The detection of delirium in admitted oncology patients: a scoping review.

Background and aim

Delirium is a neurocognitive syndrome characterised by an altered level of arousal, altered awareness and cognition, and a reduced ability to direct, focus, sustain, and shift attention [ 1 , 2 ]. Delirium is associated with increased morbidity and mortality, longer length of stay [ 3 , 4 ], and marked distress for cancer patients, their families and staff [ 5 , 6 ]. Delirium is common in hospitalised patients [ 1 , 2 ], and outcomes can be improved via prevention [ 7 , 8 ] and effective management [ 9 ]. The use of validated assessments improves detection and provides earlier identification of patients with delirium [ 10 ].

Under-diagnosis of delirium is an important issue in clinical settings [ 11 ]; outcomes are worse if the diagnosis of delirium is delayed or missed entirely [ 12 ]. Studies of general hospital patients indicate that pain, younger age, correct orientation in person, place and time, and previous psychiatric diagnosis, especially bipolar disorder or psychosis, are important risk factors for the diagnosis of delirium being missed [ 13 ]. One study has shown increasing age, poor performance on cognitive testing and lower serum albumin to be associated with a higher risk of delirium in the hospitalised cancer patients, however, less is known about factors which increases the misdiagnosis of delirium in cancer populations or whether there are specific clinical factors which can be used to mitigate risk [ 3 ].

The majority of epidemiological studies in delirium have targeted people over 65 years of age [ 14 ]. Although guidelines for the management of delirium in cancer settings exist [ 15 ], fewer studies have primarily focussed on adults (defined as 18yrs or older) in an acute hospital, oncology, inpatient setting [ 16 – 18 ]. More commonly studies including cancer patients have been in “stand-alone” palliative care units [ 19 , 20 ], or subsets of cancer inpatient cohorts on the basis of palliative care [ 21 – 23 ] or psychiatry consultation/liaison services in acute hospitals [ 11 ]. A recent review of delirium in the palliative care setting yielded a point prevalence estimate of 35% [95% confidence interval (CI) = 0.29–0.40] at inpatient admission. [ 20 ] Studies indicate that in the palliative care cancer setting at least, whilst the prevalence of delirium is high, it remains reversible in approximately half of cases [ 24 ]. These data also lend support to the case for improved detection. Of interest, reversibility in the palliative care setting although not a universal possibility, has been associated with factors such as delirium aetiology specifically opioid, or other psychoactive medication, or dehydration, and where there is a less severe cognitive disturbance or absence of organ failure [ 9 , 25 ].

We chose a scoping review methodology because initial searches yielded few returns in the target setting. We also chose to take a broad approach to clarify key concepts in delirium detection in cancer settings and identify key concepts and gaps in the evidence base [ 26 ]. Our review explores the literature in relation to delirium detection and missed delirium in the inpatient oncology setting, and clinical factors associated with misdiagnosis. The aim of this scoping review is to synthesise knowledge and identify gaps relating to detection tool selection, incidence, prevalence and reversibility of delirium in hospitalised, adult patients with cancer.

Patients and methods

The target population was admitted, adult, oncology patients in an acute-hospital or comprehensive cancer centre. The research questions were: Which instruments are most commonly used to detect delirium? Which reference standards have been used to measure rates of delirium and compare the performance of delirium screening instruments? What is the incidence and prevalence of delirium in the target setting? and What is the rate of reversibility of delirium in the target setting?

Our search strategy centred on four key domains; delirium, cancer, inpatient oncology, and delirium detection. Full inclusion criteria were: original study, English language, for inclusion the focus of the study must be syndromic delirium e.g. not: confusion, cognitive impairment, acute brain syndrome. The target population is patients with cancer and the setting is adult inpatient oncology, studies not relevant to this population were excluded. Specifically, the target setting was oncology wards in acute hospitals including tertiary referral and cancer centres. Studies of non-oncology ward patients were included if the oncology population could be abstracted from a broader study e.g., hospital-wide point prevalence, subset of cancer patients within an index population of older patients with cancer. Studies set in palliative care populations in a “stand alone” inpatient unit or hospice were only included if the setting was combined oncology and palliative care, for example a comprehensive cancer centre. To meet inclusion a delirium assessment with a validated objective tool, or clinical diagnostic criteria was also required.

Studies were excluded if they were solely conducted in the following settings or populations; haematology or non-solid haematological malignancy, non-cancer palliative care, perioperative including surgical oncology, or alcohol withdrawal delirium.

The reason for excluding non-solid haematological malignancy was the consideration that illness trajectories and treatment protocols in this population may differ a great deal from those of solid cancers, similarly for non-cancer palliative care patients. The exclusion of surgical oncology and peri-operative settings was pragmatic as those patients may be admitted to surgical wards with a different background for staff and potentially different delirium aetiologies. Understanding delirium in these patient cohorts is important and we hope that future work will address areas not included in our review as has been the case in recent multicentre delirium prevalence studies [ 27 , 28 ].

All authors and an academic liaison-librarian were involved in an iterative process to determine search terms. MEDLINE, CINAHL, PsycINFO, EMBASE and SCOPUS databases were searched. Publication date was limited from 1st of January 1996 to 12th of August 2017. A full list of keywords and Medical Sub-heading (MeSH) is available in Appendix 1.

Independent title, abstract, full-text review and cross check was carried out by MBS and IW, using COVIDENCE [ 29 ] software, with conflicts resolved by consensus. Where the same study was reported in more than one manuscript, additional information from related or subsequent publications was included where possible [ 9 , 30 – 34 ]. Study heterogeneity was not objectively tested, but the overall lower quality of several included studies and issues with reference standards seemed to suggest meta-analysis would not be meaningful, but sources of bias and generalisability were assessed using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) system.[ 35 ] Two authors (MBS and IW) independently piloted the QUADAS-2 and subsequently quality considerations and information synthesis was reviewed by all authors consensus was achieved through discussion.

For the purposes of this study, we defined a delirium reference standard as one which determined diagnostic assignment based on an instrument which used an independent reference-rater evaluation [ 36 ]. This last point, although identified in the literature was also arrived at via an iterative process that revealed unclear distinctions between screening tools and case ascertainment or diagnostic criteria upon which case identification was verified among included studies. Examples of reference standards in the basis of these criteria are the World Health Organization (WHO) International Classification of Diseases, 10th Revision (ICD-10)[ 37 ] or the American Psychiatric Association Diagnostic and Statistical Manual of Mental Disorders (DSM)[ 1 , 38 ] criteria, applied by a psychiatrist or consultant physician. On the basis of these criteria, the Confusion Assessment Method (CAM) meets reference standard criteria for case ascertainment, only in studies where reference-rater training in the use of the CAM is explicitly-stated [ 39 ]. This follows published recommendations for valid use of the CAM[ 39 ] along with diagnostic assignment in delirium research [ 36 ]. With regard to detection tool we use the term detection instrument (or tool) to include screening tools or other instruments put forward as standardised methods to identify delirium.

With regard to protocol registration, on inception authors were advised that PROSPERO did not currently accept registrations for scoping reviews and was unable to accept our application for protocol registration. The following is an accurate description of our methodology and further information is available on request. The data that support the findings of this study are available from the corresponding author.

Results

Search results

The search date was August 12, 2017. Returns were as follows: Medline (211), EMBASE (684), SCOPUS (97), PsycINFO (52) and CINAHL (47). A total of 805 studies were identified with an additional 91 titles added from hand search. Although most duplicates were removed prior, for pragmatic reasons, final removal of duplicates and screening of abstract and date of publication for hand search returns was held over until full-text review. Hand search consisted of hand search of reference lists from included studies as well as search of authors PDF library using delirium as a title search.

