Changes in Antiemetic Overuse in Response to Choosing Wisely Recommendations

Supplementary Online Content

Encinosa W, Davidoff AJ. Changes in Antiemetic Overuse in Response to Choosing Wisely Recommendations. JAMA Oncol. Published online August 11, 2016. doi:10.1001/jamaoncol.2016.2530.

eTable 1. Antineoplastics and Their Antiemetic Guidelines

eTable 2. Descriptive Statistics Sample: Means and Proportions

eTable 3. Logit-Estimated Effect of Choosing Wisely on Antiemetic Overuse

eAppendix. Log of Statistical Analyses (Stata 14.0)

This supplementary material has been provided by the authors to give readers additional information about their work.

Downloaded From: https://jamanetwork.com/ on 09/23/2021 eTable 1. Antineoplastics and Their Antiemetic Guidelinesa Chemotherapy Induced Nausea ASCO and NCCN Antiemetic 2012 and 2015 Operational & Vomiting Risk Category & Therapy Definitions of Choosing Wisely Individual Antineoplastic Agents Antiemetic Overuse IV Chemotherapy High risk A. 5HT3-RA (dolasetron, Carboplatinb granisetron, ondansetron, or Cisplatinc palonosetron), steroid Cyclophosphamidec,d (dexamethasone) and NK1-RA Use of metaclopramide, Dacarbazine (aprepitant, fosaprepitantor, or prochloperazine, or haloperidol in Doxorubicinc rotapitant); addition to recommended Irinotecanb B. Netupitant with palonosetron and antiemetics Methotrexateb,c dexamethasone; or C. 2015 NCCN: olanzapine with palonosetron and dexamethasone Moderate risk Azacitidine Same as above, with NK1-RA Same as above Bendamustine hydrochloride optional in A Oxaliplatin Low risk ASCO: dexamethasone Bortezomibe Docetaxel NCCN: dexamethasone, Fluorouracil metoclopramide or prochlorperazine 2012: use of NK1-RA or 5HT3-RA Gemcitabine Mitomycin 2015 NCCN: dexamethasone, 2015: use of NK1-RA, netupitant, Paclitaxel metoclopramide, prochlorperazine, olanzapine, or palonosetron Pemetrexed or 5HT3-RA (dolasetron, granisetron, or ondansetron) Minimal risk Bevacizumab Use of NK1-RA, steroids, netupitant, olanzapine, or 5HT3- Rituximab No routine prophylaxis Bleomycin RA; use of metaclopramide, prochloperazine, or haloperidol Trastuzumab Oral Chemotherapy High to moderate risk Use of NK1-RA, steroids, Cyclophosphamidec netupitant, olanzapine, or NCCN: 5HT3-RA c palonosetron; use of Temozolomide (dolasetron, granisetron, or metaclopramide, prochloperazine, ondansetron) or haloperidol in addition to recommended antiemetics Low to minimal risk Capecitabine Chlorambucil Erlotinib Hydroxyurea NCCN: haloperidol, 2012: use of NK1-RA, steroids, or Imatinib mesylate metoclopramide, or prochlorperazine 5HT3-RA Lenalidomide

Melphalan 2015 NCCN: haloperidol, 2015: use of NK1-RA, steroids, Mercaptopurine metoclopramide, prochlorperazine, netupitant, olanzapine, or or 5HT3-RA (dolasetron, Methotrexate palonosetron Sorafenib tosylate granisetron, or ondansetron) Sunitinib malate Thalidomide Dasatinib Pazopanib Abbreviations: 5HT3-RA, serotonin receptor antagonist; ASCO, American Society of Clinical Oncology; CINV, chemotherapy-induced nausea and vomiting; IV, intravenous; NCCN, National Comprehensive Cancer Network; NK1- RA, neurokinin-1 receptor antagonist.

Downloaded From: https://jamanetwork.com/ on 09/23/2021 aBased on ASCO and National Comprehensive Cancer Network guidelines.17-22 bThis agent is a lower risk under ASCO but under National Comprehensive Cancer Network can be high risk for certain patients, so we assign it as high risk. cThis agent’s National Comprehensive Cancer Network risk varies by dose per surface area, but we assign it to its highest risk class. dASCO: the anthracyclines combined with cyclophosphamide are included here. eBortezomib is minimal risk under ASCO.

