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Introduction
Delirium is a serious neurocognitive illness characterized by abrupt, fluctuating changes in mental status and consciousness, as well as cognitive functions, which is frequently observed in patients with advanced cancer [1]. Reported prevalence and incidence rates for delirium in this population from Sands et al. [2] range from 18 to 58% and 3.5 to 45%, respectively. Watt et al. [3] reported the median cumulative incidence and the median period prevalence of delirium in hospitalized patients was 29% and 60%, respectively. The presence of delirium in palliative setting can degrade the quality of life of patients with advanced cancer and their caregivers and make it difficult to evaluate symptoms, such as pain or dyspnea [4,5]. Moreover, delirium is associated with longer hospital stays, higher mortality and morbidity rates, and higher healthcare expenses [6]. Given the significant consequence of delirium, it is essential to predict the risk of developing delirium in advance and undertake steps to reduce it [7,8].
The etiology of delirium is a multi-factorial combination of predisposing factors, such as age or previous cognitive dysfunction and precipitating factors, such as metabolic derangements [9]. Among the potential etiologies, targeting the modifiable or reversible risk factors can be a good strategy for preventing delirium in patients with advanced cancer [7,9]. Several cohort studies of patients with advanced cancer in a palliative setting found that persistent symptoms, such as pain, drowsiness, use of medications, such as opioids and steroids, metabolic abnormalities, or dehydration were associated with an increased risk of delirium [4,10,11]. However, few studies have built a comprehensive prediction model incorporating multiple components, including predisposing and precipitating factors. Furthermore, apart from predictors of delirium in hospice or end-of-life cases, few prediction models targeted patients admitted to the general oncology ward for supportive cares. As it is often difficult to manage terminal delirium in a hospice setting, it is of value to predict delirium occurring in a relatively earlier setting [12].
Therefore, the current multicenter cohort study was performed to identify risk factors related to the occurrence of delirium and to propose a comprehensive prediction model in patients with advanced cancer who were hospitalized in oncology wards of tertiary cancer centers for supportive cares.
Materials and Methods
1. Study design and participants
This was a retrospective cohort study for hospitalized patients with advanced cancer at four tertiary cancer centers (Seoul National University Bundang Hospital, Yonsei Cancer Center, CHA University Bundang Hospital, and Seoul National University Hospital) in South Korea between January 2019 and December 2020. Among patients with advanced cancer hospitalized in oncology wards of the cancer centers in need of inpatient supportive care, patients older than 19 years were enrolled. We defined advanced cancer as a metastatic, or recurrent disease for solid tumors, relapsed/refractory disease for hematologic malignancies, and not treatable with curative intent. The exclusion criteria were as follows: (1) patients who were hard to communicate with because of active brain metastasis or leptomeningeal seeding, (2) patients who had previously been diagnosed with any neurocognitive disorder according to the code of the 10th revision of the International Classification of Diseases (ICD-10), such as dementia, Parkinson’s disease, Alzheimer’s disease other than delirium, (3) patients who were admitted due to active psychiatric symptoms resulting from underlying psychiatric disorder and (4) patients who discharged due to death. The last item of exclusion criteria was to exclude patients with terminal delirium. We defined terminal delirium as that accompanied by death within 15 days after the onset of delirium during inpatient care.
2. Study variables and data collection
Clinical and sociodemographic data were obtained from electrical medical records. Our dataset comprised general sociodemographic information (age, sex, health insurance, education level, use of glasses or hearing aids, living with family, smoking and alcohol consumption), clinical risk factors (vital signs and body mass index [BMI] at admission, laboratory findings, obesity, duration of admission, and data on chemotherapy during admission), past medical history, and concomitant drugs. Obesity was assessed in conformity with BMI, and a BMI of less than 23 kg/m2 was defined as non-obese. The laboratory findings within three days of admission were referred for analysis.
