Pediatric prognostic models predicting inhospital child mortality in resource‐limited settings: An external validation study

Abstract Background and Aims Prognostic models provide evidence‐based predictions and estimates of future outcomes, facilitating decision‐making, patient care, and research. A few of these models have been externally validated, leading to uncertain reliability and generalizability. This study aims to externally validate four models to assess their transferability and usefulness in clinical practice. The models include the respiratory index of severity in children (RISC)‐Malawi model and three other models by Lowlavaar et al. Methods The study used data from the Clinical Information Network (CIN) to validate the four models where the primary outcome was in‐hospital mortality. 163,329 patients met eligibility criteria. Missing data were imputed, and the logistic function was used to compute predicted risk of in‐hospital mortality. Models' discriminatory ability and calibration were determined using area under the curve (AUC), calibration slope, and intercept. Results The RISC‐Malawi model had 50,669 pneumonia patients who met the eligibility criteria, of which the case‐fatality ratio was 4406 (8.7%). Its AUC was 0.77 (95% CI: 0.77−0.78), whereas the calibration slope was 1.04 (95% CI: 1.00 −1.06), and calibration intercept was 0.81 (95% CI: 0.77−0.84). Regarding the external validation of Lowlavaar et al. models, 10,782 eligible patients were included, with an in‐hospital mortality rate of 5.3%. The primary model's AUC was 0.75 (95% CI: 0.72−0.77), the calibration slope was 0.78 (95% CI: 0.71−0.84), and the calibration intercept was 0.37 (95% CI: 0.28−0.46). All models markedly underestimated the risk of mortality. Conclusion All externally validated models exhibited either underestimation or overestimation of the risk as judged from calibration statistics. Hence, applying these models with confidence in settings other than their original development context may not be advisable. Our findings strongly suggest the need for recalibrating these model to enhance their generalizability. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Morris Ogero [verfasserIn] John Ndiritu [verfasserIn] Rachel Sarguta [verfasserIn] Timothy Tuti [verfasserIn] Samuel Akech [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	epidemiology model external validation pediatrics and adolescent medicine

Übergeordnetes Werk:	In: Health Science Reports - Wiley, 2018, 6(2023), 8, Seite n/a-n/a
Übergeordnetes Werk:	volume:6 ; year:2023 ; number:8 ; pages:n/a-n/a

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1002/hsr2.1433

Katalog-ID:	DOAJ092680046

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245	1	0	\|a Pediatric prognostic models predicting inhospital child mortality in resource‐limited settings: An external validation study
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520			\|a Abstract Background and Aims Prognostic models provide evidence‐based predictions and estimates of future outcomes, facilitating decision‐making, patient care, and research. A few of these models have been externally validated, leading to uncertain reliability and generalizability. This study aims to externally validate four models to assess their transferability and usefulness in clinical practice. The models include the respiratory index of severity in children (RISC)‐Malawi model and three other models by Lowlavaar et al. Methods The study used data from the Clinical Information Network (CIN) to validate the four models where the primary outcome was in‐hospital mortality. 163,329 patients met eligibility criteria. Missing data were imputed, and the logistic function was used to compute predicted risk of in‐hospital mortality. Models' discriminatory ability and calibration were determined using area under the curve (AUC), calibration slope, and intercept. Results The RISC‐Malawi model had 50,669 pneumonia patients who met the eligibility criteria, of which the case‐fatality ratio was 4406 (8.7%). Its AUC was 0.77 (95% CI: 0.77−0.78), whereas the calibration slope was 1.04 (95% CI: 1.00 −1.06), and calibration intercept was 0.81 (95% CI: 0.77−0.84). Regarding the external validation of Lowlavaar et al. models, 10,782 eligible patients were included, with an in‐hospital mortality rate of 5.3%. The primary model's AUC was 0.75 (95% CI: 0.72−0.77), the calibration slope was 0.78 (95% CI: 0.71−0.84), and the calibration intercept was 0.37 (95% CI: 0.28−0.46). All models markedly underestimated the risk of mortality. Conclusion All externally validated models exhibited either underestimation or overestimation of the risk as judged from calibration statistics. Hence, applying these models with confidence in settings other than their original development context may not be advisable. Our findings strongly suggest the need for recalibrating these model to enhance their generalizability.
