Publications

IMPORTANCE: Guidelines recommend individualizing screening mammography decisions for women aged 75 years and older. However, little pragmatic guidance is available to help counsel patients.

OBJECTIVE: To provide an evidence-based approach for individualizing decision-making about screening mammography in older women.

EVIDENCE ACQUISITION: We searched PubMed for English-language studies in peer-reviewed journals published from January 1, 1990, to February 1, 2014, to identify risk factors for late-life breast cancer in women aged 65 years and older and to quantify the benefits and harms of screening mammography for women aged 75 years and older.

FINDINGS: Age is the major risk factor for developing and dying from breast cancer. Breast cancer risk factors that reflect hormonal exposures in the distant past, such as age at first birth or age at menarche, are less predictive of late-life breast cancer than factors indicating recent hormonal exposures such as high bone mass or obesity. Randomized trials of the benefits of screening mammography did not include women older than 74 years. Thus it is not known if screening mammography benefits older women. Observational studies favor extending screening mammography to older women who have a life expectancy of more than 10 years. Modeling studies estimate 2 fewer breast cancer deaths/1000 women who in their 70s continue biennial screening for 10 years instead of stopping screening at age 69. Potential harms of continued screening over 10 years include false-positive mammograms in approximately 200/1000 women screened and overdiagnosis (ie, finding breast cancer that would not have clinically surfaced otherwise) in approximately 13/1000 women screened. Providing information about life expectancy along with potential benefits and harms of screening may help older women's decision-making about screening mammography.

CONCLUSIONS AND RELEVANCE: For women with less than a 10-year life expectancy, recommendations to stop screening mammography should emphasize increased potential harms from screening and highlight health promotion measures likely to be beneficial over the short term. For women with a life expectancy of more than 10 years, deciding whether potential benefits of screening outweigh harms becomes a value judgment for patients, requiring a realistic understanding of screening outcomes.

OBJECTIVES: To examine receipt of mammography screening according to life expectancy in women aged 75 and older.

DESIGN: Population-based survey.

SETTING: United States.

PARTICIPANTS: Community dwelling U.S. women aged 75 and older who participated in the 2008 or 2010 National Health Interview Survey.

MEASUREMENTS: Using a previously developed and validated index, women were categorized according to life expectancy (>9, 5-9, <5 years). Receipt of mammography screening in the past 2 years was examined according to life expectancy, adjusting for sociodemographic characteristics, access to care, preventive orientation (e.g., receipt of influenza vaccination), and receipt of a clinician recommendation for screening.

RESULTS: Of 2,266 respondents, 27.1% had a life expectancy of greater than 9 years, 53.4% had a life expectancy of 5 to 9 years, and 19.5% had a life expectancy of less than 5 years. Overall, 55.7% reported receiving mammography screening in the past 2 years. Life expectancy was strongly associated with receipt of screening (P < .001), yet 36.1% of women with less than 5 years life expectancy were screened, and 29.2% of women with more than 9 years life expectancy were not screened. A clinician recommendation for screening was the strongest predictor of screening independent of life expectancy. Higher educational attainment, age, receipt of influenza vaccination, and history of benign breast biopsy were also independently associated with being screened.

CONCLUSION: Despite uncertainty of benefit, many women aged 75 and older are screened with mammography. Life expectancy is strongly associated with receipt of screening, which may reflect clinicians and patients appropriately considering life expectancy in screening decisions, but 36% of women with short life expectancies are still screened, suggesting that new interventions are needed to further improve targeting of screening according to life expectancy. Decision aids and guidelines encouraging clinicians to consider patient life expectancy in screening decisions may improve care.

RATIONALE: Inpatient consultation by specialists is one of the most common medical interventions in the modern intensive care unit (ICU), but few data exist on components of high-quality consultation.

OBJECTIVES: Our objective was to use qualitative methods to develop a conceptual framework of consultative quality in critically ill patients.

METHODS: We conducted a qualitative study of medical ICU physicians at a single institution using a novel, semistructured interview guide. We elicited physicians' attitudes toward processes of obtaining specialty consultation, identified perceived elements of high-quality consults, and identified barriers to obtaining high-quality consults. We used grounded theory to identify themes.

MEASUREMENTS AND MAIN RESULTS: ICU physicians described four common reasons for involving a consulting physician: the need for clinical or procedural expertise, an explicit or implicit protocol of the institution mandating the consult, an opportunity to provide education to the primary or consulting team, and/or at the family's request. Participants identified seven components of a high-quality consult, including the consulting teams' (1) decisiveness, (2) thoroughness, (3) level of interest, (4) professionalism, (5) expertise, (6) timeliness, and (7) involvement with the family of the patient. The intensive care team, the consult team, the health system, and the temporal context in which the consultation takes place may influence the quality of the consultation.