In total, 91 studies were retained for full-text review, and 12 studies remained for data abstraction and synthesis (Fig. 1 ) Fig 1 Flow diagram of literature search. Although most duplicates were removed prior, for pragmatic reasons abstract screening for hand search returns was held over until full text review

Characteristics of included studies

Study recruitment periods ranged from 1997 to 2015. Study design of all 12 studies was observational: six were prospective, six were retrospective. Two studies were secondary analyses of data from prior prospective studies. Tables 1 and 2 provide detailed data for the included studies related to the research questions. Table 3 summarises quality considerations according to QUADAS-2 criteria [ 35 ]. Table 4 provides a summary Table 1 Study design and setting Author (Endnote reference number) Study setting Other publications same data set Study aim Study period Patient characteristics, inclusion and exclusion criteria Total number (of eligible) Study design flow of participant recruitment/administration of tools Oncology setting Gaudreau et al. JPSM [ 31 ] Haematology, oncology, internal medicine, tertiary hospital Quebec Canada Gaudreau 2005 September JCO [ 43 ] Determine delirium risk associated with medication exposure January 21, 2002, to August 4, 2003 Included: admitted, adult, histologic diagnosis of cancer n = 261 (all eligible) Prospective. Consecutive patients, Nu-DESC incorporated in routine ward care. All patients from admission to discharge for the entire study Grandahl et al. [ 40 ] Oncology ward, metropolitan cancer centre Denmark NA Examine the value of cognitive testing in delirium detection October 2011–February 2012 Included: admitted adults, histological diagnosis of cancer Excluded: non-Danish speaking. Each participant was included only once. Ward characteristics: patients with cancer who had "complications to their active treatment" or complications to their cancer n = 81 Number of eligible patients not stated Prospective. Nominated days. Ward staff identified possible cases, then MMSE, CAM, modified mini cog, digit span, and ICD 10. Not stated if consecutive patients or how many eligible patients were excluded from analysis Ljubisavljevic et al. [ 3 ] Oncology ward metropolitan cancer centre, Australia NA Define delirium risk factors Over 2 periods (ten weeks in total) Included: admitted, adult, histological diagnosis of cancer. Excluded: inability to undergo interviewing; language barrier; and refusal by the patient, family or physician, admission to a different ward n = 124 (of 156 eligible) Prospective. All patients during study period were assessed with DOSS on admission. CAM completed nightly for all patients by trained clinical nurses, patients with suspected delirium were reviewed within 24 h to confirm diagnoses of delirium based on DSM iv criteria Neefjes et al. [ 41 ] Medical oncology ward metropolitan cancer center, Netherlands NA Develop delirium prediction model Jan 1st 2011–June 30th 2012 Included: admitted, adult, solid malignancy n = 574 patients/1733 admissions (all eligible) Retrospective, All patients. Chart review of DOSS scale outcomes, recorded, twice per week on nominated shifts according to standard hospital procedures. Staff familiar with use of tool Sands et al. [ 42 ] Medical and radiation oncology ward, comprehensive cancer centre, Australia NA Test feasibility of index tool October 2004–August 2006* Included: admitted, adults, solid malignancy. Patient or proxy consent. Excluded: unable to complete tests in English n = 19 (of 33) Prospective. All patients on nominated day approached. Consenting patients were assessed in order of SQiD, MMSE, CAM, MDAS, by one blinded investigator, psychiatrist interview by one of two blinded investigators Older patients with cancer setting Bellelli et al. [27] 108 acute and 12 rehabilitation wards across participating Italian hospitals NA To determine the point prevalence of delirium in patients in index population in large multi-centre study September 30, 2015 all admissions to the participating centers from 00:00 to 23:59 Included: admitted, aged 65 years and older, native Italian speakers, patient or proxy consent. Excluded: coma, aphasia, and end-of-life status. Site recruitment by personal email to the members of four scientific associations (5000 members) 108 acute and 12 rehabilitation wards in Italian hospitals n = 323* (1867 of 2221 eligible in main study) Prospective. All consenting patients in participating centers from 00:00 to 23:59 of the index day. Data reported here is for patients with cancer diagnosis Bond et al. Oncology Nursing Forum [ 32 ] General medical wards, 3 tertiary teaching hospitals United States Bond, S. M. et al. 2008, Cancer Nursing [ 33 ] Determine delirium incidence and risk factors in index population Not reported in index study, paper with full methodology not found Secondary analysis of data. Included: admitted, age 65 or older, cancer was main diagnosis or co-morbidity n = 76 Number of eligible patients not stated. Parent study was of 627 hospitalized older adults This was a sub group with cancer Retrospective. Further methodology not established as original paper not available Hamaker et al. [ 44 ] Medical or oncology ward. 2 metropolitan academic medical centres and one tertiary teaching hospital, Netherlands NA Determine delirium prevalence in index population November 2002 to March 2006 and April 2006 to March 2008 Included: admitted, age 65 or older. Excluded: too ill, intensive care unit, coronary care unit, or transfer 48 h post admission, unable to speak or understand Dutch n = 292 number eligible not stated This was a secondary, sub-group analysis of patients with advanced cancer from prospective study. All consenting. Multidisciplinary comprehensive geriatric assessment (CGA) within 48 h of admission. (two medical specialists, a geriatric resident, a clinical nurse specialist, and two research nurses trained in geriatric medicine, who assessed for geriatric conditions including delirium) Acute palliative care de la Cruz, et al. [ 39 ] 12-bed acute palliative care inpatient unit in comprehensive cancer centre, USA. (Same centre as Shin 2014 and Mori 2011) NA Determine incidence and prevalence of delirium in index population January 2011 to December 2011 Included: admitted patients n = 609 consecutive patients > 556 total single admissions > Retrospective. Search of medical records for demographics, ECOG performance status, MDAS score, Edmonton Symptom Assessment Scale (ESAS) score [ 18 ], and discharge disposition Lawlor et al., March, Arch Int Med [ 9 ] 14-bed tertiary level Palliative Care Unit in a university affiliated teaching hospital in Canada Lawlor, P. G. et al. 2000, June, Cancer https://doi.org/10.1001/archinte.160.6.786 [ 28 ] Determine incidence, prevalence, severity and reversibility in index population February to