A simple nomogram to help guide the diagnosis of cardiac amyloidosis in patients with heart failure with preserved ejection fraction

Introduction

Cardiac amyloidosis (CA) is a progressive disorder caused by the deposition of insoluble amyloid protein in the myocardium, mainly represented by monoclonal immunoglobulin light chains (AL) and transthyretin (ATTR) amyloidosis (1). It is now considered a specific etiology of heart failure (HF) with preserved ejection fraction (HFpEF). Historically, it was a disease with very poor prognosis. Recent advances in disease-modifying therapies have greatly improved its clinical status and outcomes, rendering it a treatable disease (2,3). In comparison with therapeutic advances, great challenges for early diagnosis of CA exist. Misdiagnosis or delayed diagnosis in CA are frequent, particularly among cardiologists, to whom many patients are initially referred due to the manifestations of HF (4,5). These inaccuracies limit the prompt treatment and outcome improvement of CA. Therefore, it is of critical importance to discriminate CA from other etiologies in patients with HFpEF.

In addition to the lower awareness of CA of cardiologists, the other important reason for the low diagnosis rate of CA in HFpEF is the lack of an efficient diagnostic approach. At the cardiac level, the common findings of CA such as increased left ventricular (LV) wall thickness, abnormal diastolic function, and elevated cardiac biomarkers are also prevalent in patients with HFpEF (6,7). Current expert consensus recommends the ascertainment of CA in patients with HF and increased wall thickness (IWT) (8). In such patients without further refinement, subsequent diagnostic work-up including diphosphonate scintigraphy, monoclonal antibody assessment, and invasive biopsy would be inefficient and costly. An apical sparing pattern derived by speckle tracking echocardiography (STE) has been found to be the most accurate single echocardiographic red flag for CA detection (9), but it needs strain assessment that is not routinely performed in many echocardiographic labs. Boldrini et al. proposed IWT and amyloid light-chain (AL) scores for detecting CA, but these scores incorporate only echocardiographic variables including strain parameters and have not been validated in an HFpEF cohort (10). The Mayo Clinic recently developed a simple score for detecting high risk of CA in HFpEF, but it is only for ATTR-CA (11). In fact, ATTR-CA and AL-CA share many common clinical and echocardiographic features or red flags of CA (8), making it possible to establish a unified prediction model for both ATTR-CA and AL-CA.

Thus, in this single center study, we included patients with HFpEF and increased LV wall thickness (≥12 mm) who underwent diagnostic work-up for both AL type and ATTR type of CA. By detecting and comparing the clinical and echocardiographic features between ATTR-CA and AL-CA in this cohort, we aimed to develop a simple and unified nomogram model using easily accessible variables for identifying increased risk of both ATTR-CA and AL-CA in Chinese HFpEF patients, thereby aiding medical decision-making by cardiologists regarding the need for a systematic and costly diagnostic algorithm of CA. In addition, we tried to approximately evaluate the frequency rate of AL-CA and ATTR-CA in Chinese HFpEF patients, which had not yet been fully investigated. We present this article in accordance with the TRIPOD reporting checklist (available at https://qims.amegroups.com/article/view/10.21037/qims-2025-1-2646/rc).

Methods

Population and study design

The original cohort consisted of consecutive patients hospitalized at the Cardiology Department of Tongji Hospital, Tongji Medical College of Huazhong University of Science and Technology from April 2015 to September 2024, due to acute or chronic decompensated HF. The diagnosis of HF was made according to current guidelines (12). Patients who presented HFpEF (EF ≥40%) with increased LV wall thickness (≥12 mm) on echocardiography were then selected and screened, excluding those with severe valvular heart disease, end-stage kidney disease on hemodialysis, the presence of arrhythmia which would interfere with the imaging process and the history of HF with left ventricular ejection fraction (LVEF) <40%. The remaining patients were included if they had subsequently undergone a comprehensive diagnostic evaluation for clinically suspected CA, including serum/urine immunofixation electrophoresis, serum-free light chain assay, tissue biopsy (cardiac or extracardiac biopsy), cardiac magnetic resonance (CMR), ^99mTc-Pyrophosphate (^99mTc-PYP) scintigraphy and genetic testing for ATTR gene.

