Predictive model of pre-operative prognostic nutrition index for biochemical recurrence in patients undergoing robot-assisted laparoscopic radical prostatectomy: a retrospective clinical study

doi:10.21203/rs.3.rs-3872940/v1

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Predictive model of pre-operative prognostic nutrition index for biochemical recurrence in patients undergoing robot-assisted laparoscopic radical prostatectomy: a retrospective clinical study

https://doi.org/10.21203/rs.3.rs-3872940/v1

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Objective: To investigate the predictive value of pre-operative prognostic nutritional index (PNI) in biochemical recurrence (BCR) in patients with robot-assistedlaparoscopic radical prostatectomy (RARP) and to establish a BCR risk score model based on PNI.

Methods: The clinical data of 157 patients treated with RARP in the Department of Urology, the First Affiliated Hospital of Soochow University were retrospectively analyzed. The endpoint of observation was BCR. The area under the receiver operating characteristic (ROC) curve was evaluated to determine the optimal cutoff value for PNI. Kaplan-Meier analysis and Cox regression analysis were used to evaluate the correlation between PNI and BCR. 157 patients were divided into a training group and a validation group by a ratio of 7:3. By univariate and multivariate Cox regression analysis, independent prognostic factors were screened from the relevant clinicopathological factors, a BCR prediction model and nomogramwere established, then verified its value.

Results: According to the ROC curve, the optimal cutoff value of PNI for 157 patients in this study was 47.425. According to multivariate Cox regression analysis, PNI and prostate-specific antigen (PSA) were identified as independent prognostic factors for predicting BCR in patients treated with RARP. A BCR prediction model formula was established based on PNI and PSA. It was proved to have good predictive value in both the training group and the validation group. Nomogram was constructed to predict the BCR of patients treated with RARP at 6-, 12-, and 24-months after surgery. The results of the calibration plots showed that the nomogram performed well in the training group and the validation group.

Conclusion: PNI is an independent prognostic factor for predicting BCR in patients treated with RARP. The scoring model and nomogram based on PNI and PSA can effectively predict the risk of BCR in patients treated with RARP.

prostate cancer

biochemical recurrence

prognostic factors

prognostic nutritional index

robot-assisted laparoscopic radical prostatectomy

risk model

nomogram

Prostate cancer is the most common malignant tumor in the male genitourinary system, and its incidence rate second only to lung cancer among all malignant tumors in men (1). In recent years, with the PSA screening of high-risk population, the detection rate of prostate cancer has increased significantly, which also provides great help for the early diagnosis and treatment of prostate cancer patients. Radical prostatectomy (RP) is considered to be one of the most effective methods for the treatment of localized and locally advanced prostate cancer, but there are still 20%-40% of patients who develop BCR after RP (2, 3). Although some clinical and pathological factors, such as clinical case stage, surgical margin status and Gleason score, have been proved to be independent prognostic factors for predicting postoperative BCR of prostate cancer, they have a certain degree of invasive risk and limitations (4). Therefore, there is an urgent need to find new accurate and non-invasive prognostic indicators to predict the risk of BCR early, so as to provide the best treatment plan for patients.

The nutritional and inflammatory immune status of cancer patients have been considered to be important factors affecting the prognosis, and play an important role in the process of cancer progression (5–7). Systemic inflammation has been widely recognized as a key factor in cancer invasion, proliferation and metastasis (8). Previous studies have reported that some systemic inflammation indicators, such as neutrophil-lymphocyte ratio (NLR), platelet–lymphocyte ratio (PLR) and systemic immune-inflammation index (SII) have been used to predict the prognosis of cancer (9, 10). In addition, the deterioration of nutritional status has been a common problem in cancer patients. PNI happens to be a coefficient that combines nutritional and inflammatory immune indicators. By calculating serum albumin concentration and peripheral blood lymphocyte count, it can simply assess the condition of a patient. PNI was originally used to evaluate preoperative nutritional status and surgical complications in patients with gastrointestinal cancers. It has been proven to be a good prognostic indicator for oral cancer, breast cancer, pancreatic cancer, etc. (11–13).

