Are muscle and fat loss predictive of clinical events in pancreatic cancer? The importance of precision metrics.

BACKGROUND: Muscle and fat loss from cancer may have prognostic significance. Skeletal muscle and fat areas measured at L3 on a CT scan correlate with body muscle and fat mass. We wished to know if reduced skeletal muscle area or fat on diagnostic CT scans or changes from initial CT scans in patients with pancreatic cancer who died in 2018 and 2019 predicted mortality. METHOD: eEectronic records of 112 patients with locally advanced or metastatic pancreatic cancer were used to extract stage, age, gender, comorbidities, weight, and height at the time of the first CT scan. Survival (in days) was defined from the first CT scan to the death date. Patients had at least one CT scan of the abdomen. I. Two trained medical students read scans independently using TeraRecon software (Durham, NC). Results were averaged, and the differences determined precision. Interclass correlation coefficient (ICC), coefficient of variation, and least significant change determined the precision between readers. Independent prognostic modeling included age and BMI. RESULTS: An evaluable sample of 104 with an average age of 67, 56 were male. Nearly half had a TNM Stage of IV (45%). The average Charlson Comorbidity index is 7.2. In those undergoing repeat scans, most were in the timeframe of 60-120 days. Changes in visceral fat in men in the unadjusted Cox proportional hazard model and reduced skeletal muscle area in the age-adjusted model of men predicted mortality. In contrast, myosteatosis in women marginally predicted improved survival. ICC's precision between readers was adequate but by least significant change would have missed subtle, clinically important changes. DISCUSSION: Muscle loss during chemotherapy in men predicted mortality in men but not women. Precision is an important metric when measuring body composition. CONCLUSION: Muscle loss in men during chemotherapy of pancreatic cancer predicts mortality.

Early Increases in Blood Pressure and Major Adverse Cardiovascular Events in Patients With Renal Cell Carcinoma and Thyroid Cancer Treated With VEGFR TKIs.

BACKGROUND: Although VEGFR tyrosine kinase inhibitors (TKIs) are a preferred systemic treatment approach for patients with advanced renal cell carcinoma (RCC) and thyroid carcinoma (TC), treatment-related cardiovascular (CV) toxicity is an important contributor to morbidity. However, the clinical risk assessment and impact of CV toxicities, including early significant hypertension, among real-world advanced cancer populations receiving VEGFR TKI therapies remain understudied. METHODS: In a multicenter, retrospective cohort study across 3 large and diverse US health systems, we characterized baseline hypertension and CV comorbidity in patients with RCC and those with TC who are newly initiating VEGFR TKI therapy. We also evaluated baseline patient-, treatment-, and disease-related factors associated with the risk for treatment-related early hypertension (within 6 weeks of TKI initiation) and major adverse CV events (MACE), accounting for the competing risk of death in an advanced cancer population, after VEGFR TKI initiation. RESULTS: Between 2008 and 2020, 987 patients (80.3% with RCC, 19.7% with TC) initiated VEGFR TKI therapy. The baseline prevalence of hypertension was high (61.5% and 53.6% in patients with RCC and TC, respectively). Adverse CV events, including heart failure and cerebrovascular accident, were common (occurring in 14.9% of patients) and frequently occurred early (46.3% occurred within 1 year of VEGFR TKI initiation). Baseline hypertension and Black race were the primary clinical factors associated with increased acute hypertensive risk within 6 weeks of VEGFR TKI initiation. However, early significant "on-treatment" hypertension was not associated with MACE. CONCLUSIONS: These multicenter, real-world findings indicate that hypertensive and CV morbidities are highly prevalent among patients initiating VEGFR TKI therapies, and baseline hypertension and Black race represent the primary clinical factors associated with VEGFR TKI-related early significant hypertension. However, early on-treatment hypertension was not associated with MACE, and cancer-specific CV risk algorithms may be warranted for patients initiating VEGFR TKIs.

