Deep Learning for Echo Analysis, Tracking, and Evaluation of Mitral Regurgitation (DELINEATE-MR).

BACKGROUND: Artificial intelligence, particularly deep learning (DL), has immense potential to improve the interpretation of transthoracic echocardiography (TTE). Mitral regurgitation (MR) is the most common valvular heart disease and presents unique challenges for DL, including the integration of multiple video-level assessments into a final study-level classification. METHODS: A novel DL system was developed to intake complete TTEs, identify color MR Doppler videos, and determine MR severity on a 4-step ordinal scale (none/trace, mild, moderate, and severe) using the reading cardiologist as a reference standard. This DL system was tested in internal and external test sets with performance assessed by agreement with the reading cardiologist, weighted κ, and area under the receiver-operating characteristic curve for binary classification of both moderate or greater and severe MR. In addition to the primary 4-step model, a 6-step MR assessment model was studied with the addition of the intermediate MR classes of mild-moderate and moderate-severe with performance assessed by both exact agreement and ±1 step agreement with the clinical MR interpretation. RESULTS: A total of 61 689 TTEs were split into train (n=43 811), validation (n=8891), and internal test (n=8987) sets with an additional external test set of 8208 TTEs. The model had high performance in MR classification in internal (exact accuracy, 82%; κ=0.84; area under the receiver-operating characteristic curve, 0.98 for moderate or greater MR) and external test sets (exact accuracy, 79%; κ=0.80; area under the receiver-operating characteristic curve, 0.98 for moderate or greater MR). Most (63% internal and 66% external) misclassification disagreements were between none/trace and mild MR. MR classification accuracy was slightly higher using multiple TTE views (accuracy, 82%) than with only apical 4-chamber views (accuracy, 80%). In subset analyses, the model was accurate in the classification of both primary and secondary MR with slightly lower performance in cases of eccentric MR. In the analysis of the 6-step classification system, the exact accuracy was 80% and 76% with a ±1 step agreement of 99% and 98% in the internal and external test set, respectively. CONCLUSIONS: This end-to-end DL system can intake entire echocardiogram studies to accurately classify MR severity and may be useful in helping clinicians refine MR assessments.

Deep learning to detect left ventricular structural abnormalities in chest X-rays.

BACKGROUND AND AIMS: Early identification of cardiac structural abnormalities indicative of heart failure is crucial to improving patient outcomes. Chest X-rays (CXRs) are routinely conducted on a broad population of patients, presenting an opportunity to build scalable screening tools for structural abnormalities indicative of Stage B or worse heart failure with deep learning methods. In this study, a model was developed to identify severe left ventricular hypertrophy (SLVH) and dilated left ventricle (DLV) using CXRs. METHODS: A total of 71 589 unique CXRs from 24 689 different patients completed within 1 year of echocardiograms were identified. Labels for SLVH, DLV, and a composite label indicating the presence of either were extracted from echocardiograms. A deep learning model was developed and evaluated using area under the receiver operating characteristic curve (AUROC). Performance was additionally validated on 8003 CXRs from an external site and compared against visual assessment by 15 board-certified radiologists. RESULTS: The model yielded an AUROC of 0.79 (0.76-0.81) for SLVH, 0.80 (0.77-0.84) for DLV, and 0.80 (0.78-0.83) for the composite label, with similar performance on an external data set. The model outperformed all 15 individual radiologists for predicting the composite label and achieved a sensitivity of 71% vs. 66% against the consensus vote across all radiologists at a fixed specificity of 73%. CONCLUSIONS: Deep learning analysis of CXRs can accurately detect the presence of certain structural abnormalities and may be useful in early identification of patients with LV hypertrophy and dilation. As a resource to promote further innovation, 71 589 CXRs with adjoining echocardiographic labels have been made publicly available.

Clinical and cardiac characteristics of COVID-19 mortalities in a diverse New York City Cohort.