In recent years, robot-assisted surgery has made significant progress in various surgical fields, because it has the advantages of 3D surgical field of view, greater magnification effect, freer range of motion, more stable and more precise mechanical operation, which makes up for some of the technical defects of traditional laparoscopy, thus it can reduce the occurrence of intraoperative and postoperative complications (14). And it is more conducive to the recovery of patients. However, some prostate cancer patients treated with RARP are still unable to avoid BCR, so there is an urgent need to find an easily accessible and low-cost biomarker to predict it. To our knowledge, previous studies have evaluated PNI as a predictor of prognosis in patients with RARP (15). In this study, we will further confirm their correlation. We also included other commonly used inflammatory nutritional biomarkers, such as NLR, SII and albumin-globulin ratio (AGR). Finally, we combined the clinicopathological characteristics of the patients to screen out the independent prognostic factors for predicting BCR in patients treated with RARP, constructed a prognostic model and verify its effectiveness.

2.1 Study population

The clinical and pathological information of 157 patients treated with RARP in the Department of Urology, the First Affiliated Hospital of Soochow University from December 2019 to November 2022 were retrospectively analyzed. This study inclusion criteria: (1) Prostate biopsy was performed before surgery, and the pathological report confirmed prostate cancer; (2) According to the relevant clinical examination, computed tomography (CT), magnetic resonance imaging (MRI) and bone scan examination, in accordance with the international TNM staging of prostate cancer, have a clear clinical staging; (3) There were no obvious preoperative complications; (4) Preoperative 1 week with serological samples available; (5) Received robot-assisted radical resection of prostate cancer in the First Affiliated Hospital of Soochow University, and was discharged successfully without significant complications after surgery; (6) Postoperative pathology confirmed prostate cancer, and Gleason score was used. Exclusion criteria:(1) Lack of relevant clinical data and complete follow-up information; (2) Suffering from diseases affecting systemic nutritional and immune indexes; (3) Combined with other primary tumor diseases; (4) preoperative neoadjuvant therapy; (5) Endocrine therapy was given immediately without follow-up. The study complied with the Declaration of Helsinki and was approved by the Ethics Committee of the First Affiliated Hospital of Soochow University. All patients gave informed written consent before they were enrolled in this study.

2.2 Data collection and follow-up

Relevant clinical data of patients were collected, including age, body mass index (BMI), preoperative total PSA, free PSA (f PSA), free PSA/total PSA (f/t), clinical stage, and postoperative pathological results, including tumor nature, Gleason score, incisal margin, lymph node status, nerve invasion, seminal vesicle invasion, and vas deferens invasion. The clinical staging was performed according to the American Joint Committee on Cancer 7th edition tumor, node, and metastasis staging system (16). Preoperative complete blood count and related biochemical indexes were obtained by fasting blood samples collected in the morning within 1 week before surgery. According to the established formula to calculate the research related to nutrition and immune scores: PNI = serum albumin (g/L) + 5 *lymphocyte count (10⁹ /L); NLR = neutrophil count (10⁹ /L)/ lymphocyte count (10⁹ /L); AGR = serum albumin (g/L)/ serum globulin (g/L); SII = platelet count (10⁹ /L) *neutrophil count (10⁹ /L)/ lymphocyte count (10⁹ /L).

We obtained the required follow-up information by inquiring the patients' postoperative outpatient review data and telephone interview. Patients are usually required to retest PSA 6–8 weeks after surgery, every 3 months in the first and second years, and every 6 months thereafter. We defined BCR by a consecutive increase of PSA ≥ 0.2 ng/ml on two blood tests. The follow-up time was defined as the period from the time of RARP to BCR (1 decimal place reserved) (17).

2.3 Statistical analysis

The statistical analyses were conducted using SPSS version 27 (SPSS Inc.) and R software (3.5.2). Medians with interquartile ranges (IQRs) and frequencies were adopted to report continuous and categorical variables, respectively. The ROC curve analysis is performed to select the most suitable demarcation point for the PNI. Differences in continuous variables were analyzed using the Mann–Whitney U-test and differences in categorical data were analyzed using the x ² test. Kaplan–Meier survival analysis and log‐rank test were used to compare the effects of different PNI groups on the BCR rate of patients receiving RARP. Univariate and multivariate cox regression analyses were used to assess the influence of prognostic factors and to establish a risk prediction model. P < 0.05 was considered statistically significant.

