Predictors of postoperative delirium in patients undergoing radical prostatectomy: a prospective study

doi:10.21203/rs.3.rs-4065304/v1

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Research Article

Predictors of postoperative delirium in patients undergoing radical prostatectomy: a prospective study

https://doi.org/10.21203/rs.3.rs-4065304/v1

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Background

Analyze the risk factors for postoperative delirium (POD) in elderly patients undergoing radical prostatectomy, built a predictive nomogram model for early identification of high-risk individuals and develop strategies for preventive interventions.

Methods

A total of 156 patients was recruited and categorized according to the development of POD within 7 days. After identifying independent risk factors through univariate and multivariate logistic regression analyses, predictive models were established. The discrimination and calibration were determined by C-index and calibration curve, with five-fold cross-validation executed. A nomogram model representing the optimal model was constructed based on the results.

Results

POD occurred in 24 (15.38%) patients. Significant differences were observed in age, anxiety, physical status, sleep disorders, blood glucose, age-adjusted Charlson comorbidity index (ACCI), anticholinergic, blood loss, postoperative infection, and numerical rating scale (NRS). Logistic regression analyses showed that sleep disorders (OR:12.931, 95% CI:1.191-140.351, P = 0.035), ACCI (OR:2.608, 95% CI:1.143–5.950, P = 0.023), postoperative infection (OR:19.298, 95% CI:2.53-147.202, P = 0.04), and NRS (OR:4.033, 95% CI:1.062–15.324, P = 0.041) were independent risk factors for POD. Model 1 (postoperative infection, ACCI, preoperative sleep disorder, NRS showed better diagnostic performance than the others, of which the area under the curve (AUC) was 0.973. The best diagnostic performance was found in model 1 through five-fold cross-validation, with a C-index of 0.963.

Conclusions

This prospective cohort study highlighted that ACCI, preoperative sleep disorder, postoperative pain, and postoperative infection were identified as independent risk factors for POD. Furthermore, the nomogram derived from model 1 proved to be effective in predicting POD in elderly patients undergoing radical prostatectomy.

Prostate cancer

Elderly patients

Postoperative delirium

Risk factors

Nomogram

Prostate cancer ranks as the second most prevalent malignant neoplasm in males and stands as the fifth primary contributor to male mortality on a global scale [1]. Prostate cancer primarily affects older individuals [2]. As China's population continues to age, the incidence and mortality rates of prostate cancer are on the rise. Radical prostatectomy stands as an effective treatment for local prostate cancer.

Most postoperative delirium (POD) occurs within 3 days after operation, with a higher prevalence among elderly patients. The main characteristics of POD include acute attention disorder and cognitive dysfunction [3]. A previous study has shown that the overall incidence of POD was 19% after elective surgery and 32% after emergency surgery [4]. Researchers found that the incidence of POD in otorhinolaryngology was 12%, 13% in general surgery, 29% in aortic surgery, 50% in major abdominal surgery and 51% in cardiac surgery [5]. Delirium not only prolongs the length of hospital stay, but impacts the postoperative quality of life for patients. There is currently no perfect clinical treatment plan for POD, and the underlying mechanism remains unclear. The factors contributing to POD are multifaceted, involving preoperative, intraoperative, and postoperative elements. It is clinically significant to timely identify and manage the risk factors associated with POD in order to decrease its occurrence in elderly patients.

Predictive models have the potential to support physicians in implementing precision medicine, thus enabling the development of individualized and effective treatment strategies [6–9]. The objective of this investigation was to examine the present status and potential risk factors linked to POD in elderly individuals undergoing radical prostatectomy, as there has been little focus on them. Therefore, we aimed to gather and analyze clinical data in this prospective cohort analysis to establish a predictive model that can serve as a reference for identifying high-risk patients with POD and formulating preventive measures in future clinical practice.

