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Description of Fall-Related Transfers in the Nursing Facility: Findings From the OPTIMISTIC Project
Abstract
Falls are a leading cause for transfer of long-term care (LTC) residents to the emergency department (ED) and hospital. Reducing avoidable hospital transfers continues to be a priority. Using data from the Minimum Data Set (MDS) 3.0 and the database of the Optimizing Patient Transfers, Impacting Medical Quality, and Improving Symptoms: Transforming Institutional Care (OPTIMISTIC) demonstration project, our study team compared characteristics of LTC residents transferred to hospital settings for falls with residents transferred for other clinical reasons over 27 months. Our analysis showed that history of falls, cognitive impairment, female sex, White race, and being aged 80 years or older are all significantly predictive of fall-related transfers vs non-fall–related transfers. The majority of those transferred only needed care in the ED. Our study underscores the need for continued work to identify residents at high risk for injurious falls and hospital transfers to better inform risk stratification and interventions in the nursing facility setting.
Key words: Long-term care, falls, hospital transfer, ED visit
Citation: Ann Longterm Care. 2023.
DOI: 10.25270/altc.2023.04.001
Introduction
Falls are a leading cause for transfer of long-term care (LTC) residents to the emergency department (ED) and admission to the hospital.1,2 A fall, defined as the unplanned descent of a person to a lower level with or without injury, is a common event in nursing facilities.2-4 Of the 1.6 million residents in US nursing facilities, about half of them fall every year, and 1 in every 3 residents falls multiple times in a year.5 Factors predisposing older adults to falls can be divided into three categories: extrinsic, anticipated intrinsic, and unanticipated intrinsic. Extrinsic factors relate to the environmental causes of falls and are usually considered accidental. Physiologic or intrinsic causes can be either anticipated (such as certain medications) and unanticipated (such as sudden stroke).6,7 Among older adults, those in nursing facilities fall more frequently than those residing in the community.4,8
In a retrospective study that compared cases of falling in nursing facilities and their estimated costs, residents who fell were more likely to suffer fractures and hospitalizations than those who did not fall; in addition, falls in LTC settings are associated with higher total health care cost.9
The US Centers for Medicare & Medicaid Services (CMS) includes the percentage of LTC residents experiencing one or more falls with major injury as one of the long-stay quality measures in nursing facilities.10 As such, it is important that nursing facilities have a fall management program and create a culture of safety.5 While this is a important guideline for nursing facilities to follow, it is also crucial to prevent fall-related hospital transfers and their potential complications, especially in frail residents.11,12 However, we lack a reliable method to identify LTC residents with high risk of fall-related hospital transfers. This study aims to close this gap by illustrating variables that are associated with fall-related hospital transfers. The objectives of the study are: (1) to describe characteristics of LTC residents who were transferred to the hospital due to falls; and (2) to describe differences in characteristics of LTC residents transferred to the hospital with falls vs other non-fall reasons.
Methods
Optimizing Patient Transfers, Impacting Medical Quality, and Improving Symptoms: Transforming Institutional Care (OPTIMISTIC) is a CMS demonstration project that implemented multicomponent interventions, including clinical interventions and payment incentives, in an effort to reduce potentially avoidable hospital transfers among LTC residents.13,14 The OPTIMISTIC project was approved by the Indiana University Institutional Review Board. We investigated all hospital transfers of LTC residents enrolled in the OPTIMISTIC demonstration project from 17 nursing facilities in a 27-month period, between April 1, 2017, and June 30, 2019. Nursing facilities had an average of 141.8 Medicare- and Medicaid-certified beds (range, 89-188), and none were in rural or mostly rural Zip codes. Ownership was primarily public (56.5%), with 17.4% for profit and 26.1% nonprofit.14 The star ratings of the facilities ranged from 1 to 5, with 35.3% rated 5 stars, 23.5% rated 4 stars, 29.1% rated 3 stars, 5.9% rated 2 stars, and 5.9% rated 1 star, as of September 2016. Facilities had an average all-cause hospital transfer rate of program-eligible residents of 1.2 transfers per 1000 resident-days, an average non-White resident population of 29.0% (range, 4.3-79.2%), and an average Cognitive Function Scale score of 2.1 (range, 1.6-2.7).14
We reviewed general health and functioning data on the residents who were transferred to the hospital based on Minimum Data Set (MDS) 3.0 assessments, including data about activities of daily living (ADLs), gait abnormality, functional limitation in range of motion, use of mobility devices, fall history on admission, arthritis, stroke, Parkinson disease, incontinence, severe mental illness, and cognitive functional status.