October 1997 Included: adult, admitted, histological diagnosis of cancer. Excluded unable to speak English fluently, or unable to speak due to tracheostomy n = 104 (of 113 eligible) Prospective. Consecutive admissions, verbal consent, MMSE on admission and twice weekly. If MMSE threshold reached, DSM diagnosis by palliative care physician. If delirious then MDAS to assess severity and progress Mori et al. [ 47 ] 12 bed acute palliative care inpatient unit in comprehensive cancer centre, USA. (Same centre as Shin de la Cruz) Determine the influence of delirium severity and survival June 2006 to December 2007 Included: admitted, adult, advanced cancer. Admissions from emergency centre (EC) and outpatient clinic with ESAS data from within 24 h of APCU admission (baseline) and 3 to 5 days (follow-up) of APCU admission were included. Excluded: transfers from oncology ward excluded, missing symptom assessment score, early death or discharge n = 166 (of 181 eligible) Retrospective. Consecutive patients. In some patients, the ESAS was not completed because of the diagnosis of delirium. In such cases, other information was collected and included in analysis. Excluded patients who died before third day of APCU admission were excluded Shin et al. [ 48 ] Acute palliative care inpatient unit in comprehensive cancer centre, USA (same and Mori and de la Cruz) September 1, 2003 and August 31, 2008 Index group: Emergency centre (EC) admissions Comparator group: inpatient (IP) transfers from oncology ward n = 610 (of 612 eligible) Retrospective. Institution's database identified 2568 MDAS scores data. Unclear how many unique patients represented by these scores. Data abstracted from electronic record for patients admitted from EC or oncology ward transfers * Unpublished data:, 4AT 4 A’s Test, Nu-DESC Nursing delirium screening scale, MMSE mini-mental state exam, CAM Confusion Assessment Method, ICD 10 international classification of diseases 10th revision, DOS The Delirium Observation Screening scale, SQID Single Question in Delirium, MMSE Mini-mental state exam, MDAS Memorial Delirium Assessment Scale, CGA comprehensive geriatric assessment, ICD-10 international classification of diseases 10th revision, MMSE mini-mental state exam, ECOG Eastern Cooperative Oncology Group performance status, MDAS Memorial Delirium Assessment Scale, ESAS Edmonton Symptom Assessment Score, APCU Acute Palliative Care Unit Table 2 Patient Characteristics, Study Tools and Delirium Rates Author (endnote reference number) Cancer primary site (%) Age in yrs, mean sd (range) mlos (days) Other correlates of burden of disease Index delirium tool assessor sensitivity and specificity vs diagnostic standard Other delirium detection tools Diagnostic or research reference standard, assessor, assessor training Delirium rate test reversibility Oncology setting Gaudreau et al. JPSM [ 31 ] Hematologic 86(33%) Gastrointestinal tract 35(13.4%) Lung 21(17%) Bones/soft tissue 24 (9.2%) Genital 11(4.2%) Urinary 14(5.4%) Breast 16 (6.1%) Ovary 12(4.6%) Colorectal 26(10%) Other 16(6.1) 59.6 ± 14.3 154/261 (59%) loco regional disease only TOOL: NuDESC ASSESSOR: routine administration by bedside nurses familiar with tool. Sensitivity: 0.857 (0.654–0.950) Specificity: 0.868 (0.727–0.943) 1. CAM assessed by psychiatrist (73% of patients) 2. MDAS by research nurse 3. MDAS by psychiatrist 4. DSM-IV by research nurse 5. DSM-IV by psychiatrist CAM training not specified TOOL: CAM; ASSESSOR: research nurse; ASSESSOR TRAINING: research nurses were trained over six 2-h on- site sessions with psychiatrists in the use of the CAM, the MDAS, and the DSM-IV criteria for delirium. Inter-rater reliability: kappa = 0.89 (95% CI, 0.75–1.0) of research nurse—psychiatrist for the CAM Incidence 16.5% (43/261) on basis of NuDESC REVERSIBILITY: not reported Grandahl et al. [ 40 ] Gastrointestinal 30 (37%), Lung 28 (35%), Breast 16 (20%) Other 7 (9%) 68.5 ± 7.8 (42—86) not reported Battery of tests of cognition CAM training not specified TOOL: ICD 10 diagnosis ASSESSOR: not stated ASSESSOR TRAINING: not stated Prevalence 33% (27/81) on basis of DSM IV REVERSIBILITY: not reported Ljubisavljevic et al. [ 3 ] Haematological 70 (57%) gastro-oesophageal 23 (19%) breast 11 (9%) melanoma, osteogenic sarcoma, germ cell tumour 4 (3%) each, colon 3 (2%), other 3 (2%) 53 -SD and range not reported mean LOS 5 CNS tumour 9% NA CAM by psychiatrist for positive cases. Training not specified. Clinical review by consultant psychiatrist for all positive cases and a sample of 10 (consenting) negative cases TOOL: CAM ASSESSOR: ward nursing staff ASSESSOR TRAINING: weekly sessions prior to and throughout study period CAM completion 80% Prevalence 18% (26/145 admissions) REVERSIBILITY: not reported Neefjes et al. [ 41 ] Gastrointestinal 196 (34%) Genito-urethral 22 (4%) Head and Neck 19 (3%) Breast 9 (2%) Lung < 1 60 ± 13.1 MLOS 3 (IQR 2–6) Included: acute admission (42%) median ECOG 1, alive at discharge 96% 81% "disseminated cancer" 14/81 CNS metastases TOOL: DOSS or clinical diagnosis, and without rejection of delirium in the notes ASSESSOR: clinical nurses as part of routine care, or clinician diagnosis NA NA Incidence 3.5% all admissions 7.8% (57/730) for un-scheduled admissions on basis of DOSS REVERSIBILITY: not reported Sands et al. [ 42 ] Breast 3/18, lung 2/18 prostate 2/18. 6/18 other, unknown 3/18 * 53 ± 14.3 (30–79)* 5/19 distant metastases* Single question in delirium (SQiD), novel tool CAM administered by medical students training not specified TOOL: DSM IV criteria ASSESSOR: Psychiatrist, clinical diagnosis ASSESSOR TRAINING: [core professional competence] Prevalence 27% (5/18) on basis of DSM REVERSIBILITY: not reported Older patients with cancer Bellelli et al. [ 27 ] NA 81.2 ± 7.5* Charlson comorbidity index 5.3 + 2.1, Katz's ADL 3.8 + 2.3 Comorbid dementia 53 (16.4) TOOL 4AT ASSESSOR: attending physician NA NA OLDER CANCER Point prevalence 19.2% (62/323) on basis of 4AT REVERSIBILITY: not reported Bond et al. Oncology Nursing Forum[ 32 ] Multiple myeloma 13 (17%), Lymphoma 6 (8%), Lung cancer 11 (15%), prostate cancer 11 (15%), breast cancer 8 (11%) Other 27 (36%) 74.4 ± 7.29 (65–96) Mean LOS 9.8 APACHE II score 14.9 (moderate illness severity). IADL score of 6.8 TOOL: NEECHAM ASSESSOR and TRAINING: unable to access primary source referenced NA NA OLDER CANCER Prevalence 57% (43/76) on basis of NEECHAM REVERSIBILITY: 13/43 (30%) Hamaker et al. [ 44 ] Leukaemia 12 (4%), Pancreatic 36 (12%), Colon 32 (11%), Oesophageal 26 (9%), Cholangiocarcinoma 23 (8%), Lymphoma 21 (7%), Breast 18 (6%), Lung 18 (65), Prostate 16 (5.5%), Stomach 15 (5%), Bladder 14 (5%) 74.9 (65.0–96.2) MLOS 8 (1–80) 48% receiving supportive care only 55% receiving active[antitumour] treatment 95% living independently 43% metastatic disease at inclusion. 77% impaired ADL. Mean Charlson co-morbidity score 1.1. 