Effects of preoperative neoadjuvant chemotherapy on postoperative delirium in patients with gynecological tumor surgery: an observational study

doi:10.21203/rs.3.rs-5026040/v1

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Effects of preoperative neoadjuvant chemotherapy on postoperative delirium in patients with gynecological tumor surgery: an observational study

https://doi.org/10.21203/rs.3.rs-5026040/v1

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Purpose: To investigate whether NACT increases the incidence of POD in patients with gynecological tumors undergoing radical hysterectomy.

Methods: This study included 60 patients in the neoadjuvant chemotherapy exposure group and 60 in the non-exposure group. Preoperative cognitive function, the incidence of POD and other physiological parameters were assessed on POD-1, and POD1-POD3. Additionally, preoperative olfactory function was evaluated using an olfactory detection kit on POD-1. The primary outcome was the incidence of POD within three days after surgery.

Results：The incidence of POD was 28.3% in the exposed group and 8.3% in the non-exposed group (P = 0.005). Compared to the non-exposed group, the exposed group had higher rate of cognitive dysfunction (33.33% vs 13.33%; P = 0.010), and a higher rate of OD (25.0% vs 10.0%; P = 0.031). A restricted cubic spline analysis revealed a non-linear relationship between olfactory test scores and MoCA scores (P for overall < 0.001, P for nonlinear = 0.001). Logistic regression identified NACT, mild and moderate cognitive dysfunction, OD, and depression as independent risk factors for POD, with all factors showing significant associations (P < 0.05). The AUC of OD for predicting POD was 0.783 (95%CI: 0.656-0.909).

Conclusions: This single-blind observational study suggests that NACT increases the incidence of POD in patients with gynecological tumors undergoing radical hysterectomy. Moreover, the results indicate that preoperative OD may reflect preoperative cognitive dysfunction, and have predictive value for the incidence of POD.

postoperative delirium

neoadjuvant chemotherapy

paclitaxel

platinum agents

Postoperative delirium (POD) impedes patients’ recovery after surgery.
Neo-adjuvant chemotherapy (NACT) is often used in patients with gynecological tumor surgery.
NACT impairs preoperative cognitive function and increases the rate of POD.
Preoperative olfactory dysfunction has a predictive value on postoperative delirium.

Postoperative delirium (POD) is defined as delirium occurring within 24 to 72 hours after surgery and is a common condition [1]. POD is characterized by acute episodes of altered mental status, including fluctuating levels of inattention, confusion, and altered consciousness [2]. Patients with POD are at increased risk for complications, elevated perioperative mortality, impaired cognitive function, and diminished quality of life following hospitalization [3]. The incidence of POD in surgical patients ranges from 4–53% [4]. Preoperative cognitive dysfunction has been identified as an independent risk factor for POD [5–7].

Neoadjuvant chemotherapy (NACT) is commonly administered prior to surgery for gynecological tumors, including ovarian, endometrial, and cervical cancer [8–11]. Chemotherapy agents such as paclitaxel and platinum-based drugs are associated with chemotherapy-related cognitive impairment (CRCI), a clinical syndrome often referred to as ‘chemotherapy brain’ [12–16]. Research indicates that cognitive dysfunction resulting from preoperative chemotherapy is closely linked to an increased incidence of POD. For example, preoperative chemotherapy significantly increased the incidence of postoperative cognitive dysfunction (POCD) within 3 days after surgery in elderly cancer patients (42.3% vs 15.4%, P < 0.05 ) [17]. However, the impact of preoperative paclitaxel and platinum-based chemotherapy on the development of POD in patients with gynecological tumors remains unclear.

Studies have shown that chemotherapy can lead to cognitive impairment and is often associated with a decline in olfactory function [18–21]. Olfactory dysfunction (OD) has been identified as an early prodromal feature of neurodegenerative diseases. OD may serve as an important clinical marker and predictor of mild cognitive impairment or Alzheimer's disease [22, 23]. Studies have shown that OD is associated with impaired memory and executive function [24]. In addition, compared with elderly patients who maintained stable cognitive function after surgery, those with POCD exhibited decreased olfactory discrimination and threshold prior to surgery, indicating impaired olfactory function. The difference in olfactory function persisted for one week after surgery [25]. These results suggest that preoperative OD may be closely related to cognitive impairment following preoperative chemotherapy and surgery. However, whether the presence of OD after preoperative chemotherapy for gynecological tumors predicts the development of POD in patients remains unreported and warrants further investigation.