INTRODUCTION: Electrocardiographic characteristics in COVID-19-related mortality have not yet been reported, particularly in racial/ethnic minorities. METHODS AND RESULTS: We reviewed demographics, laboratory and cardiac tests, medications, and cardiac rhythm proximate to death or initiation of comfort care for patients hospitalized with a positive SARS-CoV-2 reverse-transcriptase polymerase chain reaction in three New York City hospitals between March 1 and April 3, 2020 who died. We described clinical characteristics and compared factors contributing toward arrhythmic versus nonarrhythmic death. Of 1258 patients screened, 133 died and were enrolled. Of these, 55.6% (74/133) were male, 69.9% (93/133) were racial/ethnic minorities, and 88.0% (117/133) had cardiovascular disease. The last cardiac rhythm recorded was VT or fibrillation in 5.3% (7/133), pulseless electrical activity in 7.5% (10/133), unspecified bradycardia in 0.8% (1/133), and asystole in 26.3% (35/133). Most 74.4% (99/133) died receiving comfort measures only. The most common abnormalities on admission electrocardiogram included abnormal QRS axis (25.8%), atrial fibrillation/flutter (14.3%), atrial ectopy (12.0%), and right bundle branch block (11.9%). During hospitalization, an additional 17.6% developed atrial ectopy, 14.7% ventricular ectopy, 10.1% atrial fibrillation/flutter, and 7.8% a right ventricular abnormality. Arrhythmic death was confirmed or suspected in 8.3% (11/133) associated with age, coronary artery disease, asthma, vasopressor use, longer admission corrected QT interval, and left bundle branch block (LBBB). CONCLUSIONS: Conduction, rhythm, and electrocardiographic abnormalities were common during COVID-19-related hospitalization. Arrhythmic death was associated with age, coronary artery disease, asthma, longer admission corrected QT interval, LBBB, ventricular ectopy, and usage of vasopressors. Most died receiving comfort measures.

Cardiac Tumors: Clinical Presentation, Diagnosis, and Management.

OPINION STATEMENT: Cardiac masses and tumors are a heterogenous group of disorders and include primary tumors (both benign and malignant), metastatic disease, and numerous masquerades such as thrombus. Clinical presentation ranges from incidental discovery on imaging tests ordered for other reasons to life-threatening presentations such as cardiac tamponade, arrhythmia, obstruction, and systemic embolization. Of the available imaging modalities, cardiac MRI is generally the most useful for assessment and helps to delineate the relevant anatomy. Due to the technical difficulties and risk of biopsy of cardiac masses, a presumptive diagnosis is typically made using imaging techniques with surgery serving both a diagnostic and curative role. Because these conditions can vary widely in their management, we recommend early involvement of a multidisciplinary group which should include a cardiologist, cardiac surgeon, and oncologist.

A retrospective comparison of skin and renal direct immunofluorescence findings in patients with glomerulonephritis in adult Henoch-Schönlein purpura.

BACKGROUND: Henoch-Sch¨onlein purpura (HSP) is a vasculitis tha tcan affect the skin and kidneys. It is characterized by immunoglobulin(Ig) A-predominant deposition in small blood vessels. To our knowledge, there has been no comparison of direct immunofluorescence (DIF) findings in skin and kidney biopsy specimens. METHODS: We retrospectively studied 21 adults with HSP who had IgA deposition in the skin and kidneys. The skin and kidney DIF findings were compared and tested for an association with the progression of renal disease. RESULTS: Mean age of the patients was 51.4 years. Follow-up data were available for 19 patients, of whom 5 had progression to chronic kidney disease or renal failure. Concordance between DIF findings onskin and renal biopsies was 100% for IgA, 80% for C3, 80% for IgG,71% for IgM and 53% for fibrinogen. A worse renal outcome was associated with renal IgG deposition (p=0.04). A trend for worse renal outcome was found with renal fibrinogen and skin IgM deposition(p=0.10 and 0.14, respectively). CONCLUSIONS: In this retrospective study of adult HSP, theconcordance between DIF findings in skin and kidney specimens was low-moderate. Further study is required to elucidate the mechanisms responsible for these differences in Ig deposition.

Role of Artificial Intelligence in Improving Syncope Management.

Syncope is common in the general population and a common presenting symptom in acute care settings. Substantial costs are attributed to the care of patients with syncope. Current challenges include differentiating syncope from its mimickers, identifying serious underlying conditions that caused the syncope, and wide variations in current management. Although validated risk tools exist, especially for short-term prognosis, there is inconsistent application, and the current approach does not meet patient needs and expectations. Artificial intelligence (AI) techniques, such as machine learning methods including natural language processing, can potentially address the current challenges in syncope management. Preliminary evidence from published studies indicates that it is possible to accurately differentiate syncope from its mimickers and predict short-term prognosis and hospitalisation. More recently, AI analysis of electrocardiograms has shown promise in detection of serious structural and functional cardiac abnormalities, which has the potential to improve syncope care. Future AI studies have the potential to address current issues in syncope management. AI can automatically prognosticate risk in real time by accessing traditional and nontraditional data. However, steps to mitigate known problems such as generalisability, patient privacy, data protection, and liability will be needed. In the past AI has had limited impact due to underdeveloped analytical methods, lack of computing power, poor access to powerful computing systems, and availability of reliable high-quality data. All impediments except data have been solved. AI will live up to its promise to transform syncope care if the health care system can satisfy AI requirement of large scale, robust, accurate, and reliable data.

