Optimizing Cancer Associated Thrombosis (CAT) Risk Assessment Model at a Safety-Net Healthcare System

Introduction: Cancer associated thrombosis is a preventable complication that impacts the quality of life of patients with cancer. The Khorana score (KS) is the most widely used risk assessment model (RAM) to predict venous thromboembolism (VTE) in ambulatory patients undergoing chemotherapy. Potential limitations of the score include modest discrimination and small proportion of patients in the highest risk subgroup. We aimed to examine if a clinical informatics approach incorporating race/ethnicity, cancer staging, type of systemic therapy, and other known VTE risk factors from the electronic health record (EHR) can improve the RAM. Methods: We performed a retrospective cohort study at Harris Health System (HHS), a safety-net healthcare system that provides care for underserved minorities and uninsured patients in Houston. We created an integrated database that linked consecutive patients with newly diagnosed invasive cancer in the cancer registry with structured data from EPIC Caboodle database 2011-2020. Inclusion/exclusion criteria are shown in Figure 1. We followed patients from time of initial systemic therapy to time of first VTE, death, or loss of follow-up. VTE was defined as radiologically confirmed pulmonary embolism (PE), proximal or distal lower extremity deep vein thrombosis (LE-DVT), catheter-related DVT (CR-DVT), or splanchnic vein thrombosis (SVT) in inpatient or outpatient setting. We used acute, chronic or historical VTE ICD9/ICD10 facility billing codes to assess for potential events and confirmed incident and recurrent events through medical record review. We used multivariable Cox regression to assess potential risk predictors. The model was built iteratively to expand upon the KS. Kaplan Meier failures curves were used to estimate the VTE incidence. C statistic was assessed with binary outcomes at 3- and 6-month. Results: A total of 4,546 patients with newly diagnosed cancer receiving 1 st line systemic therapy met the inclusion/exclusion criteria. Relevant demographics showed a median age of 54 (IQR 46-61), 57% female, 50% Hispanic, 27% Black, and 75% uninsured. Most common cancer types included breast (17%), colorectal (13%), lung (10%), and non-Hodgkin lymphoma (8%); 32% of patients had metastatic disease. First-line systemic therapy included 89% cytotoxic chemotherapy, 9% small molecule targeted +/- endocrine therapy, and 2% PD-1/PD-L1 immunotherapy. Only 1% had remote VTE history after excluding 317 patients already on therapeutic anticoagulation. Incident VTE occurred in 477 patients during a median follow-up of 11.3 months. There were 229 PE +/- other, 140 LE-DVT, 94 CR-DVT, and 14 SVT. In addition to the KS covariates (Table 1), recent cancer diagnosis (≤ 1 month) (HR 1.43, 1.16-1.76), metastatic disease (HR 1.46, 1.20-1.76), recent hospitalization (≤ 3 month) (HR 1.54, 1.24-1.90) were also associated with higher risk of VTE, whereas Hispanic ethnicity (HR 0.65, 0.51-0.84) and Asian race (HR 0.30, 0.16-0.54) were associated with a lower risk. Other appreciable predictors included targeted vs. chemotherapy (HR 0.75, 0.51-1.10) and history of PE/LE-DVT (HR 1.74, 0.93-3.27). Immunotherapy (vs. chemotherapy) and black (vs. white) were not associated with VTE. Figure 2 shows the comparison of VTE incidence using the 2 RAMs. Original KS RAM had c statistic of 0.66 and 0.62 at 3- and 6-month, respectively (Table 2). The highest risk group (3+) included 16% of patients (n=723) and 23% of all VTE (n=108). The modified RAM had better discrimination with c statistic of 0.71 and 0.67 at 3- and 6-month, respectively. The highest risk group (3+) included 33% of patients (n=1509) and 51% of all VTE (n=244). Conclusions: The KS performed reasonably well in a large safety-net healthcare system with predominantly uninsured patients with advanced cancer initiating systemic therapy. Nonetheless, simple structured data elements from the EHR such as race/ethnicity, staging, therapy type, and recent hospitalization improved the performance of the KS-based RAM and doubled the number of patients in the high-risk stratum and the number of preventable VTE. An integrated clinical informatics approach adapted to the local population can improve outcomes by identifying patients most appropriate for ambulatory thromboprophylaxis. Figure 1 Figure 1. Disclosures Carrier: Sanofi: Honoraria; Leo Pharma: Honoraria, Research Funding; Servier: Honoraria; Pfizer: Honoraria, Research Funding; Bayer: Honoraria; BMS: Honoraria, Research Funding.

