Abstract
Background:
People with Huntington’s disease (HD) often become institutionalized and more frequently die away from the home setting. The reasons behind differences in end-of-life care are poorly understood. Less than 5% of people with HD report utilization of palliative care (PC) or hospice services, regardless of the lack of curative therapies for this neurodegenerative disease. It is unknown what factors are associated with in-patient specialty PC consultation in this population and how PC might be related to discharge disposition.
Objectives:
To determine what HD-specific (e.g., psychosis) and serious illness-specific factors (e.g., resuscitation preferences) are associated with PC encounters in people with HD and explore how PC encounters are associated with discharge disposition.
Design:
We analyzed factors associated with PC consultation for people with HD using discharge data from the National Inpatient Sample and the Nationwide Inpatient Sample (NIS), Healthcare Cost and Utilization Project (HCUP), Agency for Healthcare Research and Quality. An anonymized, cross-sectional, and stratified sample of 20% of United States hospitalizations from 2007 through 2014 were included using ICD-9 codes.
Results:
8521 patients with HD were admitted to the hospital. Of those, 321 (3.8%) received specialty PC. Payer type, (specifically private insurer or other insurer as compared to Medicare), income, (specifically the top quartile as compared to the bottom quartile), mortality risk, D.N.R., aspiration pneumonia, and depression were significantly associated with PC in a multivariate model. Among those who received PC, the odds ratio (OR) of discharge to a facility was 0.43 (95% CI, 0.32–0.58), whereas the OR of discharge to home with services was2.25 (95% CI 1.57–3.23), even after adjusting for possible confounders.
Conclusions:
Among patients with HD, economic factors, depression, and serious illness-specific factors were associated with PC, and PC was associated with discharge disposition. These findings have implications for the adaptation of inpatient PC models to meet the needs of persons with HD.
Keywords: palliative care, neuropalliative care, Huntington disease, end-of-life
Introduction
Huntington’s disease (HD) is an autosomal dominant neurodegenerative disease caused by the mutant huntingtin protein.1 People with the HD gene have an almost 100% penetrance of motor symptoms, which often arise in the third to fifth decade of life. Sometimes years before motor symptoms appear, people will often exhibit apathy, impulsivity, depression, and anxiety.2 Once the motor manifestations appear, life expectancy is around 15 to 20 years with an inexorable decline in all domains, including cognitive, psychiatric, and motor.3 No disease-modifying agent or cure has been discovered. There remain few evidence-based therapies to improve the health-related quality of life (HRQOL) in HD.3 Therefore, the management of HD remains inherently supportive.4 Yet, fewer than 5% of people with the HD gene mutation report receiving palliative care (PC), and approximately 57% reported little thought to these services in a recent large multi-center cross-sectional study.5
Nationally, people with HD often die in the hospital (29.8%) or at a skilled nursing facility (SNF) (19.8%), with 23.9% dying at home.6 Fewer than 5% die with hospice care.6,7 These findings are in stark contrast to other dementias, where a majority (66.9%) die at SNFs.8 Given the high rates of in-hospital deaths for people with HD, a specific evaluation of patient characteristics and the nature of the hospitalizations that persons with HD experience are essential for understanding their potential influence on the relative under-utilization of PC.9–13
With the underutilization of PC services5 among people with HD and high institutionalization rates as the disease progresses, we sought to understand what factors were associated with PC utilization and to determine if PC encounters corresponded with discharge location from the in-patient setting. First, we examined the factors associated with PC encounters among hospitalized persons with HD.14 Similar to the disparities of PC during hospitalization for other chronic illnesses,15 we hypothesized that those who received PC encounters would likely arise from a higher median household income zip codes and exhibit depression, a prevalent co-morbidity of HD. Second, we explored how PC encounters affected discharge location. We hypothesized that even when accounting for the relevant patient, hospital-level, and serious illness-related confounders, persons with HD who had PC encounters, compared to those who did not receive PC, would have a) higher in-hospital mortality, b) higher discharge to home with services, and c) lower rates of SNF discharge.