Regarding reliability, the occurrence of the delirium was measured by the diagnostic criteria used during the routine clinical practice. Patients were included if they were diagnosed with delirium according to ICD-10 criteria, referred for psychiatric consultation due to delirium, and administered medication to control delirium during their hospital stay. Due to the retrospective nature of the study, research team including a well-trained physician and academic nurses reviewed the medical records and confirmed the occurrence of the delirium.
3. Statistical analysis
The baseline characteristics between patients with and without delirium were compared using the student’s t test or Wilcoxon’s sign rank test for continuous variables and Pearson’s chi-square test or Fisher’s exact test for categorical variables. The variables comprised the following four categories: sociodemographic data, clinical characteristics, laboratory findings, and concomitant medication. We used mean imputation techniques to handle missing data for variables with missing values of less than 10%. Variables of four categories that were associated with the occurrence of delirium were evaluated by univariate logistic regression. Variables with more than 10% missing data (education, prothrombin time, C-reactive protein) were excluded from the univariate analysis. A stepwise backward procedure was used to identify the predictors of the occurrence of delirium. All predictors achieving a p-value below 0.05 were considered and sequentially removed if the p-value in the multivariate model was above 0.05. As no significant collinearity was observed, the variables that were statistically significant in the univariate analysis among the whole classified variables were entered into the multivariate logistic regression analysis. Using multivariate logistic regression, we confirmed each predictive model by consecutively adding the variables of the four categories.
A combination of sociodemographic data and clinical characteristics was set as the basic prediction model. Additional models were developed by sequentially adding laboratory findings and concomitant medication to the basic model, and the accuracy and discriminatory power of each prediction model were identified. We evaluated the performance of the predictive model in terms of discrimination and calibration. The discriminatory ability for each prediction model was measured by a receiver operating characteristic (ROC) curve to calculate the corresponding areas under the curve value. Values above 0.7 were suitable for discrimination [13]. Calibration for the final developed model, including significant variables in four categories, was assessed graphically by plotting to evaluate the agreement between the predicted probability and actual outcome. The final developed prediction model using multivariate logistic regression was internally validated by bootstrapping (1,000 repetitions).
A two-tailed p-value <0.05 was considered statistically significant, and confidence intervals (CIs) were calculated at a 95% confidence level. All data were analyzed using Statistical Package for the Social Sciences software ver. 22.0 (IBM SPSS Statistics, Armonk, NY) and R statistics ver. 4.2.2 (R Foundation for Statistical Computing, Vienna, Austria).
Results
1. Patient characteristics between delirium and non-delirium groups
From January 2019 to December 2020, 2,152 patients were enrolled. A total of 127 patients (5.9%) developed delirium during hospitalization. The median age of patients with delirium was 69 years (range, 27 to 88 years). The delirium group had more male patients (70.1% vs. 57.7%, p=0.007), had more patients with hearing impairment (3.1% vs. 0.9%, p=0.037), had a longer length of hospital stay (13 days vs. 8 days, p < 0.001), and had fewer patients who underwent chemotherapy during hospitalization (15.7% vs. 28.8%, p=0.001) compared to the non-delirium group.
Regarding past medical history, the delirium group had a higher proportion of history of delirium (18.9% vs. 1.6%, p < 0.001), psychiatric disease (13.4% vs. 5.7%, p < 0.001), diabetes mellitus (30.7% vs. 21.6%, p=0.020), and cardiovascular disease (48.8% vs. 38.1%, p=0.019) compared to the non-delirium group. Concerning laboratory findings at admission, azotemia and electrolyte imbalances, including hyponatremia, hyperkalemia, and hypercalcemia, were more common in the delirium group than in the non-delirium group. In the delirium group, concomitant use of opioids (76.4% vs. 62.3%, p=0.002) and antibiotics (77.2% vs. 59.1%, p < 0.001) was statistically significantly higher than in the non-delirium group during hospitalization. Furthermore, the use of sedatives and antidepressants was more frequent in the delirium group (37.0% vs. 15.6%, p < 0.001) than in the non-delirium group. Baseline patient characteristics are summarized in Table 1.
Table 1.
