==== Front Ann Saudi MedAnn Saudi MedAnnals of Saudi Medicine0256-49470975-4466King Faisal Specialist Hospital and Research Centre 2941952410.5144/0256-4947.2018.15asm-1-15Original ArticleDelirium and correlates of delirium among newly admitted elderly patients: a cross-sectional study in a Saudi general hospital Alamri Sultan Hassan aAshanqity Obay Ahmed bAlshomrani Ahmad Belgeth bElmasri Abdelrahman Haytham cSaeed Muaadh Badr cYahya Sultan Ahmed bAlmasoud Abdulhakeem Ibraheem bAdam Youssouf bAlamoudi Hani Mohammed dMukhtar Abdel Moniem a a Faculty of Medicine, King Abdulaziz University, Jeddah, Makkah, Saudi Arabia b Faculty of Medicine, University of Jeddah, Jeddah, Makkah, Saudi Arabia c Faculty of Medicine, Ibn Sina National College for Medical Studies, Jeddah, Makkah, Saudi Arabia d General Surgery, King Fahd General Hospital, Jeddah, Makkah, Saudi ArabiaCorrespondence: Dr. Sultan Alamri, Faculty of Medicine, King Abdulaziz University, Jeddah, Makkah 21441, Saudi Arabia, T +966126952000 Ext 21186, shalamri1@kau.edu.sa, ORCID: http://orcid.org/0000-0003-4991-6223Jan-Feb 2018 38 1 15 21 Copyright © 2018, Annals of Saudi Medicine2018This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.BACKGROUND Delirium is a common, often undiagnosed disorder in elderly patients, but no studies have been conducted in Saudi Arabia. OBJECTIVES To determine the prevalence of delirium among elderly patients on admission and to identify associated factors. DESIGN A cross-sectional study. SETTING Tertiary care hospital, Saudi Arabia. PATIENTS AND METHODS Elderly patients were evaluated for delirium within 24 hours of admission using the Confusion Assessment Method (CAM). The medical records were also reviewed to identify associated factors and whether the diagnosis of delirium was documented by the admitting physician. MAIN OUTCOME MEASURES Prevalence of delirium. RESULTS Of 147 patients aged 60 or over screened for delirium within 24 hours of admission, 32 (21.8%) patients were identified with delirium. Seven (21.9%) of the 32 patients with delirium had documentation of their diagnosis in the patient chart by the attending physician. Univariate logistic regression identified greater age (OR=2.70, 95%-CI: 1.21–6.02), higher unemployment rate (OR=3.30, 95%-CI: 1.43–7.61), more often had 3–5 co-morbidities (OR=2.69, 95%-CI: 1.14–6.33), and more cognitive impairment (OR=38.90, 95%-CI: 8.78–172.34) as risk factors for delirium on admission. Multivariate logistic regression analysis identified greater age (OR=2.53, 95%-CI: 1.08–5.88), higher unemployment rate (OR=3.73, 95%-CI: 1.52–9.13) and 3–5 co-morbidities (OR=3.31, 95%-CI: 1.30–8.46) as risk factors for delirium. CONCLUSIONS Delirium was common and frequently not recognized in elderly patients admitted to the hospital. Administration of the CAM was very helpful in identifying delirium at admission. LIMITATIONS The main limitation of our study was the relatively small number of patients which might have limited the power to detect some associations. ==== Body Delirium is an acute neuropsychiatric disorder characterized by failure of cognition and attention. Unlike dementia, delirium develops rapidly (over several hours to days), can fluctuate in severity and may be reversed by eliminating the causative factor. In some cases, it can become chronic or result in irreversible cognitive changes.1 The occurrence of delirium is highest among the hospitalized elderly population. In fact, a recent review estimated that 50% of all hospitalized patients older than the age of 65 are affected by delirium.2 The risk is even higher when special subpopulations are considered. For example, the prevalence of delirium in intensive care units, palliative care units and post-surgical settings may be as high as 80%, 85%, 70%, respectively.3–5 Interestingly, greater than 30% of delirium cases can be prevented6,7 and hence preventive measures can reduce both the occurrence as well as adverse outcomes of delirium. In a recent systematic review, the commonest risk factors for incident delirium in hospitalized