Therefore, we designed a prospective observational cohort study to investigate whether preoperative NACT affects the incidence of POD in patients with gynecological tumors undergoing radical hysterectomy and to evaluate the predictive value of OD for POD in these patients.

2.1. Study design and ethics

This is a single-center, single-blind, prospective cohort study. Ethical approval was granted by the Institutional Review Board of Jiangsu Provincial Cancer Hospital (2023 Ke-Fast 079; November 28, 2023). This study was registered with the Chinese Clinical Trial Registry (ChiCTR2300078510). Written informed consent was obtained from all participants. The study procedures adhered to the ethical standards of the responsible committee on human experimentation (Institutional or regional) and the Declaration of Helsinki (1975, revised 2013). This clinical trial was conducted from December 2023 to March 2024.

2.2. Participants

Adult patients aged 18 to 65 years, with American Society of Anesthesiologists (ASA) physical status I to III, who were scheduled for elective radical hysterectomy, were screened for eligibility. The exposed group received NACT with a regimen of paclitaxel and platinum-based agents. Two chemotherapy regimens were used: paclitaxel (135-175mg/m ² ) administered as a continuous intravenous infusion, and either cisplatin (75mg/m ² ) or carboplatin (AUC = 5) administered as intraperitoneal injections. Both regimens were administered every 3 weeks. A radical hysterectomy was performed one week after the completion of 3 cycles of treatment. The non-exposed group consisted of patients who had not received any baseline treatments, including immunotherapy, radiotherapy, or chemotherapy.

The exclusion criteria were as follows: (I) a history of severe neurological and psychiatric disorders, including dementia or Parkinson's disease; (II) a history of cranial trauma or brain surgery within the past three months; (III) severe asthma or allergies; (IV) significant anemia; (V) preoperative use of sedative-hypnotics or anticholinergics, as well as within 72 hours postoperatively; (VI) abnormal liver or kidney function; specifically, liver dysfunction was identified by aspartate aminotransferase (AST) and alanine aminotransferase (ALT) levels exceeding 1.5 times the upper limit of normal, while kidney dysfunction was indicated by serum creatinine (Cr) levels ≥ 1.5 times the upper limit of normal; (VII) rhinitis that affects the sense of smell; (VIII) experiencing colds at the time of testing and the presence of other malignant tumors.

2.3. Inclusion and blinding

Eligible patients were evaluated by the investigators on the day prior to surgery. They were categorized into two groups: those who received neoadjuvant chemotherapy (NACT), referred to as the exposed group, and those who did not, known as the non-exposed group. This categorization followed a 1:1 ratio. Both groups underwent the same anesthesia management during the surgery. Data collection occurred one day before the surgery and continued for three days postoperatively. The study utilized a single-blind design, whereby all relevant information was gathered by data collection personnel who were unaware of the allocation scheme. While the subjects and medical staff were informed of the allocation, only the investigators involved in follow-up and statistical analyses were blinded to the group assignments.

2.4. Anesthesia management

Patients did not receive any medication before the operation. Patients were monitored using Standard ASA parameters, including invasive radial artery blood pressure, electrocardiography, capnography, and pulse oximetry. Anesthesia was induced intravenously with midazolam (0.03-0.05mg/kg), propofol (1.5-2.0 mg/kg), sufentanil (0.3–0.5 µg/kg), and rocuronium (0.6–0.9 mg/kg). Endotracheal intubation was performed for respiratory support. Anesthesia was maintained with a continuous intraoperative infusion of propofol (4–6 mg/kg/h), remifentanil (6–12 µg/kg/h), rocuronium (600 ug/kg/h), and dexmedetomidine (0.2 ug/kg/h), without the use of sevoflurane. All patients received the same protective ventilation strategy: PCV-VG mode with a tidal volume of 6 to 8 ml/kg, FiO ₂ of 60%, PEEP of 5cmH₂O, a respiratory rate of 12 to 14 breaths/min, an inspiration/expiration ratio of 1:2, and a P_ETCO₂ maintained between 35 to 45 mmHg). Intraoperative adjustments were made by modulating respiratory parameters to maintain SpO₂ at ≥ 95%. Intraoperative anesthesia depth was monitored with Sedline and maintained within the range of 25 to 50. Mean arterial pressure was maintained within ± 20% of the baseline value during surgery, with vasopressor drugs administered as needed. Intravenous ephedrine was administered to patients with hypotension, defined as a systolic blood pressure less than 80% of the baseline value or below 90 mmHg). Atropine (0.5 mg) was administered if the heart rate fell below 40 beats/min. Intraoperative temperature was maintained at ≥ 36℃ using thermal blankets.

