The Influence of Treatment Facility Volume on Survival of Non-Hodgkin Lymphoma: Determining the Volume-Outcome Relationship

Blood ◽  
2015 ◽  
Vol 126 (23) ◽  
pp. 266-266
Author(s):  
Ronald S. Go ◽  
Mohammed Al-Hamadani ◽  
Cynthia S Crowson ◽  
Nilay D Shah ◽  
Elizabeth B Habermann

Abstract Background: Non-Hodgkin lymphoma (NHL) is a relatively uncommon cancer with annual incidence of ~70,000 cases but with over 50 distinct subtypes. The goal of this study was to determine the extent to which the number of NHL patients treated annually in a facility (facility volume) affects overall survival (OS). This study used the National Cancer Data Base (NCDB), a nationwide oncology database covering 70% of the US cancer population, to address this question. Methods: We used the NCDB to identify patients with NHL diagnosed from 1998 to 2006. Year 2006 was used as a cut-off in order to allow a minimum of five years of follow-up for all patients. Only patients treated at facilities with continuous annual reporting to NCDB were included. We classified treatment facilities by quartiles based on facility volume (mean patients/year): Quartile 1 (Q1: 2-13), Quartile 2 (Q2: 14-20), Quartile 3 (Q3: 21-32) and Quartile 4 (Q4: ≥33). We used Pearson correlation methods to examine collinearity, unadjusted Kaplan-Meier methods to estimate OS rates, log rank test to compare survival distributions, and multivariable Cox proportional hazards model to examine the associations between hospital volume and OS adjusting for other covariates of interest. We also included random effects for hospital to more fully adjust for clustering of outcomes within hospitals. To examine non-linear effects of hospital volume, we utilized smoothing splines. Results: There were 278,985 NHL patients cared for at 1,151 facilities. The distribution of patients according to facility volume was Q1 (10.7%), Q2 (13.5%), Q3 (23.3%) and Q4 (52.5%) and according to facility type was academic (31.2%), comprehensive community (55.9%), community (10.6%) and other (2.3%) centers. The unadjusted median OS by facility volume was: Q1: 61.8 months, Q2: 65.9 months, Q3: 71.4 months and Q4: 83.6 months. After multivariable analysis adjusting for demographic (sex, age, race, ethnicity), socioeconomic (income, insurance type), geographic (area of residence), disease-specific (NHL subtype, stage) and facility-specific (type and location) factors, we show that facility volume remains an independent predictor of all-cause mortality. Compared to patients treated at Q4 facilities, patients treated at lower quartile facilities had a worse OS (Q3HR: 1.05 [95% CI, 1.04-1.06]; Q2HR: 1.08 [1.07-1.10]; Q1HR: 1.14 [1.11-1.17]). We adjusted for hospital as a random effect, performed sensitivity analyses removing primary payor and facility type (due to collinearity with age and facility volume, respectively), and adjusted for Charlson-Deyo co-morbidity score (available only for patients diagnosed after 2003) in secondary models and found similar results. Using smoothing splines, we found a significant non-linear effect of hospital volume on OS (P <0.001). This is depicted in the Figure wherein the hazard ratio of 1.0 corresponded to the average predicted hazard, which occurred at a hospital volume of 59 patients per year. Conclusions: Patients who were treated for NHL at higher volume facilities had longer OS than those who were treated at facilities with a lower volume. This is the first study in the US using a national sample to show that a volume-outcome relationship exists in the medical management of cancer. Figure 1. Figure 1. Disclosures No relevant conflicts of interest to declare.

Blood ◽  
2015 ◽  
Vol 126 (23) ◽  
pp. 3297-3297
Author(s):  
Aishwarya Ravindran ◽  
Wilson I. Gonsalves ◽  
Shahrukh K. Hashmi ◽  
Prashant Kapoor ◽  
Ariela L. Marshall ◽  
...  

