Risk prediction of 30-day mortality after stroke using machine learning: a nationwide registry-based cohort study.
BMC Neurol
; 22(1): 195, 2022 May 27.
Article
em En
| MEDLINE
| ID: mdl-35624434
ABSTRACT
BACKGROUNDS We aimed to develop and validate machine learning (ML) models for 30-day stroke mortality for mortality risk stratification and as benchmarking models for quality improvement in stroke care. METHODS:
Data from the UK Sentinel Stroke National Audit Program between 2013 to 2019 were used. Models were developed using XGBoost, Logistic Regression (LR), LR with elastic net with/without interaction terms using 80% randomly selected admissions from 2013 to 2018, validated on the 20% remaining admissions, and temporally validated on 2019 admissions. The models were developed with 30 variables. A reference model was developed using LR and 4 variables. Performances of all models was evaluated in terms of discrimination, calibration, reclassification, Brier scores and Decision-curves.RESULTS:
In total, 488,497 stroke patients with a 12.3% 30-day mortality rate were included in the analysis. In 2019 temporal validation set, XGBoost model obtained the lowest Brier score (0.069 (95% CI 0.068-0.071)) and the highest area under the ROC curve (AUC) (0.895 (95% CI 0.891-0.900)) which outperformed LR reference model by 0.04 AUC (p < 0.001) and LR with elastic net and interaction term model by 0.003 AUC (p < 0.001). All models were perfectly calibrated for low (< 5%) and moderate risk groups (5-15%) and ≈1% underestimation for high-risk groups (> 15%). The XGBoost model reclassified 1648 (8.1%) low-risk cases by the LR reference model as being moderate or high-risk and gained the most net benefit in decision curve analysis.CONCLUSIONS:
All models with 30 variables are potentially useful as benchmarking models in stroke-care quality improvement with ML slightly outperforming others.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Acidente Vascular Cerebral
/
Aprendizado de Máquina
Tipo de estudo:
Etiology_studies
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Incidence_studies
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Observational_studies
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Prognostic_studies
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Risk_factors_studies
Limite:
Humans
Idioma:
En
Revista:
BMC Neurol
Assunto da revista:
NEUROLOGIA
Ano de publicação:
2022
Tipo de documento:
Article
País de afiliação:
Reino Unido