The final remaining patients were included in the analysis. They were then randomly divided into the training cohort and the validation cohort at a ratio of 7:3 (Figure 1). This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments and was approved by the Ethics Committee of Tongji Hospital (No. TJ-IRB20230768) with a waiver for informed consent.

Figure 1 Flow chart for patient selection. CA, cardiac amyloidosis; HF, heart failure; LVEF, left ventricular ejection fraction.

Confirmation of ATTR-CA and AL-CA

AL-CA (n=117) was considered in the presence of monoclonal light chain, and if one of the following criteria was met (I) a cardiac biopsy demonstrating amyloid deposits (n=16); and (II) an extracardiac biopsy demonstrating amyloid deposits along with characteristic features of amyloidosis on echocardiography or CMR (n=101) (6). ATTR-CA (n=35) was considered if the absence of monoclonal protein was confirmed by serum/urine immunofixation electrophoresis and serum-free light chain assay, and if one of the following criteria was met (I) positive ^99mTc-PYP scintigraphy with either a visual score of 2–3 cardiac uptake or the heart to contralateral chest ratio >1.5 (n=29); (II) cardiac biopsy demonstrating amyloid deposits (n=2); and (III) an extracardiac biopsy demonstrating amyloid deposits along with typical findings of CA on echocardiography or CMR (n=4) (6,13,14). Wild-type (ATTRwt-CA) and hereditary ATTR-CA (ATTRm-CA) of ATTR-CA were confirmed by genetic testing.

Data collection

Demographic characteristics and laboratory tests were obtained from the electronic medical records. The laboratory results were based on the first test on admission, including hemoglobin, creatinine, N-terminal pro-brain natriuretic peptide (NT-proBNP), and high sensitivity cardiac troponin I (hs-cTnI). The estimated glomerular filtration rate (eGFR) was calculated using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) formula. Coronary artery disease was defined as a history of myocardial infarction, previous coronary vascularization, or the presence of coronary stenosis >50% on coronary angiography. Anemia was defined as hemoglobin level <120 g/L in men or <110 g/L in women.

The first standard 12-lead electrocardiograms (ECGs) at admission were used for analysis. Low QRS (Q, R, and S waves of an electrocardiogram) voltage was defined as QRS amplitude <0.5 mV in limb leads or <1 mV in the precordial leads (15). Pseudo-necrosis was defined as pathological Q waves (duration >0.04 s, voltage >1/4 R wave) or QS waves in two consecutive leads, in the absence of previous myocardial infarction or echocardiographic akinetic areas (16). Poor precordial R wave progression was defined according to DePace criteria (17).

Standard transthoracic echocardiography (TTE) at rest was performed on a single GE Vivid E9 ultrasonography machine (GE Healthcare, Chicago, IL, USA) and completed at admission in the core echocardiographic lab in the Cardiology Department of Tongji Hospital. Measurements as left ventricular end diastolic diameter (LVDd), interventricular septum thickness in diastole (IVSd), left ventricular posterior wall thickness in diastole (PWTd), LV early diastolic mitral peak flow velocity/late diastolic mitral peak flow velocity (E/A), LV E-wave/e’-wave (E/e’), right ventricular (RV) wall thickness, and tricuspid annular plane systolic excursion (TAPSE) were according to standard recommendations (18,19). RV wall thickening was defined as thickness >5 mm (20). Relative wall thickness (RWT) was calculated using the sum of IVSd and PWTd divided by LVDd. LVEF was measured and calculated by the biplane Simpson’s methods. The presence of pericardial effusion and biatrial enlargement were also evaluated. Imaging processing and strain analysis were performed offline with Echo Pac software (version:113, 2017; GE Vingmed, Horten, Norway) as described previously (21). The presence of strain apical sparing pattern was meticulously evaluated at the “bull’s eye” presentation of LV global longitudinal strain (GLS) by an experienced independent cardiologist.