elderly patients were advanced age, dementia, comorbidities, reduced activities of daily living, immobility, use of urinary catheters, high-risk medication use, certain laboratory abnormalities (levels of albumin, sodium, urea/creatinine ratio) and increased hospital stay.8 Irrespective of its etiology, delirium is associated with adverse patient outcomes. Symptoms of delirium, such as agitation and lethargy can increase risk of aspirations, ulcers, pulmonary emboli and reduced oral intake.1 In some cases, delirium can lead to long-term functional and cognitive decline, which persists even after treatment or the withdrawal of causative factors.9,10 It may also lead to longer hospital stay, institutionalization, psychological stress, and mortality.11,12 Due to the varied clinical presentations of delirium, the fluctuating symptoms and the lack of routine cognitive assessments in hospitals, the diagnosis of this condition is often difficult, leading to under recognition of the problem. Moreover, delirium is sometimes present along with pre-existing dementia, making it hard to differentiate the two.13 In the existing literature, several studies have evaluated the occurrence of delirium in hospitalized elderly patients. However, similar studies have not been conducted in Saudi Arabia. Therefore, the present study aimed to determine the prevalence of delirium among newly hospitalized patients aged 60 years and over within 24 hours of their admission to a general hospital. In addition, we aimed to identify the associated factors and to determine the recognition rate of delirium by hospital staff. PATIENTS AND METHODS Between January and March 2016, we included all patients aged 60 years and older who were admitted to the medical and surgical wards at King Fahad General Hospital, Jeddah Saudi Arabia. Patients with a language barrier, aphasia, hearing impairment, severe cognitive dysfunction, reduced level of consciousness, or with unstable medical illnesses were excluded. Eligible patients were evaluated for delirium by trained medical staff within 24 hours of admission using a protocol approved by the Institutional Review Board of Ministry of Health. The questionnaire included items on age, sex, education, marital status, employment, and living conditions. It also included selected clinical characteristics such as admission diagnosis, comorbidities, polypharmacy, premorbid functional status, and basic laboratory results. According to the American Psychiatric Association’s Diagnostic and Statistical Manual, 5th edition (DSM-5), five key characteristics are used to diagnose delirium.14 These features include attention disturbances, acute onset of symptoms, additional cognitive disturbances, and the absence of other neurocognitive disorders to explain symptoms. However, the DSM-5 is not easily applied at the patient’s bedside. Hence, several other diagnostic tools exist to identify delirium. The Confusion Assessment Method (CAM) is a standardized and validated bedside diagnostic tool that is widely used to detect delirium.15 This method relies on the following criteria for diagnosis of delirium: acute onset, fluctuating symptoms, diminished attention, disorganized thinking or altered consciousness. In a meta-analysis, CAM demonstrated high sensitivity (94%), specificity (89%) and inter-rater reliability.16 A comparison of bedside diagnostic instruments to detect delirium reported CAM as the most accurate test.17 CAM is most reliable when performed in conjunction with formal cognitive testing and by trained interviewers. The short portable mental status questionnaire (SPMSQ) is a 10-item cognitive screening questionnaire that is widely used in clinical practice to detect the presence and severity of cognitive impairment in elderly patients.18 As per the SPMSQ scoring system, 0–2 errors indicate normal mental function, 3–4 errors indicate mild cognitive impairment, 5–7 errors indicate moderate cognitive impairment, and ≥8 errors indicate severe cognitive impairment.19 The Katz Index of Independence in Basic Activities of Daily Living (BADL) is a reliable instrument to assess the functional status of elderly individuals.20 The index assesses six primary functions: bathing, dressing, toileting, transferring, continence, and feeding. The independence in each function is scored with a yes/no. A score of 6 reflects full function, a score of 4 reflects moderate functional impairment, and ≤2 indicates presence of severe functional impairment.21 We planned a cross-sectional study with consecutive patient selection as a sampling technique. Assuming 10% of our study participants had delirium, we needed to study 138 patients to estimate the prevalence of delirium with a 5% margin of error so we included 147 patients in our study. To describe our study population, we used frequencies and absolute numbers for categorical variables. Differences between two categorical variables were assessed using the two-sided chi-squared test or the two-sided Fisher exact test when the data were sparse in one or more category. Associations between delirium and risk factors were estimated by odds ratios and their corresponding 95% confidence intervals. To adjust for potential confounding variables, multivariate regression models were constructed. The dependent variable was delirium diagnosed by the screening staff that evaluated patients within 24 hours of admission at KAUH. The independent variables were age, gender, marital status, living status, educational level, occupational status, department, admission diagnosis, number of comorbidities, admission medications, Katz index 2 weeks before admission and SPMSQ at admission. A backwards elimination procedure was used to select the variables for the regression model. For all statistical tests, a P value of <.05 was defined as a level of significance. No adjustments were made for multiple comparisons. We examined potential collinearity between age and occupational status using the Pearson correlation coefficient as well as tolerance and variance inflation factor as collinearity diagnostics in multivariate regression. We used the IBM SPSS version 21 for data analysis. RESULTS Of 147 consecutive patients aged 60 or older included in our study, 65.3% (n=96) of the patients were 60 to 74 years of age and 34.7% (n=51) were 75 or older. Women accounted for 40.8% (n=60) of the study population and about two-thirds of the patients were married (65.3%, n=96). Most were living with their family prior to hospitalization (93.2%, n=137). About 50% of the patients were illiterate (n=73 and 53.7% (n=79) were employed. Most of the patients were admitted for the treatment of an acute medical illnesses (75.5%, n=111). Cardiac disorders were the most common admission diagnosis (23.8%, n=35), followed by gastrointestinal (19.7%, n=29) and neurologic disease (19.0%, n=28). Two or more comorbidities were found in 63.3% of patients and 38.1 % (n=56) were taking 5 or more medications. The mean (SD) Katz Index two weeks before admission was 3.83 (2.25) (Table 1). Of 32 (21.8%) of the 147 patients diagnosed with delirium by the screening staff, 7 (21.9%) had documentation of their diagnosis in the patient chart by the attending physician. Compared to patients without delirium, patients with delirium were older (chi-square, P=.01), educated (chi-square, P=.02), had a lower employment rate (chi-square, P=.004), and less intact cognitive function as determined by SPMSQ testing (chisquare, P=<.001). No significant differences were found in other sociodemographic and clinical characteristics of patients with and without delirium (Table 2). The most common clinical factors associated with delirium were taking three or more medications (62.5%), use of a urinary catheter (20/32, 31.3%), acute pain (10/32, 31.3%) and stroke (9/32, 28.1%). Several abnormalities were also noted on admission laboratory blood testing. Six patients (18.8%) had a low blood hemoglobin level and 9 (28.8%) had a leukocytosis. Random blood sugar, blood urea nitrogen and creatinine levels were elevated in 78.1% (n=25), 93.1% (n=30) and 87.5% (n=28), respectively (Table 3). Using univariate