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allfields	10.1002/hsr2.1433 doi (DE-627)DOAJ092680046 (DE-599)DOAJ47ab8a1152a7429ea6639104008b12ad DE-627 ger DE-627 rakwb eng Morris Ogero verfasserin aut Pediatric prognostic models predicting inhospital child mortality in resource‐limited settings: An external validation study 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background and Aims Prognostic models provide evidence‐based predictions and estimates of future outcomes, facilitating decision‐making, patient care, and research. A few of these models have been externally validated, leading to uncertain reliability and generalizability. This study aims to externally validate four models to assess their transferability and usefulness in clinical practice. The models include the respiratory index of severity in children (RISC)‐Malawi model and three other models by Lowlavaar et al. Methods The study used data from the Clinical Information Network (CIN) to validate the four models where the primary outcome was in‐hospital mortality. 163,329 patients met eligibility criteria. Missing data were imputed, and the logistic function was used to compute predicted risk of in‐hospital mortality. Models' discriminatory ability and calibration were determined using area under the curve (AUC), calibration slope, and intercept. Results The RISC‐Malawi model had 50,669 pneumonia patients who met the eligibility criteria, of which the case‐fatality ratio was 4406 (8.7%). Its AUC was 0.77 (95% CI: 0.77−0.78), whereas the calibration slope was 1.04 (95% CI: 1.00 −1.06), and calibration intercept was 0.81 (95% CI: 0.77−0.84). Regarding the external validation of Lowlavaar et al. models, 10,782 eligible patients were included, with an in‐hospital mortality rate of 5.3%. The primary model's AUC was 0.75 (95% CI: 0.72−0.77), the calibration slope was 0.78 (95% CI: 0.71−0.84), and the calibration intercept was 0.37 (95% CI: 0.28−0.46). All models markedly underestimated the risk of mortality. Conclusion All externally validated models exhibited either underestimation or overestimation of the risk as judged from calibration statistics. Hence, applying these models with confidence in settings other than their original development context may not be advisable. Our findings strongly suggest the need for recalibrating these model to enhance their generalizability. epidemiology model external validation pediatrics and adolescent medicine Medicine R John Ndiritu verfasserin aut Rachel Sarguta verfasserin aut Timothy Tuti verfasserin aut Samuel Akech verfasserin aut In Health Science Reports Wiley, 2018 6(2023), 8, Seite n/a-n/a (DE-627)101922052X (DE-600)2927182-4 23988835 nnns volume:6 year:2023 number:8 pages:n/a-n/a https://doi.org/10.1002/hsr2.1433 kostenfrei https://doaj.org/article/47ab8a1152a7429ea6639104008b12ad kostenfrei https://doi.org/10.1002/hsr2.1433 kostenfrei https://doaj.org/toc/2398-8835 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_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 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_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2023 8 n/a-n/a
spelling	10.1002/hsr2.1433 doi (DE-627)DOAJ092680046 (DE-599)DOAJ47ab8a1152a7429ea6639104008b12ad DE-627 ger DE-627 rakwb eng Morris Ogero verfasserin aut Pediatric prognostic models predicting inhospital child mortality in resource‐limited settings: An external validation study 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background and Aims Prognostic models provide evidence‐based predictions and estimates of future outcomes, facilitating decision‐making, patient care, and research. A few of these models have been externally validated, leading to uncertain reliability and generalizability. This study aims to externally validate four models to assess their transferability and usefulness in clinical practice. The models include the respiratory index of severity in children (RISC)‐Malawi model and three other models by Lowlavaar et al. Methods The study used data from the Clinical Information Network (CIN) to validate the four models where the primary outcome was in‐hospital mortality. 163,329 patients met eligibility criteria. Missing data were imputed, and the logistic function was used to compute predicted risk of in‐hospital mortality. Models' discriminatory ability and calibration were determined using area under the curve (AUC), calibration slope, and intercept. Results The RISC‐Malawi model had 50,669 pneumonia patients who met the eligibility criteria, of which the case‐fatality ratio was 4406 (8.7%). Its AUC was 0.77 (95% CI: 0.77−0.78), whereas the calibration slope was 1.04 (95% CI: 1.00 −1.06), and calibration intercept was 0.81 (95% CI: 0.77−0.84). Regarding the external validation of Lowlavaar et al. models, 10,782 eligible patients were included, with an in‐hospital mortality rate of 5.3%. The primary model's AUC was 0.75 (95% CI: 0.72−0.77), the