Association of Insurance Types and Survival in Acute Promyelocytic Leukemia (APL)

Introduction Health insurance, or lack thereof, is a significant barrier to health care access in the United States. Patients without insurance or with inadequate coverage are more likely to delay or forego treatment, even with acute illness or significant symptoms, leading to worse health outcomes. We aimed to analyze if insurance types impacted one-month mortality and overall survival (OS) in younger patients with APL. Methods We utilized National Cancer Database to identify patients <65 years who were diagnosed with APL between 2004-2015. We used multiple logistic regression analysis to evaluate the effects of insurance type on the probability of one-month mortality. OS was estimated by the Kaplan-Meier method. A full Cox regression model was used to determine the effects of insurance types on mortality. Results A total of 5380 patients were included. Median age was 44 years (0-64), 50% were female, 93% had Charlson-Deyo comorbidity index (CCI) of 0 or 1, and 58% were treated at academic centers. Insurance types included private (67%), Medicaid or other government insurance (19%), Medicare (7%) or uninsured (6%). Patients with Medicare were older and had increased comorbidities. Lower percent of patients with Medicare (23%) or Medicaid/Other government insurance (21%), compared to those with private insurance (56%) or uninsured patients (63%) were treated at academic centers. One-month mortality was higher for patients with Medicare (16%) or uninsured patients (14%), compared to those with Medicaid/Other government (8%) or private insurance (7%).Patients with Medicare (Odds ratio [OR] 1.69, 95% confidence interval [CI] 1.23-2.32, p=0.001) or uninsured patients (OR 2.23, 95% CI 1.56-3.18, p<0.0001) had worse one-month mortality compared to those with private insurance (Table 1). One-month mortality worsened with increasing comorbidities (OR 2.31 for CCI 1, OR 4.44 for CCI 2, and OR 7.02 for CCI ≥3 compared to patients with CCI of 0, p<0.0001). Female patients and patients traveling <6 miles to the treatment center had lower one-month mortality. Median follow-up for surviving patients was 5.4 years (0.008-13.9). Three-year OS was 89% for private insurance, 81% for Medicaid/Other government insurance, 63% for Medicare, and 80% for uninsured patients (Table 2, Figure 1). Patients with Medicaid/Other government insurance (hazard ratio [HR] 1.39, 95% CI 1.19-1.63 p<0.0001), and Medicare (HR 1.88, 95% CI 1.57-2.24, p<0.0001) were associated with worse OS compared to patients with private insurance (Table 3). Compared to patients ≤18 years of age, the likelihood of death was worse for patients 41-64 years (HR 0.68, 95% CI 1.47-4.90, p=0.001). OS worsened with increasing comorbidities (HR 1.71 for CCI 1, HR 2.33 for CCI 2, and HR 3.48 for CCI ≥3 compared to patients with CCI of 1, p<0.0001). Male gender (p=0.0002) was associated with decreased OS. Conclusion In one of the largest database analyses, we identified insurance status as a significant factor affecting one-month mortality and OS in APL. Our results revealed a higher one-month mortality but similar longer-term OS in uninsured patients compared to patients with private insurance, which may reflect poor access to healthcare necessary for prompt diagnosis and timely initial treatment; similar longer-term OS may reflect a higher proportion of younger patients with less comorbidities in uninsured group. Patients with Medicaid/Other government or Medicare insurance had worse OS compared to private insurance. The reasons for worse OS may be multifactorial including problems with access to quality leukemia care or drug coverage, or the effects of other differences in the patient population including income and in case of Medicare patients, older age and comorbidity burden. Our results raise concern for healthcare disparities based on insurance types and highlight challenges associated with improving OS in patients with Medicaid, Medicare, or no insurance, which comprise a significant proportion of patients with APL. Figure 1 Figure 1. Disclosures Gundabolu: Samus Therapeutics: Research Funding; Pfizer: Research Funding; BioMarin Pharmaceuticals: Consultancy; Blueprint Medicines: Consultancy; Bristol-Myers Squibb Company: Consultancy. Bhatt: Genentech: Consultancy; Abbvie: Consultancy, Research Funding; National Marrow Donor Program: Research Funding; Tolero Pharmaceuticals, Inc: Research Funding; Pfizer: Research Funding; Incyte: Consultancy, Research Funding; Jazz: Research Funding; Abbvie: Consultancy, Research Funding; Partnership for health analytic research, LLC: Consultancy; Servier Pharmaceuticals LLC: Consultancy; Rigel: Consultancy.

Abrogation of survival disparity between insured and uninsured individuals after the USPSTF's 2012 prostate-specific antigen-based prostate cancer screening recommendation.

