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Diagnosis, Risk Factors, Predisposing Factors, and Predictive Models of Delirium
EDITORIAL
Diagnosis, Risk Factors, Predisposing Factors, and Predictive Models of Delirium Josepha A. Cheong, M.D. “Thousands of tiny little creatures, some on horseback, waving arms, carrying weapons like some grand Renaissance battle were trying to turn people into zombies. Their leader was a woman with no mouth but a very precisely cut hole in her throat.” —Justin Kaplan - age 84, Pulitzer Prize winning historian and biographer, describing his experience with delirium while being hospitalized for pneumonia.1
Delirium is a clinical syndrome that is disruptive to patients, their families, and clinicians and also the cause for great morbidity and mortality for hospital patients, a disproportionate number of whom are elderly. Delirium, also referred to as acute confusional state or encephalopathy, is a clinical presentation distinguished by a disturbance of consciousness and attention, cognitive impairment, disorientation, the development of perceptual disturbances, accompanied by psychomotor hyper- or hypoactivity and disrupted sleepewake cycle. The Latin roots for the term are derived from de (“away from”) and lira (“furrow in the field”), literally meaning “to go away from a ploughed track” or “to go off a track.” In hospitalized patients, delirium occurs at rates estimated to be between 14% and 80% (medical intensive care unit patients) and is accompanied by increased morbidity and mortality than can be accounted for by the original underlying medical illness alone.2,3 In the adult general medicine population, the incidence of delirium ranges from 10% to 24%.2,4 The incidence increases greatly to an estimated 60% in the frail geriatric patient.5 This issue of the American Journal of Geriatric Psychiatry is focused on the various aspects of delirium with regard to its diagnosis, risk factors, predisposing factors, and predictive models. Each of the five featured articles
focuses on a different aspect of delirium, yet all are similar in their intent: clarifying and elucidating further the boundaries of this often missed and/or misdiagnosed, yet increasingly prevalent, clinical presentation.6e10 Additionally, the articles as a group examine delirium within both the acute and long-term institutionalized geriatric patient population. The geriatric patient is at a disproportionally increased risk of developing delirium with significantly negative impact on overall morbidity and mortality compared with the nongeriatric patient. In particular, two universally recognized risk factors for delirium are a patient’s advanced age (older than 75 years) and the presence of baseline cognitive disorder such as dementia or stroke.11 In their study, Oldenbeuving et al.6 attempt to determine the relationship between epsilon 4 allele of apolipoprotein E (APOEe4) and delirium in poststroke patients. The authors posit that given delirium’s frequency in acute-phase poststroke patients and its predictive value as a risk factor for dementia in these patients, there may be a link APOEe4, also an established risk factor for dementia and cognitive decline. Prospectively, the risk factors, incidence, and delirium outcome of 527 consecutive stroke patients were studied and analyzed. Each patient was screened for delirium twice: between days 2 and 4 and between days 5 and 7. Delirium status was assessed using the Confusion Assessment Method.12 For 353 patients, DNA samples were available for APOE typing. After a complete analysis of the data gathered and the application of rigorous statistical models, no association was found between the presence of an APOEe4 allele and delirium in the acute phase after stroke. Although two prior studies suggested a possible association between duration of
Received July 23, 2013; accepted July 24, 2013. From the Malcom Randall VA Medical Center and University of Florida College of Medicine, Gainesville, FL. Send correspondence and reprint requests to Josepha A. Cheong, M.D., Malcom Randall VA Medical Center, Section 11-A, 1601 SW Archer Rd, Gainesville, FL 32608. e-mail: jcheong@ufl.edu Ó 2013 American Association for Geriatric Psychiatry http://dx.doi.org/10.1016/j.jagp.2013.07.004
Am J Geriatr Psychiatry 21:10, October 2013