Patients

This was a single-center, observational, prospective cohort study with the approval of the Medical Ethics Committee of the First Affiliated Hospital of Guangxi Medical University (Ethics No.2024-E093-01) in accordance with the Declaration of Helsinki. Between November 2020 and January 2023, a total of 183 elderly patients who received radical prostatectomy were collected from the First Affiliated Hospital of Guangxi Medical University. The patient data was retrieved from the hospital's electronic health record system, and postoperative follow-up was conducted for 7 days. The inclusion criteria included: (1) radical prostatectomy under general anesthesia, (2) postoperative hospitalization for at least 3 days, (3) American Association of Anesthesiologists (ASA) grade I to III, (4) no language communication barriers, with certain language comprehension and reading abilities, (5) patient and family members provided consent to be involved in the research, (6) patient age ≥ 65 years old. The exclusion criteria were delineated as follows: (1) patient or their families refused to be involved in the research, (2) had a confirmed history of delirium or dementia, (3) emergency surgery, (4) entered ICU after surgery, (5) with severe visual or auditory impairments, (6) preoperative cognitive impairment is defined as having a MMSE score of 17 or lower for individuals who are illiterate, a score of 20 or lower for those with a primary school education, or a score of 24 or lower for individuals with a middle school education or higher. All patients provided signed informed consent forms.

Data collection

General clinical data of patients such as age, height, weight, education, past medical history, smoking history, drinking history, medication history, ASA grade, preoperative urinary tract infection, hospitalization days, etc. were collected. The following laboratory findings were collected: hemoglobin, serum albumin, serum potassium, serum sodium and blood glucose. Intraoperative information was gathered by searching through the Anesthesia Clinical Information System: anesthetic drugs and dosage, type of surgical procedure, duration of surgery, length of anesthesia, duration of resuscitation, blood gas analysis, and blood loss.

Assessment scales

Prior to the surgery, the patients' baseline cognitive function was assessed using the Mini-Mental State Examination (MMSE) [10, 11]. The levels of anxiety were measured using the Generalized Anxiety Disorder-7 (GAD-7) [12], while the levels of depression were evaluated using the Patient Health Questionnaire (PHQ-9) [13]. The frailty of the patients was assessed using the FRAIL Scale [14, 15], and their sleep quality was evaluated using the Pittsburgh Sleep Quality Index (PSQI) [16]. Preoperative comorbidities were determined by the age-adjusted Charlson comorbidity index (ACCI) [17], and physical status was assessed using the metablic equivalents (METs) [18]. Following the surgery, pain levels were quantified using the numerical rating scale (NRS) [19], and delirium was assessed using the 3-minute Diagnostic Interview for the Confusion Assessment Method (3D-CAM) [20, 21].

Predictive Model

Univariate and multivariate logistic regression analyses were utilized to assess potential risk factors linked to POD. Once independent risk factors had been identified, random combinations were employed to establish models. The stepwise regression approach was utilized to evaluate the models with strong differentiation. R software (version 4.3.0) was then used to evaluate and plot the ROC curve, C-index, calibration curve, and DCA curve for each model, with 5-fold cross-validation executed. The resulting data was used to generate a nomogram representing the optimal model.

Statistical analysis

The study employed the Kolmogorov-Smirnov test to assess the normality of the data. Continuous variables exhibiting normal distributions were represented as means ± standard deviations (SDs) and compared using the independent samples t-test. Skewed continuous variables were presented as medians (interquartile ranges) and analyzed using the Mann-Whitney U test. Counting data was expressed as percentages [n (%)], and group comparisons were conducted using the Chi-square test. Statistical analysis was conducted using SPSS software (version 27.0), with a significance level set at p < 0.05.

Demographic and Clinical Characteristics of Patients

Our study involved 183 patients in total, with 156 patients (85%) successfully completing the follow-up process. Among them, 24 patients (15.38%) developed POD. The demographic and clinical details of the patients can be found in Table 1. There were notable variations between the POD group and the non-POD group in age (P < 0.001), anxiety levels (P = 0.013), frailty (P < 0.001), sleep disorder (P < 0.001), blood glucose levels (P = 0.02), ACCI scores (P < 0.001) and MET scores (P < 0.001). Regarding the intraoperative risk factors outlined in Table 2, there were no statistically significant differences identified in surgical method, benzodiazepine use, electrolyte disturbances, surgical time, anesthesia time, resuscitation time, and hypotension time between the two groups. Nonetheless, there was a significantly lower proportion of anticholinergic use (P = 0.019) observed in the POD group compared to the non-POD group. Additionally, the intraoperative blood loss (P = 0.015) was significantly higher in the POD group. Table 3, which provides information on postoperative indicators, revealed significant differences in postoperative infections (P < 0.001) and postoperative pain (P < 0.001) between the two cohorts. There were no statistically significant variations observed in the levels of serum sodium, serum potassium, and serum albumin between the two groups.

