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How does age affect baseline screening mammography performance measures? A decision model.

Keen JD, Keen JE - BMC Med Inform Decis Mak (2008)

Bottom Line: For other probabilities, the model used population-based estimates for screening mammography accuracy and diagnostic mammography outcomes specific to baseline exams.The model predicts the total intervention rate = 0.013*AGE2 - 0.67*AGE + 40, or 34/1000 at age 40 to 47/1000 at age 60.Therefore, the positive biopsy (intervention) fraction varies from 6% at age 40 to 32% at age 60.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Radiology, John H. Stroger Jr. Hospital of Cook County, 1901 West Harrison Street, Chicago, IL 60612-9985, USA. jkeen@ccbhs.org

ABSTRACT

Background: In order to promote consumer-oriented informed medical decision-making regarding screening mammography, we created a decision model to predict the age dependence of the cancer detection rate, the recall rate and the secondary performance measures (positive predictive values, total intervention rate, and positive biopsy fraction) for a baseline mammogram.

Methods: We constructed a decision tree to model the possible outcomes of a baseline screening mammogram in women ages 35 to 65. We compared the single baseline screening mammogram decision with the no screening alternative. We used the Surveillance Epidemiology and End Results national cancer database as the primary input to estimate cancer prevalence. For other probabilities, the model used population-based estimates for screening mammography accuracy and diagnostic mammography outcomes specific to baseline exams. We varied radiologist performance for screening accuracy.

Results: The cancer detection rate increases from 1.9/1000 at age 40 to 7.2/1000 at age 50 to 15.1/1000 at age 60. The recall rate remains relatively stable at 142-157/1000, which varies from 73-236/1000 at age 50 depending on radiologist performance. The positive predictive value of a screening mammogram increases from 1.3% at age 40 to 9.8% at age 60, while the positive predictive value of a diagnostic mammogram varies from 2.9% at age 40 to 19.2% at age 60. The model predicts the total intervention rate = 0.013*AGE2 - 0.67*AGE + 40, or 34/1000 at age 40 to 47/1000 at age 60. Therefore, the positive biopsy (intervention) fraction varies from 6% at age 40 to 32% at age 60.

Conclusion: Breast cancer prevalence, the cancer detection rate, and all secondary screening mammography performance measures increase substantially with age.

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Related in: MedlinePlus

Predicted secondary performance measures: total intervention rate and positive biopsy fraction (TIR, PBF). The TIR is for 1000 screening mammograms for women ages 35–65 and includes the interventions of tissue biopsy and needle aspiration. The PBF percentage is a widely accepted performance measure due to the desire to avoid the high financial and emotional cost and invasive nature of negative interventions. The model predicts that the PBF does not reach the minimum recommended level of 25% until age 55. Stratifying the recommendations for screening so that only higher risk women would be encouraged to screen at younger ages would increase the prevalence and therefore improve all performance measures.
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Figure 5: Predicted secondary performance measures: total intervention rate and positive biopsy fraction (TIR, PBF). The TIR is for 1000 screening mammograms for women ages 35–65 and includes the interventions of tissue biopsy and needle aspiration. The PBF percentage is a widely accepted performance measure due to the desire to avoid the high financial and emotional cost and invasive nature of negative interventions. The model predicts that the PBF does not reach the minimum recommended level of 25% until age 55. Stratifying the recommendations for screening so that only higher risk women would be encouraged to screen at younger ages would increase the prevalence and therefore improve all performance measures.

Mentions: Figures 2 and 3 show the model-predicted primary events (CDR and recall rate) associated with baseline mammography, while Figures 4 and 5 summarize predicted secondary performance measures. Table 3 shows the regression equations for incidence, prevalence and model predictions with the associated R2 value. The increase in CDR with age generally tracks the prevalence 2nd order polynomial, while the recall rate is linear and stable with age. Both the primary events and the secondary performance measures have a wide range of values depending on radiologist performance at the 10th or 90th percentiles. Specificity dominates the recall rate by an order of magnitude: the false-positive outcomes (140/1000) and recall rate (147/1000) at age 50 are about 20 times the CDR (7.2/1000). At age 50, the recall rate range from 10th to 90th percentile is 16.3%, but this decreases only to 16.1% when using a median sensitivity at the extremes of specificity. Therefore, equal absolute percentage changes in radiologist sensitivity or specificity would have disparate effects.


