Karnofsky Performance Status (KPS) ≤60 Is Strongly Associated With Shorter Brain-Specific Progression-Free Survival Among Patients With Metastatic Breast Cancer With Brain Metastases

ObjectiveTo examine the association between Karnofsky Performance Status (“KPS”) and brain-specific progression-free survival (“bsPFS”) among patients with breast cancer brain metastases (“BCBrM”).MethodsUsing a previously compiled retrospective cohort of 683 patients who were treated for BCBrM with surgery and/or radiotherapy at the Sunnybrook Odette Cancer Centre from 2008-2018, electronic records were reviewed to impute KPS scores at the time of BCBrM diagnosis. Patients were then grouped into KPS ≤60 and KPS >60 cohorts. The dataset was analyzed to identify variables that were prognostic for bsPFS and/or overall survival (“OS”) using univariable and multivariable Cox proportional hazards models.ResultsThe mean age of patients was 57 (range 24-93). Most patients (n=622, 91%) had extracranial metastatic disease and 174 (25%) had leptomeningeal disease. 247 patients (36%) had hormone receptor (“HR”)-positive/human endothelial growth factor receptor 2 (“HER2”)-negative tumours, 189 (28%) had HER2-positive disease, and 153 (22%) had triple-negative breast cancer. Of the 331 patients (48%) who could be assigned a KPS cohort, 102 (31%) had KPS ≤60. Most patients were treated with whole brain radiotherapy (n=498, 73%) and/or stereotactic radiosurgery (“SRS”) (n=128, 19%). Median bsPFS was 9 months (95% CI 8-10 months) and median OS was not reached. In univariable analyses, KPS ≤60, presence of leptomeningeal disease, neurological symptoms, ≥2 brain metastases, and not undergoing SRS were factors associated with shorter bsPFS. In a multivariable analysis, KPS ≤60 was the only statistically significant determinant of bsPFS (HR 1.86, 95% CI 1.20-2.88). Although survival data was limited, KPS ≤60 was associated with shorter OS in both univariable (HR 3.12, 95% CI 1.85-5.26) and multivariable (HR 2.95, 95% CI 1.55-5.58) analyses.ConclusionPatients with BCBrM who have a KPS ≤60 have significantly shorter bsPFS and OS than those with KPS >60. KPS should be documented routinely at the time of diagnosis of brain metastases to improve prognostication

Selecting a BRCA risk assessment model for use in a familial cancer clinic

<p>Abstract</p> <p>Background</p> <p>Risk models are used to calculate the likelihood of carrying a <it>BRCA1 </it>or <it>BRCA2 </it>mutation. We evaluated the performances of currently-used risk models among patients from a large familial program using the criteria of high sensitivity, simple data collection and entry and <it>BRCA </it>score reporting.</p> <p>Methods</p> <p>Risk calculations were performed by applying the BRCAPRO, Manchester, Penn II, Myriad II, FHAT, IBIS and BOADICEA models to 200 non-<it>BRCA </it>carriers and 100 <it>BRCA </it>carriers, consecutively tested between August 1995 and March 2006. Areas under the receiver operating characteristic curves (AUCs) were determined and sensitivity and specificity were calculated at the conventional testing thresholds. In addition, subset analyses were performed for low and high risk probands.</p> <p>Results</p> <p>The BRCAPRO, Penn II, Myriad II, FHAT and BOADICEA models all have similar AUCs of approximately 0.75 for <it>BRCA </it>status. The Manchester and IBIS models have lower AUCs (0. and 0.47 respectively). At the conventional testing thresholds, the sensitivities and specificities for a <it>BRCA </it>mutation were, respectively, as follows: BRCAPRO (0.75, 0.62), Manchester (0.58,0.71), Penn II (0.93,0.31), Myriad II (0.71,0.63), FHAT (0.70,0.63), IBIS (0.20,0.74), BOADICEA (0.70, 0.65).</p> <p>Conclusion</p> <p>The Penn II model most closely met the criteria we established and this supports the use of this model for identifying individuals appropriate for genetic testing at our facility. These data are applicable to other familial clinics provided that variations in sample populations are taken into consideration.</p

