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 approachResearch in context
Summary: Background: A better understanding of which children are likely to die during acute illness will help clinicians and policy makers target resources at the most vulnerable children. We used machine learning to characterise mortality in the 30-days following admission and the 180-days after...
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E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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In: EClinicalMedicine - Elsevier, 2018, 57(2023), Seite 101838- |
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| Übergeordnetes Werk: |
volume:57 ; year:2023 ; pages:101838- |
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| DOI / URN: |
10.1016/j.eclinm.2023.101838 |
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| Katalog-ID: |
DOAJ080862292 |
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| 100 | 0 | |a Abdoulaye Hama Diallo |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a 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 approachResearch in context |
| 264 | 1 | |c 2023 | |
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| 520 | |a Summary: Background: A better understanding of which children are likely to die during acute illness will help clinicians and policy makers target resources at the most vulnerable children. We used machine learning to characterise mortality in the 30-days following admission and the 180-days after discharge from nine hospitals in low and middle-income countries (LMIC). Methods: A cohort of 3101 children aged 2–24 months were recruited at admission to hospital for any acute illness in Bangladesh (Dhaka and Matlab Hospitals), Pakistan (Civil Hospital Karachi), Kenya (Kilifi, Mbagathi, and Migori Hospitals), Uganda (Mulago Hospital), Malawi (Queen Elizabeth Central Hospital), and Burkina Faso (Banfora Hospital) from November 2016 to January 2019. To record mortality, children were observed during their hospitalisation and for 180 days post-discharge. Extreme gradient boosted models of death within 30 days of admission and mortality in the 180 days following discharge were built. Clusters of mortality sharing similar characteristics were identified from the models using Shapley additive values with spectral clustering. Findings: Anthropometric and laboratory parameters were the most influential predictors of both 30-day and post-discharge mortality. No WHO/IMCI syndromes were among the 25 most influential mortality predictors of mortality. For 30-day mortality, two lower-risk clusters (N = 1915, 61%) included children with higher-than-average anthropometry (1% died, 95% CI: 0–2), and children without signs of severe illness (3% died, 95% CI: 2–4%). The two highest risk 30-day mortality clusters (N = 118, 4%) were characterised by high urea and creatinine (70% died, 95% CI: 62–82%); and nutritional oedema with low platelets and reduced consciousness (97% died, 95% CI: 92–100%). For post-discharge mortality risk, two low-risk clusters (N = 1753, 61%) were defined by higher-than-average anthropometry (0% died, 95% CI: 0–1%), and gastroenteritis with lower-than-average anthropometry and without major laboratory abnormalities (0% died, 95% CI: 0–1%). Two highest risk post-discharge clusters (N = 267, 9%) included children leaving against medical advice (30% died, 95% CI: 25–37%), and severely-low anthropometry with signs of illness at discharge (46% died, 95% CI: 34–62%). Interpretation: WHO clinical syndromes are not sufficient at predicting risk. Integrating basic laboratory features such as urea, creatinine, red blood cell, lymphocyte and platelet counts into guidelines may strengthen efforts to identify high-risk children during paediatric hospitalisations. Funding: Bill & Melinda Gates Foundation OPP1131320. | ||
| 650 | 4 | |a Paediatric mortality | |
| 650 | 4 | |a Wasting | |
| 650 | 4 | |a Malnutrition | |
| 650 | 4 | |a Post-discharge mortality | |
| 650 | 4 | |a Explainable machine learning | |
| 653 | 0 | |a Medicine (General) | |
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| 700 | 0 | |a Benson O. Singa |e verfasserin |4 aut | |
| 700 | 0 | |a Blaise Siezanga Gnoumou |e verfasserin |4 aut | |
| 700 | 0 | |a Caroline Tigoi |e verfasserin |4 aut | |
| 700 | 0 | |a Catherine Achieng Otieno |e verfasserin |4 aut | |
| 700 | 0 | |a Celine Bourdon |e verfasserin |4 aut | |
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| 700 | 0 | |a Christopher Lwanga |e verfasserin |4 aut | |
| 700 | 0 | |a Daniella Brals |e verfasserin |4 aut | |
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10.1016/j.eclinm.2023.101838 doi (DE-627)DOAJ080862292 (DE-599)DOAJf829cad9bd274d33bc06d7df05a813b0 DE-627 ger DE-627 rakwb eng R5-920 Abdoulaye Hama Diallo verfasserin aut 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 approachResearch in context 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Summary: Background: A better understanding of which children are likely to die during acute illness will help clinicians and policy makers target resources at the most vulnerable children. We used machine learning to characterise mortality in the 30-days following admission and the 180-days after discharge from nine hospitals in low and middle-income countries (LMIC). Methods: A cohort of 3101 children aged 2–24 months were recruited at admission to hospital for any acute illness in Bangladesh (Dhaka and Matlab Hospitals), Pakistan (Civil Hospital Karachi), Kenya (Kilifi, Mbagathi, and Migori Hospitals), Uganda (Mulago Hospital), Malawi (Queen Elizabeth Central Hospital), and Burkina Faso (Banfora Hospital) from November 2016 to January 2019. To record mortality, children were observed during their hospitalisation and for 180 days post-discharge. Extreme gradient boosted models of death within 30 days of admission and mortality in the 180 days following discharge were built. Clusters of mortality sharing similar characteristics were identified from the models using Shapley additive values with spectral clustering. Findings: Anthropometric and laboratory parameters were the most influential predictors of both 30-day and post-discharge mortality. No WHO/IMCI syndromes were among the 25 most influential mortality predictors of mortality. For 30-day mortality, two lower-risk clusters (N = 1915, 61%) included children with higher-than-average anthropometry (1% died, 95% CI: 0–2), and children without signs of severe illness (3% died, 95% CI: 2–4%). The two highest risk 30-day mortality clusters (N = 118, 4%) were characterised by high urea and creatinine (70% died, 95% CI: 62–82%); and nutritional oedema with low platelets and reduced consciousness (97% died, 95% CI: 92–100%). For post-discharge mortality risk, two low-risk clusters (N = 1753, 61%) were defined by higher-than-average anthropometry (0% died, 95% CI: 0–1%), and gastroenteritis with lower-than-average anthropometry and without major laboratory abnormalities (0% died, 95% CI: 0–1%). Two highest risk post-discharge clusters (N = 267, 9%) included children leaving against medical advice (30% died, 95% CI: 25–37%), and severely-low anthropometry with signs of illness at discharge (46% died, 95% CI: 34–62%). Interpretation: WHO clinical syndromes are not sufficient at predicting risk. Integrating basic laboratory features such as urea, creatinine, red blood cell, lymphocyte and platelet counts into guidelines may strengthen efforts to identify high-risk children during paediatric hospitalisations. Funding: Bill & Melinda Gates Foundation OPP1131320. Paediatric mortality Wasting Malnutrition Post-discharge mortality Explainable machine learning Medicine (General) Abu Sadat Mohammad Sayeem Bin Shahid verfasserin aut Ali Fazal Khan verfasserin aut Ali Faisal Saleem verfasserin aut Benson O. Singa verfasserin aut Blaise Siezanga Gnoumou verfasserin aut Caroline Tigoi verfasserin aut Catherine Achieng Otieno verfasserin aut Celine Bourdon verfasserin aut Chris Odhiambo Oduol verfasserin aut Christina L. Lancioni verfasserin aut Christine Manyasi verfasserin aut Christine J. McGrath verfasserin aut Christopher Maronga verfasserin aut Christopher Lwanga verfasserin aut Daniella Brals verfasserin aut Dilruba Ahmed verfasserin aut Dinesh Mondal verfasserin aut Donna M. Denno verfasserin aut Dorothy I. Mangale verfasserin aut Emmanuel Chimezi verfasserin aut Emmie Mbale verfasserin aut Ezekiel Mupere verfasserin aut Gazi Md. Salahuddin Mamun verfasserin aut Issaka Ouedraogo verfasserin aut George Githinji verfasserin aut James A. Berkley verfasserin aut Jenala Njirammadzi verfasserin aut John Mukisa verfasserin aut Johnstone Thitiri verfasserin aut Jonas Haggstrom verfasserin aut Joseph D. Carreon verfasserin aut Judd L. Walson verfasserin aut Julie Jemutai verfasserin aut Kirkby D. Tickell verfasserin aut Lubaba Shahrin verfasserin aut MacPherson Mallewa verfasserin aut Md. Iqbal Hossain verfasserin aut Mohammod Jobayer Chisti verfasserin aut Molly Timbwa verfasserin aut Moses Mburu verfasserin aut Moses M. Ngari verfasserin aut Narshion Ngao verfasserin aut Peace Aber verfasserin aut Philliness Prisca Harawa verfasserin aut Priya Sukhtankar verfasserin aut Robert H.J. Bandsma verfasserin aut Roseline Maimouna Bamouni verfasserin aut Sassy Molyneux verfasserin aut Sergey Feldman verfasserin aut Shalton Mwaringa verfasserin aut Shamsun Nahar Shaima verfasserin aut Syed Asad Ali verfasserin aut Syeda Momena Afsana verfasserin aut Syera Banu verfasserin aut Tahmeed Ahmed verfasserin aut Wieger P. Voskuijl verfasserin aut Zaubina Kazi verfasserin aut In EClinicalMedicine Elsevier, 2018 57(2023), Seite 101838- (DE-627)1035271834 25895370 nnns volume:57 year:2023 pages:101838- https://doi.org/10.1016/j.eclinm.2023.101838 kostenfrei https://doaj.org/article/f829cad9bd274d33bc06d7df05a813b0 kostenfrei http://www.sciencedirect.com/science/article/pii/S2589537023000159 kostenfrei https://doaj.org/toc/2589-5370 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 57 2023 101838- |
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10.1016/j.eclinm.2023.101838 doi (DE-627)DOAJ080862292 (DE-599)DOAJf829cad9bd274d33bc06d7df05a813b0 DE-627 ger DE-627 rakwb eng R5-920 Abdoulaye Hama Diallo verfasserin aut 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 approachResearch in context 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Summary: Background: A better understanding of which children are likely to die during acute illness will help clinicians and policy makers target resources at the most vulnerable children. We used machine learning to characterise mortality in the 30-days following admission and the 180-days after discharge from nine hospitals in low and middle-income countries (LMIC). Methods: A cohort of 3101 children aged 2–24 months were recruited at admission to hospital for any acute illness in Bangladesh (Dhaka and Matlab Hospitals), Pakistan (Civil Hospital Karachi), Kenya (Kilifi, Mbagathi, and Migori Hospitals), Uganda (Mulago Hospital), Malawi (Queen Elizabeth Central Hospital), and Burkina Faso (Banfora Hospital) from November 2016 to January 2019. To record mortality, children were observed during their hospitalisation and for 180 days post-discharge. Extreme gradient boosted models of death within 30 days of admission and mortality in the 180 days following discharge were built. Clusters of mortality sharing similar characteristics were identified from the models using Shapley additive values with spectral clustering. Findings: Anthropometric and laboratory parameters were the most influential predictors of both 30-day and post-discharge mortality. No WHO/IMCI syndromes were among the 25 most influential mortality predictors of mortality. For 30-day mortality, two lower-risk clusters (N = 1915, 61%) included children with higher-than-average anthropometry (1% died, 95% CI: 0–2), and children without signs of severe illness (3% died, 95% CI: 2–4%). The two highest risk 30-day mortality clusters (N = 118, 4%) were characterised by high urea and creatinine (70% died, 95% CI: 62–82%); and nutritional oedema with low platelets and reduced consciousness (97% died, 95% CI: 92–100%). For post-discharge mortality risk, two low-risk clusters (N = 1753, 61%) were defined by higher-than-average anthropometry (0% died, 95% CI: 0–1%), and gastroenteritis with lower-than-average anthropometry and without major laboratory abnormalities (0% died, 95% CI: 0–1%). Two highest risk post-discharge clusters (N = 267, 9%) included children leaving against medical advice (30% died, 95% CI: 25–37%), and severely-low anthropometry with signs of illness at discharge (46% died, 95% CI: 34–62%). Interpretation: WHO clinical syndromes