Radiation Therapy in the Last Month of Life: Association With Aggressive Care at the End of Life.

CONTEXT: Half of the patients with cancer who undergo radiation therapy do so with palliative intent. OBJECTIVES: To determine the proportion of undergoing radiation in the last month of life, patient characteristics, cancer course, the type and duration of radiation, whether palliative care was involved, and the of radiation with aggressive cancer care metrics. METHODS: One thousand seven hundred twenty-seven patients who died of cancer between January 1, 2018, and December 31, 2019, were included. Demographics, cancer stage, palliative care referral, advance directives, use of home health care, radiation timing, and survival were collected. Type of radiation, course, and intent were reviewed. Chi-square analysis was utilized for categorical variables, and Kruskal-Wallis tests for continuous variables. A stepwise selection was used to build a Cox proportional hazard model. RESULTS: Two hundred thirty-three patients underwent radiation in the last month of life. Younger patients underwent radiation 67.3 years (SD 11.52) versus 69.2 years (SD 11.96). 42.6% had radiation within two weeks of death. The average fraction number was 5.5. Individuals undergoing radiation were more likely to start chemotherapy within the last 30 days of life, continue chemotherapy within two weeks of death, be admitted to the ICU, and have two or more hospitalizations or emergency room visits. Survival measured from the date of diagnosis was shorter for those undergoing radiation, 122 days (IQR 58-462) versus 474 days (IQR 225-1150). Palliative care consultations occurred later in those undergoing radiation therapy. CONCLUSION: Radiation therapy in the last month of life occurs in younger patients with rapidly progressive cancer, who are subject to more aggressive cancer care, and have late palliative care consults.

Validation of the Surprise Question and the Development of a Multivariable Model.

CONTEXT: The Surprise Question (SQ) (would you be surprised if this patient died within a year?) is a prognostic variable explored in chronic illnesses. Validation is limited to sensitivity, specificity, and predictive values. OBJECTIVES: Our objective is to validate the SQ in cancer patients and develop a predictive model with additional variables. METHODS: A prospective cohort study of adult (age>18) cancer patients seen between October 1, 2019, through March 31, 2021, undergoing systemic therapies had the SQ completed by oncologists prior to each change in systemic therapy. The primary outcome was survival for one year. Secondary outcomes were predictions of survival at three, six, and nine months. Patients were grouped into negative SQ (not surprised) and positive SQ (surprised). Sensitivity, specificity, predictive values, and likelihood ratios (LR) were calculated for the SQ. Additional prognostic variables were age, gender, cancer stage, line of therapy, Charleson Comorbid Index (CCI), palliative care consultation (prior to, after the SQ, or not at all), and healthcare utilization (outpatient, inpatient, and emergency department (ED). Logistic regression and receiver operating characteristics (ROC) were used for discrimination and modeling. Akaike information criterion (AIC) was used to compare the model fit as each predictor. RESULTS: 1366 patients had 1 SQ; 784 died within a year. The SQ predicted survival at one year (P = 0.008), with a positive LR of 1.459 (95%CI 1.316-1.602) and a c-statistic of 0.565 (95%CI 0.530-0.600). Additional variables increased the c-statistic to 0.648 (95% CI 0.608-0.686). The total model best predicted survival at three months, c-statistic of 0.663 (95% CI 0.616-0.706). However, the total model c-statistic remained <0.70. CONCLUSIONS: The SQ, as a single factor, poorly predicts survival and should not be used to alter therapies. Adding additional objective variables improved prognostication, but further refinement and external validation are needed.

Is Weight Loss During Chemotherapy for Pancreatic Cancer Prognostic?

