JAMDA 15 (2014) 957.e7e957.e11

JAMDA journal homepage: www.jamda.com

Original Study

A Delirium Risk Modification Program Is Associated With Hospital Outcomes James L. Rudolph MD, SM a, b, c, *, Elizabeth Archambault MSW, LICSW a, Brittany Kelly BA a, d on behalf of the VA Boston Delirium Task Force a

Geriatric Research, Education, and Clinical Center, VA Boston Healthcare System, Boston, MA Division of Aging, Brigham and Women’s Hospital, Boston, MA c Harvard Medical School, Boston, MA d School of Nursing, Science, and Health Professions, Regis College, Boston, MA b

a b s t r a c t Keywords: Delirium risk stratification prevention rehabilitation aged health care cost

Background: Delirium has been associated with negative health consequences, which can potentially be improved by delirium risk modification. This study sought to determine if a quality improvement project to identify and modify delirium risk and discharge to rehabilitation is associated with improved outcomes for patients and health care systems. Methods: In older veterans admitted to a tertiary VA hospital, delirium risk was assessed using cognitive impairment, vision impairment, and dehydration. Delirium risk was communicated to providers via electronic medical record. To modify delirium risk, interventions were provided in cognitive stimulation, sensory improvement, and sleep promotion. Primary outcomes included length of stay, restraint use, discharge to rehabilitation, and hospital variable direct costs. Outcomes were compared using a propensity-matched cohort of patients without intervention. Number of intervention categories was compared with primary outcomes. Results: Patients (n ¼ 1527) were older (78.2  8.3 years) and male (98%). Propensity-matched patients (n ¼ 566) were well matched for age, gender, cognitive deficits, vision impairment, and dehydration. Patients with interventions were discharged to rehabilitation similarly (mean difference [MD] 2.2%, 95% CI 2.56.9) and had lower lengths of stay (MD 0.7 day, 95% CI 1.3 to 0.1), lower restraint use (MD 4.0%, 95% CI 6.7 to 1.2) and trended toward lower variable direct costs (MD $1390, 95% CI 3586807). Increasing number of interventions was associated with shorter length of stay, lower rate of restraint use, and lower variable direct costs. Conclusions: This delirium risk modification project was associated with patient outcomes and reduced costs. Serious consideration should be given to delirium risk identification and modification programs. Published by Elsevier Inc. on behalf of AMDA e The Society for Post-Acute and Long-Term Care Medicine.

Older hospitalized patients are at heightened risk for adverse events. Hospital adverse events increase placement in skilled care facilities after hospital discharge.1 Although falls, pressure ulcers, and nosocomial infections are traditionally spotlighted, delirium, an acute The authors declare no conflicts of interest. This study was funded by a Department of Veterans Affairsefunded T21 Alternative to Non-institutional Long Term Care award. JLR, EA, and BK are employees of the US government. JLR also received support from a VA Career Development Award and a VA Patient Safety Center of Inquiry. The sponsor had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. The opinions expressed are those of the authors and not necessarily those of the Department of Veterans Affairs or the US government. * Address correspondence to James L. Rudolph, MD, SM, VA Boston Healthcare System GRECC, 150 S. Huntington Avenue, Boston, MA 02130. E-mail address: [email protected] (J.L. Rudolph).

change in awareness and attention,2 is an underrecognized hospital danger3 that is associated with placement.4 Delirium occurs in up to 25% of hospitalized patients,5 50% of surgical patients,6 20% of nursing home patients,7 and 75% of patients in the intensive care unit (ICU),8,9 and is associated with increased morbidity and mortality.10 Although generally thought to be an acute disorder, delirium is associated with long-term deficits in cognitive and physical function.11,12 Delirium-prevention strategies are of critical importance,13 because of the outcomes of reduced functional ability, high prevalence of cognitive impairment, and increased nursing home placement.7 Previous studies demonstrated that 14% of patients admitted to postacute facilities are delirious on admission.14 In-person screening for mental status is required15 for delirium identification, because methods such as the Minimum Data Set Resident Assessment Protocol are less likely to pick up delirium accurately.16

http://dx.doi.org/10.1016/j.jamda.2014.08.009 1525-8610/Published by Elsevier Inc. on behalf of AMDA e The Society for Post-Acute and Long-Term Care Medicine.

