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Association of preoperative frailty with risk of postoperative delirium in older patients undergoing craniotomy: a prospective cohort study

BMC Surgery volume 24, Article number: 272 (2024) Cite this article

Abstract

Background

Preoperative frailty is a risk factor associated with postoperative delirium (POD), which has attracted more attention from clinicians, but no research has shown that it is related to elderly patients undergoing craniotomy. Therefore, the aim of this study was to determine the effect of preoperative frailty on POD in older patients, especially those who underwent craniotomy.

Methods

From October 2022 to May 2023, older patients who underwent elective craniotomy were collected. Assess the occurrence of frailty using the FRAIL scale one day before surgery. Evaluate the occurrence of POD using the Confusion Assessment Method (CAM) within three days after surgery. Participants were divided into two groups, one group being POD, Logistic regression analysis was used to find the risk variables for POD, and the predictive value of preoperative frailty to POD was determined by using the operating characteristic curve of the subjects.

Results

A total of 300 patients were included in this study, among whom 83 patients (27.7%) exhibited preoperative frailty and 69 patients (23.0%) experienced POD. The results of the multivariate logistic regression analysis indicate that preoperative frailty (OR: 8.816, 95% CI: 3.972–19.572), preoperative hypoalbuminemia (OR: 0.893, 95% CI: 0.811–0.984), low BMI (OR: 0.793, 95% CI: 0.698–0.901), and prolonged operative duration (OR: 1.007, 95% CI: 1.004–1.010) are independent risk factors for POD in older patients who underwent craniotomy. We constructed a risk prediction model using these factors, which had an area under the ROC curve of 0.908 (95% CI: 0.869–0.947, P < 0.001). Preoperative frailty enhanced the discriminative ability of the prediction model by 0.037. POD was associated with a longer length of hospital stay and higher hospitalization costs.

Conclusions

Preoperative frailty is an independent risk factor for POD in older patients undergoing elective craniotomy and can predict the occurrence of POD to a certain extent. In addition, early identification of patients at risk of malnutrition and appropriate surgical planning can reduce the incidence of POD.

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Background

Delirium is an acute and reversible state of altered consciousness, characterized by inattention, impaired level of consciousness, and disturbance of cognition [1]. Postoperative delirium (POD) is one of the most common postoperative complications in the older patients [2]. In general adult patients, it occurs 2.6% to 4.3% of the time [3, 4]. The incidence of POD increases to 10.0% to 47.0% in patients 65 years of age or above [5]. Specifically, POD prevalence in older patients who underwent colorectal surgery is 21% [6], while incidence in older patients who underwent non-cardiac surgery is 18.6% [7]. POD was associated with many adverse clinical outcomes, such as postoperative complications, unscheduled intensive care unit admissions, prolonged hospital stays, and increased mortality rates [8]. However, due to the characteristics of neurosurgical diseases themselves and the handling of the brain during surgery, there is an exacerbation of neuroinflammation and oxidative reactions, making older patients who underwent neurosurgery more susceptible to POD, with its influencing factors being more complex [9]. Studies have shown that the incidence of hyperactive delirium after brain tumor resection in adults is 50.76%, hypoactive delirium is 41.67%, and mixed delirium is relatively less common at 7.57% [10].

Previous research has found that numerous risk factors for POD, including age, cognitive impairment, past medical history, and frailty [11, 12]. Frailty is a state characterized by a decline in physiological reserve capacity and a reduced ability to cope with stress, resulting from the cumulative deterioration of multiple physiological systems [13]. Research has found that the physiological reserve status of older patients has a greater impact on postoperative disease outcomes than the surgery itself, and that frailty can accurately and comprehensively assess the physiological reserve status of older adults [14]. Including frailty assessment in the preoperative evaluation and implementing early interventions can improve patients' prognoses [15]. Hemiplegia, disability, and difficulty swallowing are common symptoms in neurosurgical patients, making them more susceptible to frailty. However, despite the high incidence of preoperative frailty and POD, there is limited research on older patients who have undergone craniotomy in this specific group. Moreover, while most studies indicate a correlation between the two, some studies show no significant correlation [16]. Therefore, this study aims to investigate the impact of preoperative frailty on POD in older patients who underwent craniotomy, as well as to understand the incidence of POD, other risk factors, and clinical outcome.

