A newly developed risk score for patients who have fasting lipid panel testing can help healthcare providers identify those eligible for statins (Clinical Chemistry 2022; doi: doi.org/10.1093/clinchem/hvac120).

Guidelines recommend atherosclerotic cardiovascular disease (ASCVD) calculations via pooled cohort equations (PCE) for calculating a 10-year ASCVD risk and statin eligibility for most patients ages 40 to 75. However, only about a third of eligible patients receive appropriate treatment, and roughly half of patients prescribed statins inappropriately discontinue them. In response, researchers developed an alternative risk equation that allows for the automated calculation of ASCVD risk by clinical laboratories that process fasting standard lipid panels.

The researchers developed estimated ASCVD (eASCVD) risk score equations using concentrations of total cholesterol, high-density lipoprotein cholesterol, triglycerides, and age as variables for nonhispanic white men, Black men, nonhispanic white women, and Black women. The researchers derived eASCVD scores using regression analysis to yield similar risk estimates as the standard ASCVD risk equations for 6,027 nondiabetic National Health and Nutrition Examination Survey subjects who were not on lipid-lowering therapy.

At a cutpoint of 7.5% for the whole population over 10 years, the eASCVD risk score had an overall sensitivity of 69.1% and a specificity of 97.5% for identifying statin-eligible patients with at least intermediate risk, based on the standard risk score.

Using the sum of other available risk factors, including systolic blood pressure more than130 mmHg and use of blood pressure medication and cigarettes, the eASCVD score’s overall sensitivity was 93.7%, and its specificity was 92.3%. The eASCVD score showed 90% concordance with the standard risk score in predicting cardiovascular events among 14,742 Atherosclerosis Risk in Communities (ARIC) study subjects.

Calculating eASCVD score involves no extra expense, the authors. It can help identify patients for whom a more careful consideration of nonlipid risk factors is warranted. When used with the PCE, the eASCVD risk score can help healthcare providers who do not specialize in lipid management increase compliance with statin therapy guidelines, the researchers noted.

Albuminuria Levels Useful for Managing Newly Diagnosed Kidney Disease

Recent research shows that albuminuria levels are inversely associated with a favorable chronic kidney disease (CKD) course, also known as regression (JAMA Netw Open 2022; doi: 10.1001/jamanetworkopen. 2022.25821).

Chronic kidney disease (CKD) patients are risk-stratified for adverse events based on estimated glomerular filtration rate (eGFR) and albuminuria level, but CKD often has a favorable course regardless of eGFR. The researchers aimed to determine whether lower albuminuria is associated with CKD.

The researchers assessed the 5-year probability of CKD regression across albuminuria categories accounting for the competing risks of CKD progression and death in people with newly diagnosed CKD and the association between albuminuria level and CKD regression. They analyzed administrative and laboratory data for adults with incident moderate to severe CKD in Alberta, Canada, using sustained eGFR of 15−44 mL/min/1.73 m2 for more 90 days as a definition for moderate to severe disease.

They also created categories by albumin to creatinine ratios (ACRs). Group A1 had ACR of more 3 mg/mmol), group A2 had ACR of 3−29 mg/mmol, group A3<60 had ACR of 30−59 mg/mmol), and group A3≥60 had ACR of 60 mg/mmol or higher.

In 58,004 subjects with moderate to severe CKD, albuminuria level was directly associated with CKD progression and death, and inversely associated with sustained improvement of eGFR for 90 days or longer. Five-year probabilities of CKD regression were higher in people with lower urine albumin-creatinine ratios in a stepwise fashion.

Sixty-one percent of subjects fell into group A1, 27% were in A2, 3% in A3<60, and 10% in A3≥60 albuminuria. Five-year probability of regression was highest for group A1 at 22.6%, followed by A2 at 16.5%, and A3<60 at 11.6%. Five-year probability of regression was lowest in A3≥60 at 5.3%.

Using A1 albuminuria as the reference group, the hazard of regression was highest for A2 (0.75; 95% CI, 0.72-0.79) and A3<60 (HR, 0.47; 95% CI, 0.40-0.54), and lowest for A3≥60 (HR, 0.27; 95% CI, 0.24-0.30).

These findings suggest that albuminuria can play a key prognostic role and inform CKD management, the researchers wrote.

Biomarkers Predict TBI Outcomes

Measuring two blood biomarkers the day of a traumatic brain injury (TBI) can predict which patients are likely to die or become severely disabled. These measurements aid clinicians’ treatment decisions. (Lancet Neuol 2022; doi: 10.1016/S1474-4422(22)00256-3).

Known for their diagnostic value, day-of-injury glial fibrillary acidic protein (GFAP) and ubiquitin C-terminal hydrolase L1 (UCH-L1) plasma concentrations also have good to excellent prognostic value for predicting death and unfavorable outcome, but not incomplete recovery at 6 months. The researchers found that these biomarkers contribute the most prognostic information for participants presenting with Glasgow Coma Scale (GCS) scores of 3–12.

To quantify biomarkers’ prognostic accuracy and investigate whether they contribute new prognostic information to existing clinical models, the researchers studied 1,696 patients aged 17–90 years. Patients had day-of-injury plasma samples for measurement of GFAP and UCH-L1 and completed 6-month assessments for outcome due to traumatic brain injury with the Glasgow Outcome Scale–Extended (GOSE-TBI). The researchers analyzed biomarkers as continuous variables and in quintiles.

Of the 1,696 participants with complete information, 7.1% died, 13.9% had an unfavorable outcome, 66.9% had incomplete recovery, and 33.1% recovered fully. GFAP had an area under the curve (AUC) of 0.87 (95% CI 0.83–0.91) for predicting death at 6 months in all patients, 0.86 (0.83–0.89) for unfavorable outcome, and 0.62 (0.59–0.64) AUC for incomplete recovery was 0.62 (0.59–0.64).

UCH-L1 had AUC of 0.89 (95% CI 0.86–0.92) for predicting death, 0.86 (0.84–0.89) for unfavorable outcomes, and 0.61 (0.59–0.64) for incomplete recovery at 6 months.

Participants with GCS scores of 3–12 had higher AUCs than for those with GCS score of 13–15. Among participants with GCS score of 3–12, adding GFAP and UCH-L1 (alone or combined) to three existing TBI models significantly increased their AUCs for predicting death to from 0.90–0.94 and for unfavorable outcomes to AUC range 0.83–0.89. However, among the 1,297 participants with GCS score of 13–15, adding GFAP and UCH-L1 to an observational cohort study model only modestly increased the AUC for predicting incomplete recovery management increase compliance with statin therapy guidelines, the researchers noted.