Observational studyBMC medical research methodology2016
Predictors of in-hospital mortality following major lower extremity amputations in type 2 diabetic patients using artificial neural networks.
Observational study in BMC medical research methodology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 3 of them syntheses that pooled it.
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Who cites it
10 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- The Use of Machine Learning for Analyzing Real-World Data in Disease Prediction and Management: Systematic Review.JMIR medical informatics · 2025Pooled it
- The ability of comorbidity indices to predict mortality in an orthopedic setting: a systematic review.Systematic reviews · 2021Pooled it
- Systematic literature review of machine learning methods used in the analysis of real-world data for patient-provider decision making.BMC medical informatics and decision making · 2021Pooled it
- Evaluation of Machine Learning Model Performance in Diabetic Foot Ulcer: Retrospective Cohort Study.JMIR medical informatics · 2025Article
- Comorbid status in patients with osteomyelitis is associated with long-term incidence of extremity amputation.BMJ open diabetes research & care · 2023Observational
- Major areas of interest of artificial intelligence research applied to health care administrative data: a scoping review.Frontiers in pharmacology · 2022Article
- Artificial neural network-based models used for predicting 28- and 90-day mortality of patients with hepatitis B-associated acute-on-chronic liver failure.BMC gastroenterology · 2020Article
- Major amputations in type 2 diabetes between 2001 and 2015 in Spain: regional differences.BMC public health · 2020Observational
- Major lower extremity amputation: a contemporary analysis from an academic tertiary referral centre in a developing community.BMC surgery · 2019Article
- Noninvasive Evaluation of Liver Fibrosis Reverse Using Artificial Neural Network Model for Chronic Hepatitis B Patients.Computational and mathematical methods in medicine · 2019Article
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Authors and funding
8 authors.
Funding
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Abstract
backgroundOutcome 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.
methodsWe 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 measuresPredictors 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.
resultsFrom 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).
conclusionsElixhauser 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.
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