Wireless versus routine physiologic monitoring after cesarean delivery to reduce maternal morbidity and mortality in a resource-limited setting: protocol of type 2 hybrid effectiveness-implementation study

Adeline A Boatin, Joseph Ngonzi, Blair J Wylie, Henry M Lugobe, Lisa M Bebell, Godfrey Mugyenyi, Sudi Mohamed, Kenia Martinez, Nicholas Musinguzi, Christina Psaros, Joshua P Metlay, Jessica E Haberer, Adeline A Boatin, Joseph Ngonzi, Blair J Wylie, Henry M Lugobe, Lisa M Bebell, Godfrey Mugyenyi, Sudi Mohamed, Kenia Martinez, Nicholas Musinguzi, Christina Psaros, Joshua P Metlay, Jessica E Haberer

Abstract

Background: Women in sub-Saharan Africa have the highest rates of morbidity and mortality during childbirth globally. Despite increases in facility-based childbirth, gaps in quality of care at facilities have limited reductions in maternal deaths. Infrequent physiologic monitoring of women around childbirth is a major gap in care that leads to delays in life-saving interventions for women experiencing complications.

Methods: We will conduct a type-2 hybrid effectiveness-implementation study over 12 months to evaluate using a wireless physiologic monitoring system to detect and alert clinicians of abnormal vital signs in women for 24 h after undergoing emergency cesarean delivery at a tertiary care facility in Uganda. We will provide physiologic data (heart rate, respiratory rate, temperature and blood pressure) to clinicians via a smartphone-based application with alert notifications if monitored women develop predefined abnormalities in monitored physiologic signs. We will alternate two-week intervention and control time periods where women and clinicians use the wireless monitoring system during intervention periods and current standard of care (i.e., manual vital sign measurement when clinically indicated) during control periods. Our primary outcome for effectiveness is a composite of severe maternal outcomes per World Health Organization criteria (e.g. death, cardiac arrest, jaundice, shock, prolonged unconsciousness, paralysis, hysterectomy). Secondary outcomes include maternal mortality rate, and case fatality rates for postpartum hemorrhage, hypertensive disorders, and sepsis. We will use the RE-AIM implementation framework to measure implementation metrics of the wireless physiologic system including Reach (proportion of eligible women monitored, length of time women monitored), Efficacy (proportion of women with monitoring according to Uganda Ministry of Health guidelines, number of appropriate alerts sent), Adoption (proportion of clinicians utilizing physiologic data per shift, clinical actions in response to alerts), Implementation (fidelity to monitoring protocol), Maintenance (sustainability of implementation over time). We will also perform in-depth qualitative interviews with up to 30 women and 30 clinicians participating in the study.

Discussion: This is the first hybrid-effectiveness study of wireless physiologic monitoring in an obstetric population. This study offers insights into use of wireless monitoring systems in low resource-settings, as well as normal and abnormal physiologic parameters among women delivering by cesarean.

Trial registration: ClinicalTrials.gov , NCT04060667 . Registered on 08/01/2019.

Keywords: Cesarean delivery; Hybrid effectiveness-implementation trial; Maternal mortality; Post-operative monitoring; Wireless physiologic monitoring.

Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
a: Biosensor as worn on left arm of postpartum woman. b: Close up of biosensor and strap
Fig. 2
Fig. 2
Panel a: Smart phone log-in interface, Panel b: Patient current vital signs on smart phone app; Panel c: Historic vital sign and timeline of previous alerts
Fig. 3
Fig. 3
Spirit Flow Diagram

