Hospice Referral Research Articles

End-of-Life Health Care Service Use and Cost Among Medicare Decedents With Neurodegenerative Diseases.

Although neurodegenerative diseases are a leading cause of death, little is known about health care utilization and cost during the end-of-life (EoL) period or how it compares with that of other life-limiting conditions. We aimed to describe and compare resource utilization among US Medicare decedents with neurodegenerative diseases with decedents with cancer. We conducted a retrospective study of Medicare Part A and B beneficiaries with Alzheimer disease (AD), Parkinson disease (PD), or amyotrophic lateral sclerosis (ALS) who died in 2018. Decedents diagnosed with malignant brain tumors or pancreatic cancer served as non-neurodegenerative comparators. Descriptive analyses examined demographic and clinical characteristics in the last year of life. The probabilities and associated costs of emergency department (ED), inpatient, skilled nursing facility (SNF), and hospice utilization during the last 12 and 6 months of life were also compared between persons with neurodegenerative diseases and cancer, adjusting for sociodemographic factors and comorbidity burden. A total of 1,126,799 Medicare beneficiaries died in 2018, of which 357,926 had a qualifying diagnosis. Persons with neurodegenerative diseases were older and more frequently received Medicaid assistance than persons with brain or pancreatic cancer. In all groups, health care service utilization increased over the last year of life, and total costs were predominantly attributable to inpatient care. In the last 6 months of life, neurologist care was infrequent among patients with neurodegenerative disease (AD: 1.5%; PD: 8.6%; ALS: 32.0%). Persons with neurodegenerative diseases as compared to persons with malignant brain tumors also had greater odds of ED use (AD: adjusted odds ratio [aOR] 1.17, 95% CI 1.11-1.23; PD: aOR 1.18, 95% CI 1.11-1.25; ALS: aOR 1.11, 95% CI 1.01-1.23), lower odds of hospitalization (AD: aOR 0.64, 95% CI 0.60-0.68; PD: aOR 0.65, 95% CI 0.61-0.69; ALS: aOR 0.33, 95% CI 0.30-0.37), and lower odds of hospice enrollment (AD: aOR 0.33, 95% CI 0.31-0.36; PD: aOR 0.33, 95% CI 0.31-0.36; ALS: aOR 0.41, 95% CI 0.36-0.46). The findings were similar in pancreatic cancer. Persons with neurodegenerative diseases in the United States are more likely to visit the ED and less likely to use inpatient and hospice services at EoL than persons with brain or pancreatic cancer. These group differences may stem from prognostic uncertainty and reflect inadequate EoL care practices, requiring further investigation to ensure more timely palliative care and hospice referrals.

Updates in palliative care of gynecologic oncology patients

Purpose of review This review assesses the impact of early integration of palliative care and the disparities that exist among patients with gynecologic malignancies. It also highlights the recent advances in symptom management, goals of care communication, and end of life care. Recent findings Although palliative care has been utilized earlier, there are still barriers to its integration both nationally and worldwide, manifesting as predominantly late hospice referrals and aggressive care at the end of life. However, there are increasing efforts to promote earlier integration and discussion around goals of care through multidisciplinary approaches. Apart from symptom burdens, patients with gynecologic malignancies experience financial toxicity that impact various aspects of their lives, leading to potentially harmful choices to mitigate costs. A growing area of interest in palliative care is the realm of psychedelic-assisted therapy, which has shown benefits in treating advanced cancer patients with depression. Summary While significant strides have been made in incorporating palliative care earlier, continued research in palliative care is needed to address symptomatic and psychosocial suffering worldwide.

General versus disease-specific medical oncology consults and impact on inpatient quality of care.

321 Background: Hospitalization is a time of crisis for patients and caregivers. Subspeciality oncologists may bring more timely, effective support compared to a general oncologists. Attendings, fellows and advance practice providers (APPs) may benefit from focused expertise for consultative care and education. Methods: Subspeciality oncology consults launched July 2023, with consults staffed by 4 oncologists from: 1) breast 2) head and neck/thoracic 3) gastrointestinal and 4) genitourinary/melanoma. Previously, 3 oncologists staffed all patients, regardless of primary malignancy. Throughout the study period, oncologists were supported by at least 1 fellow or 1 APP. Using Epic and Tableau, we collected LOS, time to consult (TTC), ICU rate, hospice referral rate, 30-day readmission, 30-day emergency department (ED) visit data. Using Qualtrics, we collected fellow, attending, and APP satisfaction feedback pre- and post-intervention. Data was analyzed using R-4.40. Oncologists, fellows, and APPs completed Qualtrics surveys pre- and post-intervention regarding their experience. Results: Subspeciality oncology consults were associated with a significant decrease in LOS, TTC, and ICU rate. Median LOS decreased by 2.0 days (P<0.001), with median TTC decreasing by 1.0 hour (P=0.003). ICU rate decreased from 27.5% to 16.4% (P<0.001). There was no difference in hospice referrals or 30-day ED visits. A significant increase in 30-day readmission during post-intervention was observed (Table). Oncologists reported a high level of satisfaction with the program. Oncologist, fellow, and APP overall experience and educational rankings increased after the introduction of subspeciality consults. Inpatient productivity measured by wRVU increased by 30% following the intervention, without associated changes to outpatient productivity. Conclusions: Transition to a subspeciality oncology consult model improved inpatient quality of care, as well as clinician experience. Additionally, inpatient productivity increased without decrease in outpatient metrics. Subspeciality medical oncology consultation can improve inpatient quality of care, education, clinician satisfaction and productivity. Subspeciality oncology consult outcomes. General Consults (Baseline)* Subspeciality Consults (Intervention) P-value Median TTC (Hours) 4.0 (1.5-6.0)** 3.0 (0-5.0)** P=0.003 Median LOS (Days) 7.0 (4.0-13.0)** 5.0 (3.0-10.0)** P<0.001 ICU Rate 148 (27.5%) 161 (16.4%) P<0.001 Hospice Referral Rate 56 (10.4%) 95 (9.7%) 0.655 30-Day Readmission Rate 136 (25.3%) 301 (30.7%) 0.028 30-Day ED Rate 157 (29.2%) 315 (32.1%) 0.247 *Baseline data reflects outcomes for general oncology consult staffing, prior to initiation of subspeciality consult model (pre-intervention). **Interquartile Range (IQR).