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The association of delirium with long-term functional decline makes prevention paramount. Programs to prevent incident delirium have shown effectiveness,3 but are not widely used.17 Program adoption is limited by health care system elements, including a dearth of strong delirium advocates, the necessary upfront capital investment, as well as a lack of sustained facility commitment to realize cost savings, recognition of the importance of delirium, and a unified pathophysiology and treatment model.5,18 Medical centers may not recognize the savings actuarially, thus making the business case difficult to demonstrate.19 However, delirium remains a common, morbid, and costly patient safety emergency. Delirium-prevention programs identify patients at risk and use multicomponent nonpharmacological interventions that are imbedded in routine care. The Delirium Toolbox was designed to target 3 intervention categories (Table 1): sensory improvement, cognitive stimulation, and sleep promotion. This Delirium Toolbox was accessible to nurses on each ward so they were able to offer the appropriate supplies to patients. In addition to this tangible component, education is provided to nursing staff regarding how to identify delirium risk and intervene to manage this risk. Our program shares similar features with previous delirium-prevention programs, including delirium risk identification, multicomponent delirium risk modification, and educational initiatives. The intent of Delirium Toolbox was to develop and implement a sustainable program that mitigates delirium risk and demonstrates improved patient outcomes (lower restraint use and discharge to rehabilitation), while building a business case (decreased length of stay and variable direct cost) for medical center leadership. Secondary analyses were to determine if the number of items distributed to patients from the Delirium Toolbox was associated with improved outcomes. Methods The Delirium Toolbox was developed through a hospital-wide Healthcare Failure and Effects Mode Analysis (HFEMA) process. The Department of Veterans Affairs Boston Healthcare System (VABHS) Institutional Review Board and Research and Development Committee approved the analysis and dissemination of this project. Setting

Delirium risk screening Cognitive performance, sensory impairment, and dehydration were targeted through a brief in-person assessment to determine delirium risk. Each risk factor was assigned 1 point, and this risk prediction rule was validated in a separate cohort.20 Cognitive performance was assessed using 3 brief measures: (1) Days of the Week Backward (DOWB), (2) Months of the Year Backward (MOYB), and (3) the Clock-in-the-Box (CIB) test. Days of the Week and Months of the Year Backward are measures of attention used in previous delirium studies.21 The CIB is a modified clock-draw task (range 0e8, with 8 indicating best) that has been associated with cognitive performance in older patients.22,23 For this project, cognitive impairment was considered any error on DOWB or MOYB, or a CIB score of 4 or less. Patients unable to read the written instructions of the CIB or without access to their corrective eyewear were considered to be visually impaired. Dehydration was assessed through elevated blood urea nitrogen to creatinine ratio with 18.0 or higher considered abnormal. Delirium risk assessments were communicated to health care staff by a progress note in the electronic medical record. The delirium risk assessment was streamlined over the duration of this project (Appendix 1). Delirium toolbox interventions The Delirium Toolbox includes items to (1) correct sensory input, (2) stimulate cognition, and (3) promote sleep (Table 1). These interventions were chosen because of their correlation with modifiable delirium risk factors.3,24 Educational interventions Delirium education was provided to patients, family members, and hospital staff. Patients and families (when present) were introduced to the concept of delirium and the importance of preventing it. Nurses were targeted as the prime recipients of the educational initiative. Information about epidemiology, recognition, prevention, and management of delirium was disseminated through a multimodal educational initiative and nurse “champions” were trained to advocate for recognition of delirium risk and reinforce early intervention on their wards. Outcome Measurement

This quality improvement project was conducted at the VABHS West Roxbury campus; the 125-bed tertiary referral Veterans Affairs medical center for New England. Veterans, 65 years of age and older, admitted to an acute care medical ward, were approached for participation. Patients admitted for observation only or to an ICU, who were unable to communicate, or who had been inpatient for 48 hours or longer before screening, were excluded from the analysis. Table 1 Delirium Toolbox Items Category

Toolbox Item

Correct sensory input

Reading glasses Magnifying glasses Hearing amplifiers

Stimulate cognition

Jigsaw puzzles Crossword/word search activity Books Playing cards Modeling clay Stress balls

Promote sleep

Interventions

Earplugs Eye masks Headphones

The selected outcomes were pertinent to medical center goals. Patient outcomes were collected via electronic medical record chart review. Length of stay was calculated from the date of admission to the date of discharge. Restraint use was identified using a keyword search of the electronic medical record, because VABHS requires documentation of restraints within 12 hours of application. Discharge to rehabilitation was obtained from the electronic medical record and patients who died in the hospital were excluded. Hospital variable direct costs were collected from the VA decision support system (DSS),25 a centrally maintained administrative database. To address the limitations of DSS,26 we performed a sensitivity analysis of the cost data (Appendix 2). Statistical Analysis Overall analysis Baseline characteristics of those who received Delirium Toolbox interventions were compared with those who did not (concurrent controls), using a Student t test with the reported difference between sample means and 95% confidence intervals (95% CIs) reported. Because those with and without Delirium Toolbox interventions had

