Development and validation of a patient-reported outcome measure to assess symptom burden after chimeric antigen receptor T-cell therapy

This cross-sectional study aimed to develop and validate a patient-reported outcomes (PROs) assessment tool to assess symptom burden and daily functioning in patients after chimeric antigen receptor (CAR) T-cell therapy, the MD Anderson Symptom Inventory (MDASI-CAR). The items were generated based on literature review, content elicitation interviews with patients, and clinician's review. The patients completed the MDASI core and module, single-item quality-of-life (QoL) measure and Patient-Reported Outcomes Measurement Information System-29 (PROMIS-29). The psychometric validation analysis was based on the acceptability after item reduction process. The final 10 MDASI-CAR module items included tremors, fever/chills, headache, balance, dizziness, attention, difficulty speaking, coughing, sexual dysfunction, and diarrhoea with high internal consistency (Cronbach's alpha: MDASI Core, 0.865; MDASI Interference, 0.915; CAR-T module, 0.746). The MDASI-CAR has excellent known-group validity that was demonstrated by differentiate patients based on patient's performance status (Cohen's d for MDASI core = −1.008, interference = −0.771, module = −0.835). Criterion validity was demonstrated by the significant correlations between the MDASI-CAR composite score, the single QoL item and the relevant domains on PROMIS-29 (all p < 0.05). This study established the MDASI-CAR module as a reliable and valid PRO tool for monitoring symptom burden after CAR T-cell therapy in patients with haematological malignancies. The findings need to be validated with a longitudinal design.     

The diagnostic role of T wave morphology biomarkers in congenital and acquired long QT syndrome: A systematic review

Introduction

QTc prolongation is key in diagnosing long QT syndrome (LQTS), however 25%–50% with congenital LQTS (cLQTS) demonstrate a normal resting QTc. T wave morphology (TWM) can distinguish cLQTS subtypes but its role in acquired LQTS (aLQTS) is unclear.

Methods

Electronic databases were searched using the terms “LQTS,” “long QT syndrome,” “QTc prolongation,” “prolonged QT,” and “T wave,” “T wave morphology,” “T wave pattern,” “T wave biomarkers.” Whole text articles assessing TWM, independent of QTc, were included.

Results

Seventeen studies met criteria. TWM measurements included T-wave amplitude, duration, magnitude, Tpeak-Tend, QTpeak, left and right slope, center of gravity (COG), sigmoidal and polynomial classifiers, repolarizing integral, morphology combination score (MCS) and principal component analysis (PCA); and vectorcardiographic biomarkers. cLQTS were distinguished from controls by sigmoidal and polynomial classifiers, MCS, QTpeak, Tpeak-Tend, left slope; and COG x axis. MCS detected aLQTS more significantly than QTc. Flatness, asymmetry and notching, J-Tpeak; and Tpeak-Tend correlated with QTc in aLQTS. Multichannel block in aLQTS was identified by early repolarization (ERD_30%) and late repolarization (LRD_30%), with ERD reflecting hERG-specific blockade. Cardiac events were predicted in cLQTS by T wave flatness, notching, and inversion in leads II and V₅, left slope in lead V₆; and COG last 25% in lead I. T wave right slope in lead I and T-roundness achieved this in aLQTS.

Conclusion

Numerous TWM biomarkers which supplement QTc assessment were identified. Their diagnostic capabilities include differentiation of genotypes, identification of concealed LQTS, differentiating aLQTS from cLQTS; and determining multichannel versus hERG channel blockade.