Abstract BACKGROUND: While hematologic cancers comprise only 10% of all malignancies, they are divided into >100 distinct World Health Organization subtypes. It is known that higher volume of care is generally associated with better clinical outcomes. However, such a volume-outcome relationship in the medical management of hematologic cancers has not been rigorously explored. The American Society of Clinical Oncology (ASCO) National Census of Oncology Practices shows that the majority of hematologist-oncologists in the United States (US) have a combined hematology-oncology practice (J Oncol Pract 2013). In this study, we estimated the annual number of new and established patients with major hematologic cancers seen on average by a hematologist-oncologist in the US. METHODS: We estimated the number of hematologist-oncologists working in the US using the ASCO workforce information system data from 2011. We utilized statistics from the Surveillance Epidemiology and End Results (SEER) Program to determine the incidence and 37-year limited prevalence of hematologic cancers in 2011. We used 'first malignant tumor per site' statistics as the tumor inclusion method. For potentially curable hematologic cancers (acute lymphocytic leukemia, acute myeloid leukemia, Burkitt lymphoma, diffuse large b-cell lymphoma, Hodgkin lymphoma, and marginal zone lymphoma), we used the estimated 1-5 year survival rates from SEER and excluded patients who survived >5 years, since relapses are rare afterwards. Because prevalence estimates of chronic myelomonocytic leukemia, myelodysplastic syndromes, and certain subtypes of non-Hodgkin lymphoma are unavailable, we were unable to calculate the number of annual established cases. For myeloproliferative neoplasms, we obtained the prevalence estimate from Mehta J, et al (Leuk Lymphoma 2014). We derived the distribution of major non-Hodgkin lymphoma subtypes from the National Cancer Data Base (NCDB) Participant User File. RESULTS: The ASCO workforce information reported a total of 13,084 hematologist-oncologists working in the US in 2011. The Table summarizes the average number of specific hematologic cancer cases seen per hematologist-oncologist in 2011. CONCLUSION: Hematologic cancers are relatively rare but complex. In the US, a hematologist-oncologist on average cares for only 1-2 new patients of any subtype of hematologic cancers annually. The number of established patients is correspondingly low. These numbers are expected to vary by practice setting and disease specialization. As the diagnosis and management of hematologic cancers becomes more sophisticated, future research should explore the potential of a volume to clinical outcome relationship for these providers. Table. Hematologic Cancer Average Annual Number of Cases per Hematologist-Oncologist in the US New Cases Established Cases All Cases Acute lymphocytic leukemia 0.4 1.4 1.8 Acute myeloid leukemia 1 1.5 2.5 Chronic lymphocytic leukemia 1.1 10.7 11.8 Chronic myeloid leukemia 0.4 2.7 3.1 Chronic myelomonocytic leukemia 0.1 - - Hodgkin lymphoma 0.7 2.7 3.4 Multiple myeloma 1.6 6.3 7.9 Myelodysplastic syndromes 1.2 - - Myeloproliferative neoplasms 0.6 22.2 22.8 Non-Hodgkin lymphoma 5.1 - - Anaplastic large cell 0.1 - - Burkitt 0.1 0.3 0.4 Diffuse large B-cell 2 6 8 Follicular 1.1 - - Hairy cell leukemia 0.1 - - Lymphoplasmacytic 0.1 - - Mantle-cell 0.3 - - Marginal zone 0.5 2.1 2.6 Peripheral T-cell, not otherwise specified 0.1 - - Disclosures No relevant conflicts of interest to declare.


2019 ◽  
Author(s):  
Cécile Payet ◽  
Stéphanie Polazzi ◽  
Jean-Christophe Lifante ◽  
Eddy Cotte ◽  
Daniel Grinberg ◽  
...  