logistic regression, patients with delirium, compared to non-delirious patients, were older (OR=2.70, 95%-CI: 1.21 – 6.02, P=.01) had a higher unemployment rate (OR=3.30, 95%-CI: 1.43–7.61, P=.004), higher rate of 3–5 co-morbidities (OR=2.69, 95%-CI: 1.14–6.33, P=.02) and had more impaired cognitive function (OR=38.90, 95%-CI: 8.78 – 172.34, P<.0001) than patients without delirium. Patients with less than high school and more than high school education were less likely to have delirium (OR=0.29, 95%-CI: 0.11–0.73 and OR=0.36, 95%-CI: 0.08–1.75 respectively), compared to illiterate patients (Table 4). The slight difference in the OR might be due to the small number of patients with more than high school education (n=14), i.e. low statistical power. In the multivariate logistic regression, patients with delirium were older (OR=2.53, 95%-CI: 1.08–5.88), had a higher unemployment rate (OR=3.73, 95%-CI: 1.52 – 9.13) and a higher rate of 3–5 co-morbidities (OR=3.31, 95%-CI: 1.30 – 8.46) than patients without delirium (Nagelkerke R2= 0.192).. We found no evidence of collinearity. The Pearson correlation coefficient between age and employment status was weak (r=0.098). When we used the multivariate regression model, we found the tolerance of the variables (measure of multicollinearity) age and employment status to be 0.987 and 0.984, respectively, and the variance inflation factor of the variables age and employment status to be 1.013 and 1.016, respectively. The high values of tolerance (>0.1) and the low values of VIF (<2.5) indicated lack of collinearity. DISCUSSION Delirium was diagnosed in 21.8% of newly admitted hospitalized elderly patients by the screening group we employed with a similar frequency (21.8%) as in previous reports.2 Only 21.9% of patients identified by screening were similarly diagnosed by the treating physician. The low recognition rate points out the need for screening at-risk patients for the diagnosis of delirium within 24 hours of admission and periodically during their stay. This has been recommended by the National Institute for Health and Clinical Excellence.22 The CAM is a validated screening instrument with a sensitivity of 94–100% and specificity of 90–95% in diagnosing delirium.15,16 If administered on admission, it can facilitate the diagnosis of delirium and improve patient treatment.16 Risks factors for delirium by univariate analysis included increased age, illiteracy, unemployment, impaired cognitive functioning, and the presence of 3–5 co-morbidities. Increased age, unemployment, and the presence of 3–5 co-morbidities were confirmed as risk factors by multivariate analysis. These findings are also in accord with previous reports.2,23 The evidence from 19 studies, included in a systematic review, confirmed the effectiveness and safety of most multicomponent interventions in preventing delirium in at-risk hospitalized patients.24 Hospital Elder Life Program (HELP) is a widely recognized multicomponent intervention program to prevent delirium.2,25 It is used internationally at over 60 institutions for delirium prevention and consists of standardized interventions that address six key risk factors – cognitive impairment, sleep deprivation, immobility, diminished vision or hearing, and dehydration.26,27 A strength of this study was the evaluation of all patients admitted to the hospital that were 60 years of age or older for the diagnosis of delirium. These findings may not be comparable to community-based studies, but is more applicable to hospitalized patients undergoing evaluation for delirium. Moreover, our study used validated and reliable instruments to measure delirium and controlled for several risk factors. The main limitation of our study was the relatively small number of patients which might limited the power to detect some associations. When our study population was stratified by the presence or absence of delirium, as we showed in Table 2, contingency tables of the variables living status, department and admission diagnosis suffered from sparse data problem; with <5 expected