Predicting inadequate postoperative pain management in depressed patients: A machine learning approach
Widely-prescribed prodrug opioids (e.g., hydrocodone) require conversion by liver enzyme CYP-2D6 to exert their analgesic effects. The most commonly prescribed antidepressant, selective serotonin reuptake inhibitors (SSRIs), inhibits CYP-2D6 activity and therefore may reduce the effectiveness of pro...
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| Veröffentlicht in: | PloS one 2019-02, Vol.14 (2), p.e0210575-e0210575 |
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| creator | Parthipan, Arjun Banerjee, Imon Humphreys, Keith Asch, Steven M Curtin, Catherine Carroll, Ian Hernandez-Boussard, Tina |
| description | Widely-prescribed prodrug opioids (e.g., hydrocodone) require conversion by liver enzyme CYP-2D6 to exert their analgesic effects. The most commonly prescribed antidepressant, selective serotonin reuptake inhibitors (SSRIs), inhibits CYP-2D6 activity and therefore may reduce the effectiveness of prodrug opioids. We used a machine learning approach to identify patients prescribed a combination of SSRIs and prodrug opioids postoperatively and to examine the effect of this combination on postoperative pain control. Using EHR data from an academic medical center, we identified patients receiving surgery over a 9-year period. We developed and validated natural language processing (NLP) algorithms to extract depression-related information (diagnosis, SSRI use, symptoms) from structured and unstructured data elements. The primary outcome was the difference between preoperative pain score and postoperative pain at discharge, 3-week and 8-week time points. We developed computational models to predict the increase or decrease in the postoperative pain across the 3 time points by using the patient's EHR data (e.g. medications, vitals, demographics) captured before surgery. We evaluate the generalizability of the model using 10-fold cross-validation method where the holdout test method is repeated 10 times and mean area-under-the-curve (AUC) is considered as evaluation metrics for the prediction performance. We identified 4,306 surgical patients with symptoms of depression. A total of 14.1% were prescribed both an SSRI and a prodrug opioid, 29.4% were prescribed an SSRI and a non-prodrug opioid, 18.6% were prescribed a prodrug opioid but were not on SSRIs, and 37.5% were prescribed a non-prodrug opioid but were not on SSRIs. Our NLP algorithm identified depression with a F1 score of 0.95 against manual annotation of 300 randomly sampled clinical notes. On average, patients receiving prodrug opioids had lower average pain scores (p0.05). The machine learning algorithm accurately predicted the increase or decrease of the discharge, 3-week and 8-week follow-up pain scores when compared to the pre-operative pain score using 10-fold cross validation (mean ar |
| doi_str_mv | 10.1371/journal.pone.0210575 |
| format | Article |
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The most commonly prescribed antidepressant, selective serotonin reuptake inhibitors (SSRIs), inhibits CYP-2D6 activity and therefore may reduce the effectiveness of prodrug opioids. We used a machine learning approach to identify patients prescribed a combination of SSRIs and prodrug opioids postoperatively and to examine the effect of this combination on postoperative pain control. Using EHR data from an academic medical center, we identified patients receiving surgery over a 9-year period. We developed and validated natural language processing (NLP) algorithms to extract depression-related information (diagnosis, SSRI use, symptoms) from structured and unstructured data elements. The primary outcome was the difference between preoperative pain score and postoperative pain at discharge, 3-week and 8-week time points. We developed computational models to predict the increase or decrease in the postoperative pain across the 3 time points by using the patient's EHR data (e.g. medications, vitals, demographics) captured before surgery. We evaluate the generalizability of the model using 10-fold cross-validation