BACKGROUND: Predicting poor survival outcomes early in palliative chemotherapy is important to the timing of palliative care. Weight loss during chemotherapy if prognostic would lead to early palliative care. METHOD: We collected demographics, stage, chemotherapy, chemotherapy cycles, weight, healthcare utilization, comorbidities (Charleson Comorbid Index), tumor markers, and weight changes over 60 days. We defined 3 groups of patients: 1. Weight gain to <.5% weight loss, 2. Weight loss< 2% and 3. Weight loss of >2%. A Chi-square test assessed differences in weight during treatment. Time-to-event analysis was expressed in a Kaplan Meier curve. RESULTS: 93 individuals died of pancreatic cancer in 2018 and 2019. The median age was 71.2 years. Forty-three had stage I and II, 40 stages III and IV cancers, and 10 had unknown stages. Most received FOLFIRINOX and gemcitabine/nab-paclitaxel chemotherapy. Thirty-six gained to lost < .5% during chemotherapy,8 lost < 2% and 49 patients lost > 2% of their weight. Mortality was available in 55 of 93 patients. Median survival was 16.6 months in those with weight gain to < .5% weight loss, 17.28 months for those with < 2% weight loss, and 20.5 months for those with > 2% weight loss (P = .42). DISCUSSION: Weight loss over 60 days did not predict a poor prognosis in this small retrospective study; larger prospective studies may clarify the prognostic importance of weight loss during chemotherapy. CONCLUSION: In this small retrospective study, weight loss over 60 days did not predict poor survival.

The Financial Impact of Palliative Care and Aggressive Cancer Care on End-of-Life Health Care Costs.

BACKGROUND: Medicare cancer expenditures in the last month of life have increased. Aggressive cancer care at the end-of-life (ACEOL) is considered poor quality care. We used Geisinger Health Plan (GHP) last month's costs for cancer patients who died in 2018 and 2019 to determine the costs of and influence of Palliative Care (PC) on ACEOL. METHOD: Patients with GHP ages 18-99 who died in 2018 and 2019 were included. Demographic, clinical characteristics, and Charlson Comorbid Index were compared across care groups defined as no ACEOL indicator, 1 or more than 1 indicator. Differences between groups were compared with Kruskal-Wallis tests and one-way ANOVA for 3 groups. Median two-sample tests and independent t-tests compared groups of 2. A P-value 1. There were incremental cost increases with each additional ACEOL indicator (p = < .0001). Palliative Care <90 days before death was associated with increased costs while consultations >90 days before death lowered cost (P < .0001) due to reduced chemotherapy in the last month. Completed ADs reduced cost by $4000. DISCUSSION: ACEOL indicators multiply costs during the last month of life. Palliative care instituted >90 days before death reduces chemotherapy in the last month of life and AD reduces health care costs. CONCLUSION: Cancer health care costs increase with indicators of ACEOL. Palliative care consultations >90 days before death; ADs reduce cancer health care costs.

When does early palliative care influence aggressive care at the end of life?