We also reviewed transfer forms from the OPTIMISTIC project to gather more information about residents who were transferred to the hospital. Registered nurses (RNs) in the OPTIMSTIC project were trained in Interventions to Reduce Acute Care Transfers (INTERACT) tools,15,16 and so when these residents were transferred to the hospital, the RNs performed a root cause analysis. These data were transmitted through a secure REDCap database hosted at Indiana University.16 For this study, REDCap provided data on total falls, demographics, medication changes that the project RN noted in relation to hospital transfers and outcomes of transfers (eg, ED visit only, hospitalization for observation, or inpatient stay). Discharge diagnoses associated with the hospitalization were not captured.
We examined the variables known to affect fall risk: age, sex, race/ethnicity, functional status (eg, ADLs, gait abnormality, use of mobility device), fall history, comorbidities (eg, stroke/cerebrovascular accident, Parkinson disease, arthritis, dementia, incontinence, severe mental illness, intellectual disability, epilepsy), number of medications, and psychotropic medication usage.
A bivariate analysis of these variables was done between the group who was transferred to hospital due to falls, and the group who was transferred without fall as a reason. A logistic regression generalized estimating equations (GEE) model was used to compare characteristics. This model accounted for the non-independence structure of the data introduced by residents having more than one transfer.
Because falls, as a geriatric syndrome, has multiple facets to its etiology and high chance of confounding, a multivariable logistic regression GEE model was run to adjust for variables to illustrate potential associations of variables and falls.
Results
The 27-month study included a total of 1644 transfers among 877 residents from the nursing facility to the hospital for acute changes of condition. We divided the total group into 2 groups of hospital transfers: those with falls (n=205) and those without falls (n=1439).
Falls were the second leading cause of transfer to the hospital; the most common category was cognitive/behavioral and psychiatric reasons. Table 1 lists the most frequent reasons of transfer to the hospital.
Table 1. Reasons for Transfers From the Nursing Facility to the Hospital (N=1644) |
|
Reason for Hospital Transfers | Number of Hospital Transfers |
Cognitive/behavioral and psychiatric reasons |
298 (18.1%) |
Falls |
205 (12.5%) |
Pain |
93 (5.7%) |
Abnormal laboratory finding |
87 (5.3%) |
Shortness of breath/high respiratory rate |
76 (4.6%) |
Hypoxia |
69 (4.2%) |
Chest pain |
64 (3.9%) |
Othera |
752 (45.7%) |
aOther includes reasons for transfer that were less frequent (each category <3.5% of transfers) and were not in the top seven categories. Other reasons include non-gastrointestinal bleeding, vomiting, seizure, hypotension, tachycardia, gastrointestinal bleeding, hypertension, and urinary retention. |
The characteristics of residents transferred for falls and those transferred for non-fall reasons outlined in Table 2. The results of a bivariate comparison of variables showed that residents had at higher risk of falls with a hospital transfer in the following categories: Older adults aged 80 years or above (64% vs 38%; P < .0001), female sex (70% vs 55%; P = .0005), White race (82% vs 65%; P < .0001), history of falls (47% vs 22%; P < .0001), cognitive impairment (mild, 22.8% vs 21.8%; P = .001; moderate, 50.8% vs 31.2%; P < .0001; severe, 7.8% vs 5.0%; P = .0002), occasional incontinence (25.4% vs 21.5%; P = .012), and frequent incontinence (45% vs 34%; P = .014). Patients with a fall-related hospital transfer were more likely to have received care in the ED without hospitalization (65% vs 28%; P < .0001) compared to transferred patients without falls.