method where the holdout test method is repeated 10 times and mean area-under-the-curve (AUC) is considered as evaluation metrics for the prediction performance. We identified 4,306 surgical patients with symptoms of depression. A total of 14.1% were prescribed both an SSRI and a prodrug opioid, 29.4% were prescribed an SSRI and a non-prodrug opioid, 18.6% were prescribed a prodrug opioid but were not on SSRIs, and 37.5% were prescribed a non-prodrug opioid but were not on SSRIs. Our NLP algorithm identified depression with a F1 score of 0.95 against manual annotation of 300 randomly sampled clinical notes. On average, patients receiving prodrug opioids had lower average pain scores (p<0.05), with the exception of the SSRI+ group at 3-weeks postoperative follow-up. However, SSRI+/Prodrug+ had significantly worse pain control at discharge, 3 and 8-week follow-up (p < .01) compared to SSRI+/Prodrug- patients, whereas there was no difference in pain control among the SSRI- patients by prodrug opioid (p>0.05). The machine learning algorithm accurately predicted the increase or decrease of the discharge, 3-week and 8-week follow-up pain scores when compared to the pre-operative pain score using 10-fold cross validation (mean area under the receiver operating characteristic curve 0.87, 0.81, and 0.69, respectively). Preoperative pain, surgery type, and opioid tolerance were the strongest predictors of postoperative pain control. We provide the first direct clinical evidence that the known ability of SSRIs to inhibit prodrug opioid effectiveness is associated with worse pain control among depressed patients. Current prescribing patterns indicate that prescribers may not account for this interaction when choosing an opioid. The study results imply that prescribers might instead choose direct acting opioids (e.g. oxycodone or morphine) in depressed patients on SSRIs.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0210575</identifier><identifier>PMID: 30726237</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Adult ; Aged ; Algorithms ; Analgesics ; Annotations ; Antidepressants ; Antidepressive Agents - administration & dosage ; Care and treatment ; Complications and side effects ; Computer applications ; Cytochrome P-450 ; Data mining ; Demographic aspects ; Demographics ; Demography ; Depression (Mood disorder) ; Depression - drug therapy ; Discharge ; Drug Prescriptions ; Drug tolerance ; Electronic Health Records ; Female ; Health care facilities ; Hospital patients ; Humans ; Hydrocodone - administration & dosage ; Information processing ; Learning algorithms ; Liver ; Machine Learning ; Male ; Mathematical models ; Medical centers ; Medical schools ; Medicine and Health Sciences ; Mental depression ; Methods ; Middle Aged ; Models, Biological ; Morphine ; Narcotics ; Natural Language Processing ; Opioids ; Oxycodone ; Pain ; Pain management ; Pain Management - methods ; Pain, Postoperative - drug therapy ; Pain, Postoperative - physiopathology ; Patients ; Phenols (Class of compounds) ; Postoperative pain ; Postoperative Period ; Predictions ; Prescription writing ; Psychotropic drugs ; Selective serotonin reuptake inhibitors ; Serotonin ; Serotonin uptake inhibitors ; Serotonin Uptake Inhibitors - administration & dosage ; Signs and symptoms ; Surgery ; Test procedures ; Unstructured data</subject><ispartof>PloS one, 2019-02, Vol.14 (2), p.e0210575-e0210575</ispartof><rights>COPYRIGHT 2019 Public Library of Science</rights><rights>2019 Parthipan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2019 Parthipan et al 2019 Parthipan et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c692t-2b6fa4709af410947b0ba88787a72d245a474dee728e0b598f13303897bbe1233</citedby><cites>FETCH-LOGICAL-c692t-2b6fa4709af410947b0ba88787a72d245a474dee728e0b598f13303897bbe1233</cites><orcidid>0000-0001-6553-3455</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6364959/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6364959/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,864,885,2100,2926,23865,27923,27924,53790,53792,79371,79372</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/30726237$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Palma, Jose</contributor><creatorcontrib>Parthipan, Arjun</creatorcontrib><creatorcontrib>Banerjee, Imon</creatorcontrib><creatorcontrib>Humphreys, Keith</creatorcontrib><creatorcontrib>Asch, Steven