Delirium In ICU Patients With Malignancy: Patient Characteristics, Resource Utilization and Outcomes

Study design

This retrospective, single-center longitudinal cohort study at a University Hospital in Switzerland was part of a large Health Service Research project, which evaluated the prevention, screening and treatment of delirium in hospitalized patients. Results from the overall cohort including 10’906 hospitalized patients have been reported recently [12].

Setting

This University Hospital has approximately 39’000 admissions annually distributed across 43 departments and institutes. In the year 2014, a total of 4’002 patients were treated in one of the specialized ICUs for medical, abdominal and thoracic surgical, cardiovascular surgical, trauma surgical, and neurosurgical as well as burn patients.

In the year 2012, a concept for delirium management and Health Service Research project (Delir Path) was launched in all departments by a multi-disciplinary and multi-professional expert team. By covering all aspects from screening to pharmacological and non-pharmacological treatment its aim was the improvement of prevention, early recognition and treatment of delirium in hospitalized patients. Physicians and nurses received training via lectures, e-learning modules and bedside teaching, and had access to the developed algorithms available as pocket cards and on the hospital’s intranet. These algorithms comprised the screening of all patients with a Richmond Agitation Sedation Scale (RASS) [13] score of -3 to + 4 with the Intensive Care Delirium Screening Checklist (ICDSC) [14], performed by trained nurses once per shift. Positive delirium screening corresponding to an ICDSC score ≥ 4 was followed by appropriate pharmacological treatments and non-pharmacological measures. The drug of first choice was the neuroleptic pipamperon, which exists as tablets or as syrup and is strongly sedative while having a weak anti-psychotic action. If hallucinations occurred the neuroleptic drug haloperidol was added orally or intravenously. Vegetative symptoms were treated with intravenous clonidine or dexmedetomidine. Patients with nocturnal agitation, insomnia or increased risk of non-convulsive epileptic seizures received intravenous midazolam via a continuous infusion with doses between 0.05–0.1 mg/kg/h, which was interrupted daily at 6:00 a.m.

Participants

Data of all adult patients ≥ 18 years admitted to one of the six ICUs between 1st of January and 31st of December 2014 were included in the longitudinal cohort study. Patients from intermediate care units, patients with missing data and / or ICDSC score as well as patients without malignancy as principal diagnosis were excluded from the analysis.

Definitions of delirium and malignancy

Patients were considered delirious if the ICDSC score was ≥ 4. The ICDSC is one of the most widely used screening methods in the ICU setting and comprises eight criteria assessed over one entire nursing shift. Initial validation, meta-analysis and previous studies by our center showed that the chosen ICDSC cut-off score of ≥ 4 has good sensitivity (62%-99%) and specificity (57%-95%) as well as moderate to good reliability (κ 0.59–0.92) [14–17]. However, the different criteria of screening tests do not equally contribute to the test’s diagnostic performance [18, 19].

Patients were categorized as having malignancy when the principal diagnosis had been attributed to an ICD-10 code from the International Classification of Diseases (ICD) which belongs to the block C (“malignant neoplasms”) from chapter II (“neoplasms”).

Outcome variables

Outcome variables of interest were in-hospital mortality, ICU and hospital LOS in hours and days, respectively, duration of mechanical ventilation in hours, ICU nursing workload assessed with the Nine Equivalents Nursing Manpower Use Score (NEMS) [20], costs in the ICU and total costs per case in Swiss Francs, assessed by the hospital and provided to the Swiss Federal Statistical Office, as well as the rate of patients discharged home.

Potential confounders

The Charlson Comorbidity Index was calculated according to Quan et al. [21], with higher values signifying a higher comorbidity burden. The Simplified Acute Physiology Score II (SAPS II) indicating disease severity was computed with the worst values during the first 24 hours of the ICU stay [22]. Sepsis and shock were determined using the ICD-10 codes from principal and secondary diagnoses.

Data sources

All data are documented in the patient medical records. They refer to the Swiss Federal Statistical Office [23] medical and administrative database and the database Minimal Data Set – Intensive Care Unit (MDSi) [24]. Authorized administrative personnel extracted the data of interest and provided it to the investigators. The researchers had no possibility to identify patients from whom data were collected.