77 Background: In 2012, the U.S. Preventive Services Task Force (USPSTF) recommended against prostate-specific antigen (PSA)-based screening for prostate cancer. Studies have found that insured patients with prostate cancer have better outcomes than uninsured patients. We examined the recommendation’s effects on survival disparities based on insurance status as well as socioeconomic quintile, marital status, and housing (urban/rural). Methods: Using the SEER18 database, we examined prostate cancer-specific survival (PCSS) based on diagnostic time period and one of four factors: insurance status, socioeconomic quintile, marital status, and housing (urban/rural). The SEER-designated socioeconomic quintile was based on variables including median household income and education index. Patients were designated as belonging to the pre-USPSTF era if diagnosed in 2010-2012 or post-USPSTF era if diagnosed in 2014-2016. Disparities were measured with the Cox proportional hazards model. Results: We identified 282,994 patients diagnosed with prostate cancer. During the pre-USPSTF era, uninsured patients experienced worse PCSS compared to insured patients (adjusted HR 1.29, 95% CI 1.06-1.58, p = 0.01). This survival disparity narrowed during the post-USPSTF era as a result of decreased PCSS among insured patients combined with unchanged PCSS among uninsured patients. Moreover, the survival disparity was no longer observed during the post-USPSTF era (aHR 0.91, 95% CI 0.61-1.38, p = 0.67). The survival disparity based on socioeconomic quintile also narrowed but remained significant. In contrast, the survival disparity based on marital status widened, while housing status was not associated with survival disparities in either era. Conclusions: From the pre- to the post-USPSTF era, insured patients with prostate cancer observed a significant decrease in survival that made their survival outcomes similar to that of uninsured patients. Although the underlying reasons are not clear, the USPSTF’s 2012 PSA screening recommendation may have hindered insured patients from being regularly screened for prostate cancer and selectively led to worse outcomes for insured patients without improving the survival of uninsured patients.[Table: see text]

Follow-up colonoscopy after an abnormal stool-based colorectal cancer screening result: analysis of steps in the colonoscopy completion process

Background Delays in receiving follow-up colonoscopy after an abnormal fecal immunochemical test (FIT) result are associated with increased colorectal cancer incidence and mortality. Little is known about patterns of follow-up colonoscopy completion in federally qualified health centers. Methods We abstracted the medical records of health center patients, aged 50–75 years, who had an abnormal FIT result between August 5, 2017 and August 4, 2018 (N = 711). We assessed one-year rates of colonoscopy referral, pre-procedure visit completion, colonoscopy completion, and time to colonoscopy; associations between these outcomes and patient characteristics; and reasons for non-completion found in the medical record. Results Of the 711 patients with an abnormal FIT result, 90% were referred to colonoscopy, but only 52% completed a pre-procedure visit, and 43% completed a colonoscopy within 1 year. Median time to colonoscopy was 83 days (interquartile range: 52–131 days). Pre-procedure visit and colonoscopy completion rates were relatively low in patients aged 65–75 (vs. 50–64), who were uninsured (vs. insured) or had no clinic visit in the prior year (vs. ≥ 1 clinic visit). Common reasons listed for non-completion were that the patient declined, or the provider could not reach the patient. Discussion Efforts to improve follow-up colonoscopy rates in health centers might focus on supporting the care transition from primary to specialty gastroenterology care and emphasize care for older uninsured patients and those having no recent clinic visits. Our findings can inform efforts to improve follow-up colonoscopy uptake, reduce time to colonoscopy receipt, and save lives from colorectal cancer. Trial registration: National Clinical Trial (NCT) Identifier: NCT03925883.

Evaluation of Hospital-Based Acute Care Utilization by Uninsured Patients Enrolled in Free or Low-Cost Pharmacy Programs

Background: Research is warranted to define the role of affordable pharmacy programs in optimizing healthcare utilization for uninsured patients. Methods: This was a pre-post study including uninsured patients from an internal medicine residency clinic who enrolled in free or low-cost pharmacy programs with clinical pharmacist support. Results: In the period following program enrollment (N=116), there was a mean decrease of 0.23 acute care encounters (hospitalizations and emergency department [ED] visits) per patient (p=0.0210, 95% CI 0.04-0.43). The mean decrease for hospitalizations was also statistically significant (0.17, p=0.0052, 95% CI 0.05-0.28), but the mean decrease for ED visits was not (0.06, p=0.3771, 95% CI -0.08-0.21). Using the national average hospitalization cost of $10,700, the decrease in hospitalizations represents an estimated savings of $246,100. Conclusions: Enrollment in affordable pharmacy programs was found to be associated with decreased acute care encounters.

