2. Academic Validation
  3. Clinical and pharmacogenetics associated with recovery time from general anesthesia

Clinical and pharmacogenetics associated with recovery time from general anesthesia

  • Pharmacogenomics. 2018 Sep 1;19(14):1111-1123. doi: 10.2217/pgs-2018-0085.
Shangchen Xie 1,  Wenjuan Ma 2,  Minxue Shen 3,  Qulian Guo 2,  E Wang 2,  Changsheng Huang 2,  Yueling Wang 2,  Xiang Chen 3,  Zhaoqian Liu 1,  Wei Zhang 1,  Howard L McLeod 1 4,  Yijing He 3 4
Affiliations

Affiliations

  • 1 Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Institute of Clinical Pharmacology, Central South University, Hunan Key Laboratory of Pharmacogenetics, Changsha, Hunan, PR China.
  • 2 Department of Anesthesiology, Xiangya Hospital, Central South University, Changsha, Hunan, PR China.
  • 3 Department of Dermatology, Xiangya Hospital, Central South University, Changsha, Hunan, PR China.
  • 4 Moffitt Cancer Center, DeBartolo Family Personalized Medicine Institute, Tampa, 33612 FL, USA.
PMID: 30136624 DOI: 10.2217/pgs-2018-0085
Abstract

Aim: Delayed recovery from general anesthesia is a well-known complication that requires predictive tools and approaches. This study aimed to determine significant factors associated with postanesthesia recovery and to develop an algorithm for estimating recovery time from general anesthesia.

Materials & methods: The genotypes of patients were determined by SNaPshot or ARMS-qPCR. The algorithm was developed via machine-learning and tested by the worm plot.

Results: Results showed that OPRM1 rs1799971 (p = 0.006) and ABCG2 rs2231142 (p = 0.041) were significantly associated with recovery time. Ten factors after random forest and stepwise selection were associated with recovery time. Ten factors after random forest and stepwise selection were associated with recovery time. Meanwhile, seven factors were associated with delayed recovery.

Conclusion: This study demonstrated that both clinical and pharmacogenetic data are significantly associated with recovery from general anesthesia and provide the basis for pre-emptive prediction tools.

Keywords

general anesthesia; machine learning; pharmacogenetics; postoperative recovery.

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