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1.
Heart Fail Clin ; 17(1): 1-8, 2021 Jan.
Article in English | MEDLINE | ID: mdl-33220878

ABSTRACT

Use of cardiac magnetic resonance (CMR) to aid in diagnosis, management, and prognosis of ischemic and nonischemic cardiomyopathy has advanced tremendously in the past several decades. These advances have expanded our understanding of both ischemic and nonischemic cardiomyopathies while also allowing for new avenues of diagnosis and treatment. This review summarizes key concepts of CMR technology and CMR use in the diagnosis and prognosis in ischemic, infiltrative, inflammatory, and other nonischemic cardiomyopathies and discusses the use of CMR in the patient presenting with ventricular arrhythmia with unclear diagnosis and advances in CMR in the management cardiomyopathy.


Subject(s)
Contrast Media/pharmacology , Heart Failure/diagnosis , Magnetic Resonance Imaging, Cine/methods , Magnetic Resonance Spectroscopy/methods , Humans , Prognosis
3.
Acute Card Care ; 18(1): 25-27, 2016 Mar.
Article in English | MEDLINE | ID: mdl-27754701
4.
Chest ; 141(1): 43-50, 2012 Jan.
Article in English | MEDLINE | ID: mdl-22030803

ABSTRACT

BACKGROUND: At the onset of acute hypoxic respiratory failure, critically ill patients with acute lung injury (ALI) may be difficult to distinguish from those with cardiogenic pulmonary edema (CPE). No single clinical parameter provides satisfying prediction. We hypothesized that a combination of those will facilitate early differential diagnosis. METHODS: In a population-based retrospective development cohort, validated electronic surveillance identified critically ill adult patients with acute pulmonary edema. Recursive partitioning and logistic regression were used to develop a decision support tool based on routine clinical information to differentiate ALI from CPE. Performance of the score was validated in an independent cohort of referral patients. Blinded post hoc expert review served as gold standard. RESULTS: Of 332 patients in a development cohort, expert reviewers (κ, 0.86) classified 156 as having ALI and 176 as having CPE. The validation cohort had 161 patients (ALI = 113, CPE = 48). The score was based on risk factors for ALI and CPE, age, alcohol abuse, chemotherapy, and peripheral oxygen saturation/Fio(2) ratio. It demonstrated good discrimination (area under curve [AUC] = 0.81; 95% CI, 0.77-0.86) and calibration (Hosmer-Lemeshow [HL] P = .16). Similar performance was obtained in the validation cohort (AUC = 0.80; 95% CI, 0.72-0.88; HL P = .13). CONCLUSIONS: A simple decision support tool accurately classifies acute pulmonary edema, reserving advanced testing for a subset of patients in whom satisfying prediction cannot be made. This novel tool may facilitate early inclusion of patients with ALI and CPE into research studies as well as improve and rationalize clinical management and resource use.


Subject(s)
Acute Lung Injury/diagnosis , Critical Illness , Decision Support Systems, Clinical/statistics & numerical data , Decision Support Techniques , Early Diagnosis , Pulmonary Edema/diagnosis , Shock, Cardiogenic/complications , Acute Lung Injury/etiology , Aged , Aged, 80 and over , Diagnosis, Differential , Female , Follow-Up Studies , Humans , Male , Middle Aged , Pulmonary Edema/etiology , Reproducibility of Results , Retrospective Studies , Risk Factors , Shock, Cardiogenic/diagnosis
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