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Editorial delirium and APOEe4 allele, Oldenbeuving and her colleagues showed no such association.6,13,14 Although this study demonstrates negative results, it is significant because the study documents a reasonable approach to an important hypothesis and therefore expands knowledge in the relationship of APOEe4 and delirium in poststroke patients. Because there is growing competition for funding and citations, the perceived pressure to publish only positive-outcome studies is theorized to distort scientific literature directly.15 Negative results are mistaken to be “bad results” or are thought to imply poor study design or flawed execution. Rather, the publication of studies presenting negative results serves an important role in scientific discovery by reducing duplication of effort, which is likely to lead to acceleration of scientific progress and greater transparency and openness.16 Hatano et al.7 explore the predictive value of cerebral white matter hyperintensities (WMHs) on magnetic resonance imaging studies for delirium after cardiac surgeries. A retrospective chart review of 130 patients undergoing cardiac surgery was conducted as well as review of the brain magnetic resonance imaging scans conducted before surgery as part of the preoperative evaluation. After analysis, the prevalence of severe WMHs was found to be significantly higher in patients with delirium. In addition, further analysis of the risk factors revealed that patients with delirium had higher prevalence of pre- and postoperative lower albumin and higher creatinine values. The study also confirmed that patients whose surgical procedures were of a longer duration tended to develop delirium. Additionally, the study identified a high baseline prevalence of WMHs in patients with cardiac disease, suggesting perhaps a role of the regions of WMHs in the pathogenesis of delirium, a question for future investigation.7 In a prospective cohort study, Leung et al.8 investigate whether preoperative risk for delirium moderates the effect of postoperative pain and opioids on the development of postoperative delirium. The study was focused on patients 65 years of age and older. Using a structured interview and the Confusion Assessment Method, 581 patients were evaluated and assessed for the level of risk (high versus low) of delirium. Not surprisingly, high levels of postoperative pain and high-dose opioid usage increased the risk for postoperative delirium for all patients. In addition, the highest incidence of delirium occurred in patients with
932
all three predictors: high preoperative risk for delirium, high level of postoperative pain, and high opioid doses. This study lends support to implementing the patient safety strategy of completing a preoperative risk for delirium assessment. By anticipating which patients are likely to develop delirium postoperatively, preoperative risk assessment would allow for preemptive precautions and increased vigilance for postoperative pain monitoring and management.8 As the practice of medicine becomes more complex, the need for standardization of care has grown considerably. Nowhere is this more evident than in the development and application of algorithms and decision tree analysis in the diagnosis and management of clinical presentations. Chi-squared Automatic Interaction Detection (CHAID) is a type of decision tree technique that facilitates the discovery of the relationship between specific variables. First used in retail marketing to “profile” consumers to determine their purchasing habits, CHAID analysis in medicine can be used to determine how certain variables can predict a patient’s likelihood to develop certain clinical presentations, that is, to produce a “profile” of an at-risk patient based on specific identified traits or factors. Kobayashi et al.9 apply the CHAID model in a retrospective cohort study of all adult patients admitted to a large community hospital to predict the development of delirium. They identified four potentially predictive variables (decision nodes) in the CHAID model: delirium history, age, underlying malignancy, and activities of daily living impairment. Compared with the logistic regression method, the authors determined that CHAID analysis was easier to apply in routine clinical settings and may more likely facilitate appropriate preventative care and monitoring for identified high-risk patients. Through a post-hoc analysis of a prospective cohort assessment, von Gunten et al.10 investigate the evolution of delirium in nursing home residents to identify their possible predictors. At the conclusion of the study, four major types of delirium time courses in the nursing home setting were identified. In addition, the authors determined that subsyndromal and full delirium occur frequently in nursing homes. Key findings identified that both the degree of cognitive impairment and the presence of severe depression at initial assessment are clear predictors for the development of delirium as well as the course
Am J Geriatr Psychiatry 21:10, October 2013