Baseline characteristics of patients
| Characteristic | Total (n=2,152) | Delirium group (n=127) | Non-delirium group (n=2,025) | p-value |
|---|---|---|---|---|
| Sociodemographic characteristic | ||||
| Age (yr), median (range) | 64 (18-97) | 69 (27-88) | 63 (18-97) | < 0.001 |
| Sex | ||||
| Male | 1,257 (58.4) | 89 (70.1) | 1,168 (57.7) | 0.007 |
| Female | 895 (41.6) | 38 (29.9) | 857 (42.3) | |
| Marital status | ||||
| Married | 1,776 (82.5) | 107 (84.3) | 1,669 (82.4) | 0.532 |
| Not married | 368 (17.1) | 19 (15.0) | 349 (17.2) | |
| Unknown | 8 (0.4) | 1 (0.8) | 7 (0.3) | |
| Education (n=1,513) | ||||
| High school or less | 984 (65.0) | 51 (66.2) | 933 (65.0) | |
| College/Graduate school | 529 (35.0) | 26 (33.8) | 503 (35.0) | |
| Smoking status | ||||
| Non-smoker | 1,475 (68.5) | 74 (58.3) | 1,401 (69.2) | |
| Ex-smoker | 626 (29.1) | 45 (35.4) | 581 (28.7) | |
| Current smoker | 51 (2.4) | 8 (6.3) | 43 (2.1) | |
| Alcohol consumption (n=2,151) | ||||
| Non-drinker | 1,772 (82.4) | 98 (77.2) | 1,674 (82.7) | 0.036 |
| 1-3 times a week | 265 (12.3) | 16 (12.6) | 249 (12.3) | |
| ≥ 4 times a week | 114 (5.3) | 13 (10.2) | 101 (5.0) | |
| BMI (n=2,144) | ||||
| Obese | 355 (16.6) | 21 (16.5) | 334 (16.6) | 0.563 |
| Overweight | 343 (16.0) | 24 (18.9) | 319 (15.8) | |
| Normal weight | 980 (45.7) | 51 (40.2) | 929 (46.1) | |
| Underweight | 466 (21.7) | 31 (24.4) | 435 (21.6) | |
| Living with family | 1,430 (66.4) | 82 (64.6) | 1,348 (66.6) | 0.698 |
| National health insurance | 2,054 (95.4) | 118 (92.9) | 1,936 (95.6) | 0.182 |
| Visual impairment (wearing glasses) | 108 (5.0) | 5 (3.9) | 103 (5.1) | 0.679 |
| Hearing impairment (using hearing aids) | 22 (1.0) | 4 (3.1) | 18 (0.9) | 0.037a) |
| Clinical characteristic | ||||
| Primary cancer | ||||
| Lung | 442 (20.5) | 49 (38.6) | 393 (19.4) | < 0.001 |
| Gastroesophageal and colorectal | 610 (28.3) | 31 (24.4) | 579 (28.6) | |
| Hepatopancreatobiliary | 391 (18.2) | 21 (16.5) | 370 (18.3) | |
| Genitourinary | 148 (6.9) | 7 (5.5) | 141 (7.0) | |
| Hematology | 91 (4.2) | 3 (2.4) | 88 (4.3) | |
| Breast | 122 (5.7) | 1 (0.8) | 121 (6.0) | |
| Head and neck | 139 (6.5) | 3 (2.4) | 136 (6.7) | |
| Others | 209 (9.7) | 12 (9.4) | 197 (9.7) | |
| Past medical history | ||||
| History of delirium | 56 (2.6) | 24 (18.9) | 32 (1.6) | < 0.001a) |
| History of cardiovascular disease | 833 (38.7) | 62 (48.8) | 771 (38.1) | 0.019 |
| History of diabetes mellitus | 476 (22.1) | 39 (30.7) | 437 (21.6) | 0.020 |
| History of respiratory disease | 189 (8.8) | 15 (11.8) | 174 (8.6) | 0.255 |
| History of liver disease | 144 (6.7) | 11 (8.7) | 133 (6.6) | 0.358 |
| History of psychiatric disease | 132 (6.1) | 17 (13.4) | 115 (5.7) | < 0.001 |