Machine Learning for Targeted Advance Care Planning in Cancer Patients: A Quality Improvement Study

ContextPrognostication challenges contribute to delays in advance care planning (ACP) for patients with cancer near the end of life (EOL). ObjectivesExamine a quality improvement mortality prediction algorithm intervention's impact on ACP documentation and EOL care. MethodsWe implemented a validated mortality risk prediction machine learning model for solid malignancy patients admitted from the emergency department (ED) to a dedicated solid malignancy unit at Duke University Hospital. Clinicians received an email when a patient was identified as high-risk. We compared ACP documentation and EOL care outcomes before and after the notification intervention. We excluded patients with intensive care unit (ICU) admission in the first 24 hours. Comparisons involved chi-square/Fisher's exact tests and Wilcoxon rank sum tests; comparisons stratified by physician specialty employ Cochran-Mantel-Haenszel tests. ResultsPreintervention and postintervention cohorts comprised 88 and 77 patients, respectively. Most were White, non-Hispanic/Latino, and married. ACP conversations were documented for 2.3% of hospitalizations preintervention vs. 80.5% postintervention (P<0.001), and if the attending physician notified was a palliative care specialist (4.1% vs. 84.6%) or oncologist (0% vs. 76.3%) (P<0.001). There were no differences between groups in length of stay (LOS), hospice referral, code status change, ICU admissions or LOS, 30-day readmissions, 30-day ED visits, and inpatient and 30-day deaths. ConclusionIdentifying patients with cancer and high mortality risk via machine learning elicited a substantial increase in documented ACP conversations but did not impact EOL care. Our intervention showed promise in changing clinician behavior. Further integration of this model in clinical practice is ongoing.

Experiences and barriers in downward referral decision-making for palliative care patient caregivers under China's three-tiered linkage model: A qualitative study

ObjectiveThis study aimed to explore hospice caregivers’ downward referral decision-making experiences and barriers under the triadic linkage model in China and to analyze the deeper social dynamics of hospice referral choices. MethodsSemi-structured interviews were conducted with caregivers handling hospice referrals from two primary hospice agencies in Nanjing, China. The themes were analyzed and summarized using the Colaizzi 7-step analysis. ResultsFour themes and nine subthemes were extracted: multidimensional caregiver psychological experience (Dilemma and Guilt, Emotional Support and Psychological Adaptation), perceived disparities between referral organizations (convenience and affordability in the home community, perceived lack of primary health care resources), limitations of caregiver decision-making (cognitive comprehension bias, difficulty in information seeking, and passive acceptance of decision making), and limitations of health care referral support (lack of health care referral guidance, inadequate referral handoffs). ConclusionsFeedback from caregivers of hospice-referred patients reveals many barriers to hospice referral decision making and referral implementation. Overcoming these barriers entails efforts to change the cognitive misunderstandings regarding hospice referrals from patients' perspective, clarify the distribution of responsibilities among hospice agencies, and provide information support and decision-making assistance. These measures must be employed to improve the implementation of hospice referral, realize the multiple benefits of hierarchical diagnosis and treatment, boost patients’ satisfaction with the referrals, and ensure the rational and efficient distribution of hospice resources.

Collaboration, coordination and communication as facilitators of transitions for patients with advanced cancer: a scoping review linked to the Pal-Cycles project

BackgroundPerson-centred care is becoming increasingly recognised as an important element of palliative care. The current review syntheses evidence in relation to transitions in advanced cancer patients with palliative care needs. The review focuses on specific elements which will inform the Pal-Cycles programme, for patients with advanced cancer transitioning from hospital care to community care. Elements of transitional models for cancer patients may include, identification of palliative care needs, compassionate communication with the patient and family members, collaborative effort to establish a multi-dimensional treatment plan, review and evaluation of the treatment plan and identification of the end of life phase.MethodsA scoping review of four databases (MEDLINE, EMBASE, CINAHL, PsycINFO) was conducted to identify peer-reviewed studies published from January 2013 to October, 2022. A further hand-search of references to locate additional relevant studies was also undertaken. Inclusion criteria involved cancer patients transitions of care with a minimum of two of components from those listed above. Studies were excluded if they were literature reviews, if transition of care was related to cancer survivors, involved non-cancer patients, had paediatric population, if the transition implied a change of therapy and or a lack of physical transit to a non-hospital place of care. This review was guided by Arksey and O’Malley’s framework and narrative synthesis was used.ResultsOut of 5695 records found, 14 records were selected. Transition models identified: increases in palliative care consultations, hospice referrals, reduction in readmission rates and the ability to provide end of life care at home. Transition models highlight emotional and spiritual support for patients and families. No uniform model of transition was apparent, this depends on the healthcare system where it is implemented.ConclusionsThe findings highlight the importance of collaboration, coordination and communication as central mechanisms for transitional model for patients with advanced cancer. This may require careful planning and will need to be tailored to the contexts of each healthcare system.

