Can machine learning models improve the prediction of surgical site infection in abdominal surgery than traditional statistical models?
Objective To externally validate by revision and update the study on the efficacy of nosocomial infection control (SENIC) model of surgical site infection (SSI) using logistic regression (LR) and machine learning (ML) approaches. Methods A retrospective analysis of hospital database-derived data fro...
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2024-11-01
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| Series: | Journal of International Medical Research |
| Online Access: | https://doi.org/10.1177/03000605241293696 |
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| author | Pongsathorn Piebpien Amarit Tansawet Oraluck Pattanaprateep Anuchate Pattanateepapon Chumpon Wilasrusmee Gareth J. Mckay John Attia Ammarin Thakkinstian |
| author_facet | Pongsathorn Piebpien Amarit Tansawet Oraluck Pattanaprateep Anuchate Pattanateepapon Chumpon Wilasrusmee Gareth J. Mckay John Attia Ammarin Thakkinstian |
| author_sort | Pongsathorn Piebpien |
| collection | DOAJ |
| description | Objective To externally validate by revision and update the study on the efficacy of nosocomial infection control (SENIC) model of surgical site infection (SSI) using logistic regression (LR) and machine learning (ML) approaches. Methods A retrospective analysis of hospital database-derived data from patients that had undergone gastrointestinal, colorectal and hernia surgeries (identified by ICD-9-CM). The SENIC index was calculated and fitted in an LR. MLs were developed using decision-tree (DT), random forest (RF), extreme-gradient-boosting (XGBoost) and Naïve Bayes (NB). Results The prevalence of an SSI was 3.21% (404 of 12 596 surgeries; 95% confidence interval [CI] 2.91%, 3.53%). The C-statistic for the original SENIC model was 0.668 (95% CI 0.648, 0.688) with an observed/expected (O/E) ratio of 0.998 (interquartile range [IQR] 0.750, 1.047). An updated-SENIC-LR model with six predictors had a C-statistic of 0.768 (95% CI 0.745, 0.790) and O/E ratio of 0.999 (IQR 0.976, 1.004). The performance of MLs considering 14 predictors was poorer than the updated-SENIC-LR with C-statistics of 0.679, 0.675, 0.656 and 0.651 for NB, XGBoost, RF and DT, respectively. Overfitting was detected for ML approaches, particularly for DT, RF and XGBoost. Conclusion The updated-SENIC-LR model and NB may be useful for monitoring SSI risk following abdominal surgery. |
| format | Article |
| id | doaj-art-cd3e2186baf94e3a87e95676b22ed2e3 |
| institution | Kabale University |
| issn | 1473-2300 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Journal of International Medical Research |
| spelling | doaj-art-cd3e2186baf94e3a87e95676b22ed2e32024-11-18T08:03:33ZengSAGE PublishingJournal of International Medical Research1473-23002024-11-015210.1177/03000605241293696Can machine learning models improve the prediction of surgical site infection in abdominal surgery than traditional statistical models?Pongsathorn PiebpienAmarit TansawetOraluck PattanaprateepAnuchate PattanateepaponChumpon WilasrusmeeGareth J. MckayJohn AttiaAmmarin ThakkinstianObjective To externally validate by revision and update the study on the efficacy of nosocomial infection control (SENIC) model of surgical site infection (SSI) using logistic regression (LR) and machine learning (ML) approaches. Methods A retrospective analysis of hospital database-derived data from patients that had undergone gastrointestinal, colorectal and hernia surgeries (identified by ICD-9-CM). The SENIC index was calculated and fitted in an LR. MLs were developed using decision-tree (DT), random forest (RF), extreme-gradient-boosting (XGBoost) and Naïve Bayes (NB). Results The prevalence of an SSI was 3.21% (404 of 12 596 surgeries; 95% confidence interval [CI] 2.91%, 3.53%). The C-statistic for the original SENIC model was 0.668 (95% CI 0.648, 0.688) with an observed/expected (O/E) ratio of 0.998 (interquartile range [IQR] 0.750, 1.047). An updated-SENIC-LR model with six predictors had a C-statistic of 0.768 (95% CI 0.745, 0.790) and O/E ratio of 0.999 (IQR 0.976, 1.004). The performance of MLs considering 14 predictors was poorer than the updated-SENIC-LR with C-statistics of 0.679, 0.675, 0.656 and 0.651 for NB, XGBoost, RF and DT, respectively. Overfitting was detected for ML approaches, particularly for DT, RF and XGBoost. Conclusion The updated-SENIC-LR model and NB may be useful for monitoring SSI risk following abdominal surgery.https://doi.org/10.1177/03000605241293696 |
| spellingShingle | Pongsathorn Piebpien Amarit Tansawet Oraluck Pattanaprateep Anuchate Pattanateepapon Chumpon Wilasrusmee Gareth J. Mckay John Attia Ammarin Thakkinstian Can machine learning models improve the prediction of surgical site infection in abdominal surgery than traditional statistical models? Journal of International Medical Research |
| title | Can machine learning models improve the prediction of surgical site infection in abdominal surgery than traditional statistical models? |
| title_full | Can machine learning models improve the prediction of surgical site infection in abdominal surgery than traditional statistical models? |
| title_fullStr | Can machine learning models improve the prediction of surgical site infection in abdominal surgery than traditional statistical models? |
| title_full_unstemmed | Can machine learning models improve the prediction of surgical site infection in abdominal surgery than traditional statistical models? |
| title_short | Can machine learning models improve the prediction of surgical site infection in abdominal surgery than traditional statistical models? |
| title_sort | can machine learning models improve the prediction of surgical site infection in abdominal surgery than traditional statistical models |
| url | https://doi.org/10.1177/03000605241293696 |
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