Table 2. Descriptive Statistics and Bivariate Logistic Regression GEE Results Comparing Residents, Transferred to the Hospital for Fall vs Non-Fall Reasons (N=1644 transfers) |
|||
|
Fall (n=205) |
No Falls (n=1439) |
P Valuea |
Any medication change in 1 week of transferb |
|||
No medication change |
152 (74.1%) |
935 (65.0%) |
ref |
Any medication change |
53 (25.9%) |
504 (35.0%) |
.0081 |
Age–continuous |
|||
Mean (SD) |
81.3 (11.0) |
75.1 (12.3) |
<.0001 |
Range |
48.0-107.0 |
34.0-107.0 |
|
Age–categorized |
|||
< 80 years |
73 (35.6%) |
886 (61.6%) |
ref |
≥ 80 years |
132 (64.4%) |
553 (38.4%) |
<.0001 |
Sex |
|||
Female |
144 (70.2%) |
799 (55.5%) |
ref |
Male |
61 (29.8%) |
640 (44.5%) |
.0005 |
Race |
|||
White |
169 (82.4%) |
935 (65.0%) |
ref |
Black |
31 (15.1%) |
466 (32.4%) |
<.0001 |
Other |
5 (2.4%) |
38 (2.6%) |
.50 |
ADL function score |
|||
Mean (SD) |
18.9 (3.0) |
18.9 (3.9) |
.65 |
Range |
5.0 - 27.0 |
1.0 - 28.0 |
|
N-Miss |
0 |
1 |
|
Balance–walking |
|||
Steady at all times |
12 (5.9%) |
57 (4.0%) |
ref |
Not steady, able to stabilize without staff |
51 (25.1%) |
297 (20.8%) |
.758 |
Not steady, only able to stabilize with staff |
87 (42.9%) |
381 (26.7%) |
.765 |
Activity did not occur |
53 (26.1%) |
691 (48.5%) |
.012 |
N-Miss |
2 |
13 |
|
History of falls |
|||
No |
109 (53.2%) |
1121 (77.9%) |
ref |
Yes |
96 (46.8%) |
318 (22.1%) |
<.0001 |
Parkinson disease |
|
|
|
No |
194 (94.6%) |
1365 (94.9%) |
ref |
Yes |
11 (5.4%) |
74 (5.1%) |
.84 |
CFS |
|||
1 - Cognitively intact |
36 (18.7%) |
585 (42.0%) |
ref |
2 - Mildly impaired |
44 (22.8%) |
303 (21.8%) |
.001 |
3 - Moderately impaired |
98 (50.8%) |
435 (31.2%) |
<.0001 |
4 - Severely impaired |
15 (7.8%) |
69 (5.0%) |
.0002 |
N-Miss |
12 |
47 |
|
Depression (other than bipolar) |
|||
No |
84 (41.0%) |
635 (44.2%) |
ref |
Yes |
121 (59.0%) |
803 (55.8%) |
.44 |
N-Miss |
0 |
1 |
|
Incontinence |
|||
Always continent |
9 (4.4%) |
126 (8.8%) |
ref |
Occasionally incontinent |
52 (25.4%) |
310 (21.5%) |
.012 |
Frequently incontinent |
93 (45.4%) |
497 (34.5%) |
.014 |
Always incontinent |
48 (23.4%) |
403 (28.0%) |
.195 |
Not rated |
3 (1.5%) |
103 (7.2%) |
.185 |
Vision |
|||
Adequate |
161 (80.9%) |
1093 (79.4%) |
ref |
Impaired |
28 (14.1%) |
184 (13.4%) |
.92 |
Moderately impaired |
5 (2.5%) |
53 (3.9%) |
.46 |
Highly impaired |
4 (2.0%) |
16 (1.2%) |
.30 |
Severely impaired |
1 (0.5%) |
30 (2.2%) |
.13 |
N-Miss |
6 |
63 |
|
Hospitalization/ED visit |
|||
ED visit only |
134 (65.4%) |
407 (28.3%) |
ref |
Hospital admission |
71 (34.6%) |
1032 (71.7%) |
<.0001 |
aP value indicates statistical significance of bivariate test obtained from a logistic regression GEE model. bMedication changes that may have been associated with transfers were noted by trained nursing staff of the OPTIMISTIC project. Abbreviations: ADL, activities of daily living, CFS, Cognitive Functional Scale; ED, emergency department; ref, category specified as reference group; SD, standard deviation. |
Table 3 illustrates the results of a multivariate logistic regression GEE (n=1435, missing 209). For this statistical modeling, variables known to affect fall risk in older adults were included. The following variables were significantly seen in the group of transferred residents with falls compared with transferred residents without falls: age 80 years or older (odds ratio [OR], 2.00; P = .001), female sex (OR, 1.49; P = .05), White race (OR, 2.25; P = .000), history of falls (OR, 2.42; P = .000), and the presence of any impairment vs no impairment per the Cognitive Functional Scale score (OR, 2.94; P = .000). Documentation of medication changes related to the transfers was provided less often by the OPTIMISTIC RNs for transferred patients with falls vs those without falls (OR, 0.62; P = .009). Detailed information about the characteristics of medication changes was not available.