Utilization of Palliative Radiation in Pediatric Oncology Patients During the End-of-Life (EOL)

BackgroundSuffering at the end-of-life (EOL) can impact the perception of a “good death” and ultimately affect bereavement for families of children with cancer. Palliative radiation (pXRT) is a tool that can address pain, mitigate suffering and improve quality of life. MethodsA retrospective medical record review of pediatric oncology patients who died over an 11-year period was completed. Descriptive analysis and nonparametric tests to compare groups were used. Results2202 total deaths occurred during the study period; 167 patients met study criteria, reflecting a 7.6% incidence of pXRT use at the EOL. Most patients were white (68%) and male (59%), with a median age of 9 years. Solid tumors were most common (52%), followed by CNS tumors (38%), and leukemia (10%). pXRT was primarily used to treat pain (37%) and focused on sites including brain/spine (37%), head/neck (24%), and pelvis (12%). Mean radiation dose delivered was 23.8Gy (range: 1.8–55.8 Gy) in a median of 7 fractions (range: 1–31). Side effects were rare and 58% of patients had a decrease in reported pain scores. Additionally, 87% received a pediatric palliative care (PPC) consultation which increased the likelihood for hospice referral, documented DNR preferences and decrease episodes of CPR on the day of death. ConclusionsThere is underutilization and significant variability in the use of pXRT during EOL in pediatric oncology. Barriers to this tool may include physician perceptions, family/patient preferences, and logistical hardships. Guidelines to standardize pXRT, alongside earlier PPC integration, may guide clinician decision making and increase pXRT utilization.

Quality Improvement Study Using a Machine Learning Mortality Risk Prediction Model Notification System on Advance Care Planning in High-Risk Patients

Background: Advance care planning (ACP) is an important aspect of patient care that is underutilized. Machine learning (ML) models can help identify patients appropriate for ACP. The objective was to evaluate the impact of using provider notifications based on an ML model on the rate of ACP documentation and patient outcomes. Methods: This was a pre-post QI intervention study at a tertiary academic hospital. Adult patients admitted to general medicine teams identified to be at elevated risk of mortality using an ML model were included in the study. The intervention consisted of notifying a provider by email and page for a patient identified by the ML model. Results: A total of 479 encounters were analyzed of which 282 encounters occurred post-intervention. The covariate-adjusted proportion of higher-risk patients with documented ACP rose from 6.0% at baseline to 56.5% (Risk Ratio (RR)= 9.42, 95% CI: 4.90 - 18.11). Patients with ACP were more than twice as likely to have code status reduced when ACP was documented (29.0% vs. 10.8% RR=2.69, 95% CI: 1.64 – 4.27). Additionally, patients with ACP had twice the odds of hospice referral (22.2% vs. 12.6% Odds Ratio=2.16, 95% CI: 1.16 – 4.01). However, patients with ACP documented had a longer mean LOS (9.7 vs. 7.6 days, Event time ratio = 1.29, 95% CI: 1.10 - 1.53). Conclusion: Provider notifications using an ML model can lead to an increase in completion of ACP documentation by frontline clinicians in the inpatient setting.

Intravenous Opioid Administration During Mechanical Ventilation and Use After Hospital Discharge

Guidelines recommend an analgesia-first strategy for sedation during mechanical ventilation, but associations between opioids provided during mechanical ventilation and posthospitalization opioid-related outcomes are unclear. To evaluate associations between an intravenous opioid dose received during mechanical ventilation and postdischarge opioid-related outcomes in medical (nonsurgical) patients. This retrospective cohort study evaluated adults receiving mechanical ventilation lasting 24 hours or more for acute respiratory failure and surviving hospitalization. Participants from 21 Kaiser Permanente Northern California hospitals from January 1, 2012, to December 31, 2019, were included. Data were analyzed from October 1, 2020, to October 31, 2023. Terciles of median daily intravenous fentanyl equivalents during mechanical ventilation. The primary outcome was the first filled opioid prescription in 1 year after discharge. Secondary outcomes included persistent opioid use and opioid-associated complications. Secondary analyses tested for interaction between opioid doses during mechanical ventilation, prior opioid use, and posthospitalization opioid use. Estimates were based on multivariable-adjusted time-to-event analyses, with death as a competing risk, and censored for hospice or palliative care referral, rehospitalization with receipt of opioid, or loss of Kaiser Permanente plan membership. The study included 6746 patients across 21 hospitals (median age, 67 years [IQR, 57-76 years]; 53.0% male). Of the participants, 3114 (46.2%) filled an opioid prescription in the year prior to admission. The median daily fentanyl equivalent during mechanical ventilation was 200 μg (IQR, 40-1000 μg), with terciles of 0 to 67 μg, more than 67 to 700 μg, and more than 700 μg. Compared with patients who did not receive opioids during mechanical ventilation (n = 1013), a higher daily opioid dose was associated with opioid prescriptions in the year after discharge (n = 2942 outcomes; tercile 1: adjusted hazard ratio [AHR], 1.00 [95% CI, 0.85-1.17], tercile 2: AHR, 1.20 [95% CI, 1.03-1.40], and tercile 3: AHR, 1.25 [95% CI, 1.07-1.47]). Higher doses of opioids during mechanical ventilation were also associated with persistent opioid use after hospitalization (n = 1410 outcomes; tercile 3 vs no opioids: odds ratio, 1.44 [95% CI, 1.14-1.83]). No interaction was observed between opioid dose during mechanical ventilation, prior opioid use, and posthospitalization opioid use. In this retrospective cohort study of patients receiving mechanical ventilation, opioids administered during mechanical ventilation were associated with opioid prescriptions following hospital discharge. Additional studies to evaluate risks and benefits of strategies using lower opioid doses are warranted.

