Predictor variables for post-discharge mortality modelling in infants: a protocol development project

Abstract

BackgroundOver 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.ObjectivesTo 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.MethodsA 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.ResultsIn 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 were retained.ConclusionA systematic approach yielded 55 candidate predictor variables to use in devising predictive models for post-discharge mortality in infants in a low-resource setting.

Exploring factors influencing patient mortality and loss to follow-up in two paediatric hospital wards in Zamfara, North-West Nigeria, 2016–2018

“…We are aware that mortality after discharge is a huge problem in developing countries. However, this was beyond the scope of our study on inpatient mortality and has been described elsewhere [37,38]. We considered LTFU when there was no formal discharge, referral or death recorded, including patients with missing information on the outcome of their admission, hence, we may have overestimated LTFU in our analysis.…”

Whole blood genome-wide transcriptome profiling and metagenomics next-generation sequencing in young infants with suspected sepsis in a low-and middle-income country: A study protocol

“…The prospective nature of this study is a major strength. The data collection is informed by rigorous literature reviews 44 , 45 . Studies of neonatal sepsis in LMIC have been mostly retrospective, often starting with case selection by a positive blood culture.…”

Characterising paediatric mortality during and after acute illness in Sub-Saharan Africa and South Asia: a secondary analysis of the CHAIN cohort using a machine learning approach

“…1 Children in these settings have a high risk of death during inpatient admission and for six to twelve months following discharge. 2 , 3 The Childhood Acute Illness and Nutrition (CHAIN) Cohort is a study of children admitted to urban and rural hospitals in sub-Saharan Africa and south Asia. 4 , 5 The cohort's primary analysis used survival analysis and structural equation modelling to estimate causal pathways to death based on the UNICEF framework for child mortality.…”