Statistical analysis

Normal distribution was assessed using the Shapiro-Wilk test. Continuous variables were presented as mean ± standard deviation when normally distributed and median with interquartile range (IQR) when abnormally distributed. Differences between groups were performed with the Student’s t-test or Mann-Whitney U test, depending on the normality of the variables. Categorical variables were presented as frequencies with percentages; differences between groups were performed with the χ² test or Fisher’s exact test. Statistical analysis was performed using the software SPSS 25.0 (IBM Corp., Armonk, NY, USA) and R version 4.2.3 (R Foundation for Statistical Computing, Vienna, Austria). All statistical tests were two-tailed, and P value <0.05 was considered statistically significant.

Nomogram construction and performance assessment

In addition to demographic variables, candidate variables were chosen mainly based on reported features typical of CA, including myocardial injury biomarkers, ECG, and TTE variables. Disease history that was likely related to HF was also considered. Variables with high levels of missingness (>30% missing values) were not included. GLS, strain apical sparing pattern, and TAPSE that required sophisticated imaging techniques and were not routinely assessable in most clinical settings were also not included. Model building began by evaluating the remaining variables by univariate logistic regression analysis in the training cohort. Those with P<0.05 were further entered into the multivariate logistic analysis. Stepwise backward elimination was used to identify variables independently associated with CA, and these variables were then used to develop the nomogram for predicting the risk of CA. The receiver operator characteristic (ROC) curves and area under the curve (AUC) were used to evaluate the discriminative performance of the prediction model. To analyze the agreement between nomogram predictions and actual observations, bootstraps of 1,000 resamples were set and calibration curves were created, which were assessed with the Hosmer-Lemeshow goodness-of-fit test where a P value >0.05 indicates consistent calibration with the model. Decision curve analysis (DCA) was conducted to determine the clinical usefulness of the nomogram by quantifying the net benefits at different threshold probabilities.

Validation of the nomogram

The nomogram was tested by applying the logistic regression formula constructed in the training cohort to the validation cohort, and the performance of the nomogram was examined by evaluating its discriminative (AUC), calibration (calibration curve), and clinical usefulness (DCA).

Results

Study cohorts and their baseline characteristics in the CA group versus non-CA group

A total of 29,179 consecutive patients, admitted because of decompensated HF between April 2015 and September 2024 at the Cardiology Department of Tongji Hospital, were retrospectively screened (Figure 1). Of them, 5,689 patients presented with LVEF ≥40% and LV wall thickness ≥12 mm. Subsequently, patients with previous LVEF <40%, severe valvular heart disease, end-stage kidney disease on hemodialysis, and severe arrhythmia interfering imaging processing were excluded. Of the remaining 4,182 patients, patients were included if they underwent a comprehensive diagnostic evaluation for clinically suspected CA. Finally, a total of 337 patients were included in the analysis and they were randomly assigned to the training cohort (n=236) and the validation cohort (n=101) at a ratio of 7:3. As shown in Table 1, there were no significant differences in demographic, laboratory, ECG, and echocardiographic characteristics between the training and validation cohorts.

