Development and validation of a nomogram model for predicting postoperative delirium in older adults undergoing free flap reconstruction after oral cancer surgery

Development and validation of a nomogram model for predicting postoperative delirium in older adults undergoing free flap reconstruction after oral cancer surgery | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Development and validation of a nomogram model for predicting postoperative delirium in older adults undergoing free flap reconstruction after oral cancer surgery Chen Ying, Liu Xiaona, Zhang Aili, Wang Zengxiang, Wu Ying, Pu Yu, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4626964/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Jul, 2025 Read the published version in BMC Oral Health → Version 1 posted 14 You are reading this latest preprint version Abstract Objective : This study aimed to develop and internally validate a dynamic a nomogram model by analysing the risk factors for postoperative delirium (POD) in elderly patients undergoing free flap reconstruction for oral cancer. Methods : This was a single-centre, retrospective study. We used the convenience sampling method to select 359 elderly oral cancer patients from January 2020-August 2023 in the Oral and Maxillofacial Surgery Ward of Nanjing Stomatological Hospital as the study population. The original dataset was randomly divided into a training group (n=252) and a validation group (n=107) by a computer-generated random number sequence in a 7:3 ratio. Least Absolute Shrinkage and Selection Operator Regression (LASSO regression) were used to screen the best predictor variables. Logistic regression was used to build the model and visualized by nomogram. The performance of the model was evaluated by area under the curve (AUC), calibration curve and decision curve analysis (DCA). Results : Our prediction model showed that six variables, age, sex, marriage, preoperative anxiety, preoperative sleep disorder, and ICU length of stay, were associated with POD. The nomogram showed high predictive accuracy with an AUC of 0.82 (95% CI: 0.76-0.87) for the training group and 0.84 (95% CI: 0.76-0.92) for the internal validation group. In both the training and validation groups, there was good agreement between the predicted results and the true observations. Decision curve analyses in the training and validation groups showed that the predictive model had a good net clinical benefit. Conclusion : We developed a new predictive model to predict risk factors for POD in elderly oral cancer patients. This simple and reliable nomogram can help physicians assess POD quickly and effectively, and has the potential to be widely used in the clinic after more external validation. elderly oral cancer predictive model postoperative delirium nomogram factor Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Oral cancer is a common malignant tumour of the head and neck, with an incidence rate ranking 9th among systemic malignant tumours, accounting for about 2.9%[ 1 ]. According to the statistics of 2022, China is expected to have 32,000 cases of oral cancer, which is 1.26 times more than the USA [ 2 ]. It has been shown that nearly 30 per cent of oral cancer patients are elderly and have a more severe disease burden [ 3 ]. Currently, radical resection with microvascular free flap reconstruction is the standard treatment for patients with oral cancer [ 4 , 5 ]. Postoperative delirium (POD) is an acute fluctuating altered mental state in patients after surgical anaesthesia, often characterized by a decreased level of consciousness, impaired concentration, psychomotor disturbances and disturbances in the sleep-wake cycle [ 6 ]. It usually occurs 2–5 days after surgery and its symptoms can last from hours to weeks[ 7 ]. POD is a common surgical complication with an incidence of 4%-41% in the general population and 8%-54% in elderly patients[ 8 ]. The occurrence of POD can prolong the patient's hospital stay, increase healthcare costs, delay recovery, decrease cognitive and somatic functions, and even increase mortality and consume healthcare resources [ 9 ]. It is now widely accepted that POD may be associated with a variety of factors, including physiological stress responses, alterations in neurotransmitter transmission, and the occurrence of inflammatory mechanisms [ 10 ]. Free flap reconstruction has a higher rate of POD compared with surgery without flap or pedicled flap reconstruction [ 11 ]. Therefore, identifying and controlling risk factors remains the priority in preventing POD in elderly patients after free flap reconstruction for oral cancer. Disease prediction models can effectively screen high-risk patients compared with simple quantitative risk factors such as age and duration of surgery, among which the nomogram is a risk prediction model that can be used in oncology by integrating risk factors and achieving graphical and visualisation [ 12 ]. However, there are fewer studies on POD risk prediction in elderly oral cancer patients so far. Therefore, this study intended to establish and internally validate a dynamic nomogram model by analysing the risk factors for POD in elderly patients after free flap reconstruction for oral cancer, aiming to provide a reference for early clinical identification of people at high risk of POD. Methods Patients We used the convenience sampling method to select elderly oral cancer patients who were in the oral and maxillofacial surgery ward of Nanjing Stomatological Hospital from January 2020 to August 2023 as the study population. Our research flowchart is shown in Fig. 1 . Inclusion criteria: ① age ≥ 60 years old; ② elective general anaesthesia surgery and immediate free flap reconstruction patients; ③ histopathological or imaging diagnosis of oral cancer. Exclusion criteria: ① previous dementia, consciousness disorder, mental disorder, recurrent cancer, other active cancers; ② palliative treatment; ③ refusal to participate or inability to communicate effectively due to severe hearing impairment or speech impairment; ④ incomplete case data. This study was approved by the Ethics Committee of Nanjing Stomatological Hospital (Approval No. KY-2024NL-091). The database used for the retrospective study was the electronic medical record database of Nanjing Stomatological Hospital. As this study is a single-center retrospective study, the review committee waived the requirement for written informed consent. Patient confidential data was removed from the entire dataset before analysis. The study was conducted in accordance with the Declaration of Helsinki. A total of 359 patients were included in this study. The original dataset was randomly divided into a training group (n = 252) and a validation group (n = 107) by a computer-generated random number sequence in a 7:3 ratio. Postoperative delirium assessment The diagnosis of delirium was made by the surgeon by referring to the American Psychiatric Association's Diagnostic and Statistical Manual of Mental Disorders for criteria[ 13 ], and based on the Confusion Assessment Method-ICU (CAM-ICU). From the first postoperative day until the patient is discharged from the hospital, a trained physician conducted a daily assessment based on information from the patient's interview and reports from family members or nurses. This assessment consisted of 4 entries: ① Acute alterations or recurrent fluctuations in the state of consciousness. ② Attention deficit. ③ Altered clarity of consciousness. ④ Disordered thinking. If both ① and ② are positive and ③ or ④ is positive, delirium is diagnosed [ 14 ]. The scale has been shown to be accurate [ 15 ]. Patients with POD receive appropriate antipsychotic medications and sedatives, such as dexmedetomidine and haloperidol. Data collection Based on literature review, expert consultation, and group discussion, we designed our own data collection form for POD in elderly oral cancer patients. Five nurses from the Department of Oral and Maxillofacial Surgery were selected for uniform training before data collection, and they were qualified before case data collection. The study participants were selected in strict accordance with the inclusion and exclusion criteria. After collection, the data were coded, and the data were entered and checked by two researchers. After the entry was completed, another researcher sampled the data (20% ratio) to ensure the accuracy of data entry. Variables were collected in three sections: preoperative, intraoperative and postoperative. Preoperative variables included: age, sex, BMI, alcohol consumption history (average daily alcohol consumption greater than 1 standard drink, continuous alcohol consumption for more than 1 year and abstinence for less than 1 year; 1 standard drink is equivalent to 120 ml of wine or 360 ml of beer or 45 ml of liquor), smoking history (at least 1 cigarette per 1–3 days in the past 6 months), preexisting conditions (cardio-cerebral and cerebrovascular diseases, hypertension, diabetes, respiratory diseases, malignant tumours, other previous diseases), residence, marriage, education level, disease diagnosis, TNM stage, tumour nature, preoperative pain (assessed on admission using the Visual Analogue Scale [ 16 ]), preoperative anxiety (assessed on admission using the Self-rating