Mortality prediction using a novel combination of biomarkers in the first day of sepsis in intensive care units

Liu, Junkun; Chengwen, Bai; Li, Binbin; Shan, Aijun; Shi, Fei; Yao, Cheng‐Bao; Zhang, Yu; Wang, Jin; Chen, Weibu; Xie, Mian; Deng, Dehui

doi:10.1038/s41598-020-79843-5

Cited by 27 publications

(32 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The 90-day mortality in our retrospective cohort of Chinese patients was 31.6%, which was higher than in previous studies [2,3,5]. Sepsis patients concluded in the present study had much higher APACHE II scores and had a longer follow-up (90-day mortality) than in previous reports,which could make an explaination of these differences [14].…”

Section: Discussioncontrasting

confidence: 67%

“…Our results are consistent with this literature. Nevertheless, international normalized ratio alone cannot accurately predict sepsis outcomes [5], which our results suggest may be due to the need to take into account other independent predictors of mortality.…”

Section: Discussionmentioning

confidence: 64%

“…Several studies have analyzed risk factors of mortality in patients with sepsis [5][6][7][8][9], but most have focused on biomarkers related to in ammation or the function of certain organs. For such a complex disease, prediction algorithms may need to take a range of biomarkers into account.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Prediction of 90-day mortality among sepsis patients based on a nomogram integrating diverse clinical indices

Zeng¹,

He²,

Zhang³

et al. 2021

Preprint

View full text Add to dashboard Cite

Background Sepsis is prevalent among intensive care units and is a frequent cause of death. Several studies have identified individual risk factors or potential predictors of sepsis-associated mortality, without defining an integrated predictive model. The present work aimed to define a nomogram for reliably predicting mortality. Methods We carried out a retrospective, single-center study based on 231 patients with sepsis who were admitted to our intensive care unit between May 2018 and October 2020. Patients were randomly split into training and validation cohorts. In the training cohort, multivariate logistic regression and a stepwise algorithm were performed to identify risk factors, which were then integrated into a predictive nomogram. Nomogram performance was assessed against the training and validation cohorts based on the area under receiver operating characteristic curves (AUC), calibration plots and decision curve analysis. Results Among the 161 patients in the training cohort and 70 patients in the validation cohort, 90-day mortality was 31.6%. Older age and higher values for the international normalized ratio, lactate level, and thrombomodulin level were associated with greater risk of 90-day mortality. The nomogram showed an AUC of 0.810 (95% CI 0.739 to 0.881) in the training cohort and 0.813 (95% CI 0.708 to 0.917) in the validation cohort. The nomogram also performed well based on the calibration curve and decision curve analysis. Conclusion This nomogram may help identify sepsis patients at elevated risk of 90-day mortality, which may help clinicians allocate resources appropriately to improve patient outcomes.

show abstract

Section: Discussioncontrasting

confidence: 67%

Section: Discussionmentioning

confidence: 64%

See 1 more Smart Citation

Prediction of 90-day mortality among sepsis patients based on a nomogram integrating diverse clinical indices

Zeng¹,

He²,

Zhang³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Several studies have analyzed risk factors for mortality in patients with sepsis [ 5 – 9 ], but most have focused on biomarkers related to inflammation or the function of certain organs. For such a complex disease, prediction algorithms may need to take a range of biomarkers into account.…”

Section: Introductionmentioning

confidence: 99%

Prediction of 90-Day Mortality among Sepsis Patients Based on a Nomogram Integrating Diverse Clinical Indices