Cheong and duration of the delirium. The therapeutic implications of early prediction and monitoring of high-risk patients are valuable given the prevalence of delirium in nursing home settings.10 Regardless of clinical care setting or patient population, delirium continues to be associated with greater morbidity and mortality than can be accounted for by the underlying illness precipitating the hospitalization. As more policy, research, public demand, and legislation are placed on the importance of continuously improving the quality of care and patient safety, delirium is the focus of increasingly more qualitative and quantitative research studies in an attempt to develop greater accuracy in risk assessment, prevention, and early intervention. In 2000, the Agency for Healthcare Research and Quality commissioned the report “Making Health Care Safer: A Critical Analysis of Patient Safety Practices.”17 This landmark report analyzed and rated 80 so-called patient safety strategies. Although some success from the implementation of patient safety strategies for certain targets has been seen, studies reveal greater than expected and continued high rates of “preventable” harm in hospitals.18e21 Over the past 4 years, a project team of healthcare and patient safety experts from the RAND Corporation, Stanford University, University of CaliformaiSan Francisco, Johns Hopkins University, and the Emergency Care Research Institute in cooperation with the Agency for Healthcare Research and Quality identified delirium as 1 of 18 topics for patient safety strategies in-depth reviews.22 An in-depth systematic review of in-facility delirium prevention programs
suggests that a patient safety strategy designed to assess and remediate multiple causative factors is more likely to be effective in delirium prevention.23 The various components of these delirium prevention programs include the implementation of anesthesia protocols, medication review, mobilization, hydration, pain management, sleep cycle management, and extra nutrition. Although these multicomponent programs are effective in preventing delirium in a hospital setting, they have not been well studied in nonacute hospital settings such as palliative care and long-term care, settings with an elderly at-risk patient population. The field of geriatric psychiatry must lead the charge in further exploring and determining best practices to ensure patient safety with regard to delirium in the geriatric patient. Much of the research to date has been done in acute care settings. Less is known about delirium in the geriatric patient in a subacute or non-acutesetting. One potential research objective may be the development of a multicomponent delirium prevention program in the nonacute setting of palliative and long-term care. Another objective, as demonstrated by the five articles featured in this issue, may be the development of strategies to predict more accurately at-risk patients and to detect delirium earlier in its pathogenesis. Early detection and diagnosis are the key steps in reducing the morbidity and mortality associated with delirium in both the acute and nonacute settings. After all, as noted physician and author, Martin H. Fischer, M.D., once wrote, “Diagnosis is not the end, but the beginning of practice.”
References 1. Belluck P: Hallucinations in hospital pose risk to elderly. New York Times, June 21, 2010, p. A1 2. Speed G, Wynaden D, McGowan S, et al: Prevalence rate of delirium at two hospitals in Western Australia. Austral J Adv Nurs 2007; 25:38e43 3. Ely EW, Gautam S, Margolin R, et al: The impact of delirium in the intensive care unit on hospital length of stay. Intens Care Med 2001; 27:1892e1900 4. Gonzalez M, de Pablo J, Fuente E, et al: Instrument for detection of delirium in general hospitals: adaptation of the confusion assessment method. Psychosomatics 2004; 45:426e431 5. Francis J, Martin D, Kapoor WN: A prospective study of delirium in hospitalized elderly. JAMA 1990; 263:1097e1101 6. Oldenbeuving AW, de Kort PLM, Kappelle van LJ, et al: Delirium in the acute phase after stroke and the role of the apolipoprotein E gene. Am J Geriatr Psychiatry 2013; 21:935e937
Am J Geriatr Psychiatry 21:10, October 2013
7. Hatano Y, Narumoto J, Shibata K, et al: White matter hyperintensities predict delirium after cardiac surgery. Am J Geriatr Psychiatry 2013; 21:938e945 8. Leung JM, Sands LP, Lim E, et al: Does preoperative risk for delirium moderate the effects of postoperative pain and opiate use on postoperative delirium? Am J Geriatr Psychiatry 2013; 21: 946e956 9. Kobayashi D, Takahashi O, Arioka H, et al: A prediction rule for the development of delirium among patients in medical wards: Chisquare Automatic Interaction Detector (CHAID) decision tree analysis model. Am J Geriatr Psychiatry 2013; 21:957e962 10. von Gunten A, Mosimann UP, Antonietti JP: A longitudinal study on delirium in nursing homes. Am J Geriatr Psychiatry 2013; 21: 963e972 11. Maldonado JR: Delirium in the acute care setting: characteristics, diagnosis and treatment. Crit Care Clin 2008; 24:657e722
