Hepatocellular carcinoma (HCC) ranks as the sixth most commonly diagnosed cancer and the third deadliest globally1. Globally, the incidence rates of HCC exhibit a positive correlation with age, reaching their peak around the age of 75 years2,3. Owing to rapid aging of the global population and a record-high average life expectancy, there is a growing incidence of older patients diagnosed with HCC4,5.

Postoperative delirium (POD) is a prevalent and serious complication marked by acute and varying alterations in mental condition, attentional capabilities, and consciousness levels following liver resection6,7,8. Studies have shown a correlation between POD and unfavorable consequences, including heightened mortality rates, extended hospital stays, and elevated medical costs. Additionally, POD may contribute to lasting and more substantial declines in cognitive functions and daily life activities9,10,11,12,13.

Roughly one-third of POD cases are considered preventable, making it a suitable focus for surgical quality improvement endeavors14,15. In practice, uniformly implementing all effective delirium prevention strategies for every older surgical patient throughout their perioperative course is often impractical, despite being theoretically possible. Given the resource constraints and infrequent implementation of these interventions in most centers, recommendations have been made to focus on identifying patients with the highest risk16,17,18.

Previous research has established nomograms for POD in malignant tumors, including gynecologic cancers19, gastric cancer 20, colorectal cancer21, and head and neck cancer22. However, the accuracy of these nomograms varies widely and may not necessarily be applicable to HCC. In this study, we aimed to identify the risk factors for POD in older patients with HCC and to develop a corresponding nomogram.

Materials and methods

Patients

A nomogram was developed through a retrospective analysis of a prospectively registered database including 1109 patients with HCC at Mengchao Hepatobiliary Hospital of Fujian Medical University between March 2015 and June 2020. Concurrently, for external validation, we included data from 372 patients treated at Fujian Cancer Hospital between March 2018 and August 2020 (Fig. 1). The inclusion criteria were: (1) individuals aged 65 years and older; (2) patients who underwent elective hepatectomy; and (3) availability of sufficient data. The exclusion criteria were: (1) preoperative cognitive impairment; (2) history of severe nervous system disorders and dementia; (3) language barriers; hearing or vision impairments resulting in an inability to communicate; and (4) brain metastases.

Figure 1
figure 1

Flowchart of patient selection.

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This study was performed in accordance with the Declaration of Helsinki. Written informed consent was obtained from all participants. This research was approved by the ethics committee of Mengchao Hepatobiliary Hospital of Fujian Medical University.

Definitions

The diagnosis of POD was conducted through the application of the Confusion Assessment Method (CAM), a widely used diagnostic algorithm known for its demonstrated high sensitivity and specificity in identifying delirium23,24. POD frequently initiates in the recovery room and can persist for up to 5 days following the surgical procedure18. Assessment of POD can occur daily for a consecutive 5-day period post surgery, whenever patients manifest an abrupt alteration in mental status.

Cerebrovascular disease, as defined in this study, included conditions such as cerebral infarction, cerebral hemorrhage, stenosis (such as stenosis of the carotid, vertebral stenosis, or intracranial stenosis), and aneurysms25.

Ischemic heart disease (IHD) refers to inadequate blood supply to the heart, resulting in myocardial ischemia. This encompasses acute myocardial infarction, chronic stable angina, chronic IHD, and its associated heart failure25,26.

Chronic pulmonary disease is characterized by the presence of at least one of the following conditions: asthma, chronic obstructive pulmonary disease, and restrictive lung disease27.

Intraoperative hypotension is characterized by instances in which the patient experiences a systolic blood pressure below 80 mmHg or encounters at least one episode of systolic blood pressure that falls more than 20% below the baseline28.