Zeng

He²,

Zhang

et al. 2021

BioMed Research International

View full text Add to dashboard Cite

Background. Sepsis is prevalent among intensive care units and is a frequent cause of death. Several studies have identified individual risk factors or potential predictors of sepsis-associated mortality, without defining an integrated predictive model. The present work was aimed at defining a nomogram for reliably predicting mortality. Methods. We carried out a retrospective, single-center study based on 231 patients with sepsis who were admitted to our intensive care unit between May 2018 and October 2020. Patients were randomly split into training and validation cohorts. In the training cohort, multivariate logistic regression and a stepwise algorithm were performed to identify risk factors, which were then integrated into a predictive nomogram. Nomogram performance was assessed against the training and validation cohorts based on the area under receiver operating characteristic curves (AUC), calibration plots, and decision curve analysis. Results. Among the 161 patients in the training cohort and 70 patients in the validation cohort, 90-day mortality was 31.6%. Older age and higher values for the international normalized ratio, lactate level, and thrombomodulin level were associated with greater risk of 90-day mortality. The nomogram showed an AUC of 0.810 (95% CI 0.739 to 0.881) in the training cohort and 0.813 (95% CI 0.708 to 0.917) in the validation cohort. The nomogram also performed well based on the calibration curve and decision curve analysis. Conclusion. This nomogram may help identify sepsis patients at elevated risk of 90-day mortality, which may help clinicians allocate resources appropriately to improve patient outcomes.

show abstract

“…Sin embargo, los punta-jes predictivos de la mortalidad no han sido creados para tomar decisiones individuales a nivel clínico ni para seleccionar pacientes para ensayos clínicos [9]. Adicionalmente, se ha observado que tienen un menor desempeño cuando se evalúan bajo algunas condiciones como la sepsis [10,11]; afortunadamente, algunos biomarcadores han mostrado potencial para compensar estas limitaciones [10][11][12], como sucede con la combinación de interleuquina 6 (IL-6), pétido natriurético tipo B (BNP) e INR en la predicción de la mortalidad a 28 días por sepsis [13].…”

Section: Introductionunclassified

Ancho de distribución eritrocitaria como marcador asociado a riesgo de mortalidad en niños en cuidados intensivos

et al. 2021

View full text Add to dashboard Cite

Introducción. El ancho de distribución eritrocitaria (ADE) ha surgido recientemente como un biomarcador pronóstico de mortalidad y de otros resultados del paciente adulto crítico, pero en niños hay pocos reportes. El objetivo de este estudio fue evaluar la asociación entre el ADE y el riesgo de mortalidad en niños que ingresan a una unidad de cuidados intensivos pediátricos (UCIP). Metodología. Estudio de cohorte prospectivo con 266 pacientes que cumplieron con los criterios de inclusión entre enero y septiembre de 2018. Para el análisis estadístico se utilizó regresión logística multivariada para evaluar la asociación del ADE del primer día y la mortalidad. Se comparó el área bajo la curva ROC del ADE y del Índice Pediátrico de Mortalidad 2 (PIM2). Resultados. Se encontró que un ADE al ingreso mayor de 16,4% aumentaba la probabilidad de morir, con un OR de 2,6 (IC95% 1,17-5,9; p=0,019). La capacidad del ADE para discriminar mortalidad fue moderada (ROC 0,68; IC95% 0,59-0,76), menor que la del PIM2 (ROC 0,8; IC95% 0,73-0,86). El ADE y el PIM2 se correlacionaron de manera significativa, aunque débilmente (r=0,186; p<0,002). La correlación entre ADE y los días libres de ventilación mecánica fue débil pero significativa (r=-0,23; p<0,001). El ADE no se relacionó con los días de uso de medicamentos vasoactivos (r=0,042; p=0,63) ni con los días de estancia en UCIP (r=0,11; p=0,07). Conclusión. El ADE al ingreso se asoció con un riesgo moderado de mortalidad durante la estancia en UCIP. A pesar de que no demostró ser mejor que el PIM2 para pronosticar mortalidad, por ser un biomarcador asequible y de bajo costo, podría usarse en conjunto con PIM2 o con otros biomarcadores, con el fin de aumentar su capacidad predictiva en la mortalidad de los niños en cuidados intensivos. Se requieren más estudios que evalúen esta posibilidad en nuestro medio.

show abstract

Mortality prediction using a novel combination of biomarkers in the first day of sepsis in intensive care units

Cited by 27 publications

References 26 publications

Prediction of 90-day mortality among sepsis patients based on a nomogram integrating diverse clinical indices

Prediction of 90-day mortality among sepsis patients based on a nomogram integrating diverse clinical indices

Prediction of 90-Day Mortality among Sepsis Patients Based on a Nomogram Integrating Diverse Clinical Indices

Ancho de distribución eritrocitaria como marcador asociado a riesgo de mortalidad en niños en cuidados intensivos

Contact Info

Product

Resources

About