are not sufficient at predicting risk. Integrating basic laboratory features such as urea, creatinine, red blood cell, lymphocyte and platelet counts into guidelines may strengthen efforts to identify high-risk children during paediatric hospitalisations. Funding: Bill & Melinda Gates Foundation OPP1131320. Paediatric mortality Wasting Malnutrition Post-discharge mortality Explainable machine learning Medicine (General) Abu Sadat Mohammad Sayeem Bin Shahid verfasserin aut Ali Fazal Khan verfasserin aut Ali Faisal Saleem verfasserin aut Benson O. Singa verfasserin aut Blaise Siezanga Gnoumou verfasserin aut Caroline Tigoi verfasserin aut Catherine Achieng Otieno verfasserin aut Celine Bourdon verfasserin aut Chris Odhiambo Oduol verfasserin aut Christina L. Lancioni verfasserin aut Christine Manyasi verfasserin aut Christine J. McGrath verfasserin aut Christopher Maronga verfasserin aut Christopher Lwanga verfasserin aut Daniella Brals verfasserin aut Dilruba Ahmed verfasserin aut Dinesh Mondal verfasserin aut Donna M. Denno verfasserin aut Dorothy I. Mangale verfasserin aut Emmanuel Chimezi verfasserin aut Emmie Mbale verfasserin aut Ezekiel Mupere verfasserin aut Gazi Md. Salahuddin Mamun verfasserin aut Issaka Ouedraogo verfasserin aut George Githinji verfasserin aut James A. Berkley verfasserin aut Jenala Njirammadzi verfasserin aut John Mukisa verfasserin aut Johnstone Thitiri verfasserin aut Jonas Haggstrom verfasserin aut Joseph D. Carreon verfasserin aut Judd L. Walson verfasserin aut Julie Jemutai verfasserin aut Kirkby D. Tickell verfasserin aut Lubaba Shahrin verfasserin aut MacPherson Mallewa verfasserin aut Md. Iqbal Hossain verfasserin aut Mohammod Jobayer Chisti verfasserin aut Molly Timbwa verfasserin aut Moses Mburu verfasserin aut Moses M. Ngari verfasserin aut Narshion Ngao verfasserin aut Peace Aber verfasserin aut Philliness Prisca Harawa verfasserin aut Priya Sukhtankar verfasserin aut Robert H.J. Bandsma verfasserin aut Roseline Maimouna Bamouni verfasserin aut Sassy Molyneux verfasserin aut Sergey Feldman verfasserin aut Shalton Mwaringa verfasserin aut Shamsun Nahar Shaima verfasserin aut Syed Asad Ali verfasserin aut Syeda Momena Afsana verfasserin aut Syera Banu verfasserin aut Tahmeed Ahmed verfasserin aut Wieger P. Voskuijl verfasserin aut Zaubina Kazi verfasserin aut In EClinicalMedicine Elsevier, 2018 57(2023), Seite 101838- (DE-627)1035271834 25895370 nnns volume:57 year:2023 pages:101838- https://doi.org/10.1016/j.eclinm.2023.101838 kostenfrei https://doaj.org/article/f829cad9bd274d33bc06d7df05a813b0 kostenfrei http://www.sciencedirect.com/science/article/pii/S2589537023000159 kostenfrei https://doaj.org/toc/2589-5370 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 57 2023 101838- |
| allfields_unstemmed |
10.1016/j.eclinm.2023.101838 doi (DE-627)DOAJ080862292 (DE-599)DOAJf829cad9bd274d33bc06d7df05a813b0 DE-627 ger DE-627 rakwb eng R5-920 Abdoulaye Hama Diallo verfasserin aut 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 approachResearch in context 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Summary: Background: A better understanding of which children are likely to die during acute illness will help clinicians and policy makers target resources at the most vulnerable children. We used machine learning to characterise mortality in the 30-days following admission and the 180-days after discharge from nine hospitals in low and middle-income countries (LMIC). Methods: A cohort of 3101 children aged 2–24 months were recruited at admission to hospital for any acute illness in Bangladesh (Dhaka and Matlab Hospitals), Pakistan (Civil Hospital Karachi), Kenya (Kilifi, Mbagathi, and Migori Hospitals), Uganda (Mulago Hospital), Malawi (Queen Elizabeth Central Hospital), and Burkina Faso (Banfora Hospital) from November 2016 to January 2019. To record mortality, children were observed during their hospitalisation and for 180 days post-discharge. Extreme gradient boosted models of death within 30 days of admission and mortality in the 180 days following discharge were built. Clusters of mortality sharing similar characteristics were identified from the models using Shapley additive values with spectral clustering. Findings: Anthropometric and laboratory parameters were the most influential predictors of both 30-day and post-discharge mortality. No WHO/IMCI syndromes were among the 25 most influential mortality predictors of mortality. For 30-day mortality, two lower-risk clusters (N = 1915, 61%) included children with higher-than-average anthropometry (1% died, 95% CI: 0–2), and children without signs of severe illness (3% died, 95% CI: 2–4%). The two highest risk 30-day mortality clusters (N = 118, 4%) were characterised by high urea and creatinine (70% died, 95% CI: 62–82%); and nutritional oedema with low platelets and reduced consciousness (97% died, 95% CI: 92–100%). For post-discharge mortality risk, two low-risk clusters (N = 1753, 61%) were defined by higher-than-average anthropometry (0% died, 95% CI: 0–1%), and gastroenteritis with lower-than-average anthropometry and without major laboratory abnormalities (0% died, 95% CI: 0–1%). Two highest risk post-discharge clusters (N = 267, 9%) included children leaving against medical advice (30% died, 95% CI: 25–37%), and severely-low anthropometry with signs of illness at discharge (46% died, 95% CI: 34–62%). Interpretation: WHO clinical syndromes are not sufficient at predicting risk. Integrating basic laboratory features such as urea, creatinine, red blood cell, lymphocyte and platelet counts into guidelines may strengthen efforts to identify high-risk children during paediatric hospitalisations. Funding: Bill & Melinda Gates Foundation OPP1131320. Paediatric mortality Wasting Malnutrition Post-discharge mortality Explainable machine learning Medicine (General) Abu Sadat Mohammad Sayeem Bin Shahid verfasserin aut Ali Fazal Khan verfasserin aut Ali Faisal Saleem verfasserin aut Benson O. Singa verfasserin aut Blaise Siezanga Gnoumou verfasserin aut Caroline Tigoi verfasserin aut Catherine Achieng Otieno verfasserin aut Celine Bourdon verfasserin aut Chris Odhiambo Oduol verfasserin aut Christina L. Lancioni verfasserin aut Christine Manyasi verfasserin aut Christine J. McGrath verfasserin aut Christopher Maronga verfasserin aut Christopher Lwanga verfasserin aut Daniella Brals verfasserin