Correlation of significant delays and ethnic disparities in the receipt of treatment, palliative care, and supportive care interventions in pancreatic adenocarcinoma.

e16355 Background: In patients with pancreatic adenocarcinoma (PDAC), immediate treatment initiation is preferred. Early palliative care initiation has also improved quality of life and symptom burden. However, the timing of real-world treatment and palliative care initiation and possible ethnic disparities are not well understood. The objective of this study was to evaluate the timing of care initiation in a mainly Hispanic population of PDAC patients. Methods: We conducted a retrospective cohort study that included patients diagnosed with PDAC from 2014 to 2023 at Loma Linda University Health (the only tertiary care center for two large Hispanic-majority counties with large rural populations). Data were obtained on the presence and timing of appropriate provider referrals, care establishment, treatment, advanced care planning, and hospital admissions. We used a Wilcoxon rank-sum test to compare Hispanic and non-Hispanic patients regarding the time to treatment initiation and palliative care initiation. Results: 263 PDAC patients were identified (mean age 66, 57% female, 47% Hispanic, 67% head tumor, 78% stage III/IV). After diagnosis, 93% of patients were referred to medical oncology, 82% to surgical oncology, 61% to palliative care, and 30% to genetic testing. An advanced directive or physician orders for life-sustaining treatment was completed for 24% of patients. The time to care initiation is as follows: a median of 23 days (IQR 14, 54) from diagnosis to the first medical oncology visit, 13 days (IQR 6, 37) to the first surgical oncology visit, 73 days (IQR 24, 132) to the first palliative care visit and 133 days (IQR 47, 329) to hospice referral. 64% of hospice referrals were placed within the last two weeks of life. Hispanic patients had a significantly longer time to treatment initiation (68 days vs. 48 days, p= 0.008) but similar times to palliative care initiation (73 vs 74 days, p= 0.64). Hispanics also had a lower rate of completing genetic testing (45% to 66%, p= 0.07). Conclusions: In our retrospective cohort study, significant delays in establishing care with oncology, palliative care, and hospice initiation in patients with PDAC were noted. Low rates of advanced care planning were also noted despite guideline recommendations for early-focused symptomatic interventions and advanced care planning. Hispanic patients had significantly longer time to treatment initiation and lower rates of genetic testing. These results indicate a need for improved targeted patient care navigation for multidisciplinary PDAC management, particularly in Hispanic patients.

Real-world quality analysis of palliative care utilization in patients with brain metastases at three academic institutes.

12080 Background: Brain metastases (BMETs) are a devastating complication of cancer, occurring in up to 30-50% of patients with advanced solid tumors. Median survival after diagnosis of BMETs can be limited, with 3-6 months survival for patients with poorer performance status (Updated Recursive Partition analysis [U-RPA] classification class 2b and 3). Early integration of palliative care (PC) can improve a patient’s quality of life. Per guidelines, patients with advanced cancer should be referred to an interdisciplinary team, including PC, early in the disease. We aimed to assess the utilization and impact of PC in patients with BMETs at three academic centers. Methods: We obtained data from the electronic medical record (EMR) of all patients diagnosed with BMETS at the University of Virginia, the Medical College of Wisconsin, and the University of Vermont between 1 January 2017 and 31 December 2019. Inclusion criteria were age 18+ years, diagnosis of BMETs secondary to a solid tumor malignancy (excluding leptomeningeal metastases), and &gt;3 months of follow-up treatment. Patients were included if considered at high risk for mortality, which was defined as KPS &lt;70 (U-RPA Class 3) or KPS 70-80 and age &gt; 65 (U-RPA Class 2b). Chi-squared and Fisher’s exact tests compared survival, hospice referral, emergency visits, readmissions, and advance directives (AD) on the file between the PC and non-PC groups. Results: In total, of the 309 patients that were assessed, 220 patients were class 2b, and 89 were class 3 per U-RPA classification. The mean age was 66 in both groups, and 167 (54%) were seen by PC. The median survival for patients seen by PC was 7.8 months compared to 8.3 months (p 0.056). There was no statistically significant difference in survival between the two groups. 80% of patients in the group seen by PC enrolled with hospice care compared to 58% in the non-PC group (p 0.0002). 80% of patients had an AD on file in the group seen by PC compared to 56% in the non-PC group (p &lt;0.0001). In terms of ED visits or re-hospitalization, there was a trend for more ED visits/readmissions in the group seen by PC compared to the non-PC group (p 0.057). Conclusions: In this real-world quality analysis, we found that there is under-utilization of formal PC consultation in patients with BMETs. There was no difference in survival between groups, indicating PC is not synonymous with end-of-life or hospice care. PC was associated with more patients completing ADs and more patients using hospice. Health systems and policy initiatives promoting palliative care utilization earlier in the disease course may support patient values and preferences at the end of life. [Table: see text]