Anxiety Scale [ 17 ], with a score of ≥ 50 as anxiety), preoperative sleep quality (assessed using the Pittsburgh sleep quality index [ 18 ] to assess patients' sleep in the 1 month prior to surgery, with > 7 being classified as poor sleep quality and ≤ 7 as good sleep quality [ 19 ]), laboratory results (Na, K, Ca, ultrasensitive C-reactive protein, total protein, creatinine, D-dimer, leukocyte count, erythrocyte count, haemoglobin, erythrocyte cumulative blood pressure, monocyte percentage, neutrophil percentage). Intraoperative variables included: cervical lymph node dissection, ASA classification, tracheotomy, flap site, total input, total output, blood loss, urine output, and blood transfusion. postoperative variables included: length of hospitalization, length of ICU stay, postoperative hospitalization, postoperative pain, postoperative sleep quality, and laboratory results (Na, K, Ca, ultrasensitive C-reactive protein, total protein, creatinine, D-dimer, white blood cell count, erythrocyte count, haemoglobin, erythrocyte accumulation pressure, monocyte percentage, neutrophil percentage). Statistical analysis EpiData 3.1 was used to establish the database, and pROC, calibrate, rmda, and rms packages in IBM SPSS 23.0 and R⁃Studio (4.2.1) were used for statistical analysis and graphing. Qualitative data were expressed as frequency and percentage (%), quantitative data were expressed as median (quartile) [M (P25, P75)] for non-normal distribution, and univariate analyses were performed by Mann ⁃Whitney U test, chi-square test, and rank-sum test. Least absolute contraction and choice of operator regression (LASSO regression) were performed to screen the best predictor variables for factors that were statistically significant in the univariate analysis. Logistic regression analysis was used for influencing factor analysis to build risk prediction models. The nomogram model was constructed by R⁃Studio (4.2.1). The area under the receiver operating characteristic (ROC) curve (AUC) assessed the discrimination of the model group and validation group. The Hosmer ⁃ Lemeshow test to evaluate the goodness-of-fit of the model. We used Bootstrap self-sampling method for internal validation of the model. Calibration curves and decision curve analysis (DCA) were plotted to evaluate the calibration and clinical utility of the model, respectively. P < 0.05 was considered statistically significant difference. Results Characteristics of the study population A total of 359 elderly oral cancer patients were included in this study, including 252 (70%) in the training group and 107 (30%) in the validation group. All variables were not statistically significant in both groups of patients in the training and validation groups. ( Additional file Table 1 ) The mean age of the patients was 68 [65,72] years with a fluctuating range of 60–86 years. We found that the incidence of POD in elderly oral cancer patients was 26.5% (27.8% in the training group; 23.4% in the validation group). The majority of POD cases (93.7%) were observed within 1–5 days postoperatively. We included 66 variables divided into preoperative, intraoperative, and postoperative sections. Table 1 Baseline characteristics of training group comparing POD VS. non-POD patients. (statistically significant variable, P < 0.05) Characteristics Total(n = 252) Non-POD(n = 182) POD(n = 70) P Preoperative variables age (years) (median [IQR]) 68.00 [65.00, 72.00] 67.00 [64.25, 70.00] 70.00 [65.25, 75.75] < 0.01 sex n (%) male 134 (53.2) 83 (45.6) 51 (72.9) < 0.01 female 118 (46.8) 99 (54.4) 19 (27.1) alcohol consumption history n (%) no 194 (77.0) 157 (86.3) 37 (52.9) < 0.01 yes 58 (23.0) 25 (13.7) 33 (47.1) smoking history n (%) no 199 (79.0) 152 (83.5) 47 (67.1) < 0.01 yes 53 (21.0) 30 (16.5) 23 (32.9) hypertension n (%) no 147 (58.3) 115 (63.2) 32 (45.7) 0.01 yes 105 (41.7) 67 (36.8) 38 (54.3) marriage n (%) married 215 (85.3) 163 (89.6) 52 (74.3) < 0.01 unmarried 9 (3.6) 4 (2.2) 5 (7.1) widowed/divorced 28 (11.1) 15 (8.2) 13 (18.6) 4 74 (29.4) 51 (28.0) 23 (32.9) preoperative pain n (%) no 104 (41.3) 82 (45.1) 22 (31.4) 0.05 yes 148 (58.7) 100 (54.9) 48 (68.6) preoperative anxiety n (%) no 167 (66.3) 132 (72.5) 35 (50.0) < 0.01 yes 85 (33.7) 50 (27.5) 35 (50.0) preoperative sleep quality n (%) no 109 (43.3) 87 (47.8) 22 (31.4) 0.02 yes 143 (56.7) 95 (52.2) 48 (68.6) Intraoperative variables ASA classification n (%) ASA I 3 (1.2) 3 (1.6) 0 (0.0) < 0.01 ASA II 172 (68.3) 134 (73.6) 38 (54.3) ASA III 77 (30.6) 45 (24.7) 32 (45.7) blood loss (ml) (median [IQR]) 600.00 [500.00, 800.00] 600.00 [500.00, 787.50] 600.00 [500.00, 800.00] 0.03 Postoperative variables length of hospitalization (day) (median [IQR]) 20.00 [18.00, 23.00] 21.00 [19.00, 23.00] 19.00 [17.00, 22.00] 0.03 length of ICU stay (day) (median [IQR]) 7.00 [6.00, 8.00] 7.00 [6.00, 7.00] 7.00 [7.00, 8.00] < 0.01 hs-CRP (mg/L) (median [IQR]) 51.63 [34.81, 67.22] 49.12 [33.26, 65.71] 56.34 [41.23, 70.81] 0.04 hs-CRP = hypersensitive C-reactive protein; Univariate analysis of postoperative delirium in elderly patients with oral cancer Table 1 provided a detailed comparison of clinical characteristics between POD and non-POD patients in the training group. The results of univariate analysis showed statistically significant (P < 0.05) differences in age, sex, alcohol consumption history, smoking history, hypertension, marriage, preoperative pain, preoperative anxiety, preoperative sleep quality, ASA classification, blood loss, length of hospitalization, length of ICU stay, and postoperative ultrasensitive C-reactive protein. Variables that were statistically different in the univariate analysis were subjected to LASSO regression analysis. Lambda.min-based 10-fold cross-validation was used to select the most relevant variables. Ultimately, we screened 13 potentially influential factors. Trajectory changes for each variable are shown in Fig. 2 A, and confidence intervals for λ are displayed in Fig. 2 B. Multivariable analysis of postoperative delirium in elderly patients with oral cancer Thirteen variables screened by LASSO regression analysis were included in the logistic regression model. The results showed that age, sex, alcohol consumption history, marriage, preoperative anxiety, preoperative sleep quality, and length of ICU stay were independent influences on POD (Fig. 3 ). Development of a nomogram model for predicting postoperative delirium in elderly oral cancer patients The independent risk factors screened by multifactorial analysis in the training group were included in the prediction model, and a nomogram prediction model was established (Fig. 4 ). The indicators corresponding from top to bottom were single item score, age, sex, alcohol consumption history, marriage, preoperative anxiety, preoperative sleep quality, and length of ICU stay. Each single item is followed by a scale with corresponding values. The incidence of POD in elderly postoperative oral cancer patients can be approximated by adding the corresponding scores in the patients' corresponding indicators. The higher the total score, the higher the risk. Validation of accuracy and discrimination of nomogram model The AUC for the prediction model was 0.82 (95% Cl: 0.76–0.87), which indicated that the model had good predictive validity (Fig. 5 ). The Brier score of the calibration curve was 0.145, which indicated that the predictions were in good agreement with the true observations (Fig. 6 A). The Hosmer-Lemeshow goodness-of-fit test X 2 = 6.19, P = 0.63. this indicated good model applicability. The predictive model was internally validated using the validation set. The AUC was 0.84 (95% Cl: 0.76–0.92), which indicated that the model has good discriminatory power (Fig. 5 ). The calibration curve was close to the standard curve with a Brier score of 0.133 (Fig. 6 B). The Hosmer-Lemeshow goodness-of-fit test showed that the model fit was good (X 2 = 6.67, p = 0.57). Clinical efficacy evaluation of nomogram model The DCA showed that the nomogram model predicted the best applicability of the model when the threshold for the occurrence of POD in elderly oral cancer patients was in the range of 0.08–0.89 (Fig. 7 A). In addition, the validation group DCA showed that when the probability of POD occurrence was in the wide range of 0.06–0.73, the net benefit level of the nomogram was higher than that of the "no intervention" and "full intervention" scenarios, indicating that the model had better clinical applicability (Fig. 7 B). Discussion In the present study, the incidence of POD in elderly patients with oral cancer was 26.5%, which was higher than that reported in previous studies [ 10 , 20 ]. This discrepancy may be related to the fact that the population in this study was elderly and required free flap reconstruction with extensive and prolonged surgery. Most of the POD cases (93.7%) were observed within 1–5 days postoperatively. This suggests that clinicians should pay special attention to the possibility of POD during this time. Notably, in order to reduce the incidence of POD, it is crucial to have an early, validated and accurate tool to assess adverse outcomes. We retrospectively analysed the clinical data and laboratory indicators of 359 elderly oral cancer patients. Through a series of statistical