15% (31/201) Global cognitive impairment NA NA TOOL: CAM ASSESSOR: "nurse" ASSESSOR TRAINING: not stated OLDER CANCER Prevalence 21.5% (61/283) On basis of CGA incorporating CAM REVERSIBILITY: not reported Acute palliative care setting de la Cruz, et al. [ 22 , 46 ] Haematological 74(13%), solid tumour 382 (86%) 56.51 ± 13.85 182 (32%) died index admission ECOG > or = to 3 508/556 (91%) TOOL: MDAS cutoff ASSESSOR: daily routine, palliative care physician TOOL: DSM IV ASSESSOR: palliative care physician. Number assessed unclear NA APCU Point prevalence on admission 71% 229/556 Incidence: 16.9% 94/327 REVERSIBILITY: 26% (68/229) Lawlor et al. 2000, March, Arch Int Med [ 9 ] Lung 17 (30.4%), genitourinary 16 (28.6%), breast in 8 (14.3%), gastrointestinal in 7 (12.5%), haematologic in 4 (7.1%), head and neck in 3 (5.3%), and other in 1 (1.8%) 64.14 ± 10 distant mets: 86/104 (83%) TOOLS: MMSE with cutoff (assessor not explicit) TOOL: MDAS if DSM positive TOOL: DSM IV (not applied to all participants) ASSESSOR: palliative care physician ASSESSOR TRAINING: not stated APCU Point prevalence on admission 42% (44/104) incidence 45% (27/60) on basis of MMSE plus MDAS with cutoff REVERSIBILITY: 46/94 (49%) Mori et al. [ 47 ] Gastrointestinal 47 (28%) Lung 33 (20%) Breast 10 (6%) Haematological 11 (7%) Gynaecological 10 (6%) Head and Neck 9 (5%) Urological 23 (14%) Other 23 (14%) 59 ± 13 (Patients who died) 61.3 ± 14.4 (patients alive at discharge) MLOS 8 days (4–12) metastases 89% TOOL: MDAS ASSESSOR: daily routine, palliative care physician or clinical judgment of palliative care physicians, advanced practice nurses, or palliative care clinic nurses NA APCU Prevalence 73/166 43% on basis of MDAS cutoff REVERSIBILITY: not reported Shin et al. [ 48 ] Haematological 58 (10%) Gastrointestinal 129 (22%) Respiratory 149 (25%) Breast 42 (7%) Genitourinary/gynaecological 85 (14%) Head and Neck 41 (7%) Others 96 (16%) 58.9 (95% CI 57.8–60.0) MLOS (in APCU) 8.0 (7.6–8.4) TOOL MDAS or clinical diagnosis ASSESSOR: daily routine, palliative care physician PURPOSE: to determine influence of symptoms on survival NA APCU Period prevalence: 48% (284/610) on basis of MDAS cutoff REVERSIBILITY: not reported MLOS median length of stay *Unpublished data, ECOG Eastern Co-operative Oncology Group performance status, CAM Confusion Assessment Method, MDAS Memorial Delirium Assessment Scale, MMSE Mini-mental state exam, DSMIV Diagnostic and Statistics Manual 4th edition, ICD-10 International Classification of diseases 10th version, *for cancer patient subset personal communication, 4AT : 4 A's delirium assessment test, NEECHAM Neeson and Champagne confusion Confusion Scale, CAM Confusion Assessment Method, APACHE II Acute Physiology and Chronic Health Evaluation II Score, ECOG Eastern collaborative oncology group performance status, ADL Activity of Daily Living, CGA Comprehensive Gerriatric Assessment, MMSE mini-mental state exam, MDAS Memorial Delirium Assessment Scale, ESAS Edmonton Symptom Assessment Score, APCU Acute Palliative Care Unit Table 3 Quality assessment using QUADAS tool Author (Endnote reference number) Risk of bias patient selection Risk of bias index test Risk of bias reference standard Risk of bias flow and timing Generalisability patient selection Generalisability index test Generalisability reference standard Oncology setting Gaudreau et al. April [ 31 ] Low risk Low risk Low risk Low risk Intermediate risk Low risk Low risk Grandahl et al. [ 40 ] Low risk Intermediate risk Intermediate risk Intermediate risk Intermediate risk Low risk Low risk Ljubisavljevic et al. [ 3 ] Low risk Low risk Low risk Low risk Low risk Low risk Low risk Neefjes et al. [ 41 ] Low risk Low risk Not used Intermediate risk Intermediate risk Low risk Not used Sands et al. [ 42 ] Low risk Intermediate risk Low risk Low risk Intermediate risk Low risk Low risk Older patients with cancer setting Bellelli et al. [ 27 ] Low risk Low risk Low risk Low risk Intermediate risk (for cancer subset) Low risk Low risk Hamaker et al. [ 44 ] Low risk Not used Low risk Low risk Low risk Low risk Low risk Bond et al. [ 32 ] Insufficient information to assess Acute palliative care setting de la Cruz, et al. [ 22 , 46 ] Intermediate risk Higher risk Intermediate risk Intermediate risk Intermediate risk Intermediate risk Higher risk Lawlor et al. [ 9 ] Low risk Higher risk Low risk Higher risk Low risk Higher risk Higher risk Mori et al. 2011 [ 47 ] Intermediate risk Higher risk Higher risk Intermediate risk Intermediate risk Higher risk Higher risk Shin et al. [ 48 ] Intermediate risk Higher risk Higher risk Intermediate risk Intermediate risk Intermediate risk Higher risk Total number of studies in categories: Study Setting: Oncology (5), older patients with cancer (3), acute palliative care (4). Diagnostic reference standards (2): DSM Diagnostic and Statistics Manual (various editions ICD-10 International Classification of Diseases (10th version) and CAM by trained operator (1). Tools used for delirium detection: MDAS (4), CAM (3), DOSS (1), Cognition testing (1), 4AT (1), NEECHAM (1), NuDESCC (1) Note: (total greater than number of studies as one study used two methods) Table 4 Delirium Rate by study and tool used Author Age in yrs, mean sd (range if reported) Delirium assessment Delirium rate recruitment consecutive or non consecutive admissions Oncology inpatients Gaudreau et al. [ 31 ] 59.6 ± 14.3 NuDESC Incidence 16.5% Consecutive Grandahl et al. [ 40 ] 68.5 ± 7.8 (42—86) DSM IV Prevalence 33% Non-consecutive Ljubisavljevic et al.[ 3 ] 53 NA Prevalence 18% Consecutive Neefjes et al. [ 41 ] 60 ± 13.1 TOOL: DOSS or clinical diagnosis Incidence 3.5% all admissions 7.8% (57/730) un-scheduled admissions Consecutive Sands et al. [ 42 ] 53 ± 14.3 (30–79)* DSM IV/DSM IVR Prevalence 27% (5/18) Non-consecutive Older patients with cancer Bellelli et al. [ 27 ] 81.2 ± 7.5* TOOL 4AT Point prevalence 19.2% (62/323) Consecutive Bond et al. [ 32 ] 74.4 ± 7.29 (65–96) NEECHAM Prevalence 57% (43/76) Non-consecutive Hamaker et al. [ 44 ] 74.9 (65.0–96.2) GCA Prevalence 21.5% (61/283) Consecutive Acute palliative care setting de la Cruz et al. [ 22 , 46 ] 56.51 ± 13.85 MDAS Point prevalence on admission 71% 229/556 Incidence: 16.9% 94/327 Consecutive Lawlor et al. [ 9 ] 64.14 ± 10 MMSE Point prevalence on admission 42% (44/104) incidence 45% (27/60) Consecutive Mori et al. [ 47 ] 59 ± 13 (Patients who died) 61.3 ± 14.4 (patients alive at discharge) MDAS Prevalence 73/166 43% Consecutive Shin et al. y[ 48 ] 58.9 (95% CI 57.8–60.0) MDAS or clinical diagnosis Period prevalence: 48% (284/610) 4AT 4 As test, Nu-DESC Nursing delirium screening scale, MMSE mini-mental state exam, CAM Confusion Assessment Method, GCA Comprehensive Geriatric Assessment, ICD 10 international classification of diseases 10th revision, DOS The Delirium Observation Screening scale, MDAS Memorial Delirium Assessment Scale