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Editorial 12. Inouye SK, van Dyck CH, Alessi CA, et al: Clarifying confusion: the confusion assessment method. A new method for detection of delirium. Ann Intern Med 1990; 113:941e948 13. Adamis D, Treloar A, Martin FC, et al: APOE and cytokines as biological markers for recovery of prevalent delirium in elderly medical inpatients. Int J Geriatr Psychiatry 2007; 22: 688e694 14. Ely EW, Girard TD, Shintani AK, et al: Apolipoprotein E4 polymorphism as a genetic predisposition to delirium in critically ill patients. Crit Care Med 2007; 35:112e117 15. Fanelli D: Negative results are disappearing from most disciplines and countries. Scientometrics 2012; 90:891e904 16. Anderson G, Haiko Sprott H, Olsen BR: Opinion: Publish negative results non-confirmatory or “negative” results are not worthless. Retrieved from www.the-Scientist.com (accessed January 15, 2013) 17. Shojania KG, Duncan BW, McDonald KM, et al: Making health care safer: a critical analysis of patient safety practices. Evid Rep Technol Assess 2001; i-x:1e668
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18. Wachter RM, Pronovost P, Shekelle P: Strategies to improve patient safey: the evidence base matures. Ann Intern Med 2013; 158(5 Part 1):350e352 19. Landrigan CP, Parry GJ, Bones CB, et al: Temporal trends in rates of patient harm resulting from medical care. N Engl J Med 2010; 363:2124e2134 20. Levinson DR: Adverse events in hospitals: national incidence among Medicare beneficiaries. Washington, DC: US Department of Health and Human Services, Office of the Inspector General; November 2010. Report No. OEI-06-09-00090 21. Rennke S, Nguyen OK, Shoeb MH, et al: Hospital-initiated transitional care interventions as a patient safety strategy. A systematic review. Ann Intern Med 2013; 158:432e440 22. Shekelle PG, Pronovost PJ, Wachter RM, et al: The top patient safety strategies that can be encouraged for adoption now. Ann Intern Med 2013; 158(5 Part 2):365e368 23. Reston JT, Schoelles KM: In-facility delirium prevention programs as a patient safety strategy: a systematic review. Ann Intern Med 2013; 158(5 Part 2):375e380
Am J Geriatr Psychiatry 21:10, October 2013
Diagnosis, Risk Factors, Predisposing Factors, and Predictive Models of Delirium Josepha A. Cheong, M.D. “Thousands of tiny little creatures, some on horseback, waving arms, carrying weapons like some grand Renaissance battle were trying to turn people into zombies. Their leader was a woman with no mouth but a very precisely cut hole in her throat.” —Justin Kaplan - age 84, Pulitzer Prize winning historian and biographer, describing his experience with delirium while being hospitalized for pneumonia.1
Delirium is a clinical syndrome that is disruptive to patients, their families, and clinicians and also the cause for great morbidity and mortality for hospital patients, a disproportionate number of whom are elderly. Delirium, also referred to as acute confusional state or encephalopathy, is a clinical presentation distinguished by a disturbance of consciousness and attention, cognitive impairment, disorientation, the development of perceptual disturbances, accompanied by psychomotor hyper- or hypoactivity and disrupted sleepewake cycle. The Latin roots for the term are derived from de (“away from”) and lira (“furrow in the field”), literally meaning “to go away from a ploughed track” or “to go off a track.” In hospitalized patients, delirium occurs at rates estimated to be between 14% and 80% (medical intensive care unit patients) and is accompanied by increased morbidity and mortality than can be accounted for by the original underlying medical illness alone.2,3 In the adult general medicine population, the incidence of delirium ranges from 10% to 24%.2,4 The incidence increases greatly to an estimated 60% in the frail geriatric patient.5 This issue of the American Journal of Geriatric Psychiatry is focused on the various aspects of delirium with regard to its diagnosis, risk factors, predisposing factors, and predictive models. Each of the five featured articles
focuses on a different aspect of delirium, yet all are similar in their intent: clarifying and elucidating further the boundaries of this often missed and/or misdiagnosed, yet increasingly prevalent, clinical presentation.6e10 Additionally, the articles as a group examine delirium within both the acute and long-term institutionalized geriatric patient population. The geriatric patient is at a disproportionally increased risk of developing delirium with significantly negative impact on overall morbidity and mortality compared with the nongeriatric patient. In particular, two universally recognized risk factors for delirium are a patient’s advanced age (older than 75 years) and the presence of baseline cognitive disorder such as dementia or stroke.11 In their study, Oldenbeuving et al.6 attempt to determine the relationship between epsilon 4 allele of apolipoprotein E (APOEe4) and delirium in poststroke patients. The