After the surgical procedure, patients were transferred to the post-anesthesia care unit for recovery. The endotracheal tube was removed once the patient was fully awake, had stable circulation, restored swallowing reflex, normal tidal volume, and minute ventilation, and had airway secretions successfully aspirated. Atropine and neostigmine were administered as needed to negate any residual neuromuscular blockade. Patients were permitted to be discharged to the ward once their Alderete score exceeded 9.

For the first 48 hours after surgery, all patients received routine patient-controlled intravenous analgesia, which consisted of flurbiprofen axetil (100 mg) and dezocine (50 mg) diluted in 100 ml of normal saline. The infusion pump delivered a continuous basal rate of 1.5–2.0 ml/h, along with bolus doses of 1.0 ml and a 15-minute lock-out period. Postoperative analgesia was monitored, and corrective actions, such as adjusting the analgesic pump settings or adding tramadol or oxycodone, were taken if the Numeric Rating Scale (NRS) score was 3 or higher.

2.5. Outcome assessments and testing

The primary outcome was the incidence of POD within 3 days after surgery. POD was assessed on postoperative day 1 (POD1)、day 2 (POD2), and day 3 (POD3) using the Confusion Assessment Method(CAM)or the Confusion Assessment Method of the Intensive Care Unit (CAM-ICU). POD was defined as the occurrence of POD on any of the three days following surgery.

The secondary outcomes included the predictive value of OD for POD, postoperative nausea and vomiting (PONV), pain assessed by the NRS on POD1, POD2, POD3, and the length of hospitalization. PONV was defined as nausea and vomiting occurring within 24 hours after surgery. Cognitive evaluation was conducted using the Montreal Cognitive Assessment Scale (MoCA). Researchers assessed frailty using the Fatigue Resistance Ambulation, Illness and Loss of Weight Index (FRAIL), anxiety using the General Anxiety Disorder-7 (GAD-7), depression using the Patient Health Questionnaire-9 items (PHQ-9), and life capability using the Activities of Daily Living (ADL) scale on POD-1. Additionally, the olfactory assessment was performed qualitatively using an olfactory test wax block with 12 flavors (Jinhaimo Olfactory Disorder Auxiliary Diagnosis Card, Production Lot Number: 20230701, Jiangsu Parkinsense Biotech Co., Ltd). OD was defined as the inability to identify fewer than 8 out of odors in the olfactory test.

Other study parameters included preoperative diagnosis, gender, age, BMI, educational level, complications (such as hypertension, diabetes, and coronary heart disease), smoking status, alcohol consumption, operation duration, anesthesia duration, fluid infusion, blood loss, and the frequency of remedial analgesia.

2.6. Statistical analysis

Based on our pilot study, the sample size was determined by analyzing the prevalence of POD in the two groups. From preliminary data, we estimated an incidence of 28% in the exposed group and 4.7% in the non-exposed group. To achieve a statistical power of 90% with a two-sided significance level of 0.05, and maintaining a 1:1 ratio between the two groups, a total of 96 patients was needed for the study. The sample size calculation was conducted using Fisher’s Exact Test for Two Proportions in PASS 2021 software. To account for a potential 20% loss to follow-up or withdrawals of consent, we aimed to include 58 patients in the exposed group and 58 in the non-exposed group, bringing the total to 116 patients.

Statistical analysis and plotting were performed using SPSS version 27.0 and R version 4.4.0. The normality of the data distribution was assessed using the Shapiro-Wilk test and the Q-Q plot. Normally distributed variables were presented as mean ± standard deviation. Non-normally distributed variables were reported as median (IQR) and analyzed using the Kruskal-Wallis test. Categorical variables were expressed as counts (percentage) and compared using the Chi-square test or Fisher exact test, as appropriate. The authors considered a p-value of < 0.05 (two-tailed) statistically significant. A 3-node restricted cubic spline plot was used to assess the potential non-linear relationship between the MoCA score (represented by continuous variables) and the olfactory test score (represented by continuous variables). Univariate and multivariate logistic regression analyses were performed to identify risk factors for POD. The study planned to include 12 potential confounding factors in the univariate analysis. Variables with a p-value < 0.05 in the univariate regression analysis were included in the multivariate logistic regression analysis. For multivariable analysis, such as multiple regression, logistic regression, and factor analysis, the rules of thumb were often adopted that the sample size should be 5, 10, or 20 times the number of variables. For 12 predictors, a sample size of 60 might be acceptable, and a sample size of 120 would meet the most stringent rule of thumb. The ROC curve for OD (represented by categorical variables) as a predictor of POD was plotted. The area under the curve (AUC) was calculated, along with the sensitivity and specificity of the prediction.