Simple models vs. deep learning in detecting low ejection fraction from the electrocardiogram.

Aims: Deep learning methods have recently gained success in detecting left ventricular systolic dysfunction (LVSD) from electrocardiogram (ECG) waveforms. Despite their high level of accuracy, they are difficult to interpret and deploy broadly in the clinical setting. In this study, we set out to determine whether simpler models based on standard ECG measurements could detect LVSD with similar accuracy to that of deep learning models. Methods and results: Using an observational data set of 40 994 matched 12-lead ECGs and transthoracic echocardiograms, we trained a range of models with increasing complexity to detect LVSD based on ECG waveforms and derived measurements. The training data were acquired from the Stanford University Medical Center. External validation data were acquired from the Columbia Medical Center and the UK Biobank. The Stanford data set consisted of 40 994 matched ECGs and echocardiograms, of which 9.72% had LVSD. A random forest model using 555 discrete, automated measurements achieved an area under the receiver operator characteristic curve (AUC) of 0.92 (0.91-0.93), similar to a deep learning waveform model with an AUC of 0.94 (0.93-0.94). A logistic regression model based on five measurements achieved high performance [AUC of 0.86 (0.85-0.87)], close to a deep learning model and better than N-terminal prohormone brain natriuretic peptide (NT-proBNP). Finally, we found that simpler models were more portable across sites, with experiments at two independent, external sites. Conclusion: Our study demonstrates the value of simple electrocardiographic models that perform nearly as well as deep learning models, while being much easier to implement and interpret.

Artificial Intelligence in Cardiovascular Care-Part 2: Applications: JACC Review Topic of the Week.

Recent artificial intelligence (AI) advancements in cardiovascular care offer potential enhancements in effective diagnosis, treatment, and outcomes. More than 600 U.S. Food and Drug Administration-approved clinical AI algorithms now exist, with 10% focusing on cardiovascular applications, highlighting the growing opportunities for AI to augment care. This review discusses the latest advancements in the field of AI, with a particular focus on the utilization of multimodal inputs and the field of generative AI. Further discussions in this review involve an approach to understanding the larger context in which AI-augmented care may exist, and include a discussion of the need for rigorous evaluation, appropriate infrastructure for deployment, ethics and equity assessments, regulatory oversight, and viable business cases for deployment. Embracing this rapidly evolving technology while setting an appropriately high evaluation benchmark with careful and patient-centered implementation will be crucial for cardiology to leverage AI to enhance patient care and the provider experience.

Artificial Intelligence for Cardiovascular Care-Part 1: Advances: JACC Review Topic of the Week.

Recent artificial intelligence (AI) advancements in cardiovascular care offer potential enhancements in diagnosis, treatment, and outcomes. Innovations to date focus on automating measurements, enhancing image quality, and detecting diseases using novel methods. Applications span wearables, electrocardiograms, echocardiography, angiography, genetics, and more. AI models detect diseases from electrocardiograms at accuracy not previously achieved by technology or human experts, including reduced ejection fraction, valvular heart disease, and other cardiomyopathies. However, AI's unique characteristics necessitate rigorous validation by addressing training methods, real-world efficacy, equity concerns, and long-term reliability. Despite an exponentially growing number of studies in cardiovascular AI, trials showing improvement in outcomes remain lacking. A number are currently underway. Embracing this rapidly evolving technology while setting a high evaluation benchmark will be crucial for cardiology to leverage AI to enhance patient care and the provider experience.

Impact of cardiac size on diagnostic performance of single-photon emission computed tomography myocardial perfusion imaging: insights from the REgistry of Fast Myocardial Perfusion Imaging with NExt generation single-photon emission computed tomography.