Downloaded From: https://jamanetwork.com/ on 09/23/2021

eTable 2. Descriptive Statistics Sample: Means and Proportions Characteristic Proportion or Mean (SD) No. of patients receiving chemotherapy 678,220 Overuse 0.241 (0.429) Intravenous chemotherapy, high to moderate CINV risk 0.486 (0.500) Intravenous chemotherapy, low CINV risk 0.148 (0.355) Intravenous chemotherapy, minimal CINV risk 0.144 (0.351) Oral chemotherapy, high to moderate CINV risk 0.023 (0.150) Oral chemotherapy, low to minimal CINV risk 0.200 (0.400) Medicare enrolled 0.321 (0.467) Chemotherapy given in hospital 0.241 (0.427) Out-of-network chemotherapy 0.133 (0.340) Fee for service 0.129 (0.336) Preferred provider organization 0.633 (0.482) Point of service 0.054 (0.226) Health maintenance organization 0.132 (0.338) High deductible plan 0.051 (0.221) Union 0.129 (0.336) Hourly 0.164 (0.370) Rural 0.154 (0.361) Northeast 0.200 (0.400) North Central 0.267 (0.442) South 0.357 (0.479) West 0.176 (0.381) Age 59.5 (13.6) Female 0.582 (0.493) No. of chronic conditions = 0 0.168 (0.374) No. of chronic conditions = 1 0.668 (0.471) No. of chronic conditions = 2 0.094 (0.292) No. of chronic conditions = 3 0.046 (0.210) No. of chronic conditions ≥4 0.023 (0.151) Abbreviation: CINV, chemotherapy induced nausea and vomiting

Downloaded From: https://jamanetwork.com/ on 09/23/2021

eTable 3. Logit-Estimated Effect of Choosing Wisely on Antiemetic Overusea Characteristicb Odds Ratio (95% CI) No. of patients receiving chemotherapy 678,220 Pre-Choosing Wisely baseline period Not applicable 6-mo transition period after Choosing Wisely 0.930c (0.905, 0.956) Post-Choosing Wisely period (after 6 mo) 1.074c (1.046, 1.102) Oral chemotherapy, low to minimal CINV risk 0.566c (0.472, 0.678) Intravenous chemotherapy, high to moderate CINV risk 1.672c (1.527, 1.830) Intravenous chemotherapy, low CINV risk 2.791c (2.281, 3.415) Intravenous chemotherapy, minimal CINV risk 10.83c (3.834, 30.58) Medicare enrolled 0.898c (0.874, 0.924) Chemotherapy administered in hospital outpatient department 0.765c (0.754, 0.776) Out-of-network chemotherapy 0.889c (0.872, 0.907) Preferred provider organization 0.997 (0.974, 1.020) Point of service 0.907c (0.877, 0.938) Health maintenance organization 1.201c (1.168, 1.234) High deductible plan 1.147c (1.108, 1.188) Union 1.252c (1.221, 1.283) Hourly 1.502c (1.469, 1.535) Rural 1.069c (1.051, 1.087) North Central 1.299c (1.276, 1.323) South 0.911c (0.895, 0.927) West 1.085c (1.063, 1.107) Age 49-56 y 0.964c (0.946, 0.982) Age 57-61 y 0.942c (0.923, 0.961) Age 62-69 y 0.932c (0.908, 0.957) Age ≥70 y 0.863c (0.834, 0.894) Female 1.033c (1.017, 1.049) No. of chronic conditions = 1 0.808c (0.796, 0.821) No. of chronic conditions = 2 0.828c (0.809, 0.847) No. of chronic conditions = 3 0.784c (0.760, 0.809) No. of chronic conditions ≥4 0.761c (0.729, 0.794) Month 1.000 (1.000, 1.001) aChoosing Wisely baseline, oral chemotherapy with high to moderate CINV risk, fee for service, Northeast, age 18-48 y, and number of chronic conditions = 0 were omitted. bFixed effects for 135 antineoplastic agents and 36 cancer sites are not shown. cStatistically different from 0 at P < .01.

Downloaded From: https://jamanetwork.com/ on 09/23/2021 eAppendix. Log of Statistical Analyses (Stata 14.0)

1. Specification tests for Probit model.

Probit

xi: quietly probit overuse choosewise1 choosewise2 i.risk medicare hospital outofnetwork i.plan union hourly rural i.region i.acat i.sex i.ncc month /*cancer site fixed effects*/ cancer11_head_sum_sum cancer12_esopha_sum_sum cancer13_stomach_sum_sum cancer14_colon_sum_sum cancer15_rectum_sum_sum cancer16_liver_sum_sum cancer17_pancreas_sum_sum cancer18_gi_sum_sum cancer19_lung_sum_sum cancer20_respiratory_sum_sum cancer21_bone_sum_sum cancer22_skin_sum_sum cancer24_breast_sum_sum cancer25_uterus_sum_sum cancer26_cervix_sum_sum cancer27_ovary_sum_sum cancer28_otherf_sum_sum cancer29_prostate_sum_sum cancer30_testis_sum_sum cancer32_bladder_sum_sum cancer33_kidney_sum_sum cancer34_otheruri_sum_sum cancer35_brain_sum_sum cancer36_thyroid_sum_sum cancer37_hodgkin_sum_sum cancer38_lymphoma_sum_sum cancer390_leukemias_sum_sum cancer391_cml_sum_sum cancer40_myeloma_sum_sum cancer41_other_sum_sum cancer42_secondarym_sum_sum cancer43_mneoplasmwo_sum_sum cancer440_neoplasmsun_sum_sum cancer441_mds_sum_sum /*chemo drug fixed effects*/ i.c1 ;

linktest, nolog;