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Table 2 Comparison of Populations Characteristics

Age, y Male Cognitive impairment CIB 4 DOWB incorrect MOYB incorrect Vision impairment Dehydrationz Delirium risk, no. factors

Overall, n ¼ 1527

78.2 98.0 65.3 53.5 15.8 49.9 33.7 66.7 1.7

(8.3) (1497/1527) (951/1456) (649/1212) (208/1312) (713/1430) (514/1514) (1018/1527) (0.9)

Unmatched Analysis

Propensity-Matched Analysis

No Intervention, n ¼ 818

Delirium Toolbox Intervention, n ¼ 709

Difference* (95% CI)

No Intervention, n ¼ 566

Delirium Toolbox Intervention, n ¼ 566

Differencey (95% CI)

77.5 97.4 58.7 47.8 13.6 44.4 24.7 65.8 1.7

79.0 98.7 72.6 59.7 18.1 55.8 44.0 67.7 1.8

1.4 1.3 13.9 11.9 4.5 11.5 19.1 1.9 0.1

78.4 (8.2) 99.5 (563/566) 74.7 (423/566) 44.3 (251/566) 16.1 (91/566) 49.6 (281/566) 188/566 67.0 (379/566) 1.8 (0.8)

78.3 (8.0) 98.9 (560/566) 73.8 (418/566) 43.8 (248/566) 17.1 (97/566) 53.2 (301/566) 191/566 67.5 (382/566) 2.0 (0.8)

0.1 0.5% 0.9% 0.6% 1.1% 3.6% 0.5 0.5 0.2

(8.4) (797/818) (447/762) (297/622) (89/654) (330/744) (202/807) (538/818) (0.9)

(8.1) (700/709) (504/694) (352/590) (119/658) (383/686) (312/707) (480/709) (0.9)

(2.3 to 0.6) (0.1 to 2.7) (18.8 to 9.1) (17.5 to 6.3) (8.4 to 0.5) (0.6 to 16.6) (23.8 to 14.4) (6.7 to 2.8) (0.2 to 0.01)

(0.9 (0.5 (4.1 (5.2 (5.4 (9.4 (6.0 (5.9 (0.3

to to to to to to to to to

1.0) 1.5) 6.0) 6.4) 3.3) 2.2) 5.0) 5.0) 0.1)

CI, confidence interval; CIB, Clock-in-the-Box test; DOWB, Days of the Week Backward; MOYB, Months of the Year Backward. Results are displayed as mean (SD) or % (n/N). *Crude analysis uses a t test to compare differences between the groups. y Propensity analysis uses a paired t test with bootstrapping of the difference to obtain the 95% CI. z Dehydration is measured by a blood urea nitrogen to creatinine ratio >18.

different baseline characteristics, we performed a propensity score analysis to create comparable groups of those with and without interventions. Covariate data were infrequently missing (6%) and we performed multiple imputations (n ¼ 20) before propensity matching. The propensity analysis matched patients with and without Delirium Toolbox interventions on age, gender, cognitive performance, vision deficit, and dehydration using a radius match within a propensity score of 0.1. After propensity matching, we bootstrapped (n ¼ 50) the average treatment effect to obtain the 95% CIs. All statistical analyses were performed using STATA v11.0 (STATA, Inc, College Station, TX). Comparison of interventions The number of intervention categories (ie, 0, 1, 2, 3) was compared among the outcomes in the propensity-matched cohort. Poisson regression was selected because the number of intervention items, length of stay, and cost outcomes are count variables, non-normal, and follow a Poisson distribution. Poisson regression produces an incident rate ratio (IRR and 95% CI), which is the change per additional intervention. Results Table 2 describes the overall population, the crude comparison of those with and without toolbox interventions, and the propensitymatched analysis. In general, patients were older (mean age 78.2  8.3 years) men (98% men). Although 16% could not name the DOWB, a much higher percentage had difficulty with the MOYB (50%) and CIB (54%). Overall, 65% of patients had difficulty with the cognitive assessment. More than a third of patients had vision deficits