Statistical analyses

Characteristics of patients with malignancy were described for the entire population as well as for the subgroups of delirious and non-delirious patients. Values are depicted as numbers and percentages for categorical variables or median and interquartile range (IQR) for continuous variables. Groups were compared with Fisher’s exact test or Mann-Whitney U test depending on the variable. In unadjusted analyses comparing delirious to non-delirious patients, odds ratios (OR) and 95% confidence intervals (CI) were calculated for in-hospital mortality and for the rate of patients discharged home, and hazard ratios (HR) with 95% CI were computed for ICU and hospital LOS as well as duration of mechanical ventilation with univariate Cox regression. Regression coefficients and 95% CI were obtained from linear regression for ICU nursing workload as well as ICU and total costs. In multivariate analysis done with multivariate binary logistic regression, multivariate Cox regression and multiple linear regression, the OR, HR and regression coefficients and their 95% CI were adjusted for the following six covariates: presence of sepsis, presence of shock, emergency admission, age, Charlson Comorbidity Index and disease severity (SAPS II). However, in-hospital mortality was only adjusted for presence of sepsis and shock and SAPS II, because the number of events restricted the inclusion of more covariates. The null hypothesis was rejected with a two-sided p value < 0.05. All statistical analyses were performed with IBM SPSS Statistics, version 25, software (IBM, Armonk, NY, USA).

Participants

Descriptive data

In this study, 176/488 (36%) patients developed a delirium during their ICU stay. Patient characteristics for patients with malignancy are depicted in Table 1 for the entire population as well as for the subgroups of delirious and non-delirious patients. A comparison of delirium frequencies in patients with malignancy across different types of malignancies is shown in Fig. 2. Comparing delirium frequencies in critically ill oncological patients across the three most common types of care, the following results were obtained: 57% (n = 27/47, 95% CI 43–72%) for thoracic surgery, 40% (n = 54/136; 95% CI 31–48%) for abdominal surgery, and 22% (n = 32/145; 95% CI 15–29%) for neurosurgery. Although the Charlson Comorbidity Index was 4 (2–8) for patients with and without delirium, the groups differed significantly (p = 0.034). In contrast, no difference in rate of emergency admissions was observed between patients with (51/176 [29%]) and without (81/312 [26%]) delirium (p = 0.524). Comparing oncological patients with and without delirium, sepsis occurred in 26/176 (15%) and 6/312 (1.9%) patients (p < 0.001), while shock was diagnosed in 30/176 (6.1%) and 6/312 (1.9%) patients, respectively (p < 0.001). The SAPS II in delirious and non-delirious patients with malignancy was 41 (27–68) and 24 (17–32), respectively (p < 0.001).

Outcome data

Outcome data for patients with malignancy are provided in Table 2a for the entire population as well as for the subgroups of delirious and non-delirious patients. Adjusted results in Table 2b show that delirium was independently associated with discharge home, ICU and hospital LOS, duration of mechanical ventilation, ICU nursing workload as well as ICU and total costs. However, delirium was not independently associated with in-hospital mortality in patients with malignancy.

Key results

In this large sample analysis of 488 critically ill patients with malignancies treated in a University Hospital, 36% developed delirium in the ICU. Compared to non-delirious patients with malignancy, oncological patients with delirium were older, had a higher comorbidity burden, were more severely ill and experienced more often sepsis and shock. Delirium showed high frequencies in patients with hepatic, lung and colorectal malignancies. In addition, it was particularly frequent in patients from thoracic and abdominal surgery while it developed only in one out of five neurosurgical patients. Delirium in patients with malignancy was independently associated with lower odds to be discharged home, longer ICU and hospital LOS, longer duration of mechanical ventilation, increased ICU nursing workload as well as higher ICU and total costs. Whereas delirium was a strong marker of in-hospital mortality, multivariate analysis revealed that it was not independently associated with in-hospital mortality in this population.

Frequency

More than one out of three oncological ICU patients developed delirium during their ICU stay. This frequency of delirium in this special population of critically but not terminally ill oncological ICU patients lies between those reported for general wards and palliative care units [8]. Very limited and controversial data have been available in this particular patient population so far [9, 10, 25]. The much higher delirium frequency of 95% observed by Almeida et al. (n = 170) might be explained by three reasons: 1) by the divergent population of severely ill, mechanically ventilated patients; 2) by preventive measures including delirium monitoring, daily awakening trials and early mobilization implemented at the study hospital; 3) by patients in persistent coma or sedation until death without delirium screening potentially influencing the frequency observed in the present study [9]. The lower frequency of 23% reported by Sánchez-Hurtado et al. (n = 109) might derive from divergent baseline characteristics and screenings [10]. Their screening with the CAM-ICU once daily might have missed delirium in some cases due to its fluctuating course [15]. Gouveia et al. (n = 135) observed a similar delirium frequency of 39% to the one reported in the present study [25].