Methods
Study Design and Participants
This cross-sectional analysis used data from the National Inpatient Sample (N.I.S.) database from the Healthcare Cost and Utilization Project.16 As the largest in-patient hospital admission database, N.I.S. includes an anonymized cross-section of approximately 20% of the hospitalizations nationwide.16 We adhered to similar design approaches15 and combined data from 2007–2014 and used the International Classifications of Diseases, Ninth Revision diagnostic codes. We identified persons with HD using the ICD-9 code 333.4 and includes individuals who survived hospital discharge. Missing data was less than 5% unless otherwise noted. The Northwestern University IRB approved this study.
Definition of Study Variables
The primary outcome for our first aim was PC encounter among people with HD. The options for this variable were binary (yes/no). We utilized V66.7, which was shown to be99.1% specific for specialty PC consultation, based on a retrospective review of approximately more than 100,000 admissions in a large academic medical center in the United States between August 2013 and 2015.17 Independent variables were selected based on the presence of factors associated with hospitalization and institutionalization for HD (aspiration pneumonia, respiratory failure, and depression)18 as well as factors associated with PC consultation in chronic diseases (e.g., primary payer, median household income, risk of mortality subclass, the presence of a do-not-resuscitate order, bed size).14 Primary payers included Medicare, Medicaid, private insurance, self-pay, no charge, or other (e.g., workers compensation or Veterans Affairs). The median household income for a patient’s zip code included 4 quartiles (0 to 25th percentile, 25th to 50th percentile, 50th to 75th percentile, and greater than 75th percentile), and was based on demographic data obtained from Claritas.19 The risk of mortality subclass was calculated using the 3 M Health Information Systems Software using the All-Patient Refined Version 20 Methodology Booklet.20 The mortality classes included “no likelihood of dying, minor likelihood of dying, moderate likelihood of dying, major likelihood of dying, and extreme likelihood of dying.” Bed size was based on location and teaching status of the hospital; these metadata were obtained from AHA Annual Survey of Hospitals.21 Other diagnoses related to admission and co-morbidities used the Clinical Classification Software and AHRQ Comorbidity Index respectively (Supplementary Material). For our second aim, the primary outcome of interest was disposition location. These included routine (e.g., home or self-care), facility (e.g., SNF, intermediate care facility, or another type of facility), home health care, and death in the hospital.
Statistical Analysis
Descriptive statistics were used to summarize hospitalized persons with HD who received and did not receive PC. Mean and standard deviation was used to illustrate the distribution of continuous variables. N with percentages was used for categorical variables. We used weighted logistic regression incorporating complex survey sample designs to estimate the relationship between PC encounter and variables of interest. We included either HCUP hospital identification number before 2012 or N.I.S. hospital number after 2012 as cluster effect, stratum used to post-stratify hospital as stratification effect, and weights assigned to each discharge. Tukey’s test was used to make a pairwise comparison for posthoc analysis. To determine the final multivariable model, any covariable with a p-value <0.10 was initially included in the multivariable model. By comparing type 3 p-value and backward elimination, the final model includes primary payer, median household income, risk of mortality subclass, do-not-resuscitate order, aspiration pneumonia, respiratory failure, and depression as predictors. A similar model was performed to examine the association between PC encounter and discharge location while adjusting for the primary payer, median household income, risk of mortality subclass, do-not-resuscitate order, aspiration pneumonia, respiratory failure, and depression. Since we did not have a large portion of missingness and PROC SURVEYLOGISTIC has already accounted for the setting, we did not consider imputation. Any missing values were excluded from the analyses. P-values less than 0.05 were considered significant.
Results
Between 2007–2014, there were 8521 hospitalizations among persons with HD with a mean (standard deviation) age of 55.6 (14.8) years. Only 321 (3.8%) admissions received PC consultation. Of those who received PC consultation, Table 1 describes patient sociodemographic factors, characteristics of the hospitalization, and hospital discharge locations by PC encounter.
Table 1.
