CN111312387A - Model for predicting severity of pain of male chronic prostatitis/chronic pelvic pain syndrome and establishment of model - Google Patents

Abstract

The invention provides a model for predicting the severity of pain of male chronic prostatitis/chronic pelvic pain syndrome and establishment thereof, and relates to the field of prediction of the severity of pain of chronic prostatitis/chronic pelvic pain syndrome. The pain severity prediction model is based on the age of the patient, the grade of lecithin corpuscle in prostatic fluid, the dominance ratio OR of variables such as urine holding, anxiety OR irritability, contraception and smoking, a confidence interval of 2.5 percent and 97.5 percent and P-value, and a nomogram is established as a model to predict the pain severity of the CP/CPPS patient. And the establishment of the model mainly comprises the following steps: material selection, grouping, variable screening, variable analysis and the like. The method overcomes the defects of the prior art, can accurately predict the pain level of prostatitis through the establishment of the model, and simultaneously improves the efficiency of clinical decision.

Description

Model for predicting severity of pain of male chronic prostatitis/chronic pelvic pain syndrome and establishment of model

Technical Field

The invention relates to the field of prediction of chronic prostatitis/chronic pelvic pain syndrome pain degree, in particular to a model for predicting the male chronic prostatitis/chronic pelvic pain syndrome pain severity degree and establishment thereof.

Background

Prostatitis is a common urological disease. Studies have reported that about 35-50% of men suffer from type III prostatitis, and that the incidence of prostatitis is higher in men up to the age of 50. According to previous research work, the prevalence rate of chronic prostatitis in Chinese men is about 8.4%. According to a proposal of National Institute of Health (NIH), prostatitis is classified into four categories: of these, class III is defined as chronic prostatitis or chronic pelvic pain syndrome (CP/CPPS), the majority of cases in prostatitis. CP/CPPS has a variety of clinical manifestations, such as pelvic or perineal pain, irritative or obstructive urinary symptoms, sexual dysfunction or psychological disturbances, without any clear evidence of urinary tract infection. In most cases, chronic pelvic pain occurs with pelvic floor tenderness, and patients feel pain during palpation of the prostate. Clinically, physicians use NIH-CPSI scores to determine the severity of chronic prostatitis. Pain was graded as mild (score 0-7), moderate (score 8-13) and severe (score 14-21) on the pain scale.

Currently, nomograms are a prognostic method that can improve accuracy and make prognosis easier to understand, resulting in better clinical decision making; the method is widely applied to oncology and medicine, but no relevant model is available for accurately evaluating the prostatitis pain, namely, a model for predicting the severity of the male chronic prostatitis/chronic pelvic pain syndrome pain is established, so that a doctor can be effectively assisted to judge the prostatitis, and the clinical decision efficiency is improved.

Disclosure of Invention

Aiming at the defects of the prior art, the invention provides a model for predicting the pain severity of male chronic prostatitis/chronic pelvic pain syndrome and establishment thereof, the pain level of prostatitis can be accurately predicted through the establishment of the model, and meanwhile, the efficiency of clinical decision is improved.

In order to achieve the above purpose, the technical scheme of the invention is realized by the following technical scheme:

a model for predicting the pain severity of male chronic prostatitis/chronic pelvic pain syndrome is characterized in that a nomogram is established as a model to predict the pain severity of a CP/CPPS patient on the basis of the superiority ratio OR of the age of the patient, the lecithin corpuscle grade in prostatic fluid, urine holding, anxiety OR irritability, contraception, smoking and other variables, 2.5% and 97.5% confidence intervals and P-value.

The establishment of the pain severity prediction model comprises the following steps:

(1) selecting materials: selecting a plurality of male chronic prostatitis/chronic pelvic pain syndrome patients, and randomly dividing the patients into two groups according to the ratio of 3:1, wherein the two groups are respectively a training group and an experimental group;

(2) grouping: classifying the patients according to NIH-CPSI pain classification, wherein less than 7 of all patients are classified into mild pain groups, and more than 7 of the patients are classified into moderate to severe pain groups;

(3) screening variables: recording data of each patient, and further analyzing variables such as age, BMI, lecithin grade in prostatic fluid, leukocyte grade in urine, whether sitting is long, whether urine is held, whether anxiety or irritability exists, whether antibiotics are used, whether sexual life exists, whether contraception exists, whether past medical history exists, whether alopecia exists, whether drinking wine is drunk, whether smoking is carried out and the like;

(4) and (3) variable analysis: variables excluding the internal influence of the participants are measured by adopting a logistic regression analysis method, the variables with statistical significance are screened out, the variables are analyzed through odds ratio ORs, 2.5% and 97.5% confidence intervals CIs, assumed values P and the like, and a nomogram establishing model is established according to the analysis result.

