Aims To evaluate the implementation of the CA clinical pathway on awareness among referring cardiologists, diagnostic delay and severity of CA at diagnosis. Methods and results Patients with CA were retrospectively included in this study and divided in pre-implementation of the CA clinical pathway (2007-2018; T1) and post-implementation (2019-2020; T2). Patients’ and disease characteristics were extracted from electronic health records and compared. In total, 113 patients (mean age 67.8±8.5 years, 26% female) were diagnosed with CA (T1 (2007-2018): 56; T2 (2019-2020): 57). The number of CA diagnoses per year increased over time. Reasons for referral changed over time, with increased awareness for right ventricular hypertrophy (9% in T1 vs. 36% in T2) and unexplained heart failure with preserved ejection fraction (22% in T1 vs. 38% in T2). Comparing T1 with T2, diagnostic delay also improved (14 vs. 8 months, p < 0.01), NYHA class III (45% vs. 23%,p=0.03) and advanced CA stage (MAYO/Gillmore stage III/IV; 61% vs. 33%, p = <0.01) at time of diagnosis decreased. Conclusion After implementation of the CA clinical pathway the awareness among referring cardiologists was improved, diagnostic delay was decreased and patients had less severe CA at diagnosis. Further studies are warranted to assess the prognostic impact of CA clinical pathway implementation.

BackgroundUnexplained Left Ventricular Hypertrophy (ULVH) may be caused by genetic and non-genetic etiologies (e.g., sarcomere variants, cardiac amyloid, or Anderson-Fabry's disease). Identification of ULVH patients allows for early targeted treatment and family screening.AimTo automatically identify patients with ULVH in electronic health record (EHR) data using two computer methods: text-mining and machine learning (ML).MethodsAdults with echocardiographic measurement of interventricular septum thickness (IVSt) were included. A text-mining algorithm was developed to identify patients with ULVH. An ML algorithm including a variety of clinical, ECG and echocardiographic data was trained and tested in an 80/20% split. Clinical diagnosis of ULVH was considered the gold standard. Misclassifications were reviewed by an experienced cardiologist. Sensitivity, specificity, positive, and negative likelihood ratios (LHR+ and LHR–) of both text-mining and ML were reported.ResultsIn total, 26,954 subjects (median age 61 years, 55% male) were included. ULVH was diagnosed in 204/26,954 (0.8%) patients, of which 56 had amyloidosis and two Anderson-Fabry Disease. Text-mining flagged 8,192 patients with possible ULVH, of whom 159 were true positives (sensitivity, specificity, LHR+, and LHR– of 0.78, 0.67, 2.36, and 0.33). Machine learning resulted in a sensitivity, specificity, LHR+, and LHR– of 0.32, 0.99, 32, and 0.68, respectively. Pivotal variables included IVSt, systolic blood pressure, and age.ConclusionsAutomatic identification of patients with ULVH is possible with both Text-mining and ML. Text-mining may be a comprehensive scaffold but can be less specific than machine learning. Deployment of either method depends on existing infrastructures and clinical applications.

No abstract

No abstract