Descriptive Characteristics.
| Palliative care | ||||
|---|---|---|---|---|
| All | No | Yes | ||
| In-patient admissions | N (%) | 8521 (100.00) | 8200 (96.23) | 321 (3.77) |
| Age in years at admission (mean, S.D.) | N | 8517 | 8196 | 321 |
| Mean (SD) | 55.6 (14.8) | 55.5 (14.7) | 58.9 (14.9 | |
| Length of stay (mean, S.D.) | N | 8518 | 8197 | 321 |
| Mean (SD) | 7.80 (13.21) | 7.73 (12.44) | 9.51 (25.98) | |
| Gender | ||||
| Male | N (%) | 4004 (47.02) | 3842 (46.88) | 162 (50.47) |
| Female | N (%) | 4512 (52.98) | 4353 (53.12) | 159 (49.53) |
| Race+ | ||||
| White | N (%) | 5737 (77.92) | 5512 (77.95) | 225 (77.05) |
| Black | N (%) | 729 (9.90) | 701 (9.91) | 28 (9.59) |
| Hispanic | N (%) | 575 (7.81) | 550 (7.78) | 25 (8.56) |
| Asian or Pacific Islander/ Native American/ Other | N (%) | 322 (4.37) | 308 (4.36) | 14 (4.79) |
| Disposition of patient | ||||
| Routine | N (%) | 2741 (32.17) | 2725 (33.23) | 16 (4.98) |
| Facility | N (%) | 4431 (52.00) | 4300 (52.44) | 131 (40.81) |
| Home health care | N (%) | 1048 (12.30) | 977 (11.91) | 71 (22.12) |
| Died | N (%) | 301 (3.53) | 198 (2.41) | 103 (32.09) |
| Primary expected payer | ||||
| Medicare | N (%) | 5104 (60.00) | 4914 (60.04) | 190 (59.19) |
| Medicaid | N (%) | 1750 (20.57) | 1700 (20.77) | 50 (15.58) |
| Private insurer | N (%) | 1244 (14.62) | 1185 (14.48) | 59 (18.38) |
| Self-pay/ No charge/ Other | N (%) | 408 (4.80) | 386 (4.72) | 22 (6.85) |
| Bed size of hospital | ||||
| Small | N (%) | 1316 (15.55) | 1271 (15.61) | 45 (14.11) |
| Medium | N (%) | 2185 (25.82) | 2116 (25.99) | 69 (21.63) |
| Large | N (%) | 4960 (58.62) | 4755 (58.40) | 205 (64.26) |
| Median household income national quartile for patient ZIP Code | ||||
| 0 to 25th percentile | N (%) | 2569 (30.81) | 2492 (31.06) | 77 (24.44) |
| 26th to 50th percentile | N (%) | 2365 (28.36) | 2283 (28.46) | 82 (26.03) |
| 51st to 75th percentile | N (%) | 1877 (22.51) | 1810 (22.56) | 67 (21.27) |
| 76th to 100th percentile | N (%) | 1527 (18.31) | 1438 (17.92) | 89 (28.25) |
| All Patient Refined D.R.G.: Risk of Mortality Subclass | ||||
| Minor likelihood of dying* | N (%) | 3030 (35.56) | 2987 (36.43) | 43 (13.40) |
| Moderate likelihood of dying | N (%) | 3082 (36.17) | 3012 (36.73) | 70 (21.81) |
| Major likelihood of dying | N (%) | 1716 (20.14) | 1605 (19.57) | 111 (34.58) |
| Extreme likelihood of dying | N (%) | 693 (8.13) | 596 (7.27) | 97 (30.22) |
| DNR order | ||||
| No | N (%) | 7956 (93.37) | 7767 (94.72) | 189 (58.88) |
| Yes | N (%) | 565 (6.63) | 433 (5.28) | 132 (41.12) |
| Pneumonia (except caused by T.B. or S.T.I.s) | ||||
| No | N (%) | 7582 (88.98) | 7321 (89.28) | 261 (81.31) |
| Yes | N (%) | 939 (11.02) | 879 (10.72) | 60 (18.69) |
| Aspiration pneumonia | ||||
| No | N (%) | 7366 (86.45) | 7154 (87.24) | 212 (66.04) |
| Yes | N (%) | 1155 (13.55) | 1046 (12.76) | 109 (33.96) |
| Respiratory failure | ||||
| No | N (%) | 7629 (89.53) | 7415 (90.43) | 214 (66.67) |
| Yes | N (%) | 892 (10.47) | 785 (9.57) | 107 (33.33) |
| Septicemia (excludes labor) | ||||
| No | N (%) | 7339 (86.13) | 7116 (86.78) | 223 (69.47) |
| Yes | N (%) | 1182 (13.87) | 1084 (13.22) | 98 (30.53) |
| Urinary tract infection | ||||
| No | N (%) | 6862 (80.53) | 6597 (80.45) | 265 (82.55) |
| Yes | N (%) | 1659 (19.47) | 1603 (19.55) | 56 (17.45) |
| Fall | ||||
| No | N (%) | 7857 (92.21) | 7558 (92.17) | 299 (93.15) |
| Yes | N (%) | 664 (7.79) | 642 (7.83) | 22 (6.85) |
| AHRQ co-morbidity measure: Depression | ||||
| No | N (%) | 6851 (80.40) | 6580 (80.24) | 271 (84.42) |
| Yes | N (%) | 1670 (19.60) | 1620 (19.76) | 50 (15.58) |
| AHRQ co-morbidity measure: Psychoses | ||||
| No | N (%) | 7405 (86.90) | 7117 (86.79) | 288 (89.72) |
| Yes | N (%) | 1116 (13.10) | 1083 (13.21) | 33 (10.28) |
There are 1158 (13.59%) subjects missing in race.