Value of a multidisciplinary geriatric oncology committee on patient care in a community-based, academic cancer center.

e13771 Background: The heterogeneity in health and functional ability among older patients makes the management of cancer a unique challenge. The Geriatric Oncology Program at the University of Maryland Baltimore Washington Medical Center (BWMC) was created to optimize cancer management and recommendations for older patients. This study aimed to assess the benefits of the implementation of such a program at a community- based academic cancer center. Materials and Methods: We retrospectively analyzed patients aged ≥80 years presenting to the Geriatric Oncology Program between January 2017 and July 2022. A multidisciplinary team of specialists collectively reviewed each patient using geriatric-specific domains and stratified each patient into one of three management groups- Group 1: those deemed fit to receive standard oncologic care (SOC); Group 2: those recommended to receive optimization services prior to reassessment for SOC; and Group 3: those deemed to be best suited for supportive care and/ or hospice care. ANOVA, chi-square, and Kaplan-Meier analyses were used to assess patient outcomes. Results: Among 233 patients included, 76 (32.6%) received SOC (Group 1), 43 (18.5%) were optimized (Group 2), and 114 (49.0%) received supportive care or hospice referral (Group 3). There was no significant difference in sex, race, or age among all three groups. The Canadian Study of Health and Aging-Clinical Frailty Scale (CSHA-CFS) score was implemented in 2019 (n=90). Patients receiving supportive/ hospice care only had an average score of 5.8, while the averages for those in the optimization and SOC groups were 4.6 and 4.1, respectively (p=&lt; .001). SOC patients had the longest average survival of 2.71 years compared to the optimization (2.30 years) and supportive care groups (0.93 years) (p= &lt;0.001). 69.8% of optimized patients were deemed fit for SOC upon re-evaluation following optimization services. For all patients that underwent surgical interventions, post- operatively, 23 patients (85%) were discharged home and 4 (15%) were discharged to a rehab facility. The average survival after surgery for all patients was 3.16 years, while patients who were optimized prior to surgery had an average survival after surgery of 3.21 years. Conclusions: The present study demonstrates the need for specialized consideration of the heterogeneity that cancer diagnoses present in older individuals. The Geriatric Oncology Program at BWMC is able to maximize treatment outcomes for geriatric patients through the provision of SOC therapies and optimization services, while also minimizing unnecessary interventions on an individual patient-centric level.

Using machine learning for targeted advance care planning (ACP) conversations in patients with cancer: A quality improvement initiative.

1552 Background: Despite improvements, patients with advanced cancer often do not receive goal-concordant care at the end of life (EOL). Challenges with prognostication contribute to delays ACP conversations and result in more aggressive care, gaps in symptom management, and hospice underuse. Early identification of patients at high risk for mortality provides an avenue to implement targeted ACP conversations. Methods: We used a validated machine learning model that integrates demographics, lab values, vital signs, and medications to predict in-hospital, 30-day, and 6-month mortality risk for patients with a solid malignancy admitted from the emergency department (ED) to a dedicated solid malignancy oncology unit at Duke University Hospital. This unit includes one oncology and one palliative care attending physician, advance practice providers, and nurse coordinators. Providers received an email when a patient was identified as high risk for mortality. A pre-post study design compared ACP documentation and other EOL healthcare utilization before and after the notification intervention. The pre-intervention cohort included patients hospitalized from 1/7/2019 to 10/25/2019 and the post-intervention cohort from 9/19/2020 to 8/31/2021. We excluded patients admitted to the intensive care unit in the first 24 hours. We used chi-square or Fisher’s exact tests for categorical variables and Kruskal-Wallis tests for continuous variables; we stratified comparisons of categorical variables by physician division using Cochran-Mantel-Haenszel tests. Results: The pre-intervention cohort comprised 105 hospitalizations and 88 unique patients. Mean (SD) age was 64.9 (11.4); 60.0% (n=63) were White, 3.8% (4) Hispanic/Latino, and 65.7% (69) married. The post-intervention cohort comprised 84 hospitalizations and 77 patients. Mean (SD) age was 66.0 (12.2); 52.4% (44) were White, 2.4% (2) Hispanic/Latino, and 60.7% (51) married. Considering index hospitalizations, an ACP note was written for 2.3% (2) of hospitalizations pre-intervention vs. 80.5% (62) post-intervention (p &lt;0.0001). This relationship held even if the physician was in palliative care (4.1% [2] vs. 84.6% [33]) or oncology (0% [0] vs. 76.3% [29]) (p &lt;0.0001). Inpatient length of stay (LOS), hospice referral, code status change, ICU admission rate or LOS, 30-day readmissions, 30-day ED visits, and inpatient and 30-day deaths did not differ significantly between groups. Conclusions: Identification of hospitalized cancer patients with high mortality risk via machine learning led to a substantial increase in documented ACP conversations but did not impact EOL healthcare utilization. Further integration of the model in clinical practice is ongoing. Our intervention showed promise in changing clinician behavior, and additional work is needed to evaluate downstream impacts.

