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2024 ; 16
(7
): 2194-2201
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Influencing factors and risk prediction model for emergence agitation after
general anesthesia for primary liver cancer
#MMPMID39087110
Song SS
; Lin L
; Li L
; Han XD
World J Gastrointest Surg
2024[Jul]; 16
(7
): 2194-2201
PMID39087110
show ga
BACKGROUND: General anesthesia is commonly used in the surgical management of
gastrointestinal tumors; however, it can lead to emergence agitation (EA). EA is
a common complication associated with general anesthesia, often characterized by
behaviors, such as crying, struggling, and involuntary limb movements in
patients. If treatment is delayed, there is a risk of incision cracking and
bleeding, which can significantly affect surgical outcomes. Therefore, having a
proper understanding of the factors influencing the occurrence of EA and
implementing early preventive measures may reduce the incidence of agitation
during the recovery phase from general anesthesia, which is beneficial for
improving patient prognosis. AIM: To analyze influencing factors and develop a
risk prediction model for EA occurrence following general anesthesia for primary
liver cancer. METHODS: Retrospective analysis of clinical data from 200 patients
who underwent hepatoma resection under general anesthesia at Wenzhou Central
Hospital (January 2020 to December 2023) was conducted. Post-surgery, the
Richmond Agitation-Sedation Scale was used to evaluate EA presence, noting EA
incidence after general anesthesia. Patients were categorized by EA presence
postoperatively, and the influencing factors were analyzed using logistic
regression. A nomogram-based risk prediction model was constructed and evaluated
for differentiation and fit using receiver operating characteristics and
calibration curves. RESULTS: EA occurred in 51 (25.5%) patients. Multivariate
analysis identified advanced age, American Society of Anesthesiologists (ASA)
grade III, indwelling catheter use, and postoperative pain as risk factors for EA
(P < 0.05). Conversely, postoperative analgesia was a protective factor against
EA (P < 0.05). The area under the curve of the nomogram was 0.972 [95% confidence
interval (CI): 0.947-0.997] for the training set and 0.979 (95%CI: 0.951-1.000)
for the test set. Hosmer-Lemeshow test showed a good fit (? (2) = 5.483, P =
0.705), and calibration curves showed agreement between predicted and actual EA
incidence. CONCLUSION: Age, ASA grade, catheter use, postoperative pain, and
analgesia significantly influence EA occurrence. A nomogram constructed using
these factors demonstrates strong predictive accuracy.