Of the 176 patients screened between November 2023 and March 2024, 32 were excluded based on the established exclusion criteria, resulting in 144 enrolled in the study. However, 12 patients were excluded from allocation reasons including surgery cancellation, withdrawal of consent, and changes in surgical methods. Additionally, another 12 patients were lost to follow-up. Ultimately, 120 patients completed the study, 60 in the exposed group and 60 in the non-exposed group. The results are illustrated in Fig. 1.

3.1 POD

POD occurred in 17 of 60 patients in the exposed group and 5 of 60 patients in the non-exposed group (28.3% vs. 8.3%; P = 0.005). Specifically, there was no significant difference in the onset time or duration of delirium between the two groups. There was a significant difference in the subtype of delirium between the exposed and non-exposed group(P = 0.034): hyperactive delirium (16.67% vs 0.00%), hypoactive delirium (6.67% vs 6.67%), and mixed delirium (5.00% vs 1.67%). Further analysis demonstrated a correlation between NACT and hyperactive delirium (r = 0.302, P < 0.001). The results are shown in Table 1.

Table 1

The incidence of POD
	Exposed group (n = 60)	Non-exposed group (n = 60)	P Value
POD incidence, n (%)	17(28.33)	5(8.33)	0.005
Onset time of delirium, n (%)			1.000
POD1	14(23.33)	5(8.33)
POD2	2(3.33)	0(0.0)
POD3	1(1.67)	0(0.0)
Duration of delirium, n (%)			1.000
One days	12(20.00)	4(6.67)
Two days	4(6.67)	1(1.67)
There days	1(1.67)	0(0.0)
Subtype of delirium, n (%)			0.034
Hyperactive	10(16.67)	0(0.00)
Hypoactive	4(6.67)	4(6.67)
Mixed	3(5.00)	1(1.67)
Note: Continuous variables are described by mean (standard deviation) or median (interquartile spacing), and categorical data are described by n (%).
Abbreviations: POD, Postoperative delirium. POD1, Postoperatve Day 1; POD2, Postoperative Day 2; POD3, Postoperative Day 3.

Of the 120 patients, 22 patients (18.3%) experienced POD, and 6 of these 22 patients (27.3%) were diagnosed with persistent POD. Specifically, 19 patients developed POD on the first postoperative day. On the second postoperative day, seven patients were diagnosed with POD, including five patients with persistent POD and two newly diagnosed patients. On the third postoperative day, three patients were diagnosed with POD. It included one patient with persistent POD from the first postoperative day, one patient with persistent POD from the second postoperative day, and one newly diagnosed patient. The results are shown in Fig. 2.

3.2 Preoperative characteristics

Demographic data collected from participants included age, body mass index (BMI), sex ASA classification, educational level, comorbidities, and smoking status. The incidence of preoperative cognitive dysfunction in the exposed group was significantly higher than that in the non-exposed group (33.3% vs 13.33%; P = 0.010). Furthermore, the incidence of preoperative OD was significantly higher in the exposed group compared to the non-exposed group (25.0% vs 10.0%;P = 0.031). Other preoperative characteristics did not differ significantly between the groups. The results are shown in Table 2.