Table 1

General characteristics and preoperative factors of patients
	Total	POD			p
	(n = 156)	Yes (n = 24)	No (n = 132)
Age [median (IQR)]	156	74 (7)	69 (6)	Z = -4.102	< 0.001*
BMI (kg/m²)	156	23.45	23.83	t = -0.625	0.533
ASA, n (%)				χ² = 2.797	0.247
I	3	1 (33.33%)	2 (66.67%)
II	100	12 (12%)	88 (88%)
III	53	11 (20.8%)	42 (79.2%)
Anxiety, n (%)				χ² = 6.205	0.013*
Yes	56	14 (25%)	42 (75%)
No	100	10 (10%)	90 (90%)
Depression, n (%)				χ² = 0.837	0.36
Yes	23	5 (21.7%)	18 (78.3%)
No	133	19 (14.3%)	114 (85.7%)
Frailty, n (%)				χ² = 13.6	< 0.001*
Yes	76	20 (26.3%)	56 (73.7%)
No	80	4 (5%)	76 (95%)
Sleep disorder, n (%)				χ² = 40.789	< 0.001*
Yes	54	22 (40.7%)	32 (59.3%)
No	102	2 (1.96%)	100 (98.04%)
Smoking, n (%)				χ² = 0.222	0.638
Yes	52	7 (13.5%)	45 (86.5%)
No	104	17 (16.3% )	87 (83.7% )
Drinking, n (%)				χ² = 0.541	0.462
Yes	49	6 (12.2%)	43 (87.8%)
No	107	18 (16.8%)	89 (83.2%)
Urinary tract infection				χ² = 2.781	0.095
Yes	22	6 ( 27.3%)	16 ( 72.7%)
No	134	18 (13.4%)	116 (86.6%)
Serum sodium				χ² = 0.175	0.676
Normal	146	22 (15.1%)	124 (84.9%)
Abnormal	10	2 (20%)	8 (80%)
Serum potassium				χ² = 0.487	0.485
Normal	131	19 (14.5%)	112 (85.5%)
Abnormal	25	5 (20%)	20 (80%)
Hemoglobin [g/L, median (IQR)]	156	129 (20.5)	133 (20.5)	Z = -1.673	0.094
Blood glucose [mmol/L, median (IQR)]	156	6.47 (1.64)	5.41 (1.61)	Z = -3.125	0.02*
ACCI scores	156	7 (0)	5 (1)	Z = -6.66	< 0.001*
MET values	156	3 (2)	4 (1)	Z = -3.422	< 0.001*

POD, postoperative delirium; BMI, body mass index; ASA, American Society of Anesthesiologists; ACCI, age-adjusted Charlson comorbidity index; MET, metabolic equivalent. * p < 0.05

Table 2

Intraoperative factors of patients
	Total	POD			p
	(n = 156)	Yes (n = 24)	No (n = 132)
Types of surgery, n (%)				χ² = 1.266	0.542
Robot-assisted laparoscopic radical prostatectomy	83	14 (16.9%)	69 (83.1%)
Laparoscopic radical prostatectomy	70	9 (12.9%)	61 (87.1%)
Open radical prostatectomy	3	1 (33.33%)	2 (66.67%)
Anticholinergics				χ² = 5.522	0.019*
Yes	36	10 (27.8%)	26 (72.2%)
No	120	14 (11.7%)	106 (88.3%)
Benzodiazepines				χ² = 0.355	0.552
Yes	26	3 (11.5%)	23 (88.5%)
No	130	21 (16.2%)	109 (83.8%)
Electrolyte disturbance				χ² = 0.16	0.898
Yes	31	5 (16.1%)	26 (83.9%)
No	125	19 (15.4%)	106 (84.6%)
Duration of surgery (min)	156	284.42	248.98	t = 1.517	0.141
Duration of anesthesia (min)	156	336.67	301.21	t = 1.546	0.133
Duration of recovery [min, median (IQR)]	156	92 (35)	88 (33)	Z = -0.349	0.727
Blood loss [ml, median (IQR)]	156	200 (200)	100 (128)	Z = -2.444	0.015*
Duration of hypotension [min, median (IQR)]	156	10 (5)	10 (9)	Z = -0.432	0.666
POD, postoperative delirium. * p < 0.05