methods, we identified age, sex, alcohol consumption history, marriage, preoperative anxiety, preoperative sleep disorder, and the length of ICU stay as independent influences on POD in elderly oral cancer patients. We constructed an intuitive and accurate nomogram and performed internal validation, demonstrating the good discriminatory and clinical applicability of the model. In this study, it was found that the older the elderly patients with oral cancer, the more likely they were to develop POD, and this result was consistent with the study of Zhao et al [ 6 ]. The organism undergoes a series of changes in the brain during aging, such as changes in stress-regulated neurotransmitters, decreased cerebral blood flow, decreased cerebral vascular density, neuronal apoptosis, and alterations in cellular signal transduction systems. Therefore, the risk of delirium is higher [ 21 , 22 ]. Male is an independent predictor of POD in elderly oral cancer patients. Our findings may suggest that men do not handle postoperative psychological stress well, as well as more postoperative complications such as infections and negative emotions [ 23 ]. Crawford et al[ 24 ] reported that a study of head and neck oral cancer in the West of Scotland found that male patients were more likely to develop POD than female patients. A systematic evaluation of risk factors for POD in Stanford type A aortic coarctation suggested that the risk of POD in male patients was 1.33 times higher than in women [ 25 ]. However, the gender factor is still controversial. Hasegawa et al. showed no association between males and POD in oral cancer patients [ 10 ]. Future studies could further explore the relationship between POD and gender. The drinking problem is more prominent in China. Monitoring data revealed an upward trend in alcohol consumption among the Chinese adult population from 2015 to 2017, with its drinking rate reaching 43.7%[ 2 ]. Alcohol consumption history is one of the independent risk factors for POD in elderly oral cancer patients. This finding has been confirmed by previous studies [ 26 ]. In the case of alcohol, the toxic by-product acetaldehyde can bind to DNA, disrupt cell replication and increase the body's susceptibility to other carcinogens. What's more, there is a synergistic effect of tobacco and alcohol. The dangers of the two combined are exponentially greater. In addition, these two behavioural risks are often co-existing and interrelated [ 27 ]. We found that widowed and unmarried elderly oral cancer patients are vulnerable to POD, consistent with previous studies [ 28 ]. Marriage may be particularly important for patients with head and neck cancers, given that surgery and/or radiotherapy for head and neck cancers are often associated with local disease recurrence and significant long-term dysfunction in speech, communication and swallowing. Recent studies have also confirmed that for patients with oral cancers, marriage is associated with early stage, aggressive treatment and superior survival rates. [ 29 ]. Preoperative anxiety and preoperative sleep quality are also important risk factors for POD in elderly oral cancer patients. Preoperative anxiety has been suggested as a possible predisposing factor [ 30 ]. Anxiety may be associated with higher glucocorticoid concentrations. And metabolic disorders are also well known mechanisms leading to POD [ 31 ]. In addition, there is evidence that anxiety can promote the production of pro-inflammatory cytokines, which has been proven to be a potential marker of POD [ 32 ]. What's more, anxiety may lead to sleep disturbances. Preoperative sleep quality have long been associated with the development of POD as well [ 33 ]. Liu et al. identified that patients with preoperative sleep disorders undergoing craniotomy were 2.7 times more likely to develop POD than patients without sleep disorders[ 34 ], which is similar to the results of this study. Sleep disorders, particularly sleep fragmentation and poor sleep quality, are common among older adults. There is growing evidence that sleep disorders are associated with impairments in spatial memory, verbal fluency, attention, and executive function[ 35 ]. Therefore, the relationship between preoperative anxiety, preoperative sleep quality, and POD warrants continued research. A study of elderly gastric cancer patients reported a positive correlation between prolonged ICU stay and POD [ 36 ]. This is supported by our findings. ICU is a psychologically challenging environment. ICU patients may be frightened by the occasional shrill alarm. If a patient has a tracheotomy, then they are unable to communicate. Many patients have a urinary catheter in their urethra and are physically restrained [ 37 ]. The strengths of this study are threefold. Firstly, a wider range of independent risk factors were included in this study. All these factors were available in a timely and direct manner after hospital admission, which ensured the simplicity and timeliness of the model. Second, the use of simple and objective clinical data to construct predictive models facilitates their application to clinical practice. Finally, POD is a common problem that has been well explored in Western countries. However, there are still few reports on POD in China. We hope that our findings will fill the gaps in the incidence and risk factors of POD in elderly oral cancer patients. Several limitations should be considered. This study is a single-centre cross-sectional investigation. Therefore, the results obtained need to be further confirmed by the results of multi-centre and large-sample studies to look for more risk factors and to take early measures to prevent the occurrence of POD and slow down its development. In addition, only elderly oral cancer patients were analysed in this study. Further studies are needed to determine if this is applicable to other populations. Although internal validation assessed the robustness of the model, the nomogram model was not validated against an external dataset, which may limit the generalisability of our findings. Abbreviations POD postoperative delirium CAM-ICU Confusion Assessment Method-ICU LASSO regression Least Absolute Shrinkage and Selection Operator Regression AUC the area under the receiver operating characteristic curve DCA decision curve analysis hs-CRP hypersensitive C-reactive protein Declarations Acknowledgements The authors wish to thank the Oral and Maxillofacial Surgery Ward of Nanjing Stomatological Hospital. Author contributions CY, PY, WDN, JMP, DHY and ZHB conducted the clinical work, analysed and interpreted the patient’s data. CY, LXNA and ZAL completed data analysis and wrote first draft of manuscript. WZX and WY supervised the clinical work and critically revised the manuscript. The final manuscript was read and endorsed by all authors. Funding not fund. Data availability The study’s dataset is accessible through the corresponding author upon a reasonable request, but it is not publicly accessible due to restrictions. 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Preventable burden of head and neck cancer attributable to tobacco and alcohol between 1990 and 2039 in China. Cancer Sci. 2023;114(8):3374–84. https://doi.org/10.1111/cas.15877 . Choi NY, Kim EH, Baek CH, Sohn I, Yeon S, Chung MK. Development of a nomogram for predicting the probability of postoperative delirium in patients undergoing free flap reconstruction for head and neck cancer. Eur J Surg Oncol (EJSO). 2017;43(4):683–8. https://doi.org/10.1016/j.ejso.2016.09.018 . Schaefer EW, Wilson MZ, Goldenberg D, Mackley H, Koch W, Hollenbeak CS. Effect of marriage on outcomes for elderly patients with head and neck cancer. Head Neck. 2015;37(5):735–42. https://doi.org/10.1002/hed.23657 . Ren A, Zhang N, Zhu H, Zhou K, Cao Y, Liu J. Effects of Preoperative Anxiety on Postoperative Delirium in Elderly Patients Undergoing Elective Orthopedic Surgery: A Prospective Observational Cohort Study. Clin Interventions Aging 2021 Volume 16:549–57. https://doi.org/10.214 7/CI A.S300639. Yang K-L, Detroyer E, Van Grootven B, Tuand K, Zhao D-N, Rex S, Milisen K. Association between preoperative anxiety and postoperative delirium in older patients: a systematic review and meta-analysis. BMC Geriatr. 2023;23(1). https://doi.org/10.1186/s12877-023-03923-0 . Mou Q, Gao M, Liu X, Wei C, Lan G, Zhao X, Shan Y, Wu C. Preoperative anxiety as an independent predictor of postoperative delirium in older patients undergoing elective surgery for lumbar disc herniation. Aging Clin Exp Res. 2022;35(1):85–90. https://doi.org/10.1007/s40520-022-02278-7 . Leung JM, Sands LP, Newman S, Meckler G, Xie Y, Gay C, Lee K. Preoperative Sleep Disruption and Postoperative Delirium. J Clin Sleep Med. 2015;11(08):907–13. https://doi.org/10.5664/jcsm. 4944 . Liu Y, Zhang X, Jiang M, Zhang Y, Wang C, Sun Y, Shi Z, Wang B. Impact of Preoperative Sleep Disturbances on Postoperative Delirium in Patients with Intracranial Tumors: A Prospective, Observational, Cohort Study. Nat Sci Sleep. 2023;15:1093–105. https://doi.org/10.2147/NSS.S432829 . Guo H, Li L-H, Lv X-H, Su F-Z, Chen J, Xiao F, Shi M, Xie Y-B. Association Between Preoperative Sleep Disturbance and Postoperative Delirium in Elderly: A Retrospective Cohort Study. Nat Sci Sleep. 