A Risk Prediction Model for Invasive Fungal Disease in Critically Ill Patients in the Intensive Care Unit
Asian Nursing Research
;
: 299-303, 2018.
Artículo
en Inglés
| WPRIM
| ID: wpr-718372
ABSTRACT
PURPOSE:
Developing a risk prediction model for invasive fungal disease based on an analysis of the disease-related risk factors in critically ill patients in the intensive care unit (ICU) to diagnose the invasive fungal disease in the early stages and determine the time of initiating early antifungal treatment.METHODS:
Data were collected retrospectively from 141 critically ill adult patients with at least 4 days of general ICU stay at Sun Yat-sen Memorial Hospital, Sun Yat-sen University during the period from February 2015 to February 2016. Logistic regression was used to develop the risk prediction model. Discriminative power was evaluated by the area under the receiver operating characteristics (ROC) curve (AUC).RESULTS:
Sequential organ failure assessment (SOFA) score, antibiotic treatment period, and positive culture of Candida albicans other than normally sterile sites are the three predictors of invasive fungal disease in critically ill patients in the ICU. The model performs well with an ROC-AUC of .73.CONCLUSION:
The risk prediction model performs well to discriminate between critically ill patients with or without invasive fungal disease. Physicians could use this prediction model for early diagnosis of invasive fungal disease and determination of the time to start early antifungal treatment of critically ill patients in the ICU.
Texto completo:
Disponible
Índice:
WPRIM (Pacífico Occidental)
Asunto principal:
Candida albicans
/
Modelos Logísticos
/
Estudios Retrospectivos
/
Factores de Riesgo
/
Curva ROC
/
Sistema Solar
/
Enfermedad Crítica
/
Cuidados Críticos
/
Diagnóstico Precoz
/
Unidades de Cuidados Intensivos
Tipo de estudio:
Estudio diagnóstico
/
Estudio de etiología
/
Estudio observacional
/
Estudio pronóstico
/
Estudio de tamizaje
Límite:
Adulto
/
Humanos
Idioma:
Inglés
Revista:
Asian Nursing Research
Año:
2018
Tipo del documento:
Artículo
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