Identifying early-measured variables associated with APACHE IVa providing incorrect in-hospital mortality predictions for critical care patients

Abstract APACHE IVa provides typically useful and accurate predictions on in-hospital mortality and length of stay for patients in critical care. However, there are factors which may preclude APACHE IVa from reaching its ceiling of predictive accuracy. Our primary aim was to determine which variable...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Shuo Feng, Joel A. Dubin
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/6d6d04c0803943c2a1eb1afa4a596948
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:6d6d04c0803943c2a1eb1afa4a596948
record_format	dspace
spelling	oai:doaj.org-article:6d6d04c0803943c2a1eb1afa4a5969482021-11-14T12:22:22ZIdentifying early-measured variables associated with APACHE IVa providing incorrect in-hospital mortality predictions for critical care patients10.1038/s41598-021-01290-72045-2322https://doaj.org/article/6d6d04c0803943c2a1eb1afa4a5969482021-11-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-01290-7https://doaj.org/toc/2045-2322Abstract APACHE IVa provides typically useful and accurate predictions on in-hospital mortality and length of stay for patients in critical care. However, there are factors which may preclude APACHE IVa from reaching its ceiling of predictive accuracy. Our primary aim was to determine which variables available within the first 24 h of a patient’s ICU stay may be indicative of the APACHE IVa scoring system making occasional but potentially illuminating errors in predicting in-hospital mortality. We utilized the publicly available multi-institutional ICU database, eICU, available since 2018, to identify a large observational cohort for our investigation. APACHE IVa scores are provided by eICU for each patient’s ICU stay. We used Lasso logistic regression in an aim to build parsimonious final models, using cross-validation to select the penalization parameter, separately for each of our two responses, i.e., errors, of interest, which are APACHE falsely predicting in-hospital death (Type I error), and APACHE falsely predicting in-hospital survival (Type II error). We then assessed the performance of the models with a random holdout validation sample. While the extremeness of the APACHE prediction led to dependable predictions for preventing either type of error, distinct variables were identified as being strongly associated with the two different types of errors occurring. These included a primary set of predictors consisting of mean SpO2 and worst lactate for predicting Type I errors, and worst albumin and mean heart rate for Type II. In addition, a secondary set of predictors including changes recorded in care limitations for the patient’s treatment plan, worst pH, whether cardiac arrest occurred at admission, and whether vasopressor was provided for predicting Type I error; age, whether the patient was ventilated in day 1, mean respiratory rate, worst lactate, worst blood urea nitrogen test, and mean aperiodic vitals for Type II. The two models also differed in their performance metrics in their holdout validation samples, in large part due to the lower prevalence of Type II errors compared to Type I. The eICU database was a good resource for evaluating our objective, and important recommendations are provided, particularly identifying key variables that could lead to APACHE prediction errors when APACHE scores are sufficiently low to predict in-hospital survival.Shuo FengJoel A. DubinNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Shuo Feng Joel A. Dubin Identifying early-measured variables associated with APACHE IVa providing incorrect in-hospital mortality predictions for critical care patients
description	Abstract APACHE IVa provides typically useful and accurate predictions on in-hospital mortality and length of stay for patients in critical care. However, there are factors which may preclude APACHE IVa from reaching its ceiling of predictive accuracy. Our primary aim was to determine which variables available within the first 24 h of a patient’s ICU stay may be indicative of the APACHE IVa scoring system making occasional but potentially illuminating errors in predicting in-hospital mortality. We utilized the publicly available multi-institutional ICU database, eICU, available since 2018, to identify a large observational cohort for our investigation. APACHE IVa scores are provided by eICU for each patient’s ICU stay. We used Lasso logistic regression in an aim to build parsimonious final models, using cross-validation to select the penalization parameter, separately for each of our two responses, i.e., errors, of interest, which are APACHE falsely predicting in-hospital death (Type I error), and APACHE falsely predicting in-hospital survival (Type II error). We then assessed the performance of the models with a random holdout validation sample. While the extremeness of the APACHE prediction led to dependable predictions for preventing either type of error, distinct variables were identified as being strongly associated with the two different types of errors occurring. These included a primary set of predictors consisting of mean SpO2 and worst lactate for predicting Type I errors, and worst albumin and mean heart rate for Type II. In addition, a secondary set of predictors including changes recorded in care limitations for the patient’s treatment plan, worst pH, whether cardiac arrest occurred at admission, and whether vasopressor was provided for predicting Type I error; age, whether the patient was ventilated in day 1, mean respiratory rate, worst lactate, worst blood urea nitrogen test, and mean aperiodic vitals for Type II. The two models also differed in their performance metrics in their holdout validation samples, in large part due to the lower prevalence of Type II errors compared to Type I. The eICU database was a good resource for evaluating our objective, and important recommendations are provided, particularly identifying key variables that could lead to APACHE prediction errors when APACHE scores are sufficiently low to predict in-hospital survival.
format	article
author	Shuo Feng Joel A. Dubin
author_facet	Shuo Feng Joel A. Dubin
author_sort	Shuo Feng
title	Identifying early-measured variables associated with APACHE IVa providing incorrect in-hospital mortality predictions for critical care patients
title_short	Identifying early-measured variables associated with APACHE IVa providing incorrect in-hospital mortality predictions for critical care patients
title_full	Identifying early-measured variables associated with APACHE IVa providing incorrect in-hospital mortality predictions for critical care patients
title_fullStr	Identifying early-measured variables associated with APACHE IVa providing incorrect in-hospital mortality predictions for critical care patients
title_full_unstemmed	Identifying early-measured variables associated with APACHE IVa providing incorrect in-hospital mortality predictions for critical care patients
title_sort	identifying early-measured variables associated with apache iva providing incorrect in-hospital mortality predictions for critical care patients
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/6d6d04c0803943c2a1eb1afa4a596948
work_keys_str_mv	AT shuofeng identifyingearlymeasuredvariablesassociatedwithapacheivaprovidingincorrectinhospitalmortalitypredictionsforcriticalcarepatients AT joeladubin identifyingearlymeasuredvariablesassociatedwithapacheivaprovidingincorrectinhospitalmortalitypredictionsforcriticalcarepatients
_version_	1718429231745073152

Identifying early-measured variables associated with APACHE IVa providing incorrect in-hospital mortality predictions for critical care patients

Ejemplares similares