2024;16:389–400. https://doi.org/10.2147/NSS.S452517 . Chen J, Ji X, Xing H. Risk factors and a nomogram model for postoperative delirium in elderly gastric cancer patients after laparoscopic gastrectomy. World J Surg Oncol. 2022;20(1). https://doi.org/10.1186/s1295 7-022-02793-x . Liao K-M, Ho C-H, Lai C-C, Chao C-M, Chiu C-C, Chiang S-R, Wang J-J, Chen C-M, Cheng K-C. The association between depression and length of stay in the intensive care unit. Medicine. 2020;99(23). https://doi.org/10.1097/MD.0000000000020514 . Additional Declarations No competing interests reported. Supplementary Files Additionalfile1.docx Cite Share Download PDF Status: Published Journal Publication published 02 Jul, 2025 Read the published version in BMC Oral Health → Version 1 posted Editorial decision: Revision requested 14 Mar, 2025 Reviews received at journal 18 Feb, 2025 Reviewers agreed at journal 12 Feb, 2025 Reviews received at journal 06 Sep, 2024 Reviewers agreed at journal 03 Sep, 2024 Reviewers agreed at journal 02 Sep, 2024 Reviews received at journal 29 Aug, 2024 Reviewers agreed at journal 29 Aug, 2024 Reviewers agreed at journal 29 Aug, 2024 Reviewers invited by journal 07 Jul, 2024 Editor invited by journal 02 Jul, 2024 Editor assigned by journal 30 Jun, 2024 Submission checks completed at journal 30 Jun, 2024 First submitted to journal 23 Jun, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4626964","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":326175469,"identity":"dbfbcd9f-a7fc-478c-8bed-0edc9386ee10","order_by":0,"name":"Chen Ying","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA30lEQVRIiWNgGAWjYDACCRBhAET3zxg+SKioIUXLgTPGBg/OHCNWCxBINpwxk3zYwkxYB//s5mOPeQru2PUz9phVJDawMfC3dyfgt+TOsXRjHoNnyW3MPGY3EnfIMEicObsBrxYDiRwzaR6Dw8lsbCAtZ9iAIrmEtOR/g2jh4TErSGxjJkZLDhtIix2bBI8ZA1FaJG6kmUnOMTicwCbBliyRcOYYD0G/8M9Ifibx5s9hezb5wwc//qiokeNv78WvBQSYeBgYEhugHB6CykGA8QcDgz1RKkfBKBgFo2BkAgD5u0WXG03pPQAAAABJRU5ErkJggg==","orcid":"","institution":"Nanjing Stomatological Hospital, Nanjing University","correspondingAuthor":true,"prefix":"","firstName":"Chen","middleName":"","lastName":"Ying","suffix":""},{"id":326175470,"identity":"d11dd4d5-4425-479b-959d-a27a91261c1f","order_by":1,"name":"Liu Xiaona","email":"","orcid":"","institution":"Nanjing Stomatological Hospital, Nanjing 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University","correspondingAuthor":false,"prefix":"","firstName":"Wu","middleName":"","lastName":"Ying","suffix":""},{"id":326175474,"identity":"eafba82c-79c5-48c3-b724-9b69c899cb63","order_by":5,"name":"Pu Yu","email":"","orcid":"","institution":"Nanjing Stomatological Hospital, Nanjing University","correspondingAuthor":false,"prefix":"","firstName":"Pu","middleName":"","lastName":"Yu","suffix":""},{"id":326175475,"identity":"237261ef-3790-4bbb-9e46-aff3218d787f","order_by":6,"name":"Zhang Hongbo","email":"","orcid":"","institution":"Nanjing Stomatological Hospital, Nanjing University","correspondingAuthor":false,"prefix":"","firstName":"Zhang","middleName":"","lastName":"Hongbo","suffix":""},{"id":326175476,"identity":"24eae89a-e782-4ef1-86b9-304d60f9bd09","order_by":7,"name":"Wang Danni","email":"","orcid":"","institution":"Nanjing Stomatological Hospital, Nanjing 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02:38:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4626964/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4626964/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12903-025-06167-z","type":"published","date":"2025-07-02T15:57:43+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":60931548,"identity":"eccde82e-0bb4-4f52-bd5e-d287f8cb8c8d","added_by":"auto","created_at":"2024-07-23 17:14:18","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":87485,"visible":true,"origin":"","legend":"\u003cp\u003eThe patient enrollment process for the study\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4626964/v1/e6f05610204a23a6e5b48fc9.png"},{"id":60930149,"identity":"329ea9f9-56d9-4bcd-93a3-abcff8c4f889","added_by":"auto","created_at":"2024-07-23 16:58:18","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":59045,"visible":true,"origin":"","legend":"\u003cp\u003eBest match factor screening by lasso regression. A is the Lasso regression path diagram; B shows the plot of the best matching factors screened by the ten-fold cross validation method, and the best matching factors were selected using lambda.min as the criterion\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4626964/v1/2db02acc61a3dde49a2dc48f.png"},{"id":60931015,"identity":"0d55827b-5923-4c82-9111-6f0a6bc16cdc","added_by":"auto","created_at":"2024-07-23 17:06:18","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":140672,"visible":true,"origin":"","legend":"\u003cp\u003eForest plots of independent influencing factors for postoperative delirium in the elderly patients with oral cancer by multivariate analysis Univariate\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4626964/v1/8dadecc922012014c097b25b.png"},{"id":60930148,"identity":"8324a22c-5323-406b-9d3a-da554d8bc0cf","added_by":"auto","created_at":"2024-07-23 16:58:18","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":129620,"visible":true,"origin":"","legend":"\u003cp\u003eA nomogram based on the combination of seven indicators was developed using logistic regression analysis.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4626964/v1/2f72168564cd0605acd5a04e.png"},{"id":60930153,"identity":"d8405e4f-9c25-406c-a2a1-e490b400b153","added_by":"auto","created_at":"2024-07-23 16:58:18","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":88122,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristic curves of the model distinguishing POD from non-POD in the training (blue line) and validation (purple line) cohorts.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4626964/v1/62c4c2c6268a6f4744a6f480.png"},{"id":60930155,"identity":"abd44bb2-9b81-4785-ac45-f0d03b2e3981","added_by":"auto","created_at":"2024-07-23 16:58:18","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":33403,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration curves of the model in the training (A) and validation (B) cohorts.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4626964/v1/5be14f2077204e15e2c81c26.png"},{"id":60931013,"identity":"59671112-f8e2-4e12-95a6-2a46e08182d8","added_by":"auto","created_at":"2024-07-23 17:06:18","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":41073,"visible":true,"origin":"","legend":"\u003cp\u003eThe decision curve of the model in the training (A) and validation (B) cohort.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-4626964/v1/dbebcadba0e0e55f15733d41.png"},{"id":86179032,"identity":"fa8020f7-7b44-4220-b07d-2eef7b9cd376","added_by":"auto","created_at":"2025-07-07 16:14:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1434862,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4626964/v1/d0887edb-ebf2-43f4-a74e-585bd601b506.pdf"},{"id":60930147,"identity":"257ea772-4490-4c29-a2ed-eeb06c40b0a9","added_by":"auto","created_at":"2024-07-23 16:58:18","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":35784,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile1.docx","url":"https://assets-eu.researchsquare.com/files/rs-4626964/v1/9a19a0123d16f39296650ad9.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development and validation of a nomogram model for predicting postoperative delirium in older adults undergoing free flap reconstruction after oral cancer surgery","fulltext":[{"header":"Introduction","content":"\u003cp\u003eOral cancer is a common malignant tumour of the head and neck, with an incidence rate ranking 9th among systemic malignant tumours, accounting for about 2.9%[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. According to the statistics of 2022, China is expected to have 32,000 cases of oral cancer, which is 1.26 times more than the USA [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. It has been shown that nearly 30 per cent of oral cancer patients are elderly and have a more severe disease burden [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Currently, radical resection with microvascular free flap reconstruction is the standard treatment for patients with oral cancer [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePostoperative delirium (POD) is an acute fluctuating altered mental state in patients after surgical anaesthesia, often characterized by a decreased level of consciousness, impaired concentration, psychomotor disturbances and disturbances in the sleep-wake cycle [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. It usually occurs 2\u0026ndash;5 days after surgery and its symptoms can last from hours to weeks[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. POD is a common surgical complication with an incidence of 4%-41% in the general population and 8%-54% in elderly patients[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The occurrence of POD can prolong the patient's hospital stay, increase healthcare costs, delay recovery, decrease cognitive and somatic functions, and even increase mortality and consume healthcare resources [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. It is now widely accepted that POD may be associated with a variety of factors, including physiological stress responses, alterations in neurotransmitter transmission, and the occurrence of inflammatory mechanisms [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFree flap reconstruction has a higher rate of POD compared with surgery without flap or pedicled flap reconstruction [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Therefore, identifying and controlling risk factors remains the priority in preventing POD in elderly patients after free flap reconstruction for oral cancer. Disease prediction models can effectively screen high-risk patients compared with simple quantitative risk factors such as age and duration of surgery, among which the nomogram is a risk prediction model that can be used in oncology by integrating risk factors and achieving graphical and visualisation [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. However, there are fewer studies on POD risk prediction in elderly oral cancer patients so far. Therefore, this study intended to establish and internally validate a dynamic nomogram model by analysing the risk factors for POD in elderly patients after free flap reconstruction for oral cancer, aiming to provide a reference for early clinical identification of people at high risk of POD.