Studies were grouped into three categories on the basis of the clinical setting: (1) inpatient, acute-hospital or comprehensive cancer centre oncology ward; (2) older oncology patients (patients > 65 years, admitted to acute hospitals under any admitting team, with cancer as the primary diagnosis or co-morbidity); and (3) palliative care ward in acute-hospital or comprehensive cancer centre (APCU). The rationale for this grouping was based in the observation that clinical care for oncology patients occurs largely in one of these three settings, but that from the point of view of research, these settings tend to be studied independently; we also wanted to decrease heterogeneity within subgroups, but facilitate understanding the use of delirium detection tools across the spectrum of admitted adult patients with cancer to improve care in this cohort.

Five studies [ 3 , 31 , 40 – 42 ] were in the adult-oncology setting. Three of these approached all patients on nominated days [ 40 – 42 ], while two studies approached all admitted patients [ 3 , 43 ]. Three studies were of older cancer patients [ 27 , 32 , 44 ]. One of these was a point prevalence study in which patients over 65 years were recruited from more than 100 hospitals across several regions of Italy during one 24-h period [ 27 ]. Unpublished subset data on patients in this last cohort, were provided by the author. (personal communication G Bellelli, October 2017) [ 45 ] A further study recruited all patients aged 65 years or older admitted to the general medicine or oncology ward in two Dutch teaching hospitals [ 44 ]. The remaining study in older oncology patients, was a secondary analysis of a subset of cancer patients from a previous study, composed of patients from three North American centres [ 32 ]. Four studies [ 9 , 46 – 48 ] were in an APCU. Three of these [ 46 – 48 ] were retrospective and based in the same health care facility.

Three studies focused on patients with cancer referred to consultation psychiatry services and reported misdiagnosis of neuropsychiatric conditions, with two studies reporting a missed diagnosis of delirium in 46%, and a further study reporting 63% missed cases [ 11 , 13 , 49 ] .

Patient recruitment and demographics

Patient characteristics were described in varying detail: four studies [ 3 , 9 , 30 , 40 ] specified histological diagnosis of cancer, and three specified consecutive recruitment [ 27 , 34 , 46 ]. Four studies gave a detailed description of recruitment [ 3 , 44 , 50 ], and seven provided the number of eligible patients when providing number of participants. Eight studies [ 3 , 9 , 31 , 41 , 42 , 46 – 48 ] presented flow diagrams or data accounting for eligible patients not included in recruitment or analysis.

Ten of 12 studies reported primary cancer types. All reported age; the average of the mean age (years) in each setting were as follows: oncology 59, older cancer 78, and APCU 60. Six studies reported length of hospital stay [ 3 , 32 , 41 , 44 , 47 , 48 ]; these were reported as mean or median, and ranged from 3 to 9.8 days. Clinical information describing cancer stage, co-morbidity burden, overall illness severity, functional status or vital status at discharge, were not uniformly described. Only one of five studies in the adult oncology setting provided detailed information that described markers of burden of disease [ 41 ]. Six of 12 studies across all setting subgroups reported the stage of cancer in terms of metastatic versus loco-regional disease [ 9 , 41 – 44 , 47 ]. One study reported the number of patients receiving anti-cancer treatment [ 44 ].

Scoping questions; data relating to our four research questions

Which instruments are most commonly used to detect delirium?

Of the studies meeting our inclusion criteria, five used previously validated instruments for clinical detection of delirium: Nursing Delirium Screening scale (NuDESC, n = 1); Delirium Observational Screening Scale (DOSS, n = 1); four A’s test (4AT, n = 1); Neelon and Champagne (NEECHAM, n = 1); and, Memorial Delirium Assessment Scale (MDAS, n = 4). One study tested a novel delirium screening tool (Single Question in Delirium; SQiD) and one tested cognitive measures (Clock Drawing Test, Mini Cognitive, Digit Span Test) against a reference standard. Six studies included a second delirium detection tool, as presented in Table 2 . 2. Which reference standards have been used to measure rates of delirium?

Four of 12 studies met criteria for a delirium reference standard for case ascertainment [ 36 ]. Two studies in the adult oncology setting used diagnostic criteria, namely the ICD 10 [ 40 ] (assessor characteristics were not stated), and DSM IV/IVR (assessed by final year psychiatry fellow or psychiatrist) [ 42 ]. Two studies used the CAM in a way that met criteria for use as a reference standard, including an account of assessor training [ 3 , 31 ] .

Seven studies used a screening tool alone as the basis of case ascertainment of delirium: MDAS ( n = 4); CAM ( n = 2); DOSS ( n = 1); 4AT ( n = 1); NEECHAM ( n = 1); Nu-DESC ( n = 1), and one used a battery of tests of cognition ( n = 1). Neither of these two studies using the CAM as the basis of delirium case ascertainment, specified assessor training [ 40 , 44 ]. Of the prospective studies, Bellelli et al. used the 4AT assessed by the attending physician [ 27 ]; Lawlor et al. used DSM IV to confirm participants who had Mini-Mental State Exam (MMSE) scores above a cut-off point on first-line testing [ 9 ]; and Gaudreau et al. used the Nu-DESC, applied by trained bedside nurses familiar with this tool [ 43 ]. Of the retrospective studies, Neefjes et al. used the DOSS applied by trained bedside nurses familiar with the tool [ 41 ], and three studies used a cut-off score on the MDAS to identify delirium cases on chart review [ 46 – 48 ]. Studies comparing MDAS, MMSE, 4AT, NEECHAM tools for detection in clinical practice compared to a reference standard were not identified in our target settings, so it is not possible to ascertain the rate of missed delirium from the available literature. 3. What is the incidence and prevalence of delirium in this setting?

Rates of delirium incidence and prevalence reported by studies in this review are presented in Table 2 . Table 4 presents a summary of tools used and delirium rates established on that basis. Consecutive or non-consecutive recruitment is also reported to aid interpretation of delirium rates.

In the adult oncology setting, Neefjes et al. found a delirium incidence of 3.5 per 100 admissions or 7.8 per 100 of unscheduled admissions [ 41 ], and Gaudreau et al. reported an incidence of 16.5% [ 43 ]. Three studies in this sub-setting presented prevalence data; 18% [ 3 ], 27% [ 42 ], and 33%[ 40 ] respectively. In the APCU sub-population, prevalence rates of 42% [ 9 ], 43%,[ 47 ], 48% [ 48 ], and 58%[ 46 ] were found. The three studies of older cancer patients found prevalence rates of 19.2% [ 27 ], 21.5% [ 44 ], and 57%,[ 32 ], respectively. One study in the oncology sub-setting [ 41 ], and one in the APCU sub-setting [ 46 ], reported the frequency of delirium subtypes: hyperactive 11/52 (21%) and 61/246 (25%); hypoactive 20/52 (38%) and 73/245 (30%); mixed 18/52 (35%) and 112/246 (46%); and not known 3/52 (6%) and 73/246 (31%) respectively. 4. What is the rate of reversibility of delirium in this setting?

Delirium reversibility was reported in three studies, two of these were in APCU settings; 46/94 (49%) [ 9 ], 68/229 (26%) [ 46 ], and one in older patients with cancer 13/43 (30%)[ 33 ]. Of four studies reporting reversibility [ 9 , 32 , 46 , 47 ], two[ 9 , 32 ] did not explicitly state how reversibility was defined; one used the MDAS or clinical documentation to determine delirium reversibility and another used a MDAS cut off score at day five. Although one of these[ 47 ] referenced a predating publication, this could not be found. The other three used MDAS scores collected as part of routine clinical care to define reversibility. Bond et al. found that patients with fewer precipitating factors were more likely to have a resolution but found only prior cognitive impairment to be negatively associated with delirium reversal [ 33 ].

In the Lawlor study delirium associated with opioids and non-opioid psychoactive medication and dehydration were more likely to be reversed while non-reversed delirium was more common when associated with a respiratory infection, pulmonary cancer and metabolic causes [ 9 ].

Discussion

We identified significant knowledge gaps regarding epidemiological characteristics of delirium in oncology inpatients. A variety of delirium screening tools were identified, but few studies used accepted diagnostic or reference standards for case ascertainment. Sources of bias included study design and generalisability. A small number of eligible studies reported reversibility of delirium.

