Predicting future hospital antimicrobial resistance prevalence using machine learning
Background: Predicting antimicrobial resistance (AMR), a top global health threat, nationwide at an aggregate hospital level could help target interventions. Using machine learning, we exploit historical AMR and antimicrobial usage to predict future AMR. Methods: Antimicrobial use and AMR prevalence...
| Κύριοι συγγραφείς: | , , , , , , , , , , , |
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| Μορφή: | Journal article |
| Γλώσσα: | English |
| Έκδοση: |
Nature Research
2024
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| _version_ | 1826314739526926336 |
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| author | Vihta, K Pritchard, E Pouwels, KB Hopkins, S Guy, RL Henderson, K Chudasama, D Hope, R Muller-Pebody, B Walker, AS Clifton, D Eyre, DW |
| author_facet | Vihta, K Pritchard, E Pouwels, KB Hopkins, S Guy, RL Henderson, K Chudasama, D Hope, R Muller-Pebody, B Walker, AS Clifton, D Eyre, DW |
| author_sort | Vihta, K |
| collection | OXFORD |
| description | Background: Predicting antimicrobial resistance (AMR), a top global health threat, nationwide at an aggregate hospital level could help target interventions. Using machine learning, we exploit historical AMR and antimicrobial usage to predict future AMR. Methods: Antimicrobial use and AMR prevalence in bloodstream infections in hospitals in England were obtained per hospital group (Trust) and financial year (FY, April–March) for 22 pathogen–antibiotic combinations (FY2016-2017 to FY2021-2022). Extreme Gradient Boosting (XGBoost) model predictions were compared to the previous value taken forwards, the difference between the previous two years taken forwards and linear trend forecasting (LTF). XGBoost feature importances were calculated to aid interpretability. Results: Here we show that XGBoost models achieve the best predictive performance. Relatively limited year-to-year variability in AMR prevalence within Trust–pathogen–antibiotic combinations means previous value taken forwards also achieves a low mean absolute error (MAE), similar to or slightly higher than XGBoost. Using the difference between the previous two years taken forward or LTF performs consistently worse. XGBoost considerably outperforms all other methods in Trusts with a larger change in AMR prevalence from FY2020-2021 (last training year) to FY2021-2022 (held-out test set). Feature importance values indicate that besides historical resistance to the same pathogen–antibiotic combination as the outcome, complex relationships between resistance in different pathogens to the same antibiotic/antibiotic class and usage are exploited for predictions. These are generally among the top ten features ranked according to their mean absolute SHAP values. Conclusions: Year-to-year resistance has generally changed little within Trust–pathogen–antibiotic combinations. In those with larger changes, XGBoost models can improve predictions, enabling informed decisions, efficient resource allocation, and targeted interventions. |
| first_indexed | 2024-12-09T03:12:12Z |
| format | Journal article |
| id | oxford-uuid:b423b759-e23c-4110-abfe-9708d9f47ce5 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-12-09T03:12:12Z |
| publishDate | 2024 |
| publisher | Nature Research |
| record_format | dspace |
| spelling | oxford-uuid:b423b759-e23c-4110-abfe-9708d9f47ce52024-10-10T20:09:28ZPredicting future hospital antimicrobial resistance prevalence using machine learningJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:b423b759-e23c-4110-abfe-9708d9f47ce5EnglishJisc Publications RouterNature Research2024Vihta, KPritchard, EPouwels, KBHopkins, SGuy, RLHenderson, KChudasama, DHope, RMuller-Pebody, BWalker, ASClifton, DEyre, DWBackground: Predicting antimicrobial resistance (AMR), a top global health threat, nationwide at an aggregate hospital level could help target interventions. Using machine learning, we exploit historical AMR and antimicrobial usage to predict future AMR. Methods: Antimicrobial use and AMR prevalence in bloodstream infections in hospitals in England were obtained per hospital group (Trust) and financial year (FY, April–March) for 22 pathogen–antibiotic combinations (FY2016-2017 to FY2021-2022). Extreme Gradient Boosting (XGBoost) model predictions were compared to the previous value taken forwards, the difference between the previous two years taken forwards and linear trend forecasting (LTF). XGBoost feature importances were calculated to aid interpretability. Results: Here we show that XGBoost models achieve the best predictive performance. Relatively limited year-to-year variability in AMR prevalence within Trust–pathogen–antibiotic combinations means previous value taken forwards also achieves a low mean absolute error (MAE), similar to or slightly higher than XGBoost. Using the difference between the previous two years taken forward or LTF performs consistently worse. XGBoost considerably outperforms all other methods in Trusts with a larger change in AMR prevalence from FY2020-2021 (last training year) to FY2021-2022 (held-out test set). Feature importance values indicate that besides historical resistance to the same pathogen–antibiotic combination as the outcome, complex relationships between resistance in different pathogens to the same antibiotic/antibiotic class and usage are exploited for predictions. These are generally among the top ten features ranked according to their mean absolute SHAP values. Conclusions: Year-to-year resistance has generally changed little within Trust–pathogen–antibiotic combinations. In those with larger changes, XGBoost models can improve predictions, enabling informed decisions, efficient resource allocation, and targeted interventions. |
| spellingShingle | Vihta, K Pritchard, E Pouwels, KB Hopkins, S Guy, RL Henderson, K Chudasama, D Hope, R Muller-Pebody, B Walker, AS Clifton, D Eyre, DW Predicting future hospital antimicrobial resistance prevalence using machine learning |
| title | Predicting future hospital antimicrobial resistance prevalence using machine learning |
| title_full | Predicting future hospital antimicrobial resistance prevalence using machine learning |
| title_fullStr | Predicting future hospital antimicrobial resistance prevalence using machine learning |
| title_full_unstemmed | Predicting future hospital antimicrobial resistance prevalence using machine learning |
| title_short | Predicting future hospital antimicrobial resistance prevalence using machine learning |
| title_sort | predicting future hospital antimicrobial resistance prevalence using machine learning |
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