| History of CVA/head trauma | 175 (8.1) | 16 (12.6) | 159 (7.9) | 0.065 |
| Past history of admission | 2,016 (93.7) | 117 (92.1) | 1,899 (93.8) | 0.573 |
| Vital sign at admission | ||||
| Blood pressure | ||||
| SBP < 140 mmHg and DBP < 90 mmHg | 1,624 (75.5) | 89 (70.1) | 1,535 (75.8) | 0.166 |
| SBP ≥ 140 mmHg or DBP ≥ 90 mmHg | 528 (24.5) | 38 (29.9) | 490 (24.2) | |
| Body temperature | ||||
| Normal temperature (< 38℃) | 2,060 (95.7) | 121 (95.3) | 1,939 (95.8) | 0.819 |
| Hyperthermia (≥ 38℃) | 92 (4.3) | 6 (4.7) | 86 (4.2) | |
| Heart rate | ||||
| Normal rate (HR < 120/min) | 2,024 (94.1) | 114 (89.8) | 1,910 (94.3) | 0.050 |
| Tachycardia (HR ≥ 120/min) | 128 (5.9) | 13 (10.2) | 115 (5.7) | |
| Respiratory rate | ||||
| Normal respiratory rate (RR < 20/min) | 1,057 (49.1) | 61 (48.0) | 996 (49.2) | 0.855 |
| Tachypnea (RR ≥ 20/min) | 1,095 (40.9) | 66 (52.0) | 1,029 (50.8) | |
| Chemotherapy during hospitalization | 604 (28.1) | 20 (15.7) | 584 (28.8) | 0.001 |
| Duration of hospital admission (day), median (range) | 8 (1-117) | 13 (2-71) | 8 (1-117) | < 0.001 |
| Laboratory finding | ||||
| Complete blood test | ||||
| Hb < 8 g/dL | 183 (8.5) | 16 (12.6) | 167 (8.7) | 0.099 |
| WBC > 9.8×103/μL | 708 (32.9) | 48 (37.8) | 660 (32.6) | 0.243 |
| PLT < 150×103/μL | 559 (26.0) | 41 (32.3) | 518 (25.6) | 0.096 |
| Liver function | ||||
| ALP > 129 IU/L | 986 (45.8) | 55 (43.3) | 914 (45.1) | 0.013 |
| AST > 40 IU/L | 712 (33.1) | 41 (32.3) | 671 (33.1) | 0.847 |
| ALT > 40 IU/L | 466 (21.7) | 20 (15.7) | 446 (22.0) | 0.097 |
| Total bilirubin > 1.2 mg/dL | 488 (22.7) | 27 (21.3) | 461 (22.8) | 0.744 |
| Albumin < 3.5 g/dL | 1,240 (57.6) | 81 (6.8) | 1,159 (57.2) | 0.165 |
| Total Protein < 6.5 g/dL | 1,303 (60.5) | 80 (63.0) | 1,223 (60.4) | 0.576 |
| Prothrombin time-INR > 1.2 (n=1,851) | 559 (30.2) | 48 (41.4) | 511 (29.5) | 0.009 |
| Renal function | ||||
| BUN > 20 mg/dL | 802 (37.3) | 72 (56.7) | 730 (36.0) | < 0.001 |
| Creatinine > 1.2 mg/dL | 321 (14.9) | 27 (21.3) | 294 (14.5) | 0.041 |
| eGFR < 60 mL/min/1.732 m2 | 362 (16.8) | 28 (22.0) | 334 (16.5) | 0.112 |
| Uric acid > 8.6 mg/dL | 135 (6.3) | 6 (4.7) | 129 (6.4) | 0.573 |
| Na < 135 mEq/L | 748 (34.8) | 55 (43.3) | 693 (34.2) | 0.043 |
| K > 5.1 mEq/L | 168 (7.8) | 17 (13.4) | 151 (7.5) | 0.019 |
| Ca > 10.2 mg/dL | 81 (3.8) | 11 (8.7) | 70 (3.5) | 0.007a) |
| P > 4.5 mg/dL | 162 (7.5) | 8 (6.3) | 154 (7.6) | 0.614 |
| Glucose < 74 mg/dL | 54 (2.5) | 4 (3.1) | 50 (2.5) | 0.557a) |
| CRP > 0.3 mg/dL (n=1,928) | 1,702 (88.3) | 94 (81.0) | 1,612 (88.9) | 0.114 |
| Medication | ||||
| Concomitant drugs | ||||
| Opioid | 1,358 (63.1) | 97 (76.4) | 1,261 (62.3) | 0.002 |
| Sedative | 362 (16.8) | 47 (37.0) | 315 (15.6) | < 0.001 |