Probit regression Number of obs = 677,676

LR chi2(2) = 127628.49

Prob > chi2 = 0.0000

Log likelihood = -311249.53 Pseudo R2 = 0.1701

------

overuse | Coef. Std. Err. z P>|z| [95% Conf. Interval]

------+------

_hat | 1.015991 .0079212 128.26 0.000 1.000466 1.031516

_hatsq | .0088328 .0038959 2.27 0.023 .001197 .0164685

_cons | .0037831 .0031593 1.20 0.231 -.002409 .0099753

------

Here, _hat is the linear predicted outcome from the above first probit regression. In this Tukey-Pregibon link test, _hat is significant, indicating that the linear combination of covariates in the above probit does a good job at predicting overuse. However, _hatsq (the square of _hat) is also significant, indicating that nonlinear combinations of the covariates also predict probits of overuse. The probit model requires the probits to be a linear combination of the covariates. Thus, the probit model is mis-specified.

Downloaded From: https://jamanetwork.com/ on 09/23/2021 2. Specification tests for Logit model.

Logit

xi: quietly logit overuse choosewise1 choosewise2 i.risk medicare hospital outofnetwork i.plan union hourly rural i.region i.acat i.sex i.ncc month /*cancer site fixed effects*/ cancer11_head_sum_sum cancer12_esopha_sum_sum cancer13_stomach_sum_sum cancer14_colon_sum_sum cancer15_rectum_sum_sum cancer16_liver_sum_sum cancer17_pancreas_sum_sum cancer18_gi_sum_sum cancer19_lung_sum_sum cancer20_respiratory_sum_sum cancer21_bone_sum_sum cancer22_skin_sum_sum cancer24_breast_sum_sum cancer25_uterus_sum_sum cancer26_cervix_sum_sum cancer27_ovary_sum_sum cancer28_otherf_sum_sum cancer29_prostate_sum_sum cancer30_testis_sum_sum cancer32_bladder_sum_sum cancer33_kidney_sum_sum cancer34_otheruri_sum_sum cancer35_brain_sum_sum cancer36_thyroid_sum_sum cancer37_hodgkin_sum_sum cancer38_lymphoma_sum_sum cancer390_leukemias_sum_sum cancer391_cml_sum_sum cancer40_myeloma_sum_sum cancer41_other_sum_sum cancer42_secondarym_sum_sum cancer43_mneoplasmwo_sum_sum cancer440_neoplasmsun_sum_sum cancer441_mds_sum_sum /*chemo drug fixed effects*/ i.c1 ;

linktest, nolog;

Output:

Logistic regression Number of obs = 677,676

LR chi2(2) = 127760.99

Prob > chi2 = 0.0000

Log likelihood = -311183.28 Pseudo R2 = 0.1703

------

overuse | Coef. Std. Err. z P>|z| [95% Conf. Interval]

------+------

_hat | .9970171 .007796 127.89 0.000 .9817373 1.012297

_hatsq | -.0010047 .0022527 -0.45 0.656 -.0054199 .0034105

_cons | -.0011257 .0050848 -0.22 0.825 -.0110917 .0088402

------

Here, _hat is the linear predicted outcome from the above logit regression. In this Tukey-Pregibon link test, _hat is significant, indicating that the linear combination of covariates in the above logit does a good job at predicting overuse. Moreover, _hatsq (the square of _hat) is not significant, indicating that there are no nonlinear combinations of the covariates that also predict logits of overuse. The logit model requires the logits to be a linear combination of the covariates. Thus, the logit model here is well specified.

Downloaded From: https://jamanetwork.com/ on 09/23/2021 3. Goodness-of-fit test for Logit.

Receiver Operating Characteristic (ROC) analysis.

lroc;

Output:

Logistic model for overuse

number of observations = 677676

area under ROC curve = 0.7654

The ratio of sensitivity to one minus specificity was 0.77, which satisfies the recommended threshold of .7, indicating that the model has good ability to discriminate overusers from non-overusers. The ROC graph is below.

Downloaded From: https://jamanetwork.com/ on 09/23/2021