Limits...
Predictors of in-hospital mortality following major lower extremity amputations in type 2 diabetic patients using artificial neural networks

View Article: PubMed Central - PubMed

ABSTRACT

Background: Outcome prediction is important in the clinical decision-making process. Artificial neural networks (ANN) have been used to predict the risk of post-operative events, including survival, and are increasingly being used in complex medical decision making. We aimed to use ANN analysis to estimate predictive factors of in-hospital mortality (IHM) in patients with type 2 diabetes (T2DM) after major lower extremity amputation (LEA) in Spain.

Methods: We design a retrospective, observational study using ANN models. We used the Spanish National Hospital Discharge Database to select all hospital admissions of major LEA procedure in T2DM patients. Main outcome measures: Predictors of IHM using 4 ANN models: i) with all discharge diagnosis included in the database; ii) with all discharge diagnosis included in the database, excluding infectious diseases; iii) comorbidities included in the Charlson Comorbidities Index; iv) comorbidities included in the Elixhauser Comorbidity Index.

Results: From 2003 to 2013, 40,857 major LEAs in patients with T2DM were identified with a 10.0% IHM. We found that Elixhauser Comorbidity Index model performed better in terms of sensitivity, specificity and precision than Charlson Comorbidity Index model (0.7634 vs 0.7444; 0.9602 vs 0.9121; 0.9511 vs 0.888, respectively). The area under the ROC curve for Elixhauser comorbidity model was 91.7% (95% CI 90.3–93.0) and for Charlson comorbidity model was 88.9% (95% CI; 87.590.2) p = 0.043. Models including all discharge diagnosis with and without infectious diseases showed worse results. In the Elixhauser Comorbidity Index model the most sensitive parameter was age (variable sensitive ratio [VSR] 1.451) followed by female sex (VSR 1.433), congestive heart failure (VSR 1.341), renal failure (VSR 1.274) and chronic pulmonary disease (VSR 1.266).

Conclusions: Elixhauser Comorbidity Index is a superior comorbidity risk-adjustment model for major LEA survival prediction in patients with T2DM than Charlson Comorbidity Index model using ANN models. Female sex, congestive heart failure, and renal failure are strong predictors of mortality in these patients.

No MeSH data available.


Area under the ROC curve of the Charlson comorbidities index model and the Elixhauser comorbidities index model in predicting the in-hospital mortality rate after major LEA in patients with T2DM in Spain
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
getmorefigures.php?uid=PMC5120563&req=5

Fig1: Area under the ROC curve of the Charlson comorbidities index model and the Elixhauser comorbidities index model in predicting the in-hospital mortality rate after major LEA in patients with T2DM in Spain

Mentions: Compared with CCI model, ECI model showed a significantly better (p = 0.043) area under the ROC curve (91.7% [95% CI 90.3–93.0] vs 88.9% [95% CI; 87.5–90.2]) as can been seen in Fig. 1. The area under the curve for models 1 and 2 were 0.87 and 0.867, respectively, showing that these models are less accurate to predict IHM after major LEAS using ANN.Fig. 1


Predictors of in-hospital mortality following major lower extremity amputations in type 2 diabetic patients using artificial neural networks
Area under the ROC curve of the Charlson comorbidities index model and the Elixhauser comorbidities index model in predicting the in-hospital mortality rate after major LEA in patients with T2DM in Spain
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC5120563&req=5

Fig1: Area under the ROC curve of the Charlson comorbidities index model and the Elixhauser comorbidities index model in predicting the in-hospital mortality rate after major LEA in patients with T2DM in Spain
Mentions: Compared with CCI model, ECI model showed a significantly better (p = 0.043) area under the ROC curve (91.7% [95% CI 90.3–93.0] vs 88.9% [95% CI; 87.5–90.2]) as can been seen in Fig. 1. The area under the curve for models 1 and 2 were 0.87 and 0.867, respectively, showing that these models are less accurate to predict IHM after major LEAS using ANN.Fig. 1

View Article: PubMed Central - PubMed

ABSTRACT

Background: Outcome prediction is important in the clinical decision-making process. Artificial neural networks (ANN) have been used to predict the risk of post-operative events, including survival, and are increasingly being used in complex medical decision making. We aimed to use ANN analysis to estimate predictive factors of in-hospital mortality (IHM) in patients with type 2 diabetes (T2DM) after major lower extremity amputation (LEA) in Spain.

Methods: We design a retrospective, observational study using ANN models. We used the Spanish National Hospital Discharge Database to select all hospital admissions of major LEA procedure in T2DM patients. Main outcome measures: Predictors of IHM using 4 ANN models: i) with all discharge diagnosis included in the database; ii) with all discharge diagnosis included in the database, excluding infectious diseases; iii) comorbidities included in the Charlson Comorbidities Index; iv) comorbidities included in the Elixhauser Comorbidity Index.

Results: From 2003 to 2013, 40,857 major LEAs in patients with T2DM were identified with a 10.0% IHM. We found that Elixhauser Comorbidity Index model performed better in terms of sensitivity, specificity and precision than Charlson Comorbidity Index model (0.7634 vs 0.7444; 0.9602 vs 0.9121; 0.9511 vs 0.888, respectively). The area under the ROC curve for Elixhauser comorbidity model was 91.7% (95% CI 90.3–93.0) and for Charlson comorbidity model was 88.9% (95% CI; 87.590.2) p = 0.043. Models including all discharge diagnosis with and without infectious diseases showed worse results. In the Elixhauser Comorbidity Index model the most sensitive parameter was age (variable sensitive ratio [VSR] 1.451) followed by female sex (VSR 1.433), congestive heart failure (VSR 1.341), renal failure (VSR 1.274) and chronic pulmonary disease (VSR 1.266).

Conclusions: Elixhauser Comorbidity Index is a superior comorbidity risk-adjustment model for major LEA survival prediction in patients with T2DM than Charlson Comorbidity Index model using ANN models. Female sex, congestive heart failure, and renal failure are strong predictors of mortality in these patients.

No MeSH data available.