Abstract Background The more frequent a hospital performs a procedure, the better the outcome of the procedure; however, the mechanisms of this volume-outcome relationship have not been deeply elucidated to date. We aimed to determine whether patient outcomes improve in hospitals with a significantly increased volume of high-risk surgery over time and whether a learning effect existed at the individual hospital level. Methods We included all patients who underwent one of ten digestive, cardiovascular and orthopaedic procedures between 2010 and 2014 from the French nationwide hospitals database. For each procedure, we identified three groups of hospitals according to volume trend (increased, decreased, or no change). In-hospital mortality, reoperation, and unplanned hospital readmission within 30 days were compared between groups using Cox regressions, taking into account clustering of patients within hospitals and potential confounders. Learning effect was investigated by considering the interaction between hospital groups and procedure year. Results Over 5 years, 759,928 patients from 694 hospitals were analysed. Patients’ mortality in hospitals with procedure volume increase or decrease over time did not clearly differ from those in hospitals with unchanged volume across the studied procedures (e.g., Hazard Ratios [95%] of 1.04 [0.93-1.17] and 1.08 [0.97-1.21] respectively for colectomy). Furthermore, patient outcomes did not improve or deteriorate in hospitals with increased or decreased volume of procedures over time (e.g., 1.01 [0.95-1.08] and 0.99 [0.92-1.05] respectively for colectomy). Conclusions Trend in hospital volume over time does not appear to influence patient outcomes, which puts the relevance of the "practice-makes-perfect" dogma in question.


Blood ◽  
2007 ◽  
Vol 110 (11) ◽  
pp. 4423-4423
Author(s):  
Shaili Desai ◽  
Malek M. Safa ◽  
Rami S. Komrokji

Abstract Background: Follicular non Hodgkin lymphoma (FL) remains uncurable disease for majority of patients. Natural history of follicular lymphoma is changing with inroduction of new treatments. We examined outcome of FL among VA patients in the era of monoclonal antibodies. Methods: This was a retrospective analysis. The VA Central Cancer Registry (VACCR) database was used to identify patients with FL diagnosed between 1995 and 2005. There are approximately 120 VA medical centers diagnosing and/or treating patients with cancer. Data were analyzed using bio-statistical software SPSS. Variables included age, sex, stage of disease, histology subtype, date of diagnosis, date of last contact, vital status. Results: There were1338 patients with FL at the VACCR database between 1995 and 2005, 283 (88%) were white, 128 (10%) black patients, and 39 (2%) patients from other racial groups. The mean age at diagnosis was to 64 years. Four hundred twelve (31%) were grade I FL, 312 (23%) Grade II, 215 (16%) Grade III, and 399 (30%) FL, NOS. Four hundred twelve (30%) patients were early stage (I,II), 600 (45%) were advanced stage (III,IV) and 331 (25%) missing or unkown. No FLIPI score data were available. Median overall survival (OS) was 7 years. The median OS for early stage was 8.6 years compared to 5.5 years for advanced stage. The median OS for patients diagnosed 1995–2000 was 5.9 years compared to 9.7 years for patients diagnosed between 2001–2005. (log rank test, P value 0.025). The two groups were balanced regarding stage, race and chemotherapy. Conclusions: The survival of FL patients has improved in the VA system in the era of rituximab. Figure Figure


Blood ◽  
2013 ◽  
Vol 122 (21) ◽  
pp. 1737-1737
Author(s):  
Dianne Pulte ◽  
Hermann Brenner ◽  
Lina Jansen