calibration slope was 0.78 (95% CI: 0.71−0.84), and the calibration intercept was 0.37 (95% CI: 0.28−0.46). All models markedly underestimated the risk of mortality. Conclusion All externally validated models exhibited either underestimation or overestimation of the risk as judged from calibration statistics. Hence, applying these models with confidence in settings other than their original development context may not be advisable. Our findings strongly suggest the need for recalibrating these model to enhance their generalizability. epidemiology model external validation pediatrics and adolescent medicine Medicine R John Ndiritu verfasserin aut Rachel Sarguta verfasserin aut Timothy Tuti verfasserin aut Samuel Akech verfasserin aut In Health Science Reports Wiley, 2018 6(2023), 8, Seite n/a-n/a (DE-627)101922052X (DE-600)2927182-4 23988835 nnns volume:6 year:2023 number:8 pages:n/a-n/a https://doi.org/10.1002/hsr2.1433 kostenfrei https://doaj.org/article/47ab8a1152a7429ea6639104008b12ad kostenfrei https://doi.org/10.1002/hsr2.1433 kostenfrei https://doaj.org/toc/2398-8835 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_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 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_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2023 8 n/a-n/a
allfields_unstemmed	10.1002/hsr2.1433 doi (DE-627)DOAJ092680046 (DE-599)DOAJ47ab8a1152a7429ea6639104008b12ad DE-627 ger DE-627 rakwb eng Morris Ogero verfasserin aut Pediatric prognostic models predicting inhospital child mortality in resource‐limited settings: An external validation study 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background and Aims Prognostic models provide evidence‐based predictions and estimates of future outcomes, facilitating decision‐making, patient care, and research. A few of these models have been externally validated, leading to uncertain reliability and generalizability. This study aims to externally validate four models to assess their transferability and usefulness in clinical practice. The models include the respiratory index of severity in children (RISC)‐Malawi model and three other models by Lowlavaar et al. Methods The study used data from the Clinical Information Network (CIN) to validate the four models where the primary outcome was in‐hospital mortality. 163,329 patients met eligibility criteria. Missing data were imputed, and the logistic function was used to compute predicted risk of in‐hospital mortality. Models' discriminatory ability and calibration were determined using area under the curve (AUC), calibration slope, and intercept. Results The RISC‐Malawi model had 50,669 pneumonia patients who met the eligibility criteria, of which the case‐fatality ratio was 4406 (8.7%). Its AUC was 0.77 (95% CI: 0.77−0.78), whereas the calibration slope was 1.04 (95% CI: 1.00 −1.06), and calibration intercept was 0.81 (95% CI: 0.77−0.84). Regarding the external validation of Lowlavaar et al. models, 10,782 eligible patients were included, with an in‐hospital mortality rate of 5.3%. The primary model's AUC was 0.75 (95% CI: 0.72−0.77), the calibration slope was 0.78 (95% CI: 0.71−0.84), and the calibration intercept was 0.37 (95% CI: 0.28−0.46). All models markedly underestimated the risk of mortality. Conclusion All externally validated models exhibited either underestimation or overestimation of the risk as judged from calibration statistics. Hence, applying these models with confidence in settings other than their original development context may not be advisable. Our findings strongly suggest the need for recalibrating these model to enhance their generalizability. epidemiology model external validation pediatrics and adolescent medicine Medicine R John Ndiritu verfasserin aut Rachel Sarguta verfasserin aut Timothy Tuti verfasserin aut Samuel Akech verfasserin aut In Health Science Reports Wiley, 2018 6(2023), 8, Seite n/a-n/a (DE-627)101922052X (DE-600)2927182-4 23988835 nnns volume:6 year:2023 number:8 pages:n/a-n/a https://doi.org/10.1002/hsr2.1433 kostenfrei https://doaj.org/article/47ab8a1152a7429ea6639104008b12ad kostenfrei https://doi.org/10.1002/hsr2.1433 kostenfrei https://doaj.org/toc/2398-8835 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_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 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_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2023 8 n/a-n/a
