Coronary CT angiography versus standard of care strategies to evaluate patients with potential coronary artery disease; effect on long term clinical outcomes.

BACKGROUND: Previous studies have shown that computed tomography coronary angiography (CTA) in patients with suspected coronary artery disease (CAD) predicts short term adverse events. However, there is no current data on whether identifying atherosclerosis on CTA impacts outcomes. We performed a case-control study to assess whether information from CTA can improve outcomes. METHODS: 4244 symptomatic patients (mean age 58 ± 9, 62.5% male) without known CAD who underwent CTA (n = 2538) to rule out CAD were matched to 1706 patients who underwent standard of care in an academic cardiology clinic. Patients were propensity-matched by gender, age, ethnicity, CAD risk factors and follow-up duration. The primary outcome measure was all-cause mortality. Multivariable Cox proportional hazards models incorporated age, gender and traditional risk factors for coronary disease as well as pre-test probability of CAD. RESULTS: There were no significant differences in age, gender, conventional risk factors between groups (p > 0.05). During a mean follow up of 80 ± 11 months, the overall death rate was 6.3% (270 deaths). Death rate was significantly lower in CTA group (n = 106, 4.2%) as compared to the control group (n = 184, 10.8%, p = 0.001). Event free survival was 95.8% and 89.2% in CTA and standard of care groups, respectively. Risk-adjusted hazard ratio of death were 2.5 (95%CI: 1.6-6.7, p = 0.003) in standard of care cohort as compared to CTA group. Multivariate analysis demonstrated that undergoing coronary CTA resulted in a risk reduction of 32%, p = 0.0001. CONCLUSIONS: Improved knowledge of atherosclerosis or increased anti-atherosclerotic therapies among those undergoing CTA may have contributed to improved survival. Our results provide evidence of potential benefit from scanning for atherosclerosis with CTA in symptomatic patients. Large randomized trials are warranted.

Validation of candidate serum ovarian cancer biomarkers for early detection.

OBJECTIVE: We have previously analyzed protein profiles using Surface Enhanced Laser Desorption and Ionization Time-Of-Flight Mass Spectroscopy (SELDI-TOF-MS) [Kozak et al. 2003, Proc. Natl. Acad. Sci. U.S.A. 100:12343-8] and identified 3 differentially expressed serum proteins for the diagnosis of ovarian cancer (OC) [Kozak et al. 2005, Proteomics, 5:4589-96], namely, apolipoprotein A-I (apoA-I), transthyretin (TTR) and transferin (TF). The objective of the present study is to determine the efficacy of the three OC biomarkers for the detection of early stage (ES) OC, in direct comparison to CA125. METHODS: The levels of CA125, apoA-I, TTR and TF were measured in 392 serum samples [82 women with normal ovaries (N), 24 women with benign ovarian tumors (B), 85 women with ovarian tumors of low malignant potential (LMP), 126 women with early stage ovarian cancer (ESOC), and 75 women with late stage ovarian cancer (LSOC)], obtained through the GOG and Cooperative Human Tissue Network. Following statistical analysis, multivariate regression models were built to evaluate the utility of the three OC markers in early detection. RESULTS: Multiple logistic regression models (MLRM) utilizing all biomarker values (CA125, TTR, TF and apoA-I) from all histological subtypes (serous, mucinous, and endometrioid adenocarcinoma) distinguished normal samples from LMP with 91% sensitivity (specificity 92%), and normal samples from ESOC with a sensitivity of 89% (specificity 92%). MLRM, utilizing values of all four markers from only the mucinous histological subtype showed that collectively, CA125, TTR, TF and apoA-I, were able to distinguish normal samples from mucinous LMP with 90% sensitivity, and further distinguished normal samples from early stage mucinous ovarian cancer with a sensitivity of 95%. In contrast, in serum samples from patients with mucinous tumors, CA125 alone was able to distinguish normal samples from LMP and early stage ovarian cancer with a sensitivity of only 46% and 47%, respectively. Furthermore, collectively, apoA-I, TTR and TF (excluding CA-125) distinguished i) normal samples from samples representing all histopathologic subtypes of LMP, with a sensitivity of 73%, ii) normal samples from ESOC with a sensitivity of 84% and iii) normal samples from LSOC with a sensitivity of 97%. More strikingly, the sensitivity in distinguishing normal versus mucinous ESOC, utilizing apoA-I, TF and TTR (CA-125 excluded), was 95% (specificity 86%; AUC 95%). CONCLUSIONS: These results suggest that the biomarker panel consisting of apoA-I, TTR and TF may significantly improve early detection of OC.

Evaluation of add-on testing in the clinical chemistry laboratory of a large academic medical center: operational considerations.

CONTEXT: Physicians frequently request that additional tests be performed on an existing specimen (add-ons). In our institution, add-ons comprise approximately 1% of the specimen volume and require a disproportionate number of employees. Not only are add-on tests time-consuming and expensive, but storing routine specimens for 7 days in anticipation of add-ons consumes valuable laboratory space. DESIGN: One hundred sixty add-on tests during a 1-week period were reviewed. OBJECTIVES: To analyze the pattern of add-on testing and determine methods to improve laboratory operations. RESULTS: All add-on tests were ordered within 24 hours of receipt of the original specimen, even though specimens were retained for 7 days. At our institution, 1.5 full-time equivalents are required to complete add-on testing, which accounts for less than 1% of the specimen volume. The most common add-on tests recorded during the study period were hepatic and electrolyte/renal/glucose panels. The medicine service ordered more than 60% of the add-on tests. Five percent of add-on tests were caused by a lack of order communication, 64.7% of cardiac marker add-ons were not ordered according to the chest pain protocol, and certain ordering patterns were present. CONCLUSIONS: Routine specimens do not need to be retained for 7 days to accommodate add-on tests. Decreasing the storage time to 2 days would save space, while still maintaining regulatory compliance. Order communication with the laboratory, educating physicians about chest pain protocols, and instituting admission laboratory panels would decrease the number of add-ons in our hospital. This change would translate into a reduction in laboratory expenses and an improvement in operations.