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Postdischarge Mortality Prediction in Sub-Saharan Africa.

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Title: Postdischarge Mortality Prediction in Sub-Saharan Africa.
Authors: Madrid, Lola, Casellas, Aina, Sacoor, Charfudin, Quintó, Llorenç, Sitoe, Antonio, Varo, Rosauro, Acácio, Sozinho, Nhampossa, Tacilta, Massora, Sergio, Sigaúque, Betuel, Mandomando, Inacio, Cousens, Simon, Menéndez, Clara, Alonso, Pedro, Macete, Eusebio, Bassat, Quique
Source: Pediatrics. Jan2019, Vol. 143 Issue 1, p1-13. 13p.
Subjects: Mortality risk factors, Mortality, Malnutrition, Algorithms, Bacteremia, Diarrhea, Diseases, Hospital admission & discharge, Patients, Pneumonia, Discharge planning, Severity of illness index, Receiver operating characteristic curves, HIV seroconversion
Geographic Terms: Sub-Saharan Africa
Abstract: BACKGROUND: Although the burden of postdischarge mortality (PDM) in low-income settings appears to be significant, no clear recommendations have been proposed in relation to follow-up care after hospitalization. We aimed to determine the burden of pediatric PDM and develop predictive models to identify children who are at risk for dying after discharge. METHODS: Deaths after hospital discharge among children aged <15 years in the last 17 years were reviewed in an area under demographic and morbidity surveillance in Southern Mozambique. We determined PDM over time (up to 90 days) and derived predictive models of PDM using easily collected variables on admission. RESULTS: Overall PDM was high (3.6%), with half of the deaths occurring in the first 30 days. One primary predictive model for all ages included young age, moderate or severe malnutrition, a history of diarrhea, clinical pneumonia symptoms, prostration, bacteremia, having a positive HIV status, the rainy season, and transfer or absconding, with an area under the curve of 0.79 (0.75-0.82) at day 90 after discharge. Alternative models for all ages including simplified clinical predictors had a similar performance. A model specific to infants <3 months old was used to identify as predictors being a neonate, having a low weight-for-age z score, having breathing difficulties, having hypothermia or fever, having oral candidiasis, and having a history of absconding or transfer to another hospital, with an area under the curve of 0.76 (0.72-0.91) at day 90 of follow-up. CONCLUSIONS: Death after discharge is an important although poorly recognized contributor to child mortality. A simple predictive algorithm based on easily recognizable variables could readily be used to identify most infants and children who are at a high risk of dying after discharge. [ABSTRACT FROM AUTHOR]
Copyright of Pediatrics is the property of American Academy of Pediatrics and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: &lt;searchLink fieldCode=&quot;JN&quot; term=&quot;%22Pediatrics%22&quot;&gt;Pediatrics&lt;/searchLink&gt;. Jan2019, Vol. 143 Issue 1, p1-13. 13p.
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  Data: BACKGROUND: Although the burden of postdischarge mortality (PDM) in low-income settings appears to be significant, no clear recommendations have been proposed in relation to follow-up care after hospitalization. We aimed to determine the burden of pediatric PDM and develop predictive models to identify children who are at risk for dying after discharge. METHODS: Deaths after hospital discharge among children aged &lt;15 years in the last 17 years were reviewed in an area under demographic and morbidity surveillance in Southern Mozambique. We determined PDM over time (up to 90 days) and derived predictive models of PDM using easily collected variables on admission. RESULTS: Overall PDM was high (3.6%), with half of the deaths occurring in the first 30 days. One primary predictive model for all ages included young age, moderate or severe malnutrition, a history of diarrhea, clinical pneumonia symptoms, prostration, bacteremia, having a positive HIV status, the rainy season, and transfer or absconding, with an area under the curve of 0.79 (0.75-0.82) at day 90 after discharge. Alternative models for all ages including simplified clinical predictors had a similar performance. A model specific to infants &lt;3 months old was used to identify as predictors being a neonate, having a low weight-for-age z score, having breathing difficulties, having hypothermia or fever, having oral candidiasis, and having a history of absconding or transfer to another hospital, with an area under the curve of 0.76 (0.72-0.91) at day 90 of follow-up. CONCLUSIONS: Death after discharge is an important although poorly recognized contributor to child mortality. A simple predictive algorithm based on easily recognizable variables could readily be used to identify most infants and children who are at a high risk of dying after discharge. [ABSTRACT FROM AUTHOR]
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  Data: &lt;i&gt;Copyright of Pediatrics is the property of American Academy of Pediatrics and its content may not be copied or emailed to multiple sites without the copyright holder&#39;s express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.&lt;/i&gt; (Copyright applies to all Abstracts.)
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      – Type: doi
        Value: 10.1542/peds.2018-0606
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        Text: English
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        PageCount: 13
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      – SubjectFull: Malnutrition
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      – SubjectFull: Sub-Saharan Africa
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