4 (0.05%) subjects classified as “No class specified” were incorporated into the category: Minor likelihood of dying.
Hypothesis 1: Disparities Will Exist Among Persons With HD Who Receive PC
In bivariate analyses, gender, race, and hospital size were not associated with PC. However, primary expected payer (p = 0.01) and median household income (p < 0.01) were significantly correlated with PC (Table 2). Several other hospitalization-related factors were associated, including mortality class, D.N.R. status, sepsis, and others. In the multivariate model, after adjusting for all other variables (Table 3), the factors that remained associated with PC encounter were primary payer, with a private insurer as compared to Medicare (OR 1.86, 95% CI, 1.06–3.27), median household income, specifically the top quartile of income as compared to the bottom quartile (OR 1.77, 95% CI, 1.06–2.95), mortality class, specifically the extreme likelihood of dying as compared to the minor likelihood of dying (OR 4.73, 95% CI, 2.36–9.46), D.N.R. order (OR 8.75, 95% CI, 6.67–11.47), aspiration pneumonia (OR 1.43, 95% CI,1.07–1.92), respiratory failure (OR 1.46, 95% CI, 1.03–2.08), and depression (OR 0.70, 95% CI, 0.50–0.98).
Table 2.
Bivariate Model of Factors Associated With P.C. Encounter for People With H.D.
| 95% CI | ||||||
|---|---|---|---|---|---|---|
| Predictor | Group | N used | p-value | Odds ratio | Lower | Upper |
| Age in years at admission | 8517 | <0.001 | 1.017 | 1.008 | 1.025 | |
| Length of Stay (Days) | 8518 | 0.063 | 1.006 | 1.000 | 1.013 | |
| Gender (Reference: Male) | Female | 8516 | 0.216 | 0.857 | 0.671 | 1.094 |
| Race (Reference: White) | Black | 7363 | 0.979 | 0.988 | 0.532 | 1.838 |
| Hispanic | 1.105 | 0.564 | 2.167 | |||
| Asian or Pacific Islander | 0.917 | 0.154 | 5.469 | |||
| Native American | 1.776 | 0.179 | 17.638 | |||
| Other | 1.110 | 0.399 | 3.087 | |||
| Primary expected payer (Reference: Medicare) | Medicaid | 8506 | 0.008 | 0.761 | 0.471 | 1.229 |
| Private insurer | 1.327 | 0.809 | 2.177 | |||
| Self-pay | 0.561 | 0.129 | 2.443 | |||
| No charge | 1.558 | 0.099 | 24.644 | |||
| Other | 2.399 | 1.033 | 5.574 | |||
| Median household income (Reference: 0 to 25th percentile) | 26th to 50th percentile | 8338 | <0.001 | 1.148 | 0.721 | 1.827 |
| 51st to 75th percentile | 1.156 | 0.709 | 1.884 | |||
| 76th to 100th percentile | 1.969 | 1.249 | 3.103 | |||
| Bed size of hospital (Reference: Large) | Small | 8461 | 0.173 | 0.865 | 0.532 | 1.406 |
| Medium | 0.752 | 0.523 | 1.080 | |||
| Risk of Mortality Subclass (Reference: Minor likelihood of dying) | Moderate likelihood of dying | 8521 | <.001 | 1.612 | 0.904 | 2.872 |
| Major likelihood of dying | 4.783 | 2.715 | 8.425 | |||
| Extreme likelihood of dying | 11.219 | 6.363 | 19.778 | |||
| DNR (Reference: No) | Yes | 8521 | <.001 | 12.461 | 9.694 | 16.019 |
| Pneumonia (Reference: No) | Yes | 8521 | <.001 | 1.930 | 1.426 | 2.612 |
| Aspiration pneumonia (Reference: No) | Yes | 8521 | <.001 | 3.514 | 2.757 | 4.479 |