M</creatorcontrib><creatorcontrib>Curtin, Catherine</creatorcontrib><creatorcontrib>Carroll, Ian</creatorcontrib><creatorcontrib>Hernandez-Boussard, Tina</creatorcontrib><title>Predicting inadequate postoperative pain management in depressed patients: A machine learning approach</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>Widely-prescribed prodrug opioids (e.g., hydrocodone) require conversion by liver enzyme CYP-2D6 to exert their analgesic effects. The most commonly prescribed antidepressant, selective serotonin reuptake inhibitors (SSRIs), inhibits CYP-2D6 activity and therefore may reduce the effectiveness of prodrug opioids. We used a machine learning approach to identify patients prescribed a combination of SSRIs and prodrug opioids postoperatively and to examine the effect of this combination on postoperative pain control. Using EHR data from an academic medical center, we identified patients receiving surgery over a 9-year period. We developed and validated natural language processing (NLP) algorithms to extract depression-related information (diagnosis, SSRI use, symptoms) from structured and unstructured data elements. The primary outcome was the difference between preoperative pain score and postoperative pain at discharge, 3-week and 8-week time points. We developed computational models to predict the increase or decrease in the postoperative pain across the 3 time points by using the patient's EHR data (e.g. medications, vitals, demographics) captured before surgery. We evaluate the generalizability of the model using 10-fold cross-validation method where the holdout test method is repeated 10 times and mean area-under-the-curve (AUC) is considered as evaluation metrics for the prediction performance. We identified 4,306 surgical patients with symptoms of depression. A total of 14.1% were prescribed both an SSRI and a prodrug opioid, 29.4% were prescribed an SSRI and a non-prodrug opioid, 18.6% were prescribed a prodrug opioid but were not on SSRIs, and 37.5% were prescribed a non-prodrug opioid but were not on SSRIs. Our NLP algorithm identified depression with a F1 score of 0.95 against manual annotation of 300 randomly sampled clinical notes. On average, patients receiving prodrug opioids had lower average pain scores (p<0.05), with the exception of the SSRI+ group at 3-weeks postoperative follow-up. However, SSRI+/Prodrug+ had significantly worse pain control at discharge, 3 and 8-week follow-up (p < .01) compared to SSRI+/Prodrug- patients, whereas there was no difference in pain control among the SSRI- patients by prodrug opioid (p>0.05). The machine learning algorithm accurately predicted the increase or decrease of the discharge, 3-week and 8-week follow-up pain scores when compared to the pre-operative pain score using 10-fold cross validation (mean area under the receiver operating characteristic curve 0.87, 0.81, and 0.69, respectively). Preoperative pain, surgery type, and opioid tolerance were the strongest predictors of postoperative pain control. We provide the first direct clinical evidence that the known ability of SSRIs to inhibit prodrug opioid effectiveness is associated with worse pain control among depressed patients. Current prescribing patterns indicate that prescribers may not account for this interaction when choosing an opioid. The study results imply that prescribers might instead choose direct acting opioids (e.g. oxycodone or morphine) in depressed patients on SSRIs.