Wang O, Liu M. et al. A real-time early warning system for monitoring inpatient mortality risk: prospective study using electronic medical record data. J Med Internet Res 2019; 21 (07) e13719
- 9 Singh S, McGlennan A, England A, Simons R. A validation study of the CEMACH recommended modified early obstetric warning system (MEOWS). Anaesthesia 2012; 67 (01) 12-18
- 10 Shields LE, Wiesner S, Klein C, Pelletreau B, Hedriana HL. Use of maternal early warning trigger tool reduces maternal morbidity. Am J Obstet Gynecol 2016; 214 (04) 527.e1-527.e6
- 11 Mhyre JM, D'Oria R, Hameed AB. et al. The maternal early warning criteria: a proposal from the national partnership for maternal safety. J Obstet Gynecol Neonatal Nurs 2014; 43 (06) 771-779
- 12 Blumenthal EA, Hooshvar N, McQuade M, McNulty J. A validation study of maternal early warning systems: a retrospective cohort study. Am J Perinatol 2019; 36 (11) 1106-1114
- 13 Friedman AM. Maternal early warning systems. Obstet Gynecol Clin North Am 2015; 42 (02) 289-298
- 14 Zuckerwise LC, Lipkind HS. Maternal early warning systems-towards reducing preventable maternal mortality and severe maternal morbidity through improved clinical surveillance and responsiveness. Semin Perinatol 2017; 41 (03) 161-165
- 15 Green LJ, Mackillop LH, Salvi D. et al. Gestation-specific vital sign reference ranges in pregnancy. Obstet Gynecol 2020; 135 (03) 653-664
- 16 Loerup L, Pullon RM, Birks J. et al. Trends of blood pressure and heart rate in normal pregnancies: a systematic review and meta-analysis. BMC Med 2019; 17 (01) 167
- 17 Kumar F, Kemp J, Edwards C. et al. Pregnancy physiology pattern prediction study (4P study): protocol of an observational cohort study collecting vital sign information to inform the development of an accurate centile-based obstetric early warning score. BMJ Open 2017; 7 (09) e016034
- 18 Lappen JR, Keene M, Lore M, Grobman WA, Gossett DR. Existing models fail to predict sepsis in an obstetric population with intrauterine infection. Am J Obstet Gynecol 2010; 203 (06) 573.e1-573.e5
- 19 Goffman D, Friedman AM, Sheen JJ. et al. A framework for improving characterization of obstetric hemorrhage using informatics data. Obstet Gynecol 2019; 134 (06) 1317-1325
- 20 Baptiste C, D'Alton ME. Applying patient safety to reduce maternal mortality. Obstet Gynecol Clin North Am 2019; 46 (02) 353-365
- 21 Ferreira FL, Bota DP, Bross A, Mélot C, Vincent JL. Serial evaluation of the SOFA score to predict outcome in critically ill patients. JAMA 2001; 286 (14) 1754-1758
- 22 Knaus WA, Draper EA, Wagner DP, Zimmerman JE. APACHE II: a severity of disease classification system. Crit Care Med 1985; 13 (10) 818-829
- 23 Albright CM, Ali TN, Lopes V, Rouse DJ, Anderson BL. The Sepsis in Obstetrics Score: a model to identify risk of morbidity from sepsis in pregnancy. Am J Obstet Gynecol 2014; 211 (01) 39.e1-39.e8
- 24 Bateman BT, Mhyre JM, Hernandez-Diaz S. et al. Development of a comorbidity index for use in obstetric patients. Obstet Gynecol 2013; 122 (05) 957-965
- 25 Alam N, Hobbelink EL, van Tienhoven AJ, van de Ven PM, Jansma EP, Nanayakkara PW. The impact of the use of the Early Warning Score (EWS) on patient outcomes: a systematic review. Resuscitation 2014; 85 (05) 587-594
- 26 Bonafide CP, Lin R, Zander M. et al. Association between exposure to nonactionable physiologic monitor alarms and response time in a children's hospital. J Hosp Med 2015; 10 (06) 345-351
- 27 Bridi AC, Louro TQ, da Silva RC. Clinical alarms in intensive care: implications of alarm fatigue for the safety of patients. Rev Lat Am Enfermagem 2014; 22 (06) 1034-1040
- 28 Friedman AM, Campbell ML, Kline CR, Wiesner S, D'Alton ME, Shields LE. Implementing obstetric early warning systems. AJP Rep 2018; 8 (02) e79-e84
- 29 Singh A, Guleria K, Vaid NB, Jain S. Evaluation of maternal early obstetric warning system (MEOWS chart) as a predictor of obstetric morbidity: a prospective observational study. Eur J Obstet Gynecol Reprod Biol 2016; 207: 11-17
- 30 Arnolds DE, Smith A, Banayan JM, Holt R, Scavone BM. National partnership for maternal safety recommended maternal early warning criteria are associated with maternal morbidity. Anesth Analg 2019; 129 (06) 1621-1626
- 31 Brady PW, Muething S, Kotagal U. et al. Improving situation awareness to reduce unrecognized clinical deterioration and serious safety events. Pediatrics 2013; 131 (01) e298-e308
- 32 Khan A, Coffey M, Litterer KP. et al; the Patient and Family Centered I-PASS Study Group. Families as partners in hospital error and adverse event surveillance. JAMA Pediatr 2017; 171 (04) 372-381
- 33 Gao H, McDonnell A, Harrison DA. et al. Systematic review and evaluation of physiological track and trigger warning systems for identifying at-risk patients on the ward. Intensive Care Med 2007; 33 (04) 667-679
- 34 Chapman SM, Grocott MP, Franck LS. Systematic review of paediatric alert criteria for identifying hospitalised children at risk of critical deterioration. Intensive Care Med 2010; 36 (04) 600-611
- 35 Snowden JM, Cheng YW, Emeis CL, Caughey AB. The impact of hospital obstetric volume on maternal outcomes in term, non-low-birthweight pregnancies. Am J Obstet Gynecol 2015; 212 (03) 380.e1-380.e9
- 36 Zhang J, Meikle S, Trumble A. Severe maternal morbidity associated with hypertensive disorders in pregnancy in the United States. Hypertens Pregnancy 2003; 22 (02) 203-212
- 37 MacDonald EJ, Lepine S, Pledger M, Geller SE, Lawton B, Stone P. Pre-eclampsia causing severe maternal morbidity—a national retrospective review of preventability and opportunities for improved care. Aust N Z J Obstet Gynaecol 2019; 59 (06) 825-830
- 38 Gray KE, Wallace ER, Nelson KR, Reed SD, Schiff MA. Population-based study of risk factors for severe maternal morbidity. Paediatr Perinat Epidemiol 2012; 26 (06) 506-514
- 39 Topiwala R, Patel K, Twigg J, Rhule J, Meisenberg B. Retrospective observational study of the clinical performance characteristics of a machine learning approach to early sepsis identification. Crit Care Explor 2019; 1 (09) e0046
- 40 Linnen DT, Escobar GJ, Hu X, Scruth E, Liu V, Stephens C. Statistical modeling and aggregate-weighted scoring systems in prediction of mortality and ICU transfer: a systematic review. J Hosp Med 2019; 14 (03) 161-169