| Respiratory failure(Reference: No) | Yes | 8521 | <.001 | 4.719 | 3.678 | 6.054 |
| Septicemia (excludes labor) (Reference: No) | Yes | 8521 | <.001 | 2.899 | 2.238 | 3.756 |
| Urinary tract infection (Reference: No) | Yes | 8521 | 0.341 | 0.856 | 0.622 | 1.178 |
| Fracture of neck of femur (hip) (Reference: No) | Yes | 8521 | 0.115 | 0.204 | 0.028 | 1.472 |
| Fall (Reference: No) | Yes | 8521 | 0.487 | 0.853 | 0.545 | 1.335 |
| Depression (Reference: No) | Yes | 8521 | 0.069 | 0.746 | 0.544 | 1.023 |
| Psychoses (Reference: No) | Yes | 8521 | 0.125 | 0.755 | 0.526 | 1.082 |
Example interpretation: Risk of mortality subclass is significantly correlated to palliative care with p-value <0.0001. The estimated odds of receiving palliative care for a person with H.D. who is major likelihood of dying is 4.783 (95% CI 2.715–8.425) times compared to patients who is a minor likelihood of dying.
Table 3.
Multivariate Model for Factors Associated With P.C. Encounter for People With H.D.
| 95% CI | |||||
|---|---|---|---|---|---|
| Predictor | Group | Odds ratio | Lower | Upper | p-value |
| Primary expected payer (Reference: Medicare) | Medicaid | 1.064 | 0.627 | 1.805 | 0.001 |
| Private insurer | 1.862 | 1.062 | 3.266 | ||
| Self-pay | 1.005 | 0.212 | 4.773 | ||
| No charge | 4.993 | 0.291 | 85.560 | ||
| Other | 3.389 | 1.377 | 8.338 | ||
| Median household income (Reference: 0 to 25th percentile) | 26th to 50th percentile | 1.118 | 0.670 | 1.863 | 0.004 |
| 51st to 75th percentile | 1.009 | 0.592 | 1.720 | ||
| 76th to 100th percentile | 1.771 | 1.063 | 2.951 | ||
| Risk of Mortality Subclass (Reference: Minor likelihood of dying) | Moderate likelihood of dying | 1.388 | 0.769 | 2.505 | <0.001 |
| Major likelihood of dying | 2.994 | 1.649 | 5.435 | ||
| Extreme likelihood of dying | 4.726 | 2.361 | 9.458 | ||
| DNR (Reference: No) | Yes | 8.749 | 6.674 | 11.471 | <0.001 |
| Aspiration pneumonia (Reference: No) | Yes | 1.431 | 1.068 | 1.917 | 0.016 |
| Respiratory failure (Reference: No) | Yes | 1.464 | 1.032 | 2.077 | 0.033 |
| Depression (Reference: No) | Yes | 0.696 | 0.496 | 0.975 | 0.035 |
Hypothesis 2: PC Will be Associated With Discharge Location, Even When Accounting for Other Factors
The analysis of disposition is found in Table 4. The odds of being discharged to a facility for those people with HD who received PC was 0.43 (95% CI 0.32–0.58) times the odds of being discharged to a facility for those patients who did not receive PC, after adjusting for the primary payer, median household income, risk of mortality subclass, D.N.R., aspiration pneumonia, respiratory failure, and depression. Similarly, the odds of being discharged to home with services for these people with HD who received PC were 2.25 (95% CI1.57–3.23) times the odds of being discharged to home with home health care for those who did not receive PC after adjusting for other factors. Finally, upon adjusting for the same factors, the odds of in-hospital mortality for those who received PC was 7.56 (95% CI 5.25–10.88).
