Refining clinical algorithms for a neonatal digital platform for low-income countries: a modified Delphi technique

Mari Evans, Mark H Corden, Caroline Crehan, Felicity Fitzgerald, Michelle Heys, Mari Evans, Mark H Corden, Caroline Crehan, Felicity Fitzgerald, Michelle Heys

Abstract

Objectives: To determine whether a panel of neonatal experts could address evidence gaps in local and international neonatal guidelines by reaching a consensus on four clinical decision algorithms for a neonatal digital platform (NeoTree).

Design: Two-round, modified Delphi technique.

Setting and participants: Participants were neonatal experts from high-income and low-income countries (LICs).

Methods: This was a consensus-generating study. In round 1, experts rated items for four clinical algorithms (neonatal sepsis, hypoxic ischaemic encephalopathy, respiratory distress of the newborn, hypothermia) and justified their responses. Items meeting consensus for inclusion (≥80% agreement) were incorporated into the algorithms. Items not meeting consensus were either excluded, included following revisions or included if they contained core elements of evidence-based guidelines. In round 2, experts rated items from round 1 that did not reach consensus.

Results: Fourteen experts participated in round 1, 10 in round 2. Nine were from high-income countries, five from LICs. Experts included physicians and nurse practitioners with an average neonatal experience of 20 years, 12 in LICs. After two rounds, a consensus was reached on 43 of 84 items (52%). Per experts' recommendations, items in line with local and WHO guidelines yet not meeting consensus were still included to encourage consistency for front-line healthcare workers. As a result, the final algorithms included 53 items (62%).

Conclusion: Four algorithms in a neonatal digital platform were reviewed and refined by consensus expert opinion. Revisions to NeoTree will be made in response to these findings. Next steps include clinical validation of the algorithms.

Keywords: information technology; neonatal intensive & critical care; neonatology; public health.

Conflict of interest statement

Competing interests: CC and MH are cofounders of the NeoTree platform and continue to conduct research related to its development. FF and MH are trustees of the NeoTree Foundation. The NeoTree platform is a not-for-profit product; none of the coauthors benefit financially.

© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY. Published by BMJ.

Figures

Figure 1
Figure 1
Outcome of algorithm items after round 1 and round 2 of the Delphi technique. COIN, Care of the Infant and Newborn; MCTW, minor changes to wording.
Figure 2
Figure 2
Modification of the algorithms as a result of the Delphi technique. AB, antibiotics; BA, birth asphyxia; CPR, cardiopulmonary resuscitation; HAI, hospital-acquired infection; HIE, hypoxic ischaemic encephalopathy; LICs, low-income countries; MAS, meconium aspiration; RDN, respiratory distress of newborn; RDS, respiratory distress syndrome; RR, respiratory rate; TTN, transient tachypnoea of newborn.