Table 2

Patient characteristics among the two groups.
Variables	Exposed group (n = 49)	Non-exposed group (n = 78)	P Value
Age (y)	54.50(48.50,59.25)	54.50(48.75,58.00)	0.795
BMI (kg/m²)	23.49(3.43)	24.20(3.09)	0.241
ADL	100.00(100.00,100.00)	100.00(100.00,100.00)	0.424
Tumor type, n(%)			0.719
Ovary carcinoma	53(88.33)	50(83.33)
Endometrial carcinoma	5(8.33)	7(11.67)
Cervix carcinoma	2(3.33)	3(5.00)
Number of novel coronavirus Pneumonia infections	1.00(1.00,1.25)	1.00(1.00,2.00)	0.168
ASA, n (%)			0.115
I	4(6.67)	2(3.33)
II	30(50.00)	41(68.33)
III	26(43.33)	17(28.33)
Cognitive function, n(%)			0.024
Normal	40(66.67)	52(86.67)
Mild cognitive dysfunction	11(18.33)	6(10.00)
Moderate cognitive dysfunction	9(15.00)	2(3.33)
Total cognitive dysfunction,n(%)	20(33.33)	8(13.33)	0.010
Olfactory function, n (%)			0.031
Normal	45(75.00)	54(90.00)
Dysfunction	15(25.00)	6(10.00)
Hypertension, n (%)			0.345
Yes	9(15.00)	13(21.67)
No	51(85.00)	47(78.33)
Diabetes, n (%)			0.543
Yes	5(8.33)	7(11.67)
No	55(91.67)	53(88.33)
Cardiovascular disease (%)			1.000
Yes	1(1.67)	0(0.00)
No	59(98.33)	60(100.00)
Cerebral infarction (%)			1.000
Yes	0(0.00)	1(1.67)
no	60(100.00)	59(98.33)
Anemia (%)			0.471
Yes	12(20.00)	9(15.00)
No	48(80.00)	51(85.00)
Asthma (%)			1.000
Yes	1(1.67)	0(0.00)
No	59(98.33)	60(100.00)
Rhinitis (%)			0.408
Yes	6(10.00)	9(15.00)
No	54(90.00)	51(85.00)
Education level, n (%)			0.165
Primary school and below	24(40.00)	32(53.33)
Junior high school and high school	19(31.67)	19(31.67)
University and above	17(28.33)	9(15.00)
Frailty index, n (%)			0.775
Normal	38(63.33)	36(60.00)
Pre-frailty	20(33.33)	23(38.33)
Frailty	2(3.33)	1(1.67)
Smoking status, n (%)			0.714
Yes	3(5.00)	5(8.33)
No	57(95.00)	55(91.67)
Drinking status, n (%)			1.000
Yes	1(1.67)	2(3.33)
No	59(98.33)	58(96.67)
Depression (%)			0.066
Yes	12(20.00)	21(35.00)
No	48(80.00)	39(65.00)
Anxiety (%)			0.194
Yes	28(46.76)	21(35.0)
No	32(53.33)	39(65.0)
Note: Continuous variables are described by mean (standard deviation) or median (interquartile spacing), and categorical data are described by n (%).
Abbreviations: ASA, American Society of Anesthesiologists; BMI, body mass index. MoCA, Montreal Cognitive Assessment Scale. ADL, Activity of Daily Living Scale.

3.2. Surgical data

Intraoperative data did not show any statistically significant differences. However, PONV occurred 19 of 60 patients (31.67%) in the exposed group and in 9 of 60 patients (15.00%) in the non-exposed group (P = 0.031). Additionally, the short-term prognosis in the exposed group was comparatively unfavorable, with a significantly longer duration of hospitalization compared to the non-exposed group (13.00 (11.00, 15.00) vs 10(9.00, 13.25); P = 0.001). However, no statistically significant difference was observed between the two groups in NRS pain scores. The results are shown in Table 3.

Table 3

Clinical Characteristics and Outcomes of the Patients among the two groups.
	Exposed group (n = 49)	Non-exposed group (n = 78)	P Value
Operation duration(h)	3.67(2.92,4.58)	3.35(2.88,4.09)	0.442
Anesthesia duration(h)	3.08(2.46,3.89)	2.87(2.48,3.52)	0.510
Remifentanil dosage(mg)	2.17(1.53,2.48)	1.81(1.56,2.27)	0.199
Midazolam dosage(mg)	1.00(1.00,2.00)	1.00(1.00,2.00)	0.186
Dexmedetomidine dosage(mg)	36.90 (29.58,46.71)	34.38 (29.76,42.21)	0.510
Blood loss(ml)	200.00 (100.00,400.00)	200.00 (100.00,400.00)	0.975
Urine volume(ml)	300.00 (200.00,475.00)	300.00 (200.00,400.00)	0.722
Fluid infusion
Crystalloid(ml)	2000.00 (1500.00,2500.00)	2000.00 (1500.00,2500.00)	0.843
Colloidal solution(ml)	0.00(0.00,500.00)	0.00(0.00,500.00)	0.085
Red cell concentrated(u)	0.00(0.00,0.00)	0.00(0.00,0.00)	0.309
Plasma(ml)	0.00(0.00,0.00)	0.00(0.00,0.00)	0.435
ICU (%)	7(11.67)	5(8.33)	0.543
PONV(%)	19(31.67)	9(15.00)	0.031
Hospitalization duration(d)	13.00(11.00,15.00)	10.00(9.00,13.25)	0.001
NRS
POD-1	0.00(0.00,1.75)	0.00(0.00,0.00)	0.714
POD1	2.00(2.00,3.00)	3.00(2.00,3.00)	0.433
POD2	2.00(2.00,3.00)	2.00(2.00,3.00)	0.769
POD3	1.00(1.00,2.00)	1.00(1.00,2.00)	0.097
Times of remedial analgesia	7.50(5.00,11.25)	6.00(3.00,11.00)	0.153
Note: Continuous variables are described by mean (standard deviation) or median (interquartile spacing), and categorical data are described by n (%).
Abbreviations: ICU, Intensive Care Unit; NRS, Numeric Rating Scale; POD-1, Preoperative Day 1; POD1, Postoperatve Day 1; POD2, Postoperative Day 2; POD3, Postoperative Day 3.