Methods

Study design and setting

This is a prospective cohort study assessing older patients who underwent elective craniotomy in the neurosurgery department of a tertiary hospital in China from October 2022 to May 2023. Inclusion criteria were as follows: (1) Participants were aged ≥ 60 years and underwent elective craniotomy. (2) Volunteer to participate in this study. The exclusion criteria included: (1) Emergency surgery, where preoperative frailty information could not be collected; (2) Preoperative Glasgow Coma Scale (GCS) score < 15, making cooperation with the investigation and assessment impossible; And (3) patients with hearing or speech impairments or low literacy levels, who were unable to complete the survey. Finally, 300 participants were included in this research, as shown in Fig. 1.

Fig.1
figure 1

Flow diagram of patient inclusion and exclusion

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All researchers were trained before the start of the study. All data were reviewed for quality and consistency. The study was conducted with the permission of the Ethics Committee of the First Affiliated Hospital of Army Medical University ((A)KY2023020). Written, informed consent was obtained from all patients before enrollment. This study was conducted in accordance with the ethical guidelines of the Helsinki Declaration and reported in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) checklist.

Sample size calculation

Based on a previous study reporting an incidence of POD at 19.2%, our current study requires a sample size of 239 to estimate the expected incidence with a 5% margin of error at a 95% confidence level. We plan to recruit approximately 300 patients, anticipating a potential 20% attrition rate, which may be attributed to the inability to assess delirium as patients further adapt to sedation and ventilation protocols.

Data collection

Fundamental and clinical data

Patients' characteristics were collected using an electronic medical record system. Fundamental data included age, gender, classification of disease, smoking and drinking history, and comorbidity. The clinical data included body mass index (BMI), preoperative albumin, preoperative hemoglobin, preoperative white blood cell count, preoperative ASA classification (American Society of Anesthesiologists), preoperative anxiety and depression (Hospital Anxiety and Depression Scale) [17], operative duration, and preoperative pain assessment (Facial Pain Scale) [18].

Preoperative frailty assessment

According to medical advice, the surgical information for the patient was reviewed. One day prior to the surgery, researchers conducted a face-to-face frailty assessment with the patient in the ward using the FRAIL scale. The FRAIL scale assesses the following: fatigue, resistance (ability to climb one flight of stairs), walking ability (ability to walk more than 500 m), disease status (having a diagnosis of a disease more than five times in the past or present), and weight loss (greater than 5%). Each item is scored 1 point, with a maximum of 5 points. A higher score indicates a greater degree of frailty. A score of 3 or above indicates frailty, 1 or 2 points indicate pre-frailty, and 0 points indicate no frailty [19].

Postoperative delirium assessment

Within three days post-surgery, another trained researcher assessed the occurrence of POD using the Confusion Assessment Method (CAM). This assessment was conducted through the medical record system and bedside evaluations twice daily, from 08:00 to 10:00 and from 18:00 to 20:00. Patients who were missed during bedside screening were supplemented and assessed by researchers based on the content of the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-V), utilizing medical information from three days post-surgery to identify POD using keywords such as "combative behavior," "confusion," "agitation," "inappropriate behavior," "delirium," "disorientation," "inattention," "hallucinations," and "mental status change." CAM is a widely accepted delirium assessment tool that is reliable and efficient in detecting delirium in a variety of healthcare settings [20, 21]. The CAM scale includes four aspects: (1) acute onset and fluctuating course, (2) inattention, (3) disorganized thinking, and (4) altered level of consciousness. A diagnosis of POD is made when the presence of characteristics (1) and (2) is accompanied by either characteristic (3) or (4).

Postoperative outcome

Postoperative outcomes included postoperative complications, which were classified using the Clavien-Dindo classification criteria [22], the length of hospital stay, and the cost of hospitalization.

Statistical analysis

SPSS version 26.0 was utilized for conducting statistical analyses. Categorical data were examined using Fisher's exact test or the chi-squared test and were expressed as frequencies (percentages). Based on the distribution of the data, continuous variables were compared using the independent T-test or the Mann–Whitney U test. Continuous variables were represented as the mean ± SD. Statistically significant indicators from the univariate analysis were incorporated into a multivariate logistic regression analysis to identify risk factors for POD. The receiver operating characteristic (ROC) curve was plotted to assess the predictive value of these risk factors for POD. Statistical significance was set at P < 0.05.