References

    1. WHO . Newborns: reducing mortality, 2019. Available: [Accessed May 2020].
    1. Lawn JE, Blencowe H, Oza S. Every newborn: progress, priorities, and potential beyond survival. Lancet 2014;384:189–205.
    1. WHO . Levels & trends in child mortality: report 2019. Estimates developed by the UN Inter-agency Group for Child Mortality Estimation, 2019. Available: [Accessed May 2020].
    1. WHO . WHO guidelines Approved by the guidelines review committee, 2015. Available: [Accessed Jun 2019].
    1. WHO . WHO recommendations on newborn health: guidelines approved by the WHO guidelines review Committee, 2017. Available: [Accessed Feb 2021].
    1. Becker JU, Theodosis C, Jacob ST. Surviving sepsis in low-income and middle-income countries: new directions for care and research. Lancet Infect Dis 2009;9:577–82.
    1. TDV Global . Evaluation of the impact of WHO publications, 2016. Available: [Accessed Feb 2021].
    1. Hall CS, Fottrell E, Wilkinson S, et al. . Assessing the impact of mHealth interventions in low- and middle-income countries–what has been shown to work? Glob Health Action 2014;7:25606. 10.3402/gha.v7.25606
    1. Crehan C, Kesler E, Nambiar B. The NeoTree application: developing an integrated mHealth solution to improve quality of newborn care and survival in a district hospital in Malawi. BMJ Glob Health 2019;4:e000860.
    1. WHO . Pocket book of hospital care for children: guidelines for the management of common childhood illnesses. 2nd edn, 2013.
    1. COIN Course . Care of the infant and newborn in Malawi. The coin course participants manual, 2017. Available: [Accessed Jun 2018].
    1. Sinha IP, Smyth RL, Williamson PR. Using the Delphi technique to determine which outcomes to measure in clinical trials: recommendations for the future based on a systematic review of existing studies. PLoS Med 2011;8:e1000393.
    1. Hsu CC, Sandford BA. The Delphi technique: making sense of consensus. Prac Assess Res Evaluat 2007;12.
    1. Akins RB, Tolson H, Cole BR. Stability of response characteristics of a Delphi panel: application of bootstrap data expansion. BMC Med Res Methodol 2005;5:37. 10.1186/1471-2288-5-37
    1. WHO . Recommendations for management of common childhood conditions: evidence for technical update of pocket book recommendations: newborn conditions, dysentery, pneumonia, oxygen use and delivery, common causes of fever, severe acute malnutrition and supportive care, 2012. Available: [Accessed Jun 2019].
    1. European Medicines Agency . Report on the expert meeting on neonatal and paediatric sepsis, 2010. Available: [Accessed Jul 2019].
    1. Obiero CW, Seale AC, Berkley JA. Empiric treatment of neonatal sepsis in developing countries. Pediatr Infect Dis J 2015;34:659–61. 10.1097/INF.0000000000000692
    1. Babakus E, Mangold WG. Adapting the SERVQUAL scale to hospital services: an empirical investigation. Health Serv Res 1992;26:767–86.
    1. Thompson CM, Puterman AS, Linley LL, et al. . The value of a scoring system for hypoxic ischaemic encephalopathy in predicting neurodevelopmental outcome. Acta Paediatr 1997;86:757–61. 10.1111/j.1651-2227.1997.tb08581.x
    1. Ballard JL, Khoury JC, Wedig K. New Ballard score, expanded to include extremely premature infants. J Pediatr 1991;119:417–23.
    1. Sarnat HB, Sarnat MS. Neonatal encephalopathy following fetal distress: a clinical and electroencephalographic study. Arch Neurol 1976;33:696–705.
    1. Ungerer RLS, Lincetto O, McGuire W, et al. . Prophylactic versus selective antibiotics for term newborn infants of mothers with risk factors for neonatal infection. Cochrane Database Syst Rev 2004;4:CD003957. 10.1002/14651858.CD003957.pub2
    1. Bhagwani DK, Sharma M, Dolker S, et al. . To study the correlation of thompson scoring in predicting early neonatal outcome in post asphyxiated term neonates. J Clin Diagn Res 2016;10:SC16–19. 10.7860/JCDR/2016/22896.8882
    1. Costa S, Rocha G, Leitão A, et al. . Transient tachypnea of the newborn and congenital pneumonia: a comparative study. J Matern Fetal Neonatal Med 2012;25:992–4. 10.3109/14767058.2011.604366
    1. Tikmani SS, Muhammad AA, Shafiq Y. Ambulatory treatment of fast breathing in young infants aged. Clin Infect Dis 2017;64:184–9.
    1. van Haasteren A, Gille F, Fadda M. Development of the mHealth APP Trustworthiness checklist. Digit Health 2019;5:205520761988646. 10.1177/2055207619886463
    1. Kamath-Rayne BD, Thukral A, Visick MK, et al. . Helping babies breathe, second edition: a model for strengthening educational programs to increase global newborn survival. Glob Health Sci Pract 2018;6:538–51. 10.9745/GHSP-D-18-00147
    1. Balikuddembe MS, Tumwesigye NM, Wakholi PK, et al. . Expert perspectives on essential parameters to monitor during childbirth in low resource settings: a Delphi study in sub-Saharan Africa. Reprod Health 2019;16:119. 10.1186/s12978-019-0786-6
    1. Housseine N, Punt MC, Browne JL, et al. . Delphi consensus statement on intrapartum fetal monitoring in low-resource settings. Int J Gynaecol Obstet 2019;146:8–16. 10.1002/ijgo.12724
    1. Bakibinga P, Kamande E, Omuya M, et al. . The role of a decision-support smartphone application in enhancing community health volunteers' effectiveness to improve maternal and newborn outcomes in Nairobi, Kenya: quasi-experimental research protocol. BMJ Open 2017;7:e014896. 10.1136/bmjopen-2016-014896
    1. Fung JST, Akech S, Kissoon N, et al. . Determining predictors of sepsis at triage among children under 5 years of age in resource-limited settings: a modified Delphi process. PLoS One 2019;14:e0211274. 10.1371/journal.pone.0211274
    1. Pellé KG, Rambaud-Althaus C, D’Acremont V, et al. . Electronic clinical decision support algorithms incorporating point-of-care diagnostic tests in low-resource settings: a target product profile. BMJ Glob Health 2020;5:e002067. 10.1136/bmjgh-2019-002067

Source: PubMed

Sponsors en medewerkers

Medische omstandigheden

Geneesmiddelinterventies

3
Abonneren