Prediction of perioperative transfusions using an artificial neural network.
BACKGROUND:Accurate prediction of operative transfusions is essential for resource allocation and identifying patients at risk of postoperative adverse events. This research examines the efficacy of using artificial neural networks (ANNs) to predict transfusions for all inpatient operations. METHODS...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2020-01-01
|
| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0229450 |
| _version_ | 1818933529737691136 |
|---|---|
| author | Steven Walczak Vic Velanovich |
| author_facet | Steven Walczak Vic Velanovich |
| author_sort | Steven Walczak |
| collection | DOAJ |
| description | BACKGROUND:Accurate prediction of operative transfusions is essential for resource allocation and identifying patients at risk of postoperative adverse events. This research examines the efficacy of using artificial neural networks (ANNs) to predict transfusions for all inpatient operations. METHODS:Over 1.6 million surgical cases over a two year period from the NSQIP-PUF database are used. Data from 2014 (750937 records) are used for model development and data from 2015 (885502 records) are used for model validation. ANN and regression models are developed to predict perioperative transfusions for surgical patients. RESULTS:Various ANN models and logistic regression, using four variable sets, are compared. The best performing ANN models with respect to both sensitivity and area under the receiver operator characteristic curve outperformed all of the regression models (p < .001) and achieved a performance of 70-80% specificity with a corresponding 75-62% sensitivity. CONCLUSION:ANNs can predict >75% of the patients who will require transfusion and 70% of those who will not. Increasing specificity to 80% still enables a sensitivity of almost 67%. The unique contribution of this research is the utilization of a single ANN model to predict transfusions across a broad range of surgical procedures. |
| first_indexed | 2024-12-20T04:49:50Z |
| format | Article |
| id | doaj.art-10983986de1b43ba9909bcdc681e32e8 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-20T04:49:50Z |
| publishDate | 2020-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-10983986de1b43ba9909bcdc681e32e82022-12-21T19:52:54ZengPublic Library of Science (PLoS)PLoS ONE1932-62032020-01-01152e022945010.1371/journal.pone.0229450Prediction of perioperative transfusions using an artificial neural network.Steven WalczakVic VelanovichBACKGROUND:Accurate prediction of operative transfusions is essential for resource allocation and identifying patients at risk of postoperative adverse events. This research examines the efficacy of using artificial neural networks (ANNs) to predict transfusions for all inpatient operations. METHODS:Over 1.6 million surgical cases over a two year period from the NSQIP-PUF database are used. Data from 2014 (750937 records) are used for model development and data from 2015 (885502 records) are used for model validation. ANN and regression models are developed to predict perioperative transfusions for surgical patients. RESULTS:Various ANN models and logistic regression, using four variable sets, are compared. The best performing ANN models with respect to both sensitivity and area under the receiver operator characteristic curve outperformed all of the regression models (p < .001) and achieved a performance of 70-80% specificity with a corresponding 75-62% sensitivity. CONCLUSION:ANNs can predict >75% of the patients who will require transfusion and 70% of those who will not. Increasing specificity to 80% still enables a sensitivity of almost 67%. The unique contribution of this research is the utilization of a single ANN model to predict transfusions across a broad range of surgical procedures.https://doi.org/10.1371/journal.pone.0229450 |
| spellingShingle | Steven Walczak Vic Velanovich Prediction of perioperative transfusions using an artificial neural network. PLoS ONE |
| title | Prediction of perioperative transfusions using an artificial neural network. |
| title_full | Prediction of perioperative transfusions using an artificial neural network. |
| title_fullStr | Prediction of perioperative transfusions using an artificial neural network. |
| title_full_unstemmed | Prediction of perioperative transfusions using an artificial neural network. |
| title_short | Prediction of perioperative transfusions using an artificial neural network. |
| title_sort | prediction of perioperative transfusions using an artificial neural network |
| url | https://doi.org/10.1371/journal.pone.0229450 |
| work_keys_str_mv | AT stevenwalczak predictionofperioperativetransfusionsusinganartificialneuralnetwork AT vicvelanovich predictionofperioperativetransfusionsusinganartificialneuralnetwork |