Noninvasive hematocrit assessment for cardiovascular magnetic resonance extracellular volume quantification using a point-of-care device and synthetic derivation

Abstract Background Calculation of cardiovascular magnetic resonance (CMR) extracellular volume (ECV) requires input of hematocrit, which may not be readily available. The purpose of this study was to evaluate the diagnostic accuracy of ECV calculated using various noninvasive measures of hematocrit compared to ECV calculated with input of laboratory hematocrit as the reference standard. Methods One hundred twenty three subjects (47.7 ± 14.1 years; 42% male) were prospectively recruited for CMR T1 mapping between August 2016 and April 2017. Laboratory hematocrit was assessed by venipuncture. Noninvasive hematocrit was assessed with a point-of-care (POC) device (Pronto-7® Pulse CO-Oximeter®, Masimo Personal Health, Irvine, California, USA) and by synthetic derivation based on the relationship with blood pool T1 values. Left ventricular ECV was calculated with input of laboratory hematocrit (Lab-ECV), POC hematocrit (POC-ECV), and synthetic hematocrit (synthetic-ECV), respectively. Statistical analysis included Wilcoxon signed-rank test, Bland-Altman analysis, receiver-operating curve analysis and intra-class correlation (ICC). Results There was no significant difference between Lab-ECV and POC-ECV (27.1 ± 4.7% vs. 27.3 ± 4.8%, p = 0.106), with minimal bias and modest precision (bias − 0.18%, 95%CI [− 2.85, 2.49]). There was no significant difference between Lab-ECV and synthetic-ECV (26.7 ± 4.4% vs. 26.5 ± 4.3%, p = 0.084) in subjects imaged at 1.5 T, although bias was slightly higher and limits of agreement were wider (bias 0.23%, 95%CI [− 2.82, 3.27]). For discrimination of abnormal Lab-ECV ≥30%, POC-ECV had good diagnostic performance (sensitivity 85%, specificity 96%, accuracy 94%, and AUC 0.902) and synthetic-ECV had moderate diagnostic performance (sensitivity 71%, specificity 98%, accuracy 93%, and AUC 0.849). POC-ECV had excellent test-retest (ICC 0.994, 95%CI[0.987, 0.997]) and inter-observer agreement (ICC 0.974, 95%CI[0.929, 0.991]). Conclusions Myocardial ECV can be accurately and reproducibly calculated with input of hematocrit measured using a noninvasive POC device, potentially overcoming an important barrier to implementation of ECV. Further evaluation of synthetic ECV is required prior to clinical implementation

Chronic lung allograft dysfunction phenotype and prognosis by machine learning CT analysis

Background Chronic lung allograft dysfunction (CLAD) is the principal cause of graft failure in lung transplant recipients and prognosis depends on CLAD phenotype. We used a machine learning computed tomography (CT) lung texture analysis tool at CLAD diagnosis for phenotyping and prognostication compared with radiologist scoring. Methods This retrospective study included all adult first double lung transplant patients (January 2010-December 2015) with CLAD (censored December 2019) and inspiratory CT near CLAD diagnosis. The machine learning tool quantified ground-glass opacity, reticulation, hyperlucent lung and pulmonary vessel volume (PVV). Two radiologists scored for ground-glass opacity, reticulation, consolidation, pleural effusion, air trapping and bronchiectasis. Receiver operating characteristic curve analysis was used to evaluate the diagnostic performance of machine learning and radiologist for CLAD phenotype. Multivariable Cox proportional hazards regression analysis for allograft survival controlled for age, sex, native lung disease, cytomegalovirus serostatus and CLAD phenotype. Results 88 patients were included (57 bronchiolitis obliterans syndrome (BOS), 20 restrictive allograft syndrome (RAS)/mixed and 11 unclassified/undefined) with CT a median 9.5 days from CLAD onset. Radiologist and machine learning parameters phenotyped RAS/mixed with PVV as the strongest indicator (area under the curve (AUC) 0.85). Machine learning hyperlucent lung phenotyped BOS using only inspiratory CT (AUC 0.76). Radiologist and machine learning parameters predicted graft failure in the multivariable analysis, best with PVV (hazard ratio 1.23, 95% CT 1.05-1.44; p=0.01). Conclusions Machine learning discriminated between CLAD phenotypes on CT. Both radiologist and machine learning scoring were associated with graft failure, independent of CLAD phenotype. PVV, unique to machine learning, was the strongest in phenotyping and prognostication

Adherence to clinical practice guidelines for pulmonary valve intervention after tetralogy of Fallot repair: A nationwide cohort studyCentral MessagePerspective

Objectives: To determine guideline adherence pertaining to pulmonary valve replacement (PVR) referral after tetralogy of Fallot (TOF) repair. Methods: Children and adults with cardiovascular magnetic resonance imaging scans and at least moderate pulmonary regurgitation were prospectively enrolled in the Comprehensive Outcomes Registry Late After TOF Repair (CORRELATE). Individuals with previous PVR were excluded. Patients were classified according to presence (+) versus absence (−) of PVR and presence (+) versus absence (−) of contemporaneous guideline satisfaction. A validated score (specific activity scale [SAS]) classified adult symptom status. Results: In total, 498 participants (57% male, mean age 32 ± 14 years) were enrolled from 14 Canadian centers (2013-2020). Mean follow-up was 3.8 ± 1.8 years. Guideline criteria for PVR were satisfied for the majority (n = 422/498, 85%), although referral for PVR occurred only in a minority (n = 167/498, 34%). At PVR referral, most were asymptomatic (75% in SAS class 1). One participant (0.6%) received PVR without meeting criteria (PVR+/indication–). The remainder (n = 75/498, 15%) did not meet criteria for and did not receive PVR (PVR–/indication–). Abnormal cardiovascular imaging was the most commonly cited indication for PVR (n = 61/123, 50%). The SAS class and ratio of right to left end-diastolic volumes were independent predictors of PVR in a multivariable analysis (hazard ratio, 3.33; 95% confidence interval, 1.92-5.8, P < .0001; hazard ratio, 2.78; 95% confidence interval, 2.18-3.55, P < .0001). Conclusions: Although a majority of patients met guideline criteria for PVR, only a minority were referred for intervention. Abnormal cardiovascular imaging was the most common indication for referral. Further research will be necessary to establish the longer-term clinical impact of varying PVR referral strategies