BACKGROUND: Early palliative care improves patient quality of life and influences cancer care. The time frame of early has not been established. Eight quality measures reflect aggressive care at the end of life. We retrospectively reviewed patients who died with cancer between January 1, 2018, through December 31, 2019, and compared the timing of palliative care consultation, advance directives (AD), and home palliative care with aggressive care at the end of life (ACEOL). METHODS: Patients without ACEOL indicators were compared to patients with one or more than one indicator of ACEOL. The proportion of patients who received palliative care, completed AD, and the timing of palliative care and AD (less than 30 days, 30-90 days, and greater than 90 days prior to death) was compared for patients who had ACEOL versus those who did not. Chi-square analysis was used for categorical data, one-way ANOVA for continuous variables, and odds ratio (OR) with confidence intervals (CI) was reported as a measure of effect size. A p value ≤ 0.05 was considered significant. RESULTS: 1727 patients died, 46% were female, and the mean age was 69 (SD 11.91). Seventy-one percent had a palliative care consult, 26% completed AD, and 888 (51.4%) had at least one indicator of ACEOL. The most common indicator of ACEOL was new chemotherapy within 30 days of death, in 571 of 888 (64%) of patients experiencing ACEOL. ADs completed at any time reduced ACEOL (OR 0.80, 95%CI 0.64-0.99). Palliative care initiated at 30 days was associated with a greater risk of ACEOL (OR 5.32, 95% CI 3.94-7.18) and initiated between 30 and 90 days (OR 1.39, 95% CI 1.07-1.80) compared to no palliative care but was associated with reduced chemotherapy as an indicator of ACEOL when > 90 days (OR 0.46, 95% CI 0.38-0.57) before death. DISCUSSION: Completed ADs were associated with reduced chemotherapy in the last 30 days of life and reduced ICU admissions. This may reflect goals of care and end-of-life discussions and transition of care to comfort measures. Palliative care paradoxically when initiated within 90 days before death was associated with greater ACEOL compared to no palliative care. This may be due to consultation late in the course of illness with a focus on crisis management in patients frequently utilizing the health care system. There is an associated reduction in the use of chemotherapy in the last 30 days of life if palliative care is consulted 90 days prior to death. CONCLUSIONS: An initial palliative care consult greater than 90 days before death and ADs completed at any time during the disease trajectory was associated only with reduced chemotherapy in the last 30 days of life compared with no palliative care among the 7 ACEOL indicators. ADs were associated with reduced ICU admissions. Most palliative care consults occurred within 90 days of death and a palliative care consult within 90 days of death is not an optimal utilization of services.

The Influence of Palliative Care in Hospital Length of Stay and the Timing of Consultation.

BACKGROUND: Inpatient palliative care may reduce length-of-stay, costs, mortality, and prevent readmissions. Timing of consultation may influence outcomes. The aim of this study was to explore the timing of consultation and its influences patient outcomes. METHOD: This retrospective study of hospital consultations between July 1, 2019 and December 31, 2019 compared patients seen within 72 hours of admission with those seen after 72 hours. Outcomes length of stay and mortality. Chi-square analyses for categorical variables and independent t-tests for continuous normally distributed variables were done. For nonparametrically distributed outcome variables, Wilcoxon rank sum test was used. For mortality, a time-to-event analysis was used. 30-day readmissions were assessed using the Fine-Gray sub-distribution hazard model. Multiple regression models were used, controlling for other variables. RESULTS: 696 patients were seen, 424 within 72 hours of admission. The average age was 73 and 50.6% were female. Consultation within 72 hours was not associated with a shorter stay for cancer but was for patients with non-cancer illnesses. Inpatient mortality and 30-days mortality were reduced but there was a higher 30-day readmission rate. DISCUSSION: Palliative consultations within 72 hours of admission was associated with lower hospital stays and inpatient mortality but increased the risk of readmission. Benefits were largely observed in patients followed in continuity. CONCLUSION: Early inpatient palliative care consultation was associated with reduced hospital mortality, 30-day mortality and length of stay particularly if patients were seen by palliative care prior to hospitalization.

Caregiver engagement practices in National Cancer Institute Clinical Oncology Research Program settings: Implications for research to advance the field.

BACKGROUND: Supportive care interventions have demonstrated benefits for both informal and/or family cancer caregivers and their patients, but uptake generally is poor. To the authors' knowledge, little is known regarding the availability of supportive care services in community oncology practices, as well as engagement practices to connect caregivers with these services. METHODS: Questions from the National Cancer Institute Community Oncology Research Program (NCORP)'s 2017 Landscape Survey examined caregiver engagement practices (ie, caregiver identification, needs assessment, and supportive care service availability). Logistic regression was used to assess the relationship between the caregiver engagement outcomes and practice group characteristics. RESULTS: A total of 204 practice groups responded to each of the primary outcome questions. Only 40.2% of practice groups endorsed having a process with which to systematically identify and document caregivers, although approximately 76% were routinely using assessment tools to identify caregiver needs and approximately 63.7% had supportive care services available to caregivers. Caregiver identification was more common in sites affiliated with a critical access hospital (odds ratio [OR], 2.44; P = .013), and assessments were less common in safety-net practices (OR, 0.41; P = .013). Supportive care services were more commonly available in the Western region of the United States, in practices with inpatient services (OR, 2.96; P = .012), and in practices affiliated with a critical access hospital (OR, 3.31; P = .010). CONCLUSIONS: Although many practice groups provide supportive care services, fewer than one-half systematically identify and document informal cancer caregivers. Expanding fundamental engagement practices such as caregiver identification, assessment, and service provision will be critical to support recent calls to improve caregivers' well-being and skills to perform caregiving tasks.