Selecting a BRCA risk assessment model for use in a familial cancer clinic

Abstract Background Risk models are used to calculate the likelihood of carrying a BRCA1 or BRCA2 mutation. We evaluated the performances of currently-used risk models among patients from a large familial program using the criteria of high sensitivity, simple data collection and entry and BRCA score reporting. Methods Risk calculations were performed by applying the BRCAPRO, Manchester, Penn II, Myriad II, FHAT, IBIS and BOADICEA models to 200 non-BRCA carriers and 100 BRCA carriers, consecutively tested between August 1995 and March 2006. Areas under the receiver operating characteristic curves (AUCs) were determined and sensitivity and specificity were calculated at the conventional testing thresholds. In addition, subset analyses were performed for low and high risk probands. Results The BRCAPRO, Penn II, Myriad II, FHAT and BOADICEA models all have similar AUCs of approximately 0.75 for BRCA status. The Manchester and IBIS models have lower AUCs (0. and 0.47 respectively). At the conventional testing thresholds, the sensitivities and specificities for a BRCA mutation were, respectively, as follows: BRCAPRO (0.75, 0.62), Manchester (0.58,0.71), Penn II (0.93,0.31), Myriad II (0.71,0.63), FHAT (0.70,0.63), IBIS (0.20,0.74), BOADICEA (0.70, 0.65). Conclusion The Penn II model most closely met the criteria we established and this supports the use of this model for identifying individuals appropriate for genetic testing at our facility. These data are applicable to other familial clinics provided that variations in sample populations are taken into consideration

Preferences for Active and Aggressive Intervention among Patients with Advanced Cancer

BACKGROUND: Intrinsic to Patient-Centered Care is being respectful and responsive to individual patient preferences, expressed needs, and personal values. Establishing a patient\u27s preferences for active and aggressive intervention is imperative and foundational to the development of advance care planning. With the increasing awareness and acceptance of palliative philosophies of care, patients with advanced cancer are increasingly transitioning from active and aggressive medical management (AAMM) to conservative palliative management (CPM). METHODS: A cross-sectional study based on a prospective and sequential case series of patients referred to a regional palliative medicine consultative program was assembled between May 1, 2005 and June 30, 2006. Patients and/or their substitute decision makers (SDM) completed a questionnaire, at baseline, that assessed their preferences for AAMM en route to their eventual deaths. Seven common interventions constituting AAMM were surveyed: cardiopulmonary resuscitation (CPR) & mechanical ventilation (MV), chemotherapy, antibiotics, anticoagulants, blood transfusions, feeding tubes, and artificial hydration. Multivariable analyses were conducted on the seven interventions individually as well as on the composite score that summed preferences for the seven interventions. RESULTS: 380 patients with advanced cancer agreed to participate in the study. A trend to desire a mostly conservative palliative approach was noted as 42% of patients desired one or fewer interventions. At baseline, most patients and their SDM\u27s were relatively secure about decisions pertaining to the seven interventions as the rates of being undecided ranged from a high of 23.4% for chemotherapy to a low of 3.9% for feeding tubes. Multivariable modeling showed that more AAMM was preferred by younger patients (P \u3c 0.0001), non-Caucasians (P = 0.042), patients with higher baseline Palliative Performance Scale scores (P = 0.0002) and where a SDM was involved in the decision process (p = 0.027). Non-statistically significant trends to prefer more AAMM was observed with male gender (p = 0.077) and higher levels of the Charlson Comorbidity index (p = 0.059). There was no association between treatment preferences and cancer class. CONCLUSIONS: Although the majority of patients with advanced cancer in this study expressed preferences for CPM, younger age, higher baseline PPSv2, and involvement of SDMs in the decision process were significantly associated with preferences for AAMM