Preferably, the inclination data in the 15 variables in the step (3) is analyzed after being subjected to logarithmic conversion or being encoded into a classification variable.

Preferably, the statistically significant variables in step (4) are age, small body level of lecithin in prostate fluid, urine holding, anxiety or irritability, contraception and smoking.

The detection method of the prediction model is to check the calibration of the nomogram of the model through a calibration curve.

Preferably, the specific detection mode of the prediction model is as follows:

(1) evaluating the discrimination and stability of the nomogram by calculating the receiver operating characteristic ROC curve and the area AUC under the ROC curve;

(2) the clinical utility of the nomograms was examined by decision curve analysis of DCA.

The invention provides a model for predicting the severity of male chronic prostatitis/chronic pelvic pain syndrome pain and establishment thereof, compared with the prior art, the model has the advantages that: the model adopts multiple logistic regression analysis to establish a nomogram, the model comprises the variables of age, small body level of lecithin in prostatic fluid, urine suffocation, emotional anxiety or dysphoria, contraception and smoking behavior and the like, and simultaneously the model shows good discrimination, the area under the ROC curve (AUC) of a training queue is 0.736, the area under the ROC curve of a verification queue is 0.716, and the nomogram is consistent with the results suggested by a calibration graph and a decision curve, so that the prediction accuracy of the model is realized, and the model is convenient for clinical use.

Drawings

FIG. 1: prognostic analysis of the patient;

FIG. 2: a new nomogram for predicting risk of pain severity in CP/CPPS patients, wherein the upper panel represents the scale for estimating the risk score for each variable and the lower panel corresponds to pain severity in CP/CPPS patients;

FIG. 3: calibration curve of in-line prediction nomogram: calibrating curves of the line graphs in the training queue (A) and the verification queue (B);

FIG. 4: predicting the performance of the nomogram: ROC curves, ROC, receiver operating characteristics of the line graphs in training queue (a) and validation queue (B).

FIG. 5: decision curve analysis of the prediction nomogram: DCA of the line graphs in training cohort (A) and validation cohort (B).

Detailed Description

In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention are clearly and completely described below in conjunction with the embodiments of the present invention, and it is obvious that the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

Example 1:

the establishment of a model for predicting the severity of chronic prostatitis/chronic pelvic pain syndrome pain in men:

(1) crowd selection

From 3 months in 2019 to 10 months in 2019, 322 CP/CPPS patients who visit the first subsidiary hospital of the medical university in anhui were selected and relevant information of the patients was recorded (the study was approved by the review board of the first subsidiary hospital institution of the medical university in anhui); of the 322 patients investigated, 50 patients were excluded due to the absence of baseline values for continuous variables. The main point of the experiment is the pain degree of CP/CPPS patients, patients are randomly divided into two groups according to the ratio of 3:1, namely a training group and a verification group, as shown in figure 1;

(2) variable recording

Obtaining effective data from the acquired CP/CPPS patient data, selecting 15 variables for further analysis, and carrying out logarithmic conversion or coding on the inclination data into classification variables, wherein the detailed information is shown in the following table 1: (wherein the body weight index is the weight divided by the height squared and EPS is prostatic fluid)

Table 1: selection of 15 variables for analysis and testing

(3) Variable distribution:

according to NIH-CPSI pain typing, all patients were divided into two groups: CP/CPPS patients with mild pain (less than 7 points) and patients with moderate to severe pain (greater than 7 points), the distribution of the variables among CP/CPPS patients is shown in Table 2:

table 2: baseline patient and disease characteristics for training and experimental groups