allfieldsGer	10.1002/hsr2.1433 doi (DE-627)DOAJ092680046 (DE-599)DOAJ47ab8a1152a7429ea6639104008b12ad DE-627 ger DE-627 rakwb eng Morris Ogero verfasserin aut Pediatric prognostic models predicting inhospital child mortality in resource‐limited settings: An external validation study 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background and Aims Prognostic models provide evidence‐based predictions and estimates of future outcomes, facilitating decision‐making, patient care, and research. A few of these models have been externally validated, leading to uncertain reliability and generalizability. This study aims to externally validate four models to assess their transferability and usefulness in clinical practice. The models include the respiratory index of severity in children (RISC)‐Malawi model and three other models by Lowlavaar et al. Methods The study used data from the Clinical Information Network (CIN) to validate the four models where the primary outcome was in‐hospital mortality. 163,329 patients met eligibility criteria. Missing data were imputed, and the logistic function was used to compute predicted risk of in‐hospital mortality. Models' discriminatory ability and calibration were determined using area under the curve (AUC), calibration slope, and intercept. Results The RISC‐Malawi model had 50,669 pneumonia patients who met the eligibility criteria, of which the case‐fatality ratio was 4406 (8.7%). Its AUC was 0.77 (95% CI: 0.77−0.78), whereas the calibration slope was 1.04 (95% CI: 1.00 −1.06), and calibration intercept was 0.81 (95% CI: 0.77−0.84). Regarding the external validation of Lowlavaar et al. models, 10,782 eligible patients were included, with an in‐hospital mortality rate of 5.3%. The primary model's AUC was 0.75 (95% CI: 0.72−0.77), the calibration slope was 0.78 (95% CI: 0.71−0.84), and the calibration intercept was 0.37 (95% CI: 0.28−0.46). All models markedly underestimated the risk of mortality. Conclusion All externally validated models exhibited either underestimation or overestimation of the risk as judged from calibration statistics. Hence, applying these models with confidence in settings other than their original development context may not be advisable. Our findings strongly suggest the need for recalibrating these model to enhance their generalizability. epidemiology model external validation pediatrics and adolescent medicine Medicine R John Ndiritu verfasserin aut Rachel Sarguta verfasserin aut Timothy Tuti verfasserin aut Samuel Akech verfasserin aut In Health Science Reports Wiley, 2018 6(2023), 8, Seite n/a-n/a (DE-627)101922052X (DE-600)2927182-4 23988835 nnns volume:6 year:2023 number:8 pages:n/a-n/a https://doi.org/10.1002/hsr2.1433 kostenfrei https://doaj.org/article/47ab8a1152a7429ea6639104008b12ad kostenfrei https://doi.org/10.1002/hsr2.1433 kostenfrei https://doaj.org/toc/2398-8835 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_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 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_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2023 8 n/a-n/a
allfieldsSound	10.1002/hsr2.1433 doi (DE-627)DOAJ092680046 (DE-599)DOAJ47ab8a1152a7429ea6639104008b12ad DE-627 ger DE-627 rakwb eng Morris Ogero verfasserin aut Pediatric prognostic models predicting inhospital child mortality in resource‐limited settings: An external validation study 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background and Aims Prognostic models provide evidence‐based predictions and estimates of future outcomes, facilitating decision‐making, patient care, and research. A few of these models have been externally validated, leading to uncertain reliability and generalizability. This study aims to externally validate four models to assess their transferability and usefulness in clinical practice. The models include the respiratory index of severity in children (RISC)‐Malawi model and three other models by Lowlavaar et al. Methods The study used data from the Clinical Information Network (CIN) to validate the four models where the primary outcome was in‐hospital mortality. 163,329 patients met eligibility criteria. Missing data were imputed, and the logistic function was used to compute predicted risk of in‐hospital mortality. Models' discriminatory ability and calibration were determined using area under the curve (AUC), calibration slope, and intercept. Results The RISC‐Malawi model had 50,669 pneumonia patients who met the eligibility criteria, of which the case‐fatality ratio was 4406 (8.7%). Its AUC was 0.77 (95% CI: 0.77−0.78), whereas the calibration slope was 1.04 (95% CI: 1.00 −1.06), and calibration intercept was 0.81 (95% CI: 0.77−0.84). Regarding the external validation of Lowlavaar et al. models, 10,782 eligible patients were included, with an in‐hospital mortality rate of 5.3%. The primary model's AUC was 0.75 (95% CI: 0.72−0.77), the calibration slope was 0.78 (95% CI: 0.71−0.84), and the calibration intercept was 0.37 (95% CI: 0.28−0.46). All models markedly underestimated the risk of mortality. Conclusion All externally validated models exhibited either underestimation or overestimation of the risk as judged from calibration statistics. Hence, applying these models with confidence in settings other than their original development context may not be advisable. Our findings strongly suggest the need for recalibrating these model to enhance their generalizability. epidemiology