Missed Venous Thromboembolism After Major Cancer Surgery Ryon EL, - PowerPoint PPT Presentation

Missed Venous Thromboembolism After Major Cancer Surgery Ryon EL, Parreco JP, Goel N, Eidelson SA, Byers PM, Yeh DD, Namias N, Rattan R. 1

No disclosures 2

��� ��� ���� Venous thromboembolism (VTE) after major cancer • surgery (MCS) is significant 1/3 - 30 days post-discharge 1 • 2/3 - 90 days post-discharge 2 • Risk continues to rise up to 1y 3 • Up to 1/3 of postoperative readmissions occur at • different hospital 4 1. Merkow RP et al, Ann Surg 2011; 254(1). 2. Bouras G et al, PLoS One 2015; 10(12). 3. Wun T, White RH, Best Pract Res Clin Haematol 2009; 22(1). 4. Rattan R et al, Surg Inf 2017; 18(8). 3

��� ��� ���� • No national studies tracking different hospital readmission for postoperative VTE • True rate of VTE after major cancer surgery unknown 4

� �� ���� • 2010-2014 Nationwide Readmissions Database • Adults after MCS without VTE during index admission – Hysterectomy – Colectomy – Lung resection – Cystectomy – Pancreatectomy – Esophagectomy – Gastrectomy – Prostatectomy • 1y VTE rates, risk factors, costs 5

����� � � n=1,238,706 • 30d readmission 10.6% (130,774) • 1y readmission 21.5% (266,861) • 1y readmission to different hospital 24.8% (66,292) • 1y readmission with VTE 1.9% (22,746) • 1y readmission with VTE to different hospital 28.2% (6,413) 6

1 � ����� � �� � ��� �� �� � � ���� � ��� ��� � � � �� ���� �� � Procedure 1y VTE rate (%) Colectomy 2.0 Cystectomy 5.3 Esophagectomy 2.6 Gastrectomy 2.7 Hysterectomy 1.9 Lung resection 2.0 Pancreatectomy 3.7 Prostatectomy 0.8 7

1 � ����� � �� � �� ��� � �� � �� �� � �� � � ���� � ��� ��� � � � �� ���� �� � Risk factor Overall Different hospital Hospitalization >7d 1.60 [1.55-1.65] 0.84 [0.78-0.90] Public hospital NS 1.43 [1.31-1.57] For-profit hospital NS 1.26 [1.14-1.41] Urban teaching hospital -- 1.23 [1.14-1.32] Medicare 1.11 [1.06-1.16] NS Medicaid 1.15 [1.08-1.22] NS Lowest income quartile 1.05 [1.01-1.09] 1.16 [1.07-1.27] 8

����� � �� � ���� � ���� ��� � • 1y VTE readmission cost $83.5 Million • 1y VTE readmission to different hospital cost $21.5 Million • 1y VTE readmission costs previously hidden 25.7% 9

���� � ��� ��� • Nearly 1 in 3 VTEs after MCS are hidden • Missed VTEs costs $21.5mn annually • Poorer patients experience more fragmentation of care • Hospital type affects risk of hidden VTE 10

Questions 11

Missed Venous Thromboembolism After Major Cancer Surgery Ryon EL, Parreco JP, Goel N, Eidelson SA, Byers PM, Yeh DD, Namias N, Rattan R. 12

Development of Malignancy-Risk Gene Signature Assay for Predicting Breast Cancer Risk James Sun MD, Dung-Tsa Chen PhD, Jiannong Li PhD, Weihong Sun MD, Sean J. Yoder MS, Tania E. Mesa MA, Marek Wloch MS, Richard Roetzheim MD, Christine Laronga MD, M. Catherine Lee MD March 23, 2019 Florida Chapter American College of Surgeons Annual Meeting Orlando, FL

Disclosures No proprietary or commercial interests • • Supported by the NCI (R21CA198762-02) • Supported by the Tissue Core, Molecular Genomics Core Facilities, and the Biostatistics and Bioinformatics Shared Resources (P30-CA076292) • Malignancy-Risk signature patent (#9195796) owned by Moffitt Cancer Center

Background • Gail Model most commonly used tool to estimate breast cancer risk • Malig ignan ancy-risk isk (MR) gene s signature re 1 – Distinguishes histologically-normal tissue at increased cancer risk – Genes associated with cell cycle/proliferation functions • Goal al: compare MR gene signature to Gail Model as predictor of breast cancer risk 1 Chen D-T, Nasir A, Culhane A, et al. Proliferative genes dominate malignancy-risk gene signature in histologically-normal breast tissue. Breast Cancer Res Treat . 2010;119(2):335-346.