3.3 The correlation between cognitive function and olfactory function

We used restricted cubic splines to flexibly model and visualize the relationship between predicted olfactory test scores and MoCA scores. In our analysis, we selected three knots at the 25th, 50th, and 75th percentiles of the olfactory test scores (8, 10, and 12, respectively) to segment the range of olfactory test scores. After adjusting for potential confounding factors (chemotherapy, frailty, education, depression, BMI, preoperative pain NRS score) with P<0.1 in the linear regression model, the restricted cubic spline analysis revealed a non-linear relationship between olfactory test scores and MoCA scores (P for overall < 0.001, P for nonlinear = 0.001). When the olfactory test score was ≤ 8, a strong positive correlation was observed between preoperative olfactory test scores and MoCA scores. Each standard deviation increase in the olfactory scores was associated with an increase in MoCA scores of β(95%CI) = 3.29 (1.42–5.16). The average cognitive score of patients with a predicted olfactory test score of 8 was 23.07. The average cognitive score of patients with a predicted olfactory test score of 10 was 25.96. The average cognitive score of patients with a predicted olfactory test score of 12 was 26.65. The results are shown in Fig. 3.

3.4 Variables associated with POD

The variable assignments for the binary logistics regression analysis were as follows: NACT (0 = No, 1 = Yes); OD (0 = No, 1 = Yes); Depression (0 = No, 1 = Yes); Education level (0 = Primary school and below, 1 = Junior high school and high school, 2 = University and above); Preoperative cognitive function (0 = Normal, 1 = Mild cognitive dysfunction, 2 = Moderate cognitive dysfunction); Frailty status (0 = Normal, 1 = Pre-frailty, 2 = Frailty).

In this study, risk factors related to POD reported in previous studies (age, BMI, preoperative cognitive dysfunction, education level, depression, frailty, operation duration, intraoperative dexmedetomidine dosage, and intraoperative midazolam dosage) as well as new variables introduced in this study (NACT and OD) were included in the univariate analysis. Factors associated with P ≤ 0.05 in the univariate analysis, including NACT, cognitive dysfunction, OD, education level, depression, and frailty, were further included in the multivariable logistic regression analysis. Among these, NACT (OR:6.817, 95%CI: 1.022–45.476, P = 0.047); preoperative mild cognitive dysfunction (OR:6.507, 95%CI༚1.258–33.664, P = 0.026); preoperative moderate cognitive dysfunction (OR:11.614, 95%CI:1.419–95.07, P = 0.022); OD (OR:5.889, 95%CI:1.200-28.898, P = 0.029); depression (OR:9.037, 95%CI:1.164–70.163, P = 0.035) were identified as significant independent risk factors for POD. The results are shown in Table 4.

Table 4

Analysis of risk factors of POD
	β	P	RR	95% CI
Neoadjuvant chemotherapy	1.919	0.047	6.817	1.022–45.476
Cognitive dysfunction		0.021
Mild cognitive dysfunction	1.873	0.026	6.507	1.258–33.664
Moderate cognitive dysfunction	2.452	0.022	11.614	1.419–95.079
Olfactory dysfunction	1.773	0.029	5.889	1.200-28.898
Depression	2.201	0.035	9.037	1.164–70.163
Education level		0.594
Junior high school and high school	-0.654	0.532	0.520	0.067–4.037
University and above	-1.022	0.349	0.360	0.042–3.055
Frailty status		0.849
Pre-frailty	0.514	0.568	1.672	0.287–9.750
Frailty	0.070	0.980	1.073	0.005-254.797
Abbreviations: β, Beta; P, P-value; RR, Relative Risk; 95% CI, 95% Confidence Interval.

3.5 Diagnostic value of OD

The optimal cut-off value for POD-1 OD was 0.5. The AUC for predicting POD at three days post-surgery was 0.783(95%CI: 0.656–0.909), with a sensitivity of 63.64% and a specificity of 92.86%. Preoperative olfactory function level demonstrated predictive ability for POD. The results are shown in Fig. 4.

