Prediction of complication related death after radical cystectomy for bladder cancer with machine learning methodology.
Scand J Urol
; 53(5): 325-331, 2019 Oct.
Article
em En
| MEDLINE
| ID: mdl-31552774
ABSTRACT
Purpose:
To create a pre-operatively usable tool to identify patients at high risk of early death (within 90 days post-operatively) after radical cystectomy and to assess potential risk factors for post-operative and surgery related mortality.Materials andmethods:
Material consists of 1099 consecutive radical cystectomy (RC) patients operated at 16 different hospitals in Finland 2005-2014. Machine learning methodology was utilized. For model building and testing, the data was randomly divided into training data (n = 733, 66.7%) and independent testing data (n = 366, 33.3%). To predict the risk of early death after RC from baseline variables, a binary classifier was constructed using logistic regression with lasso regularization. Finally, a user-friendly risk table was constructed for practical use.Results:
The model resulted in an area under the receiver operating characteristic curve (AUROC) of 0.73 (95% CI = 0.59-0.87). The strongest risk factors were American Society of Anesthesiologists physical status classification (ASA), congestive heart failure (CHF), age adjusted Charlson comorbidity index (ACCI) and chronic pulmonary disease.Conclusion:
This study with a novel methodological approach adds CHF and chronic pulmonary disease to previously known independent prognostic risk factors for early death after RC. Importantly, the risk prediction tool uses purely pre-operative data and can be used before surgery.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Complicações Pós-Operatórias
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Neoplasias da Bexiga Urinária
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Cistectomia
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Aprendizado de Máquina
Tipo de estudo:
Etiology_studies
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Observational_studies
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Prognostic_studies
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Risk_factors_studies
Limite:
Aged
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Aged80
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Female
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Humans
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Male
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Middle aged
Idioma:
En
Revista:
Scand J Urol
Ano de publicação:
2019
Tipo de documento:
Article
País de afiliação:
Finlândia