Prediction of opioid-related outcomes in a medicaid surgical population: Evidence to guide postoperative opiate therapy and monitoring.
<h4>Background</h4>Treatment of surgical pain is a common reason for opioid prescriptions. Being able to predict which patients are at risk for opioid abuse, dependence, and overdose (opioid-related adverse outcomes [OR-AE]) could help physicians make safer prescription decisions. We aim...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2023-08-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1011376 |
| _version_ | 1797674047185616896 |
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| author | Oualid El Hajouji Ran S Sun Alban Zammit Keith Humphreys Steven M Asch Ian Carroll Catherine M Curtin Tina Hernandez-Boussard |
| author_facet | Oualid El Hajouji Ran S Sun Alban Zammit Keith Humphreys Steven M Asch Ian Carroll Catherine M Curtin Tina Hernandez-Boussard |
| author_sort | Oualid El Hajouji |
| collection | DOAJ |
| description | <h4>Background</h4>Treatment of surgical pain is a common reason for opioid prescriptions. Being able to predict which patients are at risk for opioid abuse, dependence, and overdose (opioid-related adverse outcomes [OR-AE]) could help physicians make safer prescription decisions. We aimed to develop a machine-learning algorithm to predict the risk of OR-AE following surgery using Medicaid data with external validation across states.<h4>Methods</h4>Five machine learning models were developed and validated across seven US states (90-10 data split). The model output was the risk of OR-AE 6-months following surgery. The models were evaluated using standard metrics and area under the receiver operating characteristic curve (AUC) was used for model comparison. We assessed calibration for the top performing model and generated bootstrap estimations for standard deviations. Decision curves were generated for the top-performing model and logistic regression.<h4>Results</h4>We evaluated 96,974 surgical patients aged 15 and 64. During the 6-month period following surgery, 10,464 (10.8%) patients had an OR-AE. Outcome rates were significantly higher for patients with depression (17.5%), diabetes (13.1%) or obesity (11.1%). The random forest model achieved the best predictive performance (AUC: 0.877; F1-score: 0.57; recall: 0.69; precision:0.48). An opioid disorder diagnosis prior to surgery was the most important feature for the model, which was well calibrated and had good discrimination.<h4>Conclusions</h4>A machine learning models to predict risk of OR-AE following surgery performed well in external validation. This work could be used to assist pain management following surgery for Medicaid beneficiaries and supports a precision medicine approach to opioid prescribing. |
| first_indexed | 2024-03-11T21:54:27Z |
| format | Article |
| id | doaj.art-26b14681868a484aa0c934e7a6c83067 |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-03-11T21:54:27Z |
| publishDate | 2023-08-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-26b14681868a484aa0c934e7a6c830672023-09-26T05:30:55ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582023-08-01198e101137610.1371/journal.pcbi.1011376Prediction of opioid-related outcomes in a medicaid surgical population: Evidence to guide postoperative opiate therapy and monitoring.Oualid El HajoujiRan S SunAlban ZammitKeith HumphreysSteven M AschIan CarrollCatherine M CurtinTina Hernandez-Boussard<h4>Background</h4>Treatment of surgical pain is a common reason for opioid prescriptions. Being able to predict which patients are at risk for opioid abuse, dependence, and overdose (opioid-related adverse outcomes [OR-AE]) could help physicians make safer prescription decisions. We aimed to develop a machine-learning algorithm to predict the risk of OR-AE following surgery using Medicaid data with external validation across states.<h4>Methods</h4>Five machine learning models were developed and validated across seven US states (90-10 data split). The model output was the risk of OR-AE 6-months following surgery. The models were evaluated using standard metrics and area under the receiver operating characteristic curve (AUC) was used for model comparison. We assessed calibration for the top performing model and generated bootstrap estimations for standard deviations. Decision curves were generated for the top-performing model and logistic regression.<h4>Results</h4>We evaluated 96,974 surgical patients aged 15 and 64. During the 6-month period following surgery, 10,464 (10.8%) patients had an OR-AE. Outcome rates were significantly higher for patients with depression (17.5%), diabetes (13.1%) or obesity (11.1%). The random forest model achieved the best predictive performance (AUC: 0.877; F1-score: 0.57; recall: 0.69; precision:0.48). An opioid disorder diagnosis prior to surgery was the most important feature for the model, which was well calibrated and had good discrimination.<h4>Conclusions</h4>A machine learning models to predict risk of OR-AE following surgery performed well in external validation. This work could be used to assist pain management following surgery for Medicaid beneficiaries and supports a precision medicine approach to opioid prescribing.https://doi.org/10.1371/journal.pcbi.1011376 |
| spellingShingle | Oualid El Hajouji Ran S Sun Alban Zammit Keith Humphreys Steven M Asch Ian Carroll Catherine M Curtin Tina Hernandez-Boussard Prediction of opioid-related outcomes in a medicaid surgical population: Evidence to guide postoperative opiate therapy and monitoring. PLoS Computational Biology |
| title | Prediction of opioid-related outcomes in a medicaid surgical population: Evidence to guide postoperative opiate therapy and monitoring. |
| title_full | Prediction of opioid-related outcomes in a medicaid surgical population: Evidence to guide postoperative opiate therapy and monitoring. |
| title_fullStr | Prediction of opioid-related outcomes in a medicaid surgical population: Evidence to guide postoperative opiate therapy and monitoring. |
| title_full_unstemmed | Prediction of opioid-related outcomes in a medicaid surgical population: Evidence to guide postoperative opiate therapy and monitoring. |
| title_short | Prediction of opioid-related outcomes in a medicaid surgical population: Evidence to guide postoperative opiate therapy and monitoring. |
| title_sort | prediction of opioid related outcomes in a medicaid surgical population evidence to guide postoperative opiate therapy and monitoring |
| url | https://doi.org/10.1371/journal.pcbi.1011376 |
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