(34%). Dehydration was present in 66.7% of participants. In the unmatched analysis, Delirium Toolbox interventions were provided to patients who were older (79.0  8.1 vs 77.5  8.4 years, P < .001) and had higher delirium risk (31.9% vs 16.5% high risk, P < .001). The intervention group also tended to be male (99% vs 97%, P ¼ .07). In the propensity-matched cohort, there were no significant differences between those with and without Delirium Toolbox interventions with respect to age, gender, cognitive performance, vision deficits, dehydration, or delirium risk factors. The outcomes of the unmatched and propensity-matched analysis are listed in Table 3. In the unmatched analysis, there was a shorter length of stay and lower rate of restraint use. In the propensity analysis, Delirium Toolbox interventions are associated with a shorter length of stay (0.7 days, 95% CI 1.3 to 0.1) and lower rate of restraint use (4.0%, 95% CI 6.7 to 1.2), but not with discharge to rehabilitation (2.2%, 95% CI 2.56.9) or variable direct cost ($1390, 95% CI 3586e807). The impact of the number and intervention type is displayed in Table 4. A greater number of interventions was correlated (r ¼ 0.24, P < .001) with increasing delirium risk. The number of intervention categories is associated with significant IRR reductions in length of stay, restraint use, and variable direct costs. Discussion Delirium is a dangerous condition, which commonly affects older, hospitalized patients, and is associated with negative long-term health consequences. This quality improvement project was designed to identify and modify delirium risk proactively so as to improve patient outcomes and lower hospital costs. In a propensity

Table 3 Adjusted Outcomes for Comparison of Intervention Outcomes

Length of stay, d Restraint use Rehabilitation discharge Cost, $

Unmatched Analysis

Matched Propensity Analysis

No Intervention, n ¼ 818

Intervention, n ¼ 709

Difference* (95% CI)

No Intervention, n ¼ 566

Intervention, n ¼ 566

Differencey (95% CI)

5.0 7.0 17.8 10,674

4.5 2.8 21.6 9205

0.53 4.1% 3.4% 1469

5.1 6.9% 17.9% 10,836

4.4 2.8% 20.1% 9446

0.7 4.0% 2.2% 1390

(5.8) (57/818) (143/805) (20,395)

(4.8) (20/709) (149/701) (14,060)

(1.07 to 0.0001) (6.3% to 2.0%) (0.5% to 7.4%) (3253 to 315)

(5.9) (39/566) (100/557) (22,317)

CI, confidence interval. Results are displayed as mean (SD) or % (n/N). *Crude analysis uses a t test to compare differences between the groups. y Propensity analysis uses a paired t test with bootstrapping of the difference to obtain the 95% CI.

(4.7) (16/566) (113/561) (14,937)

(1.3 to 0.1) (6.7% to 1.2%) (2.5% to 6.9%) (3586 to 807)

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Table 4 Impact of Interventions Outcomes

No Intervention, n ¼ 566

No. of Intervention Categories (Propensity-Matched Cohort)*

IRRy (95% CI)

Length of stay, d Restraint use Rehabilitation discharge Variable direct cost, $

0 Reference 5.1 (5.9) 6.9 (39/566) 17.9 (100/557) 10,836 (22,317)

1 n ¼ 363 4.5 (4.7) 2.8 (14/363) 20.0 (72/361) 9488 (14,784)

0.91 0.61 1.05 0.91

2 n ¼ 174 4.5 (4.9) 3.1 (5/174) 21.6 (37/171) 10,071 (16,223)

3 n ¼ 29 3.6 (3.7) 3.4 (1/29) 13.9 (5/29) 5223 (5483)

(0.880.94) (0.480.78) (0.981.13) (0.850.97)

CI, confidence interval; IRR, incidence rate ratio. Results are displayed as mean (SD) or % (n/N). *The number of intervention categories is the sum of categories that were provided to the patient. y The analyses used a Poisson regression. The IRR presented is per additional intervention category.