aut Dilruba Ahmed verfasserin aut Dinesh Mondal verfasserin aut Donna M. Denno verfasserin aut Dorothy I. Mangale verfasserin aut Emmanuel Chimezi verfasserin aut Emmie Mbale verfasserin aut Ezekiel Mupere verfasserin aut Gazi Md. Salahuddin Mamun verfasserin aut Issaka Ouedraogo verfasserin aut George Githinji verfasserin aut James A. Berkley verfasserin aut Jenala Njirammadzi verfasserin aut John Mukisa verfasserin aut Johnstone Thitiri verfasserin aut Jonas Haggstrom verfasserin aut Joseph D. Carreon verfasserin aut Judd L. Walson verfasserin aut Julie Jemutai verfasserin aut Kirkby D. Tickell verfasserin aut Lubaba Shahrin verfasserin aut MacPherson Mallewa verfasserin aut Md. Iqbal Hossain verfasserin aut Mohammod Jobayer Chisti verfasserin aut Molly Timbwa verfasserin aut Moses Mburu verfasserin aut Moses M. Ngari verfasserin aut Narshion Ngao verfasserin aut Peace Aber verfasserin aut Philliness Prisca Harawa verfasserin aut Priya Sukhtankar verfasserin aut Robert H.J. Bandsma verfasserin aut Roseline Maimouna Bamouni verfasserin aut Sassy Molyneux verfasserin aut Sergey Feldman verfasserin aut Shalton Mwaringa verfasserin aut Shamsun Nahar Shaima verfasserin aut Syed Asad Ali verfasserin aut Syeda Momena Afsana verfasserin aut Syera Banu verfasserin aut Tahmeed Ahmed verfasserin aut Wieger P. Voskuijl verfasserin aut Zaubina Kazi verfasserin aut In EClinicalMedicine Elsevier, 2018 57(2023), Seite 101838- (DE-627)1035271834 25895370 nnns volume:57 year:2023 pages:101838- https://doi.org/10.1016/j.eclinm.2023.101838 kostenfrei https://doaj.org/article/f829cad9bd274d33bc06d7df05a813b0 kostenfrei http://www.sciencedirect.com/science/article/pii/S2589537023000159 kostenfrei https://doaj.org/toc/2589-5370 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 57 2023 101838- |
| allfieldsGer |
10.1016/j.eclinm.2023.101838 doi (DE-627)DOAJ080862292 (DE-599)DOAJf829cad9bd274d33bc06d7df05a813b0 DE-627 ger DE-627 rakwb eng R5-920 Abdoulaye Hama Diallo verfasserin aut 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 approachResearch in context 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Summary: Background: A better understanding of which children are likely to die during acute illness will help clinicians and policy makers target resources at the most vulnerable children. We used machine learning to characterise mortality in the 30-days following admission and the 180-days after discharge from nine hospitals in low and middle-income countries (LMIC). Methods: A cohort of 3101 children aged 2–24 months were recruited at admission to hospital for any acute illness in Bangladesh (Dhaka and Matlab Hospitals), Pakistan (Civil Hospital Karachi), Kenya (Kilifi, Mbagathi, and Migori Hospitals), Uganda (Mulago Hospital), Malawi (Queen Elizabeth Central Hospital), and Burkina Faso (Banfora Hospital) from November 2016 to January 2019. To record mortality, children were observed during their hospitalisation and for 180 days post-discharge. Extreme gradient boosted models of death within 30 days of admission and mortality in the 180 days following discharge were built. Clusters of mortality sharing similar characteristics were identified from the models using Shapley additive values with spectral clustering. Findings: Anthropometric and laboratory parameters were the most influential predictors of both 30-day and post-discharge mortality. No WHO/IMCI syndromes were among the 25 most influential mortality predictors of mortality. For 30-day mortality, two lower-risk clusters (N = 1915, 61%) included children with higher-than-average anthropometry (1% died, 95% CI: 0–2), and children without signs of severe illness (3% died, 95% CI: 2–4%). The two highest risk 30-day mortality clusters (N = 118, 4%) were characterised by high urea and creatinine (70% died, 95% CI: 62–82%); and nutritional oedema with low platelets and reduced consciousness (97% died, 95% CI: 92–100%). For post-discharge mortality risk, two low-risk clusters (N = 1753, 61%) were defined by higher-than-average anthropometry (0% died, 95% CI: 0–1%), and gastroenteritis with lower-than-average anthropometry and without major laboratory abnormalities (0% died, 95% CI: 0–1%). Two highest risk post-discharge clusters (N = 267, 9%) included children leaving against medical advice (30% died, 95% CI: 25–37%), and severely-low anthropometry with signs of illness at discharge (46% died, 95% CI: 34–62%). Interpretation: WHO clinical syndromes are not sufficient at predicting risk. Integrating basic laboratory features such as urea, creatinine, red blood cell, lymphocyte and platelet counts into guidelines may strengthen efforts to identify high-risk children during paediatric hospitalisations. Funding: Bill & Melinda Gates Foundation OPP1131320. Paediatric mortality Wasting Malnutrition Post-discharge mortality Explainable machine learning Medicine (General) Abu Sadat Mohammad Sayeem Bin Shahid verfasserin aut Ali Fazal Khan verfasserin aut Ali Faisal Saleem verfasserin aut Benson O. Singa verfasserin aut Blaise Siezanga Gnoumou verfasserin aut Caroline Tigoi verfasserin aut Catherine Achieng Otieno verfasserin aut Celine Bourdon verfasserin aut Chris Odhiambo Oduol verfasserin aut Christina L. Lancioni verfasserin aut Christine Manyasi verfasserin aut Christine J. McGrath verfasserin aut Christopher Maronga verfasserin aut Christopher Lwanga verfasserin aut Daniella Brals verfasserin aut Dilruba Ahmed verfasserin aut Dinesh Mondal verfasserin aut Donna M. Denno verfasserin aut Dorothy I. Mangale verfasserin aut Emmanuel Chimezi verfasserin aut Emmie Mbale verfasserin aut Ezekiel Mupere verfasserin aut Gazi Md. Salahuddin Mamun verfasserin aut Issaka Ouedraogo verfasserin aut George Githinji verfasserin aut James A. Berkley verfasserin aut Jenala Njirammadzi verfasserin aut John Mukisa verfasserin aut Johnstone Thitiri verfasserin aut Jonas Haggstrom verfasserin aut Joseph D. Carreon verfasserin aut Judd L. Walson verfasserin aut Julie Jemutai verfasserin aut Kirkby D. Tickell verfasserin aut Lubaba Shahrin verfasserin aut MacPherson Mallewa verfasserin aut Md. Iqbal Hossain verfasserin aut Mohammod Jobayer Chisti verfasserin aut Molly Timbwa verfasserin aut Moses Mburu verfasserin aut Moses M. Ngari verfasserin aut Narshion Ngao verfasserin aut Peace Aber verfasserin aut Philliness Prisca Harawa verfasserin aut Priya Sukhtankar verfasserin aut Robert H.J. Bandsma verfasserin aut Roseline Maimouna Bamouni verfasserin aut Sassy Molyneux verfasserin aut Sergey Feldman verfasserin aut Shalton Mwaringa verfasserin aut Shamsun Nahar Shaima verfasserin aut Syed Asad Ali verfasserin aut Syeda Momena Afsana verfasserin aut Syera Banu verfasserin aut Tahmeed Ahmed verfasserin aut Wieger P. Voskuijl verfasserin aut Zaubina Kazi verfasserin aut In EClinicalMedicine Elsevier, 2018 57(2023), Seite 101838- (DE-627)1035271834 25895370 nnns volume:57 year:2023 pages:101838- https://doi.org/10.1016/j.eclinm.2023.101838 kostenfrei https://doaj.org/article/f829cad9bd274d33bc06d7df05a813b0 kostenfrei http://www.sciencedirect.com/science/article/pii/S2589537023000159 kostenfrei https://doaj.org/toc/2589-5370 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 57 2023 101838- |
