dc.contributor.author | Nemetchek, Brooklyn R | |
dc.contributor.author | Liang, Li(Danny) | |
dc.contributor.author | Kissoon, Niranjan | |
dc.contributor.author | Ansermino, J Mark | |
dc.contributor.author | Kabakyenga, Jerome | |
dc.contributor.author | Lavoie, Pascal M | |
dc.contributor.author | Kerry, Susan Fowler | |
dc.contributor.author | Wiens, Matthew O | |
dc.date.accessioned | 2022-05-25T07:35:21Z | |
dc.date.available | 2022-05-25T07:35:21Z | |
dc.date.issued | 2018 | |
dc.identifier.citation | Nemetchek, B. R., Liang, L. D., Kissoon, N., Ansermino, M. J., Kabakyenga, J., Lavoie, P. M., ... & Wiens, M. O. (2018). Predictor variables for post-discharge mortality modelling in infants: a protocol development project. African Health Sciences, 18(4), 1214-1225. | en_US |
dc.identifier.uri | http://ir.must.ac.ug/xmlui/handle/123456789/2036 | |
dc.description.abstract | Background: Over two-thirds of the five million annual deaths in children under five occur in infants, mostly in developing countries and many after hospital discharge. However, there is a lack of understanding of which children are at higher risk based on early clinical predictors. Early identification of vulnerable infants at high-risk for death post-discharge is important in order to craft interventional programs.
Objectives: To determine potential predictor variables for post-discharge mortality in infants less than one year of age who are likely to die after discharge from health facilities in the developing world.
Methods: A two-round modified Delphi process was conducted, wherein a panel of experts evaluated variables selected from a systematic literature review. Variables were evaluated based on (1) predictive value, (2) measurement reliability, (3) availability, and (4) applicability in low-resource settings.
Results: In the first round, 18 experts evaluated 37 candidate variables and suggested 26 additional variables. Twenty-seven variables derived from those suggested in the first round were evaluated by 17 experts during the second round. A final total of 55 candidate variables was retained.
Conclusion: A systematic approach yielded 55 candidate predictor variables to use in devising predictive models for post-discharge mortality in infants in a low-resource setting. | en_US |
dc.description.sponsorship | Thrasher Research Fund | en_US |
dc.language.iso | en_US | en_US |
dc.publisher | African Health Sciences | en_US |
dc.subject | Candidate predictor variables | en_US |
dc.subject | Pediatrics | en_US |
dc.subject | Neonatal | en_US |
dc.subject | Infants | en_US |
dc.subject | Prediction | en_US |
dc.subject | Post-discharge mortality | en_US |
dc.subject | Sepsis | en_US |
dc.title | Predictor variables for post-discharge mortality modelling in infants: a protocol development project | en_US |
dc.type | Article | en_US |