How does age affect baseline screening mammography performance measures? A decision model.

Keen JD, Keen JE - BMC Med Inform Decis Mak (2008)

Predicted secondary performance measures: total intervention rate and positive biopsy fraction (TIR, PBF). The TIR is for 1000 screening mammograms for women ages 35–65 and includes the interventions of tissue biopsy and needle aspiration. The PBF percentage is a widely accepted performance measure due to the desire to avoid the high financial and emotional cost and invasive nature of negative interventions. The model predicts that the PBF does not reach the minimum recommended level of 25% until age 55. Stratifying the recommendations for screening so that only higher risk women would be encouraged to screen at younger ages would increase the prevalence and therefore improve all performance measures.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2563001&req=5

Figure 5: Predicted secondary performance measures: total intervention rate and positive biopsy fraction (TIR, PBF). The TIR is for 1000 screening mammograms for women ages 35–65 and includes the interventions of tissue biopsy and needle aspiration. The PBF percentage is a widely accepted performance measure due to the desire to avoid the high financial and emotional cost and invasive nature of negative interventions. The model predicts that the PBF does not reach the minimum recommended level of 25% until age 55. Stratifying the recommendations for screening so that only higher risk women would be encouraged to screen at younger ages would increase the prevalence and therefore improve all performance measures.
Mentions: Figures 2 and 3 show the model-predicted primary events (CDR and recall rate) associated with baseline mammography, while Figures 4 and 5 summarize predicted secondary performance measures. Table 3 shows the regression equations for incidence, prevalence and model predictions with the associated R2 value. The increase in CDR with age generally tracks the prevalence 2nd order polynomial, while the recall rate is linear and stable with age. Both the primary events and the secondary performance measures have a wide range of values depending on radiologist performance at the 10th or 90th percentiles. Specificity dominates the recall rate by an order of magnitude: the false-positive outcomes (140/1000) and recall rate (147/1000) at age 50 are about 20 times the CDR (7.2/1000). At age 50, the recall rate range from 10th to 90th percentile is 16.3%, but this decreases only to 16.1% when using a median sensitivity at the extremes of specificity. Therefore, equal absolute percentage changes in radiologist sensitivity or specificity would have disparate effects.

Bottom Line: For other probabilities, the model used population-based estimates for screening mammography accuracy and diagnostic mammography outcomes specific to baseline exams.The model predicts the total intervention rate = 0.013*AGE2 - 0.67*AGE + 40, or 34/1000 at age 40 to 47/1000 at age 60.Therefore, the positive biopsy (intervention) fraction varies from 6% at age 40 to 32% at age 60.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Radiology, John H. Stroger Jr. Hospital of Cook County, 1901 West Harrison Street, Chicago, IL 60612-9985, USA. jkeen@ccbhs.org

ABSTRACT

Background: In order to promote consumer-oriented informed medical decision-making regarding screening mammography, we created a decision model to predict the age dependence of the cancer detection rate, the recall rate and the secondary performance measures (positive predictive values, total intervention rate, and positive biopsy fraction) for a baseline mammogram.

Methods: We constructed a decision tree to model the possible outcomes of a baseline screening mammogram in women ages 35 to 65. We compared the single baseline screening mammogram decision with the no screening alternative. We used the Surveillance Epidemiology and End Results national cancer database as the primary input to estimate cancer prevalence. For other probabilities, the model used population-based estimates for screening mammography accuracy and diagnostic mammography outcomes specific to baseline exams. We varied radiologist performance for screening accuracy.

Results: The cancer detection rate increases from 1.9/1000 at age 40 to 7.2/1000 at age 50 to 15.1/1000 at age 60. The recall rate remains relatively stable at 142-157/1000, which varies from 73-236/1000 at age 50 depending on radiologist performance. The positive predictive value of a screening mammogram increases from 1.3% at age 40 to 9.8% at age 60, while the positive predictive value of a diagnostic mammogram varies from 2.9% at age 40 to 19.2% at age 60. The model predicts the total intervention rate = 0.013*AGE2 - 0.67*AGE + 40, or 34/1000 at age 40 to 47/1000 at age 60. Therefore, the positive biopsy (intervention) fraction varies from 6% at age 40 to 32% at age 60.

Conclusion: Breast cancer prevalence, the cancer detection rate, and all secondary screening mammography performance measures increase substantially with age.

Show MeSH
Related in: MedlinePlus