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatients\u003c/h2\u003e \u003cp\u003e We used the convenience sampling method to select elderly oral cancer patients who were in the oral and maxillofacial surgery ward of Nanjing Stomatological Hospital from January 2020 to August 2023 as the study population. Our research flowchart is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eInclusion criteria: ① age\u0026thinsp;\u0026ge;\u0026thinsp;60 years old; ② elective general anaesthesia surgery and immediate free flap reconstruction patients; ③ histopathological or imaging diagnosis of oral cancer. Exclusion criteria: ① previous dementia, consciousness disorder, mental disorder, recurrent cancer, other active cancers; ② palliative treatment; ③ refusal to participate or inability to communicate effectively due to severe hearing impairment or speech impairment; ④ incomplete case data.\u003c/p\u003e \u003cp\u003e This study was approved by the Ethics Committee of Nanjing Stomatological Hospital (Approval No. KY-2024NL-091). The database used for the retrospective study was the electronic medical record database of Nanjing Stomatological Hospital. As this study is a single-center retrospective study, the review committee waived the requirement for written informed consent. Patient confidential data was removed from the entire dataset before analysis. The study was conducted in accordance with the Declaration of Helsinki. A total of 359 patients were included in this study. The original dataset was randomly divided into a training group (n\u0026thinsp;=\u0026thinsp;252) and a validation group (n\u0026thinsp;=\u0026thinsp;107) by a computer-generated random number sequence in a 7:3 ratio.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003ePostoperative delirium assessment\u003c/h2\u003e \u003cp\u003eThe diagnosis of delirium was made by the surgeon by referring to the American Psychiatric Association's Diagnostic and Statistical Manual of Mental Disorders for criteria[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], and based on the Confusion Assessment Method-ICU (CAM-ICU). From the first postoperative day until the patient is discharged from the hospital, a trained physician conducted a daily assessment based on information from the patient's interview and reports from family members or nurses. This assessment consisted of 4 entries: ① Acute alterations or recurrent fluctuations in the state of consciousness. ② Attention deficit. ③ Altered clarity of consciousness. ④ Disordered thinking. If both ① and ② are positive and ③ or ④ is positive, delirium is diagnosed [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The scale has been shown to be accurate [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Patients with POD receive appropriate antipsychotic medications and sedatives, such as dexmedetomidine and haloperidol.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eData collection\u003c/h2\u003e \u003cp\u003eBased on literature review, expert consultation, and group discussion, we designed our own data collection form for POD in elderly oral cancer patients. Five nurses from the Department of Oral and Maxillofacial Surgery were selected for uniform training before data collection, and they were qualified before case data collection. The study participants were selected in strict accordance with the inclusion and exclusion criteria. After collection, the data were coded, and the data were entered and checked by two researchers. After the entry was completed, another researcher sampled the data (20% ratio) to ensure the accuracy of data entry.\u003c/p\u003e \u003cp\u003eVariables were collected in three sections: preoperative, intraoperative and postoperative.\u003c/p\u003e \u003cp\u003ePreoperative variables included: age, sex, BMI, alcohol consumption history (average daily alcohol consumption greater than 1 standard drink, continuous alcohol consumption for more than 1 year and abstinence for less than 1 year; 1 standard drink is equivalent to 120 ml of wine or 360 ml of beer or 45 ml of liquor), smoking history (at least 1 cigarette per 1\u0026ndash;3 days in the past 6 months), preexisting conditions (cardio-cerebral and cerebrovascular diseases, hypertension, diabetes, respiratory diseases, malignant tumours, other previous diseases), residence, marriage, education level, disease diagnosis, TNM stage, tumour nature, preoperative pain (assessed on admission using the Visual Analogue Scale [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]), preoperative anxiety (assessed on admission using the Self-rating Anxiety Scale [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], with a score of \u0026ge;\u0026thinsp;50 as anxiety), preoperative sleep quality (assessed using the Pittsburgh sleep quality index [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] to assess patients' sleep in the 1 month prior to surgery, with \u0026gt;\u0026thinsp;7 being classified as poor sleep quality and \u0026le;\u0026thinsp;7 as good sleep quality [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]), laboratory results (Na, K, Ca, ultrasensitive C-reactive protein, total protein, creatinine, D-dimer, leukocyte count, erythrocyte count, haemoglobin, erythrocyte cumulative blood pressure, monocyte percentage, neutrophil percentage).\u003c/p\u003e \u003cp\u003eIntraoperative variables included: cervical lymph node dissection, ASA classification, tracheotomy, flap site, total input, total output, blood loss, urine output, and blood transfusion.\u003c/p\u003e \u003cp\u003epostoperative variables included: length of hospitalization, length of ICU stay, postoperative hospitalization, postoperative pain, postoperative sleep quality, and laboratory results (Na, K, Ca, ultrasensitive C-reactive protein, total protein, creatinine, D-dimer, white blood cell count, erythrocyte count, haemoglobin, erythrocyte accumulation pressure, monocyte percentage, neutrophil percentage).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eEpiData 3.1 was used to establish the database, and pROC, calibrate, rmda, and rms packages in IBM SPSS 23.0 and R⁃Studio (4.2.1) were used for statistical analysis and graphing. Qualitative data were expressed as frequency and percentage (%), quantitative data were expressed as median (quartile) [M (P25, P75)] for non-normal distribution, and univariate analyses were performed by Mann ⁃Whitney U test, chi-square test, and rank-sum test. Least absolute contraction and choice of operator regression (LASSO regression) were performed to screen the best predictor variables for factors that were statistically significant in the univariate analysis. Logistic regression analysis was used for influencing factor analysis to build risk prediction models. The nomogram model was constructed by R⁃Studio (4.2.1).\u003c/p\u003e \u003cp\u003eThe area under the receiver operating characteristic (ROC) curve (AUC) assessed the discrimination of the model group and validation group. The Hosmer ⁃ Lemeshow test to evaluate the goodness-of-fit of the model. We used Bootstrap self-sampling method for internal validation of the model. Calibration curves and decision curve analysis (DCA) were plotted to evaluate the calibration and clinical utility of the model, respectively. P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant difference.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eCharacteristics of the study population\u003c/h2\u003e \u003cp\u003eA total of 359 elderly oral cancer patients were included in this study, including 252 (70%) in the training group and 107 (30%) in the validation group. All variables were not statistically significant in both groups of patients in the training and validation groups. (\u003cb\u003eAdditional file\u003c/b\u003e Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) The mean age of the patients was 68 [65,72] years with a fluctuating range of 60\u0026ndash;86 years. We found that the incidence of POD in elderly oral cancer patients was 26.5% (27.8% in the training group; 23.4% in the validation group). The majority of POD cases (93.7%) were observed within 1\u0026ndash;5 days postoperatively. We included 66 variables divided into preoperative, intraoperative, and postoperative sections.