Delirium is a multifactorial syndrome. The relationship of delirium risk with demographic factors such as age and clinical factors (e.g. cancer diagnosis), is complex. One of the studies in the older cancer sub-group provided comparative figures of delirium point prevalence in cancer and non-cancer patients; 19.2% ( n = 323) for patients with cancer compared to 23.9% ( n = 1544) of the patients with no cancer diagnosis ( p = 0.06) [ 27 ]. Within the included studies, but also in non-cancer settings reported elsewhere, multiple delirium risk factors such as co-morbidities, presence of physiological disturbance and medication burden are factors that seem to be constants in understanding overall delirium risk. [ 7 , 9 , 13 , 41 , 43 , 51 – 59 ]

Delirium screening tools have been developed, and validated, predominantly in older, hospitalised patients [ 60 – 64 ]. The tools for which psychometric properties have been assessed in cancer in-patients in the acute setting, are the Nu-DESC and MDAS [ 31 , 34 , 65 ]. There is a clear rationale for use of the Nu-DESC as a delirium screening tool [ 31 ]. The 4AT has been tested in “stand-alone” palliative care, inpatient settings[ 66 ] and for older adults admitted to hospital [ 50 , 67 , 68 ]. The MMSE can be used to screen for cognitive impairment it has been found to have poor performance as a bedside tool for identifying delirium [ 69 , 70 ].

The Confusion Assessment Method [ 36 ] (CAM) has several versions [ 67 ], and has well-established psychometric properties [ 61 ]. In the main, studies have supported the use of the CAM for delirium screening in research settings providing there is strict adherence to operator training, however, one study suggests even in the context of strict adherence to CAM training, sensitivity of the CAM may not be sustained [ 68 ]. Our review found that most studies using the CAM for case ascertainment did not describe the training staff underwent [ 3 , 37 , 39 , 41 ] and one, described difficulty in attaining adequate training in a clinically embedded research context [ 42 , 71 ].

Four of the twelve included studies used the MDAS as a basis for case confirmation of delirium [ 9 , 46 – 48 ]. The MDAS was designed specifically to rate delirium severity[ 65 ], it has face validity and uptake, further formal validation studies for its use as a delirium screening tool would build on the existing psychometric data and help to reinforce the attributes of the tools [ 64 ]. One perspective looks at the balance between the positive features of usability of the MDAS compared with some other tools, and the effect of the breakdown and operationalisation of delirium features within the MDAS which does not support the syndromic nature of delirium diagnosis in terms of coexisting core features. Although it identifies delirium symptoms, regardless of the cut-off score specified to identify delirium, the MDAS risks false positive results, as patients with delirium symptoms who do not fit the core diagnostic criteria for syndromic delirium as characterised by coexistent core features may be labelled case positive. Several studies in this review used the MDAS alone for case ascertainment, which may bias reported detection rates [ 48 , 64 ].

Clinical operationalisation appeared to be the major driver of choice of delirium screening tools. Delirium diagnosis is complex, multidimensional and not intuitive for bedside staff. DSM 5 criteria require 5 characteristics and so while screening tools may gain in usability through operationalisation they lose precise application of the necessarily coexistent core features that define delirium. In the research setting, we found clear demarcation between the index tool and the chosen reference standard was not always evident. A blurring of the distinction between screening tools and diagnostic reference standards used for case confirmation for validation purposes was found. More specifically where references standards were other than DSM or ICD based reporting of reference-rater training was at times lacking. The importance of tool selection to fit the intended purpose is an important finding of our review.

In APCUs delirium rates were higher than in oncology inpatients but given methodological constraints in studies within this setting, results may not be representative. The use of the MDAS may have contributed to inflated delirium rates reflecting the way the tool is operationalised. In the older cancer patient cohorts, differences in delirium incidence and prevalence might be accounted for by study heterogeneity and patient recruitment. This is an important issue for future work, as establishing delirium incidence and prevalence in inpatient oncology settings is an important step in management. Better understanding of how to use available tools will improve management and inform education initiatives in this setting.

Criteria for delirium reversal were inadequately defined in studies, making it difficult to compare delirium reversibility across studies. These data may be further constrained by retrospective methodology, the absence of a diagnostic reference standard, or study flow reliant on clinical documentation. Ascertainment of delirium reversibility requires prospective, longitudinal study design, use of a robust diagnostic standard and explicit definition of delirium reversal. Assessment of delirium reversibility is an important issue for consideration in the design of future studies.

Patient selection, choice of the delirium screening tool and the choice of the diagnostic reference standard, were all identified as a source of bias on QUADAS-2 criteria [ 35 ]. Recruitment flow was also an important consideration. For example, patient selection methods at times risked exclusion of potentially delirious patients due to retrospective design, convenience sampling, and ascertainment bias.

Adherence to consensus recommendations for reporting patient characteristics and wherever possible the use of assessment tools and delirium reference standards will improve epidemiological studies of delirium in this setting [ 36 , 72 , 73 ] .

Limitations to our review include those related to the methodology of the original studies as well as a limitation to the English language. The search was limited to publications between 1996 and 2017. The discussion has aimed to identify recent updates in the area, again these are largely limited to aged care or stand-alone settings, with one systematic review of delirium in palliative care fining an incidence of 9–57% across hospital palliative care consultative services, with a majority of patients having cancer diagnoses [ 20 ]. a further systematic review, again in the palliative care setting, identified 14 delirium detection tools and heterogeneity of methods [ 23 ], Important questions for future work include which tools translate well to inpatient oncology from aged care and stand-alone inpatient palliative care settings, which tools are most suitable for patients, carers and staff, and which reference standards are most appropriate. Requirements for clinical and research uses of detection tools will differ according to purpose, however establishing methodical approaches to the detection of delirium in either setting is a prerequisite to determining the incidence, prevalence and reversibility of delirium for oncology inpatients. Maintaining a clear accountability for the validation and purpose of the tool, and its psychometric characteristics when applying it to clinical screening/detection is critical as is the requirement in research uses to select a reference standard with established reference-rater methodology, is extremely important.

Choosing a tool for delirium detection in the clinical oncology setting will vary according to operational issues such as staff training and preference, however, it is important that tools are fit for purpose, and where possible, have been validated in the same clinical setting. While patient profiles may be similar across palliative care, aged care and some oncology inpatient settings, staff competencies will be more specifically related to setting. Delirium detection and diagnosis must be a core competency for clinical teams in acute settings, however, operational characteristics may render a tool selection may vary according to operational setting, the exact tool chosen is not as important as the review of characteristics that makes it fit for purpose/setting.

Our review, found gaps in the validation of tools in for use in oncology inpatients. At present extrapolation from findings in other acute hospital settings, such as aged care, may help support a more robust selection for this population for the time being. As further validation occurs in acute oncology settings the evidence base for selection tools to detect the presence or resolution of delirium in this clinical setting should improve.

The knowledge gaps identified to generate new hypotheses for future investigation. We recommend the optimal description of patient characteristics, selection of delirium detection tools appropriate to the setting, use of reproducible methods of patient selection and diagnostic assignment using a reference standard with appropriate reference rater methodology. Our results indicate that a determination of the incidence, prevalence, and reversibility of delirium in the inpatient cancer population is both lacking and overdue. Addressing these knowledge gaps will help to provide a more robust evidence base to inform ongoing efforts for effective prevention, detection and management of delirium in the inpatient oncology setting.

Supplementary Information

Below is the link to the electronic supplementary material. Supplementary file1 (DOCX 120 KB) Supplementary file2 (DOCX 1050 KB) Supplementary file3 (DOCX 14 KB)

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Acknowledgements

The authors are grateful for assistance from Elaine Tam, Academic Liaison Librarian, Medicine and Health, Health Sciences, University Library, University of Sydney in the selection of databases for inclusion as well as keywords and search iterations.