Technical validation • Materials and Methods – Paired fresh frozen and FFPE • malignant tumor and benign breast tissue from same patient – Standard RNA extraction protocol – Custom NanoString CodeSet for gene expression • Statistical Analysis – Principal component analysis

Correlation of FFPE/FF specimens

Materials and Methods

Results Model MR a alone ne Gail il alone ne Combin ined AUC 0.61 0.68 0.71

Conclusions • Demonstrated feasibility of FFPE-based assay for breast cancer risk – Personalized: benign breast biopsy tissue • Limit mitat atio ions Thank you! – Low sample size – Scant cellularity of archived tissue • Combination model (MR score + Gail Model) best predictive value – Further investigation required

Is it Wise to Omit Sentinel Node Biopsy in Elderly Patients with Breast Cancer? James Sun MD, Brittany J. Mathias MD, Weihong Sun MD, William J. Fulp MS, Jun-Min Zhou PhD, Christine Laronga MD, Loretta S. Loftus MD, M. Catherine Lee MD March 23, 2019 Florida Chapter American College of Surgeons Annual Meeting Orlando, FL

Disclosures • No relevant financial disclosures • Supported by the Biostatistics and Bioinformatics Shared Resources (P30-CA076292)

Background • SSO’s Choosing Wisely Recommendations (July 2016) • Identified common practices that may not be necessary – Routine SLNB is not recommended in cN 0 patients ≥ 70 years old with hormone receptor (HR) positive breast cancer • Referenced sources: – CALGB 9343 1 – Martelli 2011 2 1. Hughes, Kevin S., et al. "Lumpectomy plus tamoxifen with or without irradiation in women age 70 years or older with early breast cancer: long-term follow-up of CALGB 9343." Journal of Clinical Oncology 31.19 (2013): 2382. 2. Martelli, Gabriele, et al. "Axillary dissection versus no axillary dissection in elderly patients with breast cancer and no palpable axillary nodes: results after 15 years of follow-up." Annals of surgical oncology 18.1 (2011): 125-133.

Objectives • The recommendation to omit SLNB in elderly patients is poorly supported • Is there a benefit of SLNB in elderly patients? • Evaluated patients by SLN-status – How does this affect management? – Does this change outcomes?

Methods • Single-institution retrospective review • Patients treated 1998-2016 • Age  70 years, unilateral breast cancer with resection, positive SLNB • Subset of hormone receptor-positive patients analyzed • Compared by SLN status to assess differences in treatment

Results: Comparison by SLNB status (n=500) Va Variable Negative Po Positive ve P-value P- ER ER 0.92 n = 345 (69% n = 345 (69%) n = 155 n = 155 (3 (31% 1%) Negative Nega 40 (12%) 17 (11%) Ag Age a at 74 yrs (72-78) 75 yrs (72-79) 0.27 Po Positive ve 301 (88%) 138 (89%) diagnosis diagnosis PR PR 0.44 (IQR) (IQR) Nega Negative 87 (26%) 34 (22%) Tu Tumor s size: 1.3 cm (0.8-2) 2 cm (1.4-2.5) <0.0001 <0.000 Po Positive ve 253 (74%) 121 (78%) median median Her2 Her2 0.17 (IQR) (IQR) Positive Po ve 27 (8%) 19 (12%) Tumor grade Tu 0. 0.003 003 Nega Negative 306 (92%) 133 (88%) 1 85 (28%) 25 (18%) XRT XRT 0. 0.0008 0008 2 154 (50%) 65 (46%) Ye Yes 242 (78%) 93 (62%) 3 68 (22%) 51 (36%) No No 69 (22%) 56 (38%) T s T stage 0.0005 0. 0005 Chemo. Chemo. <0.000 <0.0001 1 278 (81%) 80 (52%) Ye Yes 23 (7%) 51 (34%) 2 62 (18%) 68 (44%) No No 290 (93%) 99 (66%) 3 5 (1%) 7 (4%) Hormone Hormone 0.04 0.04 Ov Overa erall ll Ye Yes 229 (73%) 125 (82%) Stage Sta <0.0001 <0.000 No No 85 (27%) 27 (18%) 1 281 (81%) 26 (17%) 2 64 (19%) 97 (63%) Recurrence: 38/500 3 0 (0%) 32 (20%) Locoregional: 11/500 (2.2%) Distant: 27/500 (5.4%)

Results: Comparison by SLNB status, HR+ patients (n=371) Va Variable Negative Po Positive ve P-value P- Her2 Her2 0.73 n = 251 n = 25 1 (68% (68%) n = 1 n = 120 0 (32%) 2%) Yes Ye 11 (5%) 7 (6%) Ag Age a at 74 yrs (72-78) 75 yrs (72-79.2) 0.18 No No 233 (95%) 110 (94%) diagnosis diagnosis XRT XRT 0.05 (IQR) (IQR) Yes Ye 174 (76%) 75 (66%) Tu Tumor s size: 1.2 cm (0.7-1.9) 2 cm (1.45-2.5) <0.000 <0.0001 No No 54 (24%) 39 (34%) median median Chemo. Chemo. <0.0001 <0.000 (IQR) (IQR) Yes Ye 8 (3%) 31 (27%) Tumor grade Tu 0.27 No No 222 (97%) 84 (73%) 1 70 (32%) 22 (20%) Hormone Hormone 0.04 0.04 2 124 (56%) 56 (51%) Ye Yes 190 (83%) 107 (92%) 3 28 (12%) 31 (29%) No No 40 (17%) 10 (7%) T stage T s 0. 0.0005 0005 1 205 (82%) 62 (52%) 2 43 (17%) 54 (45%) 3 3 (1%) 4 (3%) Ov Overall all Sta Stage <0.0001 <0.000 1 206 (82%) 20 (17%) 2 45 (18%) 77 (64%) Recurrence: 18/371 3 0 (0%) 23 (19%) Locoregional: 4/371 (1.1%) Distant: 14/370 (3.8%)