Jaffe, Allan S.

Introduction: Biomarkers can facilitate the prediction of events in primary prevention. Few studies have been conducted in the community setting with analyses that incorporate many biomarkers. Hypothesis: We tested whether a multi biomarker model would outperform clinical factors and individual biomarkers for predicting MACE and Stroke/MI in subjects without known CAD. Methods: A retrospective cohort study assessed composite outcomes MACE (MI, CABG, PCI, stroke, and death) and Stroke/MI. Cox proportional hazard models were fit for individual biomarkers, adjusted for age, gender, BMI, hypertension, CAD, and GFR. Multi biomarker models were developed based on AIC values from the single-biomarker models. Bootstrap tests compared survival C-statistics and relative Integrated Discrimination Index (IDI). Results: 1131 patients in Olmsted County, MN were studied. Median age was 63.1 years, 97.5% were Caucasian, and 52.2% were female. Median BMI was 27.8. Kaplan Meier rates of MACE and Stroke/MI were 29.2% (CI = 26.5-31.8%) and 12.3% (CI = 10.3% - 14.3%), at 10 years. Adjusted NT proBNP models showed a 31% increased risk of MACE/death (HR = 1.31; CI = 1.17-1.46) and a 31% increased risk for stroke/MI (HR = 1.31; CI = 1.08-1.58). Adjusted Ceramide score models showed a 13% increased risk of MACE/death (HR = 1.13; CI = 1.04-1.24) and a 29% increased risk for stroke/MI (HR = 1.29; CI = 1.11-1.51). NT proBNP and the Ceramide score with clinical factors was better than clinical factors alone for MACE/Death (Relative IDI = 29.7%; p = 0.003) and Stroke/MI (Relative IDI = 36.1%; p = 0.034). This combined model did not outperform the strongest single biomarker model for Stroke/MI or MACE/Death. Other biomarkers were not significant when added to the combined model. Discussion: Ceramide score and NT proBNP alone and together improve the prediction of MACE and Stroke/MI in a community primary prevention cohort.

Abstract Background Artificial intelligence (AI) promises to become an important tool in the practice of laboratory medicine. AI programs are available online that can provide concise medical and laboratory information within seconds after a question is submitted. Methods We posed the following contemporary and relevant question for clinical chemists using an AI program: “Should labs eliminate CK-MB testing?” The results of this inquiry were critically reviewed by a cardiologist, emergency department physician, and clinical laboratorians for accuracy and appropriateness. Results An AI report answering this question was generated within 15 s. The experts felt that the information was inaccurate with the statements that CK-MB results are released earlier than cardiac troponin (cTn), and are more useful for detection of myocardial injury in patients with renal insufficiencies. The summary omitted discussion on the ongoing debate for periprocedural detection of acute myocardial infarction (MI), the perceived value for detection of reinfarction, the value in risk stratification compared to cTn, the economic justification for testing both CK-MB and cTn, and medicolegal aspects of testing when results are discordant. Conclusions At this time, AI does not appear to be ready to be used by clinical laboratories for answering important practice questions.

Abstract Background Transthyretin amyloid cardiomyopathy (ATTR-CM) is an increasingly recognized cause of heart failure (1,2). 99mTc- pyrophosphate cardiac scintigraphy (PYP) enables accurate, noninvasive diagnosis of ATTR-CM when coupled with appropriate clinical and laboratory evaluations to rule out light chain amyloidosis (3,4). High sensitivity cardiac troponin T (hs-cTnT) is routinely assessed for diagnostic purposes when ATTR-CM is suspected in clinical practice. Purpose To investigate the diagnostic performance of hs-cTnT in patients undergoing PYP for suspected ATTR-CM. Methods Observational study of patients undergoing PYP at a United States (US) cardiac amyloidosis referral center from 2013 to September 2022 and with at least one hs-cTnT value available within 1 year. 3-hour planar imaging followed by single photon-emission computed tomography with computed tomography was performed. Final diagnoses of ATTR-CM were validated using all clinical information. The lowest reportable clinical value for hs-cTnT in the US is <6 ng/L (limit of quantitation). Results A total of 1572 patients underwent PYP and had a hs-cTnT value available within the study period. Of these, ATTR-CM was diagnosed in 475 patients (30%). Median (IQR) hs-cTnT was 48 (31-67) ng/L in patients with ATTR-CM and 26 (13-48) ng/L in those without. The area under the curve of hs-cTnT for the diagnosis of ATTR-CM was 0.69 (0.67-0.72). Among patients with a very low hs-cTnT value, i.e. <6 ng/L (n=100, 6.4% of the overall cohort), 7 (7%) patients had a final ATTR-CM diagnosis while 93 (93%) did not. Therefore, a threshold of <6 ng/L demonstrated a negative predictive value (NPV) of 93% (IQR 88, 98) and a sensitivity of 99% (97, 100) for ruling out ATTR-CM. The highest NPV was achieved with a threshold of 12 ng/L; 269 (17%) patients had hs-cTnT below this value and, of those, only 16 (6%) had a final diagnosis of ATTR-CM. This resulted in a NPV of 94% (91, 97) and a sensitivity of 97% (95, 98) for ruling out ATTR-CM. When considering progressively increasing hs-cTnT values as potential thresholds to aid in the prediction of ATTR-CM (Figure), we observed a progressive increase in specificity (above 90% for hs-cTnT values > 95 ng/L), but the positive predictive value (PPV) remained low (below 50%). Conclusions Among patients undergoing PYP for suspected ATTR-CM at a US referral center, a very low hs-cTnT value was present in a minority of patients but demonstrated good performance in ruling out ATTR-CM. The highest NPV (94%) was achieved with a threshold of < 12 ng/L. Higher hs-cTnT values showed a good specificity, but PPV remained low. Therefore, while a very low hs-cTnT might be of help in identifying those at low risk of ATTR-CM, the ubiquitous presence of increased hs-cTnT in many of these patients suggests the need for a multiparametric diagnostic approach to make the diagnosis.hs-cTnT in patients undergoing PYP scan

