The factors influencing the effect of periprostatic nerve block anesthesia and the establishment of a predictive model and efficacy verification
10.3760/cma.j.cn112330-20230304-00069
- VernacularTitle:前列腺周围神经阻滞麻醉效果的影响因素及其预测模型的建立和效能验证
- Author:
Xiao TAN
1
;
Xuefei DING
;
Yang LUAN
;
Shengming LU
;
Liangyong ZHU
;
Yuexing HAN
;
Haopeng CHEN
;
Zhong LIU
;
Zhenhao WU
;
Yueqi WU
Author Information
1. 江苏省苏北人民医院泌尿外科，扬州 225001
- Keywords:
Prostatic neoplasms;
Carcinoma;
Puncture biopsy;
Anesthesia;
Nomograph
- From:
Chinese Journal of Urology
2023;44(12):917-921
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To investigate the factors affecting the effect of periprostatic nerve block (PNB), establish a prediction model of pain degree, and verify the prediction efficiency.Methods:The clinical data of 314 patients who underwent transperineal prostate biopsy in our hospital from June 2022 to January 2023 were retrospectively analyzed. The median age was 71 (65, 76) years, the median prostate-specific antigen (PSA) was 14.6 (10.70, 24.65) ng/ml, and the median puncture needle number was 21 (19, 23) needles, median prostate volume 45.86 (31.52, 67.96) ml, median body mass index (BMI)24.02(22.97, 25.33)kg/m 2, including 109 patients with a history of diabetes, 90 patients with a history of surgery, and 57 patients with a history of severe trauma. The patients were divided into mild pain group (1-3 points), moderate pain group (4-6 points) and severe pain group (7-10 points) according to the intraoperative visual analogue scale (VAS). According to the clinical characteristics, the factors affecting the effect of PNB were analyzed by univariate analysis and multiple ordered logistic regression method. R language was used to construct a nomogram model for predicting PNB effect, receiver operating characteristic (ROC) curve and calibration curve were drawn, and Hosmer-Lemeshow test was carried out to verify the prediction efficiency of the model. Results:The results of univariate analysis showed that 171 patients in the mild pain group had a median age of 71 (65, 75) years, a median PSA14.5 (9.6, 24.6) ng/ml, a median number of puncture needles of 20 (18, 22), and a median prostate volume of 34.94 (26.36, 45.12) ml, median BMI24.17(23.14, 25.79)kg/m 2, including 74 patients with a history of diabetes, 51 patients with a history of surgery, and 40 patients with a history of severe trauma; There were 110 patients in the moderate pain group, the median age was 71 (65, 76) years, the median PSA14.8 (11.03, 24.27) ng/ml, the median number of puncture needles was 23 (20, 24) needles, median prostatic volume 63.24 (49.14, 78.72) ml, median BMI23.91(22.58, 24.88)kg/m 2, including 26 patients with a history of diabetes, 29 patients with a history of surgery, and 10 patients with a history of severe trauma; In the severe pain group, 33 patients had a median age of 73 (67, 78) years, a median PSA14.6 (10.85, 34.80) ng/ml, and a median puncture needle number of 23 (22.5, 24) needles, median prostate volume 70.64 (61.50, 104.51) ml, median BMI24.32(23.00, 26.06)kg/m 2, including 9 patients with a history of diabetes, 10 patients with a history of surgery, and 7 patients with a history of severe trauma. The results of univariate analysis showed that the number of puncture needles ( P<0.01), prostate volume ( P<0.01), history of diabetes ( P=0.002) and history of major trauma ( P= 0.009) were the factors affecting the effect of PNB. Multiple logistic regression analysis showed that puncture needle number ( P=0.009), prostate volume ( P<0.01) and diabetes history ( P=0.041) were independent risk factors for PNB effect. The area under ROC curve (AUC) of the moderate and above pain prediction model was 0.872, P<0.01; the area under ROC curve of the severe pain prediction model was 0.817, P<0.01; the result of Hosmer-Lemeshow test of the moderate and above pain prediction model was χ2=5.001, P=0.757. The results of the severe pain prediction model were χ2=4.452 and P=0.814. The calibration curve was established, which showed that the prediction probability of pain degree was in good agreement with the actual risk. Conclusions:The number of puncture needles, prostate volume and history of diabetes are the risk factors affecting the effect of PNB. The prediction model of PNB effect based on this model can be used to predict the pain degree of patients undergoing prostate biopsy after PNB.