authors posit that given delirium’s frequency in acute-phase poststroke patients and its predictive value as a risk factor for dementia in these patients, there may be a link APOEe4, also an established risk factor for dementia and cognitive decline. Prospectively, the risk factors, incidence, and delirium outcome of 527 consecutive stroke patients were studied and analyzed. Each patient was screened for delirium twice: between days 2 and 4 and between days 5 and 7. Delirium status was assessed using the Confusion Assessment Method.12 For 353 patients, DNA samples were available for APOE typing. After a complete analysis of the data gathered and the application of rigorous statistical models, no association was found between the presence of an APOEe4 allele and delirium in the acute phase after stroke. Although two prior studies suggested a possible association between duration of
Received July 23, 2013; accepted July 24, 2013. From the Malcom Randall VA Medical Center and University of Florida College of Medicine, Gainesville, FL. Send correspondence and reprint requests to Josepha A. Cheong, M.D., Malcom Randall VA Medical Center, Section 11-A, 1601 SW Archer Rd, Gainesville, FL 32608. e-mail: jcheong@ufl.edu Ó 2013 American Association for Geriatric Psychiatry http://dx.doi.org/10.1016/j.jagp.2013.07.004
Am J Geriatr Psychiatry 21:10, October 2013
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Editorial delirium and APOEe4 allele, Oldenbeuving and her colleagues showed no such association.6,13,14 Although this study demonstrates negative results, it is significant because the study documents a reasonable approach to an important hypothesis and therefore expands knowledge in the relationship of APOEe4 and delirium in poststroke patients. Because there is growing competition for funding and citations, the perceived pressure to publish only positive-outcome studies is theorized to distort scientific literature directly.15 Negative results are mistaken to be “bad results” or are thought to imply poor study design or flawed execution. Rather, the publication of studies presenting negative results serves an important role in scientific discovery by reducing duplication of effort, which is likely to lead to acceleration of scientific progress and greater transparency and openness.16 Hatano et al.7 explore the predictive value of cerebral white matter hyperintensities (WMHs) on magnetic resonance imaging studies for delirium after cardiac surgeries. A retrospective chart review of 130 patients undergoing cardiac surgery was conducted as well as review of the brain magnetic resonance imaging scans conducted before surgery as part of the preoperative evaluation. After analysis, the prevalence of severe WMHs was found to be significantly higher in patients with delirium. In addition, further analysis of the risk factors revealed that patients with delirium had higher prevalence of pre- and postoperative lower albumin and higher creatinine values. The study also confirmed that patients whose surgical procedures were of a longer duration tended to develop delirium. Additionally, the study identified a high baseline prevalence of WMHs in patients with cardiac disease, suggesting perhaps a role of the regions of WMHs in the pathogenesis of delirium, a question for future investigation.7 In a prospective cohort study, Leung et al.8 investigate whether preoperative risk for delirium moderates the effect of postoperative pain and opioids on the development of postoperative delirium. The study was focused on patients 65 years of age and older. Using a structured interview and the Confusion Assessment Method, 581 patients were evaluated and assessed for the level of risk (high versus low) of delirium. Not surprisingly, high levels of postoperative pain and high-dose opioid usage increased the risk for postoperative delirium for all patients. In addition, the highest incidence of delirium occurred in patients with
932
all three predictors: high preoperative risk for delirium, high level of postoperative pain, and high opioid doses. This study lends support to implementing the patient safety strategy of completing a preoperative risk for delirium assessment. By anticipating which patients are likely to develop delirium postoperatively, preoperative risk assessment would allow for preemptive precautions and increased vigilance for postoperative pain monitoring and management.8 As the practice of medicine becomes more complex, the need for standardization of care has grown considerably. Nowhere is this more evident than in the development and application of algorithms and decision tree analysis in the diagnosis and management of clinical presentations. Chi-squared Automatic Interaction Detection (CHAID) is a type of decision tree technique that facilitates the discovery of the relationship between specific variables. First used