AIMS: Variation in diagnostic performance of single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) has been observed, yet the impact of cardiac size has not been well characterized. We assessed whether low left ventricular volume influences SPECT MPI's ability to detect obstructive coronary artery disease (CAD) and its interaction with age and sex. METHODS AND RESULTS: A total of 2066 patients without known CAD (67% male, 64.7 ± 11.2 years) across nine institutions underwent SPECT MPI with solid-state scanners followed by coronary angiography as part of the REgistry of Fast Myocardial Perfusion Imaging with NExt Generation SPECT. Area under receiver-operating characteristic curve (AUC) analyses evaluated the performance of quantitative and visual assessments according to cardiac size [end-diastolic volume (EDV); <20th vs. ≥20th population or sex-specific percentiles], age (<75 vs. ≥75 years), and sex. Significantly decreased performance was observed in patients with low EDV compared with those without (AUC: population 0.72 vs. 0.78, P = 0.03; sex-specific 0.72 vs. 0.79, P = 0.01) and elderly patients compared with younger patients (AUC 0.72 vs. 0.78, P = 0.03), whereas males and females demonstrated similar AUC (0.77 vs. 0.76, P = 0.67). The reduction in accuracy attributed to lower volumes was primarily observed in males (sex-specific threshold: EDV 0.69 vs. 0.79, P = 0.01). Accordingly, a significant decrease in AUC, sensitivity, specificity, and negative predictive value for quantitative and visual assessments was noted in patients with at least two characteristics of low EDV, elderly age, or male sex. CONCLUSION: Detection of CAD with SPECT MPI is negatively impacted by small cardiac size, most notably in elderly and male patients.

Histopathology and correlates of systemic disease in adult Henoch-Schönlein purpura: a retrospective study of microscopic and clinical findings in 68 patients at Mayo Clinic.

BACKGROUND: The histopathology of Henoch-Schönlein purpura (HSP) is well defined, but specific markers have not been correlated with systemic involvement. OBJECTIVE: We sought to evaluate whether histopathologic markers were associated with renal or other systemic involvement in adult HSP. METHODS: We retrospectively reviewed clinical information and pathology slides of 68 adult patients with HSP seen at Mayo Clinic between 1992 and 2011. RESULTS: Of the 68 patients, mean age was 45.8 years and 41 (60%) of the patients were male. Renal involvement was observed in 30 patients (44%), gastrointestinal tract in 27 (40%), joint in 32 (47%), and any systemic signs in 52 (76%). Patients who were older than 40 years and had leukocytoclastic vasculitis with an absence of eosinophils on skin biopsy specimen had higher rates of renal involvement than those who did not have both of these features (75% vs 27%; P < .001). Patients with skin biopsy specimens showing leukocytoclastic vasculitis and an absence of histiocytes had higher rates of gastrointestinal tract involvement (P = .03). Age of 40 years or younger was associated with increased risk for gastrointestinal tract involvement and a nonsignificant trend for joint involvement (P = .004 and P = .06, respectively). LIMITATIONS: This study is retrospective, and the causative factors of HSP were unable to be determined in many patients. CONCLUSION: Patients older than 40 years with HSP who had an absence of eosinophils on skin biopsy specimen had a nearly 3-times increased risk of renal involvement compared with patients who did not have both features.

Topical amitriptyline combined with ketamine for the treatment of erythromelalgia: a retrospective study of 36 patients at Mayo Clinic.

BACKGROUND: Erythromelalgia is an uncommon neurovascular disorder characterized by redness, increased skin temperature, and pain that usually occurs in the extremities. Treatment remains challenging because of its varying response to medical therapy. The objective of this study was to assess the response of erythromelalgia to compounded topical amitriptyline-ketamine. METHODS: We retrospectively evaluated 36 patients with erythromelalgia who were treated with compounded topical amitriptyline-ketamine from January 1, 2004, through January 31, 2011. RESULTS: Thirty-two patients (89%) were female. Mean (standard deviation) age was 44.7 (15.8) years (range, 5-74 years). Patients applied the medication 1 to 6 times per day (median, 5 times). One patient (3%) had complete relief from symptoms, 14 (39%) had substantial relief, 12 (33%) had some relief, 7 (19%) had no relief, and 2 (6%) had local worsening of symptoms. No patients had systemic adverse effects. CONCLUSIONS: A majority of patients with erythromelalgia (75%) reported improvement in pain with topical application of a compounded amitriptyline-ketamine formulation. The medication was well tolerated.

Correlates of systemic disease in adult Henoch-Schönlein purpura: a retrospective study of direct immunofluorescence and skin lesion distribution in 87 patients at Mayo Clinic.

