Slow Dynamics of Acute Postoperative Pain Intensity Time Series Determined via Wavelet Analysis Are Associated With the Risk of Severe Postoperative Day 30 Pain
Evidence suggests that increased early postoperative pain (POP) intensities are associated with increased pain in the weeks following surgery. However, it remains unclear which temporal aspects of this early POP relate to later pain experience. In this prospective cohort study, we used wavelet analy...
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| Veröffentlicht in: | Anesthesia and analgesia 2021-05, Vol.132 (5), p.1465-1474 |
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| creator | Baharloo, Raheleh Principe, Jose C. Fillingim, Roger B. Wallace, Margaret R. Zou, Baiming Crispen, Paul L. Parvataneni, Hari K. Prieto, Hernan A. Machuca, Tiago N. Mi, Xinlei Hughes, Steven J. Murad, Gregory J. A. Rashidi, Parisa Tighe, Patrick J. |
| description | Evidence suggests that increased early postoperative pain (POP) intensities are associated with increased pain in the weeks following surgery. However, it remains unclear which temporal aspects of this early POP relate to later pain experience. In this prospective cohort study, we used wavelet analysis of clinically captured POP intensity data on postoperative days 1 and 2 to characterize slow/fast dynamics of POP intensities and predict pain outcomes on postoperative day 30.
The study used clinical POP time series from the first 48 hours following surgery from 218 patients to predict their mean POP on postoperative day 30. We first used wavelet analysis to approximate the POP series and to represent the series at different time scales to characterize the early temporal profile of acute POP in the first 2 postoperative days. We then used the wavelet coefficients alongside demographic parameters as inputs to a neural network to predict the risk of severe pain 30 days after surgery.
Slow dynamic approximation components, but not fast dynamic detailed components, were linked to pain intensity on postoperative day 30. Despite imbalanced outcome rates, using wavelet decomposition along with a neural network for classification, the model achieved an F score of 0.79 and area under the receiver operating characteristic curve of 0.74 on test-set data for classifying pain intensities on postoperative day 30. The wavelet-based approach outperformed logistic regression (F score of 0.31) and neural network (F score of 0.22) classifiers that were restricted to sociodemographic variables and linear trajectories of pain intensities.
These findings identify latent mechanistic information within the temporal domain of clinically documented acute POP intensity ratings, which are accessible via wavelet analysis, and demonstrate that such temporal patterns inform pain outcomes at postoperative day 30. |
| doi_str_mv | 10.1213/ANE.0000000000005385 |
| format | Article |
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The study used clinical POP time series from the first 48 hours following surgery from 218 patients to predict their mean POP on postoperative day 30. We first used wavelet analysis to approximate the POP series and to represent the series at different time scales to characterize the early temporal profile of acute POP in the first 2 postoperative days. We then used the wavelet coefficients alongside demographic parameters as inputs to a neural network to predict the risk of severe pain 30 days after surgery.
Slow dynamic approximation components, but not fast dynamic detailed components, were linked to pain intensity on postoperative day 30. Despite imbalanced outcome rates, using wavelet decomposition along with a neural network for classification, the model achieved an F score of 0.79 and area under the receiver operating characteristic curve of 0.74 on test-set data for classifying pain intensities on postoperative day 30. The wavelet-based approach outperformed logistic regression (F score of 0.31) and neural network (F score of 0.22) classifiers that were restricted to sociodemographic variables and linear trajectories of pain intensities.
These findings identify latent mechanistic information within the temporal domain of clinically documented acute POP intensity ratings, which are accessible via wavelet analysis, and demonstrate that such temporal patterns inform pain outcomes at postoperative day 30.</description><identifier>ISSN: 0003-2999</identifier><identifier>EISSN: 1526-7598</identifier><identifier>DOI: 10.1213/ANE.0000000000005385</identifier><identifier>PMID: 33591118</identifier><language>eng</language><publisher>United States: Lippincott Williams & Wilkin</publisher><subject>Aged ; Female ; Humans ; Male ; Middle Aged ; Neural Networks, Computer ; Pain Measurement ; Pain Perception ; Pain Threshold ; Pain, Postoperative - diagnosis ; Pain, Postoperative - etiology ; Pain, Postoperative - physiopathology ; Pain, Postoperative - psychology ; Predictive Value of Tests ; Prospective Studies ; Recovery of Function ; Severity of Illness Index ; Time Factors ; Wavelet Analysis</subject><ispartof>Anesthesia and analgesia, 2021-05, Vol.132 (5), p.1465-1474</ispartof><rights>Lippincott Williams & Wilkin</rights><rights>Copyright © 2021 International Anesthesia Research Society.