Inpatient outcomes of patients with new solid tumors admitted to a comprehensive cancer center.

e23056 Background: Hospitalized cancer patients demonstrate multiple challenges including management of acute illnesses, establishment of the cancer diagnosis, creation of a goal concordant care plan, and post-hospital disposition. Yet, there has been limited research regarding characteristics and outcomes of new to hospital oncology patients. Methods: We performed a retrospective chart review of patients with suspicion or confirmed solid tumor cancer diagnosis who were admitted without an established MD Anderson Cancer Center oncologist. We assessed length of stay, ICU utilization, 30-day readmission rate, inpatient mortality, and discharge disposition. We compared the results to our group’s established oncology patients. Statistical analysis was performed using Chi-squared testing. Results: Between November 2022 and January 2023, 201 unique new patients were reviewed out of 1635 admissions. Of those 103 (51.2%) were males, median age of 60 (range: 22-79), White including Hispanics 118 (59%), African Americans 46 (23%), others 33 (18%). Of the study population, 88 (43.8%) did not have a confirmed cancer diagnosis, of which we established a cancer diagnosis in 68 (77.3%) and ruled out cancer in 8 (4%). Of the 113 (56.2%) diagnosed at outside facilities, 63 (55.8%) were treatment naive and 22 (19.4%) had their cancer diagnosis revised. Of 175 patients which had staging information, 150 (85.7%) were stage 4. During the index admission, 45 (22.4%) patients had cancer-directed therapy. The average and geometric mean length of stay (days) for new patients was 12.1 and 8.8, respectively, compared to the established group of 8.2 and 6.1 (p &lt; 0.001). Inpatient mortality was higher in new patients (10.5% vs 6.8%, p &lt; 0.001) and they were more likely to be referred to hospice services (17.4% vs 8.3%, p &lt; 0.001). Furthermore, hospice referral was higher in the unestablished, previously treated group compared to treatment naive patients (28% vs 13.9%, p = 0.003), however inpatient mortality was similar (12% vs 9.9%, p = 0.63). ICU stay, 30-day readmission rate, discharge to home vs placement were similar in new and established patients (Table 1). Conclusions: Admitted new oncology patients typically present with advanced disease and had longer length of stay, mortality rates, and hospice referrals compared to established patients. Further research is needed to identify and improve care barriers to new inpatients, focusing on improving workflow process and timely goal concordant care discussions. [Table: see text]

Patterns of referral to supportive oncology (SO) practice embedded in a cancer center and impact of timing of referrals on end-of-life outcomes.

e24064 Background: ASCO guidelines recommend early palliative care (PC) for patients with advanced cancer, defined as within eight weeks of diagnosis, to enhance survival and quality of life. It is unclear which subsets of patients are referred “late” to PC and how timing of referral impacts cancer outcomes and end-of-life care. Methods: Patients &gt;18 years with first SO consult between 01/01/2021-06/30/2023 were included. Data on patient demographics, acute healthcare and hospice utilization, cancer treatment and mortality was extracted from the electronic medical record (EMR) and claims database. Early referral to SO was defined as visit &lt; 3 months after diagnosis of advanced disease, delayed referrals &gt; 3 months and late consults as &lt; 30 days from death. We used descriptive statistics to describe patients’ sociodemographics, clinical characteristics and healthcare utilization. Multinomial logistic regression analysis was utilized to evaluate how patient characteristics influenced the timing of referrals. To examine the effects of late referral on inpatient (IP) and intensive care unit (ICU) stay durations, we applied a hurdle model. Results: Total sample included 1229 patients; majority had ECOG &lt; 2 and received one line of treatment at time of consult, 87% had solid tumors, of whom 74% had stage 4 disease. The solid tumors most referred were gastrointestinal cancers (25%), lung cancer (14%) and head and neck cancers (13%). Early referral rates across cancer groups differed significantly 53% Head and Neck, 48% GI, 46% Lung, 43% GYN, 29% GU, 11% Breast, and 31% Other (p &lt; 0.001). The most common reason for referral was physical symptom management (98%). The median ESAS symptom burden was 5 (range 0-10) symptoms. At time of analysis, 431 (35.1%) patients had died of which 256 (59%) were in-hospital deaths. Of the deceased patients, late referral ( &lt; 30 days from death) showed a trend, although not statistically significant, toward higher in-hospital mortality rates, (Odds Ratio: 1.13; 95% CI: 0.57-2.24), and a trend toward reduced likelihood of hospice enrollment(Odds Ratio: 0.73; 95% CI: 0.36-1.49). Notably, late referral significantly increased the duration of IP stays (p &lt; 0.001) and ICU stays (p = 0.004) in last 30 days of life, without change in admission to IP (p = 0.68) or ICU (p = 0.86). Conclusions: The delivery of the guideline-directed "early” consult among patients with advanced cancer is variable. Preliminary analysis showed a trend towards higher in-hospital mortality rates and lower hospice referral for patients referred &lt; 30 days from death among “late” consults. Further subgroup analysis is ongoing. [Table: see text]

Value of a multidisciplinary geriatric oncology committee on patient care in a community-based, academic cancer center