BACKGROUND: Detection of IgM in lesional skin of adult patients with Henoch-Schönlein purpura via direct immunofluorescence (DIF) has been associated with the presence of renal disease. OBJECTIVE: We sought to examine whether DIF findings of skin biopsy specimens and distribution of skin lesions were associated with the presence of systemic disease, including renal, gastrointestinal tract, and joint involvement. METHODS: We performed a retrospective review of adult patients with Henoch-Schönlein purpura seen at Mayo Clinic between 1992 and 2011. RESULTS: Of the 87 patients (mean age, 46.1 years), 51 (59%) were male. A total of 39 patients (45%) had renal disease; 32 (37%), gastrointestinal tract involvement; 39 (45%), joint involvement; and 65 (75%), some systemic involvement. In all, 61 patients (70%) had cutaneous lesions above the waist. The DIF findings showed the presence of IgA in all 87 patients (100%). In addition, findings were positive for IgM in 32 patients (37%); IgG in 3 patients (3%); C3 in 75 patients (87%); and fibrinogen in 78 patients (92%). IgM was not found to be significantly associated with renal disease (P = .10); however, absence of fibrinogen was correlated with presence of renal involvement (P = .04). No other correlations were detected between DIF findings and systemic disease. Lesions above the waist were not significantly associated with renal (P = .12) or any (P = .76) systemic involvement. LIMITATIONS: This study is retrospective. CONCLUSIONS: Neither IgM in lesional skin nor distribution of skin lesions above the waist was a reliable indicator of renal or systemic disease in adults with Henoch-Schönlein purpura.

Relation of Body Mass Index to Transthyretin Cardiac Amyloidosis Particularly in Black and Hispanic Patients (from the SCAN-MP Study).

Heart failure with preserved ejection fraction is a heterogeneous clinical syndrome that includes distinct subtypes with different pathophysiologies, genetics, and treatment. Distinguishing heart failure with preserved ejection fraction caused by transthyretin cardiac amyloidosis (ATTR-CA) is critical given its specific treatment. We analyzed a single-center retrospective cohort to determine the association of body mass index (BMI) with a composite of either ATTR-CA or the valine-to-isoleucine substitution (Val122Ile) variant genotype (ATTR-CA+Val122Ile). These BMI differences were prospectively evaluated in the multicenter Screening for Cardiac Amyloidosis using nuclear imaging for Minority Populations (SCAN-MP) study of Black and Hispanic patients with heart failure. The association of BMI with ATTR-CA+Val122Ile was compared by Wilcoxon rank sum analysis and combined with age, gender, and maximum left ventricle wall thickness in multivariable logistic regression. In the retrospective analysis (n = 469), ATTR-CA+Val122Ile was identified in n = 198 (40%), who had a lower median BMI (25.8 kg/m2, interquartile range [IQR] 23.4 to 28.9) than other patients (27.1 kg/m2, IQR 23.9 to 32.0) (p <0.001). In multivariable logistic regression, BMI <30 kg/m2 (odds ratio 2.6, 95% confidence interval 1.5 to 4.5) remained independently associated with ATTR-CA+Val122Ile with a greater association in Black and Hispanic patients (odds ratio 5.8, 95% confidence interval 1.7 to 19.6). In SCAN-MP (n = 201), 17 (8%) had either ATTR-CA (n = 10) or were Val122Ile carriers (n = 7) with negative pyrophosphate scans. BMI was lower (25.4 kg/m2 [IQR 24.3 to 28.2]) in ATTR-CA+Val122Ile patients than in non-amyloid patients (32.7 kg/m2 [28.3 to 38.6]) (p <0.001), a finding that persisted in multivariable analysis (p = 0.002). In conclusion, lower BMI is associated with ATTR-CA+Val122Ile in heart failure with increased left ventricle wall thickness, particularly in Black and Hispanic patients, and may aid in the identification of those benefiting from ATTR-CA evaluation.

Meta-Analysis of Point-of-Care Lung Ultrasonography Versus Chest Radiography in Adults With Symptoms of Acute Decompensated Heart Failure.