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3986-7300fdad3b675ea2edae5bcb5ed946c0e7472a53a6137299dda81ca3788935f23</citedby><cites>FETCH-LOGICAL-c3986-7300fdad3b675ea2edae5bcb5ed946c0e7472a53a6137299dda81ca3788935f23</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttp://ovidsp.ovid.com/ovidweb.cgi?T=JS&NEWS=n&CSC=Y&PAGE=fulltext&D=ovft&AN=00000539-202105000-00036$$EHTML$$P50$$Gwolterskluwer$$H</linktohtml><link.rule.ids>314,780,784,4606,27922,27923,65231</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/33591118$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Baharloo, Raheleh</creatorcontrib><creatorcontrib>Principe, Jose C.</creatorcontrib><creatorcontrib>Fillingim, Roger B.</creatorcontrib><creatorcontrib>Wallace, Margaret R.</creatorcontrib><creatorcontrib>Zou, Baiming</creatorcontrib><creatorcontrib>Crispen, Paul L.</creatorcontrib><creatorcontrib>Parvataneni, Hari K.</creatorcontrib><creatorcontrib>Prieto, Hernan A.</creatorcontrib><creatorcontrib>Machuca, Tiago N.</creatorcontrib><creatorcontrib>Mi, Xinlei</creatorcontrib><creatorcontrib>Hughes, Steven J.</creatorcontrib><creatorcontrib>Murad, Gregory J. A.</creatorcontrib><creatorcontrib>Rashidi, Parisa</creatorcontrib><creatorcontrib>Tighe, Patrick J.</creatorcontrib><title>Slow Dynamics of Acute Postoperative Pain Intensity Time Series Determined via Wavelet Analysis Are Associated With the Risk of Severe Postoperative Day 30 Pain</title><title>Anesthesia and analgesia</title><addtitle>Anesth Analg</addtitle><description>Evidence suggests that increased early postoperative pain (POP) intensities are associated with increased pain in the weeks following surgery. However, it remains unclear which temporal aspects of this early POP relate to later pain experience. In this prospective cohort study, we used wavelet analysis of clinically captured POP intensity data on postoperative days 1 and 2 to characterize slow/fast dynamics of POP intensities and predict pain outcomes on postoperative day 30.
The study used clinical POP time series from the first 48 hours following surgery from 218 patients to predict their mean POP on postoperative day 30. We first used wavelet analysis to approximate the POP series and to represent the series at different time scales to characterize the early temporal profile of acute POP in the first 2 postoperative days. We then used the wavelet coefficients alongside demographic parameters as inputs to a neural network to predict the risk of severe pain 30 days after surgery.
Slow dynamic approximation components, but not fast dynamic detailed components, were linked to pain intensity on postoperative day 30. Despite imbalanced outcome rates, using wavelet decomposition along with a neural network for classification, the model achieved an F score of 0.79 and area under the receiver operating characteristic curve of 0.74 on test-set data for classifying pain intensities on postoperative day 30. The wavelet-based approach outperformed logistic regression (F score of 0.31) and neural network (F score of 0.22) classifiers that were restricted to sociodemographic variables and linear trajectories of pain intensities.
These findings identify latent mechanistic information within the temporal domain of clinically documented acute POP intensity ratings, which are accessible via wavelet analysis, and demonstrate that such temporal patterns inform pain outcomes at postoperative day 30.</description><subject>Aged</subject><subject>Female</subject><subject>Humans</subject><subject>Male</subject><subject>Middle Aged</subject><subject>Neural Networks, Computer</subject><subject>Pain Measurement</subject><subject>Pain Perception</subject><subject>Pain Threshold</subject><subject>Pain, Postoperative - diagnosis</subject><subject>Pain, Postoperative - etiology</subject><subject>Pain, Postoperative - physiopathology</subject><subject>Pain, Postoperative - psychology</subject><subject>Predictive Value of Tests</subject><subject>Prospective Studies</subject><subject>Recovery of Function</subject><subject>Severity of Illness Index</subject><subject>Time Factors</subject><subject>Wavelet Analysis</subject><issn>0003-2999</issn><issn>1526-7598</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNpdkc1u1DAUhS0EokPhDRDykk2Kf8ZJvIw6BSpVgJiiLq07zo3G1EkG25lR3oZHxUOHH9Ub61jnniPfj5DXnF1wweW75tPVBfvvKFmrJ2TBlSiLSun6KVnkV1kIrfUZeRHj9yw5q8vn5ExKpTnn9YL8XPvxQFfzAL2zkY4dbeyUkH4ZYxp3GCC5fVbgBno9JByiSzO9dT3SNQaHka4wYejdgC3dO6B3sEePiTYD-Dm6SJuAtIlxtA5S9ty5tKVpi_Sri_fHujXuMTzuW8FMJftd-5I868BHfHW6z8m391e3lx-Lm88fri-bm8JKXecfS8a6Flq5KSuFILAFVBu7UdjqZWkZVstKgJJQclnllbQt1NyCrOpaS9UJeU7ePuTuwvhjwphM76JF72HAcYpGLDXjQlRlla3LB6sNY4wBO7MLrocwG87MkY3JbMxjNnnszalh2vTY_h36A-Nf7mH0eanx3k8HDGaL4NPWnIJ0IZjgTGVRHPmW8hdw15rF</recordid><startdate>20210501</startdate><enddate>20210501</enddate><creator>Baharloo, Raheleh</creator><creator>Principe, Jose C.