| Antidepressant | 249 (11.6) | 22 (17.3) | 227 (11.2) | 0.044 |
| AED | 271 (12.6) | 19 (15.0) | 252 (12.4) | 0.408 |
| Cholinergic | 172 (8.0) | 8 (6.3) | 164 (8.1) | 0.508 |
| Anti-cholinergic | 159 (7.4) | 6 (4.7) | 153 (7.6) | 0.295 |
| Antibiotics use during hospital admission | 1,295 (60.2) | 98 (77.2) | 1,197 (59.1) | < 0.001 |
Values are presented as number (%) unless otherwise indicated. AED, antiepileptic drug; ALP, alkaline phosphatase; ALT, alanine aminotransferase; AST, aspartate aminotransferase; BMI, body mass index; BUN, blood urea nitrogen; CRP, C-reactive protein; CVA, cerebrovascular accident; DBP, diastolic blood pressure; eGFR, estimated glomerular filtration rate; Hb, hemoglobin; HR, heart rate; INR, international normalized ratio; PLT, platelet; RR, respiratory rate; SBP, systolic blood pressure; WBC, white blood cell.
2. Factors associated with the occurrence of delirium
By univariate analysis, among sociodemographic variables, age over 65, male sex, hearing impairment, smoking status, and non-obese range of BMI were associated with a higher risk of developing delirium. Among clinical variables, previous history of delirium, cardiovascular disease, diabetes mellitus, psychiatric diseases, length of hospital stay, or chemotherapy during hospitalization were significantly associated factors. Regarding laboratory findings, leukocytosis, elevated alkaline phosphatase, azotemia, and electrolyte imbalance were associated with delirium occurrence. The use of opioids, sedatives, antidepressants, and antibiotics during hospitalization were significant factors with delirium risk.
Multivariate analysis for variables among all four categories—sociodemographic features, clinical characteristics, laboratory findings, and medication—revealed that the occurrence of delirium significantly increased in patients whose age was over 65 years (odds ratio [OR], 1.86; 95% CI, 1.22 to 2.87; p=0.004), were non-obese (OR, 1.70; 95% CI, 1.07 to 2.80; p=0.029), had hearing impairment (OR, 3.38; 95% CI, 0.89 to 10.34; p=0.046), had a previous history of delirium (OR, 11.90; 95% CI, 6.28 to 22.42; p < 0.001), were hospitalized for longer than 8 days (OR, 3.24; 95% CI, 2.14 to 5.01; p < 0.001), who did not receive chemotherapy during hospitalization (OR, 1.77; 95% CI, 1.07 to 3.07; p=0.032), had elevated blood urea nitrogen (BUN) level (OR, 1.58; 95% CI, 1.06 to 2.36; p=0.024), had hypercalcemia (OR, 2.15; 95% CI, 0.98 to 4.33; p=0.042), and used sedatives (OR, 3.15; 95% CI 2.04 to 4.83; p < 0.001) (Table 2). The results of multivariate analysis on the risk of delirium according to the combination of each categorized variable were additionally presented as supplementary data (S1-S3 Tables).
Table 2.
