Development of a predictive risk model for all-cause mortality in patients with diabetes in Hong Kong.
BMJ Open Diabetes Res Care
; 9(1)2021 06.
Article
em En
| MEDLINE
| ID: mdl-34117050
ABSTRACT
INTRODUCTION:
Patients with diabetes mellitus are risk of premature death. In this study, we developed a machine learning-driven predictive risk model for all-cause mortality among patients with type 2 diabetes mellitus using multiparametric approach with data from different domains. RESEARCH DESIGN ANDMETHODS:
This study used territory-wide data of patients with type 2 diabetes attending public hospitals or their associated ambulatory/outpatient facilities in Hong Kong between January 1, 2009 and December 31, 2009. The primary outcome is all-cause mortality. The association of risk variables and all-cause mortality was assessed using Cox proportional hazards models. Machine and deep learning approaches were used to improve overall survival prediction and were evaluated with fivefold cross validation method.RESULTS:
A total of 273 678 patients (mean age 65.4±12.7 years, male 48.2%, median follow-up 142 (IQR=106-142) months) were included, with 91 155 deaths occurring on follow-up (33.3%; annualized mortality rate 3.4%/year; 2.7 million patient-years). Multivariate Cox regression found the following significant predictors of all-cause mortality age, male gender, baseline comorbidities, anemia, mean values of neutrophil-to-lymphocyte ratio, high-density lipoprotein-cholesterol, total cholesterol, triglyceride, HbA1c and fasting blood glucose (FBG), measures of variability of both HbA1c and FBG. The above parameters were incorporated into a score-based predictive risk model that had a c-statistic of 0.73 (95% CI 0.66 to 0.77), which was improved to 0.86 (0.81 to 0.90) and 0.87 (0.84 to 0.91) using random survival forests and deep survival learning models, respectively.CONCLUSIONS:
A multiparametric model incorporating variables from different domains predicted all-cause mortality accurately in type 2 diabetes mellitus. The predictive and modeling capabilities of machine/deep learning survival analysis achieved more accurate predictions.Palavras-chave
Texto completo:
1
Bases de dados:
MEDLINE
Assunto principal:
Diabetes Mellitus Tipo 2
Tipo de estudo:
Etiology_studies
/
Prognostic_studies
/
Risk_factors_studies
Limite:
Aged
/
Humans
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Male
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Middle aged
País/Região como assunto:
Asia
Idioma:
En
Revista:
BMJ Open Diabetes Res Care
Ano de publicação:
2021
Tipo de documento:
Article
País de afiliação:
Hong Kong