Acute decompensated heart failure (ADHF) is a primary cause of older adults presenting to the emergency department with acute dyspnea. Point-of-care lung ultrasound (LUS) has shown comparable or superior diagnostic accuracy in comparison with a chest x-ray (CXR) in patients presenting with symptoms of ADHF. The systematic review and meta-analysis aimed to elucidate the sensitivity and specificity of LUS in comparison with CXR for diagnosing ADHF and summarize the rapidly growing body of evidence in this domain. A total of 5 databases were searched through February 18, 2021, to identify observational studies that reported on the use of LUS compared with CXR in diagnosing ADHF in patients presenting with shortness of breath. Meta-analysis was conducted on the sensitivities and specificities of each diagnostic method. A total of 8 studies reporting on 2,787 patients were included in this meta-analysis. For patients presenting with signs and symptoms of ADHF, LUS was found to be more sensitive than CXR (91.8% vs 76.5%) and more specific than CXR (92.3% vs 87.0%) for the detection of cardiogenic pulmonary edema. In conclusion, LUS is more sensitive and specific than CXR in detecting pulmonary edema. This highlights the importance of sonographic B-lines, along with the accurate interpretation of clinical data, in the diagnosis of ADHF. In addition to its convenience, reduced costs, and reduced radiation exposure, LUS should be considered an effective alternative to CXR for evaluating patients with dyspnea in the setting of ADHF.

Deep Learning Electrocardiographic Analysis for Detection of Left-Sided Valvular Heart Disease.

BACKGROUND: Valvular heart disease is an important contributor to cardiovascular morbidity and mortality and remains underdiagnosed. Deep learning analysis of electrocardiography (ECG) may be useful in detecting aortic stenosis (AS), aortic regurgitation (AR), and mitral regurgitation (MR). OBJECTIVES: This study aimed to develop ECG deep learning algorithms to identify moderate or severe AS, AR, and MR alone and in combination. METHODS: A total of 77,163 patients undergoing ECG within 1 year before echocardiography from 2005-2021 were identified and split into train (n = 43,165), validation (n = 12,950), and test sets (n = 21,048; 7.8% with any of AS, AR, or MR). Model performance was assessed using area under the receiver-operating characteristic (AU-ROC) and precision-recall curves. Outside validation was conducted on an independent data set. Test accuracy was modeled using different disease prevalence levels to simulate screening efficacy using the deep learning model. RESULTS: The deep learning algorithm model accuracy was as follows: AS (AU-ROC: 0.88), AR (AU-ROC: 0.77), MR (AU-ROC: 0.83), and any of AS, AR, or MR (AU-ROC: 0.84; sensitivity 78%, specificity 73%) with similar accuracy in external validation. In screening program modeling, test characteristics were dependent on underlying prevalence and selected sensitivity levels. At a prevalence of 7.8%, the positive and negative predictive values were 20% and 97.6%, respectively. CONCLUSIONS: Deep learning analysis of the ECG can accurately detect AS, AR, and MR in this multicenter cohort and may serve as the basis for the development of a valvular heart disease screening program.

False positive technetium-99m pyrophosphate scintigraphy in a patient with cardiac amyloidosis light chain: Case report.

INTRODUCTION: Patients with cardiac amyloidosis light chain (AL) present with negative Tc-99m pyrophosphate (PYP) scintigraphy (absent or mild heart uptake). On the contrary, patients with cardiac amyloidosis transthyretin (ATTR) present with positive Tc-99m PYP scanning (intensive heart uptake). We present a false positive Tc-99m PYP scintigraphy (grade 2, the heart-to-contralateral ratio is 1.65) in a patient with AL. PATIENT CONCERNS: A 42-year-old Chinese man complained of effort intolerance, chest discomfort, and short of breath progressively over 1 year. New York Heart Association Class III. Physical examination showed legs swelling. Laboratory revealed elevated brain natriuretic peptide of 23,031 ng/mL (0-88) and Troponin-T of 273.4 ng/mL (0-14). DIAGNOSIS: Cardiac amyloidosis light chain. Evidences: free light chains (FLCs): decreased serum free kappa/lambda ratio of 0.043 (0.31-1.56). Immunofixation electrophoresis: a positive lambda light chain monoclonal protein. Cardiac biopsy: HE: Ambiguity Congo red strain. Myocardial immunofluorescence: positive lambda light chain. Myocardial immunohistochemistry: positive lambda light chain, negative kappa light chain, and TTR. INTERVENTIONS: Furosemide 40 mg qd, torasemide 20 mg qd, spirolactone 20 mg qd, potassium chloride 10 mL per 500 mL urine, atorvastatin calcium tablet 20 mg qd, aspirin enteric-coated tablets 100 mg qd during the 2-weeks in-hospital. OUTCOMES: The patient died 2 months later after discharge. CONCLUSION: False positive Tc-99m PYP scintigraphy may rarely presented in patients with cardiac amyloidosis light chain. So, the clonal plasma cell process based on the FLCs and immunofixation is a base to rule out AL cardiac amyloidosis when we interpret a positive Tc-99m PYP scintigraphy.