Habermann, Elizabeth B.

Background National Comprehensive Cancer Network (NCCN) guidelines recommend accurate clinical staging, perioperative therapy, and complete lymphadenectomy for patients with stage II to III gastric cancer. However, national compliance remains low. It was hypothesized that integrated cancer networks might improve compliance and outcomes within the community. Methods Patients with stage II to III gastric adenocarcinoma undergoing curative‐intent resection (National Cancer Data Base, 2006‐2015) were examined. Guideline compliance was defined as any perioperative adjunctive therapy, complete lymphadenectomy, complete clinical staging, and complete compliance (all measures). Univariate comparisons and multivariable regression were used to assess factors associated with compliance, and Kaplan‐Meier analysis was used to assess survival. Results There were 27,210 patients identified: 7235 (26.6%) underwent surgery alone, whereas 19,975 (73.4%) received additional therapy. Half (53.1%) had complete lymphadenectomies, whereas complete clinical staging was available for 65.5%. Overall compliance with all 3 measures was 30.1%. Compliance improved by approximately 20% for each measure across the 10‐year study period. Although patients treated at academic programs were most likely to receive concordant care in an adjusted analysis, those treated at integrated care networks were more likely to receive guideline‐concordant care (odds ratio [OR], 0.69) than those treated at comprehensive community programs (OR, 0.48) or community programs (OR, 0.45; all P values <.001). The median overall survival was 45.5 months for patients who received guideline‐concordant care and 32.0 months for those who did not ( P < .001, reference for all ORs: academic programs). Conclusions Compliance with guidelines was associated with improved outcomes. Although the rate of compliance with NCCN guidelines is improving, integrated care networks may be an important way of improving the quality of gastric cancer care within the community.

Objective: Determine if routine ordering of postoperative day 1 (POD 1) serum laboratory tests after elective colorectal surgery are clinically warranted and valuable given the associated costs of these lab tests. Summary of Background Data: Routine postoperative serum laboratory tests are a part of many colorectal surgery order sets. Whether these protocolized lab tests represent cost-effective care is unknown. Methods: Patients undergoing elective colorectal surgery between January 1, 2015 and December 31, 2017 at our institution were identified. The protocolized POD 1 lab tests obtained as part of the postoperative order set were reviewed to determine the rate of abnormal values and any intervention in response. Costs associated with protocolized laboratory testing were calculated using dollar amounts representing 2017 outpatient Medicare reimbursement. Results: A total of 2252 patients were identified with 8205 total lab test values. Of these, only 4% were abnormal (3% of hemoglobin values, 6% of creatinine values, 3% of potassium of values, and 3% of glucose values), and only 1% were actively intervened upon. The total aggregate cost of the protocolized POD 1 laboratory tests in these years was $64,000 based on Medicare outpatient reimbursement dollars. Conclusions: Routine POD 1 lab tests after elective colorectal surgery are rarely abnormal, and they even less frequently require active intervention beyond rechecking. This results in increased resource utilization and cost of care without appreciable impact on clinical care, and is not cost-effective. Protocolized POD 1 laboratory testing should be replaced with clinically-based criteria to trigger serum laboratory investigations.

Background Implant-based breast reconstruction (IBR) accounts for 70% of postmastectomy reconstructions in the United States. Improving the quality of surgical care in IBR patients through accurate measurements of outcomes is necessary. The purpose of this study is to compare the American College of Surgeons National Surgical Quality Improvement Program (NSQIP) data from our institution to our complete institutional health records database. Methods Data were collected and recorded for all patients undergoing IBR at our institution from 2015 to 2017. The data were completely identified and compared with our institutional NSQIP database for demographics and complications. Results The electronic health records data search identified 768 IBR patients in 3 years and NSQIP reported on 229 (30%) patients. Demographics were reported similarly among the 2 databases. Rates of tissue expander/implant infections (5.9% vs 1.8%; P = 0.003) and wound dehiscence (3.5% vs 0.4%; P = 0.003) were not reported similarly between our database and NSQIP. However, the rates of hematoma (2.7% vs 1.8%) and skin flap necrosis (2.5% vs 1.8%) were comparable between the two databases. In our database, 43% of all complications presented after 30 days of surgery, beyond NSQIP's capture period. Conclusions Databases built on partial sampling, such as the NSQIP, may be useful for demographic analyses, but fall short of providing data for complications after IBR, such as infections and wound dehiscence. These results highlight the utility and importance of complete databases. National comparisons of clinical outcomes for IBR should be interpreted with caution when using partial databases.

