This study of the relationship between clinical frailty, delirium and hospital mortality is part of a larger, nurse-led interventional study, to reduce the burden of delirium in the adult ICU setting, which has been described previously (Lynch et al., 2020). In brief, the Delirium in ICU (Deli) Study in a randomised stepped-wedge intervention trial, including the four adult intensive care units across the South Western Sydney Local Health District (SWSLHD). The intervention is a nurse-led non-pharmacological bundle of care, to reduce the incidence of delirium among adults admitted to the ICU. The data for this specific study of the relationship between frailty, delirium and hospital mortality, is based on the baseline period (pre-intervention phase) of the larger Deli Study. This sub-study was planned prior to the commencement of data collection on the 1st of May 2019.
Subjects and setting
The South Western Sydney Local Health District (SWSLHD) provides public hospital services for around a million residents, with five acute care hospitals, with approximately 230,000 separations each year. There are four adult ICUs (one tertiary referral and three metropolitan), with between 80 to 250 admissions each per month.
This project was considered by the South Western Sydney Local Health District Human Research Ethics Committee and was determined to meet the requirements of the National Statement on Ethical Conduct in Human Research (2007). Due to the nursing intervention being implemented among all admission, and the use of routinely collected ICU and hospital separation data, the need for individual patient consent was wavered (HREC ref: HE18/169). Australian New Zealand Clinical Trials Registry (ANZCTR) -
(ref no. ACTRN12618000411246p).
Inclusion and exclusion criteria
Consecutive patients admitted during the study period were enrolled in the study, excluding patients with delirium on admission, those not expected to stay in the ICU very long, and any patient that we would not be able to asses for delirium. This includes: (1) patients at the end-of-life, and not expected to survive 24-hours; (2) patients not expected to stay in the ICU for at least 24-hours; (3) patients with acute or chronic neurological conditions that may prevent assessment of delirium (traumatic brain injury, intra-cerebral haemorrhage, ischaemic stroke, central nervous system infection, hypoxic brain injury, hepatic encephalopathy, severe mental disability, serious receptive aphasia, severe dementia); and (4) patients which persistent coma, preventing the assessment of delirium.
Specific data collected for the study included age, sex, admission date and discharge from ICU and hospital, ICU and hospital outcomes, and clinical frailty status on admission to intensive care, along with identification of an acute episode of delirium. Other general characteristics of the patients on admission to ICU were collected from the Hospital Health Information Exchange (HIE), and the Australian and New Zealand Adult ICU data collection. History of comorbid conditions was obtained using ICD-10-AM codes, a Charlson Index was calculated, using the method suggested by Quan et al (Quan et al., 2011).
Assessment of clinical frailty
Clinical frailty status, was assessed on admission to the ICU by the admitting medical officer, either directly from the patient, their family, and review of any previous medical notes. Frailty was collected using Rockwood’s Clinical Frailty Score (Rockwood et al., 2005). Frailty status was based on the patient’s level of physical function in the 2-months prior to their admission to hospital for the index ICU stay during the study period. Admissions with a Clinical Frailty Score (CFS) of five or more were classified as frail (Bagshaw et al., 2014, Rockwood et al., 2005).
Identification of delirium
The Confusion Assessment Method (CAM) was used to identify acute episodes of delirium among any patient who appears to be disorientated or confused, or who has any change in behaviour, or level of consciousness (Inouye et al., 1990) during an ICU stay. The CAM is based on four main area of assessment: (1) acute onset and fluctuating course (Is there evidence of an acute change in mental status from baseline? If so, did the abnormal behaviour fluctuate during the day?); (2) Inattention (did the patient have difficulty focussing attention during the interview?); (3) Disorganised thinking (was the patient’s thinking disorganised?); and, (4) Altered level of consciousness (overall, how would you rate the patient’s level of consciousness?) (Inouye et al., 1990). Patients who were rousable (Richmond Agitation and Sedation Scale ≥ -3) were assessed for the presence of delirium using the CAM (Inouye et al., 1990) or CAM-ICU (Ely et al., 2001). Both versions have been validated as a reliable (kappa = 0.96; 95% CI 0.91-0.99) and valid (sensitivity 0.81-0.82, and specificity 0.99) tool for diagnosing delirium in the ICU setting (Ely et al., 2001) (Shi et al., 2013). Our hospital based electronic medical record system (eMR) currently only offers the CAM for documentation. However, all ICU staff are trained to use of both the CAM and the CAM-ICU (for example when a patient is unable to verbalise, the ‘inattention’ and ‘disorganised thinking’ components of the CAM are assessed using the CAM-ICU approach. We have a single standard delirium policy and protocol that is used across our four adult ICUs. We did not perform any specific reliability assessments of the CAM and ICU-CAM during the study period.
Delirium status was assessed each shift by nursing and/or medical staff (shifts range from 8 to 12-hours in duration), or when there was an acute change in mental status. On each morning of admission during an ICU stay (up to maximum of 21-days) patients were recorded as delirium yes, if at least one episode was recognized by clinical staff during that last 24-hour period, or delirium free. Each recorded delirium event were further categorised to be of a hypoactive, hyperactive, or mixed nature (Ely et al., 2001).
Outcomes of interest
The outcomes of interest for our analysis were: (1) clinical frailty status on admission to ICU; (2) rates of acute episodes of delirium in the ICU; (3) rates of ICU mortality; (4) length of stay in the ICU and hospital; and (5) Hospital mortality.
The sample size planned for the overall baseline and intervention phase of the Deli study was based on monthly admissions between 80 and 125 (adults, aged 16-years or more) patients from the four ICUs included in the 12-month study (Lynch et al., 2020). Our, local health district ICU data estimated approximately 80% of admissions was among patients aged 50-year or more, and that after application of the inclusion and exclusion criteria, approximately 70% of admissions would be included in our study. Using the baseline (6-month) period of the Deli study, we estimated approximately 1,008 patients (aged 50-years or more) would be included in our analysis of the relationship between frailty, delirium and hospital mortality. A post-hoc power calculation based on a 15% rate of hospital mortality among non-frail patients, and a 33% rate of frailty (Bagshaw et al., 2014), our estimated sample size of 1,008 for the baseline period, would have a power of 0.79 to detect an 50% increase in risk of hospital mortality among frail patients compared to non-frail patients.
Characteristics of patients admitted to the four adult ICUs during the baseline 6-month period of the Deli study are presented using descriptive statistics. Risk of hospital mortality was based on at least one episode of delirium in the ICU during the study period, and frailty status. Crude and adjusted Rate Ratios (RR), and 95% confidence intervals (95% CI), were estimated using a generalised-linear-model (Poisson error) (Breslow and Day, 1980). Due to the potential complex relationship between frailty, delirium and subsequent risk of mortality, the role of delirium being an effect modifier of the risk of death due to frailty was assessed by including an interaction term between frailty and delirium, and hospital death. A p-value of < 0.1 was used to confirm interaction between frailty and delirium, and then crude and adjusted (adjusted for age and sex) models were estimated for delirium free, and delirium patients (Breslow and Day, 1980). Data imputation was not planned for missing data. All data management and analyses were performed using the R-statistical language (R Core Team, 2018).