In the training cohort, 106 patients were diagnosed with CA. Compared to non-CA patients, those with CA were more often males (71.7% vs. 56.9%, P=0.019), with lower incidences of hypertension (24.5% vs. 65.4%, P<0.001) and diabetes mellitus (13.2% vs. 26.9%, P=0.010), and higher levels of myocardial injury markers [NT-proBNP: 5,811 (IQR, 2,881, 10,868) vs. 3,340 (IQR, 1,207, 10,391) ng/L, P=0.019; and hs-cTnI: 131.6 (IQR, 56.8, 286.1) vs. 45.6 (IQR, 22.0, 149.3) ng/L, P=0.001, respectively]. In term of ECG features, patients with CA were more likely to have higher rates of low QRS voltage (17.0% vs. 6.9%, P=0.016), pseudo-necrosis (29.2% vs. 16.2%, P=0.016), and poor precordial R wave progression (40.6% vs. 13.8%, P<0.001), compared with non-CA patients. Regarding echocardiographic features, patients with CA showed more severe systolic dysfunction (LVEF: 50%, IQR, 41–58% vs. 55%, IQR, 45–60%, P=0.035), more severe myocardial hypertrophy [IVSd: 17 (IQR, 14–19) vs. 14 (IQR, 12–16) mm; PWTd: 16 (IQR, 14–18) vs. 12 (IQR, 11–15) mm; RWT: 0.78 (IQR, 0.64–0.91) vs. 0.57 (IQR, 0.50–0.65), P<0.001, respectively], more severe impaired myocardial relaxation [E/A: 2.45 (IQR, 1.50–3.29) vs. 1.16 (IQR, 0.69–1.98); E/e’: 29 (IQR, 22–40) vs. 24 (IQR, 17–29), P<0.001, respectively] and more severe RV systolic dysfunction [TAPSE: 11 (IQR, 9–13) vs. 14 (IQR, 12–16) mm, P<0.001]. Meanwhile, STE revealed that strain apical sparing pattern was significantly higher (84.5% vs. 40.5%; P<0.001) and GLS showed a clear trend towards its reduction [−7.7% (IQR, −9.7%, −5.9%) vs. −10.1% (IQR, −12.6%, −7.4%), P<0.001] in the CA group compared with the non-CA group. Other detailed characteristics are shown in Table S1.

In the validation cohort, 46 patients were diagnosed with CA. The baseline characteristics of patients in this cohort were similar to those of the training cohort (Table S1).

Comparison of features and frequency rates of AL-CA versus ATTR-CA in the study cohort

Among the 337 patients who underwent a complete diagnostic evaluation for suspected CA, 152 received a confirmed diagnosis of CA. Of whom, 35 (23%) were identified as ATTR-CA and 117 (77%) as AL-CA. Genetic testing was performed in 17 patients with ATTR-CA, revealing that 11 had hereditary ATTR-CA (ATTRm-CA) and six had wild-type ATTR-CA (ATTRwt-CA).

Regarding the demographic, laboratory, ECG, and echocardiographic features, there were no significant differences between patients with ATTR-CA and those with AL-CA, except for myocardial injury markers. Compared to patients with ATTR-CA, patients with AL-CA showed more severe myocardial injuries [NT-proBNP: 6,499 (IQR, 3,558–13,878) vs. 3,403 (IQR, 1,324–6,896) ng/L, P=0.001; and hs-cTnI: 177.5 (IQR, 67.6–323.6) vs. 96.9 (IQR, 27.5–238.8) ng/L, P=0.045, respectively]. The details of the comparison are shown in Table S2.

Predictive variables screening and nomogram construction

Predictive variables for CA were further determined by univariate and multivariate logistic regression models in the training cohort, and multivariate odds ratios (ORs) were calculated for the risk factors used to build the nomogram. The results of the univariate analysis showed that male sex, the absence of hypertension, the absence of diabetes mellitus, low QRS voltage, pseudo-necrosis, atrial fibrillation/flutter, poor precordial R wave progression, LVEF, LVDd, IVSd, PWTd, RWT, E/A, E/e’, RV wall thickening, biatrial enlargement, and pericardial effusion were predictive for CA (Table 2). Further multivariate logistic regression analysis indicated that the five variables, including the absence of hypertension, poor precordial R wave progression, RWT, E/A, and RV wall thickening were independent predictors of CA.