| allfieldsSound |
10.1016/j.eclinm.2023.101838 doi (DE-627)DOAJ080862292 (DE-599)DOAJf829cad9bd274d33bc06d7df05a813b0 DE-627 ger DE-627 rakwb eng R5-920 Abdoulaye Hama Diallo verfasserin aut 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 approachResearch in context 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Summary: Background: A better understanding of which children are likely to die during acute illness will help clinicians and policy makers target resources at the most vulnerable children. We used machine learning to characterise mortality in the 30-days following admission and the 180-days after discharge from nine hospitals in low and middle-income countries (LMIC). Methods: A cohort of 3101 children aged 2–24 months were recruited at admission to hospital for any acute illness in Bangladesh (Dhaka and Matlab Hospitals), Pakistan (Civil Hospital Karachi), Kenya (Kilifi, Mbagathi, and Migori Hospitals), Uganda (Mulago Hospital), Malawi (Queen Elizabeth Central Hospital), and Burkina Faso (Banfora Hospital) from November 2016 to January 2019. To record mortality, children were observed during their hospitalisation and for 180 days post-discharge. Extreme gradient boosted models of death within 30 days of admission and mortality in the 180 days following discharge were built. Clusters of mortality sharing similar characteristics were identified from the models using Shapley additive values with spectral clustering. Findings: Anthropometric and laboratory parameters were the most influential predictors of both 30-day and post-discharge mortality. No WHO/IMCI syndromes were among the 25 most influential mortality predictors of mortality. For 30-day mortality, two lower-risk clusters (N = 1915, 61%) included children with higher-than-average anthropometry (1% died, 95% CI: 0–2), and children without signs of severe illness (3% died, 95% CI: 2–4%). The two highest risk 30-day mortality clusters (N = 118, 4%) were characterised by high urea and creatinine (70% died, 95% CI: 62–82%); and nutritional oedema with low platelets and reduced consciousness (97% died, 95% CI: 92–100%). For post-discharge mortality risk, two low-risk clusters (N = 1753, 61%) were defined by higher-than-average anthropometry (0% died, 95% CI: 0–1%), and gastroenteritis with lower-than-average anthropometry and without major laboratory abnormalities (0% died, 95% CI: 0–1%). Two highest risk post-discharge clusters (N = 267, 9%) included children leaving against medical advice (30% died, 95% CI: 25–37%), and severely-low anthropometry with signs of illness at discharge (46% died, 95% CI: 34–62%). Interpretation: WHO clinical syndromes are not sufficient at predicting risk. Integrating basic laboratory features such as urea, creatinine, red blood cell, lymphocyte and platelet counts into guidelines may strengthen efforts to identify high-risk children during paediatric hospitalisations. Funding: Bill & Melinda Gates Foundation OPP1131320. Paediatric mortality Wasting Malnutrition Post-discharge mortality Explainable machine learning Medicine (General) Abu Sadat Mohammad Sayeem Bin Shahid verfasserin aut Ali Fazal Khan verfasserin aut Ali Faisal Saleem verfasserin aut Benson O. Singa verfasserin aut Blaise Siezanga Gnoumou verfasserin aut Caroline Tigoi verfasserin aut Catherine Achieng Otieno verfasserin aut Celine Bourdon verfasserin aut Chris Odhiambo Oduol verfasserin aut Christina L. Lancioni verfasserin aut Christine Manyasi verfasserin aut Christine J. McGrath verfasserin aut Christopher Maronga verfasserin aut Christopher Lwanga verfasserin aut Daniella Brals verfasserin aut Dilruba Ahmed verfasserin aut Dinesh Mondal verfasserin aut Donna M. Denno verfasserin aut Dorothy I. Mangale verfasserin aut Emmanuel Chimezi verfasserin aut Emmie Mbale verfasserin aut Ezekiel Mupere verfasserin aut Gazi Md. Salahuddin Mamun verfasserin aut Issaka Ouedraogo verfasserin aut George Githinji verfasserin aut James A. Berkley verfasserin aut Jenala Njirammadzi verfasserin aut John Mukisa verfasserin aut Johnstone Thitiri verfasserin aut Jonas Haggstrom verfasserin aut Joseph D. Carreon verfasserin aut Judd L. Walson verfasserin aut Julie Jemutai verfasserin aut Kirkby D. Tickell verfasserin aut Lubaba Shahrin verfasserin aut MacPherson Mallewa verfasserin aut Md. Iqbal Hossain verfasserin aut Mohammod Jobayer Chisti verfasserin aut Molly Timbwa verfasserin aut Moses Mburu verfasserin aut Moses M. Ngari verfasserin aut Narshion Ngao verfasserin aut Peace Aber verfasserin aut Philliness Prisca Harawa verfasserin aut Priya Sukhtankar verfasserin aut Robert H.J. Bandsma verfasserin aut Roseline Maimouna Bamouni verfasserin aut Sassy Molyneux verfasserin aut Sergey Feldman verfasserin aut Shalton Mwaringa verfasserin aut Shamsun Nahar Shaima verfasserin aut Syed Asad Ali verfasserin aut Syeda Momena Afsana verfasserin aut Syera Banu verfasserin aut Tahmeed Ahmed verfasserin aut Wieger P. Voskuijl verfasserin aut Zaubina Kazi verfasserin aut In EClinicalMedicine Elsevier, 2018 57(2023), Seite 101838- (DE-627)1035271834 25895370 nnns volume:57 year:2023 pages:101838- https://doi.org/10.1016/j.eclinm.2023.101838 kostenfrei https://doaj.org/article/f829cad9bd274d33bc06d7df05a813b0 kostenfrei http://www.sciencedirect.com/science/article/pii/S2589537023000159 kostenfrei https://doaj.org/toc/2589-5370 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 57 2023 101838- |
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In EClinicalMedicine 57(2023), Seite 101838- volume:57 year:2023 pages:101838- |