Risk factor for occurrence of delirium on univariate analysis and multivariate analysis for all variables
AED, antiepileptic drug; ALP, alkaline phosphatase; ALT, alanine aminotransferase; AST, aspartate aminotransferase; BMI, body mass index; BUN, blood urea nitrogen; CI, confidence interval; CVA, cerebrovascular accident; DBP, diastolic blood pressure; eGFR, estimated glomerular filtration rate; Hb, hemoglobin; HR, heart rate; OR, odds ratio; PLT, platelet; RR, respiratory rate; SBP, systolic blood pressure; WBC, white blood cell.
3. Development of the prediction model for delirium and prediction ability
Table 3 lists the validity and accuracy statistics of the prediction model’s performance. The area under the ROC (AUROC) was approximately 0.8 in all four prediction models (Fig. 1A-D), and negative predictive values were greater than the positive predictive values in the overall prediction model. The highest AUROC of the predictive model was 0.831 (95% CI, 0.794 to 0.868), observed for the combination of all four classification variables, indicating good discriminant ability. Therefore, we designated the combination of all four categorized variables (sociodemographic data, clinical characteristics, laboratory findings, and concomitant medications) as the final prediction model. The sensitivity and specificity of the final prediction model were 80.3% and 72.0%, respectively. We performed internal validation of the final model using the bootstrap with 1,000 repetitions. The calibration plot of the internal validation set using bootstrapping resampling illustrated good accordance between the predicted and actual probabilities (Fig. 2).
Fig. 1.
Receiver operating characteristic curves of predictive models according to variables. (A) Sociodemographic+clinical characteristics. (B) Sociodemographic+clinical+laboratory characteristics. (C) Sociodemographic+clinical+concomitant medication characteristics. (D) Sociodemographic+clinical+laboratory+concomitant medication characteristics. AUROC, area under the receiver operating characteristic curve; CI, confidence interval.
Table 3.
Performance comparison of predictive models according to sociodemographic, clinical, laboratory and concomitant medication characteristics
Discussion
This multicenter, retrospective cohort study developed and internally validated a delirium prediction model for patients with advanced cancer who were hospitalized in oncology wards in need of inpatient supportive care using predictors that can be readily measured. The prediction model, which consisted of four predisposing factors (old age, non-obesity, hearing difficulty, and previous history of delirium) and five precipitating factors (longer length of hospital stay, non-receipt of chemotherapy at this admission, uremia, hypercalcemia, and using sedatives), showed good performance and good discrimination capability. This model may contribute to timely and proper management of delirium by analyzing the risk factors and early detection of delirium, as compared to predicting irreversible terminal delirium.
Age [14], hearing impairment [10,14], and past delirium history [15] have been well identified as delirium risk factors in previous research of patients with advanced cancer, and were eventually incorporated in our study’s predictive model. However, our findings regarding the negative association between obesity and delirium risk are quite intriguing. Though obesity is a well-known risk factor for chronic cognitive impairment [16], mixed results have been reported about the association of BMI with delirium [17,18]. Existing research involving postoperative delirium [19] or intensive care unit delirium [20,21] as an outcome variable found that having a high BMI was a protective factor for delirium. “Obesity paradox,” which is a controversial issue, may support those results [22]. To our knowledge, as the association of BMI with delirium has not been well established in patients with advanced cancer, our findings, which reveal that non-obese patients were 1.7 times more likely than obese patients to develop delirium, are the first to show that such an obesity paradox may be applied to delirium in patients with advanced cancer [22,23]. Further research is required to elucidate and validate the correlation between delirium and BMI in patients with advanced cancer and the associated mechanisms.
Our findings suggesting a hospital stay of more than 8 days increases the risk of delirium are consistent with the findings of previous studies that indicated a positive relationship between hospital stay length and delirium risk [24,25]. Their relationship has not been fully clarified so far. When compared to those who do not have delirium, patients with delirium may have a longer hospital stay as a result of their poor outcome [24]. The length of hospital stay, however, may be a risk factor for delirium because the hospital environment itself may be a risk of delirium.
