Pediatric in-Hospital Death from Infectious Disease in Uganda: Derivation of Clinical Prediction Models

Abstract

BackgroundPediatric hospital mortality from infectious diseases in resource constrained countries remains unacceptably high. Improved methods of risk-stratification can assist in referral decision making and resource allocation. The purpose of this study was to create prediction models for in-hospital mortality among children admitted with suspected infectious diseases.MethodsThis two-site prospective observational study enrolled children between 6 months and 5 years admitted with a proven or suspected infection. Baseline clinical and laboratory variables were collected on enrolled children. The primary outcome was death during admission. Stepwise logistic regression minimizing Akaike’s information criterion was used to identify the most promising multivariate models. The final model was chosen based on parsimony.Results1307 children were enrolled consecutively, and 65 (5%) of whom died during their admission. Malaria, pneumonia and gastroenteritis were diagnosed in 50%, 31% and 8% of children, respectively. The primary model included an abnormal Blantyre coma scale, HIV and weight-for-age z-score. This model had an area under the curve (AUC) of 0.85 (95% CI, 0.80–0.89) with a sensitivity and specificity of 83% and 76%, respectively. The positive and negative predictive values were 15% and 99%, respectively. Two alternate models with similar performance characteristics were developed withholding HIV and weight-for-age z-score, for use when these variables are not available.ConclusionsRisk stratification of children admitted with infectious diseases can be calculated based on several easily measured variables. Risk stratification at admission can be used for allocation of scarce human and physical resources and to guide referral among children admitted to lower level health facilities.

Severely malnourished children with a low weight-for-height have similar mortality to those with a low mid-upper-arm-circumference: II. Systematic literature review and meta-analysis

“…In two of the papers the All-whz/muac gave a larger difference in CFR than the S-whz/muac comparison; in 6 of the papers S-whz/muac CFR comparison was larger than the All-whz/muac comparison. In the other 2 papers by Lowlaavar et al [ 47 ] and Berkley et al [ 49 ] the direction of the difference was actually reversed (in opposite directions), so by inclusion of children with both deficits into each of the single groups contrary conclusions would have been reached. These latter studies are examples of Simpson’s paradox, an extreme form of confounding [ 33 , 66 ], but inclusion of S-both into the whz v muac comparison led to erroneous results in every study.…”

“…Two of the studies, Lowlaavar [ 47 ] and Sachdeva [ 51 ], had extremely short observation periods for children in hospital before they died or were discharged. They also have a very high mortality which calls into question the severity of SAM, the admission criteria and the quality of treatment given.…”

“…The report by Lowlaavar [ 47 ] is particularly illustrative of the errors that can occur. There were no deaths at all in the children with S-muac and a CFR in the S-whz children of over 6%.…”

Contribution of Malaria to Inhospital Mortality in Papua New Guinean Children from a Malaria-Endemic Area: A Prospective Observational Study

“…In a comparable study in hospitalized Kenyan children, impaired consciousness as defined by inability to localize pain was also the most important risk factor for mortality and, consistent with this, a major variable in a multivariable prediction score developed by the investigators. 7 The degree of coma is an adverse prognostic indicator in severe pediatric infections in general, [35][36][37] as well as in severe malaria. 38 We have found previously that PNG children with severe malaria had lower proportions of cerebral malaria and hypoglycemia than African children, 39 but that severe malaria due to mixed P. falciparum/ P. vivax infections appeared to have a poorer prognosis.…”

Smart triage: triage and management of sepsis in children using the point-of-care Pediatric Rapid Sepsis Trigger (PRST) tool

“…Over the past 10 years, we have developed, implemented, and evaluated the core technology of the Digital Triaging Platform including vital sign measurement devices (PhoneOx [10] and RRate [11]) and the mobile application and dashboard [12][13][14]. We have already identified candidate predictor variables using a modified Delphi process [15], and developed a risk prediction model based on the need for admission using predictors collected in over 1000 children at a Kenyan hospital [16].…”