A nomogram including the above five variables was constructed to predict the risk of CA. A final score was derived by adding all the points associated with the five variables, and the higher the score, the higher the risk of CA (Figure 2). For example, a patient presented with HFpEF (EF ≥40%) and had thickened LV wall thickness (≥12 mm), had no history of hypertension, and poor precordial R wave progression on the ECG. Echocardiographic exam revealed RV wall thickening, with RWT =0.76 and E/A =2.33. By adding the corresponding points for the variables on the nomogram (dotted red line in Figure 2), this patient’s final score was approximately 253 and thus the estimated risk of CA was about 92.2% (red arrow in Figure 2). Thus, this patient was warranted to undergo further CA diagnostic work-up. As shown in Figure 3, the patient was confirmed to be an ATTR-CA patient.

Figure 2 Nomogram for predicting the risk of CA in HFpEF patients. CA, cardiac amyloidosis; E/A, early diastolic mitral peak flow velocity/late diastolic mitral peak flow velocity; HFpEF, heart failure with preserved ejection fraction; RWT, relative wall thickness; RV, right ventricular.

Figure 3 An HFpEF patient with the estimated risk of CA was 92.2% and a positive ^99mTc-PYP scintigraphy. (A) Parasternal long axis and four-chamber view showed increased biventricular thickening with a typical apical sparing pattern of LV global longitudinal strains with 2D-strain using speckle tracking echocardiography. (B) ^99mTc-PYP SPECT showed grade 3 radiotracer uptake on the Perugini scale at 1 and 3 h, and 1 h heart-to-lung ratio of 2.46 and 3 h of 2.43. (C) ECG presented low QRS voltage and poor precordial R wave progression. (D) ^99mTc-PYP SPECT-CT (axial, sagittal, and coronal plains) demonstrated Perugini grade 3 abnormal cardiac uptake of radiotracer. 2D, two-dimensional; ^99mTc-PYP, ^99mTc-pyrophosphate; CA, cardiac amyloidosis; CT, computed tomography; ECG, electrocardiogram; HFpEF, heart failure with preserved ejection fraction; LV, left ventricular; QRS, Q, R, and S waves of an electrocardiogram; SPECT, single-photon emission tomography.

Performance and validation of the nomogram

In the training cohort, the nomogram possessed good discriminative performance for predicting the risk of CA in HFpEF patients with an AUC of 0.881 (Figure 4A). Meanwhile, predictive accuracy was maintained in validation cohort with an AUC of 0.854 (Figure 4B). The calibration curves showed apparent line and bias-corrected line similar to the ideal line, demonstrating good concordance between predicted frequency and observed frequency extent of CA in the training and validation cohort (Figure 4C,4D). Additionally, the Hosmer-Lemeshow goodness-of-fit test showed P values of 0.405 for the training cohort and of 0.541 for the validation cohort, indicating that the model exhibited good calibration. The DCA curve revealed that the use of the nomogram can result in more net benefit than the all-or-none risk of CA for all threshold probabilities (Figure 4E,4F), suggesting that the nomogram model was clinically useful.

Figure 4 Evaluation of the performance of the nomogram to predict the risk of CA in HFpEF patients. (A) ROC curve of the training cohort. (B) ROC curve of the validation cohort. (C) Calibration curve of the training cohort. (D) Calibration curve of the validation cohort. (E) DCA of the training cohort. (F) DCA of the validation cohort. AUC, area under the curve; CA, cardiac amyloidosis; DCA, decision curve analysis; HFpEF, heart failure with preserved ejection fraction; ROC, receiver operating characteristic.

Discussion

The main findings of the present study were as follows: (I) in Chinese HFpEF patients, the prevalence of AL-CA was significantly higher than that of ATTR-CA, and ATTR-CA and AL-CA exhibited comparable clinical and echocardiographic characteristics; and (II) a simplified and unified nomogram model was developed and validated to identify increased risk of both AL-CA and ATTR-CA in Chinese HFpEF patients, which further guide medical decision-making by cardiologists on systematic diagnostic algorithm for CA to ensure prompt early diagnosis.