(4) Logistic regression analysis

From the above table it is clear that age, the lecithin corpuscle grade in prostatic fluid, urine holding, anxiety OR irritability, contraception and smoking, etc. are statistically significant and can be incorporated into the prediction model, and that the OR, 2.5% and 97.5% confidence intervals and P-values for each variable are shown in table 3 below:

table 3: pain related factors

Parameters	OR	2.5％CI	97.5％CI	P-value
					Age	0.691	1.959	21.350	0.003*
Lecithingrade	1.191	1.257	45.826	0.036*
					Holdoffpissy	2.916	1.201	7.249	0.020*
Anxietyorirritability	4.985	2.748	12.985	0.008*
					Contraception	2.201	1.208	5.684	0.016*
Smoking	1.968	1.323	11.057	0.015*

(5) Establishing a model:

based on the 6 variables obtained from the multiple logistic regression analysis, a nomogram was established that could be used to predict the severity of pain in CP/CPPS patients, as shown in FIG. 2.

Example 2:

verification of the model obtained in example 1 above:

(1) a calibration curve and an ROC curve are adopted to evaluate the calibration and discrimination capability of the nomogram; from fig. 3A, it can be seen that the calibration curve shows good consistency in the training queue;

(2) meanwhile, the ROC curve known in FIG. 4A confirms that the predicted value AUC of the nomogram is 0.737;

(3) the validation cohort was used to validate the calibration and discrimination ability of the nomograms and it was found that the calibration curve (fig. 3B) and AUC values (fig. 4B) from the validation cohort showed similar results to the training cohort.

In conclusion, the nomograms of the present invention are well predictive of pain severity in CP/CPPS patients.

It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.

The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

Claims (6)

1. The model for predicting the pain severity of the male chronic prostatitis/chronic pelvic pain syndrome is characterized in that a nomogram is established as a model for predicting the pain severity of a CP/CPPS patient on the basis of the P-value and the prevalence ratio OR of variables such as age of the patient, the grade of lecithin bodies in prostatic fluid, urine holding, anxiety OR irritability, contraception and smoking, a confidence interval of 2.5 percent and a confidence interval of 97.5 percent.

2. The establishment of a model for predicting the severity of chronic prostatitis/chronic pelvic pain syndrome pain in men is characterized in that: the establishment of the pain severity prediction model comprises the following steps:

(1) selecting materials: selecting a plurality of male chronic prostatitis/chronic pelvic pain syndrome patients, and randomly dividing the patients into two groups according to the ratio of 3:1, wherein the two groups are respectively a training group and an experimental group;

(2) grouping: classifying the patients according to NIH-CPSI pain classification, wherein less than 7 of all patients are classified into mild pain groups, and more than 7 of the patients are classified into moderate to severe pain groups;

(3) screening variables: recording data of each patient, and further analyzing variables such as age, BMI, lecithin grade in prostatic fluid, leukocyte grade in urine, whether sitting is long, whether urine is held, whether anxiety or irritability exists, whether antibiotics are used, whether sexual life exists, whether contraception exists, whether past medical history exists, whether alopecia exists, whether drinking wine is drunk, whether smoking is carried out and the like;

(4) and (3) variable analysis: variables excluding the internal influence of the participants are measured by adopting a logistic regression analysis method, the variables with statistical significance are screened out, the variables are analyzed through odds ratio ORs, 2.5% and 97.5% confidence intervals CIs, assumed values P and the like, and a nomogram establishing model is established according to the analysis result.

3. The establishment of the model for predicting the severity of pain in male chronic prostatitis/chronic pelvic pain syndrome according to claim 2, wherein: and (4) carrying out logarithmic conversion or coding on the inclination data in the 15 variables in the step (3) into classified variables and then analyzing.

4. The establishment of the model for predicting the severity of pain in male chronic prostatitis/chronic pelvic pain syndrome according to claim 2, wherein: the statistically significant variables in step (4) are age, small body level of lecithin in the prostatic fluid, urine holding, anxiety or irritability, contraception and smoking.

5. The establishment of the model for predicting the severity of pain in male chronic prostatitis/chronic pelvic pain syndrome according to claim 2, wherein: the detection method of the prediction model is to check the calibration of the nomogram of the model through a calibration curve.

6. The establishment of the model for predicting the severity of pain in male chronic prostatitis/chronic pelvic pain syndrome according to claim 5, wherein: the specific detection mode of the prediction model is as follows:

(1) evaluating the discrimination and stability of the nomogram by calculating the receiver operating characteristic ROC curve and the area AUC under the ROC curve;

(2) the clinical utility of the nomograms was examined by decision curve analysis of DCA.

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