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Postdischarge mortality in children with acute infectious diseases: derivation of postdischarge mortality prediction models.

Wiens MO, Kumbakumba E, Larson CP, Ansermino JM, Singer J, Kissoon N, Wong H, Ndamira A, Kabakyenga J, Kiwanuka J, Zhou G - BMJ Open (2015)

Bottom Line: None.Identification of at-risk children is critical in developing postdischarge interventions.Improved discharge planning and care could be provided for high-risk children.

View Article: PubMed Central - PubMed

Affiliation: School of Population and Public Health, University of British Columbia, Vancouver, British Columbia, Canada.

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Related in: MedlinePlus

Performance of the primary prediction model derived with data from admission (AUC, area under the curve; NPV, negative predictive value; PPV, positive predictive value; ROC, receiver operating characteristic; Sens, sensitivity; Spec, specificity).
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BMJOPEN2015009449F2: Performance of the primary prediction model derived with data from admission (AUC, area under the curve; NPV, negative predictive value; PPV, positive predictive value; ROC, receiver operating characteristic; Sens, sensitivity; Spec, specificity).

Mentions: One primary model and three alternate models of equal sensitivity were developed for the prediction of 6-month postdischarge mortality (table 3). Two alternate models were developed while systematically excluding oxygen saturation, and HIV status, respectively, since these may not be routinely available in all clinical settings. A fourth model was developed excluding variables most likely to change over the course of admission (ie, clinical variables), giving the model utility for variables collected at any time throughout the hospital stay. The primary model included MUAC in mm, oxygen saturation (SpO2) at admission (per cent), time since previous hospitalisation, the presence of abnormal BCS score at admission, and HIV status. The area under the ROC curve was 0.82 (95% CI 0.76 to 0.87) (figure 2). The model, at a cut-off of >80% sensitivity, had a final sensitivity of 82% (95% CI 0.75% to 0.87%) and a specificity of 66% (95% CI 64% to 69%). In a population similar to this model derivation cohort, we would expect the positive predictive value to be 11.1% and the negative predictive value to be 98.6% (table 4). The final model equation for the primary model was: logit(p)=7.71 + (−0.041; MUAC) + (−0.041; SpO2) + (−0.28; time period since last hospitalisation) + (1.09; HIV positive) + (0.87; BCS<5).


Postdischarge mortality in children with acute infectious diseases: derivation of postdischarge mortality prediction models.

Wiens MO, Kumbakumba E, Larson CP, Ansermino JM, Singer J, Kissoon N, Wong H, Ndamira A, Kabakyenga J, Kiwanuka J, Zhou G - BMJ Open (2015)

Performance of the primary prediction model derived with data from admission (AUC, area under the curve; NPV, negative predictive value; PPV, positive predictive value; ROC, receiver operating characteristic; Sens, sensitivity; Spec, specificity).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4663423&req=5

BMJOPEN2015009449F2: Performance of the primary prediction model derived with data from admission (AUC, area under the curve; NPV, negative predictive value; PPV, positive predictive value; ROC, receiver operating characteristic; Sens, sensitivity; Spec, specificity).
Mentions: One primary model and three alternate models of equal sensitivity were developed for the prediction of 6-month postdischarge mortality (table 3). Two alternate models were developed while systematically excluding oxygen saturation, and HIV status, respectively, since these may not be routinely available in all clinical settings. A fourth model was developed excluding variables most likely to change over the course of admission (ie, clinical variables), giving the model utility for variables collected at any time throughout the hospital stay. The primary model included MUAC in mm, oxygen saturation (SpO2) at admission (per cent), time since previous hospitalisation, the presence of abnormal BCS score at admission, and HIV status. The area under the ROC curve was 0.82 (95% CI 0.76 to 0.87) (figure 2). The model, at a cut-off of >80% sensitivity, had a final sensitivity of 82% (95% CI 0.75% to 0.87%) and a specificity of 66% (95% CI 64% to 69%). In a population similar to this model derivation cohort, we would expect the positive predictive value to be 11.1% and the negative predictive value to be 98.6% (table 4). The final model equation for the primary model was: logit(p)=7.71 + (−0.041; MUAC) + (−0.041; SpO2) + (−0.28; time period since last hospitalisation) + (1.09; HIV positive) + (0.87; BCS<5).

Bottom Line: None.Identification of at-risk children is critical in developing postdischarge interventions.Improved discharge planning and care could be provided for high-risk children.

View Article: PubMed Central - PubMed

Affiliation: School of Population and Public Health, University of British Columbia, Vancouver, British Columbia, Canada.

Show MeSH
Related in: MedlinePlus