Statistical analysis

The data were analyzed using both IBM SPSS 24.0 (IBM Corp) and R software (version 4.1.1). Parameters with a normal distribution are expressed as mean ± standard deviation and analyzed using the Student t-test. Parameters not following a normal distribution are expressed as median and interquartile range and analyzed using the Mann–Whitney test. Categorical variables are presented as frequency and percentage, and their comparisons were conducted using the chi-squared test. The least absolute shrinkage and selection operator (LASSO) regression model was used to select the optimal predictive variables. Subsequently, the identified key features were integrated into multivariable logistic regression analysis. Forest plots, constructed using GraphPad Prism, were used to visualize the results. Predictors found to be statistically significant were used to establish a nomogram system for diagnosing POD. The discriminative performance of the nomogram was assessed using the area under the receiver operating characteristic curve (AUC). Calibration curves were generated to evaluate the concordance between predicted and observed probabilities. Additionally, a decision curve analysis (DCA) was conducted to assess the clinical utility of the nomogram. Statistical significance was defined as a two-tailed P-value < 0.05.

Results

Patient characteristics

In the training set, the overall rate of POD was 13.3% (148 of 1109 patients). In the validation set, this rate was slightly higher at 16.4% (61 of 372 patients). Notable differences between the two sets included factors such as sex, body mass index, presence of hypertension, history of previous abdominal surgery, platelet levels, blood urea nitrogen, duration of the surgical procedure, and a requirement for intraoperative blood transfusion (Table 1).

Table 1 Patients’ background characteristics in the training and validation sets.
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Selection of independent predictors for POD and development of the nomogram in the training set

From a pool of 40 related dependent variables, eight potential predictors with non-zero coefficients in the LASSO regression model were selected based on data from the training set. These predictors include age, diabetes mellitus, history of cerebrovascular disease, smoking, ASA classification, albumin level, tumor number, and surgical approach (Fig. 2). To facilitate analysis and clinical implementation, continuous variables were dichotomized according to the ROC curve analysis. The optimal cutoff values for age and albumin level were determined to be 71 (AUC = 0.711) and 37.2 g/L (AUC = 0.688), respectively (Fig. 3). The multivariable logistic regression analysis incorporated these predictors, with age, history of cerebrovascular disease, ASA classification, albumin level, and surgical approach recognized as independent risk factors for POD (Fig. 4).

Figure 2
figure 2

Clinicopathologic feature selection using the LASSO regression model. (A) The smallest lambda is determined through tenfold cross-validation. (B) LASSO coefficient profiles of 40 signatures. When the smallest lambda equals 0.015, the eight coefficients with non-zero values are selected.

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Figure 3
figure 3

Receiver operating characteristic (ROC) curves of the age (A) and albumin level (B). AUC Area under the ROC curve.

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Figure 4
figure 4

Forest plot of independent predictors of postoperative delirium. ASA American Society of Anesthesiologists, HR Hazard ratio, CI Confidence interval.

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Construction and validation of the nomogram

The nomogram was created using these independent factors as its foundation (Fig. 5). The AUCs of the nomogram model were 0.798 (95% CI 0.752–0.843) in the training set and 0.808 (95% CI 0.754–0.861) in the validation set (Fig. 6). The calibration curves generated by the nomogram exhibited strong concordance between observed outcome frequencies and predicted probabilities in both sets (Fig. 7). The results of DCA are shown in Fig. 8, which demonstrated that the nomogram used in our study had superior effectiveness compared with treating all patients or providing no treatment. This advantage was observed when the threshold probability ranged from 9 to 91% in the training set and from 5 to 87% in the validation set.

Figure 5
figure 5

Nomogram for estimating the likelihood of POD in older patients diagnosed with HCC. ASA American Society of Anesthesiologists.

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Figure 6
figure 6

Receiver operating characteristic (ROC) curves in the training set (A) and validation set (B). AUC Area under the ROC curve.

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Figure 7
figure 7

Calibration curves of the nomogram in the training set (A) and validation set (B). The horizontal axis depicts the anticipated likelihood of POD, and the vertical axis illustrates the actual occurrence of diagnosed POD relative to the total cases. The diagonal dashed line represents the perfect prediction of the ideal model. The solid line represents the prediction of the nomogram; a closer fit to the diagonal dashed line represents the result after bias correction by bootstrapping (1000 repetitions).

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Figure 8
figure 8

Decision curve analysis of the nomogram in the training set (A) and validation set (B). The net benefit is quantified along the y-axis. The red line denotes predictions from the nomogram, the green line signifies the assumption of POD occurrence in all patients, and the blue line signifies the assumption of no POD occurrence in any patient.