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Paediatric mortality Wasting Malnutrition Post-discharge mortality Explainable machine learning Medicine (General) |
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Abdoulaye Hama Diallo @@aut@@ Abu Sadat Mohammad Sayeem Bin Shahid @@aut@@ Ali Fazal Khan @@aut@@ Ali Faisal Saleem @@aut@@ Benson O. Singa @@aut@@ Blaise Siezanga Gnoumou @@aut@@ Caroline Tigoi @@aut@@ Catherine Achieng Otieno @@aut@@ Celine Bourdon @@aut@@ Chris Odhiambo Oduol @@aut@@ Christina L. Lancioni @@aut@@ Christine Manyasi @@aut@@ Christine J. McGrath @@aut@@ Christopher Maronga @@aut@@ Christopher Lwanga @@aut@@ Daniella Brals @@aut@@ Dilruba Ahmed @@aut@@ Dinesh Mondal @@aut@@ Donna M. Denno @@aut@@ Dorothy I. Mangale @@aut@@ Emmanuel Chimezi @@aut@@ Emmie Mbale @@aut@@ Ezekiel Mupere @@aut@@ Gazi Md. Salahuddin Mamun @@aut@@ Issaka Ouedraogo @@aut@@ George Githinji @@aut@@ James A. Berkley @@aut@@ Jenala Njirammadzi @@aut@@ John Mukisa @@aut@@ Johnstone Thitiri @@aut@@ Jonas Haggstrom @@aut@@ Joseph D. Carreon @@aut@@ Judd L. Walson @@aut@@ Julie Jemutai @@aut@@ Kirkby D. Tickell @@aut@@ Lubaba Shahrin @@aut@@ MacPherson Mallewa @@aut@@ Md. Iqbal Hossain @@aut@@ Mohammod Jobayer Chisti @@aut@@ Molly Timbwa @@aut@@ Moses Mburu @@aut@@ Moses M. Ngari @@aut@@ Narshion Ngao @@aut@@ Peace Aber @@aut@@ Philliness Prisca Harawa @@aut@@ Priya Sukhtankar @@aut@@ Robert H.J. Bandsma @@aut@@ Roseline Maimouna Bamouni @@aut@@ Sassy Molyneux @@aut@@ Sergey Feldman @@aut@@ Shalton Mwaringa @@aut@@ Shamsun Nahar Shaima @@aut@@ Syed Asad Ali @@aut@@ Syeda Momena Afsana @@aut@@ Syera Banu @@aut@@ Tahmeed Ahmed @@aut@@ Wieger P. Voskuijl @@aut@@ Zaubina Kazi @@aut@@ |
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2023-01-01T00:00:00Z |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ080862292</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230331015322.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230310s2023 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.eclinm.2023.101838</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ080862292</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJf829cad9bd274d33bc06d7df05a813b0</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">R5-920</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Abdoulaye Hama Diallo</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">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 approachResearch in context</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Summary: Background: A better understanding of which children are likely to die during acute illness will help clinicians and policy makers target resources at the most vulnerable children. We used machine learning to characterise mortality in the 30-days following admission and the 180-days after discharge from nine hospitals in low and middle-income countries (LMIC). Methods: A cohort of 3101 children aged 2–24 months were recruited at admission to hospital for any acute illness in Bangladesh (Dhaka and Matlab Hospitals), Pakistan (Civil Hospital Karachi), Kenya (Kilifi, Mbagathi, and Migori Hospitals), Uganda (Mulago Hospital), Malawi (Queen Elizabeth Central Hospital), and Burkina Faso (Banfora Hospital) from November 2016 to January 2019. To record mortality, children were observed during their hospitalisation and for 180 days post-discharge. Extreme gradient boosted models of death within 30 days of admission and mortality in the 180 days following discharge were built. Clusters of mortality sharing similar characteristics were identified from the models using Shapley additive values with spectral clustering. Findings: Anthropometric and laboratory parameters were the most influential predictors of both 30-day and post-discharge mortality. No WHO/IMCI syndromes were among the 25 most influential mortality predictors of mortality. For 30-day mortality, two lower-risk clusters (N = 1915, 61%) included children with higher-than-average anthropometry (1% died, 95% CI: 0–2), and children without signs of severe illness (3% died, 95% CI: 2–4%). The two highest risk 30-day mortality clusters (N = 118, 4%) were characterised by high urea and creatinine (70% died, 95% CI: 62–82%); and nutritional oedema with low platelets and reduced consciousness (97% died, 95% CI: 92–100%). For post-discharge mortality risk, two low-risk clusters (N = 1753, 61%) were defined by higher-than-average anthropometry (0% died, 95% CI: 0–1%), and gastroenteritis with lower-than-average anthropometry and without major laboratory abnormalities (0% died, 95% CI: 0–1%). Two highest risk post-discharge clusters (N = 267, 9%) included children leaving against medical advice (30% died, 95% CI: 25–37%), and severely-low anthropometry with signs of illness at discharge (46% died, 95% CI: 34–62%). Interpretation: WHO clinical syndromes are not sufficient at predicting risk. Integrating basic laboratory features such as urea, creatinine, red blood cell, lymphocyte and platelet counts into guidelines may strengthen efforts to identify high-risk children during paediatric hospitalisations. Funding: Bill & Melinda Gates Foundation OPP1131320.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Paediatric mortality</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Wasting</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Malnutrition</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Post-discharge mortality</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Explainable machine learning</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Medicine (General)</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Abu Sadat Mohammad Sayeem Bin Shahid</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Ali Fazal Khan</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Ali Faisal Saleem</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Benson O. 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R5-920 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 approachResearch in context Paediatric mortality Wasting Malnutrition Post-discharge mortality Explainable machine learning |
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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 approachResearch in context |