3.1 Basic information

A total of 157 patients with RARP were included in this study, and detailed clinicopathological characteristics of all patients were summarized in Table 1. The median age was 71 years, median BMI was 23.875 kg/m², median PSA was 12.538ng/ml and median f/t value was 0.104. The median values of PNI, AGR, NLR and SII were 48.70, 1.7, 2.41 and 482.14. In addition, 71 patients (45.2%) had positive surgical margins, 109 patients (69.4%) had nerve invasion, 30 patients (19.1%) had seminal vesicle invasion and 16 patients (10.2%) had vas deferens invasion. The clinical staging between T3 and T4 was 72.0%. Lymph node metastasis occurred in 9 patients (5.7%).

Table 1

Characteristics of the 157 participants.
Variable	Total n = 157	%
Age (years)
≤ 70	77	49.0
>70	80	51.0
BMI (kg/m2)
<18.5	2	1.3
18.5(inclusive)-24	82	52.2
24 (inclusive)-28	62	39.5
≥ 28	11	7.0
Clinical staging
T1	0	0
T2	44	28.0
T3	104	66.2
T4	9	5.7
PNI
≤ 47.425	53	33.8
>47.425	104	66.2
Gleason score
6	7	4.4
7	119	75.8
8	15	9.6
9	16	10.2
Margin status
Negative	86	54.8
Positive	71	45.2
Nerve invasion
Negative	48	30.6
Positive	109	69.4
Seminal vesicle invasion
Negative	127	80.9
Positive	30	19.1
Vas deferens invasion
Negative	141	89.8
Positive	16	10.2
Lymph node status
Negative	148	94.3
Positive	9	5.7
BCR
No	106	67.5
Yes	51	32.5
Age (years) median (IQR)	71(66–75)
BMI (kg/m2) median (IQR)	23.875(22.039–25.374)
PSA (ng/ml) median (IQR)	12.538(7.961–23.500)
f/t median (IQR)	0.104(0.076–0.149)
AGR median (IQR)	1.7(1.5–1.9)
PNI median (IQR)	48.70(45.25–50.95)
NLR median (IQR)	2.41(1.73–3.27)
SII median (IQR)	482.14 (321.39-713.71)

3.2 Comparison of clinicopathological data between BCR group and non-BCR group

The correlation between BCR and clinical characteristics was confirmed by the x² test and Mann–Whitney U test. The clinical data of patients in different BCR groups are shown in the Table 2. The results showed that the BCR group had higher clinical stage (P = 0.006), higher PSA value (P < 0.001), higher Gleason score (P < 0.001), higher NLR score (P = 0.008), higher margin positive rate (P = 0.002), higher lymph node positive rate (P < 0.001), higher invasion rate of nerve, seminal vesicle and vas deferens (P = 0.015, P < 0.001, P < 0.001), but lower PNI value (P < 0.001) compared with the non-BCR group. There was no significant difference in age, BMI, f/t, AGR and SII scores in different groups (Table 2).

Table 2

Comparison of different BCR group.
Variable	BCR group	non-BCR group	P
Number of cases	51	106
Age (years) median (IQR)	72(67,76)	70(66,74)	0.092
BMI (kg/m2) median (IQR)	24.424(21.671,25.344)	23.577(22.039,25.454)	0.464
PSA (ng/ml) median (IQR)	24.820(15.048,46.987)	9.696(6.598,15.397)	<0.001
f/t median (IQR)	0.091(0.064,0.142)	0.113(0.078,0.159)	0.062
PNI median (IQR)	44.85(43.25,48.70)	49.53(47.98,51.64)	<0.001
AGR median (IQR)	1.7(1.4,1.9)	1.7(1.5,1.9)	P = 0.064
NLR median (IQR)	2.65(2.08,3.70)	2.29(1.58,3.03)	P = 0.008
SII median (IQR)	528.27(358.22,751.01)	458.42(291.92,671.39)	P = 0.113
Clinical staging
T1-T2	7	37
T3-T4	44	69	0.006
Gleason score
≤ 7	28	98
>7	23	8	<0.001
Margin status
Negative	19	67
Positive	32	39	0.002
Nerve invasion
Negative	9	39
Positive	42	67	0.015
Seminal vesicle invasion
Negative	32	95
Positive	19	11	<0.001
Vas deferens invasion
Negative	37	104
Positive	14	2	<0.001
Lymph node status
Negative	42	106
Positive	9	0	<0.001