References

    1. World Health Organization . Trends in maternal mortality: 1990 to 2015: estimates by WHO, UNICEF, UNFPA, World Bank Group and the United Nations population division. Geneva: World Health ORganization; 2015.
    1. Alkema L, Chou D, Hogan D, Zhang S, Moller A-B, Gemmill A, et al. Global, regional, and national levels and trends in maternal mortality between 1990 and 2015, with scenario-based projections to 2030: a systematic analysis by the UN maternal mortality estimation inter-agency group. Lancet. 2016;387(10017):462–474. doi: 10.1016/S0140-6736(15)00838-7.
    1. Organization WH. Neonatal and perinatal mortality: country, regional and global estimates. 2006.
    1. World Health Organization. Trends in maternal mortality 2000 to 2017: estimates by WHO, UNICEF, UNFPA, World Bank Group and the United Nations Population Division: executive summary. World Health Organization; 2019.
    1. Ronsmans C, Graham WJ. Lancet maternal survival series steering g. maternal mortality: who, when, where, and why. Lancet. 2006;368(9542):1189–1200. doi: 10.1016/S0140-6736(06)69380-X.
    1. Li X, Fortney J, Kotelchuck M, Glover L. The postpartum period: the key to maternal mortality. Int J Gynecol Obstet. 1996;54(1):1–10. doi: 10.1016/0020-7292(96)02667-7.
    1. Miller S, Abalos E, Chamillard M, Ciapponi A, Colaci D, Comandé D, et al. Beyond too little, too late and too much, too soon: a pathway towards evidence-based, respectful maternity care worldwide. Lancet. 2016;388(10056):2176–2192. doi: 10.1016/S0140-6736(16)31472-6.
    1. Campbell OMR, Graham WJ. Strategies for reducing maternal mortality: getting on with what works. Lancet. 2006;368(9543):1284–1299. doi: 10.1016/S0140-6736(06)69381-1.
    1. Kruk ME, Gage AD, Arsenault C, Jordan K, Leslie HH, Roder-DeWan S, et al. High-quality health systems in the sustainable development goals era: time for a revolution. Lancet Glob Health. 2018;6(11):e1196–ee252. doi: 10.1016/S2214-109X(18)30386-3.
    1. Association of Women’s Health Obstetric and Neonatal Nurses. Guidelines for Professional Registered Nurse Staffing for Perinatal and Neonatal Nurses. Washington D.C: Association of Women's Health, Obstetric and Neonatal Nurses; 2010.
    1. Say L, Chou D, Gemmill A, Tuncalp O, Moller AB, Daniels J, et al. Global causes of maternal death: a WHO systematic analysis. Lancet Glob Health. 2014;2(6):e323–e333. doi: 10.1016/S2214-109X(14)70227-X.
    1. 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. European Journal of Obstetrics & Gynecology and Reproductive Biology. 2016;207:11–17. doi: 10.1016/j.ejogrb.2016.09.014.
    1. Friedman AM. Maternal early warning systems. Obstet Gynecol Clin N Am. 2015;42(2):289–298. doi: 10.1016/j.ogc.2015.01.006.
    1. Kause J, Smith G, Prytherch D, Parr M, Flabouris A, Hillman K. A comparison of antecedents to cardiac arrests, deaths and emergency intensive care admissions in Australia and New Zealand, and the United Kingdom—the ACADEMIA study. Resuscitation. 2004;62(3):275–282. doi: 10.1016/j.resuscitation.2004.05.016.
    1. Burch V, Tarr G, Morroni C. Modified early warning score predicts the need for hospital admission and inhospital mortality. Emerg Med J. 2008;25(10):674–678. doi: 10.1136/emj.2007.057661.
    1. Buist M, Bernard S, Nguyen TV, Moore G, Anderson J. Association between clinically abnormal observations and subsequent in-hospital mortality: a prospective study. Resuscitation. 2004;62(2):137–141. doi: 10.1016/j.resuscitation.2004.03.005.
    1. Goldhill DR, Worthington L, Mulcahy A, Tarling M, Sumner A. The patient-at-risk team: identifying and managing seriously ill ward patients. Anaesthesia. 1999;54(9):853–860. doi: 10.1046/j.1365-2044.1999.00996.x.
    1. Goldhill D, McNarry A. Physiological abnormalities in early warning scores are related to mortality in adult inpatients. Br J Anaesth. 2004;92(6):882–884. doi: 10.1093/bja/aeh113.
    1. Smith GB. In-hospital cardiac arrest: is it time for an in-hospital ‘chain of prevention’? Resuscitation. 2010;81(9):1209–1211. doi: 10.1016/j.resuscitation.2010.04.017.
    1. Ball C, Kirkby M, Williams S. Effect of the critical care outreach team on patient survival to discharge from hospital and readmission to critical care: non-randomised population based study. Bmj. 2003;327(7422):1014. doi: 10.1136/bmj.327.7422.1014.
    1. Bristow PJ, Hillman KM, Chey T, Daffurn K, Jacques TC, Norman SL, et al. Rates of in-hospital arrests, deaths and intensive care admissions: the effect of a medical emergency team. Med J Aust. 2000;173(5):236–240. doi: 10.5694/j.1326-5377.2000.tb125627.x.