IntroductionThe heterogeneity in health and functional ability among older patients makes the management of cancer a unique challenge. The Geriatric Oncology Program at the University of Maryland Baltimore Washington Medical Center (BWMC) was created to optimize cancer management for older patients. This study aimed to assess the benefits of the implementation of such a program at a community-based academic cancer center. Materials and MethodsWe analyzed patients aged ≥80 years presenting to the Geriatric Oncology Program between 2017 and 2022. A multidisciplinary team of specialists collectively reviewed each patient using geriatric-specific domains and stratified each patient into one of three management groups- Group 1: those deemed fit to receive standard oncologic care (SOC); Group 2: those recommended to receive optimization services prior to reassessment for SOC; and Group 3: those deemed to be best suited for supportive care and/or hospice care. ResultsThe study cohort consisted of 233 patients, of which 76 (32.6%) received SOC, 43 (18.5%) were optimized, and 114 (49.0%) received supportive care or hospice referral. Among the optimized patients, 69.8% were deemed fit for SOC upon re-evaluation following their respective optimization services. The Canadian Study of Health and Aging-Clinical Frailty Scale (CSHA-CFS) score was implemented in 2019 (n = 90). Patients receiving supportive/hospice care only had an average score of 5.8, while the averages for those in the optimization and SOC groups were 4.6 and 4.1, respectively (p ≤0.001). Patients receiving SOC had the longest average survival of 2.71 years compared to the optimization (2.30 years) and supportive care groups (0.93 years) (p ≤0.001). For all patients that underwent surgical interventions post-operatively, 23 patients (85%) were discharged home and four (15%) were discharged to a rehabilitation facility. DiscussionThe present study demonstrates the profound impact that the complexities in health status and frailty among older individuals can have during cancer management. The Geriatric Oncology Program at BWMC maximized treatment outcomes for older adults through the provision of SOC therapies and optimization services, while also minimizing unnecessary interventions on an individual patient-centric level.

End of life care in chronic limb threatening ischemia: a retrospective cohort study

ObjectiveChronic limb-threatening ischemia (CLTI) is the end-stage of peripheral artery disease, defined as 2 or more weeks of rest pain and/or tissue loss from objectively proven obstructive arterial disease. Mortality is high, yet literature on end-of-life and palliative care in this setting is scarce. Palliative care is care that is aimed at alleviating the physiological and psychological symptoms and addresses the social and spiritual needs of patients and family/carers. End-of-life care is an umbrella term for palliative care given alongside disease-modifying care. The aim of this study was to evaluate predictors of end-of-life and palliative care and corresponding outcomes using surgical revascularization and amputation as surrogate markers for CLTI. MethodsThis was a retrospective single-center cohort study of people with CLTI who underwent either surgical revascularization (bypass) or amputation between January 2018 and December 2019. Palliative care input was defined as a dedicated palliative care consultation by a palliative care professional, medical doctor, surgeon, or advanced care practitioner. Data was collected on patient demographics, cause and place of death, and palliative and end-of-life care outcomes, which included documentation of preferred place of care/death and care priorities, family involvement in advance care planning discussions, key workers allocated to coordinate palliative care, use of community palliative care registers, hospice referral, time spent in hospital and the intensive care unit toward the end of life, and realization of documented care objectives. The data was analyzed to determine predictors of palliative care and tangible outcomes. ResultsOne-hundred and eighty-six patients were included (116 bypass and 70 amputation) with a median age of 67.5 years (interquartile range, 58-76 years) and most were male (73.7%) with a median survival of 46 months (interquartile range, 21-54 months) and a 2-year survival of 72% (95% confidence interval, 66%-79%). Palliative care consultations occurred for 10.8% of patients, with the median time from consultation to death of 5 days. Of the deceased, only a few had preferred place of care/death (8.1%), care priorities (18.3%), and family involvement during advance care planning (15.1%) documented in their notes. Cause of death was most commonly due to sepsis (n = 30), malignancy (n = 11), and myocardial infarction (n = 10). Mortality and in-hospital death were significantly higher in patients who underwent amputation in comparison to bypass surgery. There was an association of both cardiac and renal dysfunction with palliative care input. Patients with palliative care input were more likely to have documentation of preferred place of care/death, care priorities, and resuscitation decisions; and family involvement in advance care planning decisions. However, this did not translate into a reduction in hospital readmission or time spent in the hospital or intensive care unit towards the end of life. ConclusionsPatients with CLTI are not being offered palliative care in line with The National Institute for Health and Care Excellence or The Vascular Society of Great Britain and Ireland guidance. There was an association between palliative care input and end-of-life care quality. However, input occurred too close to death to have a very significant effect. Further research, development of clinical pathways and services, and better integration between palliative and vascular services is needed to improve the end-of-life care of patients living with CLTI.

Evaluation of an outreach programme for patients with COVID-19 in an integrated healthcare delivery system: a retrospective cohort study

ObjectivesIn the first year of the COVID-19 pandemic, health systems implemented programmes to manage outpatients with COVID-19. The goal was to expedite patients’ referral to acute care and prevent overcrowding...

Decision curve analysis based on summary data.

