The association of traditional cardiovascular disease (CVD) risk factors with outcomes of Takotsubo syndrome (TTS) is not well-defined. This study examined how modifiable CVD risk factors affect composite cardiovascular outcomes in TTS hospitalizations. TTS admissions were identified using ICD-10 codes and compared for demographics and comorbidities using the 2019 National Inpatient Sample. A multivariable regression examined the association of traditional CVD risk variables with adverse composite cardiovascular outcomes in TTS, controlling for confounders including sociodemographic or hospital-level characteristics and other relevant comorbidities. A total of 16,055 (38.1%) of the 41,855 adult TTS admissions had composite cardiovascular outcomes (TACCO). The TACCO cohort was 81.5% white, 77.3% female, and 72 years old. This group had higher rates of diabetes and peripheral vascular disease (PVD). The results showed that a higher prevalence of diabetes with chronic complications (OR = 1.18) and complicated hypertension (HTN) (OR = 1.1) predicted TACCO, whereas tobacco use disorder (OR = 0.84), hyperlipidemia (OR = 0.76), and uncomplicated HTN (OR = 0.65) (p < 0.001) showed a paradoxical effect with TACCO. TACCO had fewer routine discharges (35.3% vs. 63.4%), longer stays (6 vs. 3 days), and higher median hospital costs (78,309 USD vs. 44,966 USD). This population-based study found that complicated HTN and DM with chronic complications are strongly associated with adverse cardiovascular outcomes in TTS hospitalizations. But still, some risk factors, such as hyperlipidemia and uncomplicated HTN, have counterintuitive effects that require further evaluation. To prevent cardiac events in TTS patients, traditional CVD risk factors must be addressed.

Cardiac amyloidosis was long considered rare and untreatable, but both assumptions have given way, and the disease is now recognized as an underdiagnosed and increasingly treatable cause of heart failure. The available therapies work by slowing amyloid deposition, so their benefit depends on early diagnosis, yet the condition is still identified late and usually only after referral for specialized imaging. Artificial intelligence has started to shorten this path. Models built on the electrocardiogram and the echocardiogram detect amyloid infiltration with high accuracy and have reached external validation, while artificial-intelligence-based analysis of heart sound, the cheapest and most widely available cardiac signal, has not yet been directly evaluated in this disease. The physiological rationale for studying it is strong, because a ventricle stiffened by amyloid is expected to alter the low-frequency diastolic sounds that reflect ventricular compliance, although whether these changes are specific to amyloidosis remains unknown. This review brings together the acoustic manifestations of cardiac amyloidosis, the signal-processing and machine learning methods relevant to heart sounds, the clinical evidence across imaging and acoustic modalities, and the practical and equity questions that deployment raises. Heart sound analysis remains the most scalable and least explored opportunity in the field. We outline the mechanistic rationale, the available methods, the lessons from the more mature electrocardiographic and echocardiographic literature, and a set of minimum design requirements for a first disease-specific study.