    1. Kenward G, Castle N, Hodgetts T, Shaikh L. Evaluation of a medical emergency team one year after implementation. Resuscitation. 2004;61(3):257–263. doi: 10.1016/j.resuscitation.2004.01.021.
    1. Wilkinson H. Saving mothers’ lives. Reviewing maternal deaths to make motherhood safer: 2006–2008. BJOG Int J Obstet Gynaecol. 2011;118(11):1402–1403. doi: 10.1111/j.1471-0528.2011.03097.x.
    1. Mhyre JM, D’oria R, Hameed AB, Lappen JR, Holley SL, Hunter SK, et al. The maternal early warning criteria: a proposal from the national partnership for maternal safety. J Obstet Gynecol Neonatal Nurs. 2014;43(6):771–779. doi: 10.1111/1552-6909.12504.
    1. Carle C, Alexander P, Columb M, Johal J. Design and internal validation of an obstetric early warning score: secondary analysis of the intensive care National Audit and research Centre case mix Programme database. Anaesthesia. 2013;68(4):354–367. doi: 10.1111/anae.12180.
    1. World Health Organization . Standards for improving quality of maternal and newborn care in health facilities. Geneva, Switzerland: World Health Organization; 2016.
    1. National Collaborating Centre for Women's and Children's Health. Intrapartm Care: Care of health women and their babies during childbirth. National Institute for Health and Care Excellence; 2014.
    1. Centre for Clinical Practice at NICE. Acutely ill patients in hospital: Recognition of and response to acute illness in adults in hospital. 2007.
    1. Sahandi R, Noroozi S, Roushan G, Heaslip V, Liu Y. Wireless technology in the evolution of patient monitoring on general hospital wards. Journal of medical engineering & technology. 2010;34(1):51–63. doi: 10.3109/03091900903336902.
    1. Ngonzi J. A functionality and acceptability study of wireless maternal vital sign monitor in a Tertiary University teaching hospital in rural Uganda. J Womens Health Gyn. 2017;1:1–8.
    1. Mugyenyi GR, Ngonzi J, Wylie B, Haberer J, Boatin A. Quality of vital sign monitoring during facility-based childbirth in Uganda: an opportunity for improvement. Pan Afr Med J. 2020;ACCEPTED.
    1. Semrau KE, Hirschhorn LR, Marx Delaney M, Singh VP, Saurastri R, Sharma N, et al. Outcomes of a coaching-based WHO safe childbirth checklist program in India. N Engl J Med. 2017;377(24):2313–24. doi: 10.1056/NEJMoa1701075.
    1. Vousden N, Lawley E, Nathan HL, Seed PT, Gidiri MF, Goudar S, et al. Effect of a novel vital sign device on maternal mortality and morbidity in low-resource settings: a pragmatic, stepped-wedge, cluster-randomised controlled trial. Lancet Glob Health. 2019;7(3):e347–ee56. doi: 10.1016/S2214-109X(18)30526-6.
    1. Boatin AA, Wylie B, Goldfarb I, Azevedo R, Pittel E, Ng C, et al. Wireless Vital Sign Monitoring in Pregnant Women: A Functionality and Acceptability Study. Telemedicine and e-Health. 2016;22(7).
    1. Ngonzi J, Boatin AA, Mugyenyi G, Wylie B, Haberer J. A functionality and acceptability study of wireless maternal vital sign monitor in a Tertiary University teaching hospital in rural Uganda. J Womens Health Gyn. 2017;1:1–8.
    1. Reed MJ, McGrath M, Black PL, Lewis S, McCann C, Whiting S, et al. Detection of physiological deterioration by the SNAP40 wearable device compared to standard monitoring devices in the emergency department: the SNAP40-ED study. Diagnostic and prognostic research. 2018;2(1):1–9. doi: 10.1186/s41512-018-0040-7.
    1. Seymour CW, Liu VX, Iwashyna TJ, Brunkhorst FM, Rea TD, Scherag A, et al. Assessment of clinical criteria for sepsis: for the third international consensus definitions for Sepsis and septic shock (Sepsis-3) Jama. 2016;315(8):762–774. doi: 10.1001/jama.2016.0288.
    1. Gutierrez G, Reines H, Wulf-Gutierrez ME. Clinical review: hemorrhagic shock. Crit Care. 2004;8(5):373. doi: 10.1186/cc2851.
    1. Obstetricians ACo, Gynecologists. Hypertension in pregnancy. Report of the American College of Obstetricians and Gynecologists’ task force on hypertension in pregnancy. Obstetrics and gynecology. 2013;122(5):1122.
    1. Vogel JP, Souza JP, Mori R, Morisaki N, Lumbiganon P, Laopaiboon M, et al. Maternal complications and perinatal mortality: findings of the World Health Organization multicountry survey on maternal and newborn health. BJOG: an international journal of obstetrics and gynaecology. 2014;121(Suppl 1):76–88. doi: 10.1111/1471-0528.12633.
    1. Souza JP, Gulmezoglu A, Lumbiganon P, Laopaiboon M, Carroli G, Fawole B, et al. Caesarean section without medical indications is associated with an increased risk of adverse short-term maternal outcomes: the 2004-2008 WHO global survey on maternal and perinatal health. BMC Med. 2010;8:71. doi: 10.1186/1741-7015-8-71.