Results

General characteristics of patients and univariate results

Demographic and clinical characteristics of patients are shown in Table 1. The average age was 67.1 (4.6), 52.3% were male (n = 157). Diagnoses included 156 (52.0%) with intracranial tumors, 65 (21.7%) with cerebrovascular diseases, and 36 (12.0%) with functional neurosurgical disorders. Among the older patients who underwent craniotomy, 83 (27.7%) exhibited preoperative frailty, and 69 (23.0%) developed postoperative delirium (POD). Patients will be divided into a POD group and a non-POD group based on the occurrence of POD. Compared with the non-POD group, gender, preoperative frailty, preoperative anxiety, preoperative depression, BMI, preoperative ASA classification, postoperative complications, preoperative albumin, preoperative hemoglobin, and operative duration were statistically significant in the POD group. The clinical outcome data reveal that the POD group had significantly higher levels of postoperative complications, longer hospital stays, and greater hospitalization costs than the non-POD group, as shown in Table 1.

Table 1 Baseline characteristics of patients in different postoperative delirium groups (n = 300)
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Potential risk factors and clinical outcomes of POD

We conducted a logistic regression analysis using variables that demonstrated statistically significant differences in the univariate analysis as independent variables, with the occurrence of POD as the dependent variable. As shown in Table 2, the independent factors influencing POD include preoperative albumin, BMI, preoperative frailty, and operative duration. Furthermore, Table 2 also indicates that the length of hospital stay and hospitalization costs, which are related to clinical adverse outcomes, are significantly associated with POD.

Table 2 Multivariable logistic regression analysis of factors influencing POD and clinical outcomes in older patients who underwent craniectomy (n = 300)
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Predictive efficacy of preoperative frailty for POD

We constructed a logistic regression model with POD as the dependent variable and statistically significant indicators from Table 1 as covariates. Nine preoperative statistical indicators, including gender, anxiety, depression, BMI, albumin, hemoglobin, ASA classification, operative duration, and frailty, were used to predict the prognosis of older patients who underwent craniotomy (Table 3). Figure 2 displays the predicted values calculated using the ROC curve. The basic prediction model (preoperative anxiety, ASA grading, preoperative albumin, BMI, and operative duration) had an area under the curve (AUC) of 0.871 (95% CI: 0.827 ~ 0.915, P < 0.001). After adding preoperative frailty assessment to the basic prediction model, the discrimination of the new prediction model improved by 0.037 (AUC = 0.908, 95% CI: 0.869 ~ 0.947, P < 0.001). Models 1 and 2 had statistical significance (P = 0.009). The results indicate that preoperative frailty assessment can improve prognostic prediction in older patients who underwent craniotomy.

Table 3 Prediction models for prognosis in older patients with craniotomy (n = 300)
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Fig. 2
figure 2

ROC curves were used to evaluate the clinical prediction models. Model 1 is a basic prediction model, including preoperative anxiety, preoperative ASA grading, preoperative albumin, BMI, operative duration, and the AUC = 0.871 (95% CI: 0.827 ~ 0.915, P < 0.001). Model 2 adds preoperative frailty assessment to the basic prediction model, and the AUC = 0.908 (95% CI: 0.869 ~ 0.947, P < 0.001). AUC, area under the ROC curve; POD, postoperative delirium

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Discussion

In this prospective observational cohort study, we observed an association between preoperative frailty in older patients and the incidence of POD following elective craniotomy. We identified preoperative low albumin, low BMI, and prolonged operative duration as risk factors for POD. Furthermore, POD was associated with adverse clinical outcomes, including prolonged hospital stays and increased hospitalization costs.

Research results indicated that the incidence of POD in older patients who underwent craniotomy is 23.0%, which is higher than the 19.6% reported in other studies on intracranial surgery [23]. The main reason for the differences may be that the average age of the subjects in this study is higher. Older patients are at a higher risk of POD due to reduced metabolic stress responses and adaptability, which are accompanied by brain atrophy and a major drop in physical activity [24]. Furthermore, we recognized that the diagnosis of delirium in patients following neurosurgical craniotomy remains controversial since it is difficult to distinguish whether delirium is caused by structural brain injury or systemic effects. However, recognizing that delirium is a distinct phenotype as defined in the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders, extensive efforts should be made to better describe this phenomenon, especially when delirium is detected in patients with intracranial lesions or structural brain injuries [25].