Over the past decade, CA has been found to be a more frequent etiology of HFpEF. The prevalence of ATTR-CA in HFpEF has been widely investigated, reporting a range of 5% to 17% (22-24). In comparison, there have been few studies for AL-CA and the true incidence of AL-CA in HFpEF remains unknown. Hahn et al. performed endomyocardial biopsy on 108 patients with HFpEF (EF ≥50%) and found that 11 of these patients had ATTR-CA, whereas only three had AL-CA (25). In histological analysis of LV specimens from 109 patients with antemortem diagnosis of HFpEF without clinically apparent cardiac amyloid involvement, ATTR-CA was identified in 18 cases, whereas AL-CA was only detected in one case (26). Based on the limited data, ATTR-CA seems to be the most common form of CA in HFpEF patients, with AL-CA being a poorly represented type. However, in our cohort of Chinese HFpEF patients (EF ≥40%) with IWT (≥12 mm), we found a significantly higher prevalence of AL-CA compared with ATTR-CA. Among the 152 consecutive cases diagnosed with CA, only 35 (23%) were ATTR-CA, and 117 (77%) were AL-CA. Whether the higher prevalence of AL-CA than ATTR-CA is solely caused by the racial disparities, or the true prevalence of AL-CA has been underestimated remains undetermined. Firstly, referral bias may have influenced our findings. Our cohort of patients were from a large tertiary comprehensive hospital that does receive referrals for HF cases with hematological abnormalities suggestive of AL-CA. This could affect the observed subtype distribution in our cohort. However, it is important to note that in the study, only patients who initially met the diagnosis criteria of HFpEF were selected and screened, which mitigates specialty-specific selection bias to the greatest extent. In addition to referral patterns, the exclusion of patients with severe valvular heart disease, mainly aortic stenosis, will also lower the prevalence of ATTR-CA theoretically in the cohort, since the coexistence of aortic stenosis and ATTR-CA is not infrequent as reported in the literature (6). However, in the real practice of our center, no concurrent cases were identified until the end of our study, suggesting limited practical impact on the reported subtype distribution. Finally, the limitations of this single-center study are recognized, and further large-scale, multicenter epidemiological studies are needed to elucidate the true incidence of AL-CA and ATTR-CA in a broader Chinese HFpEF population and to investigate the potential influence of racial disparities.

Characteristic clinical presentations of CA have long been described, such as discordant QRS voltage with increased LV wall thickness, conduction disorder, pseudo-necrosis or poor R progression in the precordial leads, diastolic dysfunction, increased RV wall thickness, bi-atrial enlargement, pericardial effusion, decreased GLS with relative apical sparing pattern, and elevated cardiac biomarkers (6,8,14,20). The overall clinical features observed in our CA patients were similar with those reported in the literature. However, in our study, we found that the median onset age of ATTR-CA and AL-CA was comparable, at approximately 60 years, which was different from previous studies. Prior studies have suggested that ATTR-CA, particularly ATTRwt-CA, is a late-onset disease with symptoms usually presenting in patients 70 years of age or older (27,28). Meanwhile, AL-CA generally manifests an earlier onset, primarily affecting patients older than 60 years (29). The early onset age observed in our study may be attributed to the higher proportion of ATTRm-CA in our HFpEF-ATTR cohort, and ATTRm-CA patients are more likely to present with cardiac symptoms earlier. Future multicenter research that involves wider areas of China is needed to confirm our findings. In addition, AL-CA patients had higher levels of NT-proBNP and cardiac troponin I in comparison with ATTR-CA, as shown by our and the previous findings (30,31). The potential mechanism remains unclear, and direct cytotoxicity of light chains has been suggested as a contributing factor (30).

