Effect of precise nursing service mode on postoperative urinary incontinence prevention in patients with prostate disease

BACKGROUND Patients with benign prostatic disease often experience detrusor morphological changes and dysfunction.In severe cases,it leads to bladder detrusor dysfunction,resulting in dysuria,frequent urination,urgent urination,incomplete urination,and other symptoms including renal function injury.An operation to restore normal urination function and to control postoperative complications,as far as possible,is the most common method for benign prostatic disease.AIM To observe the effect of precise nursing service mode on postoperative urinary incontinence prevention in patients with prostate disease.METHODS In total,130 patients diagnosed with benign prostatic disease,from January 2018 to June 2021,in our hospital,were selected and divided into observation and control groups according to their treatment options.Sixty-five cases in the control group were given routine nursing mode intervention and 65 cases in the observation group received precise nursing service mode intervention.The intervention with the observation group included psychological counseling about negative emotions,pelvic floor exercises,and post-hospital discharge care.The complications of the two groups were counted,and the general postoperative conditions of the two groups were recorded.The urinary flow dynamics indexes of the two groups were detected,and differences in clinical international prostate system score(IPSS)and urinary incontinence quality of life questionnaire(I-QOL)scores were evaluated.RESULTS Postoperative exhaust time(18.65±3.23 h and 24.63±4.51 h),the time of indwelling catheter(4.85±1.08 d and 5.63±1.24 d),and hospitalization time(8.78±2.03 d and 10.23±2.28 d)in the observation group were lower than in the control group.The difference was statistically significant(P<0.05).After the operation,the maximum urinary flow rate(Qmax)increased(P<0.05),the residual urine volume(RUV)decreased(P<0.05),and the maximum closed urethral pressure(MUCP)was not statistically significant(P>0.05)compared to pre-operation.The Qmax of the observation group was higher than that of the control group,while the RUV was lower than that of the control group.There was no significant difference in MUCP between the observation and control groups(P>0.05).The I-QOL score of the two groups improved(P<0.05),and the IPSS decreased(P<0.05).After the operation,the I-QOL score of the observation group was higher than that of the control group,and the IPSS was lower than that of the control group(P<0.05).There were no significant differences in the incidence of urethral injury(1.54%and 3.08%),bladder spasm(0.00%and 1.54%),and secondary bleeding(1.54%and 4.62)between the observation and control groups(P>0.05).CONCLUSION The precise nursing service mode can reduce the incidence of postoperative urinary incontinence in patients with prostate disease,thus improving postoperative urodynamics and rehabilitation,and quality of life.

Ontology-Driven Discovering Model for Geographical Information Services

This paper proposes an ontology-driven discovering model for the geographical information services to improve their recall ratio and precision ratio. This model uses the geographical information service ontology. In this paper, first we study the multilevel matching arithmetic of geographical information services. This arithmetic is used for filtering and matching the services in the service register center according to the similarity between services selected and services requested from the definition of the function similarity and credit standing similarity. The matching arithmetic, geographical information service ontology and semantic description constitute the discovering model. Finally, we test and analyze the model from the recall ratio, precision ratio, responsivity and load balance. The result indicates that the ontology-driven discovering model is excellent in recall ratio and precision ratio, and can maintain the dynamic load balance of service copy.