Table 4.
Adjusted Analysis of Hospital Disposition Among People With H.D. Who Received Palliative Care Consultation.
| 95% CI | ||||||
|---|---|---|---|---|---|---|
| Outcome | Predictor | N | P-value | Odds ratio | Lower | Upper |
| Discharge Home | Palliative Care | 8323 | <.001 | 0.175 | 0.099 | 0.307 |
| Discharge to Facility | 8323 | <.001 | 0.432 | 0.323 | 0.579 | |
| Discharge Home with Home Care | 8323 | <.001 | 2.250 | 1.567 | 3.229 | |
| Death in Hospital | 8323 | <.001 | 7.557 | 5.251 | 10.875 | |
Example interpretation: The odds of death for those patients who received palliative care referral is 7.56 (95% CI 5.25–10.88) times the odds of death for those patients who did not receive palliative care referral with a p-value <0.001 after controlling for primary expected payer, median household income, risk of mortality subclass, D.N.R., aspiration pneumonia, respiratory failure, and depression.
Discussion
This is the first nationally representative study of PC encounters among hospitalized people with HD. These data demonstrate an economic and psychosocial disparity among persons with HD who receive PC and those who do not. Our results suggest that people with HD who live in areas where the median household income is in the bottom quartile had a lower odds of receiving PC during hospitalization, which may reflect a lack of availability of PC services or other unmeasured factors. These findings build upon work in other serious illnesses, such as heart failure, where zip codes with higher median household income were positively associated with PC encounters.15 We also found that comorbid depression, a highly prevalent symptom in HD, was associated with lower PC use. Further study is needed to understand any barriers or implicit biases that may impact the use of PC.
Our analysis indicates that inpatient PC encounters were associated with discharge location. PC was significantly associated with fewer discharges to a facility and more discharges with home health care services, even when adjusting for other factors. These home health care services could include home hospice, though the N.I.S. does not include hospice enrollment or patient outcomes (e.g., death) after discharge. Notably, inpatient PC encounters were also associated with a high odds of in-hospital death, independent of the risk of mortality subclass, suggesting other potential contributing factors (e.g., the inpatient hospitalization itself may serve as a trigger point to refine goals of care with the assistance of specialty PC consultation). Though only 11% of persons with HD have thought about their death location, and fewer than 10% have established home care services, existing reports suggest home as advantageous to receiving dignified end-of-life care in those with serious illness,22 including people with HD.14
These data also build on a retrospective study with 59 people hospitalized with HD. It compared clinical and demographic factors associated with discharge disposition (SNF vs. home).23 It found that (1) male gender; (2) longer in-patient stays; (3) psychosocial difficulties (e.g., dissolution of financial and support networks); and (4) behavioral issues (e.g., impulsivity) were associated with discharge to a SNF. Caregiver distress was a fifth associated factor; however, it did not remain significant after adjusting for multiple comparisons.
Our study was limited because the data do not account for repeated hospitalizations, suicidality, initial location before hospital admission, patient values and preferences for care, HD staging and functional level, family (caregiver) distress/experience with HD, the primary reason for admission, or hospice use. Causality also cannot be inferred based on this data and no information is available on the initiating recipient (e.g., patient, care partner, or physician) of the consultation. Therefore, future work might prospectively examine how these additional factors influence discharge disposition, including hospice use and place of death. Further, additional work might explore the utility of primary neuropalliative assessments, including symptom assessments or the use of the “surprise question” to trigger PC consultation and support goalconcordant care in this population.24 Since there are no evidence-based PC models in existence for this population in the outpatient setting,5,25,26 where most care presides, efforts are, therefore, warranted to adapt and pilot PC models (e.g., meaning-centered) to people with the HD genetic mutation, which could be interchangeable among various environments (e.g., outpatient and inpatient), and stages of illness (prodromal, early, and late).27
In conclusion, these data are a compelling first step showcasing sociodemographic and psychosocial factors were associated with PC utilization among hospitalized persons with HD. People whose household resides in the bottom income quartile, even independent of the hospital’s size or location, were negatively associated with PC encounters. Similarly, Medicare and the presence of co-morbid depression were also inversely correlated. Inpatient PC was positively associated with discharge to home with services (potentially including home hospice) and negatively associated with discharge to a facility. Taken together, our findings can direct efforts to offer PC interventions for this population equitably.28
Supplementary Material
Acknowledgments
Source of the data is the National Inpatient Sample (N.I.S.), Healthcare Cost and Utilization Project (HCUP), Agency for Healthcare Research and Quality.