model external validation pediatrics and adolescent medicine Medicine R John Ndiritu verfasserin aut Rachel Sarguta verfasserin aut Timothy Tuti verfasserin aut Samuel Akech verfasserin aut In Health Science Reports Wiley, 2018 6(2023), 8, Seite n/a-n/a (DE-627)101922052X (DE-600)2927182-4 23988835 nnns volume:6 year:2023 number:8 pages:n/a-n/a https://doi.org/10.1002/hsr2.1433 kostenfrei https://doaj.org/article/47ab8a1152a7429ea6639104008b12ad kostenfrei https://doi.org/10.1002/hsr2.1433 kostenfrei https://doaj.org/toc/2398-8835 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_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 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_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2023 8 n/a-n/a
language	English
source	In Health Science Reports 6(2023), 8, Seite n/a-n/a volume:6 year:2023 number:8 pages:n/a-n/a
sourceStr	In Health Science Reports 6(2023), 8, Seite n/a-n/a volume:6 year:2023 number:8 pages:n/a-n/a
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	epidemiology model external validation pediatrics and adolescent medicine Medicine R
isfreeaccess_bool	true
container_title	Health Science Reports
authorswithroles_txt_mv	Morris Ogero @@aut@@ John Ndiritu @@aut@@ Rachel Sarguta @@aut@@ Timothy Tuti @@aut@@ Samuel Akech @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	101922052X
id	DOAJ092680046
language_de	englisch
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">DOAJ092680046</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240412192115.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">240412s2023 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1002/hsr2.1433</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ092680046</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ47ab8a1152a7429ea6639104008b12ad</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="100" ind1="0" ind2=" "><subfield code="a">Morris Ogero</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Pediatric prognostic models predicting inhospital child mortality in resource‐limited settings: An external validation study</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">Abstract Background and Aims Prognostic models provide evidence‐based predictions and estimates of future outcomes, facilitating decision‐making, patient care, and research. A few of these models have been externally validated, leading to uncertain reliability and generalizability. This study aims to externally validate four models to assess their transferability and usefulness in clinical practice. The models include the respiratory index of severity in children (RISC)‐Malawi model and three other models by Lowlavaar et al. Methods The study used data from the Clinical Information Network (CIN) to validate the four models where the primary outcome was in‐hospital mortality. 163,329 patients met eligibility criteria. Missing data were imputed, and the logistic function was used to compute predicted risk of in‐hospital mortality. Models' discriminatory ability and calibration were determined using area under the curve (AUC), calibration slope, and intercept. Results The RISC‐Malawi model had 50,669 pneumonia patients who met the eligibility criteria, of which the case‐fatality ratio was 4406 (8.7%). Its AUC was 0.77 (95% CI: 0.77−0.78), whereas the calibration slope was 1.04 (95% CI: 1.00 −1.06), and calibration intercept was 0.81 (95% CI: 0.77−0.84). Regarding the external validation of Lowlavaar et al. models, 10,782 eligible patients were included, with an in‐hospital mortality rate of 5.3%. The primary model's AUC was 0.75 (95% CI: 0.72−0.77), the calibration slope was 0.78 (95% CI: 0.71−0.84), and the calibration intercept was 0.37 (95% CI: 0.28−0.46). All models markedly underestimated the risk of mortality. Conclusion All externally validated models exhibited either underestimation or overestimation of the risk as judged from calibration statistics. Hence, applying these models with confidence in settings other than their original development context may not be advisable. 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author	Morris Ogero
spellingShingle	Morris Ogero misc epidemiology misc model external validation misc pediatrics and adolescent medicine misc Medicine misc R Pediatric prognostic models predicting inhospital child mortality in resource‐limited settings: An external validation study
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topic_title	Pediatric prognostic models predicting inhospital child mortality in resource‐limited settings: An external validation study epidemiology model external validation pediatrics and adolescent medicine
topic	misc epidemiology misc model external validation misc pediatrics and adolescent medicine misc Medicine misc R
topic_unstemmed	misc epidemiology misc model external validation misc pediatrics and adolescent medicine misc Medicine misc R