in retail marketing to “profile” consumers to determine their purchasing habits, CHAID analysis in medicine can be used to determine how certain variables can predict a patient’s likelihood to develop certain clinical presentations, that is, to produce a “profile” of an at-risk patient based on specific identified traits or factors. Kobayashi et al.9 apply the CHAID model in a retrospective cohort study of all adult patients admitted to a large community hospital to predict the development of delirium. They identified four potentially predictive variables (decision nodes) in the CHAID model: delirium history, age, underlying malignancy, and activities of daily living impairment. Compared with the logistic regression method, the authors determined that CHAID analysis was easier to apply in routine clinical settings and may more likely facilitate appropriate preventative care and monitoring for identified high-risk patients. Through a post-hoc analysis of a prospective cohort assessment, von Gunten et al.10 investigate the evolution of delirium in nursing home residents to identify their possible predictors. At the conclusion of the study, four major types of delirium time courses in the nursing home setting were identified. In addition, the authors determined that subsyndromal and full delirium occur frequently in nursing homes. Key findings identified that both the degree of cognitive impairment and the presence of severe depression at initial assessment are clear predictors for the development of delirium as well as the course
Am J Geriatr Psychiatry 21:10, October 2013
Cheong and duration of the delirium. The therapeutic implications of early prediction and monitoring of high-risk patients are valuable given the prevalence of delirium in nursing home settings.10 Regardless of clinical care setting or patient population, delirium continues to be associated with greater morbidity and mortality than can be accounted for by the underlying illness precipitating the hospitalization. As more policy, research, public demand, and legislation are placed on the importance of continuously improving the quality of care and patient safety, delirium is the focus of increasingly more qualitative and quantitative research studies in an attempt to develop greater accuracy in risk assessment, prevention, and early intervention. In 2000, the Agency for Healthcare Research and Quality commissioned the report “Making Health Care Safer: A Critical Analysis of Patient Safety Practices.”17 This landmark report analyzed and rated 80 so-called patient safety strategies. Although some success from the implementation of patient safety strategies for certain targets has been seen, studies reveal greater than expected and continued high rates of “preventable” harm in hospitals.18e21 Over the past 4 years, a project team of healthcare and patient safety experts from the RAND Corporation, Stanford University, University of CaliformaiSan Francisco, Johns Hopkins University, and the Emergency Care Research Institute in cooperation with the Agency for Healthcare Research and Quality identified delirium as 1 of 18 topics for patient safety strategies in-depth reviews.22 An in-depth systematic review of in-facility delirium prevention programs
suggests that a patient safety strategy designed to assess and remediate multiple causative factors is more likely to be effective in delirium prevention.23 The various components of these delirium prevention programs include the implementation of anesthesia protocols, medication review, mobilization, hydration, pain management, sleep cycle management, and extra nutrition. Although these multicomponent programs are effective in preventing delirium in a hospital setting, they have not been well studied in nonacute hospital settings such as palliative care and long-term care, settings with an elderly at-risk patient population. The field of geriatric psychiatry must lead the charge in further exploring and determining best practices to ensure patient safety with regard to delirium in the geriatric patient. Much of the research to date has been done in acute care settings. Less is known about delirium in the geriatric patient in a subacute or non-acutesetting. One potential research objective may be the development of a multicomponent delirium prevention program in the nonacute setting of palliative and long-term care. Another objective, as demonstrated by the five articles featured in this issue, may be the development of strategies to predict more accurately at-risk patients and to detect delirium earlier in its pathogenesis. Early detection and diagnosis are the key steps in reducing the morbidity and mortality associated with delirium in both the acute and nonacute settings. After all, as noted physician and author, Martin H. Fischer, M.D., once wrote, “Diagnosis is not the end, but the beginning of practice.”