participants per cell. For these variables, we refrained from using the chi-square test to investigate the difference between participants with and without delirium and instead we used and reported the results of the Fisher exact test. Furthermore, based on one previous study, we assumed 10% prevalence of delirium when we calculated the required sample size of our study. However, our study found 21.8% prevalence of delirium. Therefore, we recommend that future studies conduct sample size calculations based on a systematic and critical review of all previous studies. Delirium was common and frequently not recognized in elderly patients admitted to the hospital. Greater age, unemployment, and the presence of 3–5 co-morbidities were risk factors for delirium in these patients. The use of confusion assessment method (CAM) to screen elderly patients for delirium on admission and then periodically is in keeping with published guidelines and should be widely adopted. Although, delirium preventive strategies have been successfully implemented in many countries, the cost-effectiveness of such programs needs to be proven in the Saudi hospital care system prior to adoption. Acknowledgments The authors would like to thank the Department of Medical Research and Studies, MOH, Jeddah, Kingdom of Saudi Arabia for the ethical approval of this study and the administration of King Fahad General Hospital for its support and cooperation during the data collection phase of this research project. Conflict of interest The authors reported no conflict of interests. Table 1 General characteristics of study participants. Variable Result Variable Result Gender Department Male 87 (59.2) Medical 98 (66.7) Female 60 (40.8) Surgical 49 (33.3) Age Admission diagnosis 60–74 years 96 (65.3) Cardiac 35 (23.8) >74 years 51 (34.7) Endocrine 6 (4.1) Marital status Gastrointestinal 29 (19.7) Unmarried 51 (34.7) Hematologic 2 (1.4) Married 96 (65.3) Infection 7 (4.8) Living status Musculoskeletal 6 (4.1) With family 137 (93.2) Neurologic 28 (19.0) Alone 10 (6.8) Respiratory 12 (8.2) Educational status Urinary 18 (12.2) Illiterate 73 (49.7) Vascular 4 (2.7) ≤High school 60 (40.8) Number of Comorbidities >High school 14 (9.5) None 14 (9.5) Occupational status 1 Co-morbidity 40 (27.2) Employed 79 (53.7) 2 Co-morbidities 60 (40.8) Unemployed 68 (46.3) 3 Co-morbidities 23 (15.7) Katz Index 2 weeks before admission 3.83 (2.25) 4 Co-morbidities 8 (5.4) 5 Co-morbidities 2 (1.4) SPMSQ at admission Admission medications No deficit 85 (57.8) <5 Medications 91 (61.9) Mild deficit 25 (17.0) >5 Medications 56 (38.1) Moderate deficit 13 (8.8) Admission diagnosis Severe deficit 24 (16.3) No delirium 115 (78.2) Delirium 32 (21.8) Chart documentation Delirium not documented 25 (17.0) Delirium documented 7 (4.8) Patients without delirium 115 (78.2) Data are mean (standard deviation) (Katz index) or number (percentage). Table 2 Characteristics of study participants with and without delirium. Variable Patients without delirium (n=115) Patients with delirium (n=32) P value Age (years)  60–74 81 (84.4) 15 (15.6)  >74 34 (66.7) 17 (33.3) .01 Gender  Male 72 (82.8) 15 (17.2)  Female 43 (71.7) 17 (28.3) .11 Marital status  Unmarried 36 (70.6) 15 (29.4)  Married 79 (82.3) 17 (17.7) .10 Living status  With family 109 (79.6) 28 (20.4)  Alone 6 (60.0) 4 (40.0) .23* Educational status  Illiterate 50 (68.5) 23 (31.5)  ≤High school 53 (88.3) 7 (11.7)  >High school 12 (85.7) 2 (14.3) .02 Occupational status  Employed 69 (87.3) 10 (12.7)  Unemployed 46 (67.6) 22 (32.4) .004 Department  Medical 73 (74.5) 25 (25.5)  Surgical 42 (85.7) 7 (14.3) .14* Admission diagnosis  Cardiac 32 (91.4) 3 (8.6)  Endocrine 3 (50.0) 3 (50.0)  Gastrointestinal 22 (75.9) 7 (24.1)  Hematologic 2 (100.0) 0 (0.0)  Infection 6 (85.7) 1 (14.3) Musculoskeletal 6 (100.0) 0 (0.0)  Neurologic 17 (60.7) 11 (39.3)  Respiratory 9 (75.0) 3 (25.0)  Urinary 16 (88.9) 2 (11.1)  Vascular 2 (50.0) 2 (50.0) .047* Number of comorbidities  0–2 94 (82.5) 20 (17.5)  3–5 21 (63.6) 12 (36.4) .02 Admission medications  <5 72 (79.1) 19 (20.9)  >5 