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- Lawson, Claire A, et al.
(författare)
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Patient-Reported Status and Heart Failure Outcomes in Asia by Sex, Ethnicity, and Socioeconomic Status
- 2023
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Ingår i: JACC. Asia. - : Elsevier. - 2772-3747. ; 3:3, s. 349-362
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Tidskriftsartikel (refereegranskat)abstract
- Background: In heart failure (HF), symptoms and health-related quality of life (HRQoL) are known to vary among different HF subgroups, but evidence on the association between changing HRQoL and outcomes has not been evaluated.Objectives: The authors sought to investigate the relationship between changing symptoms, signs, and HRQoL and outcomes by sex, ethnicity, and socioeconomic status (SES).Methods: Using the ASIAN-HF (Asian Sudden Cardiac Death in Heart Failure) Registry, we investigated associations between the 6-month change in a "global" symptoms and signs score (GSSS), Kansas City Cardiomyopathy Questionnaire overall score (KCCQ-OS), and visual analogue scale (VAS) and 1-year mortality or HF hospitalization.Results: In 6,549 patients (mean age: 62 ± 13 years], 29% female, 27% HF with preserved ejection fraction), women and those in low SES groups had higher symptom burden but lower signs and similar KCCQ-OS to their respective counterparts. Malay patients had the highest GSSS (3.9) and lowest KCCQ-OS (58.5), and Thai/Filipino/others (2.6) and Chinese patients (2.7) had the lowest GSSS scores and the highest KCCQ-OS (73.1 and 74.6, respectively). Compared to no change, worsening of GSSS (>1-point increase), KCCQ-OS (≥10-point decrease) and VAS (>1-point decrease) were associated with higher risk of HF admission/death (adjusted HR: 2.95 [95% CI: 2.14-4.06], 1.93 [95% CI: 1.26-2.94], and 2.30 [95% CI: 1.51-3.52], respectively). Conversely, the same degrees of improvement in GSSS, KCCQ-OS, and VAS were associated with reduced rates (HR: 0.35 [95% CI: 0.25-0.49], 0.25 [95% CI: 0.16-0.40], and 0.64 [95% CI: 0.40-1.00], respectively). Results were consistent across all sex, ethnicity, and SES groups (interaction P > 0.05).Conclusions: Serial measures of patient-reported symptoms and HRQoL are significant and consistent predictors of outcomes among different groups with HF and provide the potential for a patient-centered and pragmatic approach to risk stratification.
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| 2. |
- Myhre, Peder L., et al.
(författare)
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External validation of a deep learning algorithm for automated echocardiographic strain measurements
- 2024
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Ingår i: EUROPEAN HEART JOURNAL - DIGITAL HEALTH. - 2634-3916. ; 5:1, s. 60-68
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Tidskriftsartikel (refereegranskat)abstract
- Aims Echocardiographic strain imaging reflects myocardial deformation and is a sensitive measure of cardiac function and wall-motion abnormalities. Deep learning (DL) algorithms could automate the interpretation of echocardiographic strain imaging.Methods and results We developed and trained an automated DL-based algorithm for left ventricular (LV) strain measurements in an internal dataset. Global longitudinal strain (GLS) was validated externally in (i) a real-world Taiwanese cohort of participants with and without heart failure (HF), (ii) a core-lab measured dataset from the multinational prevalence of microvascular dysfunction-HF and preserved ejection fraction (PROMIS-HFpEF) study, and regional strain in (iii) the HMC-QU-MI study of patients with suspected myocardial infarction. Outcomes included measures of agreement [bias, mean absolute difference (MAD), root-mean-squared-error (RMSE), and Pearson's correlation (R)] and area under the curve (AUC) to identify HF and regional wall-motion abnormalities. The DL workflow successfully analysed 3741 (89%) studies in the Taiwanese cohort, 176 (96%) in PROMIS-HFpEF, and 158 (98%) in HMC-QU-MI. Automated GLS showed good agreement with manual measurements (mean +/- SD): -18.9 +/- 4.5% vs. -18.2 +/- 4.4%, respectively, bias 0.68 +/- 2.52%, MAD 2.0 +/- 1.67, RMSE = 2.61, R = 0.84 in the Taiwanese cohort; and -15.4 +/- 4.1% vs. -15.9 +/- 3.6%, respectively, bias -0.65 +/- 2.71%, MAD 2.19 +/- 1.71, RMSE = 2.78, R = 0.76 in PROMIS-HFpEF. In the Taiwanese cohort, automated GLS accurately identified patients with HF (AUC = 0.89 for total HF and AUC = 0.98 for HF with reduced ejection fraction). In HMC-QU-MI, automated regional strain identified regional wall-motion abnormalities with an average AUC = 0.80.Conclusion DL algorithms can interpret echocardiographic strain images with similar accuracy as conventional measurements. These results highlight the potential of DL algorithms to democratize the use of cardiac strain measurements and reduce time-spent and costs for echo labs globally. Graphical Abstract
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