Funding
The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: HDL is supported in part by the National Institutes of Health (K76AG054782). The contents do not represent the views of the funder, the Department of Veterans Affairs, or the United States Government.
Declaration of Conflicting Interests
The authors declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: Dr. Sokol is an ad-hoc consultant for Tikvah for Parkinson in the range of $0-$499, ad-hoc consultant for the American Film Institute on end-of-life care/palliative care in the range of $500-$999; and receives financial support from the Northwestern PSTP Program in Neurology as well as the R25 NCI 2R25CA190169. Ms. Yeh has no relevant financial or non-financial disclosures. Dr. Bega has received personal compensation for consulting, serving on a scientific advisory board, speaking, or other activities with Speaker: Teva Pharmaceuticals, Acorda Therapeutics, Neurocrine Biosciences, Adamas Pharmaceuticals Consulting: Biogen Pharmaceuticals, Amgen Pharmaceuticals, Acadia Pharmaceuticals, Genentech, Inc, G.E. Healthcare, Gerson Lehrman Group, Guidepoint, L.E.K. C., and has received personal compensation in an editorial capacity for Editor: Annals of Clinical & Translational Neurology. Dr. Kluger received research grant support from the National Institute of Aging, National Institute of Nursing Research, and Patient-Centered Outcomes Research Institute; he has received speaker honoraria from the Parkinson’s Foundation. Dr. Lum is supported in part by the National Institutes of Health K76AG054782 and R01AG066804. The contents do not represent the views of funders, the U.S. Department of Veterans Affairs, or the United States Government.
Footnotes
Supplemental Material
Supplemental material for this article is available online.
References
- 1.Ross CA, Tabrizi SJ. Huntington’s disease: from molecular pathogenesis to clinical treatment. Lancet Neurol. 2011;10(1): 83–98. [DOI] [PubMed] [Google Scholar]
- 2.Paulsen JS, Langbehn DR, Stout JC, et al. Detection of Huntington’s disease decades before diagnosis: the predict-HD study. J Neurol Neurosurg Psychiatry. 2008;79(8):874–880. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.McColgan P, Tabrizi SJ. Huntington’s disease: a clinical review. Eur J Neurol. 2018;25(1):24–34. [DOI] [PubMed] [Google Scholar]
- 4.Carlozzi NE, Boileau NR, Paulsen JS, et al. End-of-life measures in Huntington disease: HDQLIFE meaning and purpose, concern with death and dying, and end of life planning. J Neurol. 2019; 266(10):2406–2422. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Downing NR, Goodnight S, Chae S, et al. Factors associated with end-of-life planning in Huntington disease. Am J Hosp Palliat Care. 2018;35(3):440–447. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Rodrigues FB, Abreu D, Damásio J, et al. ; REGISTRY Investigators of the European Huntington’s Disease Network. Survival, mortality, causes and places of death in a European Huntington’s disease prospective cohort. Mov Disord Clin Pract. 2017;4(5): 737–742. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Johnson MO, Frank S, Mendlik M, Casarett D. Utilization of hospice services in a population of patients with Huntington’s disease. J Pain Symptom Manage. 2018;55(2):440–443. [DOI] [PubMed] [Google Scholar]
- 8.Mitchell SL, Teno JM, Miller SC, Mor V. A national study of the location of death for older persons with dementia. J Am Geriatr Soc. 2005;53(2):299–305. [DOI] [PubMed] [Google Scholar]
- 9.Dubinsky RM. No going home for hospitalized Huntington’s disease patients. Mov Disord. 2005;20(10):1316–1322. [DOI] [PubMed] [Google Scholar]