To our knowledge, this is the first prospective observational cohort study to investigate the impact of NACT on the incidence of POD in patients with gynecological reproductive system tumors undergoing radical hysterectomy. We analyzed 120 female patients who underwent radical hysterectomy under general anesthesia. Specifically, the incidence of POD in the exposed group was higher than in the non-exposed group three days after surgery (28.3% vs 8.3%). In addition, the study found that NACT can increase the incidence of preoperative cognitive dysfunction (33.33% vs 13.33%) and increase the incidence of preoperative OD (25.0% vs 10.0%). The restricted cubic spline plot revealed a significant nonlinear relationship between cognitive and olfactory function. An olfactory test score of 8 was associated with an average MoCA score of 23.07, indicating mild cognitive dysfunction. When the olfactory score was < 8, indicating OD, it also suggested cognitive dysfunction. Furthermore, in patients with OD, the association between olfactory function and cognitive function was particularly evident (per standard deviation: β = 3.29, 95% CI: 1.419–5.161). In this study, logistic regression analysis further identified preoperative cognitive dysfunction and OD as independent risk factors for POD. The results suggest that POD can be predicted by using simple olfactory function tests as a substitute for complex cognitive scale assessments before surgery.

The 'chemobrain' phenomenon is widely recognized. However, discussions about cognitive effects related to cancer treatment are often overlooked, and cognitive assessments are not routinely incorporated into clinical indicators following cancer treatment [26]. Studies have found that chemotherapy drugs may affect the integrity of the blood-brain barrier, exert direct toxic effects on the central nervous system, and induce neuroinflammation, which can lead to neurocognitive impairment [27–30]. Previous studies have shown that the incidence of cognitive dysfunction after NACT ranges from 27–75% [31–33]. The incidence of POCD in patients receiving NACT was 59.4% [34]. One study reported an incidence of POD of 28% in patients undergoing cytoreductive surgery combined with intraperitoneal hyperthermic perfusion chemotherapy [35]. The impact of neoadjuvant chemotherapy (NACT) on postoperative delirium (POD) has not been previously documented in the literature, making this study a novel contribution to the field. We investigated the relationship between NACT, preoperative cognitive function, and the incidence of POD. The results indicated that the incidence of preoperative cognitive dysfunction and POD was higher in the exposed group compared to the non-exposed group. In patients with POD, NACT may initially impair cognitive function, with subsequent surgical and anesthetic interventions potentially exacerbating brain injury, and leading to further cognitive dysfunction.

Preoperative cognitive dysfunction is categorized into mild, moderate, and severe levels [36]. This study excluded patients with severe cognitive dysfunction and identified mild and moderate cognitive dysfunction as independent predictors of POD. Compared with individuals with normal cognitive function, those with preoperative cognitive dysfunction had a higher incidence of POD after surgery [37–40]. Considering the POD risk, mild cognitive dysfunction was associated with a 1.873-fold increased risk compared to individuals with normal cognitive function, while moderate cognitive dysfunction was associated with a 2.452-flod increased risk. Compared to mild cognitive dysfunction, moderate cognitive dysfunction was associated with a higher risk of POD. Varpaei and coworkers suggested that a greater degree of preoperative cognitive decline correlates with a higher incidence of POD [41].

OD is a well-established biomarker for neurodegenerative diseases, indicating a significant association with cognitive impairment [42, 43]. In this study, patients underwent olfactory function testing before surgery. The results showed that the incidence of preoperative OD was higher in the exposed group (25%) compared to the non-exposed group (10%). The dual effects of chemotherapy drugs on cognitive and olfactory function can be attributed to their neurotoxicity, primarily resulting in oxidative damage to hippocampal neurons and impairment of olfactory memory associated with the neocortex [44]. OD can serve as an indicator of the extent of cognitive impairment. In this study, preoperative cognitive dysfunction was observed in 14 (66.67%) of the 21 patients with preoperative OD. Restricted cubic spline analysis further demonstrated the correlation between olfactory and cognitive function, with higher olfactory test scores indicating better cognitive performance. Interestingly, OD may identify patients with unrecognized or subclinical cognitive impairment before surgery, predicting their susceptibility to perioperative stress and subsequent POD. Two studies on cardiac surgery found that preoperative olfactory impairment might identify unrecognized individuals who were prone to POD [45, 46]. In addition, OD also holds predictive value for POD in patients with neurodegenerative diseases. Kim, M.S.et al. found that olfactory impairment could help identify Parkinson's disease patients who were susceptible to POD [47]. This study supported the above findings, showing that preoperative OD was an independent predictor of POD. The AUC for OD in predicting POD was 0.783 (95%CI: 0.656–0.909). The sensitivity of OD for predicting POD was 63.64%, while the specificity was 92.86%.