3.3 Optimal PNI cutoff value before treatment

157 patients ranged from 1.7 to 47.0 months, with a median follow-up time of 15.2 months. Among them, 51 cases experienced BCR. ROC curves were drawn based on whether BCR occurred at the end of follow-up. It was found that when PNI = 47.425, the AUC was 0.799 (95% confidence interval: 0.726–0.873, Youden index = 0.532, sensitivity = 70.0%, specificity = 83.2%, p < 0.001) (Fig. 1). Therefore, PNI = 47.425 was determined as the optimal cutoff value. Among 157 patients, 53 patients (33.8%) were in the low PNI group (PNI ≤ 47.425), and 104 patients (66.2%) in the high PNI group (PNI > 47.425).

3.4 Clinicopathological features in different PNI groups

The correlation between PNI values and clinical characteristics was confirmed by x² test and Mann-Whitney U test. The clinical data of patients in different PNI groups are shown in the Table 3. The results showed that compared with the high PNI group, the low PNI group had an older age (P = 0.004), a higher Gleason score (P < 0.001), and a higher BCR rate (P < 0.001), higher seminal vesicle and vas deferens invasion rate (P = 0.036, P = 0.002), higher PSA value (P = 0.022), higher NLR score (P < 0.001) and higher SII score (P = 0.022). There was no statistical significance between different PNI groups in terms of BMI, clinical stage, f/t, positive resection margin rate, nerve invasion rate, lymph node positive rate and AGR score. Kaplan-Meier analysis of all patients showed that patients in the low PNI group had a lower BCR-free survival rate compared with the high PNI group. The log-rank test results showed that the difference between the two groups was statistically significant (x² = 21.292, p < 0.001) (Fig. 2).

Table 3

Comparison of different PNI group.
Variable	Low PNI group (PNI ≤ 47.425)	High PNI group (PNI>47.425)	P
Number of cases	53	104
Age (years) median (IQR)	73(69,76)	70(66,73)	0.004
BMI (kg/m2) median (IQR)	23.665(21.593,25.438)	23.875(22.111,25.388)	0.640
PSA (ng/ml) median (IQR)	15.157(9.400,26.454)	11.303(7.053,21.024)	0.022
f/t median (IQR)	0.095(0.072,0.138)	0.108(0.076,0.151)	0.318
AGR median (IQR)	1.6(1.4,1.9)	1.7(1.5,1.9)	0.054
NLR median (IQR)	2.95(2.16,4.49)	2.27(1.57,2.90)	<0.001
SII median (IQR)	579.55(355.25,980.07)	458.42(294.75,656.11)	0.022
Clinical staging
T1-T2	15	29
T3-T4	38	75	0.956
Gleason score
≤ 7	34	92
>7	19	12	<0.001
Margin status
Negative	26	60
Positive	27	44	0.304
Nerve invasion
Negative	15	33
Positive	38	71	0.659
Seminal vesicle invasion
Negative	38	89
Positive	15	15	0.036
Vas deferens invasion
Negative	42	99
Positive	11	5	0.002
Lymph node status
Negative	47	101
Positive	6	3	0.074
BCR
No	18	88
Yes	35	16	<0.001