Abstract Background There are no robust data addressing the United States (US) experience with the 0/2 hours (h) European Society of Cardiology (ESC) algorithm using high-sensitivity cardiac troponin T (hs-cTnT). Purpose To evaluate the diagnostic performance of the 0/2h ESC-recommended absolute deltas in a US population. Methods Observational US cohort study of ED patients undergoing 0 and 2 h hs-cTnT measurements. The 99th percentiles upper reference limits (URL) used were 10 ng/L for women and 15 ng/L for men. Diagnostic outcomes were the adjudicated diagnoses of acute myocardial infarction (MI, type 1 or 2). Results A total of 1084 patients met inclusion criteria, amongst which 49% were women. Chest discomfort was reported in 521 (48%) patients. Among patients with a baseline hs-cTnT≤99th URL (n=434), 4 MIs occurred. To rule-out MI, an absolute 0-2h delta change <4 ng/L resulted in a sensitivity of 100% (95% CI: 39.8, 100) and a negative predictive value (NPV) of 100% (95% CI: 99.1, 100). To rule-in MI, an absolute 0-2h delta ≥10 ng/L had a specificity of 99.8 % (95% CI: 98.7, 99.9) and positive predictive value (PPV) of 75% (95% CI: 19.4, 99.4). Among patients with a baseline hs-cTnT >99th URL (n=650), 110 MIs occurred. In these patients, both rule-out (sensitivity 65%, NPV 91%) and rule-in (specificity 7.2%, PPV 11%) were poor using the ESC-recommended absolute deltas. Among patients with modest hs-cTnT increases (99th URL to 100 ng/L) neither the ESC-recommended absolute nor the Fourth Universal Definition of MI (UDMI)-recommended relative deltas provided acceptable performance. Among patients with marked hs-cTnT increases at presentation (>100 ng/L), a relative delta change >20% as recommended by the UDMI resulted in a superior rule-in performance, with a specificity of 92.0% (95% CI 74.0, 99.0) and PPV of 84.6% (57.4, 95.7). Conclusions In our unselected US-cohort of patients undergoing hs-cTnT, the 0/2h ESC algorithm had a good diagnostic performance in patients without myocardial injury at presentation. Conversely, in patients with myocardial injury at baseline, the ESC-recommended absolute deltas performed poorly to determine who had MI. In patients with marked hs-cTnT increases >100 ng/L at presentation, the UDMI recommended 20% relative change showed good rule-in performance.