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of training group comparing POD VS. non-POD patients. (statistically significant variable, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal(n\u0026thinsp;=\u0026thinsp;252)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNon-POD(n\u0026thinsp;=\u0026thinsp;182)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePOD(n\u0026thinsp;=\u0026thinsp;70)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePreoperative variables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eage (years) (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e68.00 [65.00, 72.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e67.00 [64.25, 70.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e70.00 [65.25, 75.75]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003esex n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e134 (53.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e83 (45.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e51 (72.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003efemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e118 (46.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e99 (54.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19 (27.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ealcohol consumption history n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e194 (77.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e157 (86.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e37 (52.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e58 (23.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25 (13.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e33 (47.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003esmoking history n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e199 (79.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e152 (83.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e47 (67.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e53 (21.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30 (16.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e23 (32.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ehypertension n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e147 (58.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e115 (63.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e32 (45.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003e0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e105 (41.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e67 (36.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e38 (54.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003emarriage n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e215 (85.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e163 (89.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e52 (74.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eunmarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9 (3.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4 (2.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5 (7.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ewidowed/divorced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28 (11.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15 (8.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13 (18.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e74 (29.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e51 (28.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e23 (32.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003epreoperative pain n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e104 (41.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e82 (45.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e22 (31.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e148 (58.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e100 (54.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e48 (68.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003epreoperative anxiety n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e167 (66.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e132 (72.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e35 (50.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e85 (33.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50 (27.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e35 (50.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003epreoperative sleep quality n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e109 (43.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e87 (47.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e22 (31.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.02\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e143 (56.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e95 (52.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e48 (68.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIntraoperative variables\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eASA classification n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eASA I\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3 (1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3 (1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eASA II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e172 (68.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e134 (73.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e38 (54.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eASA III\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e77 (30.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e45 (24.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e32 (45.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eblood loss (ml) (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e600.00 [500.00, 800.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e600.00 [500.00, 787.50]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e600.00 [500.00, 800.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.03\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePostoperative variables\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elength of hospitalization\u003c/p\u003e \u003cp\u003e(day) (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.00 [18.00, 23.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.00 [19.00, 23.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19.00 [17.00, 22.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.03\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elength of ICU stay (day)\u003c/p\u003e \u003cp\u003e(median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.00 [6.00, 8.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.00 [6.00, 7.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.00 [7.00, 8.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehs-CRP (mg/L) (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51.63 [34.81, 67.22]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e49.12 [33.26, 65.71]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e56.34 [41.23, 70.81]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.04\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003ehs-CRP\u0026thinsp;=\u0026thinsp;hypersensitive C-reactive protein;\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eUnivariate analysis of postoperative delirium in elderly patients with oral cancer\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e provided a detailed comparison of clinical characteristics between POD and non-POD patients in the training group. The results of univariate analysis showed statistically significant (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) differences in age, sex, alcohol consumption history, smoking history, hypertension, marriage, preoperative pain, preoperative anxiety, preoperative sleep quality, ASA classification, blood loss, length of hospitalization, length of ICU stay, and postoperative ultrasensitive C-reactive protein. Variables that were statistically different in the univariate analysis were subjected to LASSO regression analysis. Lambda.min-based 10-fold cross-validation was used to select the most relevant variables. Ultimately, we screened 13 potentially influential factors. Trajectory changes for each variable are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, and confidence intervals for λ are displayed in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eMultivariable analysis of postoperative delirium in elderly patients with oral cancer\u003c/h2\u003e \u003cp\u003eThirteen variables screened by LASSO regression analysis were included in the logistic regression model. The results showed that age, sex, alcohol consumption history, marriage, preoperative anxiety, preoperative sleep quality, and length of ICU stay were independent influences on POD (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eDevelopment of a nomogram model for predicting postoperative delirium in elderly oral cancer patients\u003c/h2\u003e \u003cp\u003eThe independent risk factors screened by multifactorial analysis in the training group were included in the prediction model, and a nomogram prediction model was established (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The indicators corresponding from top to bottom were single item score, age, sex, alcohol consumption history, marriage, preoperative anxiety, preoperative sleep quality, and length of ICU stay. Each single item is followed by a scale with corresponding values. The incidence of POD in elderly postoperative oral cancer patients can be approximated by adding the corresponding scores in the patients' corresponding indicators. The higher the total score, the higher the risk.