Predictive model for urosepsis in patients with Upper Urinary Tract Calculi based on ultrasonography and urinalysis using artificial intelligence learning
Int. braz. j. urol
;
49(2): 221-232, March-Apr. 2023. tab, graf
Artigo
em Inglês
|
LILACS-Express
| LILACS
| ID: biblio-1440240
ABSTRACT
ABSTRACT Purpose To construct a predicting model for urosepsis risk for patients with upper urinary tract calculi based on ultrasound and urinalysis. Materials and Methods A retrospective study was conducted in patients with upper urinary tract calculi admitted between January 2016 and January 2020. The patients were randomly grouped into the training and validation sets. The training set was used to identify the urosepsis risk factors and construct a risk prediction model based on ultrasound and urinalysis. The validation set was used to test the performance of the artificial neural network (ANN). Results Ultimately, 1716 patients (10.8% cases and 89.2% control) were included. Eight variables were selected for the model sex, age, body temperature, diabetes history, urine leukocytes, urine nitrite, urine glucose, and degree of hydronephrosis. The area under the receiver operating curve in the validation and training sets was 0.945 (95% CI 0.903-0.988) and 0.992 (95% CI 0.988-0.997), respectively. Sensitivity, specificity, and Yuden index of the validation set (training set) were 80.4% (85.9%), 98.2% (99.0%), and 0.786 (0.849), respectively. Conclusions A preliminary screening model for urosepsis based on ultrasound and urinalysis was constructed using ANN. The model could provide risk assessments for urosepsis in patients with upper urinary tract calculi.
Texto completo:
DisponíveL
Índice:
LILACS (Américas)
Tipo de estudo:
Estudo observacional
/
Estudo prognóstico
/
Fatores de risco
Idioma:
Inglês
Revista:
Int. braz. j. urol
Assunto da revista:
Urologia
Ano de publicação:
2023
Tipo de documento:
Artigo
/
Documento de projeto
País de afiliação:
China
Instituição/País de afiliação:
Shantou Central Hospital/CN
/
Sun Yat-sen University/CN
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