Citations

Purpose of review The success of LDL cholesterol (LDL-C) as a predictor of atherosclerotic cardiovascular disease and a therapeutic target is indisputable. Apolipoprotein B (apoB) is a more contemporary and physiologically relevant measure of atherogenic lipoproteins. This report summarizes recent comparisons of apoB and LDL-C as biomarkers of cardiovascular risk. Recent findings Multiple recent reports have found that LDL-C methods perform poorly at low concentrations (<70 mg/dl). Several meta-analyses from randomized controlled trials and large prospective observational studies have found that apoB and LDL-C provide equivalent information on risk of cardiovascular disease. More innovative analyses have asserted that apoB is a superior indicator of actual risk when apoB and LDL-C disagree. Summary ApoB is more analytically robust and standardized biomarker than LDL-C. Large population studies have found that apoB is at worst clinically equivalent to LDL-C and likely superior when disagreement exists. Realistically, many obstacles prevent the wide spread adoption of apoB and for now providers and their patients must weigh the costs and benefits of apoB.

Abstract BACKGROUND We assessed the impact of clinical decision support (CDS) rules within the electronic health record for ionized calcium (iCa), serum magnesium (Mg), and N-terminal pro-B-type natriuretic peptide (NT-proBNP) in intensive care unit (ICU) inpatients at a large academic center. METHODS A repeat order for measurement of iCa or Mg placed within 24 (iCa) or 48 (Mg) h of a previously nonactionable result, or additional orders for NT-proBNP beyond 1 within a single hospitalization, triggered a CDS pop-up alert showing the prior result and offering the opportunity to cancel the order or to place the order after entering an indication for repeat testing. The number of tests performed for each of these analytes and incidence of adverse clinical outcomes potentially associated with hypocalcemia or hypomagnesemia were compared between the 90-day period before CDS implementation and two 90-day periods immediately following. RESULTS iCa test volumes decreased by 48%, Mg by 39%, and NT-proBNP by 28% in the 90-day period immediately following implementation and remained decreased by 54%, 49%, and 22%, respectively, during the following 90-day period (all P values &lt;0.0002). Adverse clinical outcomes potentially associated with hypocalcemia or hypomagnesemia did not increase (all P-values &gt;0.17). CONCLUSIONS Implementation of CDS dramatically decreased repeat testing of iCa, Mg, and NT-proBNP without adversely impacting clinical outcomes in the ICU. Expansion of the rules from the ICU units to include the entire hospitalized patient population and expansion to additional analytes is expected to lead to further reductions in testing.

BACKGROUNDThe objective of this study was to demonstrate that computer‐extracted image phenotypes (CEIPs) of biopsy‐proven breast cancer on magnetic resonance imaging (MRI) can accurately predict pathologic stage.METHODSThe authors used a data set of deidentified breast MRIs organized by the National Cancer Institute in The Cancer Imaging Archive. In total, 91 biopsy‐proven breast cancers were analyzed from patients who had information available on pathologic stage (stage I, n = 22; stage II, n = 58; stage III, n = 11) and surgically verified lymph node status (negative lymph nodes, n = 46; ≥ 1 positive lymph node, n = 44; no lymph nodes examined, n = 1). Tumors were characterized according to 1) radiologist‐measured size and 2) CEIP. Then, models were built that combined 2 CEIPs to predict tumor pathologic stage and lymph node involvement, and the models were evaluated in a leave‐1‐out, cross‐validation analysis with the area under the receiver operating characteristic curve (AUC) as the value of interest.RESULTSTumor size was the most powerful predictor of pathologic stage, but CEIPs that captured biologic behavior also emerged as predictive (eg, stage I and II vs stage III demonstrated an AUC of 0.83). No size measure was successful in the prediction of positive lymph nodes, but adding a CEIP that described tumor “homogeneity” significantly improved discrimination (AUC = 0.62; P = .003) compared with chance.CONCLUSIONSThe current results indicate that MRI phenotypes have promise for predicting breast cancer pathologic stage and lymph node status. Cancer 2016;122:748–757. © 2015 American Cancer Society.

Abstract The proliferation of cardiac-related biomarkers and advocacy for their use has often come without adequate discussion of limitations in the interpretation of values and their best use in heart failure (HF) patients to provide a balanced assessment of how cardiac biomarkers are advocated for use in HF and areas where we would argue there are no gaps in knowledge. We include suggestions to address these issues. We have focused on peer-reviewed publications over the period 2000 to present. Most studies have used samples at one or at most two points in time to define risk. Although biomarkers might add to the magnitude of risk, it is unclear how often they lead to changes in treatment. We suggest that defining the use of serial biomarker testing over time would be more helpful. To do this, it is necessary to take into account the biomarker's analytical and biological variability in addition to its ability to define and monitor therapy. These factors are often overlooked leading to conclusions that may be statistically significant but not clinically or analytically robust. An appreciation of the value and limitations of biomarker use is important to all clinicians who manage HF patients. If the proper studies are done so that biomarkers are used optimally, they will likely be helpful in defining when and how to intervene. If we continue as we have, we will continue to have ambiguity about the use of these valuable probes in the assessment and management of HF.