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eValidation of accuracy and discrimination of nomogram model\u003c/h2\u003e \u003cp\u003eThe AUC for the prediction model was 0.82 (95% Cl: 0.76\u0026ndash;0.87), which indicated that the model had good predictive validity (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The Brier score of the calibration curve was 0.145, which indicated that the predictions were in good agreement with the true observations (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). The Hosmer-Lemeshow goodness-of-fit test X\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;6.19, P\u0026thinsp;=\u0026thinsp;0.63. this indicated good model applicability.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe predictive model was internally validated using the validation set. The AUC was 0.84 (95% Cl: 0.76\u0026ndash;0.92), which indicated that the model has good discriminatory power (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The calibration curve was close to the standard curve with a Brier score of 0.133 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). The Hosmer-Lemeshow goodness-of-fit test showed that the model fit was good (X\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;6.67, p\u0026thinsp;=\u0026thinsp;0.57).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eClinical efficacy evaluation of nomogram model\u003c/h2\u003e \u003cp\u003eThe DCA showed that the nomogram model predicted the best applicability of the model when the threshold for the occurrence of POD in elderly oral cancer patients was in the range of 0.08\u0026ndash;0.89 (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). In addition, the validation group DCA showed that when the probability of POD occurrence was in the wide range of 0.06\u0026ndash;0.73, the net benefit level of the nomogram was higher than that of the \"no intervention\" and \"full intervention\" scenarios, indicating that the model had better clinical applicability (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn the present study, the incidence of POD in elderly patients with oral cancer was 26.5%, which was higher than that reported in previous studies [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. This discrepancy may be related to the fact that the population in this study was elderly and required free flap reconstruction with extensive and prolonged surgery. Most of the POD cases (93.7%) were observed within 1\u0026ndash;5 days postoperatively. This suggests that clinicians should pay special attention to the possibility of POD during this time. Notably, in order to reduce the incidence of POD, it is crucial to have an early, validated and accurate tool to assess adverse outcomes.\u003c/p\u003e \u003cp\u003eWe retrospectively analysed the clinical data and laboratory indicators of 359 elderly oral cancer patients. Through a series of statistical methods, we identified age, sex, alcohol consumption history, marriage, preoperative anxiety, preoperative sleep disorder, and the length of ICU stay as independent influences on POD in elderly oral cancer patients. We constructed an intuitive and accurate nomogram and performed internal validation, demonstrating the good discriminatory and clinical applicability of the model.\u003c/p\u003e \u003cp\u003eIn this study, it was found that the older the elderly patients with oral cancer, the more likely they were to develop POD, and this result was consistent with the study of Zhao et al [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The organism undergoes a series of changes in the brain during aging, such as changes in stress-regulated neurotransmitters, decreased cerebral blood flow, decreased cerebral vascular density, neuronal apoptosis, and alterations in cellular signal transduction systems. Therefore, the risk of delirium is higher [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMale is an independent predictor of POD in elderly oral cancer patients. Our findings may suggest that men do not handle postoperative psychological stress well, as well as more postoperative complications such as infections and negative emotions [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Crawford et al[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] reported that a study of head and neck oral cancer in the West of Scotland found that male patients were more likely to develop POD than female patients. A systematic evaluation of risk factors for POD in Stanford type A aortic coarctation suggested that the risk of POD in male patients was 1.33 times higher than in women [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. However, the gender factor is still controversial. Hasegawa et al. showed no association between males and POD in oral cancer patients [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Future studies could further explore the relationship between POD and gender.\u003c/p\u003e \u003cp\u003eThe drinking problem is more prominent in China. Monitoring data revealed an upward trend in alcohol consumption among the Chinese adult population from 2015 to 2017, with its drinking rate reaching 43.7%[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Alcohol consumption history is one of the independent risk factors for POD in elderly oral cancer patients. This finding has been confirmed by previous studies [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. In the case of alcohol, the toxic by-product acetaldehyde can bind to DNA, disrupt cell replication and increase the body's susceptibility to other carcinogens. What's more, there is a synergistic effect of tobacco and alcohol. The dangers of the two combined are exponentially greater. In addition, these two behavioural risks are often co-existing and interrelated [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWe found that widowed and unmarried elderly oral cancer patients are vulnerable to POD, consistent with previous studies [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Marriage may be particularly important for patients with head and neck cancers, given that surgery and/or radiotherapy for head and neck cancers are often associated with local disease recurrence and significant long-term dysfunction in speech, communication and swallowing. Recent studies have also confirmed that for patients with oral cancers, marriage is associated with early stage, aggressive treatment and superior survival rates. [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePreoperative anxiety and preoperative sleep quality are also important risk factors for POD in elderly oral cancer patients. Preoperative anxiety has been suggested as a possible predisposing factor [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Anxiety may be associated with higher glucocorticoid concentrations. And metabolic disorders are also well known mechanisms leading to POD [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. In addition, there is evidence that anxiety can promote the production of pro-inflammatory cytokines, which has been proven to be a potential marker of POD [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. What's more, anxiety may lead to sleep disturbances. Preoperative sleep quality have long been associated with the development of POD as well [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Liu et al. identified that patients with preoperative sleep disorders undergoing craniotomy were 2.7 times more likely to develop POD than patients without sleep disorders[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], which is similar to the results of this study. Sleep disorders, particularly sleep fragmentation and poor sleep quality, are common among older adults. There is growing evidence that sleep disorders are associated with impairments in spatial memory, verbal fluency, attention, and executive function[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Therefore, the relationship between preoperative anxiety, preoperative sleep quality, and POD warrants continued research.