Author contributions

This review was conducted as part of research undertaken for a Master of Philosophy at the University of Sydney completed in 2019 by the first author which is available at https://ses.library.usyd.edu.au/handle/2123/18948 . All authors contributed to development of the Bodleian question, analysis, and final manuscript for this scoping review. Assistance from Mrs Elaine Tam Sydney University Library is gratefully acknowledged. Data abstraction and analysis was performed by Dr. MBS and Dr. IW and reviewed by all authors.

Funding

Not applicable.

Declarations

Conflict of interest

No authors have conflicting/competing interest.

Availability of data and material

Upon request.

Code availability

Not applicable.

Ethics approval

Not applicable.

Consent to participate

Not applicable.

Consent for publication

All authors give consent.

References

1. American Psychiatric Association (2013) Diagnostic and statistical manual of mental disorders (DSM-5®). American Psychiatric Pub.

2. The European Delirium Association and The American Delirium Society The DSM-5 criteria, level of arousal and delirium diagnosis: inclusiveness is safer BMC Med 2014 12 1 141 25300023 10.1186/s12916-014-0141-2 PMC4177077

3. Ljubisavljevic V Kelly B Risk factors for development of delirium among oncology patients Gen Hosp Psychiatry 2003 25 5 345 352 12972226 10.1016/s0163-8343(03)00070-7

4. Dharmarajan K Pathway from delirium to death: potential in-hospital mediators of excess mortality J Am Geriatr Soc 2017 65 5 1026 1033 28039852 10.1111/jgs.14743 PMC5435507

5. Breitbart W Gibson C Tremblay A The delirium experience: delirium recall and delirium-related distress in hospitalized patients with cancer, their spouses/caregivers, and their nurses Psychosomatics 2002 43 3 183 194 12075033 10.1176/appi.psy.43.3.183

6. Cohen MZ Delirium in advanced cancer leading to distress in patients and family caregivers J Palliat Care 2009 25 3 164 171 19824277

7. Inouye SK A multicomponent intervention to prevent delirium in hospitalized older patients N Engl J Med 1999 340 9 669 676 10053175 10.1056/NEJM199903043400901

8. Ogawa A Quality of care in hospitalized cancer patients before and after implementation of a systematic prevention program for delirium: the DELTA exploratory trial Support Care Cancer 2019 27 2 557 565 30014193 10.1007/s00520-018-4341-8

9. Lawlor PG Occurrence, causes, and outcome of delirium in patients with advanced cancer: a prospective study Arch Intern Med 2000 160 6 786 794 10737278 10.1001/archinte.160.6.786

10. Inouye SK Nurses' recognition of delirium and its symptoms: comparison of nurse and researcher ratings Arch Intern Med 2001 161 20 2467 2473 11700159 10.1001/archinte.161.20.2467

11. Swigart SE Misdiagnosed delirium in patient referrals to a university-based hospital psychiatry department Psychosomatics 2008 49 2 104 108 18354062 10.1176/appi.psy.49.2.104

12. Bellelli G Under-detection of delirium and impact of neurocognitive deficits on in-hospital mortality among acute geriatric and medical wards Eur J Intern Med 2015 26 9 696 704 26333532 10.1016/j.ejim.2015.08.006

13. Kishi Y Delirium: patient characteristics that predict a missed diagnosis at psychiatric consultation Gen Hosp Psychiatry 2007 29 5 442 445 17888812 10.1016/j.genhosppsych.2007.05.006

14. Hempenius L Long term outcomes of a geriatric liaison intervention in frail elderly cancer patients PLoS ONE 2016 11 2 e0143364 26901417 10.1371/journal.pone.0143364 PMC4762573

15. Bush SH Delirium in adult cancer patients: ESMO clinical practice guidelines Ann Oncol 2018 29 iv143 iv165 29992308 10.1093/annonc/mdy147

16. Spiller JA Keen JC Hypoactive delirium: assessing the extent of the problem for inpatient specialist palliative care Palliat Med 2006 20 1 17 23 16482754 10.1191/0269216306pm1097oa

17. Hey J The detection, documentation and management of delirium in 3 palliative care settings Psychooncology 2013 22 18 19

18. Hosie A Delirium prevalence, incidence, and implications for screening in specialist palliative care inpatient settings: a systematic review Palliat Med 2013 27 6 486 498 22988044 10.1177/0269216312457214

19. Cobb JL Delirium in patients with cancer at the end of life Cancer Pract 2000 8 4 172 177 11898256 10.1046/j.1523-5394.2000.84006.x

20. Watt CL The incidence and prevalence of delirium across palliative care settings: a systematic review Palliat Med 2019 33 8 865 877 31184538 10.1177/0269216319854944 PMC6691600

21. Barnes J Kite S Kumar M The recognition and documentation of delirium in hospital palliative care inpatients Palliat Support Care 2010 8 2 133 136 20307363 10.1017/S1478951509990873

22. de la Cruz M The frequency of missed delirium in patients referred to palliative care in a comprehensive cancer center Support Care Cancer 2015 23 8 2427 2433 25617070 10.1007/s00520-015-2610-3

23. Watt CL Delirium screening tools validated in the context of palliative care: a systematic review Palliat Med 2021 35 4 683 696 33588640 10.1177/0269216321994730

24. Gagnon P Delirium in terminal cancer: a prospective study using daily screening, early diagnosis, and continuous monitoring J Pain Symptom Manag 2000 19 6 412 426 10.1016/s0885-3924(00)00143-3 10908822

25. Leonard M Reversibility of delirium in terminally ill patients and predictors of mortality Palliat Med 2008 22 7 848 854 18755829 10.1177/0269216308094520

26. Aromataris E, MZe (2017) Joanna Briggs Institute reviewer’s manual. The Joanna Briggs Institute

27. Bellelli G “Delirium Day”: a nationwide point prevalence study of delirium in older hospitalized patients using an easy standardized diagnostic tool BMC Med 2016 14 106 27430902 10.1186/s12916-016-0649-8 PMC4950237

28. Shenkin SD Delirium detection in older acute medical inpatients: a multicentre prospective comparative diagnostic test accuracy study of the 4AT and the confusion assessment method BMC Med 2019 17 1 1 14 31337404 10.1186/s12916-019-1367-9 PMC6651960

29. Veritas Health Innovation, M., Australia (2017) Covidence systematic review software. www.covidence.org . Cited 2017

30. Gaudreau JD Association between psychoactive medications and delirium in hospitalized patients: a critical review Psychosomatics 2005 46 4 302 316 16000673 10.1176/appi.psy.46.4.302

31. Gaudreau JD Fast, systematic, and continuous delirium assessment in hospitalized patients: the nursing delirium screening scale J Pain Symptom Manag 2005 29 4 368 375 10.1016/j.jpainsymman.2004.07.009 15857740

32. Bond SM Neelon VJ Belyea MJ Delirium in hospitalized older patients with cancer Oncol Nurs Forum 2006 33 6 1075 1083 17149391 10.1188/06.ONF.1075-1083

33. Bond SM Neelon VJ Delirium resolution in hospitalized older patients with cancer Cancer Nurs 2008 31 6 444 451 18987511 10.1097/01.NCC.0000339249.45453.97

34. Lawlor PG Clinical utility, factor analysis, and further validation of the memorial delirium assessment scale in patients with advanced cancer: Assessing delirium in advanced cancer Cancer 2000 88 12 2859 2867 10870073

35. Whiting PF QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies Ann Intern Med 2011 155 8 529 536 22007046 10.7326/0003-4819-155-8-201110180-00009