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 &lt;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.

Abstract Background: Unlike acute lymphoblastic leukemia, data on acute myelogenous leukemia (AML) in the adolescents and young adults (AYA) are limited and generally derived from clinical trials, which are mostly conducted in academic centers. It is unclear whether the choice of treatment facility influences clinical outcome independent of disease biology and socio-demographic factors. This study used the National Cancer Data Base, a nationwide oncology database covering 70% of the US cancer population, to address this question. Methods: We identified 11,712 AYA patients who were diagnosed with AML from 2003-2011. We excluded patients who were: a) 15-17 years of age (n = 3,104) as treatment facility data were not available for age <18 years; b) not treated at the reporting facilities (n = 415) as follow-up might be incomplete; c) treated at treatment facilities with ≤100 new cancer cases/year (n = 18); and d) those without follow-up data (n = 47). Treatment facilities were classified as academic, comprehensive community (>500 new cancer cases/year), or community (101-500 new cancer cases/year) cancer programs. AMLs harboring t(15;17), inv(16), or t(8;21) were defined as good-risk, while the rest were considered non-good risk. Overall survival from the time of AML diagnosis was the primary outcome. We used multivariate imputation by chained equation to impute missing values, weighted Cox regression to obtain average hazard ratios (HRs), and pooled Wald c2 statistics to detect interaction. Results: We included 8,128 AYA patients with AML in the study and 25.2% were good risk. Among the good risk AMLs, 11.6%, 12.3%, and 76.1% had t(8;21), inv16, and t(15;17), respectively. The distribution of patients by treatment facility were academic (64.1%), comprehensive community (32.9%), and community (3.0%). After detecting an interaction between AML risk and type of treatment facility (P=0.038), we stratified treatment facility by AML risk and found significant differences in overall survival among good risk (P=0.02) but not non-good risk AML (P=0.19). This survival difference persisted even after adjusting for demographic (age, sex, race/ethnicity), socioeconomic (income, education, medical insurance), geographic (area of residence, travel distance), co-morbidities (Charlson-Deyo score), and treatment (receipt of chemotherapy) factors. Patients with good risk AML treated in comprehensive community cancer programs had a higher risk of death compared to those treated in academic programs (adjusted HR: 1.41, 95% CI: 1.11-1.79). For community cancer programs, the risk of death was also higher but did not reach statistical significance (adjusted HR: 1.22, 95% CI: 0.68-2.21). ). Subset analysis showed that the survival difference among those with good risk AML was observed in t(15;17) or acute promyelocytic leukemia (APL; P=0.02) but not in t(8;21) or inv16 (P=0.79). The disparity occurred primarily during the first month of APL diagnosis, wherein the risk of death was two-fold higher in community programs (Figure). Conclusions: Among AYA patients with AML, treatment at academic cancer programs was associated with a better survival, but only for the APL subgroup. Because APL comprises a fifth of all AMLs in the AYA and is a highly curable cancer, health care delivery research in APL should become a priority in this age group. Figure 1. Figure 1. Disclosures Al-Kali: Celgene: Research Funding.

