Machine learning-based prediction of in-hospital death for patients with takotsubo syndrome: The InterTAK-ML model

AIMS Takotsubo syndrome (TTS) is associated with a substantial rate of adverse events. We sought to design a machine learning (ML)-based model to predict the risk of in-hospital death and to perform a clustering of TTS patients to identify different risk profiles. METHODS AND RESULTS A ridge logistic regression-based ML model for predicting in-hospital death was developed on 3482 TTS patients from the International Takotsubo (InterTAK) Registry, randomly split in a train and an internal validation cohort (75% and 25% of the sample size, respectively) and evaluated in an external validation cohort (1037 patients). Thirty-one clinically relevant variables were included in the prediction model. Model performance represented the primary endpoint and was assessed according to area under the curve (AUC), sensitivity and specificity. As secondary endpoint, a K-medoids clustering algorithm was designed to stratify patients into phenotypic groups based on the 10 most relevant features emerging from the main model. The overall incidence of in-hospital death was 5.2%. The InterTAK-ML model showed an AUC of 0.89 (0.85-0.92), a sensitivity of 0.85 (0.78-0.95) and a specificity of 0.76 (0.74-0.79) in the internal validation cohort and an AUC of 0.82 (0.73-0.91), a sensitivity of 0.74 (0.61-0.87) and a specificity of 0.79 (0.77-0.81) in the external cohort for in-hospital death prediction. By exploiting the 10 variables showing the highest feature importance, TTS patients were clustered into six groups associated with different risks of in-hospital death (28.8% vs. 15.5% vs. 5.4% vs. 1.0.8% vs. 0.5%) which were consistent also in the external cohort. CONCLUSION A ML-based approach for the identification of TTS patients at risk of adverse short-term prognosis is feasible and effective. The InterTAK-ML model showed unprecedented discriminative capability for the prediction of in-hospital death

Inhibition of the Soluble Epoxide Hydrolase Promotes Albuminuria in Mice with Progressive Renal Disease

Epoxyeicotrienoic acids (EETs) are cytochrome P450-dependent anti-hypertensive and anti-inflammatory derivatives of arachidonic acid, which are highly abundant in the kidney and considered reno-protective. EETs are degraded by the enzyme soluble epoxide hydrolase (sEH) and sEH inhibitors are considered treatment for chronic renal failure (CRF). We determined whether sEH inhibition attenuates the progression of CRF in the 5/6-nephrectomy model (5/6-Nx) in mice. 5/6-Nx mice were treated with a placebo, an ACE-inhibitor (Ramipril, 40 mg/kg), the sEH-inhibitor cAUCB or the CYP-inhibitor fenbendazole for 8 weeks. 5/6-Nx induced hypertension, albuminuria, glomerulosclerosis and tubulo-interstitial damage and these effects were attenuated by Ramipril. In contrast, cAUCB failed to lower the blood pressure and albuminuria was more severe as compared to placebo. Plasma EET-levels were doubled in 5/6 Nx-mice as compared to sham mice receiving placebo. Renal sEH expression was attenuated in 5/6-Nx mice but cAUCB in these animals still further increased the EET-level. cAUCB also increased 5-HETE and 15-HETE, which derive from peroxidation or lipoxygenases. Similar to cAUCB, CYP450 inhibition increased HETEs and promoted albuminuria. Thus, sEH-inhibition failed to elicit protective effects in the 5/6-Nx model and showed a tendency to aggravate the disease. These effects might be consequence of a shift of arachidonic acid metabolism into the lipoxygenase pathway

Prognostic impact of acute pulmonary triggers in patients with Takotsubo syndrome : new insights from the International Takotsubo Registry

© 2021 The Authors. ESC Heart Failure published by John Wiley & Sons Ltd on behalf of European Society of Cardiology. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License.Aims: Acute pulmonary disorders are known physical triggers of takotsubo syndrome (TTS). This study aimed to investigate prevalence of acute pulmonary triggers in patients with TTS and their impact on outcomes. Methods and results: Patients with TTS were enrolled from the International Takotsubo Registry and screened for triggering factors and comorbidities. Patients were categorized into three groups (acute pulmonary trigger, chronic lung disease, and no lung disease) to compare clinical characteristics and outcomes. Of the 1670 included patients with TTS, 123 (7%) were identified with an acute pulmonary trigger, and 194 (12%) had a known history of chronic lung disease. The incidence of cardiogenic shock was highest in patients with an acute pulmonary trigger compared with those with chronic lung disease or without lung disease (17% vs. 10% vs. 9%, P = 0.017). In-hospital mortality was also higher in patients with an acute pulmonary trigger than in the other two groups, although not significantly (5.7% vs. 1.5% vs. 4.2%, P = 0.13). Survival analysis demonstrated that patients with an acute pulmonary trigger had the worst long-term outcome (P = 0.002). The presence of an acute pulmonary trigger was independently associated with worse long-term mortality (hazard ratio 2.12, 95% confidence interval 1.33-3.38; P = 0.002). Conclusions: The present study demonstrates that TTS is related to acute pulmonary triggers in 7% of all TTS patients, which accounts for 21% of patients with physical triggers. The presence of acute pulmonary trigger is associated with a severe in-hospital course and a worse long-term outcome.C. T. has been supported by the H.H. Sheikh Khalifa binHamad Al-Thani Research Programme and the Swiss HeartFoundation. The InterTAK Registry is supported by the BissDavies Charitable Trust. L. S. M. has been supported by EUHORIZON 2020(SILICOFCM ID777204)info:eu-repo/semantics/publishedVersio