Table 3

Postoperative factors of patients
	Total	POD			p
	(n = 156)	Yes (n = 24)	No (n = 132)
Serum sodium				χ²=0.292	0.589
Normal	152	23 (15.1%)	129 (84.9%)
Abnormal	4	1 (25%)	3 (75%)
Serum potassium				χ²=0.093	0.76
Normal	113	18 (15.9%)	95 (84.1%)
Abnormal	43	6 (14%)	37 (86%)
Postoperative infection				χ²=77.46	< 0.001*
Yes	15	14 (93.3%)	1 (6.7%)
No	141	10 (7.1%)	131(92.9%)
Serum albumin [mg/L, median (IQR)]	156	33.83	35.02	t=-1.317	0.19
NRS scores [median (IQR)]	156	4.14 (1)	4 (1.26)	Z=-4.155	< 0.001*

POD, postoperative delirium; NRS, numerical rating scale. * p < 0.05

Feature Selection and Prediction Model Construction

Subsequently, eleven possible risk factors were included in logistic regression analysis, as demonstrated in Table 4. The results revealed that ACCI (OR: 2.608, 95%CI: 1.143–5.950, P = 0.023), preoperative sleep disorder (OR: 12.931, 95%CI: 1.191-140.351, P = 0.035), postoperative pain (OR:4.033, 95%CI: 1.062–15.324, P = 0.041), and postoperative infection (OR:19.298, 95%CI: 2.53-147.202, P = 0.04) were found to be independent risk factors for POD. These risk factors were randomly combined to establish models, and five models with good differentiation were selected for further analysis. These models include: Model 1 (PI + ACCI + PSD + NRS), Model 2 (PI + ACCI + PSD), Model 3 (PI + PSD + NRS), Model 4 (PI + PSD), Model 5 (ACCI + NRS).

Table 4

Multivariate logistic regression analyses of POD
Variables	OR (95%CI)	P
Age	0.938 (0.626–1.406)	0.757
Anxiety	0.442 (0.061–3.191)	0.418
Frailty	0.632 (0.054–7.376)	0.714
Sleep disorder	12.931 (1.191-140.351)	0.035*
Blood glucose	1.423 (0.704–2.879)	0.326
ACCI	2.608 (1.143–5.950)	0.023*
MET	0.776 (0.162–3.724)	0.751
Anticholinergics	3.103 (0.597–16.143)	0.178
Blood loss	1.001 (0.999–1.002)	0.213
Postoperative pain (NRS)	4.033 (1.062–15.324)	0.041*
Postoperative infection	19.298 (2.53-147.202)	0.04*

POD, postoperative delirium; ACCI, age-adjusted Charlson comorbidity index; MET, metabolic equivalent; NRS, numerical rating scale. * p < 0.05

Construction of POD Nomogram

The results of ROC curve for each model are presented in Fig. 1. Model 1 in Table 5 showed the highest AUC of 0.973 (95%CI: 0.949–0.996) in predicting the occurrence of POD. Furthermore, all combined models exhibited better diagnostic efficacy compared to individual indicators. The C-index and the calibration curve for each model are shown in Table 6 and Fig. 2, highlighting their robust consistency both in theory and application. Following internal verification via five-fold cross-validation, a new C-index was obtained as presented in Table 7. Model 1 continued to exhibit the utmost diagnostic efficiency with a new C-index of 0.963 (95%CI: 0.944–0.983), substantiating its exceptional innate repeatability. Furthermore, the decision curve analysis (DCA) indicated that the net benefit of using the Model 1 to predict POD was superior to other models, as shown in Fig. 3. Based on Model 1, a nomogram was constructed in Fig. 4 to provide a more precise prediction of individualized risk of POD.