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title	Pediatric prognostic models predicting inhospital child mortality in resource‐limited settings: An external validation study
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title_full	Pediatric prognostic models predicting inhospital child mortality in resource‐limited settings: An external validation study
author_sort	Morris Ogero
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author_browse	Morris Ogero John Ndiritu Rachel Sarguta Timothy Tuti Samuel Akech
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author-letter	Morris Ogero
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title_sort	pediatric prognostic models predicting inhospital child mortality in resource‐limited settings: an external validation study
title_auth	Pediatric prognostic models predicting inhospital child mortality in resource‐limited settings: An external validation study
abstract	Abstract Background and Aims Prognostic models provide evidence‐based predictions and estimates of future outcomes, facilitating decision‐making, patient care, and research. A few of these models have been externally validated, leading to uncertain reliability and generalizability. This study aims to externally validate four models to assess their transferability and usefulness in clinical practice. The models include the respiratory index of severity in children (RISC)‐Malawi model and three other models by Lowlavaar et al. Methods The study used data from the Clinical Information Network (CIN) to validate the four models where the primary outcome was in‐hospital mortality. 163,329 patients met eligibility criteria. Missing data were imputed, and the logistic function was used to compute predicted risk of in‐hospital mortality. Models' discriminatory ability and calibration were determined using area under the curve (AUC), calibration slope, and intercept. Results The RISC‐Malawi model had 50,669 pneumonia patients who met the eligibility criteria, of which the case‐fatality ratio was 4406 (8.7%). Its AUC was 0.77 (95% CI: 0.77−0.78), whereas the calibration slope was 1.04 (95% CI: 1.00 −1.06), and calibration intercept was 0.81 (95% CI: 0.77−0.84). Regarding the external validation of Lowlavaar et al. models, 10,782 eligible patients were included, with an in‐hospital mortality rate of 5.3%. The primary model's AUC was 0.75 (95% CI: 0.72−0.77), the calibration slope was 0.78 (95% CI: 0.71−0.84), and the calibration intercept was 0.37 (95% CI: 0.28−0.46). All models markedly underestimated the risk of mortality. Conclusion All externally validated models exhibited either underestimation or overestimation of the risk as judged from calibration statistics. Hence, applying these models with confidence in settings other than their original development context may not be advisable. Our findings strongly suggest the need for recalibrating these model to enhance their generalizability.
abstractGer	Abstract Background and Aims Prognostic models provide evidence‐based predictions and estimates of future outcomes, facilitating decision‐making, patient care, and research. A few of these models have been externally validated, leading to uncertain reliability and generalizability. This study aims to externally validate four models to assess their transferability and usefulness in clinical practice. The models include the respiratory index of severity in children (RISC)‐Malawi model and three other models by Lowlavaar et al. Methods The study used data from the Clinical Information Network (CIN) to validate the four models where the primary outcome was in‐hospital mortality. 163,329 patients met eligibility criteria. Missing data were imputed, and the logistic function was used to compute predicted risk of in‐hospital mortality. Models' discriminatory ability and calibration were determined using area under the curve (AUC), calibration slope, and intercept. Results The RISC‐Malawi model had 50,669 pneumonia patients who met the eligibility criteria, of which the case‐fatality ratio was 4406 (8.7%). Its AUC was 0.77 (95% CI: 0.77−0.78), whereas the calibration slope was 1.04 (95% CI: 1.00 −1.06), and calibration intercept was 0.81 (95% CI: 0.77−0.84). Regarding the external validation of Lowlavaar et al. models, 10,782 eligible patients were included, with an in‐hospital mortality rate of 5.3%. The primary model's AUC was 0.75 (95% CI: 0.72−0.77), the calibration slope was 0.78 (95% CI: 0.71−0.84), and the calibration intercept was 0.37 (95% CI: 0.28−0.46). All models markedly underestimated the risk of mortality. Conclusion All externally validated models exhibited either underestimation or overestimation of the risk as judged from calibration statistics. Hence, applying these models with confidence in settings other than their original development context may not be advisable. Our findings strongly suggest the need for recalibrating these model to enhance their generalizability.