\u003c/p\u003e \u003cp\u003eA study of elderly gastric cancer patients reported a positive correlation between prolonged ICU stay and POD [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. This is supported by our findings. ICU is a psychologically challenging environment. ICU patients may be frightened by the occasional shrill alarm. If a patient has a tracheotomy, then they are unable to communicate. Many patients have a urinary catheter in their urethra and are physically restrained [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe strengths of this study are threefold. Firstly, a wider range of independent risk factors were included in this study. All these factors were available in a timely and direct manner after hospital admission, which ensured the simplicity and timeliness of the model. Second, the use of simple and objective clinical data to construct predictive models facilitates their application to clinical practice. Finally, POD is a common problem that has been well explored in Western countries. However, there are still few reports on POD in China. We hope that our findings will fill the gaps in the incidence and risk factors of POD in elderly oral cancer patients.\u003c/p\u003e \u003cp\u003eSeveral limitations should be considered. This study is a single-centre cross-sectional investigation. Therefore, the results obtained need to be further confirmed by the results of multi-centre and large-sample studies to look for more risk factors and to take early measures to prevent the occurrence of POD and slow down its development. In addition, only elderly oral cancer patients were analysed in this study. Further studies are needed to determine if this is applicable to other populations. Although internal validation assessed the robustness of the model, the nomogram model was not validated against an external dataset, which may limit the generalisability of our findings.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePOD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003epostoperative delirium\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCAM-ICU\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfusion Assessment Method-ICU\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLASSO regression\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLeast Absolute Shrinkage and Selection Operator Regression\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAUC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ethe area under the receiver operating characteristic curve\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDCA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003edecision curve analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ehs-CRP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehypersensitive C-reactive protein\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors wish to thank the Oral and Maxillofacial Surgery Ward of Nanjing Stomatological Hospital.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCY, PY, WDN, JMP, DHY and ZHB conducted the clinical work, analysed and interpreted the patient\u0026rsquo;s data. CY, LXNA and ZAL completed data analysis and wrote first draft of manuscript. WZX and WY supervised the clinical work and critically revised the manuscript. The final manuscript was read and endorsed by all authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003enot fund.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study\u0026rsquo;s dataset is accessible through the corresponding author upon a reasonable request, but it is not publicly accessible due to restrictions. This is because it includes information that might jeopardize the privacy of the research participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHuman Ethics and Consent to Participate declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethics Committee of Nanjing Stomatological Hospital (Approval No. KY-2024NL-091). As this study is a single-centre retrospective study, the review committee waived the requirement for written informed consent. Patient confidential data was removed from the entire dataset before analysis. The study was\u0026nbsp;\u003c/p\u003e\n\u003cp\u003econducted in accordance with the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSiegel RL, Miller KD, Jemal A. 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Medicine. 2020;99(23). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1097/MD.0000000000020514\u003c/span\u003e\u003cspan address=\"10.1097/MD.0000000000020514\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-oral-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ohea","sideBox":"Learn more about [BMC Oral Health](http://bmcoralhealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ohea/default.aspx","title":"BMC Oral Health","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"elderly, oral cancer, predictive model, postoperative delirium, nomogram, factor","lastPublishedDoi":"10.21203/rs.3.rs-4626964/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4626964/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective\u003c/strong\u003e: This study aimed to develop and internally validate a dynamic a nomogram model by analysing the risk factors for postoperative delirium (POD) in elderly patients undergoing free flap reconstruction for oral cancer.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: This was a single-centre, retrospective study. We used the convenience sampling method to select 359 elderly oral cancer patients from January 2020-August 2023 in the Oral and Maxillofacial Surgery Ward of Nanjing Stomatological Hospital as the study population. The original dataset was randomly divided into a training group (n=252) and a validation group (n=107) by a computer-generated random number sequence in a 7:3 ratio. Least Absolute Shrinkage and Selection Operator Regression (LASSO regression) were used to screen the best predictor variables. Logistic regression was used to build the model and visualized by nomogram. The performance of the model was evaluated by area under the curve (AUC), calibration curve and decision curve analysis (DCA).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: Our prediction model showed that six variables, age, sex, marriage, preoperative anxiety, preoperative sleep disorder, and ICU length of stay, were associated with POD. The nomogram showed high predictive accuracy with an AUC of 0.82 (95% CI: 0.76-0.87) for the training group and 0.84 (95% CI: 0.76-0.92) for the internal validation group. In both the training and validation groups, there was good agreement between the predicted results and the true observations. Decision curve analyses in the training and validation groups showed that the predictive model had a good net clinical benefit.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: We developed a new predictive model to predict risk factors for POD in elderly oral cancer patients. This simple and reliable nomogram can help physicians assess POD quickly and effectively, and has the potential to be widely used in the clinic after more external validation.\u003c/p\u003e","manuscriptTitle":"Development and validation of a nomogram model for predicting postoperative delirium in older adults undergoing free flap reconstruction after oral cancer surgery","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-23 16:58:13","doi":"10.21203/rs.3.rs-4626964/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-03-14T12:19:52+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-02-18T18:45:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"180587170332839588641944006782516036976","date":"2025-02-12T13:31:44+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-06T21:09:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"131103223048849685221635535222948938400","date":"2024-09-03T13:47:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"109364504611540258092146742556587466010","date":"2024-09-02T20:52:35+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-29T19:56:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"213941878318016192335111023266115449968","date":"2024-08-29T08:12:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"58462747591570426046582352190082716864","date":"2024-08-29T07:31:11+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-07-07T13:26:59+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-07-02T13:41:21+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-06-30T11:39:04+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-06-30T11:38:48+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Oral Health","date":"2024-06-24T02:31:01+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-oral-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ohea","sideBox":"Learn more about [BMC Oral Health](http://bmcoralhealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ohea/default.aspx","title":"BMC Oral Health","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"78e135ea-b285-4582-926a-eecdffa07924","owner":[],"postedDate":"July 23rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-07-07T16:03:07+00:00","versionOfRecord":{"articleIdentity":"rs-4626964","link":"https://doi.org/10.1186/s12903-025-06167-z","journal":{"identity":"bmc-oral-health","isVorOnly":false,"title":"BMC Oral Health"},"publishedOn":"2025-07-02 15:57:43","publishedOnDateReadable":"July 2nd, 2025"},"versionCreatedAt":"2024-07-23 16:58:13","video":"","vorDoi":"10.1186/s12903-025-06167-z","vorDoiUrl":"https://doi.org/10.1186/s12903-025-06167-z","workflowStages":[]},"version":"v1","identity":"rs-4626964","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4626964","identity":"rs-4626964","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}