Case studies

Predictors of mortality following amputation in type 2 diabetic patients

Outcome prediction is very important in the clinical decision-making process. By analyzing risk factors, you can predict the outcome of a determined event, such as in-hospital mortality after a medical operation.

This can be used to decide wether to perform a certain operation or not depending on the outcome prediction.

In order to improve their improve their clinical decision process, Artelnics worked with Hospital Rey Juan Carlos, located in Madrid, to predict in-hospital mortality following major lower extremity amputations (LEA) in type 2 diabetic patients using artificial intelligence.

Data set

In this project, we used the Spanish National Hospital Discharge Database (period 2003-2013) to select all hospital admissions of major LEA procedure in T2DM patients.

The data file analyzed included all procedures of major amputation, defined as any LEA through or proximal to the ankle joint. The final sample included 40,857 patients who undergone major amputation procedure between January 1, 2003 and December 31, 2013.

Data analysis

Our team elaborated different neural network models to analyze which one offered the best results. The two models that offered the best result were:

Both models used a standard feed-forward, back-propagation neural network, each with its own inputs, trained using the quasi-Newton training algorithm and optimized using model selection algorithms.

In order to comparate the models, a comparison of performance in terms of binary classification tests was made:

Indices Description Charlson Elixhauser
Accuracy rate Ratio of instances correctly classified 0.8300.861
Error rate Ratio of instances misclassified 0.169 0.138
Sensitivity Portion of actual positives which are predicted positive 0.7440.763
Specifity Portion of actual negative predicted negative 0.912 0.960

In order to have a better knowledge about which model to use, we studied the ROC curves.

ROC curve CCI model

The ECI model (that you can see above) showed a better area under the ROC curve 91.7% compared with the CCI model (91.7% [95% CI 90.3–93.0] vs 88.9% [95% CI; 87.5–90.2]), which means that it will correctly predict more cases.

After knowing that the CCI offered better results, Artelnics studied the different variable sensitive ratios (VSR) to know which variables are the best predictors for IHM following major ELA.

Variable ranking CCI variables Variable sensitive ratios
1st Age 1.577
2nd Female 1.559
3rd Myocardial infarction 1.477
4th Renal disease 1.456
5th Congestive heart failure 1.447
6th Moderate or severe liver disease 1.412
7th Metastatic solid tumor 1.362

Neural Network

Now that we have determined that the model to use is the Charlson comordibity index, we can design a neural network able to predict in-hospital mortality in the studied cases. The inputs of the neural network correspond to the different variables the CCI considers. Then, the 2 hidden layers will make the necessary calculations to predict the output, mortality.

Mortality neural network

Conclusions

With our work, we compared the two most commonly used comordibity risk ajustment models, CCI and ECI, to determine which one adjusts better to type 2 diabetes mortality following major lower extremity amputation, underlying that some of the patient conditions make them more likely than others to have more health complications.

It can be seen from the study that the Charlson comordibity offers slightly better results than can result into less errors.

IHM after major LEA in type 2 diabetes patients ranges from 7% to 12.4%. A further investigation in the motives behind the decision to amputate would explain the difference in post-operative mortality rates.

One of the variables that affect the most is age. Older age is associated to a higher prevalence of comorbid conditions, which are as well associated to higher IHM rates. It can also be seen that women have higher mortality rates in type 2 diabetes. In this graph, you can see the neural network created with the Charlson comordibity index variables.

In conclusion, the predictors analyzed in our study could and should be applied in the clinical decision making to reduce mortality risk in type 2 diabetes patients.

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References