The results of our study, which indicated that patients who did not receive chemotherapy during hospitalization had a higher risk of delirium, reflect the circumstance of patients who were unable to receive chemotherapy rather than the effect of chemotherapy itself on the risk of delirium [14]. It has been reported that certain anticancer drugs that pass through the brain blood barrier can increase the risk of hyperactive delirium [26]; however, our study did not assess the influence of each patient’s specific anticancer drug on the risk of delirium. As this study focuses on patients with advanced cancer admitted to oncology wards of cancer centers, it is speculated that the risk of delirium may increase if chemotherapy is challenging because of a specific reason (e.g., deteriorating systemic conditions, low benefits when comparing gains and losses). However, the lack of investigation into the reason why the patients did not receive chemotherapy in our study makes it difficult to interpret the results. This aspect requires further research.
High BUN and hypercalcemia, which were incorporated in our final model, were previously referred to as acute renal failure, dehydration, and metabolic derangement, and were thought to present cognitive impairment as a common symptom. While previous research did not differentiate between specific laboratory parameters, instead of focusing on the overall impact of metabolic abnormalities [10,14,26], this study demonstrated that high BUN and hypercalcemia were significant in multivariate models after adjusting for several variables that were significant in the univariate analysis. As BUN and calcium can be easily measured during admission, using them to predict delirium can assist in identifying high-risk patients.
Our study’s strength is that it includes medication use, a risk factor for delirium that is frequently ignored in its predictive models. According to previous research [3,27], opioids are a well-known risk factor for delirium in patients with advanced cancer. However, in our study, sedatives were identified as predictors included in the final model, while opioids were not included as significant predictors after adjusting for multiple variables. The use of sedatives, such as benzodiazepines could be related to the mechanism of delirium development via overstimulation of the cortical gamma-aminobutyric acid system [28]. Our findings are consistent with those of a previous report [15] that hospitalized cancer patients exposed to benzodiazepines in excess of 2 mg per day are twice as likely to develop delirium as those who have not, as well as previous studies that used postoperative delirium as an outcome variable [29,30]. These findings suggest that more attention should be paid to the development of delirium in patients with advanced cancer who are administered sedatives, and that efforts should be made to limit the use of sedatives when they are not necessary.
In this study, some limitations must be addressed. First, this study contained only the training set without a validation cohort because of the limited number of participants though we internally validated the models through cross-validation with bootstrapped 95% CI. The validation step is needed using further cohort (internal) or independent data (external). Second, the incidence of delirium in this study was relatively lower (5.9%) than that reported in previous studies [2,3]. This low incidence rate may be due to the use of medical records to identify delirium, which would have missed cases. We also excluded patients who have neurocognitive disorders, which could be a risk factor for delirium. This may limit the generalizability of the data. Third, though we have attempted to contain several factors that can be potentially related to the occurrence of delirium, some relevant factors, such as the use of restraints or performance status of the patient might be missing [7]. Fourth, the retrospective design of this study may underestimate the occurrence of the hypoactive subtype of delirium. Not all patients wore the aid device for visual and hearing impairments, but impairments of visual and hearing were evaluated based on wearing devices due to the retrospective nature of the review of electrical medical records. Moreover, patient-reported outcomes, such as symptoms or quality of life could also not be fully obtained in this study. Development of a prediction model for delirium using further prospective study would help resolve these limitations.
Despite these limitations, exploring the predictive factors associated with the occurrence of delirium in patients with advanced cancer is warranted. The strength of our model is that we selected variables that are easily quantified among comprehensive characteristics in patient groups drawn from a large multicenter cohort. We also believe that given the predicted results of the model involve delirium that occurred before terminal delirium, this model will help us predict clinical outcomes that are more preventable.
In conclusion, we developed a model to predict the occurrence of delirium in patients with advanced cancer who were hospitalized in oncology wards of the cancer centers using sociodemographic and clinical features, including laboratory abnormalities and use of concomitant medication. This model could help clinicians estimate the risk of hospitalized patients developing delirium and pay special attention to those at high risk. However, it needs external validation and a prospective cohort study before considering the application in real practice setting.
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