A validated value-based model to improve hospital-wide perioperative outcomes: adaptability to combined medical/surgical inpatient cohorts.

OBJECTIVES: Authors hypothesized that building safe hospital systems to improve value-based surgical outcomes is predicated on workflow redesign for dynamic risk stratification, coupled with "real-time" mitigation of risk. We developed a comanagement model for hospitalized surgical cohort, and determined whether this iterative process redesign for surgery will be adaptable to disparate hospital systems and will be beneficial for combined medical/surgical adult inpatients. CONTEXT: Concerns about preventable harm in hospitalized patients have generated a plethora of both, process-driven and outcome-based strategies in US Healthcare. Although comparison between hospitals is a common mechanism to drive quality, other innovative approaches are needed for real-time risk mitigation to improve outcomes. METHODS: Prospective implementation of Surgical Continuum of Care (SCoC) model in hospitals initially for surgery patients; subsequently Continuum of Care (CoC) for medical/surgical population. Redesign of hospital care delivery model: patient cohorting, floor-based team building, and intensivist/hospitalist staffing of progressive care unit (PCU). Work flow redesign for clinical effectiveness: multidisciplinary team rounds, acuity stratified care rounding based on dynamic risk assessment into a novel HAWK (high risk)/DOVE (low risk) patient grouping, intensivist/hospitalist comanagement of surgical patients, and targeted response. STUDY: Pre- and postintervention with concurrent cohort control design. SETTING: Academic medical centers for SCoC and integrated health system hospital for CoC. PATIENT GROUPS: SCoC Pilot Study-Campus A: Preintervention control group 1998-2000, Intervention Group 2001-2004; Campus B: Comparator Control Group 1998-2004. SCoC Validation Study-Campus C: Preintervention Group 2001-2005; Intervention Group 2006-2008. CoC Study-Campus D: Hospital-wide Group 2009. METRICS: Mortality, length of stay (LOS): overall, surgical intensive care unit and PCU, readmission rates, and cost. Case mix index for risk adjustment. RESULTS: Total >100,000 admissions. There was a significant reduction in overall surgical mortality in both, pilot (P < 0.002) and validation (P < 0.02) SCoC studies and overall hospital mortality in the medical/surgical CoC study (risk-adjusted mortality index progressively declined in CoC study from 1.16 pre-CoC to 0.77 six months post-CoC implementation; significant at 75% confidence level). Case mix index was unchanged during study period in each campus. Nested study in validation cohort of hospital-wide versus surgery alone (observed/expected mortality index) demonstrated significant benefit to SCoC in intervention group. The mortality benefit was primarily derived from risk-stratified rounding and actively managing risk prone population in the PCU. Surgical intensive care unit, PCU, and total hospital patient-days significantly decreased in SCoC pilot study (P < 0.05), reflecting enhanced throughput. LOS reduction benefit persisted in SCoC validation and CoC studies. In addition to decreased LOS, cost savings were in PCU (range, $851,511-2,007,388) and top diagnosis-related groups, for example, $452 K/yr for diagnosis-related group 148. CONCLUSIONS: SCoC is patient-centered, outcomes-driven, value-based approach for hospital-wide surgical patient safety. The principles of this value paradigm are adaptable to other hospitals as demonstrated in our longitudinal study in 3 hospital systems, and the initial experience of CoC suggests that this model will have benefit beyond surgical hospital cohort.