- 10.Wheelock VL, Tempkin T, Marder K, et al. ; Huntington Study Group. Predictors of nursing home placement in Huntington disease. Neurology. 2003;60(6):998–1001. [DOI] [PubMed] [Google Scholar]
- 11.Rosenblatt A, Kumar BV, Margolis RL, Welsh CS, Ross CA. Factors contributing to institutionalization in patients with Huntington’s disease. Mov Disord. 2011;26(9):1711–1716. [DOI] [PubMed] [Google Scholar]
- 12.Hamedani AG, Pauly M, Thibault DP, Gonzalez-Alegre P, Willis AW. Inpatient gastrostomy in Huntington’s disease: nationwide analysis of utilization and outcomes compared to amyotrophic lateral sclerosis. Clin Park Relat Disord. 2020;3:100041. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Winder JY, Achterberg WP, Roos RAC. Marriage as protector for nursing home admission in Huntington’s disease. J Huntingtons Dis. 2018;7(3):251–257. [DOI] [PubMed] [Google Scholar]
- 14.Report to the field promoting excellence in palliative and end-of-life care in Huntington’s disease. In: Promoting Excellence in End-of-Life Care; 2004. [Google Scholar]
- 15.Patel B, Secheresiu P, Shah M, et al. Trends and predictors of palliative care referrals in patients with acute heart failure. Am J Hosp Palliat Care. 2019;36(2):147–153. [DOI] [PubMed] [Google Scholar]
- 16.Agency for Healthcare Research and Quality [Internet]. Accessed November 12, 2020. https://www.hcup-us.ahrq.gov/nisoverview.jsp
- 17.Hua M, Li G, Clancy C, Morrison RS, Wunsch H. Validation of the v66.7 code for palliative care consultation in a single academic medical center. J Palliat Med. 2017;20(4):372–377. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Zarowitz BJ, O’Shea T, Nance M. Clinical, demographic, and pharmacologic features of nursing home residents with Huntington’s disease. J Am Med Dir Assoc. 2014;15(6):423–428. [DOI] [PubMed] [Google Scholar]
- 19.Claritas I The Claritas: Demographic Update Methodology. Claritas Inc. [Google Scholar]
- 20.APR-DRGs V20 Methodology Booklet [Internet]. Accessed May 5, 2021]. https://www.hcup-us.ahrq.gov/db/nation/nis/v261_aprdrg_meth_ovrview.pdf
- 21.HOSP_BEDSIZE—Bedsize of hospital [Internet]. Accessed June 21, 2021. https://www.hcup-us.ahrq.gov/db/vars/hosp_bedsize/nisnote.jsp#general
- 22.Teno JM, Clarridge BR, Casey V, et al. Family perspectives on end-of-life care at the last place of care. JAMA. 2004;291(1): 88–93. [DOI] [PubMed] [Google Scholar]
- 23.Fisher F, Andrews S, Churchyard A, Mathers S. Home or residential care? The role of behavioral and psychosocial factors in determining discharge outcomes for inpatients with Huntington’s disease. J Huntingtons Dis. 2012;1(2):187–193. [DOI] [PubMed] [Google Scholar]
- 24.Lakin JR, Robinson MG, Bernacki RE, et al. Estimating 1-year mortality for high-risk primary care patients using the “surprise” question. JAMA Intern Med. 2016;176(12):1863–1865. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Dawson S, Kristjanson LJ, Toye CM, Flett P. Living with Huntington’s disease: need for supportive care. Nurs Health Sci. 2004; 6(2):123–130. [DOI] [PubMed] [Google Scholar]
- 26.Carlozzi NE, Hahn EA, Frank SA, et al. A new measure for end of life planning, preparation, and preferences in Huntington disease: HDQLIFE end of life planning. J Neurol. 2018;265(1):98–107. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Sokol LL, Troost JP, Kluger BM, et al. Meaning and purpose in Huntington disease: a longitudinal study of its impact on quality of life. Ann Clin Transl Neurol. 2021. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Sokol LL, Lum HD, Creutzfeldt CJ, et al. Meaning and dignity therapies for psychoneurology in neuropalliative care: a vision for the future. J Palliat Med. 2020;23(9):1155–1156. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.