Table 3. Multivariable Logistic Regression GEE Model Results of Transfer Due to Falls Adjusted for Demographics and Other Characteristics (N=1435 transfers)a |
|||
Variable |
OR |
95% CI |
P Value |
Medication change |
0.62 |
0.43 – 0.89 |
.009 |
Age 80 years or older |
2.00 |
1.31 – 3.06 |
.001 |
Female sex |
1.49 |
1 – 2.24 |
.050 |
White race |
2.25 |
1.43 – 3.53 |
.000 |
ADL score |
0.98 |
0.93 – 1.03 |
.424 |
History of falls |
2.42 |
1.72 – 3.42 |
.000 |
Parkinson disease |
0.98 |
0.50 – 1.90 |
.948 |
Stroke or TIA |
0.76 |
0.41 – 1.41 |
.381 |
CFS score, any impairment |
2.95 |
1.85 – 4.69 |
.000 |
Anxiety disorder |
1.04 |
0.69 – 1.56 |
.844 |
Psychotic disorder |
1.27 |
0.68 – 2.38 |
.452 |
Schizophrenia |
0.98 |
0.48 – 2.00 |
.950 |
Depression |
1.17 |
0.81 – 1.69 |
.405 |
Incontinence, frequently/always incontinent |
0.75 |
0.48 – 1.16 |
.196 |
Vision impairment |
0.79 |
0.48 – 1.29 |
.345 |
Received antipsychotic, anxiety, or hypnotic medications |
1.16 |
0.75 – 1.79 |
.501 |
Impairment of upper or lower extremity |
0.77 |
0.49 – 1.22 |
.268 |
aData for 209 of the 1644 transfers were missing. Abbreviations: ADL, activities of daily living; CFS, Cognitive Functional Score; CI, confidence interval; OR, odds ratio; TIA, transient ischemic attack. |
Discussion
This study evaluated MDS variables and hospital transfer data from 17 nursing facilities and compared transfers to the hospital that were related to falls with those which were not. The study adds three key messages for LTC practice: (1) Among documented transfers, history of falls and cognitive impairment are highly correlated with fall-related hospital transfers, in addition to demographic characteristics of female sex, White race, and age 80 years or older; (2) most patients transferred to the hospital due to falls used ED services only and returned back to the facility, compared with residents transferred for non-fall reasons; and (3) analysis of MDS and hospital transfer data may suggest a cohort of LTC residents who have a higher risk of fall-related hospital transfer. The findings of our study are relevant for LTC facilities seeking to develop a focused program to address the problem of falls and fall-related transfers. These variables could trigger nursing facilities to be proactive in identifying individuals with high risk of fall-related transfers.
The analysis showed that most residents (65%) who were transferred to the hospital due to falls used ED stay/observation only, whereas residents who were transferred due to non-fall reasons were more frequently hospitalized (72%). This is consistent with another study (n=1224) that showed patients with fall-related ED transfers were less likely to be admitted to the hospital (OR, 0.18; 95% CI=0.12-0.27; P < .001).17 This suggests a lower acuity of fall-related hospital transfers compared with non-fall transfers and raises possible opportunities to prevent such transfers. Further investigations are needed to gauge a nursing facility’s ability to triage residents with falls for the need of hospital transfer, including on-site strategies for assessment and evaluation. A comprehensive geriatric assessment of frail LTC residents may help improve fall prevention programs and minimize hospital transfers.18 It is also worthwhile to explore whether the absence of a health care provider at the time of the fall may also increase the incidence of ED and hospital transfers, as reported in the literature.2 Lack of timely availability of imaging, ability to treat skin injury from falls in the facility, and fears of malpractice liability may also contribute to a facility’s decision to transfer residents with falls to the hospital. A study in assisted living residents who were transferred to the ED for falls reported that shared decision-making between paramedics and the resident’s primary care physician can prevent hospital transfers of residents with falls.19
Our study illustrates how data from MDS 3.0 and hospital transfers can be used to identify risks for falls related to transfers among LTC residents, while previous studies have highlighted a list of risk factors predisposing an individual LTC resident to fall. For example, a study of a single nursing facility chain in California investigated MDS and EMR data to identify residents with high fall risk in their facilities.20 Our retrospective study of 17 nursing facilities focused on falls that led to hospital transfers as the outcome compared with transfer for non-fall reasons. Thus, our study provides new, relevant information for potential quality improvement in LTC facilities. In addition to fall management programs to address all falls in the facility, including falls not associated with hospital transfers, this study nudges facilities to focus on a cohort of high-risk residents who have variables identified from MDS 3.0 and hospital transfer information.