</description><subject>Adult</subject><subject>Aged</subject><subject>Algorithms</subject><subject>Analgesics</subject><subject>Annotations</subject><subject>Antidepressants</subject><subject>Antidepressive Agents - administration & dosage</subject><subject>Care and treatment</subject><subject>Complications and side effects</subject><subject>Computer applications</subject><subject>Cytochrome P-450</subject><subject>Data mining</subject><subject>Demographic aspects</subject><subject>Demographics</subject><subject>Demography</subject><subject>Depression (Mood disorder)</subject><subject>Depression - drug therapy</subject><subject>Discharge</subject><subject>Drug Prescriptions</subject><subject>Drug tolerance</subject><subject>Electronic Health Records</subject><subject>Female</subject><subject>Health care facilities</subject><subject>Hospital patients</subject><subject>Humans</subject><subject>Hydrocodone - administration & dosage</subject><subject>Information processing</subject><subject>Learning algorithms</subject><subject>Liver</subject><subject>Machine Learning</subject><subject>Male</subject><subject>Mathematical models</subject><subject>Medical centers</subject><subject>Medical schools</subject><subject>Medicine and Health Sciences</subject><subject>Mental depression</subject><subject>Methods</subject><subject>Middle Aged</subject><subject>Models, Biological</subject><subject>Morphine</subject><subject>Narcotics</subject><subject>Natural Language Processing</subject><subject>Opioids</subject><subject>Oxycodone</subject><subject>Pain</subject><subject>Pain management</subject><subject>Pain Management - methods</subject><subject>Pain, Postoperative - drug therapy</subject><subject>Pain, Postoperative - physiopathology</subject><subject>Patients</subject><subject>Phenols (Class of compounds)</subject><subject>Postoperative pain</subject><subject>Postoperative Period</subject><subject>Predictions</subject><subject>Prescription writing</subject><subject>Psychotropic drugs</subject><subject>Selective serotonin reuptake inhibitors</subject><subject>Serotonin</subject><subject>Serotonin uptake inhibitors</subject><subject>Serotonin Uptake Inhibitors - administration & dosage</subject><subject>Signs and symptoms</subject><subject>Surgery</subject><subject>Test procedures</subject><subject>Unstructured data</subject><issn>1932-6203</issn><issn>1932-6203</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>DOA</sourceid><recordid>eNqNk9-PlDAQx4nReOfpf2CUxMTow66lBQr3YLK5-GOTS87467UZ6MB2Ay3Xlov-9xaXuyzmHgwPwMxnvsN86UTR84SsE8aTd3szWg3dejAa14QmJOPZg-g0KRld5ZSwh0fPJ9ET5_aEZKzI88fRCSOc5pTx06j5YlGq2ivdxkqDxOsRPMaDcd4MaMGrm_AGSsc9aGixR-0DGEscLDqHMiS9CkF3Hm8CU--UxrhDsHqShGGwJgSfRo8a6Bw-m-9n0Y-PH75ffF5dXn3aXmwuV3VeUr-iVd5AykkJTZqQMuUVqaAoeMGBU0nTLCRTichpgaTKyqJJGCOsKHlVYUIZO4teHnSHzjgxW-QETXjOCQ_jB2J7IKSBvRis6sH-FgaU-BswthVgvao7FA1WtUwTWULVpBIopLVkNVLSFIyShASt93O3sepR1sEGC91CdJnRaidacyNylqdlVgaBN7OANdcjOi965WrsOtBoxum7i0DSkmUBffUPev90M9VCGEDpxoS-9SQqNllwLZhFp7bre6hwSexVHc5To0J8UfB2URAYj798C6NzYvvt6_-zVz-X7OsjdofQ-Z0z3eiV0W4JpgewtsY5i82dyQkR0zrcuiGmdRDzOoSyF8c_6K7o9vyzPwv9BgQ</recordid><startdate>20190206</startdate><enddate>20190206</enddate><creator>Parthipan, Arjun</creator><creator>Banerjee, Imon</creator><creator>Humphreys, Keith</creator><creator>Asch, Steven M</creator><creator>Curtin, Catherine</creator><creator>Carroll, Ian</creator><creator>Hernandez-Boussard, Tina</creator><general>Public Library of Science</general><general>Public Library of Science (PLoS)</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>IOV</scope><scope>ISR</scope><scope>3V.</scope><scope>7QG</scope><scope>7QL</scope><scope>7QO</scope><scope>7RV</scope><scope>7SN</scope><scope>7SS</scope><scope>7T5</scope><scope>7TG</scope><scope>7TM</scope><scope>7U9</scope><scope>7X2</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8C1</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>D1I</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>H94</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB.</scope><scope>KB0</scope><scope>KL.