To realize the potential of precision medicine, predictive models should be integrated within the framework of decision analysis, such as the decision curve analysis (DCA). To date, its application has required individual patient data (IPD) that are often unavailable. Performing DCA using aggregate data without requiring IPD may advance the goals of precision medicine. We present a statistical framework demonstrating that DCA can be conducted by using only the mean and standard deviation (SD) from the raw probabilities of the predictive model. We tested our theoretical framework by performing extensive simulations and comparing the aggregate-based DCA with IPD DCA. The latter was conducted using IPD from four predictive models that employed logistic regression, Coxor competing risk time-to-event modeling including(a) statins for primary prevention of cardiovascular disease (n = 4859), (b) hospice referral for terminally ill patients (n = 9104), (c) use of thromboprophylaxis for preventing venous thromboembolismin patients with cancer (n = 425)and (d) prevention of sinusoidal obstruction syndromeafter hematopoietic cell transplantation (SCT) (n = 80). Simulations assuming perfect calibration showed that regardless of which probability distributions informed the predictive models, the differences in DCA were negligible. Similarly, for the adequately powered models, the results of DCA based on the summary data were similar to IPD-derived DCA. The inherent instability of the predictive models, based on the smaller sample sizes, resulted in a somewhat larger discrepancy between aggregate and IPD-based DCA. DCA informed by adequately powered and well-calibrated models using only summary statistical estimates (mean and SD) approximates well models using IPD. Use of aggregate data will facilitate broader integration of predictive with decision modeling toward the goals of individualized decision-making.

Removing Transfusion Dependence As a Barrier to Hospice Enrollment (BRUOG-407)

Background: Patients with hematologic malignancies (HM) receive more aggressive care at the end of life (EOL) and have suboptimal hospice utilization when compared to patients with solid tumors. One reason for this is a high rate of transfusion dependence (TD). Many hospice agencies decline to provide palliative transfusions because of cost, philosophical objections, and/or logistical barriers, and as a result, many TD patients delay hospice enrollment or never enroll at all. In response to a call from ASH for novel care models to address this issue, we designed a pilot study of an open access transfusion model in hospice care. Our hypothesis was that the provision of transfusions for patients with HM on hospice would be feasible and would result in timely hospice referral and less aggressive EOL care. Methods: We conducted a single-center, prospective pilot study [NCT05063591] enrolling adults with aggressive HM who were TD and hospice-eligible. We concurrently enrolled patients' primary caregivers. Patients were evaluated weekly during a clinic visit or telephone call to assess potential symptoms related to anemia or thrombocytopenia. Relevant symptoms triggered a CBC assessment, and, if appropriate, administration of a transfusion of red blood cells (RBCs) and/or platelets. Caregivers completed the Caregiver Evaluation of Quality of EOL Care survey (CEQUEL, Higgins et al. PLoS One, 2013) ≥1 month after the patient death, to assess perception of the patient's EOL care. The primary outcome was feasibility of the care model, defined as 70% rate of successful enrollment after screening. Secondary outcomes included standard EOL care quality measures: hospice length of stay (LOS), inpatient days and ICU days in the last 30 days of life, inpatient deaths, and the use of chemotherapy in the last 14 days of life. Exploratory outcomes included caregiver's assessment of EOL care quality. Results: Of 22 patients and 9 caregivers screened, 10 patients (45%) and 8 caregivers enrolled. 4 screened patients died before consenting. The median patient age was 80 (range, 69-90). Most patients (90%) had a myeloid malignancy (AML, MDS, or CMML). The mean number of transfusions on study was 2.4 units of RBCs (range, 0-9) and 1.2 units of platelets (range, 0-10) ( Figure). The most common transfusion-triggering symptom was fatigue (N=40 transfusion events), followed by shortness of breath (N=15). The median LOS on hospice was 13 days (range, 4-103; Table). 60% of patients spent &amp;gt;9 days on hospice, and no patient spent ≤ 3 days on hospice. All enrolled patients died outside of the hospital: 60% at home and 40% at inpatient hospice. No patient received chemotherapy within 14 days of death, and 1 (10%) was admitted to the ICU within 30 days of death (prior to trial enrollment). There were no disenrollments due to transfusion reactions. One patient (10%) revoked hospice for hospitalization, but re-enrolled and died in inpatient hospice. When caregivers evaluated their loved one's EOL care, 100% reported that the patient experienced a peaceful death; 100% felt that medical intervention did not result in increased suffering; 86% agreed that EOL was not prolonged by medical intervention longer than they would have wished, and 86% felt that the patient suffered much less than expected. The mean CEQUEL score was 24 (range, 20-26, higher scores indicating a better perception of EOL care). Conclusion: Providing blood transfusions to patients with HM on hospice was feasible in our symptom-driven care model. The enrollment rate was short of the 70% goal, but some screened patients died before enrolling, suggesting that hospice was considered too late in the disease course, and that additional factors beyond TD may contribute to suboptimal hospice utilization among these patients. When compared with our population-based study describing EOL outcomes of patients with HM (Egan et al., Blood Adv. 2020), our current outcomes compare favorably, with longer median time on hospice (13 vs 9 days), fewer hospital deaths (0 vs 27%), and less chemotherapy at the EOL (0 vs 13%). Importantly, caregiver perception of EOL care quality was favorable. Our findings illustrate the potential of palliative blood transfusions to improve the EOL care quality for HM patients on hospice, and highlight the need for earlier hospice referral while pursuing care models that address the specific needs of this unique TD population.