Abstract Background Guidelines suggest that the diagnosis of acute myocardial infarction (MI) is likely when high concentrations of high-sensitivity cardiac troponin (hs-cTn) are present in the initial sample. These concentration thresholds are much higher than the 99th percentile and are said to provide a high positive predictive value (PPV) for acute MI1. Purpose Evaluate the PPV of recommended baseline hs-cTnT thresholds (>52 and >100 ng/L) for the diagnosis of acute MI from our multi-center United States (US) experience. Methods Retrospective, multicenter (n=22), observational biomarker study involving consecutive, adult patients presenting to the emergency department (ED) in whom at least 1 hs-cTnT measurement was obtained within 12 hours of presentation (CV DataMart Biomarker cohort). The primary endpoint was also evaluated in an adjudicated cohort of consecutive patients presenting to the ED with at least 1 hs-cTnT >99th percentile (ACTION cohort). These cases were adjudicated based on the Fourth Universal Definition of MI by trained physicians. The primary efficacy endpoint was the PPV of acute MI (type 1 or 2), type 1 MI and type 2 MI during index hospitalization using baseline hs-cTnT thresholds of >52 ng/L and >100 ng/L. Results The CV DataMart Biomarker cohort consisted of 143,709 patients, amongst whom 3,003 (2.1%) were diagnosed with acute MI using ICD codes. In the CV DataMart Biomarker cohort, baseline hs-cTnT concentrations of >52 ng/L and >100 ng/L resulted in PPVs of 12% (95% CI: 11, 12) and 17% (95% CI: 17, 19) for acute MI, respectively. In patients with chest pain and baseline hs-cTnT concentrations >52 ng/L, PPV for acute MI was 17% (95% CI: 15, 18), and in those with hs-cTnT concentrations >100 ng/L, PPV was 22% (95% CI: 19, 25). The adjudicated ACTION cohort consisted of 2,107 patients, amongst whom 155 (7.4%) were diagnosed with acute MI including 64 (41%) with type 1 MI and 91 (59%) with type 2 MI. In the ACTION cohort, baseline hs-cTnT concentrations of >52 ng/L and >100 ng/L resulted in PPVs of 46% (95% CI: 40, 53) and 62% (95% CI: 53, 70) for acute MI, respectively. In patients with chest pain and baseline hs-cTnT concentrations >52 ng/L, PPV was 67% (95% CI: 57, 76) for acute MI, 52% (95% CI: 41, 62) for type 1 MI and 15% (95% CI: 8.4, 24) for type 2 MI. In those with chest pain and baseline hs-cTnT concentrations >100 ng/L, PPV was 77% (95% CI: 65, 87) for acute MI, 64% (95% CI: 51, 75) for type 1 MI and 14% (95% CI 6.4, 24) for type 2 MI (Figure). Conclusion Using previously recommended high-risk hs-cTnT thresholds of 52 and 100 ng/L in US patients provides reasonable performance to rule-in acute MI when used in patients with chest pain. When used more broadly across all-comers, the diagnostic performance is poor.

Abstract Background Analytical outliers (fliers) occur with most troponin methods and can adversely affect patient management. We compared the analytical outlier rates for the Roche 5th generation Troponin T STAT (cTnT) reformulated (biotin-insensitive) reagent using a Cobas e601 analyzer (Indianapolis, IN) to rates observed in a previous study using the biotin-sensitive reagent on a Cobas e411 analyzer. Methods Plasma separator tubes (PST) were collected from hospital patients with orders for clinical cTnT testing. PST samples were centrifuged for 3 min at 4000g and analyzed using the biotin-insensitive cTnT Gen 5 assay on a Roche Cobas e601 immunoassay system. If samples displayed measurable cTnT Gen 5 (≥6 ng/L), samples were stored at 2–8 °C within 2 h of initial measurement (N = 2555). Within 24 h of initial analysis, samples were warmed to room temperature, aliquoted, re-centrifuged at 4000g for 3 min, and cTNT was re-measured on the same analyzer. Outlier rates were compared to a previous study (N = 1997) using nearly identical study design but using biotin-sensitive Roche 5th Gen cTnT reagent on an e411 analyzer. We defined analytical outliers by three methods: Repeat value (after aliquoting and re-centrifugation) that differed from the initial value for all samples with measurable cTnT by a critical difference (CD): where CD = z × √2 × SDanalytical, with a z-value of 3.719 and using SD from best fit line of precision vs cTnT concentration. Precision experiments were performed using four plasma pools in 5 × 5 precision experiment on the e411 analyzer. Using the same CD but considering only initial cTnT values <100 ng/L, those are most likely to be clinically significant or lead to misinterpretation. Using CD for initial values <100 ng/L, and a fixed 10% difference between replicates for initial values ≥100 ng/L, accounting for our practice of reporting the cTnT “delta” in percent (rather than absolute) for values ≥100 ng/L. Comparisons of outlier rates between the biotin-sensitive and biotin-insensitive reagents were performed using Pearson’s chi-squared test. Results Using the CD the total number of outliers that were observed for the biotin-insensitive reagent on the e601 was 208/2555 (8.1%), compared to 68/1997 (3.4%) on the biotin-sensitive reagent measured on the e411 (P < 0.0001). Outliers were observed in 138 (6.4%) of samples with initial values < 100 ng/L using the biotin-insensitive reagent on the e601 compared to 45 (2.6%) of such samples on the e411 (P < 0.0001). Using the Z score method to define outliers for initial values <100 ng/L and a fixed 10% to define outliers for initial values ≥100 ng/L, the outlier rate was 151 (5.9%) for the biotin-insensitive reagent on the e601 compared to 53 (2.7%) on the e411 (P < 0.0001). Conclusions Analytical outliers occurred more frequently using the reformulated (biotin-insensitive) reagent on the Roche Cobas e601 analyzer, compared to outlier rates observed for biotin-sensitive reagent on the Cobas e411.