</scope><scope>L6V</scope><scope>LK8</scope><scope>M0K</scope><scope>M0S</scope><scope>M1P</scope><scope>M7N</scope><scope>M7P</scope><scope>M7S</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PATMY</scope><scope>PDBOC</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0001-6553-3455</orcidid></search><sort><creationdate>20190206</creationdate><title>Predicting inadequate postoperative pain management in depressed patients: A machine learning approach</title><author>Parthipan, Arjun ; Banerjee, Imon ; Humphreys, Keith ; Asch, Steven M ; Curtin, Catherine ; Carroll, Ian ; Hernandez-Boussard, Tina</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c692t-2b6fa4709af410947b0ba88787a72d245a474dee728e0b598f13303897bbe1233</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Adult</topic><topic>Aged</topic><topic>Algorithms</topic><topic>Analgesics</topic><topic>Annotations</topic><topic>Antidepressants</topic><topic>Antidepressive Agents - 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methods</topic><topic>Pain, Postoperative - drug therapy</topic><topic>Pain, Postoperative - physiopathology</topic><topic>Patients</topic><topic>Phenols (Class of compounds)</topic><topic>Postoperative pain</topic><topic>Postoperative Period</topic><topic>Predictions</topic><topic>Prescription writing</topic><topic>Psychotropic drugs</topic><topic>Selective serotonin reuptake inhibitors</topic><topic>Serotonin</topic><topic>Serotonin uptake inhibitors</topic><topic>Serotonin Uptake Inhibitors - administration & dosage</topic><topic>Signs and symptoms</topic><topic>Surgery</topic><topic>Test procedures</topic><topic>Unstructured data</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Parthipan, Arjun</creatorcontrib><creatorcontrib>Banerjee, Imon</creatorcontrib><creatorcontrib>Humphreys, Keith</creatorcontrib><creatorcontrib>Asch, Steven M</creatorcontrib><creatorcontrib>Curtin, Catherine</creatorcontrib><creatorcontrib>Carroll, Ian</creatorcontrib><creatorcontrib>Hernandez-Boussard, Tina</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Gale In Context: Opposing Viewpoints</collection><collection>Gale In Context: Science</collection><collection>ProQuest Central (Corporate)</collection><collection>Animal Behavior Abstracts</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Biotechnology Research Abstracts</collection><collection>Nursing & Allied Health Database</collection><collection>Ecology Abstracts</collection><collection>Entomology Abstracts (Full archive)</collection><collection>Immunology Abstracts</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Agricultural Science Collection</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Public Health Database</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Materials Science Collection</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Materials Science Database</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>Meteorological & Geoastrophysical Abstracts - 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Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Parthipan, Arjun</au><au>Banerjee, Imon</au><au>Humphreys, Keith</au><au>Asch, Steven M</au><au>Curtin, Catherine</au><au>Carroll, Ian</au><au>Hernandez-Boussard, Tina</au><au>Palma, Jose</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Predicting inadequate postoperative pain management in depressed patients: A machine learning approach</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2019-02-06</date><risdate>2019</risdate><volume>14</volume><issue>2</issue><spage>e0210575</spage><epage>e0210575</epage><pages>e0210575-e0210575</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>Widely-prescribed prodrug opioids (e.g., hydrocodone) require conversion by liver enzyme CYP-2D6 to exert their analgesic effects. The most commonly prescribed antidepressant, selective serotonin reuptake inhibitors (SSRIs), inhibits CYP-2D6 activity and therefore may reduce the effectiveness of prodrug opioids. We used a machine learning approach to identify patients prescribed a combination of SSRIs and prodrug opioids postoperatively and to examine the effect of this combination on postoperative pain control. Using EHR data from an academic medical center, we identified patients receiving surgery over a 9-year period. We developed and validated natural language processing (NLP) algorithms to extract depression-related information (diagnosis, SSRI use, symptoms) from structured and unstructured data elements. The primary outcome was the difference between preoperative pain score and postoperative pain at discharge, 3-week and 8-week time