Table 5

AUC of fusion model and single variable
	AUC	95%CI
ACCI	0.916	0.870–0.962
Preoperative sleep disorder (PSD)	0.837	0.758–0.917
Postoperative infection (PI)	0.765	0.640–0.890
NRS	0.783	0.698–0.867
Model 1 (PI + ACCI + PSD + NRS)	0.973	0.949–0.996
Model 2 (PI + ACCI + PSD)	0.968	0.924–0.994
Model 3 (PI + PSD + NRS)	0.956	0.926–0.986
Model 4 (PI + PSD)	0.918	0.876–0.959
Model 5 (ACCI + NRS)	0.936	0.898–0.973

AUC, area under the curve; CI, confidence interval; ACCI, age-adjusted Charlson comorbidity index; NRS, numerical rating scale

Table 6

C-index of fusion model and single variable
	C-index	95%CI
ACCI	0.916	0.870–0.962
Preoperative sleep disorder (PSD)	0.837	0.758–0.917
Postoperative infection (PI)	0.765	0.640–0.890
NRS	0.783	0.698–0.867
Model 1 (PI + ACCI + PSD + NRS)	0.973	0.949–0.996
Model 2 (PI + ACCI + PSD)	0.968	0.924–0.994
Model 3 (PI + PSD + NRS)	0.956	0.926–0.986
Model 4 (PI + PSD)	0.918	0.876–0.959
Model 5 (ACCI + NRS)	0.936	0.898–0.973

C-index, concordance index; CI, confidence interval; ACCI, age-adjusted Charlson comorbidity index; NRS, numerical rating scale

Table 7

C-index of models after five-fold cross-validation
	C-index	95%CI
Model 1 (PI + ACCI + PSD + NRS)	0.963	0.944–0.983
Model 2 (PI + ACCI + PSD)	0.957	0.934–0.981
Model 3 (PI + PSD + NRS)	0.941	0.920–0.962
Model 4 (PI + PSD)	0.915	0.890–0.940
Model 5 (ACCI + NRS)	0.938	0.915–0.961

C-index, concordance index; CI, confidence interval; ACCI, age-adjusted Charlson comorbidity index; NRS, numerical rating scale

In this prospective cohort study, the incidence of POD was 15.38%, a figure that similar to the previously reported 19% incidence in a retrospective study of elderly cancer patients [22]. Our study identified ACCI, preoperative sleep disorder, postoperative pain, and postoperative infection as factors that can predict the occurrence of POD. We constructed and validated a predictive model for POD and depicted the impact of different factors on POD through a nomogram.

At present, most research on POD focuses on patients undergoing cardiac and orthopedic surgeries [23]. The prevalence of POD in cardiac surgery patients varies from 25–50%[24], while orthopedic surgery patients have a POD incidence of 17.3% [25]. There is a noticeable disparity in the occurrence rates of POD among different surgical procedures, indicating that the nature of the surgery itself may pose a considerable risk factor for POD. Therefore, developing a predictive model for POD in elderly patients after a specific type of surgery may be more feasible and valuable in clinical practice.

A recent study extracted 576 variables and concluded that older age was the most influential factor in the onset of POD [26]. Similarly, studies found that older age and the presence of comorbidities were associated with an increased risk of POD [27, 28]. Elderly patients typically have multiple comorbidities and tend to become more frail as they age [29]. ACCI is a reliable predictor of overall survival and mortality across various surgeries, and it has the potential to accurately predict the onset of POD [30]. Researchers observed that 25% of spinal surgery patients developed POD, and 24% were frail, with frail patients having a 6.6 times higher probability of developing POD compared to robust patients [31]. In the older population undergoing major planned noncardiac surgical procedures, the probability of developing POD was 2.7 times higher in frail patients compared to robust patients. However, frailty was not found to be related to postoperative cognitive decline [32].

According to previous studies, preoperative anxiety has been recognized as a contributing factor for POD [33, 34]. There is an evident association between preoperative anxiety and sleep disorder [35]. Postoperative sleep disorder was among one of the risk factors for POD in elderly patients who underwent elective spinal surgery [36]. Another study also determined that sleep disorder increased the probability of developing POD [37]. Perioperative individuals with sleep disorders demonstrated heightened neuroinflammation, increased oxidative stress, compromised blood-brain barrier integrity, and impaired glymphatic pathway function, and accumulation of amyloid-beta proteins, all of which were linked to postoperative neurocognitive dysfunction [38]. Moreover, patients who experienced sleep disturbances prior to surgery exhibited notably elevated occurrences of delayed neurocognitive improvement and postoperative infection compared to those without sleep disorders [39]. Melatonin, a hormone produced by the pineal gland, has been shown to regulate sleep disturbances. Studies demonstrated that melatonin can notably reduce the occurrence of delirium [40], suggesting that improving perioperative sleep may decrease the likelihood of POD and postoperative cognitive dysfunction (POCD). Our study solely concentrated on the influence of preoperative sleep disorder on POD and did not assess postoperative sleep quality. Subsequent research should further investigate the connection between postoperative sleep disorder and POD.