analysis, patients who received delirium risk modification items had an associated shorter length of stay, lower rates of restraint use, and lower variable direct costs. Patient-centered programs that identify and mitigate delirium risk appear to benefit patient and hospital outcomes. The Delirium Toolbox project is patient-centered, as it delivers interventions to high-risk patients at a high-risk time. Although a causal relationship between Delirium Toolbox interventions and the outcomes cannot be established in this quality improvement project, the lower length of stay, rate of restraint use, and variable direct costs suggest that delirium risk modification leads to health system savings. Regardless of the business case for delirium risk modification, the early identification of elders at risk for negative outcomes in the hospital is critical to improving the hospital experience.27 Past literature supports similar efforts in surgical care,28 medication safety,29 pressure ulcer prevention,30 and falls prevention.31 Previous research has identified that delirium prevention works best in patients at intermediate risk for developing delirium.32,33 Current recommendations for the initial steps of delirium prevention and treatment include nonpharmacological strategies.6,10 By providing nurses with the knowledge to identify risk and tools to mitigate that risk, the Delirium Toolbox appears to improve patient care. Finally, past retrospective studies have demonstrated that the incidence of delirium identified in the medical record is lower than delirium identified by clinical interview, resulting in an underreporting of delirium.34,35 The Delirium Toolbox emphasis on delirium risk identification, together with modification, has the potential to improve the presence of medical record documentation regarding delirium. The finding of a strong association with sleep promotion items from the Delirium Toolbox suggests that other interventions to improve sleep hygiene and sensory improvement in the hospital should be strongly considered. Sleep interventions consisted of earplugs, eye masks, and headphones. The findings are consistent with other studies that demonstrated that earplugs in an ICU reduced the incidence of delirium.36 At a cost of $0.15 per pair of earplugs, the affordability of sleep promotion measures is compelling. Strengths demonstrated in this work include a robust sample size, the recording of delirium risk factors and outcomes, a grassroots effort to make interventions accessible for all patients, the propensity-matched analysis, and the comprehensive educational initiatives throughout the medical center. The major limitation of this project is related to the quality improvement nature of the work. The comparison group was not a randomized control group. As a result, we used a propensity-matching strategy on available variables. However, the possibility for residual confounding by unknown variables exists. Data gathering focused on outcomes associated with patient safety and cost, and were used to make the case for sustainability within the medical center. The generalizability of this work is limited by the implementation in a single-site VA hospital. In general, the VA population is older, male, and has more comorbidities than the general US population.37,38

Conclusions Among patients who received the Delirium Toolbox intervention, hospital stays were shorter and there was a lower rate of restraint use. The 3 categories of Delirium Toolbox interventions (cognitive stimulation, sensory improvement, and sleep promotion) can be administered by nursing staff, and demonstrate a positive association with patient outcomes. Because of the dangerous and costly nature of delirium, all patients should be screened for delirium risk, and have the risk modified when possible.

Acknowledgments We are indebted to the veterans who participated in our Delirium Toolbox program. We thank Jane A. Driver, MD, Edward R. Marcantonio, MD, Peter Mills, PhD, and Kelly Doherty, BA, for their thoughtful reviews and comments.

Supplementary Data Supplementary Data related to this article can be found online at http://dx.doi.org/10.1016/j.jamda.2014.08.009

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27. Davidoff F, Batalden P, Stevens D, et al. Publication guidelines for improvement studies in health care: Evolution of the squire project. Ann Intern Med 2008; 149:670e676. 28. Dimick JB, Weeks WB, Karia RJ, et al. Who pays for poor surgical quality? Building a business case for quality improvement. J Am Coll Surg 2006;202: 933e937. 29. Leendertse AJ, Egberts AC, Stoker LJ, van den Bemt PM. Frequency of and risk factors for preventable medication-related hospital admissions in the Netherlands. Arch Intern Med 2008;168:1890e1896. 30. Thomas DR. The new F-tag 314: Prevention and management of pressure ulcers. J Am Med Dir Assoc 2006;7:523e531. 31. Tinetti ME. Clinical practice. Preventing falls in elderly persons. N Engl J Med 2003;348:42e49. 32. Inouye SK, Bogardus ST Jr, Charpentier PA, et al. A multicomponent intervention to prevent delirium in hospitalized older patients. N Engl J Med 1999;340: 669e676. 33. Marcantonio ER, Flacker JM, Wright RJ, Resnick NM. Reducing delirium after hip fracture: A randomized trial. J Am Geriatr Soc 2001;49:516e522. 34. Inouye SK, Leo-Summers L, Zhang Y, et al. A chart-based method for identification of delirium: Validation compared with interviewer ratings using the confusion assessment method. J Am Geriatr Soc 2005;53:312e318. 35. Rudolph JL, Harrington MB, Lucatorto MA, et al. Validation of a medical recordbased delirium risk assessment. J Am Geriatr Soc 2011;59:S289eS294. 36. Van Rompaey B, Elseviers MM, Van Drom W, et al. The effect of earplugs during the night on the onset of delirium and sleep perception: A randomized controlled trial in intensive care patients. Crit Care 2012;16:R73. 37. Asch SM, McGlynn EA, Hogan MM, et al. Comparison of quality of care for patients in the Veterans Health Administration and patients in a national sample. Ann Intern Med 2004;141:938e945. 38. Jha AK, Wright SM, Perlin JB. Performance measures, vaccinations, and pneumonia rates among high-risk patients in Veterans Administration health care. Am J Public Health 2007;97:2167e2172.

A delirium risk modification program is associated with hospital outcomes.

Delirium has been associated with negative health consequences, which can potentially be improved by delirium risk modification. This study sought to ...
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