CONCLUSIONS: Several key factors are necessary for a consult to be judged high quality. An opportunity exists to develop an instrument to assess and to improve specialty consultations in the ICU based on these findings.

BACKGROUND: Data are needed on how life expectancy affects treatment decisions among women ≥80 years with early stage breast cancer. METHODS: We used the linked Surveillance Epidemiology and End Results-Medicare claims dataset from 1992-2005 to identify women aged ≥80 newly diagnosed with lymph node negative, estrogen receptor positive tumors, ≤5 centimeters. To estimate life expectancy, we matched these women to women of similar age, region, and insurance, not diagnosed with breast cancer. We examined 5-year mortality of matched controls by illness burden (measured with the Charlson Comorbidity Index [CCI]) using Kaplan-Meier statistics. We examined treatments received by estimated life expectancy within CCI levels. We further examined factors associated with receipt of radiotherapy after breast conserving surgery (BCS). RESULTS: Of 9,932 women, 39.6% underwent mastectomy, 30.4% received BCS plus radiotherapy, and 30.0% received BCS alone. Estimated 5-year mortality was 72% for women with CCIs of 3+, yet 38.0% of these women underwent mastectomy and 22.9% received radiotherapy after BCS. Conversely, estimated 5-year mortality was 36% for women with CCIs of 0 and 26.6% received BCS alone. Age 80-84, urban residence, higher grade, recent diagnosis, mammography use, and low comorbidity, were factors associated with receiving radiotherapy after BCS. Among women with CCIs of 3+ treated with BCS, 36.9% underwent radiotherapy. CONCLUSIONS: Many women aged ≥80 with limited life expectancies receive radiotherapy after BCS for treatment of early stage breast cancers while many in excellent health do not. More consideration needs to be given to patient life expectancy when considering breast cancer treatments. KEY WORDS: Breast cancer, older women, treatment, life expectancy, radiation.

OBJECTIVES: To identify factors that influence the breast cancer treatment decisions of women aged 80 and older.

DESIGN: Medical record review.

SETTING: One academic primary care clinic and two community health centers in Boston.

PARTICIPANTS: Sixty-five women aged 80 and older diagnosed with breast cancer between 1994 and 2004 and followed through June 30, 2010.

MEASUREMENTS: Data were abstracted on breast cancer characteristics, comorbidities, treatments received, and outcomes. Notes from primary care physicians, oncologists, and breast surgeons were reviewed to determine factors involved in treatment decision-making.

RESULTS: Median age at diagnosis was 84.0 (interquartile range 82.0-86.3), 55 (84.6%) were non-Hispanic white, and 40 (61.5%) had at least one comorbidity. Nine women were diagnosed with ductal carcinoma in situ, 42 with a new primary invasive breast cancer, eight with a second primary, and six with a breast cancer recurrence. Sixty-three (96.9%) received some type of treatment. Fifty-six (86.2%) had at least one detailed physician note on treatment decision-making in their charts. The main categories found to influence participant, family, and physician treatment decision-making were tumor characteristics, ratio of treatment benefits to risks, logistics (e.g., transportation, finances), and participant age, health (including a concurrent diagnosis), and psychosocial characteristics. Family was involved in treatment discussions for 46 (70.8%) participants.

CONCLUSION: The quality of physician documentation about decision-making in these women was high. A great amount of thoughtful and complex decision-making involving patients, family, and physicians occurs after a woman aged 80 and older is diagnosed with breast cancer.

CONTEXT: To better target services to those who may benefit, many guidelines recommend incorporating life expectancy into clinical decisions.

OBJECTIVE: To assess the quality and limitations of prognostic indices for mortality in older adults through systematic review.

DATA SOURCES: We searched MEDLINE, EMBASE, Cochrane, and Google Scholar from their inception through November 2011.

STUDY SELECTION: We included indices if they were validated and predicted absolute risk of mortality in patients whose average age was 60 years or older. We excluded indices that estimated intensive care unit, disease-specific, or in-hospital mortality.

DATA EXTRACTION: For each prognostic index, we extracted data on clinical setting, potential for bias, generalizability, and accuracy.

RESULTS: We reviewed 21,593 titles to identify 16 indices that predict risk of mortality from 6 months to 5 years for older adults in a variety of clinical settings: the community (6 indices), nursing home (2 indices), and hospital (8 indices). At least 1 measure of transportability (the index is accurate in more than 1 population) was tested for all but 3 indices. By our measures, no study was free from potential bias. Although 13 indices had C statistics of 0.70 or greater, none of the indices had C statistics of 0.90 or greater. Only 2 indices were independently validated by investigators who were not involved in the index's development.

CONCLUSION: We identified several indices for predicting overall mortality in different patient groups; future studies need to independently test their accuracy in heterogeneous populations and their ability to improve clinical outcomes before their widespread use can be recommended.