Researchers discovered that after surgery, the pain experienced by the patient may lead to an increased likelihood of POD. However, the effect of pain on other postoperative neurocognitive disorders remained uncertain [41]. Advanced age, smoking, cognitive impairment, and postoperative pain were identified as risk factors for subsyndromal delirium in a descriptive correlational study [42]. Postoperative acute pain could worsen neuroinflammation and associated cognitive functional impairments in an animal study [43]. However, studies found that preoperative chronic pain was not an independent predictor of POD [44]. Our research found that elevated postoperative pain levels in elderly individuals were linked to a higher likelihood of developing POD. The association between resting pain and POD was even more pronounced, with higher scores and longer duration of resting pain potentially elevating the risk of POD.

In our study, we found that postoperative infection was the most prominent independent indicator for POD. Factors such as trauma, surgical manipulation, intraoperative bleeding, hypoxia, electrolyte disturbances, acid-base imbalance, and the use of specific anesthetic drugs can stimulate the body's immune system and peripheral inflammatory response, resulting in the liberation of a substantial quantity of inflammatory mediators. These factors can disrupt the blood-brain barrier, allowing the inflammatory factors to reach the brain parenchymal cells through various pathways and ultimately contributing to the occurrence of POD [45]. Previous research has shown an association between POD and POCD with the levels of inflammatory markers in the peripheral tissues. Certain markers, including IL-6 and CRP, have been associated with both POD and POCD [46]. Additionally, studies have linked the elevation of inflammatory markers CPAR and S100B to the occurrence of POD [47]. Furthermore, it has been discovered that the axon guidance molecule Netrin-1 possesses properties that are anti-inflammatory and neuroprotective. Netrin-1 was found to decrease the levels of IL-6 and HMGB-1 in peripheral blood, prefrontal cortex, and hippocampus, while concurrently increasing the expression of IL-10. Additionally, Netrin-1 was observed to diminish the activation of microglial cells in the prefrontal cortex and hippocampus, and ameliorate behavior characteristic of POD in mice [48]. Another study observed a significant association between the increase in systemic immune-inflammation index and the presence of POD [49], although this finding was not validated in our study. The study has a limited sample size and did not encompass perioperative drug use that lacked statistical significance. Subsequent research should enlarge the sample size and include it. Moreover, the onset of POD varies, and may occur beyond the duration of our follow-up. Many assessment scales rely on subjective accounts from patients, potentially leading to biased outcomes. It is expected that more comprehensive research will be conducted to provide early intervention for high-risk patients with POD in the future.

In this research, we have emphasized that factors such as ACCI, preoperative sleep disorder, postoperative pain, and postoperative infection independently contribute to the risk of POD in elderly patients with prostate cancer. Furthermore, we have developed a nomogram model based on these factors, which demonstrates an effective ability to predict the occurrence of POD in this patient population.

Acknowledgements

None.

Funding

This work was supported by the Special Fund of Neurotoxicity of General Anesthetics and Its Prevention and Treatment Innovation Team of the First Affiliated Hospital of Guangxi Medical University (No. YYZS2022001), the Guangxi Clinical Research Center for Anesthesiology (No. GK AD22035214) and the Key Project of Natural Science Foundation of Guangxi (No. 2020GXNSFDA238025).

Author Contribution

Hao Wang, Yubo Xie, Jie Chen and Sining Pan designed the experiments; Hao Wang, Jie Chen and Sining Pan analyzed the data and drafted the work; Hao Wang, Jie Chen, Sining Pan, Yanhua Chen, Yinying Qin, Jing Chen and Tianxiao Liu performed the experiments and prepared the figures and tables.

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Predictors of postoperative delirium in patients undergoing radical prostatectomy: a prospective study

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Abstract

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Introduction

Material and methods

Patients

Data collection

Assessment scales

Predictive Model

Statistical analysis

Results

Demographic and Clinical Characteristics of Patients

Feature Selection and Prediction Model Construction

Construction of POD Nomogram

Discussion

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Author Contribution

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