3.5 Establishment and verification of the BCR risk score model

157 patients were divided into training group and validation group according to the ratio of 7:3. The clinical data of patients in different groups are shown in the Table 4. Among them, there were 110 patients in the training group and 47 patients in the validation group. There was no statistical difference in each variable between the two groups (P > 0.05). As shown in the Table 5, according to univariate Cox regression analysis, in the training group, clinical stage (P = 0.038 )、PSA (P < 0.001 ), Gleason score (P < 0.001), PNI (P < 0.001), NLR (P = 0.012), positive resection margin (P = 0.011), positive lymph node (P < 0.001), nerve invasion (P = 0.043), seminal vesicle invasion (P < 0.001 ) and vas deferens invasion (P < 0.001 ) had a significant correlation with BCR. The above factors were included in multiple Cox regression analysis to further determine independent predictors. It was determined that PSA (P = 0.014) and PNI (P = 0.002) were independent predictors of BCR in patients with RARP. Then a BCR prediction model formula was established and the risk score was calculated (risk score = 0.021* PSA-0.170* PNI). The AUC values of this model in the training group and validation group were 0.870 (95% confidence interval: 0.804–0.937, sensitivity = 82.4%, specificity = 80.3%) and 0.898 (95% confidence interval: 0.800-0.996, sensitivity = 2.4%, specificity = 86.7%) respectively (Fig. 3). Based on the above results of Cox regression analysis, we combined the two independent predictive factors of PSA and PNI to construct a nomogram to predict the probability of BCR in patients with RARP at 6-, 12-, and 24-months after surgery (Fig. 4). The results showed that for every 2 points decreases in PNI, the nomogram score increased by 9 points; for every 10 ng/ml increase in PSA, the nomogram score increased by 5 points. And the corresponding BCR probability of patients increased at 6-, 12-, and 24-months after surgery. Based on calibration plots (Fig. 5), the predicted 6-, 12-, and 24-months BCR probabilities of the nomogram performed well in both the training and validation group.

Table 4

Comparison of training group and validation group.
Variable	Training group	Validation group	P
Number of cases	110	47
Age (years) median (IQR)	71(67,75)	70(64,73)	0.085
BMI (kg/m2) median (IQR)	23.559(21.664,25.359)	24.167(22.857,25.977)	0.237
PSA (ng/ml) median (IQR)	12.476(7.927,23.065)	13.060(8.000,24.820)	0.833
f/t (ng/ml) median (IQR)	0.099(0.075,0.149)	0.116(0.078,0.150)	0.747
PNI (ng/ml) median (IQR)	48.90(45.28,50.81)	47.90(44.85,51.15)	0.472
AGR (ng/ml) median (IQR)	1.7(1.5,1.9)	1.7(1.5,1.9)	0.466
NLR (ng/ml) median (IQR)	2.40(1.78,3.19)	2.41(1.73,3.62)	0.513
SII (ng/ml) median (IQR)	469.00(294.33,682.88)	520.97(358.22,770.86)	0.297
Clinical staging
T1-T2	29	15
T3-T4	81	32	0.478
Gleason score
≤ 7	90	36
>7	20	11	0.452
Margin status
Negative	62	24
Positive	48	23	0.541
Nerve invasion
Negative	32	16
Positive	78	31	0.537
Seminal vesicle invasion
Negative	91	36
Positive	19	11	0.371
Vas deferens invasion
Negative	100	41
Positive	10	6	0.682
Lymph node status
Negative	105	43
Positive	5	4	0.546
BCR
No	76	30
Yes	34	17	0.519

Table 5

Univariate and multivariate analysis of factors associated with BCR in RARP patients.
Variable	Univariate analysis HR (95%CI)	P	Multivariate analysis HR (95%CI)	P
Age (continuous variable)	1.032(0.976–1.090)	0.269
BMI (continuous variable)	1.049(0.920–1.195)	0.474
PSA (continuous variable)	1.024(1.014–1.035)	<0.001	1.021(1.004–1.038)	0.014
f/t (continuous variable)	0.113(0.001–11.317)	0.354
PNI (continuous variable)	0.817(0.757–0.882)	<0.001	0.869(0.794–0.950)	0.002
AGR (continuous variable)	0.519(0.147–1.827)	0.307
NLR (continuous variable)	1.253(1.051–1.495)	0.012	1.133(0.914–1.405)	0.253
SII (continuous variable)	1.000(1.000-1.001)	0.185
Clinical staging (T1–T2/T3–T4)	0.331(0.117–0.940)	0.038	1.182(0.091–15.335)	0.898
Gleason score (≤ 7/>7)	0.212(0.107–0.419)	<0.001	0.773(0.222–2.690)	0.686
Margin status (negative/positive)	0.406(0.203–0.813)	0.011	1.135(0.439–2.935)	0.793
Nerve invasion (negative/positive)	0.375(0.145–0.969)	0.043	2.190(0.209–22.949)	0.513
Seminal vesicle invasion (negative/positive)	0.265(0.132–0.530)	<0.001	0.604(0.190–1.917)	0.392
Vas deferens invasion (negative/positive)	0.236(0.106–0.525)	<0.001	0.822(0.232–2.911)	0.761
Lymph nodes (negative/positive)	0.191(0.073–0.501)	<0.001	0.921(0.282–3.009)	0.892