points. We developed computational models to predict the increase or decrease in the postoperative pain across the 3 time points by using the patient's EHR data (e.g. medications, vitals, demographics) captured before surgery. We evaluate the generalizability of the model using 10-fold cross-validation method where the holdout test method is repeated 10 times and mean area-under-the-curve (AUC) is considered as evaluation metrics for the prediction performance. We identified 4,306 surgical patients with symptoms of depression. A total of 14.1% were prescribed both an SSRI and a prodrug opioid, 29.4% were prescribed an SSRI and a non-prodrug opioid, 18.6% were prescribed a prodrug opioid but were not on SSRIs, and 37.5% were prescribed a non-prodrug opioid but were not on SSRIs. Our NLP algorithm identified depression with a F1 score of 0.95 against manual annotation of 300 randomly sampled clinical notes. On average, patients receiving prodrug opioids had lower average pain scores (p<0.05), with the exception of the SSRI+ group at 3-weeks postoperative follow-up. However, SSRI+/Prodrug+ had significantly worse pain control at discharge, 3 and 8-week follow-up (p < .01) compared to SSRI+/Prodrug- patients, whereas there was no difference in pain control among the SSRI- patients by prodrug opioid (p>0.05). The machine learning algorithm accurately predicted the increase or decrease of the discharge, 3-week and 8-week follow-up pain scores when compared to the pre-operative pain score using 10-fold cross validation (mean area under the receiver operating characteristic curve 0.87, 0.81, and 0.69, respectively). Preoperative pain, surgery type, and opioid tolerance were the strongest predictors of postoperative pain control. We provide the first direct clinical evidence that the known ability of SSRIs to inhibit prodrug opioid effectiveness is associated with worse pain control among depressed patients. Current prescribing patterns indicate that prescribers may not account for this interaction when choosing an opioid. The study results imply that prescribers might instead choose direct acting opioids (e.g. oxycodone or morphine) in depressed patients on SSRIs.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>30726237</pmid><doi>10.1371/journal.pone.0210575</doi><tpages>e0210575</tpages><orcidid>https://orcid.org/0000-0001-6553-3455</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1932-6203 |
| ispartof | PloS one, 2019-02, Vol.14 (2), p.e0210575-e0210575 |
| issn | 1932-6203 1932-6203 |
| language | eng |
| recordid | cdi_plos_journals_2176707538 |
| source | MEDLINE; DOAJ Directory of Open Access Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; Public Library of Science (PLoS); PubMed Central; Free Full-Text Journals in Chemistry |
| subjects | Adult Aged Algorithms Analgesics Annotations Antidepressants Antidepressive Agents - administration & dosage Care and treatment Complications and side effects Computer applications Cytochrome P-450 Data mining Demographic aspects Demographics Demography Depression (Mood disorder) Depression - drug therapy Discharge Drug Prescriptions Drug tolerance Electronic Health Records Female Health care facilities Hospital patients Humans Hydrocodone - administration & dosage Information processing Learning algorithms Liver Machine Learning Male Mathematical models Medical centers Medical schools Medicine and Health Sciences Mental depression Methods Middle Aged Models, Biological Morphine Narcotics Natural Language Processing Opioids Oxycodone Pain Pain management Pain Management - methods Pain, Postoperative - drug therapy Pain, Postoperative - physiopathology Patients Phenols (Class of compounds) Postoperative pain Postoperative Period Predictions Prescription writing Psychotropic drugs Selective serotonin reuptake inhibitors Serotonin Serotonin uptake inhibitors Serotonin Uptake Inhibitors - administration & dosage Signs and symptoms Surgery Test procedures Unstructured data |
| title | Predicting inadequate postoperative pain management in depressed patients: A machine learning approach |
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