References 1. Belluck P: Hallucinations in hospital pose risk to elderly. New York Times, June 21, 2010, p. A1 2. Speed G, Wynaden D, McGowan S, et al: Prevalence rate of delirium at two hospitals in Western Australia. Austral J Adv Nurs 2007; 25:38e43 3. Ely EW, Gautam S, Margolin R, et al: The impact of delirium in the intensive care unit on hospital length of stay. Intens Care Med 2001; 27:1892e1900 4. Gonzalez M, de Pablo J, Fuente E, et al: Instrument for detection of delirium in general hospitals: adaptation of the confusion assessment method. Psychosomatics 2004; 45:426e431 5. Francis J, Martin D, Kapoor WN: A prospective study of delirium in hospitalized elderly. JAMA 1990; 263:1097e1101 6. Oldenbeuving AW, de Kort PLM, Kappelle van LJ, et al: Delirium in the acute phase after stroke and the role of the apolipoprotein E gene. Am J Geriatr Psychiatry 2013; 21:935e937
Am J Geriatr Psychiatry 21:10, October 2013
7. Hatano Y, Narumoto J, Shibata K, et al: White matter hyperintensities predict delirium after cardiac surgery. Am J Geriatr Psychiatry 2013; 21:938e945 8. Leung JM, Sands LP, Lim E, et al: Does preoperative risk for delirium moderate the effects of postoperative pain and opiate use on postoperative delirium? Am J Geriatr Psychiatry 2013; 21: 946e956 9. Kobayashi D, Takahashi O, Arioka H, et al: A prediction rule for the development of delirium among patients in medical wards: Chisquare Automatic Interaction Detector (CHAID) decision tree analysis model. Am J Geriatr Psychiatry 2013; 21:957e962 10. von Gunten A, Mosimann UP, Antonietti JP: A longitudinal study on delirium in nursing homes. Am J Geriatr Psychiatry 2013; 21: 963e972 11. Maldonado JR: Delirium in the acute care setting: characteristics, diagnosis and treatment. Crit Care Clin 2008; 24:657e722
933
Editorial 12. Inouye SK, van Dyck CH, Alessi CA, et al: Clarifying confusion: the confusion assessment method. A new method for detection of delirium. Ann Intern Med 1990; 113:941e948 13. Adamis D, Treloar A, Martin FC, et al: APOE and cytokines as biological markers for recovery of prevalent delirium in elderly medical inpatients. Int J Geriatr Psychiatry 2007; 22: 688e694 14. Ely EW, Girard TD, Shintani AK, et al: Apolipoprotein E4 polymorphism as a genetic predisposition to delirium in critically ill patients. Crit Care Med 2007; 35:112e117 15. Fanelli D: Negative results are disappearing from most disciplines and countries. Scientometrics 2012; 90:891e904 16. Anderson G, Haiko Sprott H, Olsen BR: Opinion: Publish negative results non-confirmatory or “negative” results are not worthless. Retrieved from www.the-Scientist.com (accessed January 15, 2013) 17. Shojania KG, Duncan BW, McDonald KM, et al: Making health care safer: a critical analysis of patient safety practices. Evid Rep Technol Assess 2001; i-x:1e668
934
18. Wachter RM, Pronovost P, Shekelle P: Strategies to improve patient safey: the evidence base matures. Ann Intern Med 2013; 158(5 Part 1):350e352 19. Landrigan CP, Parry GJ, Bones CB, et al: Temporal trends in rates of patient harm resulting from medical care. N Engl J Med 2010; 363:2124e2134 20. Levinson DR: Adverse events in hospitals: national incidence among Medicare beneficiaries. Washington, DC: US Department of Health and Human Services, Office of the Inspector General; November 2010. Report No. OEI-06-09-00090 21. Rennke S, Nguyen OK, Shoeb MH, et al: Hospital-initiated transitional care interventions as a patient safety strategy. A systematic review. Ann Intern Med 2013; 158:432e440 22. Shekelle PG, Pronovost PJ, Wachter RM, et al: The top patient safety strategies that can be encouraged for adoption now. Ann Intern Med 2013; 158(5 Part 2):365e368 23. Reston JT, Schoelles KM: In-facility delirium prevention programs as a patient safety strategy: a systematic review. Ann Intern Med 2013; 158(5 Part 2):375e380
Am J Geriatr Psychiatry 21:10, October 2013