PURPOSE: There is increasing interest in using administrative data to examine treatment-related complications that lead to emergency department (ED) visits or hospitalizations (H). The purpose of this study was to evaluate the reliability of billing codes for identifying chemotherapy-related acute care visits (CRVs) among women with early-stage breast cancer. MATERIALS AND METHODS: The cohort was identified by using deterministically linked health databases and consisted of women who were diagnosed with early-stage breast cancer who started adjuvant chemotherapy between 2007 and 2009 in Ontario, Canada. A random sample of 496 patient cases was chosen as the validation cohort. Sensitivity (SN) and specificity (SP) were calculated for three scenarios: chemotherapy-related ED visit, chemotherapy-related H, and febrile neutropenia (FN)-related visit. For FN-related visits, three definitions were considered: general, moderate, and strict. RESULTS: The administrative cohort consisted of 8,359 patients, 43.4% of whom had at least one ED or H, including 1,496 women who had multiple visits that resulted in 6,293 unique visits. Of these, 73.1% were considered CRVs. The algorithm performed well in identifying CRVs that included H either from ED (SN, 90%; SP, 100%) or directly from home (SN, 91%; SP, 93%), but less well for ED visits that did not result in H (SN, 65%; SP, 80%). Depending on which FN algorithm was used, 4.8% to 24% of visits were considered related. The moderate FN algorithm provided the best tradeoff between SN (69% to 97%) and SP (83% to 98%). CONCLUSION: Administrative data can be valuable in evaluating chemotherapy-related serious events. Algorithm validation in other cohorts is needed.
PURPOSE: There is increasing interest in using administrative data to examine treatment-related complications that lead to emergency department (ED) visits or hospitalizations (H). The purpose of this study was to evaluate the reliability of billing codes for identifying chemotherapy-related acute care visits (CRVs) among women with early-stage breast cancer. MATERIALS AND METHODS: The cohort was identified by using deterministically linked health databases and consisted of women who were diagnosed with early-stage breast cancer who started adjuvant chemotherapy between 2007 and 2009 in Ontario, Canada. A random sample of 496 patient cases was chosen as the validation cohort. Sensitivity (SN) and specificity (SP) were calculated for three scenarios: chemotherapy-related ED visit, chemotherapy-related H, and febrile neutropenia (FN)-related visit. For FN-related visits, three definitions were considered: general, moderate, and strict. RESULTS: The administrative cohort consisted of 8,359 patients, 43.4% of whom had at least one ED or H, including 1,496 women who had multiple visits that resulted in 6,293 unique visits. Of these, 73.1% were considered CRVs. The algorithm performed well in identifying CRVs that included H either from ED (SN, 90%; SP, 100%) or directly from home (SN, 91%; SP, 93%), but less well for ED visits that did not result in H (SN, 65%; SP, 80%). Depending on which FN algorithm was used, 4.8% to 24% of visits were considered related. The moderate FN algorithm provided the best tradeoff between SN (69% to 97%) and SP (83% to 98%). CONCLUSION: Administrative data can be valuable in evaluating chemotherapy-related serious events. Algorithm validation in other cohorts is needed.
Authors: M Powis; P Groome; N Biswanger; C Kendell; K M Decker; E Grunfeld; M L McBride; R Urquhart; M Winget; G A Porter; M K Krzyzanowska Journal: Curr Oncol Date: 2019-10-01 Impact factor: 3.677
Authors: Kelvin K W Chan; Helen Guo; Sierra Cheng; Jaclyn M Beca; Ruby Redmond-Misner; Wanrudee Isaranuwatchai; Lucy Qiao; Craig Earle; Scott R Berry; James J Biagi; Stephen Welch; Brandon M Meyers; Nicole Mittmann; Natalie Coburn; Jessica Arias; Deborah Schwartz; Wei F Dai; Scott Gavura; Robin McLeod; Erin D Kennedy Journal: Cancer Med Date: 2019-11-13 Impact factor: 4.452
Authors: Wei Fang Dai; Jaclyn M Beca; Chenthila Nagamuthu; Ning Liu; Claire de Oliveira; Craig C Earle; Maureen Trudeau; Rebecca E Mercer; Kelvin K W Chan Journal: JAMA Netw Open Date: 2022-02-01
Authors: Patti Ann Groome; Mary L McBride; Li Jiang; Cynthia Kendell; Kathleen M Decker; Eva Grunfeld; Monika K Krzyzanowska; Marcy Winget Journal: Int J Popul Data Sci Date: 2018-11-12