Pressure injury prediction models for critically-ill patients should consider both the case-mix and local factors

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00064203%3A_____%2F21%3A10459104" target="_blank" >RIV/00064203:_____/21:10459104 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216208:11130/21:10459104

Výsledek na webu

<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=DpOeaQZUjK" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=DpOeaQZUjK</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.iccn.2021.103033" target="_blank" >10.1016/j.iccn.2021.103033</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Pressure injury prediction models for critically-ill patients should consider both the case-mix and local factors

Popis výsledku v původním jazyce

Pressure injuries in intensive care unit (ICU) patients are associated with unfavourable outcomes Models for predicting ICU-acquired pressure injury have been developed using machine learning and classic regression techniques, respectively resulting in areas under the receiver operating characteristic curve (AUC) of 0.79 and 0.89 , but also resulting in large differences among predictors identified. The latter may, at least partially, be associated with the fact that these models were not built for distinct subgroups within the highly-heterogeneous ICU population. Alderden and colleagues (2018) used surgical and cardiovascular-surgical ICU patient data; Ladios-Martin et al. (2020) used data from a mixed ICU population. Such generalised approaches often originate from the researchers&apos; lack of sufficient data for distinct ICU subpopulations.

Název v anglickém jazyce

Pressure injury prediction models for critically-ill patients should consider both the case-mix and local factors

Popis výsledku anglicky

Pressure injuries in intensive care unit (ICU) patients are associated with unfavourable outcomes Models for predicting ICU-acquired pressure injury have been developed using machine learning and classic regression techniques, respectively resulting in areas under the receiver operating characteristic curve (AUC) of 0.79 and 0.89 , but also resulting in large differences among predictors identified. The latter may, at least partially, be associated with the fact that these models were not built for distinct subgroups within the highly-heterogeneous ICU population. Alderden and colleagues (2018) used surgical and cardiovascular-surgical ICU patient data; Ladios-Martin et al. (2020) used data from a mixed ICU population. Such generalised approaches often originate from the researchers&apos; lack of sufficient data for distinct ICU subpopulations.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

30223 - Anaesthesiology

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Intensive and Critical Care Nursing

ISSN

0964-3397

e-ISSN

—

Svazek periodika

65

Číslo periodika v rámci svazku

August

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

2

Strana od-do

103033

Kód UT WoS článku

000661431800015

EID výsledku v databázi Scopus

2-s2.0-85103927355