Abstract Background Low-density lipoprotein cholesterol (LDL-C) is the primary biomarker for assessing risk for atherosclerotic cardiovascular disease (ASCVD). However, multiple large studies have demonstrated greater accuracy using Apolipoprotein B (apoB). One obstacle to its widespread use is the lack of guideline endorsed apoB treatment targets. A recently published solution has proposed using population percentiles to calculate LDL-C using apoB values (PMID: 36366949). The LDL-C concentration correlated to an individual’s ApoB percentile may more appropriately reflect ASCVD risk compared to lipid panel LDL-C. Here we evaluated this approach using a cohort where LDL-C was measured with the gold-standard beta-quantification method (LDL-CBQ). Method Results from clinically ordered apoB, LDL-CBQ, and lipid panel were included (n = 18 852). Samples with triglycerides >1000 mg/dL or LpX were excluded. Total cholesterol, high-density lipoprotein cholesterol (HDL-C), triglycerides (TG), and apoB were measured using Roche Cobas c501 analyzers. LDL-CBQ was performed using Beckman LE-80K ultracentrifuge and dextran sulfate-Mg/Cl precipitation. ApoB equivalent LDL-C (eqLDL-CapoB) was calculated according to linear regression of LDL-C percentiles against apoB percentiles described by the equation eqLDL-CapoB = 1.45(apoB)-25. Concordance was determined based on clinical thresholds for LDL-CBQ and eqLDL-CapoB. Results Median (IQR) apoB and LDL-CBQ was 98 (79−119) mg/dL and 120 (94−150) mg/dL, respectively. LDL-CBQ and eqLDL-CapoB were concordant in 60% of samples overall. Discordance between eqLDL-CapoB and LDL-CBQ shifted from primarily overestimating to underestimating as LDL-CBQ increased. Discordant eqLDL-CapoB was directly associated with TG and inversely associated with HDL-C. Conclusion The eqLDL-CapoB calculation is a useful tool to present apoB results in a more familiar context of LDL-C-equivalent units. The concordance findings largely confirm previously published results using eqLDL-CapoB. Furthermore, the pattern of HDL-C and TG suggests that a discordantly elevated eqLDL-CapoB is an indicator of a higher risk lipid profile. More investigation is needed to determine if eqLDL-CapoB is appropriate for widespread adoption.

Abstract Background There is growing evidence that low-density lipoprotein-triglyceride (LDL-TG) may be a better biomarker to define atherosclerotic cardiovascular disease (ASCVD) risk than LDL-cholesterol (LDL-C), which is currently used to stratify risk and as a treatment goal. Because LDL-TG typically requires additional advanced lipid testing, we developed an equation for estimating LDL-TG (eLDL-TG) from the results of the standard lipid panel. Methods Results from a general dyslipidemic population (N = 40 202), which were obtained with beta-quantification (BQ) reference method, were randomly divided into a training and validation datasets and used to develop the equation for estimating LDL-TG shown in Fig. 1A. Association of Sampson-NIH-LDL-C (NIH-LDL-C) and eLDL-TG with ASCVD risk markers was done in the National Heart and Nutrition Examination Survey (NHANES) (N = 37 053) and with ASCVD events in the Atherosclerosis Risk in Communities (ARIC) study (N = 14 749). Results The comparison between eLDL-TG and measured LDL-TG was only modest (R2 = 0.601, slope = 0.611), but eLDL-TG (Fig. 1B) showed better risk stratification than NIH-LDL-C (Fig. 1C) by survival curve analysis in ARIC. Moreover, receiver-operating characteristic (ROC) analysis showed that eLDL-TG was better than all other lipid-marker tests for ASCVD events (AUC: eLDL-TG = 0.633, HDL-C = 0.627, sdLDL-C = 0.621, remnant-cholesterol = 0.613, NonHDL-C = 0.613, TG = 0.603, apoB = 0.598, NIH-LDL-C = 0.590, TC = 0.568). In NHANES, eLDL-TG was more strongly associated with hypertriglyceridemia, obesity, diabetes, metabolic syndrome and increased high-sensitivity C-reactive protein than NIH-LDL-C. Inclusion of eLDL-TG in a Cox-Proportional Hazards Model along with other conventional risk factors (age, sex, race, diabetes, systolic blood pressure, smoking, HDL-C, and total cholesterol) did not further improve risk prediction. Conclusions Like LDL-C, LDL-TG can also be estimated from the results of the standard lipid panel. It is a better univariate risk marker than LDL-C and hence can be useful for initial ASCVD risk stratification.