POD not only increases the risk of other postoperative complications and mortality but also significantly prolongs patients' hospital stays, increasing hospitalization costs. Our research findings further confirm that POD patients experience a significant increase in both the length of hospital stay and hospitalization costs, which is consistent with the existing literature [26]. Therefore, in order to lower medical expenditures and enhance clinical outcomes, we stress the significance of putting early POD screening and intervention mechanisms into place for senior patients following cranial surgery.

The prevalence of preoperative frailty in our study group was 27.7%. Similar to research indicating 25.4% had preoperative frailty in elective brain tumor resections [27]. Preoperative frailty was independently associated with POD, and patients with preoperative frailty were 6.5 times more likely to develop POD. Frailty, a long-term manifestation of the exhaustion of physical reserves that develops very slowly [28]. Older patients with frailty have reduced physiological reserves, decreased body regulation, and are prone to central nervous system abnormalities, which lead to POD [29]. A prediction model, developed using a binary regression method, showed that model 2, which included a base model and the addition of a frailty assessment, had an AUC of 0.908 for ROC, outperforming model 1. The result validated that, in older patients who underwent craniotomy, preoperative frailty screening might predict the likelihood of POD. Surgical prehabilitation aims to enhance baseline function and physiological reserves before surgery in order to promote recovery and reduce postoperative complications, such as critical early mobilization, nutritional support, and targeted psychological interventions [30, 31]. Preoperative frailty is a significant modifiable factor for older surgical patients, and doctors should include preoperative frailty assessments as part of routine examinations, correcting frailty early before surgery. However, it is important to consider the unique impact of different types of diseases on frailty, as controlling frailty at various levels may be more beneficial for the prognosis of postoperative delirium, thereby ensuring surgical safety and postoperative recovery outcomes for patients [32].

Furthermore, this study identified several independent factors of POD, including preoperative hypoalbuminemia, low BMI, and prolonged operative duration, consistent with findings in other articles [33]. Hypoalbuminemia indicates poor nutritional status and also signifies an acute-phase response and immune system activation. Such activation can interact with inflammatory factors in the central nervous system, like IL-6, potentially leading to POD [34]. A higher BMI may protect against POD through cerebrospinal fluid biomarkers such as T-tau and P-tau [35]. Malnutrition is a common clinical manifestation in patients with head and neck cancer. Patients with preoperative hypoalbuminemia and low BMI are at a high risk of frailty and malnutrition, which may lead to poor prognosis. The Buzzby Nutritional Risk Index (NRI), which combines serum albumin with weight loss, has shown effectiveness in nutritional risk assessment [36, 37]. In the future, the NRI index could be considered a replacement for albumin and body mass index in early nutritional screening. Prolonged operative duration can trigger stress responses, leading to changes in brain neurotransmitters, abnormal brain excitation, and hypoxia in brain tissue, all of which can induce POD. For neurosurgical procedures, the surgeries are intricate and complex, with longer operation times, making intraoperative anesthesia management more challenging, which may lead to a higher incidence of POD. Research has shown that each additional 0.5 h of surgery time raises the risk of POD by 6% [38]. The decrease in self-regulation ability and cerebrovascular elasticity in elderly surgical patients increases the risk of POD [39]. Therefore, in order to ensure the efficiency and safety of the surgery, surgeons could establish multidisciplinary collaboration with departments such as nutrition and anesthesiology, tailor surgical plans to the individual needs of older patients, conduct comprehensive preoperative nutritional assessments, and implement early nutritional interventions.

Moreover, this study found no association between age and POD, which contradicts previous research in other populations [40]. The main explanation could be that this study covered older people with a very low average age of 67 years, ranging from 60 to 82 years, which may not capture the specific impact of advanced age on POD. Another reason may be that age is not significantly correlated with POD itself and may not be a major influencing factor. Kappen et al. reported a systematic review of 21 research articles on POD in neurosurgery, and the results did not find a significant correlation between age and POD [41]. Further research is needed to validate and explore this correlation.

