A Biomarker-Based Diagnostic Model for Cardiac Dysfunction in Childhood Cancer Survivors.

Background: Childhood cancer survivors at risk for heart failure undergo lifelong echocardiographic surveillance. Previous studies reported the limited diagnostic accuracy of N-terminal pro-B-type natriuretic peptide (NT-proBNP) and high-sensitivity cardiac troponin T (hs-cTnT) in detecting left ventricular (LV) dysfunction. However, potential enhanced diagnostic accuracy through the combination of biomarkers and clinical characteristics has been suggested. Objectives: The aim of this study was to develop and internally validate a diagnostic model that combines cardiac biomarkers with clinical characteristics for effectively ruling in or ruling out LV dysfunction in childhood cancer survivors. Methods: A multicenter cross-sectional study included 1,334 survivors (median age 34.2 years) and 278 siblings (median age 36.8 years). Logistic regression models were developed and validated through bootstrapping, combining biomarkers with clinical characteristics. Results: Abnormal NT-proBNP levels were observed in 22.1% of survivors compared with 5.4% of siblings, whereas hs-cTnT levels exceeding 10 ng/L were uncommon in both survivors (5.9%) and siblings (5.0%). The diagnostic models demonstrated improvement upon the addition of NT-proBNP and hs-cTnT to clinical characteristics, resulting in an increased C statistic from 0.69 to 0.73 for LV ejection fraction (LVEF) <50% and a more accurate prediction of more severe LV dysfunction, with the C statistic increasing from 0.80 to 0.86 for LVEF <45%. For LVEF <50% (prevalence 10.9%), 16.9% of survivors could be effectively ruled out with high sensitivity (95.4%; 95% CI: 90.4%-99.3%) and negative predictive value (97.5%; 95% CI: 94.6%-99.7%). Similarly, for LVEF <45% (prevalence 3.4%), 53.0% of survivors could be ruled out with moderate to high sensitivity (91.1%; 95% CI: 79.2%-100%) and high negative predictive value (99.4%; 95% CI: 98.7%-100%). Conclusions: The biomarker-based diagnostic model proves effective in ruling out LV dysfunction, offering the potential to minimize unnecessary surveillance echocardiography in childhood cancer survivors. External validation is essential to confirm these findings. (Early Detection of Cardiac Dysfunction in Childhood Cancer Survivors; A DCOG LATER Study; https://onderzoekmetmensen.nl/nl/trial/23641).

Presence and utility of electrocardiographic abnormalities in long-term childhood cancer survivors.

BACKGROUND: We assessed the prevalence and diagnostic value of ECG abnormalities for cardiomyopathy surveillance in childhood cancer survivors. METHODS: In this cross-sectional study, 1381 survivors (≥5 years) from the Dutch Childhood Cancer Survivor Study part 2 and 272 siblings underwent a long-term follow-up ECG and echocardiography. We compared ECG abnormality prevalences using the Minnesota Code between survivors and siblings, and within biplane left ventricular ejection fraction (LVEF) categories. Among 880 survivors who received anthracycline, mitoxantrone or heart radiotherapy, logistic regression models using least absolute shrinkage and selection operator identified ECG abnormalities associated with three abnormal LVEF categories (<52% in male/<54% in female, <50% and <45%). We assessed the overall contribution of these ECG abnormalities to clinical regression models predicting abnormal LVEF, assuming an absence of systolic dysfunction with a <1% threshold probability. RESULTS: 16% of survivors (52% female, mean age 34.7 years) and 14% of siblings had major ECG abnormalities. ECG abnormalities increased with decreasing LVEF. Integrating selected ECG data into the baseline model significantly improved prediction of sex-specific abnormal LVEF (c-statistic 0.66 vs 0.71), LVEF <50% (0.66 vs 0.76) and LVEF <45% (0.80 vs 0.86). While no survivor met the preset probability threshold in the first two models, the third model used five ECG variables to predict LVEF <45% and was applicable for ruling out (sensitivity 93%, specificity 56%, negative predictive value 99.6%). Calibration and internal validation tests performed well. CONCLUSION: A clinical prediction model with ECG data (left bundle branch block, left atrial enlargement, left heart axis, Cornell's criteria for left ventricular hypertrophy and heart rate) may aid in ruling out LVEF <45%.