In recent years, the incidence of prostate cancer has shown a significant upward trend worldwide (18). Many countries and regions have included PSA screening in routine physical examinations for older men. This will help improve the detection rate of prostate cancer, diagnose and treat of early prostate cancer, especially prostate cancer of clinical significance. Although with the development of RP technology, prostate cancer patients can obtain good tumor control and achieve radical results, there are still some patients who will inevitably experience BCR, or even fail to take appropriate measures in time, leading to the progression of the disease (19, 20). Therefore, how to accurately predict which patients will have BCR has become a major problem for clinicians. The purpose of this study was to examine the predictive value of the PNI for BCR in patients undergoing RARP. PNI combines serum albumin concentration and lymphocyte count. It is a simple and effective objective data assessment system that can objectively reflect the patients’ preoperative nutritional and inflammatory immune status. At the same time, it overcomes the invasiveness risk caused by previous predictive factors, which can be measured with a simple blood test.

Many researchers currently believe that inflammation has a certain relationship with the recurrence and metastasis of tumors (21). Because the uncontrolled persistence of inflammatory responses may lead to severe cell and genome damage, resulting in wanton cell proliferation and genome instability, and increasing the risk of malignant tumors (22). Previous studies have shown that inflammation may be one of the causes of prostate cancer. There may be a certain relationship between prostatitis and the occurrence and development of prostate cancer (23). Among numerous inflammatory cells, lymphocytes play an important role in tumor immune surveillance (24). They can inhibit tumor proliferation and metastasis by promoting cytotoxic cell death and the production of cytokines (7). The reduction of lymphocytes will lead to immune responses. Studies have shown that lymphopenia is an independent prognostic factor for overall and progression-free survival in cancer patients (25–27).

There is also a close relationship between the nutritional status and prognosis of cancer patients (28). Malignant tumor itself is a chronic wasting disease, especially in advanced patients, who often develop cachexia. Malnutrition can also inhibit the function of the immune system to a certain extent, leading to the recurrence and progression of tumors (29). In clinical practice, we usually use serum albumin concentration to evaluate the nutritional status of patients. Low preoperative albumin may affect the enzyme production ability and self-repair ability of tissues and organs, which may lead to a poor prognosis (30). Liu et al. (31) conducted a meta-analysis on 23 studies and found that low preoperative serum albumin levels were associated with poor prognosis of urothelial cancer.

PNI on the prognosis of different tumors has been analyzed in some previous studies. Xu et al. (12) conducted a prognostic analysis on 508 patients after radical breast cancer resection. They found that higher PNI was associated with better disease-free survival (DFS). Kubota et al. (11) analyzed the prognosis of 183 cases of oral cancer and found that higher pre-treatment PNI was associated with better OS, while lower pre-treatment PNI and higher treatment SII were associated with worse DFS. The correlation between many urinary tumors and PNI has also been gradually discovered. Kim et al. (32) showed that the OS and cancer-specific survival rate of renal cell carcinoma patients in the low PNI group were relatively poor. KARSIYAKALI et al. (33) found that PNI can be used to predict tumor stage in patients with primary bladder cancer, and lower PNI level is associated with higher stage disease. In a 2021 study, Li et al. (15) have evaluated the impact of PNI on BCR in patients with RARP. They used a cutoff value of 46.03 to divide 136 patients into a high PNI group and a low PNI group, and Cox proportional hazard analysis confirmed that PNI is an independent prognostic factor for predicting BCR in patients with RARP.