The impact of payer status on hospital admissions: evidence from an academic medical center

Background There are plenty of studies investigating the disparity of payer status in accessing to care. However, most studies are either disease-specific or cohort-specific. Quantifying the disparity from the level of facility through a large controlled study are rare. This study aims to examine how the payer status affects patient hospitalization from the perspective of a facility. Methods We extracted all patients with visiting record in a medical center between 5/1/2009-4/30/2014, and then linked the outpatient and inpatient records three year before target admission time to patients. We conduct a retrospective observational study using a conditional logistic regression methodology. To control the illness of patients with different diseases in training the model, we construct a three-dimension variable with data stratification technology. The model is validated on a dataset distinct from the one used for training. Results Patients covered by private insurance or uninsured are less likely to be hospitalized than patients insured by government. For uninsured patients, inequity in access to hospitalization is observed. The value of standardized coefficients indicates that government-sponsored insurance has the greatest impact on improving patients’ hospitalization. Conclusion Attention is needed on improving the access to care for uninsured patients. Also, basic preventive care services should be enhanced, especially for people insured by government. The findings can serve as a baseline from which to measure the anticipated effect of measures to reduce disparity of payer status in hospitalization.

Insurance Status Effect on Laryngeal Cancer Survival: A Population Based Study

Objective: To analyze insurance status effect on overall survival (OS) and disease-specific survival (DSS) in laryngeal cancer. Study Design: Cross-sectional population analysis. Setting: Surveillance, Epidemiology, and End Results (SEER) database. Participants: Laryngeal cancer patients from 2007 to 2016. Main Outcome Measures: Kaplan-Meier method with log-rank statistic analyzed OS and DSS by insurance status. Multivariable cox proportional hazard modeling generated survival prognostic factors. Results: Of 19 667 laryngeal cancer cases, initial disease presentation was stage I: 7770 patients (39.5%), stage II: 3337 patients (17.0%), stage III: 3289 patients (16.7%), and stage IV: 5226 patients (26.6%). Patients had non-Medicaid insurance (15 523, 78.9%), had Medicaid (3306, 16.8%), or were uninsured (891, 4.5%). Mean and median OS for insured, Medicaid, and uninsured patients were 60.5, 49.6, and 56.6 and 74.0, 40.0, and 65.0 months, respectively. Following multivariable analysis, OS for insured, Medicaid, and uninsured patients was stage I: 87.9, 82.8, and 88.4 ( P < .001), stage II: 79.1, 75.1, and 78.3 ( P = .12), stage III: 68.7, 66.1, and 72.1 ( P = .11), and stage IV: 57.1, 51.7, and 50.3 ( P < .001) months. DSS mean survival times were 77.0, 65.8, and 67.7 months ( P < .001) for insured, Medicaid, and uninsured patients. Age (HR: 1.02/year, P < .001) and black (HR: 1.15, P = .001) compared to white race predicted worse survival. Compared to insured status, Medicaid insurance carried a death hazard ratio of 1.40 ( P < .001) and uninsured status had a death hazard ratio of 1.40 ( P < .001). Conclusion: Insured laryngeal cancer patients had prolonged OS and DSS compared to Medicaid and uninsured patients. Medicaid patients had equivalent survival outcomes to uninsured patients. Level of Evidence: 2c.

The Center for Surgical Health: A Compassion-Driven Urban Surgical Mission

We are the Center for Surgical Health (CSH), an academic community partnership that supports, educates, and advocates for vulnerable Philadelphians with surgical diseases, founded in 2016 by Dr. Jon B. Morris, a leader in surgical education and a general surgeon at the University of Pennsylvania, and Dr. Alan Herbst, a current third-year Penn general surgery resident. At the time, Dr. Morris, raised in a Reform Jewish household, had been participating in an RCIA Program to convert to Catholicism. The mission of providing surgery to uninsured patients, primarily undocumented individuals, by helping them obtain insurance and see Penn providers was seen by Dr. Morris as a form of Catholic charity, which he has continued to remain dedicated to as his faith in Jesus Christ has deepened. Dr. Herbst, now Associate Director of Clinics for the CSH, recalls working with Dr. Morris as a sub-intern during his conversion, beginning with passion and a neon poster board inviting people to “See the Surgeon.” Since that time, the CSH has grown from an organization with 10 volunteers, called “personal patient navigators,” who provide insurance support and advocacy at every step of the perioperative continuum, to one with over 50, who have now seen 156 patients and assisted in providing 49 needed procedures. Much of this growth has been brought about through the dedication and vision of Dr. Matthew Goldshore, the Deputy Director of the CSH and a fifth-year Penn general surgery resident, as well as Dr. Carrie Z. Morales, Associate Deputy Director of the CSH and a recent Perelman School of Medicine graduate. Through their leadership, and the talent and commitment of other members of the CSH board, overseen by Director Dr. Morris, the CSH now has policy and research divisions, a surgical equity curriculum, and continues to develop new ways of providing better care.