Establishment of predicting equation for individual sufentanil dosage postoperatively based on gene polymorphisms

Background Postoperative analgesia is widely used for patients undergoing major surgeries. Individual differences in genetic polymorphisms may be obstructive factors for accurately anesthetics using. However, the equation for predicting sufentanil dosage postoperatively based on genetic design has b...

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Veröffentlicht in:	Pain practice 2022-01, Vol.22 (1), p.39-46
Hauptverfasser:	Cheng, Huawei, Zeng, Rong, Kong, Lingsuo, Ding, Conglan, He, Yifu, Zhuang, Wei, Sun, Yancai
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Sprache:	eng
Schlagworte:	Analgesia, Patient-Controlled Analgesics, Opioid - therapeutic use ATP Binding Cassette Transporter, Subfamily B - genetics Catechol O-Methyltransferase - genetics Cytochrome P-450 CYP3A - genetics gene polymorphisms Humans individual dosage Lung Neoplasms - surgery multiple linear regression equation Pain Management Pain, Postoperative - drug therapy Pain, Postoperative - genetics Pharmacogenomic Testing Polymorphism, Genetic Receptors, Opioid, mu - genetics sufentanil Sufentanil - administration & dosage
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creator	Cheng, Huawei Zeng, Rong Kong, Lingsuo Ding, Conglan He, Yifu Zhuang, Wei Sun, Yancai
description	Background Postoperative analgesia is widely used for patients undergoing major surgeries. Individual differences in genetic polymorphisms may be obstructive factors for accurately anesthetics using. However, the equation for predicting sufentanil dosage postoperatively based on genetic design has been established yet. Our aim was to establish sufentanil dosage postoperatively prediction equation based on patients’ genetic polymorphisms. Methods One hundred forty patients with total gastrectomy and radical resection of pulmonary carcinoma were included. To establish sufentanil dosage postoperatively for patients with gastric cancer, we collected patients’ basic information and CYP3A4*1G, COMTVal158Met, OPRM1A118G, and ABCB1C3435T gene sequencing results. To verify this equation, we put patients’ with lung cancer surgeries information into it. Results The sufentanil dosage prediction equation postoperatively was y = 4.104 − 0.222 × (gender) + 0.021 × (OPRM1A118G) + 0.249 × (ABCB1C3435T). Patients’ with lung cancer surgeries information were substituted into it. The results showed no significant differences between predicted and actual sufentanil dosage (p > 0.05). Conclusion We established the prediction equation for individual sufentanil dosage postoperatively based on gene polymorphisms. The results showed this prediction equation was valid, which might be used for different types of surgeries. We established an equation for individual dosage of sufentanil for postoperative analgesia based on gene polymorphisms. The results show that the prediction equation is valid, the information might be used for different types of postoperative analgesia, and the painful patients will have great potential safe and personalized pain control after analgesic therapy. It might also have potential as a clinical tool.
doi_str_mv	10.1111/papr.13030
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Individual differences in genetic polymorphisms may be obstructive factors for accurately anesthetics using. However, the equation for predicting sufentanil dosage postoperatively based on genetic design has been established yet. Our aim was to establish sufentanil dosage postoperatively prediction equation based on patients’ genetic polymorphisms. Methods One hundred forty patients with total gastrectomy and radical resection of pulmonary carcinoma were included. To establish sufentanil dosage postoperatively for patients with gastric cancer, we collected patients’ basic information and CYP3A41G, COMTVal158Met, OPRM1A118G, and ABCB1C3435T gene sequencing results. To verify this equation, we put patients’ with lung cancer surgeries information into it. Results The sufentanil dosage prediction equation postoperatively was y = 4.104 − 0.222 × (gender) + 0.021 × (OPRM1A118G) + 0.249 × (ABCB1C3435T). Patients’ with lung cancer surgeries information were substituted into it. The results showed no significant differences between predicted and actual sufentanil dosage (p > 0.05). Conclusion We established the prediction equation for individual sufentanil dosage postoperatively based on gene polymorphisms. The results showed this prediction equation was valid, which might be used for different types of surgeries. We established an equation for individual dosage of sufentanil for postoperative analgesia based on gene polymorphisms. The results show that the prediction equation is valid, the information might be used for different types of postoperative analgesia, and the painful patients will have great potential safe and personalized pain control after analgesic therapy. It might also have potential as a clinical tool.