Cardiac resynchronization therapy in inotrope-dependent heart failure: a meta-analysis.

AIMS: The viability of cardiac resynchronization therapy (CRT) in inotrope-dependent heart failure (HF) has been a matter of debate. METHODS AND RESULTS: We searched Medline, EMBASE, Scopus, and the Cochrane Library until 31 December 2022. Studies were included if (i) HF patients required inotropic support at CRT implantation; (ii) patients were ≥18 years old; and (iii) they provided a clear definition of 'inotrope dependence' or 'inability to wean'. A meta-analysis was performed in R (Version 3.5.1). Nineteen studies comprising 386 inotrope-dependent HF patients who received CRT (mean age 64.4 years, 76.9% male) were included. A large majority survived until discharge at 91.1% [95% confidence interval (CI): 81.2% to 97.6%], 89.3% were weaned off inotropes (95% CI: 77.6% to 97.0%), and mean discharge time post-CRT was 7.8 days (95% CI: 3.9 to 11.7). After 1 year of follow-up, 69.7% survived (95% CI: 58.4% to 79.8%). During follow-up, the mean number of HF hospitalizations was reduced by 1.87 (95% CI: 1.04 to 2.70, P < 0.00001). Post-CRT mean QRS duration was reduced by 29.0 ms (95% CI: -41.3 to 16.7, P < 0.00001), and mean left ventricular ejection fraction increased by 4.8% (95% CI: 3.1% to 6.6%, P < 0.00001). The mean New York Heart Association (NYHA) class post-CRT was 2.7 (95% CI: 2.5 to 3.0), with a pronounced reduction of individuals in NYHA IV (risk ratio = 0.27, 95% CI: 0.18 to 0.41, P < 0.00001). On univariate analysis, there was a higher prevalence of males (85.7% vs. 40%), a history of left bundle branch block (71.4% vs. 30%), and more pronounced left ventricular end-diastolic dilation (274.3 ± 7.2 vs. 225.9 ± 6.1 mL). CONCLUSIONS: CRT appears to be a viable option for inotrope-dependent HF, with some of these patients seeming more likely to respond.

The treatment of symptomatic fat necrosis: A review and introduction of a new treatment algorithm.

OBJECTIVE: Fat necrosis (FN) is a well-known complication in plastic surgery. Excision of symptomatic FN has been the gold standard, but it often results in contour irregularities that require subsequent treatment. Different alternative surgical and nonsurgical management strategies have been described; however, there are currently no guidelines. This literature review aims to provide an overview of available treatment options and current management standards to support clinical decision making. METHOD: A literature search in the databases PubMed, Embase (via Ovid), and Web Of Science was carried out to identify eligible articles. The search strategy included combinations of the following terms: "Fat necrosis "AND (treatment OR management OR therapy). Six articles discussing or reporting management strategies of FN in a plastic surgery context were included. RESULTS: A variety of techniques were used to manage symptomatic FN. Asymptomatic or small lesions can be treated conservatively. Oil cyst and moderately sized areas of symptomatic FN can effectively be treated by aspiration, ultrasound-assisted liposuction, or needle aeration. Calcified and large areas of FN require excision and debridement of necrotic fat tissue CONCLUSION: At present, there is no consensus on the management of symptomatic FN. The authors propose a new classification system to aid the guidance of management of symptomatic FN.

Role of artificial intelligence in defibrillators: a narrative review.

Automated external defibrillators (AEDs) and implantable cardioverter defibrillators (ICDs) are used to treat life-threatening arrhythmias. AEDs and ICDs use shock advice algorithms to classify ECG tracings as shockable or non-shockable rhythms in clinical practice. Machine learning algorithms have recently been assessed for shock decision classification with increasing accuracy. Outside of rhythm classification alone, they have been evaluated in diagnosis of causes of cardiac arrest, prediction of success of defibrillation and rhythm classification without the need to interrupt cardiopulmonary resuscitation. This review explores the many applications of machine learning in AEDs and ICDs. While these technologies are exciting areas of research, there remain limitations to their widespread use including high processing power, cost and the 'black-box' phenomenon.