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Discussion

The incidence of POD was 13.3% in the training set and 16.1% in the validation set. Consistent with prior research, Nomi et al. reported a POD rate of 14.2%29. Yoshimura et al. found a POD rate of 17.0% in patients undergoing hepatectomy30. However, Ishihara et al. reported a lower POD incidence of 7.5%6, and Chen et al. found this to be 8.4%31. Variability in POD rates may be attributed to several factors: a lack of consistent definitions and assessment methods for POD by researchers, the diverse clinical characteristics of patients, and inaccuracies in estimation owing to the use of retrospective research methods.

This research involved developing and validating a new nomogram for predicting POD in older HCC patients. The model demonstrated strong capabilities in both discrimination and calibration, which showed its clinical value. The model's robustness was further enhanced by external validation, affirming its applicability across different patient groups and clinical settings. To our knowledge, this represents the inaugural predictive model for POD in individuals diagnosed with HCC.

Previous studies have indicated a correlation between the risk of POD and the emergence of postoperative complications32,33. However, such data are not accessible before or during surgery and thus cannot be integrated into predictive models.

In this study, a significant correlation between advanced age and an increased risk of POD was observed. Older patients experience a decline in physical capabilities, brain tissue integrity, and stress response regulation, along with diminished levels of key central neurotransmitters like acetylcholine and epinephrine34. Age-related constriction of blood vessels reduces cerebral oxygenation, which can potentially lead to postoperative cerebral impairment35,36. Moreover, alterations in drug metabolism and response owing to aging may increase the adverse effects of medications, thereby increasing the likelihood of POD37.

The present study identified that a history of cerebrovascular disease is an independent risk factor for POD. Cerebrovascular disease can lead to cognitive impairment, dementia, and neurocognitive deficits, which is postulated to increase delirium possibly through altered brain networks and a reduced ability to integrate sensory inputs38. Long-term susceptibility to delirium should be regarded as an integral aspect of the overall cerebrovascular disease burden39,40. Several studies have indicated that cognitive dysfunction and reduced functional capacity are associated with a heightened risk of POD41,42,43.

The ASA physical status classification system is commonly applied to evaluate a patient's ability to withstand anesthesia, primarily based on their overall compromised health and the presence of multiple comorbidities44. Research has indicated that an ASA classification ≥ 3 is associated with an increased risk of complications and decreased overall survival after hepatectomy45,46,47. Our study indicated that an ASA classification ≥ 3 is a risk factor for POD, as evidenced in numerous studies on this topic48,49,50,51. Whereas we found no statistically significant differences in common comorbidities such as diabetes and hypertension between the groups, it is considered that the cumulative impact of various comorbidities might heighten baseline vulnerability in older patients. This susceptibility, combined with the stress of surgery, could be a contributing factor to the development of POD52,53.

Numerous research findings indicate that a lower patient albumin level increases their likelihood of experiencing POD, a conclusion that aligns with the findings of our study6,54,55,56,57. Hypoalbuminemia affects drug metabolism, antioxidant defense, and toxin processing because albumin is the primary transport protein in blood plasma. Reduced albumin levels may result in cognitive dysfunction owing to toxic effects and oxidative injuries58,59. Appropriate medical intervention can yield lower albumin levels, potentially aiding in the reduction of POD risk.

This study showed that an open approach independently increases the risk of POD. A laparoscopic approach may reduce operative stress and postoperative systemic inflammation, which are known to be linked to the occurrence of POD29,60,61,62.

There are a number of limitations in this study. First, this research was a retrospective evaluation conducted using a prospectively registered database. Recognizing the intrinsic biases inherent in this type of study design is crucial. Prior studies have highlighted several risk factors linked to POD, including preoperative depression and anxiety63,64,65,66,67,68. Nevertheless, these factors were not incorporated into our analysis owing to certain constraints. Second, the experiment was conducted in only two centers, both of which are located in the same city. To further validate the model, it is necessary to use a more extensive sample size and conduct studies across various centers in different regions.

Conclusion

In older patients with HCC, factors such as age, cerebrovascular disease history, ASA classification, albumin levels, and the type of surgical procedure are identified as independent predictors of POD. In this study, we developed and externally validated a new, precise nomogram for personalized assessment and clinical decision-making.