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Abdoulaye Hama Diallo Abu Sadat Mohammad Sayeem Bin Shahid Ali Fazal Khan Ali Faisal Saleem Benson O. Singa Blaise Siezanga Gnoumou Caroline Tigoi Catherine Achieng Otieno Celine Bourdon Chris Odhiambo Oduol Christina L. Lancioni Christine Manyasi Christine J. McGrath Christopher Maronga Christopher Lwanga Daniella Brals Dilruba Ahmed Dinesh Mondal Donna M. Denno Dorothy I. Mangale Emmanuel Chimezi Emmie Mbale Ezekiel Mupere Gazi Md. Salahuddin Mamun Issaka Ouedraogo George Githinji James A. Berkley Jenala Njirammadzi John Mukisa Johnstone Thitiri Jonas Haggstrom Joseph D. Carreon Judd L. Walson Julie Jemutai Kirkby D. Tickell Lubaba Shahrin MacPherson Mallewa Md. Iqbal Hossain Mohammod Jobayer Chisti Molly Timbwa Moses Mburu Moses M. Ngari Narshion Ngao Peace Aber Philliness Prisca Harawa Priya Sukhtankar Robert H.J. Bandsma Roseline Maimouna Bamouni Sassy Molyneux Sergey Feldman Shalton Mwaringa Shamsun Nahar Shaima Syed Asad Ali Syeda Momena Afsana Syera Banu Tahmeed Ahmed Wieger P. Voskuijl Zaubina Kazi |
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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 approachresearch in context |
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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 approachResearch in context |
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Summary: Background: A better understanding of which children are likely to die during acute illness will help clinicians and policy makers target resources at the most vulnerable children. We used machine learning to characterise mortality in the 30-days following admission and the 180-days after discharge from nine hospitals in low and middle-income countries (LMIC). Methods: A cohort of 3101 children aged 2–24 months were recruited at admission to hospital for any acute illness in Bangladesh (Dhaka and Matlab Hospitals), Pakistan (Civil Hospital Karachi), Kenya (Kilifi, Mbagathi, and Migori Hospitals), Uganda (Mulago Hospital), Malawi (Queen Elizabeth Central Hospital), and Burkina Faso (Banfora Hospital) from November 2016 to January 2019. To record mortality, children were observed during their hospitalisation and for 180 days post-discharge. Extreme gradient boosted models of death within 30 days of admission and mortality in the 180 days following discharge were built. Clusters of mortality sharing similar characteristics were identified from the models using Shapley additive values with spectral clustering. Findings: Anthropometric and laboratory parameters were the most influential predictors of both 30-day and post-discharge mortality. No WHO/IMCI syndromes were among the 25 most influential mortality predictors of mortality. For 30-day mortality, two lower-risk clusters (N = 1915, 61%) included children with higher-than-average anthropometry (1% died, 95% CI: 0–2), and children without signs of severe illness (3% died, 95% CI: 2–4%). The two highest risk 30-day mortality clusters (N = 118, 4%) were characterised by high urea and creatinine (70% died, 95% CI: 62–82%); and nutritional oedema with low platelets and reduced consciousness (97% died, 95% CI: 92–100%). For post-discharge mortality risk, two low-risk clusters (N = 1753, 61%) were defined by higher-than-average anthropometry (0% died, 95% CI: 0–1%), and gastroenteritis with lower-than-average anthropometry and without major laboratory abnormalities (0% died, 95% CI: 0–1%). Two highest risk post-discharge clusters (N = 267, 9%) included children leaving against medical advice (30% died, 95% CI: 25–37%), and severely-low anthropometry with signs of illness at discharge (46% died, 95% CI: 34–62%). Interpretation: WHO clinical syndromes are not sufficient at predicting risk. Integrating basic laboratory features such as urea, creatinine, red blood cell, lymphocyte and platelet counts into guidelines may strengthen efforts to identify high-risk children during paediatric hospitalisations. Funding: Bill & Melinda Gates Foundation OPP1131320. |
| abstractGer |
Summary: Background: A better understanding of which children are likely to die during acute illness will help clinicians and policy makers target resources at the most vulnerable children. We used machine learning to characterise mortality in the 30-days following admission and the 180-days after discharge from nine hospitals in low and middle-income countries (LMIC). Methods: A cohort of 3101 children aged 2–24 months were recruited at admission to hospital for any acute illness in Bangladesh (Dhaka and Matlab Hospitals), Pakistan (Civil Hospital Karachi), Kenya (Kilifi, Mbagathi, and Migori Hospitals), Uganda (Mulago Hospital), Malawi (Queen Elizabeth Central Hospital), and Burkina Faso (Banfora Hospital) from November 2016 to January 2019. To record mortality, children were observed during their hospitalisation and for 180 days post-discharge. Extreme gradient boosted models of death within 30 days of admission and mortality in the 180 days following discharge were built. Clusters of mortality sharing similar characteristics were identified from the models using Shapley additive values with spectral clustering. Findings: Anthropometric and laboratory parameters were the most influential predictors of both 30-day and post-discharge mortality. No WHO/IMCI syndromes were among the 25 most influential mortality predictors of mortality. For 30-day mortality, two lower-risk clusters (N = 1915, 61%) included children with higher-than-average anthropometry (1% died, 95% CI: 0–2), and children without signs of severe illness (3% died, 95% CI: 2–4%). The two highest risk 30-day mortality clusters (N = 118, 4%) were characterised by high urea and creatinine (70% died, 95% CI: 62–82%); and nutritional oedema with low platelets and reduced consciousness (97% died, 95% CI: 92–100%). For post-discharge mortality risk, two low-risk clusters (N = 1753, 61%) were defined by higher-than-average anthropometry (0% died, 95% CI: 0–1%), and gastroenteritis with lower-than-average anthropometry and without major laboratory abnormalities (0% died, 95% CI: 0–1%). Two highest risk post-discharge clusters (N = 267, 9%) included children leaving against medical advice (30% died, 95% CI: 25–37%), and severely-low anthropometry with signs of illness at discharge (46% died, 95% CI: 34–62%). Interpretation: WHO clinical syndromes are not sufficient at predicting risk. Integrating basic laboratory features such as urea, creatinine, red blood cell, lymphocyte and platelet counts into guidelines may strengthen efforts to identify high-risk children during paediatric hospitalisations. Funding: Bill & Melinda Gates Foundation OPP1131320. |