</description><identifier>ISSN: 1530-7085</identifier><identifier>EISSN: 1533-2500</identifier><identifier>DOI: 10.1111/papr.13030</identifier><identifier>PMID: 33977649</identifier><language>eng</language><publisher>United States</publisher><subject>Analgesia, Patient-Controlled ; Analgesics, Opioid - therapeutic use ; ATP Binding Cassette Transporter, Subfamily B - genetics ; Catechol O-Methyltransferase - genetics ; Cytochrome P-450 CYP3A - genetics ; gene polymorphisms ; Humans ; individual dosage ; Lung Neoplasms - surgery ; multiple linear regression equation ; Pain Management ; Pain, Postoperative - drug therapy ; Pain, Postoperative - genetics ; Pharmacogenomic Testing ; Polymorphism, Genetic ; Receptors, Opioid, mu - genetics ; sufentanil ; Sufentanil - administration & dosage</subject><ispartof>Pain practice, 2022-01, Vol.22 (1), p.39-46</ispartof><rights>2021 World Institute of Pain</rights><rights>2021 World Institute of Pain.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3950-318261e756e6ea804fa1735821f5f2fdc19e97ca304381c7ea6a8d95765526c73</citedby><cites>FETCH-LOGICAL-c3950-318261e756e6ea804fa1735821f5f2fdc19e97ca304381c7ea6a8d95765526c73</cites><orcidid>0000-0002-6453-0743</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1111%2Fpapr.13030$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1111%2Fpapr.13030$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,776,780,1411,27901,27902,45550,45551</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/33977649$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Cheng, Huawei</creatorcontrib><creatorcontrib>Zeng, Rong</creatorcontrib><creatorcontrib>Kong, Lingsuo</creatorcontrib><creatorcontrib>Ding, Conglan</creatorcontrib><creatorcontrib>He, Yifu</creatorcontrib><creatorcontrib>Zhuang, Wei</creatorcontrib><creatorcontrib>Sun, Yancai</creatorcontrib><title>Establishment of predicting equation for individual sufentanil dosage postoperatively based on gene polymorphisms</title><title>Pain practice</title><addtitle>Pain Pract</addtitle><description>Background Postoperative analgesia is widely used for patients undergoing major surgeries. Individual differences in genetic polymorphisms may be obstructive factors for accurately anesthetics using. However, the equation for predicting sufentanil dosage postoperatively based on genetic design has been established yet. Our aim was to establish sufentanil dosage postoperatively prediction equation based on patients’ genetic polymorphisms. Methods One hundred forty patients with total gastrectomy and radical resection of pulmonary carcinoma were included. To establish sufentanil dosage postoperatively for patients with gastric cancer, we collected patients’ basic information and CYP3A41G, COMTVal158Met, OPRM1A118G, and ABCB1C3435T gene sequencing results. To verify this equation, we put patients’ with lung cancer surgeries information into it. Results The sufentanil dosage prediction equation postoperatively was y = 4.104 − 0.222 × (gender) + 0.021 × (OPRM1A118G) + 0.249 × (ABCB1C3435T). Patients’ with lung cancer surgeries information were substituted into it. The results showed no significant differences between predicted and actual sufentanil dosage (p > 0.05). Conclusion We established the prediction equation for individual sufentanil dosage postoperatively based on gene polymorphisms. The results showed this prediction equation was valid, which might be used for different types of surgeries. We established an equation for individual dosage of sufentanil for postoperative analgesia based on gene polymorphisms. The results show that the prediction equation is valid, the information might be used for different types of postoperative analgesia, and the painful patients will have great potential safe and personalized pain control after analgesic therapy. It might also have potential as a clinical tool.</description><subject>Analgesia, Patient-Controlled</subject><subject>Analgesics, Opioid - therapeutic use</subject><subject>ATP Binding Cassette Transporter, Subfamily B - genetics</subject><subject>Catechol O-Methyltransferase - genetics</subject><subject>Cytochrome P-450 CYP3A - genetics</subject><subject>gene polymorphisms</subject><subject>Humans</subject><subject>individual dosage</subject><subject>Lung Neoplasms - surgery</subject><subject>multiple linear regression equation</subject><subject>Pain Management</subject><subject>Pain, Postoperative - drug therapy</subject><subject>Pain, Postoperative - genetics</subject><subject>Pharmacogenomic Testing</subject><subject>Polymorphism, Genetic</subject><subject>Receptors, Opioid, mu - genetics</subject><subject>sufentanil</subject><subject>Sufentanil - administration & dosage</subject><issn>1530-7085</issn><issn>1533-2500</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp90E9LwzAYBvAgipvTix9AchShM2mapj2OMf_AwCF6Lln7ZoukTZe0k357u3V6NJc38P7e5_AgdEvJlPbvsZa1m1JGGDlDY8oZC0JOyPnxTwJBEj5CV95_EUJFytglGjGWChFH6RjtFr6Ra6P9toSqwVbh2kGh80ZXGwy7VjbaVlhZh3VV6L0uWmmwb1WPZaUNLqyXG8C19Y2twfV8D6bDa-mhwP3lBqrD1nSldfVW-9JfowsljYeb05ygz6fFx_wlWL49v85nyyBnKScBo0kYUxA8hhhkQiIlqWA8CaniKlRFTlNIRS4ZiVhCcwEylkmRchFzHsa5YBN0P-TWzu5a8E1Wap-DMbIC2_os7BmN4iRJe_ow0NxZ7x2orHa6lK7LKMkOFWeHirNjxT2-O-W26xKKP_rbaQ_oAL61ge6fqGw1W70PoT_98ojo</recordid><startdate>202201</startdate><enddate>202201</enddate><creator>Cheng, Huawei</creator><creator>Zeng, Rong</creator><creator>Kong, Lingsuo</creator><creator>Ding, Conglan</creator><creator>He, Yifu</creator><creator>Zhuang, Wei</creator><creator>Sun, Yancai</creator><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><orcidid>https://orcid.org/0000-0002-6453-0743</orcidid></search><sort><creationdate>202201</creationdate><title>Establishment