36. Neufeld KJ Delirium diagnosis methodology used in research: a survey-based study Am J Geriatr Psychiatry 2014 22 12 1513 1521 24745562 10.1016/j.jagp.2014.03.003 PMC4164600

37. World Health Organization. International Classification of Diseases (ICD) information sheet. http://www.who.int/classifications/icd/factsheet/en/index.html . Cited Jan 2013

38. Castillo RJ, Sattler D, Shabatay V, Kramer G, American Psychiatric Association (2000) Diagnostic and statistical manual of mental disorders (DSM-IV-TR)

39. Inouye SK Clarifying confusion: the confusion assessment method. A new method for detection of delirium Ann Intern Med 1990 113 12 941 948 2240918 10.7326/0003-4819-113-12-941

40. Grandahl MG Prevalence of delirium among patients at a cancer ward: clinical risk factors and prediction by bedside cognitive tests Nord J Psychiatry 2016 70 6 413 417 26882016 10.3109/08039488.2016.1141982

41. Neefjes ECW et al (2017) Identification of patients with cancer with a high risk to develop delirium. Cancer Med 10.1002/cam4.1106 PMC5548884 28688161

42. Sands MB Single question in delirium (SQiD): testing its efficacy against psychiatrist interview, the confusion assessment method and the memorial delirium assessment scale Palliat Med 2010 24 6 561 565 20837733 10.1177/0269216310371556

43. Gaudreau JD Psychoactive medications and risk of delirium in hospitalized cancer patients J Clin Oncol 2005 23 27 6712 6718 16170179 10.1200/JCO.2005.05.140

44. Hamaker ME The value of a comprehensive geriatric assessment for patient care in acutely hospitalized older patients with cancer Oncologist 2011 16 10 1403 1412 21914699 10.1634/theoncologist.2010-0433 PMC3228071

45. Bellelli G (2017) Prevalence of delirium in cancer patient sub-set of delirium day study. In: Sands MB (Ed.)

46. de la Cruz M The frequency, characteristics, and outcomes among cancer patients with delirium admitted to an acute palliative care unit Oncologist 2015 20 12 1425 1431 26417036 10.1634/theoncologist.2015-0115 PMC4679079

47. Mori M Changes in symptoms and inpatient mortality: a study in advanced cancer patients admitted to an acute palliative care unit in a comprehensive cancer center J Palliat Med 2011 14 9 1034 1041 21834647 10.1089/jpm.2010.0544

48. Shin SH Characteristics and outcomes of patients admitted to the acute palliative care unit from the emergency center J Pain Symptom Manag 2014 47 6 1028 1034 10.1016/j.jpainsymman.2013.07.015 24246788

49. Armstrong SC Cozza KL Watanabe KS The misdiagnosis of delirium Psychosomatics 1997 38 5 433 439 9314712 10.1016/S0033-3182(97)71420-8

50. Bellelli G Validation of the 4AT, a new instrument for rapid delirium screening: a study in 234 hospitalised older people Age Ageing 2014 43 4 496 502 24590568 10.1093/ageing/afu021 PMC4066613

51. Elie M Prevalence and detection of delirium in elderly emergency department patients CMAJ 2000 163 8 977 981 11068569 PMC80546

52. Morrison C Identification and management of delirium in the critically ill patient with cancer AACN Clin Issues 2003 14 1 92 111 12574707 10.1097/00044067-200302000-00011

53. Gaudreau JD Impact on delirium detection of using a sensitive instrument integrated into clinical practice Gen Hosp Psychiatry 2005 27 3 194 199 15882766 10.1016/j.genhosppsych.2005.01.002

54. Gaudreau JD Opioid medications and longitudinal risk of delirium in hospitalized cancer patients Cancer 2007 109 11 2365 2373 17469164 10.1002/cncr.22665

55. Agar M Lawlor P Delirium in cancer patients: a focus on treatment-induced psychopathology Curr Opin Oncol 2008 20 4 360 366 18525328 10.1097/CCO.0b013e328302167d

56. Uchida M Prevalence, associated factors and course of delirium in advanced cancer patients Asia Pac J Clin Oncol 2012 8 177

57. Barron EA Holmes J Delirium within the emergency care setting, occurrence and detection: a systematic review Emerg Med J 2013 30 4 263 268 22833596 10.1136/emermed-2011-200586

58. Alexander K Shahrokni A Korc-Grodzicki B Delirium in elderly patients undergoing intraabdominal cancer surgery-associated factors and consequences Eur Geriatr Med 2015 6 S76

59. Waked WJ Recognizing encephalopathy and delirium in the cardiopulmonary rehabilitation setting Rehabil Psychol 2015 60 2 201 210 26120746 10.1037/rep0000026

60. Inouye SK Westendorp RG Saczynski JS Delirium in elderly people Lancet 2014 383 9920 911 922 23992774 10.1016/S0140-6736(13)60688-1 PMC4120864

61. Hendry K Evaluation of delirium screening tools in geriatric medical inpatients: a diagnostic test accuracy study Age Ageing 2016 45 6 832 837 27503794 10.1093/ageing/afw130

62. Hall RJ Meagher DJ MacLullich AM Delirium detection and monitoring outside the ICU Best Pract Res Clin Anaesthesiol 2012 26 3 367 383 23040287 10.1016/j.bpa.2012.07.002

63. De J Wand AP Delirium screening: a systematic review of delirium screening tools in hospitalized patients Gerontologist 2015 55 6 1079 1099 26543179 10.1093/geront/gnv100

64. Wong CL Does this patient have delirium? Value of bedside instruments JAMA 2010 304 7 779 786 20716741 10.1001/jama.2010.1182

65. Breitbart W The Memorial Delirium Assessment scale J Pain Symptom Manag 1997 13 3 128 137 10.1016/s0885-3924(96)00316-8 9114631

66. Baird L Spiller JA A quality improvement approach to cognitive assessment on hospice admission: could we use the 4AT or Short CAM? BMJ Open Qual 2017 6 2 153 10.1136/bmjoq-2017-000153 PMC5609350 28959786

67. De J Validating the 4A's test in screening for delirium in a culturally diverse geriatric inpatient population Int J Geriatr Psychiatry 2017 32 12 1322 1329 27766672 10.1002/gps.4615

68. Tieges Z Diagnostic accuracy of the 4AT for delirium detection in older adults: systematic review and meta-analysis Age Ageing 2021 50 3 733 743 33951145 10.1093/ageing/afaa224 PMC8099016

69. Grassi L Assessing delirium in cancer patients: the Italian versions of the Delirium Rating Scale and the Memorial Delirium Assessment Scale J Pain Symptom Manag 2001 21 1 59 68 10.1016/s0885-3924(00)00241-4 11223315

70. Mitchell AJ The Mini-Mental State Examination as a diagnostic and screening test for delirium: systematic review and meta-analysis Gen Hosp Psychiatry 2014 36 6 627 633 25267696 10.1016/j.genhosppsych.2014.09.003

71. Sands MB “SQiD, the Single Question in Delirium; can a single question help clinicians to detect delirium in hospitalised cancer patients?" running heading Single Question in Delirium” (Bcan-D-20-01665) BMC Cancer 2021 21 1 75 33461523 10.1186/s12885-020-07504-x PMC7814717

72. Sigurdardottir KR The European Association for Palliative Care basic dataset to describe a palliative care cancer population: results from an international Delphi process Palliat Med 2014 28 6 463 473 24503559 10.1177/0269216314521264

73. Tayjasanant S Bruera E Hui D How far along the disease trajectory? An examination of the time-related patient characteristics in the palliative oncology literature Support Care Cancer 2016 24 9 3997 4004 27129839 10.1007/s00520-016-3225-z PMC4967383