Abstract Background: The optimal follow-up of MGUS patients, including frequency of visits and type of tests to order, is unknown because there are no evidence-based data available for guidance. Nevertheless, there are currently 4 clinical practice guidelines that are based on expert-consensus: UK-Nordic (2009), International Myeloma Working Group (IMWG; 2010), International Expert Consensus (IEC; 2010) and European Myeloma Network (EMN; 2014). Most guidelines recommend annual follow-up with laboratory tests until life expectancy becomes limited. How MGUS patients are followed in the US and whether clinical practice guidelines are being followed remain largely unknown. Methods: We conducted a retrospective analysis of adult (age >18 years) patients from 2006-2012 using theOptum Labs Data Warehouse,a large database including administrative claims information on >150 million privately insured and Medicare Advantage enrollees throughout the US. We identified patients with an incident diagnosis of MGUS (ICD9: 273.1) who had enrollment periods of>1 year pre- and>2 years post-diagnosis in order to exclude patients with a prior history or subsequent diagnosis of lymphoplasmacytic malignancy. We determined the overall outpatient follow-up practice patterns (visits or tests performed for MGUS), their variations over time and by geo-demographic subgroups, and concordance with current clinical practice guidelines Results: There were 10,349 MGUS patients who met the study criteria. The mean interval per MGUS follow-up visit was 13.1 months. The distribution of patients by mean interval between visits was: q<6 months (39.9%), q6-12 months (10.7%), q13-24 months (2.6%) and q>24 months (46.8%). There was a significant trend towards a higher proportion of patients being followed q<6 over time from 36.2% in 2006 to 43.7% in 2013 (P=0.01). Patients ≥70 years were more likely followed q<6 months, while those from the Northeast or ≤50 years were more likely followed q>24 months compared to their counterparts (P<0.01). Over half of patients≥80 years were being followed either q<6 (44.7%) or q6-12 (11.6%) months (Figure 1). Only about half of the patients diagnosed in 2013 were concordant with any of the available guidelines regardless of whether the guidelines were more aggressive (UK-Nordic/IEC) or more conservative (IMWG/EMN) in their follow-up recommendations (54.6% and 56.5%, respectively). The tests ordered during follow-up are shown in Figure 2. The most common tests were complete blood count, calcium, and creatinine as they were ordered in over half of the follow-up visits (56.5% to 68.4%). In 11.6% of the follow-up, a face-to-face visit was not associated with any ancillary test, while in 31.5%, the follow-up was comprised of ancillary tests only without a face-to-face visit. The test combination of complete blood count, calcium, creatinine, and serum protein electrophoresis with or without free light chain was ordered in 25.0% of the visits. Conclusions: In the US, the MGUS follow-up practice patterns varied demographically and geographically. Only half of the patients were followed according to current guideline recommendations. A large proportion of patients with limited life expectancy continued to undergo close follow-ups. Figure 1 Figure 1. Figure 2 Figure 2. Disclosures No relevant conflicts of interest to declare.

Abstract Background: Prior studies on the risk of progression of monoclonal gammopathy of undetermined significance (MGUS) into lymphoplasmacytic malignancies (LPMs) were almost exclusively in the White populations, comprised of limited number of patients residing in relatively small geographic areas, or included patients mostly detected by serologic screening as part of clinical study (as opposed to detected clinically during evaluation of symptoms suspicious for LPMs). Similar epidemiologic studies in a large, racially diverse population are lacking. Methods: We conducted a retrospective analysis of incident MGUS patients diagnosed from 2006-2013 using theOptum Labs Data Warehouse, a large database including administrative claims information on >110 million privately insured and Medicare Advantage enrollees throughout the United States. We identified patients with a diagnosis of MGUS (ICD9: 273.1) with 1-5 years of continuous enrollment. Each enrollment cohort based on the duration of follow-up (1-, 2-, 3-, 4-, or 5-year) was analyzed individually to help estimate the progression to cancer. We excluded patients who were diagnosed with LPMs within three months of MGUS diagnosis. We calculated the rate of progression into LPMs (chronic lymphocytic leukemia [CLL], light chain amyloidosis [AL], multiple myeloma [MM], non-Hodgkin lymphoma [NHL], and Waldenström macroglobulinemia [WM]) expressed as number per 100 person-years. Results: There were 14,728 MGUS patients for a total of 21,288 person-years of follow-up. The distributions of the number of patients and events according to the cohorts classified by duration of follow-up were: 1-year (14,728 patients; 243 events), 2-year (10,644 patients; 307 events), 3-year (7,333 patients; 299 events), 4-year (4,497 patients; 237 events), and 5-year (2,960 patients; 191 events). The rates of progression to LPMs were consistently highest during the first year after MGUS diagnosis (~2.00), generally declined by half during the second year and remained fairly stable thereafter (~1.00; Table 1). The rates of progression into LPMs overall as well as by demographics are shown in Table 2. The risk of progression was significantly higher among men (P < 0.01) and older patients (>50 years; P = 0.03) compared to their counterparts but similar among races (P = 0.15). Of the 243 patients who progressed, the distribution of LPMs was MM (70.0%), NHL (13.2%), WM (12.3%), AL (4.1%), and CLL (0.4%). Conclusions: Among patients withMGUS, the risk of transformation into LPMs is continuous although twice as high during the first year after MGUS diagnosis compared to subsequent years. The risk of transformation was higher among men and those who were older but did not differ among the racial subgroups. Our findings can be used to develop an optimal risk-based MGUS follow-up strategy that incorporates not only serum biomarkers but also demographic factors. Disclosures No relevant conflicts of interest to declare.