| abstract_unstemmed |
Summary: Background: A better understanding of which children are likely to die during acute illness will help clinicians and policy makers target resources at the most vulnerable children. We used machine learning to characterise mortality in the 30-days following admission and the 180-days after discharge from nine hospitals in low and middle-income countries (LMIC). Methods: A cohort of 3101 children aged 2–24 months were recruited at admission to hospital for any acute illness in Bangladesh (Dhaka and Matlab Hospitals), Pakistan (Civil Hospital Karachi), Kenya (Kilifi, Mbagathi, and Migori Hospitals), Uganda (Mulago Hospital), Malawi (Queen Elizabeth Central Hospital), and Burkina Faso (Banfora Hospital) from November 2016 to January 2019. To record mortality, children were observed during their hospitalisation and for 180 days post-discharge. Extreme gradient boosted models of death within 30 days of admission and mortality in the 180 days following discharge were built. Clusters of mortality sharing similar characteristics were identified from the models using Shapley additive values with spectral clustering. Findings: Anthropometric and laboratory parameters were the most influential predictors of both 30-day and post-discharge mortality. No WHO/IMCI syndromes were among the 25 most influential mortality predictors of mortality. For 30-day mortality, two lower-risk clusters (N = 1915, 61%) included children with higher-than-average anthropometry (1% died, 95% CI: 0–2), and children without signs of severe illness (3% died, 95% CI: 2–4%). The two highest risk 30-day mortality clusters (N = 118, 4%) were characterised by high urea and creatinine (70% died, 95% CI: 62–82%); and nutritional oedema with low platelets and reduced consciousness (97% died, 95% CI: 92–100%). For post-discharge mortality risk, two low-risk clusters (N = 1753, 61%) were defined by higher-than-average anthropometry (0% died, 95% CI: 0–1%), and gastroenteritis with lower-than-average anthropometry and without major laboratory abnormalities (0% died, 95% CI: 0–1%). Two highest risk post-discharge clusters (N = 267, 9%) included children leaving against medical advice (30% died, 95% CI: 25–37%), and severely-low anthropometry with signs of illness at discharge (46% died, 95% CI: 34–62%). Interpretation: WHO clinical syndromes are not sufficient at predicting risk. Integrating basic laboratory features such as urea, creatinine, red blood cell, lymphocyte and platelet counts into guidelines may strengthen efforts to identify high-risk children during paediatric hospitalisations. Funding: Bill & Melinda Gates Foundation OPP1131320. |
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10.1016/j.eclinm.2023.101838 |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ080862292</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230331015322.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230310s2023 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.eclinm.2023.101838</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ080862292</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJf829cad9bd274d33bc06d7df05a813b0</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">R5-920</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Abdoulaye Hama Diallo</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">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 approachResearch in context</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Summary: Background: A better understanding of which children are likely to die during acute illness will help clinicians and policy makers target resources at the most vulnerable children. We used machine learning to characterise mortality in the 30-days following admission and the 180-days after discharge from nine hospitals in low and middle-income countries (LMIC). Methods: A cohort of 3101 children aged 2–24 months were recruited at admission to hospital for any acute illness in Bangladesh (Dhaka and Matlab Hospitals), Pakistan (Civil Hospital Karachi), Kenya (Kilifi, Mbagathi, and Migori Hospitals), Uganda (Mulago Hospital), Malawi (Queen Elizabeth Central Hospital), and Burkina Faso (Banfora Hospital) from November 2016 to January 2019. To record mortality, children were observed during their hospitalisation and for 180 days post-discharge. Extreme gradient boosted models of death within 30 days of admission and mortality in the 180 days following discharge were built. Clusters of mortality sharing similar characteristics were identified from the models using Shapley additive values with spectral clustering. Findings: Anthropometric and laboratory parameters were the most influential predictors of both 30-day and post-discharge mortality. No WHO/IMCI syndromes were among the 25 most influential mortality predictors of mortality. For 30-day mortality, two lower-risk clusters (N = 1915, 61%) included children with higher-than-average anthropometry (1% died, 95% CI: 0–2), and children without signs of severe illness (3% died, 95% CI: 2–4%). The two highest risk 30-day mortality clusters (N = 118, 4%) were characterised by high urea and creatinine (70% died, 95% CI: 62–82%); and nutritional oedema with low platelets and reduced consciousness (97% died, 95% CI: 92–100%). For post-discharge mortality risk, two low-risk clusters (N = 1753, 61%) were defined by higher-than-average anthropometry (0% died, 95% CI: 0–1%), and gastroenteritis with lower-than-average anthropometry and without major laboratory abnormalities (0% died, 95% CI: 0–1%). Two highest risk post-discharge clusters (N = 267, 9%) included children leaving against medical advice (30% died, 95% CI: 25–37%), and severely-low anthropometry with signs of illness at discharge (46% died, 95% CI: 34–62%). Interpretation: WHO clinical syndromes are not sufficient at predicting risk. Integrating basic laboratory features such as urea, creatinine, red blood cell, lymphocyte and platelet counts into guidelines may strengthen efforts to identify high-risk children during paediatric hospitalisations. 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| score |
7.4028378 |