    1. Lumbiganon P, Laopaiboon M, Gulmezoglu AM, Souza JP, Taneepanichskul S, Pang RY, et al. Method of delivery and pregnancy outcomes in Asia: the WHO global survey on maternal and perinatal health 2007-08. Lancet. 2010;375(9713):490–499. doi: 10.1016/S0140-6736(09)61870-5.
    1. Litorp H, Kidanto HL, Roost M, Abeid M, Nystrom L, Essen B. Maternal near-miss and death and their association with caesarean section complications: a cross-sectional study at a university hospital and a regional hospital in Tanzania. BMC pregnancy and childbirth. 2014;14(244):(23 July 2014).
    1. Tunçalp Ö, Hindin MJ, Adu-Bonsaffoh K, Adanu RM. Assessment of maternal near-miss and quality of care in a hospital-based study in Accra, Ghana. Int J Gynecol Obstet. 2013;123(1):58–63. doi: 10.1016/j.ijgo.2013.06.003.
    1. Say L, Souza JP, Pattinson RC. Maternal near miss – towards a standard tool for monitoring quality of maternal health care. Best Pract Res Clin Obstet Gynecol. 2009;23.
    1. World Health Organization. Evaluating the quality of care for severe pregnancy complications. The WHO near-miss approach for maternal health. Geneva: World Health Organization; 2011.
    1. Nakimuli A, Nakubulwa S, Kakaire O, Osinde MO, Mbalinda SN, Nabirye RC, et al. Maternal near misses from two referral hospitals in Uganda: a prospective cohort study on incidence, determinants and prognostic factors. BMC pregnancy and childbirth. 2016;16(1):24. doi: 10.1186/s12884-016-0811-5.
    1. Nelissen EJT, Mduma E, Ersdal HL, Evjen-Olsen B, Roosmalen JJMV, Stekelenburg J. Maternal near miss and mortality in a rural referral hospital in northern Tanzania: a cross-sectional study. BMC pregnancy and childbirth. 2013;13(141):(4 July 2013).
    1. Jilcott S, Ammerman A, Sommers J, Glasgow RE. Applying the RE-AIM framework to assess the public health impact of policy change. Ann Behav Med. 2007;34(2):105–114. doi: 10.1007/BF02872666.
    1. Kessler RS, Purcell EP, Glasgow RE, Klesges LM, Benkeser RM, Peek C. What does it mean to “employ” the RE-AIM model? Evaluation & the health professions. 2013;36(1):44–66. doi: 10.1177/0163278712446066.
    1. Gaglio B, Shoup JA, Glasgow RE. The RE-AIM framework: a systematic review of use over time. Am J Public Health. 2013;103(6):e38–e46. doi: 10.2105/AJPH.2013.301299.
    1. Venkatesh V, Davis FD. A model of the antecedents of perceived ease of use: development and test. Decis Sci. 1996;27(3):451–481. doi: 10.1111/j.1540-5915.1996.tb01822.x.
    1. Campbell JI, Aturinda I, Mwesigwa E, Burns B, Santorino D, Haberer JE, et al. The technology acceptance model for resource-limited settings (TAM-RLS): a novel framework for Mobile health interventions targeted to low-literacy end-users in resource-limited settings. AIDS Behav. 2017:1–12.
    1. Davis FD. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 1989:319–40.
    1. Creswell JW, Poth CN. Qualitative inquiry and research design: choosing among five approaches: sage publications; 2017.
    1. Glaser B, Strauss A. Discovering grounded theory. Chicago, IL. 1967.
    1. Harris PA, Taylor R, Minor BL, Elliott V, Fernandez M, O'Neal L, et al. The REDCap consortium: Building an international community of software platform partners. Journal of biomedical informatics. 2019;95:103208.
    1. Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research electronic data capture (REDCap)—a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform. 2009;42(2):377–381. doi: 10.1016/j.jbi.2008.08.010.
    1. Strauss A, Corbin JM. Basics of qualitative research: grounded theory procedures and techniques: sage publications, Inc; 1990.
    1. Miles MB, Huberman AM, Saldana J. Qualitative data analysis: sage; 2013.
    1. Hughes NJ, Namagembe I, Nakimuli A, Sekikubo M, Moffett A, Patient CJ, et al. Decision-to-delivery interval of emergency cesarean section in Uganda: a retrospective cohort study. BMC pregnancy and childbirth. 2020;20:1–10. doi: 10.1186/s12884-020-03010-x.
    1. World Health Organization. Framing the health workforce agenda for the Sustainable Development Goals: biennium report 2016-2017 Geneva: World Health Organization; 2017.
    1. Curran GM, Bauer M, Mittman B, Pyne JM, Stetler C. Effectiveness-implementation hybrid designs: combining elements of clinical effectiveness and implementation research to enhance public health impact. Med Care. 2012;50(3):217. doi: 10.1097/MLR.0b013e3182408812.

Source: PubMed

Sponsors and Collaborators

Medical Conditions

Drug Interventions

3
Subscribe