Hepatic and colorectal as well as lung cancer patients often developed delirium while delirium was less frequent in brain cancer patients. Studies on general ward and ICU patients who had undergone oncological surgery reported the following delirium frequencies: 7% for primary pulmonary malignancy [26], 8% for hepatocellular carcinoma [27], 7% for glioblastoma [28] and 14% for colorectal carcinoma [29]. Due to the exclusive inclusion of ICU patients, delirium was more frequent in the present study. To the best of our knowledge, this study is the first to report ICU delirium frequencies on different malignancy types. Although the small sample sizes of subgroups limited our study’s results on delirium frequency in malignancy types, this study reports high frequencies of ICU delirium and suggests the presence of differences in frequency of delirium between malignancy types.

Resource utilization

Resource utilization with respect to ICU and hospital LOS as well as ICU nursing workload and duration of mechanical ventilation was increased in delirious patients with malignancy when compared to non-delirious patients. Accordingly, also ICU and total costs per case were increased. Whereas delirium in oncological ICU patients has been associated with longer ICU and hospital LOS and duration of mechanical ventilation by Sánchez-Hurtado et al. [10], Almeida et al. did not find any significant association [9]. However, the latter study was significantly underpowered for the comparison of patients with and without delirium. While the findings presented in this study are consistent with previous publications on general ICU populations [1, 5, 6] and while longer hospital LOS has been reported in delirious palliative care unit patients [30], this large study adds new data on resources utilized by delirious ICU patients with malignancy. Since ICU LOS and duration of mechanical ventilation are related to complications such as nosocomial infections and ventilator related lung injury, these results have relevant implications for clinical routine. In addition, due to growing healthcare costs in developed countries and an increasing number of oncological patients admitted to the ICU [11], the higher costs caused by oncological ICU patients have important implications on health care systems.

Outcome

In the present study, while being a strong marker of higher in-hospital mortality, delirium was not independently associated with in-hospital mortality in critically ill patients with malignancy. Although this supports findings from Sanchez-Hurtado et al. [10], it contradicts results from unadjusted analysis published by Almeida et al. and Praça et al. [9, 31]. However, while Almeida et al. reported lack of power for the comparison of delirious and non-delirious patients, Praça et al. studied a terminally ill population discharged from the ICU after a decision to withhold life-sustaining therapies. Whereas two of the three studies from palliative care units reporting adjusted results did not observe an independent relationship between delirium and in-hospital mortality [32, 33], Shin et al. showed an independent association between delirium and in-hospital mortality (OR [95% CI] 0.394 [0.244–0.635], p = 0.0003) [34]. Our data suggest that delirium might not be independently associated with short-term mortality in critically ill oncological patients, as has been previously shown by Klein Klouwenberg et al. for medical and surgical ICU patients [35]. Thus, delirium probably constitutes rather a marker for particularly severe illness and increased mortality than being independently related to increased in-hospital mortality. Nevertheless, delirium should be met with systematic delirium management including preventive measures, early recognition and therapy, since different interventions have been demonstrated to reduce delirium frequency in general ICU studies [36–38]. This reduction in delirium frequency might result in an improvement of adverse long-term outcomes such as mortality and cognitive impairment since these are potentially less influenced by the severity of the precipitating, acute illness. Future studies should address the independence of the association between delirium and these adverse long-term outcomes.

Limitations

This study is limited by its retrospective and observational design. While this enabled a larger sample size providing a representative overview of delirium in critically ill oncological patients, the use of pre-existing data led to several noteworthy limitations: 1) it impeded management of delirium as a time-dependent variable in multivariate analysis for which time-varying variables would have been needed. This restricted generalizability and causal inferences. 2) it limited the choice of variables included. Therefore, neither delirium treatment nor long-term consequences such as cognitive impairment, psychopathologies, impact on quality of life and long-term mortality can be addressed by the present study. 3) we had to deal with a considerable number of missing ICDSC data in the medical ICU, which may have led to an underrepresentation of medical ICU patients, impeding generalizability of results concerning this subgroup. Despite the high number of patients, the inclusion of more variables in the multivariate statistics of in-hospital mortality was restricted by the small number of deaths since approximately ten events are required for every variable included.

In this study, delirium diagnosis was defined with the ICDSC at a cut-off score ≥ 4. We showed in a previously published study that the ICDSC had a higher sensitivity and was more accurate as screening tool compared to the CAM-ICU [15]. Nevertheless, the chosen cut-off score of the ICDSC could have resulted in an overestimation of the true delirium frequency.