Abstract Background Lack of access to health insurance is known to be a poor prognostic indicator in many conditions. Here, we examine survival in patients with non-Hodgkin lymphoma (NHL) in the US by insurance status, including no insurance, Medicaid only, and other insurance (private or Medicare) to explore the extent of this effect in NHL patients. Methods Data were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Information regarding patients' insurance status became available in the SEER database in the most recent dataset and is available from 2007 on. The SEER17 database was used to provide the maximum number of patients. Period analysis was performed to estimate 1-year survival and complete analysis was used to estimate 3-year survival. Patients with a diagnosis of NHL diagnosed between 2007 and 2010 with follow up available through 2010 were included. Because patients over age 65 are almost all qualified for Medicare, patients age 15-64 were evaluated. Because NHL is a heterogeneous condition with subtypes having varied survival, we examined survival in diffuse large B-cell lymphoma (DLBCL) specifically as well. Results Relatively few patients were listed as uninsured, with less than 10% of patients being listed as uninsured in the SEER database (as opposed to over 20% in the US population at large for patients under age 65.) Initial age stratification showed that there was very little difference in survival for patients age 45-64 and therefore these patients were considered as a single age category to improve statistical power. For NHL overall, survival was much lower for both uninsured patients and patients with Medicaid as compared to patients with other forms of insurance at both 1- and 3-years and for each age group examined (Table). For patients age 15-44, 1-year relative survival was 15.1 and 17.7 percentage points lower for uninsured and Medicaid patients, respectively, than for patients with other forms of insurance. The differences for 3-year survival were 18.8 and 22.9% units, respectively, uninsured and Medicaid patients. Three year relative survival for patients age 15-44 without insurance was 67.8%. For comparison, 3-year survival for the same age group in 1978-80 was 69%. A similar pattern was seen for patients with DLBCL, with survival being higher for patients with private insurance compared with patients who were uninsured or insured by Medicaid. One year survival estimates were 12.8 and 13.4% units lower, respectively, for uninsured patients ages 15-44 and 45-64 as compared to patients with private insurance or Medicare. Three year survival estimates were 14.2 and 14.1% units higher, respectively, for the same comparison. Conclusions Lack of insurance was associated with severe compromise of survival for patients with lymphoma. Survival is much lower for patients with Medicaid as opposed to other forms of insurance, possibly because of the extreme poverty required for Medicaid eligibility in most states, which may compromise patients' ability to be compliant with care. Furthermore, patients with Medicaid may have only been granted Medicaid after the diagnosis of cancer and been previously uninsured, which may be a risk for presentation at a later stage of disease. Further evaluation of the reasons for the low survival for patients with Medicaid and implementation of comprehensive coverage for medical care for uninsured patients are urgently needed to reduce this disparity. Disclosures: No relevant conflicts of interest to declare.


Neurosurgery ◽  
2017 ◽  
Vol 80 (4) ◽  
pp. 534-542 ◽  
Author(s):  
Aziz S. Alali ◽  
David Gomez ◽  
Victoria McCredie ◽  
Todd G. Mainprize ◽  
Avery B. Nathens

Abstract BACKGROUND: The hospital volume–outcome relationship in severe traumatic brain injury (TBI) population remains unclear. OBJECTIVE: To examine the relationship between volume of patients with severe TBI per hospital and in-hospital mortality, major complications, and mortality following a major complication (ie, failure to rescue). METHODS: In a multicenter cohort study, data on 9255 adults with severe TBI were derived from 111 hospitals participating in the American College of Surgeons Trauma Quality Improvement Program over 2009-2011. Hospitals were ranked into quartiles based on their volume of severe TBI during the study period. Random-intercept multilevel models were used to examine the association between hospital quartile of severe TBI volume and in-hospital mortality, major complications, and mortality following a major complication after adjusting for patient and hospital characteristics. In sensitivity analyses, we examined these associations after excluding transferred cases. RESULTS: Overall mortality was 37.2% (n = 3447). Two thousand ninety-eight patients (22.7%) suffered from 1 or more major complication. Among patients with major complications, 27.8% (n = 583) died. Higher-volume hospitals were associated with lower mortality; the adjusted odds ratio of death was 0.50 (95% confidence interval: 0.29-0.85) in the highest volume quartile compared to the lowest. There was no significant association between hospital-volume quartile and the odds of a major complication or the odds of death following a major complication. After excluding transferred cases, similar results were found. CONCLUSION: High-volume hospitals might be associated with lower in-hospital mortality following severe TBI. However, this mortality reduction was not associated with lower risk of major complications or death following a major complication.


2017 ◽  
Vol 198 (1) ◽  
pp. 92-99 ◽  
Author(s):  
Boris Gershman ◽  
Sarah K. Meier ◽  
Molly M. Jeffery ◽  
Daniel M. Moreira ◽  
Matthew K. Tollefson ◽  
...  

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