of predicting equation for individual sufentanil dosage postoperatively based on gene polymorphisms</title><author>Cheng, Huawei ; Zeng, Rong ; Kong, Lingsuo ; Ding, Conglan ; He, Yifu ; Zhuang, Wei ; Sun, Yancai</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3950-318261e756e6ea804fa1735821f5f2fdc19e97ca304381c7ea6a8d95765526c73</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Analgesia, Patient-Controlled</topic><topic>Analgesics, Opioid - therapeutic use</topic><topic>ATP Binding Cassette Transporter, Subfamily B - genetics</topic><topic>Catechol O-Methyltransferase - genetics</topic><topic>Cytochrome P-450 CYP3A - genetics</topic><topic>gene polymorphisms</topic><topic>Humans</topic><topic>individual dosage</topic><topic>Lung Neoplasms - surgery</topic><topic>multiple linear regression equation</topic><topic>Pain Management</topic><topic>Pain, Postoperative - drug therapy</topic><topic>Pain, Postoperative - genetics</topic><topic>Pharmacogenomic Testing</topic><topic>Polymorphism, Genetic</topic><topic>Receptors, Opioid, mu - genetics</topic><topic>sufentanil</topic><topic>Sufentanil - administration & dosage</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Cheng, Huawei</creatorcontrib><creatorcontrib>Zeng, Rong</creatorcontrib><creatorcontrib>Kong, Lingsuo</creatorcontrib><creatorcontrib>Ding, Conglan</creatorcontrib><creatorcontrib>He, Yifu</creatorcontrib><creatorcontrib>Zhuang, Wei</creatorcontrib><creatorcontrib>Sun, Yancai</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>Pain practice</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Cheng, Huawei</au><au>Zeng, Rong</au><au>Kong, Lingsuo</au><au>Ding, Conglan</au><au>He, Yifu</au><au>Zhuang, Wei</au><au>Sun, Yancai</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Establishment of predicting equation for individual sufentanil dosage postoperatively based on gene polymorphisms</atitle><jtitle>Pain practice</jtitle><addtitle>Pain Pract</addtitle><date>2022-01</date><risdate>2022</risdate><volume>22</volume><issue>1</issue><spage>39</spage><epage>46</epage><pages>39-46</pages><issn>1530-7085</issn><eissn>1533-2500</eissn><abstract>Background Postoperative analgesia is widely used for patients undergoing major surgeries. Individual differences in genetic polymorphisms may be obstructive factors for accurately anesthetics using. However, the equation for predicting sufentanil dosage postoperatively based on genetic design has been established yet. Our aim was to establish sufentanil dosage postoperatively prediction equation based on patients’ genetic polymorphisms. Methods One hundred forty patients with total gastrectomy and radical resection of pulmonary carcinoma were included. To establish sufentanil dosage postoperatively for patients with gastric cancer, we collected patients’ basic information and CYP3A4*1G, COMTVal158Met, OPRM1A118G, and ABCB1C3435T gene sequencing results. To verify this equation, we put patients’ with lung cancer surgeries information into it. Results The sufentanil dosage prediction equation postoperatively was y = 4.104 − 0.222 × (gender) + 0.021 × (OPRM1A118G) + 0.249 × (ABCB1C3435T). Patients’ with lung cancer surgeries information were substituted into it. The results showed no significant differences between predicted and actual sufentanil dosage (p > 0.05). Conclusion We established the prediction equation for individual sufentanil dosage postoperatively based on gene polymorphisms. The results showed this prediction equation was valid, which might be used for different types of surgeries. We established an equation for individual dosage of sufentanil for postoperative analgesia based on gene polymorphisms. The results show that the prediction equation is valid, the information might be used for different types of postoperative analgesia, and the painful patients will have great potential safe and personalized pain control after analgesic therapy. It might also have potential as a clinical tool.</abstract><cop>United States</cop><pmid>33977649</pmid><doi>10.1111/papr.13030</doi><tpages>0</tpages><orcidid>https://orcid.org/0000-0002-6453-0743</orcidid></addata></record>
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subjects	Analgesia, Patient-Controlled Analgesics, Opioid - therapeutic use ATP Binding Cassette Transporter, Subfamily B - genetics Catechol O-Methyltransferase - genetics Cytochrome P-450 CYP3A - genetics gene polymorphisms Humans individual dosage Lung Neoplasms - surgery multiple linear regression equation Pain Management Pain, Postoperative - drug therapy Pain, Postoperative - genetics Pharmacogenomic Testing Polymorphism, Genetic Receptors, Opioid, mu - genetics sufentanil Sufentanil - administration & dosage
title	Establishment of predicting equation for individual sufentanil dosage postoperatively based on gene polymorphisms
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