Limitations

This study has several limitations. Firstly, this study only assessed the data on postoperative delirium within three days, which may have overlooked some delirium cases occurring later. However, the initial three days post-surgery are widely recognized as the peak period for delirium occurrence, and early assessment is known to be effective. Secondly, as a prospective single-center study, the generalizability of our findings remains to be fully validated. Therefore, future multi-center studies are crucial to assess the external validity of our results. Thirdly, this study did not account for all potential risk factors for postoperative delirium reported in the literature, including preoperative vision and hearing impairments, a history of long-term medication use, intraoperative blood oxygen and blood pressure monitoring, and anesthesia conditions.

Conclusions

Older craniotomy patients have a higher incidence of POD, which can lead to prolonged postoperative hospital stays and increased hospitalization costs. The results of this study indicate that preoperative frailty is a potentially modifiable risk factor for POD in older craniotomy patients and can predict the occurrence of POD to a certain extent. In the future, stratified management can be implemented for patients with preoperative frailty to further observe whether intervention measures can effectively reduce the risk of POD and improve medical outcomes. Additionally, the results show that preoperative low albumin, low BMI, and prolonged operation time are also risk factors for POD. Further research is required to confirm these findings, and more patients with disorders in specific medical sub-disciplines should be investigated to develop specialized scales for neurosurgery.

Availability of data and materials

The data supporting the findings of this study are available upon request from the corresponding author. Due to privacy and ethical restrictions, the data are not publicly accessible.

Data availability

>The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.

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Funding

This study was supported by the Chongqing Brain Science Collaborative Innovation Center (grant number not applicable).

Author information

Author notes

  1. Li Wei and Miao Liu contributed equally to this work..

Authors and Affiliations

  1. Department of Neurosurgery, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, 400038, China

    Li Wei, Miao Liu, Shisi Zhang, Yujie Chen, Min Wu, Xue Yang & Jishu Xian

  2. Chongqing Clinical Research Center for Neurosurgery, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, 400038, China

    Li Wei, Miao Liu, Shisi Zhang, Yujie Chen, Min Wu, Xue Yang & Jishu Xian

  3. Chongqing Key Laboratory of Precision Neuromedicine and Neuroregenaration, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, 400038, China

    Li Wei, Miao Liu, Shisi Zhang, Yujie Chen, Min Wu, Xue Yang & Jishu Xian

  4. Chongqing Brain Science Collaborative Innovation Center, Chongqing, 400038, China

    Li Wei, Miao Liu, Shisi Zhang, Yujie Chen, Min Wu, Xue Yang & Jishu Xian

  5. Department of Nursing, Qionglai Medical Center Hospital, Qionglai, 611500, Sichuan Province, China

    Xiaomei Chen

  6. Department of Urology, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, 400038, China

    Jia Liu

  7. Nursing School, Third Military Medical University (Army Medical University), Chongqing, 400038, China

    Yuxuan He

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  1. Li Wei
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  2. Miao Liu
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Contributions

Li Wei: Conceptualization, methodology, formal analysis, investigation, data curation, writing-original draft. Miao Liu: methodology, investigation, data curation, writing-original draft. Shisi Zhang: investigation, data curation. Yujie Chen: methodology, writing-review & editing, supervision. Min Wu: investigation, data curation. Xiaomei Chen: writing-original draft,visualization. Jia Liu: investigation. Yuxuan He: data curation. Xue Yang: Conceptualization, methodology,supervision. Jishu Xian: Conceptualization, methodology, writing-review & editing, supervision, project administration. All authors reviewed and approved the final manuscript.

Corresponding authors

Correspondence to Xue Yang or Jishu Xian.

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Ethics approval and consent to participate

This study was approved by the South West Hospital Institutional Ethics Committee (ethics code: (A)KY2023020) and has been conducted in compliance with the Helsinki Declaration (1996). Before surgery, the patients or their family members provided written informed consent.

Competing interests

The authors declare no competing interests.

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Wei, L., Liu, M., Zhang, S. et al. Association of preoperative frailty with risk of postoperative delirium in older patients undergoing craniotomy: a prospective cohort study. BMC Surg 24, 272 (2024). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12893-024-02573-2

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  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12893-024-02573-2

Keywords

BMC Surgery

ISSN: 1471-2482

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