The above results suggest that olfactory assessment might be a valuable preoperative screening tool for identifying patients at increased risk of postoperative complications and cognitive dysfunction following NACT. Compared to complex cognitive test scales, olfactory assessment is more interesting, straightforward, and feasible for patients. Effective olfactory stimulation and training, as a safe, economical, and convenient intervention, can improve olfactory and cognitive function in patients [25, 48, 49]. However, further studies are needed to explore effective olfactory stimulation and training for the recovery of POCD, which could be a key focus for future research.

Our study employed stringent inclusion and exclusion criteria, excluding patients with preoperative high-risk factors that could impact olfactory and cognitive function. This study was conducted during the COVID-19 novel coronavirus epidemic. Considering that COVID-19 patients might experience olfactory and gustatory disorders and that persistent OD could occur even after the resolution of respiratory symptoms [50]. We collected and analyzed data on preoperative COVID-19 infection among patients and excluded this factor as a potential source of interference. We also terminated follow-up for suspected stroke during surgery, although no patient dropped out of the study for this reason. In addition, we carefully considered the age range of the included patients. Current research showed that age over 65 was a risk factor for POD [51]. Considering the prevalence of gynecological tumors among middle-aged women, our study excluded patients over 65 years of age and included women aged 18 to 65 years. Therefore, the results applied only to this specific population. At the same time, this age criterion explained why the incidence of POD in patients with gynecological tumors in the non-exposed group was lower than that reported in previous studies (17.5%) [52, 53]. Furthermore, the levels of depression in the two patient groups were balanced and comparable. However, consistent with previous studies, our study identified depression as an independent risk factor for POD [54–57]. Although the impact of chemotherapeutic drugs on mood was still controversial [58, 59], assessing preoperative depression and enhancing perioperative management was beneficial for preventing POD.

However, this study also has some limitations. First, as a prospective single-center study focused specifically on patients undergoing radical hysterectomy, the sample size was small, which might limit the generalizability of the findings. Future studies should include larger sample sizes, multicenter designs, and various types of surgical patients. Secondly, we assessed POD once daily for three days. Given the episodic nature of delirium, this approach might have missed cases occurring at night or beyond the 3-day observation period. Third, we used an olfactory detection kit that could only determine the presence of OD, providing relatively simple measurement results. It is necessary to further analyze the capabilities of various aspects of OD (severity, olfactory recognition, and olfactory identification). Considering that the olfactory test required patient cooperation and that the reliability of the test results in patients with POD was limited, we evaluated only preoperative olfactory function. Therefore, we cannot analyze the correlation between postoperative olfactory function and POD. Finally, this study focused on a short-term follow-up of POD and did not monitor patients' conditions after discharge. Future research should incorporate long-term follow-up to evaluate the effects of POD on patients' long-term prognosis.

This single-blind observational study suggests that NACT increases the incidence of POD in patients with gynecological tumors undergoing radical hysterectomy. Moreover, the results indicate that preoperative OD may reflect preoperative cognitive dysfunction, and have predictive value for the incidence of POD.

Author Contribution

Yiwen Yang: Conceptualization, Methodology, Investigation, Data curation, Formal analysis, Writing – original draft, Writing – review & editing.Jiahui Chen: Writing – review & editing. Qian Wen : Investigation, Data curation, Formal analysis, Writing – review & editing. Guangshan Jin: Conceptualization, Methodology, Investigation, Data curation, Writing –review & editing. Fuqiang Liu:Data curation, Formal analysis, Software. Ling Yu: Conceptualization, Methodology, Investigation, Writing – review & editing.Jianhua He: Conceptualization, Methodology, Investigation, Data curation, Supervision, Writing – review & editing.All authors reviewed the manuscript.

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No competing interests reported.

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Effects of preoperative neoadjuvant chemotherapy on postoperative delirium in patients with gynecological tumor surgery: an observational study

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Abstract

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Highlights

1. Introduction

2. Methods and materials

2.1. Study design and ethics

2.2. Participants

2.3. Inclusion and blinding

2.4. Anesthesia management

2.5. Outcome assessments and testing

2.6. Statistical analysis

3. Results

3.1 POD

3.2 Preoperative characteristics

3.2. Surgical data

3.3 The correlation between cognitive function and olfactory function

3.4 Variables associated with POD

3.5 Diagnostic value of OD

4. Discussion

5. Conclusion

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Author Contribution

References

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