Our study has several limitations, including that we were unable to capture detailed information about residents’ medications or whether any changes to their medications were made before the falls. Hence, we were unable to find correlations of medication changes and fall-related hospital transfers, although there exists significant literature on medications predisposing LTC residents to falls. Medications are known as one of the intrinsic and modifiable risk factors that can predispose an individual to falls.7 The top five medicine classes that increase risk for LTC residents are antidepressants, antipsychotics, benzodiazepines, hypnotics, and antihypertensives.21,22 Some medication changes are also reported as a potential risk factor for falls among LTC residents. For example, fall risk may increase in the days after psychotropic medication changes23,24 as well as after intensification of antihypertensive medication.25 Our study looked at any potential implications of medication changes that happened within 1 week of transfer to the hospital as per analysis of trained RNs, and medication changes were found to be associated with a lower risk of transfers. Medication changes were more common in the transferred residents due to non-fall causes, probably because the transfers were related treatment changes in conditions such as congestive heart failure exacerbation and infections. Our study was not designed to capture data on all medication changes that happened in the facilities or to investigate the details of these changes that are associated with adverse outcomes, such as falls or other changes of conditions. Hence, this study was unable to illustrate any relationship between medication changes and fall-related transfers. In addition, this study only examined residents who were transferred to the hospital and excluded residents who were not transferred to the hospital after a fall or non-fall–related change in condition. The study did not examine hospital diagnoses data, so we could not ascertain or compare prevalence of key diagnoses that lead to hospitalizations, such as urinary tract infection, exacerbation of chronic obstructive lung disease, decompensated congestive heart failure, or acute coronary syndromes. These study sites had trained OPTIMISTIC RNs and nurse practitioners who were dedicated to prevent hospital transfers by proactively supervising changes in residents’ conditions. This analysis did not compare outcomes in sites with or without this support staff, however, which may limit generalizability.
Overall, this study presents a silver lining to nursing facility teams, who can incorporate these findings into developing care plans to identify the group of residents with high risk for fall-related transfers and address this risk with a multifaceted solution. These variables may also help in discussions with family and caregivers about hospital transfer risks. The key risk factors identified in this study—aged 80 years or older, female sex, White race, history of falls, and cognitive impairment—were found to be most strongly associated with hospital transfers due to falls compared with transfers due to other reasons, and they are consistent with the risk factors identified in established fall prevention programs.5 The study highlights the need to further investigate the reasons for hospital transfers with falls only needing ED care and those requiring hospitalization, with the potential to identify a larger group of residents who may be safely assessed within the facility and avoid the transfer altogether. Continued work to identify residents at higher risk for injurious falls and hospital transfers may inform risk stratification and interventions in the nursing facility setting.
Affiliations, Disclosures, & Correspondence
Kamal C Wagle, MD, MPH1,2 • Greg A Sachs, MD1,3 • Timothy E Stump, MA4 • Wanzhu Tu, PhD3,4 • Erin O’Kelly Phillips, MPH3 • Kristi M Lieb, MD1,5 • Kathleen Unroe, MD, MHA1,3
Affiliations:
1Division of General Internal Medicine and Geriatrics, Indiana University School of Medicine, Indianapolis, IN
2Indiana University Health Physicians
3Indiana University Center for Aging Research, Regenstrief Institute, Inc, Indianapolis, IN
4Department of Biostatistics, Indiana University School of Medicine, Indianapolis, IN
5Veterans Health Administration, US Department of Veterans Affairs, Richard L VA Medical Center, Indianapolis, IN
Disclosures:
This work was supported by the US Department of Health and Human Services, Centers for Medicare & Medicaid Services (Funding Opportunity 1E1CMS331488). The opinions expressed in this article are the author's own and do not reflect the view of the US Department of Health and Human Services, Centers for Medicare & Medicaid Services. Dr Unroe is the CEO of Probari, a health care company founded to disseminate the OPTIMISTIC clinical care model.
Acknowledgements:
The study team would like to acknowledge John Price for help with data collection and Liza Cohen for manuscript preparation for submission.
Address correspondence to:
Kamal Wagle
720 Eskenazi Avenue, F2-600
Indianapolis, IN
317-880-8842
kwagle@iu.edu
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