Novel available disease-modifying therapies have emphasized the importance of early recognition and changed management of CA (32). However, misdiagnosis or delayed diagnosis in CA is frequent, particularly among cardiologists, to whom many patients are initially referred due to the manifestations of HF (4,5). Patients with CA were likely included in previous HFpEF clinical trials, helping to explain the absence of medication efficacy for HFpEF in trials to date (33). Thus, there is a critical need to develop a tool for cardiologists to identify high risk of CA and prompt early diagnosis in HFpEF. Although previous studies have reported several diagnostic scores or approaches for CA, their utility is limited. The IWT and AL scores by Boldrini et al. rely on strain parameters that are not routinely available in most echocardiographic labs, which undermines their universal application (10). Predictive artificial intelligence algorithms have been proposed to identify CA (34,35), yet their clinical application is also limited due to the complex diagnostic codes and models. The Mayo score is easy to use, but it detects ATTR-CA only (11) and thus could cause missed diagnosis of AL-CA. In this study, we tried to develop a simple and unified tool for detecting both ATTR-CA and AL-CA in HFpEF.

In our cohort of HFpEF patients, ATTR-CA and AL-CA exhibited comparable clinical and echocardiographic features, suggesting the feasibility of a unified screening model to identify both types of CA. Among the variables assessed, we found that those indicating more severe diastolic dysfunction and concentric hypertrophy showed the best predictive performance, which is similar to the results of previous studies (10,36). Moreover, HFpEF patients with CA had fewer comorbidities, especially hypertension, demonstrated pronounced wall thickening for both LV and RV, and had small or non-dilated LV, which underscores the unique pathophysiological mechanism of HF in CA such as amyloid infiltration. In addition, poor precordial R wave progression was also independently predictive of CA in our cohorts. Thereafter, a simple nomogram model was constructed for identifying high risk of CA in Chinese HFpEF patients by using five variables, including the absence of hypertension, poor precordial R wave progression, RWT, E/A, and RV wall thickening. These variables were all easily accessible from electronic records and advanced cardiac imaging parameters were not required. This model had good predictive performance on the ROC and calibration curves and could be well applied in clinical practice, as indicated by the DCA curves. Through the nomogram model, each patient will have an individualized score and corresponding risk of CA. If a patient has a higher score, indicating a high risk of CA, further examinations including CMR, diphosphonate scintigraphy, serum or urine monoclonal light chains, and possibly histological examinations are mandatory. In contrast, when the risk of CA is low, we recommend that clinicians refrain from performing further diagnostic work-up due to the low likelihood of CA and reconsider the differential diagnosis of hypertrophic phenotype.

Study limitations

The present study had some limitations. First, this study was a retrospective study and the validity of the retrospective data was limited. Second, it was a single center study and selection bias cannot be excluded. Third, some patients in our cohort were diagnosed as CA by the presence or absence of monoclonal light chain in combination with positive findings of CMR, which are not gold criteria for diagnosing CA according to current guidelines. However, they only accounted for a small proportion of total CA patients in the study. Fourth, the prediction model was established in a cohort from a single center and validated by internal cohort. Therefore, its clinical utility needs further validation by external cohorts. Fifth, the study was completed in a Chinese HFpEF cohort. Whether the results have broad representativeness and apply to patients of other ethnicities is unknown and further study is needed to clarify it.

Conclusions

In this study, we confirmed that the CA is an important and specific etiology of HFpEF. Interestingly, in this cohort of Chinese HFpEF patients, the prevalence of AL-CA was higher than that of ATTR-CA, which is different from the limited data available before. We also found ATTR-CA and AL-CA actually shared many common features and thus it is feasible to establish a unified model for detecting CA. Finally, by integrating five routinely accessible variables, including clinical and echocardiographic variables, we established a simple nomogram model that may aid in identifying HFpEF patients who are at increased risk of CA and should undergo additional evaluation to confirm or exclude CA.

Acknowledgments

The authors would like to thank all the patients who participated in this study.

Footnote

Reporting Checklist: The authors have completed the TRIPOD reporting checklist. Available at https://qims.amegroups.com/article/view/10.21037/qims-2025-1-2646/rc

Data Sharing Statement: Available at https://qims.amegroups.com/article/view/10.21037/qims-2025-1-2646/dss

Funding: None.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-2025-1-2646/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Ethics Committee of Tongji Hospital (No. TJ-IRB20230768) with a waiver for informed consent.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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