OBJECTIVES: To further validate an index predicting mortality in community-dwelling older adults.

DESIGN: A comparison of the performance of the index in predicting mortality among new respondents to the National Health Interview Survey (NHIS, 2001-2004) with that of respondents from the original development and validation cohorts (1997-2000) and a test of its performance over extended follow-up (up to 9 years) using the original cohorts. Follow-up mortality data were available through 2006.

SETTING: NHIS.

PARTICIPANTS: Twenty-two thousand fifty-seven new respondents to the NHIS (2001-2004) and 24,139 respondents from the original development and validation cohorts (1997-2000).

MEASUREMENTS: A risk score was calculated for each respondent based on the presence or absence of 11 factors (function, illnesses, behaviors, demographics) that make up the index. Using the Kaplan-Meier method, 5-year mortality estimates were computed for the new and original cohort respondents and 9-year mortality estimates for the original cohorts.

RESULTS: New respondents were similar to original cohort respondents but were slightly more likely to be aged 85 and older, report diabetes mellitus, and have a body mass index of 25.0 kg/m² or greater. The model performed as well in the new cohort as it had in the original cohort. New respondents with risk scores of 0 to 1 had a 2% risk of 5-year mortality, whereas respondents who scored 18 or higher had a 69% risk of 5-year mortality (range 3-71% risk of 5-year mortality in the development cohort). The index also demonstrated excellent calibration and discrimination in predicting 9-year mortality (range 7% risk for scores of 0-1 to 92% risk for scores of ≥ 18, original validation cohort extended).

CONCLUSION: These results further justify use of this index to estimate life expectancy in clinical decision-making.

PURPOSE: To understand the impact of breast cancer on older women's survival, we compared survival of older women diagnosed with breast cancer with matched controls. METHODS Using the linked 1992 to 2003 Surveillance, Epidemiology, and End Results (SEER) -Medicare data set, we identified women age 67 years or older who were newly diagnosed with ductal carcinoma in situ (DCIS) or breast cancer. We identified women not diagnosed with breast cancer from the 5% random sample of Medicare beneficiaries residing in SEER areas.We matched patient cases to controls by birth year and registry (99% or 66,039 [corrected] patient cases matched successfully). We assigned the start of follow-up for controls as the patient cases' date of diagnosis. Mortality data were available through 2006. We compared survival of women with breast cancer by stage with survival of controls using multivariable proportional hazards models adjusting for age at diagnosis, comorbidity, prior mammography use, and sociodemographics. We repeated these analyses stratifying by age.

RESULTS: Median follow-up time was 7.7 years. Differences between patient cases and controls in sociodemographics and comorbidities were small (< 4%). Women diagnosed with DCIS (adjusted hazard ratio [aHR], 0.7; 95% CI, 0.7 to 0.7) or stage I disease (aHR, 0.8; 95% CI, 0.8 to 0.8) had slightly lower mortality than controls.Women diagnosed with stage II disease or higher had greater mortality than controls (stage II disease:aHR, 1.2; 95% CI, 1.2 to 1.2). The association of a breast cancer diagnosis with mortality declined with age among women with advanced disease [corrected].

CONCLUSION: Compared with matched controls, a diagnosis of DCIS or stage I breast cancer in older women is associated with better [corrected] survival, whereas a diagnosis of stage II or higher breast cancer is associated with worse survival.

PURPOSE: For patients who elect to have prostate cancer screening, the optimal time to discontinue screening is unknown. Our objective was to describe rates and predictors of prostate-specific antigen (PSA) screening among older men in the United States.

METHODS: Data were extracted from the population-based 2000 and 2005 National Health Interview Survey (NHIS). PSA screening was defined as a PSA test as part of a routine exam within the past year. Demographic, socioeconomic, and functional characteristics were collected, and a validated 5-year estimated life expectancy was calculated. Age-specific rates of PSA screening were determined, and sampling weight-adjusted multivariate regressions were fitted to determine predictors of screening among men age 70 years or older.

RESULTS: The PSA screening rate was 24.0% in men age 50 to 54 years, and it increased steadily with age until a peak of 45.5% among age 70 to 74 years. Screening rates then gradually declined by age, and 24.6% of men age 85 years or older reported being screened. Among men age 70 years or older, screening rates varied by estimated 5-year life expectancy: rates were 47.3% in men with high life expectancies (≤ 15% probability of 5-year mortality), 39.2% in men with intermediate life expectancies (16% to 48% probability), and 30.7% in men with low life expectancies (> 48% probability; P < .001). In multivariate analysis, estimated life expectancy and age remained independently associated with PSA screening (P < .001 for each).

CONCLUSION: Rates of PSA screening in the United States are associated with age and estimated life expectancy, but excessive PSA screening in elderly men with limited life expectancies remains a significant problem. The merits and limitations of PSA should be discussed with all patients considering prostate cancer screening.