</creator><creator>Fillingim, Roger B.</creator><creator>Wallace, Margaret R.</creator><creator>Zou, Baiming</creator><creator>Crispen, Paul L.</creator><creator>Parvataneni, Hari K.</creator><creator>Prieto, Hernan A.</creator><creator>Machuca, Tiago N.</creator><creator>Mi, Xinlei</creator><creator>Hughes, Steven J.</creator><creator>Murad, Gregory J. A.</creator><creator>Rashidi, Parisa</creator><creator>Tighe, Patrick J.</creator><general>Lippincott Williams & Wilkin</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>7X8</scope></search><sort><creationdate>20210501</creationdate><title>Slow Dynamics of Acute Postoperative Pain Intensity Time Series Determined via Wavelet Analysis Are Associated With the Risk of Severe Postoperative Day 30 Pain</title><author>Baharloo, Raheleh ; Principe, Jose C. ; Fillingim, Roger B. ; Wallace, Margaret R. ; Zou, Baiming ; Crispen, Paul L. ; Parvataneni, Hari K. ; Prieto, Hernan A. ; Machuca, Tiago N. ; Mi, Xinlei ; Hughes, Steven J. ; Murad, Gregory J. 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A.</creatorcontrib><creatorcontrib>Rashidi, Parisa</creatorcontrib><creatorcontrib>Tighe, Patrick J.</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>Anesthesia and analgesia</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Baharloo, Raheleh</au><au>Principe, Jose C.</au><au>Fillingim, Roger B.</au><au>Wallace, Margaret R.</au><au>Zou, Baiming</au><au>Crispen, Paul L.</au><au>Parvataneni, Hari K.</au><au>Prieto, Hernan A.</au><au>Machuca, Tiago N.</au><au>Mi, Xinlei</au><au>Hughes, Steven J.</au><au>Murad, Gregory J. A.</au><au>Rashidi, Parisa</au><au>Tighe, Patrick J.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Slow Dynamics of Acute Postoperative Pain Intensity Time Series Determined via Wavelet Analysis Are Associated With the Risk of Severe Postoperative Day 30 Pain</atitle><jtitle>Anesthesia and analgesia</jtitle><addtitle>Anesth Analg</addtitle><date>2021-05-01</date><risdate>2021</risdate><volume>132</volume><issue>5</issue><spage>1465</spage><epage>1474</epage><pages>1465-1474</pages><issn>0003-2999</issn><eissn>1526-7598</eissn><abstract>Evidence suggests that increased early postoperative pain (POP) intensities are associated with increased pain in the weeks following surgery. However, it remains unclear which temporal aspects of this early POP relate to later pain experience. In this prospective cohort study, we used wavelet analysis of clinically captured POP intensity data on postoperative days 1 and 2 to characterize slow/fast dynamics of POP intensities and predict pain outcomes on postoperative day 30.
The study used clinical POP time series from the first 48 hours following surgery from 218 patients to predict their mean POP on postoperative day 30. We first used wavelet analysis to approximate the POP series and to represent the series at different time scales to characterize the early temporal profile of acute POP in the first 2 postoperative days. We then used the wavelet coefficients alongside demographic parameters as inputs to a neural network to predict the risk of severe pain 30 days after surgery.
Slow dynamic approximation components, but not fast dynamic detailed components, were linked to pain intensity on postoperative day 30. Despite imbalanced outcome rates, using wavelet decomposition along with a neural network for classification, the model achieved an F score of 0.79 and area under the receiver operating characteristic curve of 0.74 on test-set data for classifying pain intensities on postoperative day 30. The wavelet-based approach outperformed logistic regression (F score of 0.31) and neural network (F score of 0.22) classifiers that were restricted to sociodemographic variables and linear trajectories of pain intensities.
These findings identify latent mechanistic information within the temporal domain of clinically documented acute POP intensity ratings, which are accessible via wavelet analysis, and demonstrate that such temporal patterns inform pain outcomes at postoperative day 30.</abstract><cop>United States</cop><pub>Lippincott Williams & Wilkin</pub><pmid>33591118</pmid><doi>10.1213/ANE.0000000000005385</doi><tpages>10</tpages><oa>free_for_read</oa></addata></record> |
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| subjects | Aged Female Humans Male Middle Aged Neural Networks, Computer Pain Measurement Pain Perception Pain Threshold Pain, Postoperative - diagnosis Pain, Postoperative - etiology Pain, Postoperative - physiopathology Pain, Postoperative - psychology Predictive Value of Tests Prospective Studies Recovery of Function Severity of Illness Index Time Factors Wavelet Analysis |
| title | Slow Dynamics of Acute Postoperative Pain Intensity Time Series Determined via Wavelet Analysis Are Associated With the Risk of Severe Postoperative Day 30 Pain |
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