Derivation and internal validation of a data-driven prediction model to guide frontline health workers in triaging children under-five in Nairobi, Kenya

Wellcome Open Research

article

Alishah Mawji¹  Samuel Akech³  Paul Mwaniki³  Dustin Dunsmuir⁴  Jeffrey Bone⁵  Matthew O. Wiens²  Matthias Görges¹  David Kimutai⁶  Niranjan Kissoon²  Mike English³  Mark J. Ansermino¹ 

[1] Department of Anesthesiology, Pharmacology & Therapeutics, University of British Columbia;Centre for International Child Health, BC Children’s Hospital Research Institute;Kenya Medical Research Institute/Wellcome Trust Research Programme;Digital Health Innovation Lab, BC Children’s Hospital Research Institute;Department of Obstetrics and Gynaecology, University of British Columbia;Mbagathi County Referral Hospital;Department of Pediatrics, University of British Columbia;Centre for Tropical Medicine and Global Health, Nuffield Department of Clinical Medicine, University of Oxford

关键词: sepsis;    prediction;    risk;    model;    triage;    children;    developing countries;   

DOI  :  10.12688/wellcomeopenres.15387.3

学科分类：内科医学

来源: Wellcome

【 摘 要 】

Background: Many hospitalized children in developing countries die from infectious diseases. Early recognition of those who are critically ill coupled with timely treatment can prevent many deaths. A data-driven, electronic triage system to assist frontline health workers in categorizing illness severity is lacking. This study aimed to develop a data-driven parsimonious triage algorithm for children under five years of age.Methods: This was a prospective observational study of children under-five years of age presenting to the outpatient department of Mbagathi Hospital in Nairobi, Kenya between January and June 2018. A study nurse examined participants and recorded history and clinical signs and symptoms using a mobile device with an attached low-cost pulse oximeter sensor. The need for hospital admission was determined independently by the facility clinician and used as the primary outcome in a logistic predictive model. We focused on the selection of variables that could be quickly and easily assessed by low skilled health workers.Results: The admission rate (for more than 24 hours) was 12% (N=138/1,132). We identified an eight-predictor logistic regression model including continuous variables of weight, mid-upper arm circumference, temperature, pulse rate, and transformed oxygen saturation, combined with dichotomous signs of difficulty breathing, lethargy, and inability to drink or breastfeed. This model predicts overnight hospital admission with an area under the receiver operating characteristic curve of 0.88 (95% CI 0.82 to 0.94). Low- and high-risk thresholds of 5% and 25%, respectively were selected to categorize participants into three triage groups for implementation. Conclusion: A logistic regression model comprised of eight easily understood variables may be useful for triage of children under the age of five based on the probability of need for admission. This model could be used by frontline workers with limited skills in assessing children. External validation is needed before adoption in clinical practice.

【 授权许可】

CC BY