abstract_unstemmed	Abstract Background and Aims Prognostic models provide evidence‐based predictions and estimates of future outcomes, facilitating decision‐making, patient care, and research. A few of these models have been externally validated, leading to uncertain reliability and generalizability. This study aims to externally validate four models to assess their transferability and usefulness in clinical practice. The models include the respiratory index of severity in children (RISC)‐Malawi model and three other models by Lowlavaar et al. Methods The study used data from the Clinical Information Network (CIN) to validate the four models where the primary outcome was in‐hospital mortality. 163,329 patients met eligibility criteria. Missing data were imputed, and the logistic function was used to compute predicted risk of in‐hospital mortality. Models' discriminatory ability and calibration were determined using area under the curve (AUC), calibration slope, and intercept. Results The RISC‐Malawi model had 50,669 pneumonia patients who met the eligibility criteria, of which the case‐fatality ratio was 4406 (8.7%). Its AUC was 0.77 (95% CI: 0.77−0.78), whereas the calibration slope was 1.04 (95% CI: 1.00 −1.06), and calibration intercept was 0.81 (95% CI: 0.77−0.84). Regarding the external validation of Lowlavaar et al. models, 10,782 eligible patients were included, with an in‐hospital mortality rate of 5.3%. The primary model's AUC was 0.75 (95% CI: 0.72−0.77), the calibration slope was 0.78 (95% CI: 0.71−0.84), and the calibration intercept was 0.37 (95% CI: 0.28−0.46). All models markedly underestimated the risk of mortality. Conclusion All externally validated models exhibited either underestimation or overestimation of the risk as judged from calibration statistics. Hence, applying these models with confidence in settings other than their original development context may not be advisable. Our findings strongly suggest the need for recalibrating these model to enhance their generalizability.
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title_short	Pediatric prognostic models predicting inhospital child mortality in resource‐limited settings: An external validation study
url	https://doi.org/10.1002/hsr2.1433 https://doaj.org/article/47ab8a1152a7429ea6639104008b12ad https://doaj.org/toc/2398-8835
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up_date	2024-07-04T02:06:37.833Z
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fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">DOAJ092680046</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240412192115.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">240412s2023 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1002/hsr2.1433</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ092680046</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ47ab8a1152a7429ea6639104008b12ad</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="100" ind1="0" ind2=" "><subfield code="a">Morris Ogero</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Pediatric prognostic models predicting inhospital child mortality in resource‐limited settings: An external validation study</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">Abstract Background and Aims Prognostic models provide evidence‐based predictions and estimates of future outcomes, facilitating decision‐making, patient care, and research. A few of these models have been externally validated, leading to uncertain reliability and generalizability. This study aims to externally validate four models to assess their transferability and usefulness in clinical practice. The models include the respiratory index of severity in children (RISC)‐Malawi model and three other models by Lowlavaar et al. Methods The study used data from the Clinical Information Network (CIN) to validate the four models where the primary outcome was in‐hospital mortality. 163,329 patients met eligibility criteria. Missing data were imputed, and the logistic function was used to compute predicted risk of in‐hospital mortality. Models' discriminatory ability and calibration were determined using area under the curve (AUC), calibration slope, and intercept. Results The RISC‐Malawi model had 50,669 pneumonia patients who met the eligibility criteria, of which the case‐fatality ratio was 4406 (8.7%). Its AUC was 0.77 (95% CI: 0.77−0.78), whereas the calibration slope was 1.04 (95% CI: 1.00 −1.06), and calibration intercept was 0.81 (95% CI: 0.77−0.84). Regarding the external validation of Lowlavaar et al. models, 10,782 eligible patients were included, with an in‐hospital mortality rate of 5.3%. The primary model's AUC was 0.75 (95% CI: 0.72−0.77), the calibration slope was 0.78 (95% CI: 0.71−0.84), and the calibration intercept was 0.37 (95% CI: 0.28−0.46). All models markedly underestimated the risk of mortality. Conclusion All externally validated models exhibited either underestimation or overestimation of the risk as judged from calibration statistics. Hence, applying these models with confidence in settings other than their original development context may not be advisable. Our findings strongly suggest the need for recalibrating these model to enhance their generalizability.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">epidemiology</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">model external validation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">pediatrics and adolescent medicine</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Medicine</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">R</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">John Ndiritu</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Rachel Sarguta</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield 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Pediatric prognostic models predicting inhospital child mortality in resource‐limited settings: An external validation study

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