In this study, the optimal cutoff value of PNI was 47.425, then patients were divided into high PNI group and low PNI group. Data analysis confirmed that PNI is an independent prognostic factor for predicting BCR in patients with RARP. Patients with a low level of PNI have a higher rate of BCR after RARP, which is similar to the conclusion of previous studies. This also shows that there is a correlation between the patients’ preoperative nutrition and inflammatory immune status and prognosis. We can take certain intervention measures during the perioperative period to increase the patients’ albumin and lymphocyte levels, thereby improving the treatment effect and long-term prognosis. In addition, we also found that PSA is an independent prognostic factor in predicting BCR in patients with RARP, and PSA levels are positively correlated with the rate of BCR. Thereafter, we established a BCR prediction score model based on PNI and PSA. And its predictive value was confirmed in both the training group and the validation group. In addition, we also constructed a nomogram to predict the probability of BCR in patients with RARP at 6-, 12-, and 24-months after surgery. According to the results of the calibration plots, nomogram performed well in predicting the probability of BCR in 6-, 12-, and 24-months in both the training group and the validation group. So, we can use this model to predict the BCR risk of patients, consequently identifying high-risk patients as early as possible and helping them optimize treatment plans to obtain better survival results.

This study shows that PNI is an independent predictor of BCR after surgery in patients with RARP, and lower PNI is associated with BCR. The risk score model and nomogram established based on PNI and PSA can effectively predict the risk of biochemical recurrence of prostate cancer patients after RARP. Therefore, in clinical practice, we can apply this scoring model to identify high-risk patients who may experience BCR, so as to reduce the postoperative BCR rate of patients with RARP.

PNI Prognostic Nutritional Index

BCR Biochemical Recurrence

RARP Robot-Assisted Laparoscopic Radical Prostatectomy

ROC Receiver Operating Characteristic

PSA Prostate-Specific Antigen

RP Radical Prostatectomy

NLR Neutrophil-Lymphocyte Ratio

PLR Platelet–Lymphocyte Ratio

SII Systemic Immune-Inflammation Index

AGR Albumin-Globulin Ratio

CT Computed Tomography

MRI Magnetic Resonance Imaging

BMI Body Mass Index

f PSA Free Prostate-Specific Antigen

f/t Free Prostate-Specific Antigen /Total Prostate-Specific Antigen

IQRs Interquartile Ranges

HR Hazard Ratio

Author contributions

All authors contributed to the study of conception and design. Xiaojun Zhao coordinated and managed all parts of the study. Yifan Zhao carried out the literature search. All authors conducted data collection and performed preliminary data preparations. Yifan Zhao conducted data analyses and contributed to the interpretation of data. Yifan Zhao wrote the draft of the paper and all authors provided substantive feedback on the paper and contributed to the final manuscript. All authors read and approved the final manuscript.

Funding

None applicable.

Data availability

All data is available from corresponding author on reasonable request.

Ethics approval and consent to participate

The experimental protocol was developed in accordance with the ethical guidelines of the Declaration of Helsinki and was approved by the Human Ethics Committee of the First Affiliated Hospital of Soochow University, the name of the institutional Ethics committee. Written informed consent was obtained from individual or guardian participants.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

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No competing interests reported.

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Predictive model of pre-operative prognostic nutrition index for biochemical recurrence in patients undergoing robot-assisted laparoscopic radical prostatectomy: a retrospective clinical study

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Abstract

Figures

1. Introduction

2. Patients and methods

2.1 Study population

2.2 Data collection and follow-up

2.3 Statistical analysis

3. Results

3.1 Basic information

3.2 Comparison of clinicopathological data between BCR group and non-BCR group

3.3 Optimal PNI cutoff value before treatment

3.4 Clinicopathological features in different PNI groups

3.5 Establishment and verification of the BCR risk score model

4. Discussion

5. Conclusion

Abbreviations

Declarations

References

Additional Declarations

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