scholarly journals Correction: Incorporating Breast Cancer Recurrence Events Into Population-Based Cancer Registries Using Medical Claims: Cohort Study (Preprint)

2020 ◽  
Author(s):  
Teresa A'mar ◽  
J David Beatty ◽  
Catherine Fedorenko ◽  
Daniel Markowitz ◽  
Thomas Corey ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cohort Study ◽  
Health Research ◽  
Cancer Recurrence ◽  
Cancer Registries ◽  
Population Based ◽  
Breast Cancer Recurrence ◽  
Kaiser Permanente ◽  
Medical Claims ◽  
Research Institute

UNSTRUCTURED In “Incorporating Breast Cancer Recurrence Events Into Population-Based Cancer Registries Using Medical Claims: Cohort Study (J Med Internet Res Ca 2020;6(2):e18143)” the authors noted two errors. The metadata erroneously listed only Drs. A’mar and Etzioni as having contributed equally; this has been corrected to reflect that Drs. A’mar, Chubak, and Etzioni contributed equally. In addition, Dr. Chubak’s affiliation has been corrected from “Washington Health Research Institute, Kaiser Permanente” to “Kaiser Permanente Washington Health Research Institute.”

scholarly journals Correction: Incorporating Breast Cancer Recurrence Events Into Population-Based Cancer Registries Using Medical Claims: Cohort Study

JMIR Cancer ◽  
10.2196/23821 ◽  
2020 ◽  
Vol 6 (2) ◽  
pp. e23821
Author(s):  
Teresa A'mar ◽  
J David Beatty ◽  
Catherine Fedorenko ◽  
Daniel Markowitz ◽  
Thomas Corey ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cohort Study ◽  
Cancer Recurrence ◽  
Cancer Registries ◽  
Population Based ◽  
Breast Cancer Recurrence ◽  
Medical Claims

scholarly journals Incorporating Breast Cancer Recurrence Events Into Population-Based Cancer Registries Using Medical Claims: Cohort Study (Preprint)

2020 ◽  
Author(s):  
Teresa A'mar ◽  
J David Beatty ◽  
Catherine Fedorenko ◽  
Daniel Markowitz ◽  
Thomas Corey ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Data Mining ◽  
Predictive Value ◽  
Puget Sound ◽  
Cancer Recurrence ◽  
Cancer Registries ◽  
Population Based ◽  
Medical Claims ◽  
Predicted Probability ◽  
Second Breast Cancer

BACKGROUND There is a need for automated approaches to incorporate information on cancer recurrence events into population-based cancer registries. OBJECTIVE The aim of this study is to determine the accuracy of a novel data mining algorithm to extract information from linked registry and medical claims data on the occurrence and timing of second breast cancer events (SBCE). METHODS We used supervised data from 3092 stage I and II breast cancer cases (with 394 recurrences), diagnosed between 1993 and 2006 inclusive, of patients at Kaiser Permanente Washington and cases in the Puget Sound Cancer Surveillance System. Our goal was to classify each month after primary treatment as pre- versus post-SBCE. The prediction feature set for a given month consisted of registry variables on disease and patient characteristics related to the primary breast cancer event, as well as features based on monthly counts of diagnosis and procedure codes for the current, prior, and future months. A month was classified as post-SBCE if the predicted probability exceeded a probability threshold (PT); the predicted time of the SBCE was taken to be the month of maximum increase in the predicted probability between adjacent months. RESULTS The Kaplan-Meier net probability of SBCE was 0.25 at 14 years. The month-level receiver operating characteristic curve on test data (20% of the data set) had an area under the curve of 0.986. The person-level predictions (at a monthly PT of 0.5) had a sensitivity of 0.89, a specificity of 0.98, a positive predictive value of 0.85, and a negative predictive value of 0.98. The corresponding median difference between the observed and predicted months of recurrence was 0 and the mean difference was 0.04 months. CONCLUSIONS Data mining of medical claims holds promise for the streamlining of cancer registry operations to feasibly collect information about second breast cancer events.

scholarly journals Incorporating Breast Cancer Recurrence Events Into Population-Based Cancer Registries Using Medical Claims: Cohort Study

JMIR Cancer ◽  
10.2196/18143 ◽  
2020 ◽  
Vol 6 (2) ◽  
pp. e18143
Author(s):  
Teresa A'mar ◽  
J David Beatty ◽  
Catherine Fedorenko ◽  
Daniel Markowitz ◽  
Thomas Corey ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Data Mining ◽  
Predictive Value ◽  
Puget Sound ◽  
Cancer Recurrence ◽  
Cancer Registries ◽  
Population Based ◽  
Medical Claims ◽  
Predicted Probability ◽  
Second Breast Cancer

Background There is a need for automated approaches to incorporate information on cancer recurrence events into population-based cancer registries. Objective The aim of this study is to determine the accuracy of a novel data mining algorithm to extract information from linked registry and medical claims data on the occurrence and timing of second breast cancer events (SBCE). Methods We used supervised data from 3092 stage I and II breast cancer cases (with 394 recurrences), diagnosed between 1993 and 2006 inclusive, of patients at Kaiser Permanente Washington and cases in the Puget Sound Cancer Surveillance System. Our goal was to classify each month after primary treatment as pre- versus post-SBCE. The prediction feature set for a given month consisted of registry variables on disease and patient characteristics related to the primary breast cancer event, as well as features based on monthly counts of diagnosis and procedure codes for the current, prior, and future months. A month was classified as post-SBCE if the predicted probability exceeded a probability threshold (PT); the predicted time of the SBCE was taken to be the month of maximum increase in the predicted probability between adjacent months. Results The Kaplan-Meier net probability of SBCE was 0.25 at 14 years. The month-level receiver operating characteristic curve on test data (20% of the data set) had an area under the curve of 0.986. The person-level predictions (at a monthly PT of 0.5) had a sensitivity of 0.89, a specificity of 0.98, a positive predictive value of 0.85, and a negative predictive value of 0.98. The corresponding median difference between the observed and predicted months of recurrence was 0 and the mean difference was 0.04 months. Conclusions Data mining of medical claims holds promise for the streamlining of cancer registry operations to feasibly collect information about second breast cancer events.

scholarly journals Opioids and breast cancer recurrence: A Danish population-based cohort study

Cancer ◽  
2015 ◽  
Vol 121 (19) ◽  
pp. 3507-3514 ◽  
Author(s):  
Deirdre P. Cronin-Fenton ◽  
Uffe Heide-Jørgensen ◽  
Thomas P. Ahern ◽  
Timothy L. Lash ◽  
Peer M. Christiansen ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cohort Study ◽  
Cancer Recurrence ◽  
Population Based ◽  
Breast Cancer Recurrence ◽  
Danish Population

scholarly journals Cohort study of adherence to adjuvant endocrine therapy, breast cancer recurrence and mortality

2013 ◽  
Vol 108 (7) ◽  
pp. 1515-1524 ◽  
Author(s):  
B Makubate ◽  
P T Donnan ◽  
J A Dewar ◽  
A M Thompson ◽  
C McCowan
Keyword(s):  
Breast Cancer ◽  
Cohort Study ◽  
Endocrine Therapy ◽  
Cancer Recurrence ◽  
Adjuvant Endocrine Therapy ◽  
Breast Cancer Recurrence
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