43 (76.8) 13 (23.2) .74 Katz Index 2 weeks before admission  Independent 32 (78.0) 9 (22.0)  Dependent 83 (78.3) 23 (21.7) .97 SPMSQ at Admission  Intact 83 (97.6) 2 (2.4)  Impaired 32 (51.6) 30 (48.4) < .001 Data are number (percentage). * P value of Fisher exact test. Table 3 Clinical characteristics of patients with delirium (n=32). Variable Frequency (%) Risk factors Pain 31.3 Malnutrition 15.6 Use of restraints 0.0 Use of urinary catheter 31.3 Alterations in oxygenation 9.4 Infection 15.6 Changes to electrolyte or acid base 3.1 Withdrawal from alcohol or Benzodiazepine 0.0 Introduction of three or more medications 62.5 Stroke 28.1 Acute fracture 9.4 Heart failure 3.1 Liver failure 6.3 Kidney failure 9.4 Hypoglycemia 0.0 Invasive procedures 9.4 Surgery 15.6 Table 3A Clinical characteristics of patients with delirium (n=32). Variable Frequency (%) Normal Low High Blood test Hemoglobin level 81.3 18.8 0.0 White blood cell 68.8 3.1 28.1 Random blood sugar 21.9 0.0 78.1 Blood urea nitrogen 6.3 0.0 93.8 Creatinine level 12.5 0.0 87.5 Alanine transaminase 90.6 6.3 3.1 Aspartate transaminase 84.4 6.3 9.4 Blood calcium 90.6 9.4 0.0 Table 4 Factors associated with delirium in univariate analysis. Variable Odds Ratio 95% Confidence interval P value Age (years) 60–74 (Reference) 1 - - >74 2.7 1.21–6.02 .02 Gender Male (Reference) 1 - - Female 1.9 0.86–4.18 .11 Marital status Unmarried (Reference) 1 - - Married 0.52 0.23–1.15 .11 Living status With family (Reference) 1 - - Alone 2.6 0.69–9.83 .16 Educational status Illiterate (Reference) 1 - - ≤High School 0.29 0.11–0.73 .01 >High School 0.36 0.08–1.75 .21 Occupational status Employed (Reference) 1 - - Unemployed 3.3 1.43–7.61 .01 Admission reason Elective (Reference) 1 - - Acute Not estimable Not estimable .1 Department Medical (Reference) 1 - - Surgical 0.49 0.19–1.22 .13 Number of comorbidities 0–2 (Reference) 1 - 3–5 2.69 1.14–6.33 .02 Number of admission medications <5 (Reference) 1 - - >5 1.15 0.52–2.55 .74 Katz Index 2 weeks before admission Independent (Reference) 1 - - Dependent 0.99 0.41–2.36 .97 SPMSQ at admission Intact Cognitive Functioning (Reference) 1 - - Impaired Cognitive Functioning 38.9 8.78–172.34 <.001 ==== Refs REFERENCES 1 Fong TG Tulebaev SR Inouye SK Delirium in elderly adults: diagnosis, prevention and treatment Nature Reviews Neurology 2009 5 4 210 20 19347026 2 Inouye SK Westendorp RGJ Saczynski JS Delirium in elderly people The Lancet 2014 383 9920 911 22 3 Morandi A Jackson JC Delirium in the intensive care unit: a review Neurol Clin 2011 29 4 749 63 22032658 4 Guenther U Radtke FM Delirium in the postanaesthesia period Current Opinion in Anesthesiology 2011 24 6 670 5 21971396 5 Casarett DJ Inouye SK Diagnosis and management of delirium near the end of life Ann Intern Med 2001 135 1 32 40 11434730 6 Young J Murthy L Westby M Akunne A O’Mahony R Diagnosis, prevention, and management of delirium: summary of NICE guidance BMJ: British Medical Journal 2010 341 c3704 20667955 7 Marcantonio ER Delirium Ann Intern Med 2011 154 11 ITC6 1 21646553 8 Ahmed S Leurent B Sampson EL Risk factors for incident delirium among older people in acute hospital medical units: a systematic review and meta-analysis Age Ageing 2014 43 3 326 33 24610863 9 Siddiqi N House AO Holmes JD Occurrence and outcome of delirium in medical inpatients: a systematic literature review Age Ageing 2006 35 4 350 64 16648149 10 Hsieh SJ Madahar P Hope AA Zapata J Gong MN Clinical deterioration in older adults with delirium during early hospitalisation: a prospective cohort study BMJ open 2015 5 9 e007496 11 Han JH Eden S Shintani A Morandi A Schnelle J Dittus RS Delirium in older emergency department patients is an independent predictor of hospital length of stay Acad Emerg Med 2011 18 5 451 7 21521405 12 Salluh JIF Wang H Schneider EB Nagaraja N Yenokyan G Damluji A Outcome of delirium in critically ill patients: systematic review and meta-analysis BMJ 2015 350 h2538 26041151 13 Martins S Fernandes L Delirium in elderly people: a review. 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