Adding the value of the Charlson Comorbidity Index to the GRACE score for mortality prediction in acute coronary syndromes.

BACKGROUND: Scarce and conflicting data still exist about the role of the Charlson Comorbidity Index (CCI) when added to the traditional Global Registry of Acute Coronary Events (GRACE) risk score for outcome prediction in patients with acute coronary syndrome (ACS). METHODS: All consecutive admissions due to ACS, from 1 January 2018 to 31 December 2020 were retrospectively reviewed from an internal database of a tertiary cardiac center in Salerno (Italy). Logistic and Cox proportional regression analyses were performed in order to assess the contribution of the CCI on 30-day and long-term mortality. The CCI adding value to the GRACE score was analyzed with several measures of improvement in discrimination: increase in the area under the receiver-operating characteristic curve (AUC), the integrated discrimination improvement (IDI), and the categorical and continuous net reclassification improvement (cNRI) more than 0. Robustness of the results was assessed through an internal validation procedure with bootstrapping. RESULTS: One thousand three hundred and ten patients were identified. The median age was 68 (58-78) years. One hundred and twenty (9.2%) and 113 (9.5%) deaths occurred, respectively, during the first 30 days from admission and during long-term follow-up (median follow-up time: 13 months; interquartile range: 9-24). After multivariate regression analysis, the CCI was not associated with short-term mortality, while it was significantly and independently associated with long-term mortality along with the GRACE score (hazard ratio: 1.34, 95% confidence interval: 1.22-1.47; P  < 0.001). An additive effect of CCI with the GRACE risk score was observed in predicting long-term mortality: AUC from 0.768 to 0.819 ( P  = 0.003), category-based NRI: 0.215, cNRI>0: 0.669 ( P  < 0.001), IDI: 0.066 ( P  < 0.001). CONCLUSION: The CCI is a predictor of long-term mortality and improves risk stratification of patients with ACS over the GRACE risk score.

[Implantable cardioverter-defibrillator explosion after gunshot: when the device protects not only from arrhythmic death]. / Esplosione di defibrillatore impiantabile in seguito a colpo d'arma da fuoco: quando il defibrillatore non previene soltanto la morte aritmica.

A 70-year-old man presented to the emergency department with accidental gunshot wound at left hemithorax and left shoulder/arm. Initial clinical assessment showed stable vital signs and an implantable cardioverter-defibrillator (ICD) protruding outside from large wound in the infraclavicular region. The ICD, previously implanted for secondary prevention of ventricular tachycardia, appeared burned and the battery was exploded. Urgent chest computed tomography scan was performed with evidence of left humeral fracture without significant arterial injury. The ICD generator was disconnected from passive fixation leads and removed. The patient was stabilized and the humeral fracture was fixed. Then lead extraction was successfully performed in a hybrid operating room with cardiac surgery standby. The patient was discharged in good clinical conditions after reimplantation of a novel ICD in the right infraclavicular region.Emerging technologies are promising in making lead extraction safer and more accessible for patients worldwide. This case report provides the most up-to-date indications and procedural approaches for lead extraction and insights on the future trends in this field.

Artificial Intelligence in Hypertension Management: An Ace up Your Sleeve.

Arterial hypertension (AH) is a progressive issue that grows in importance with the increased average age of the world population. The potential role of artificial intelligence (AI) in its prevention and treatment is firmly recognized. Indeed, AI application allows personalized medicine and tailored treatment for each patient. Specifically, this article reviews the benefits of AI in AH management, pointing out diagnostic and therapeutic improvements without ignoring the limitations of this innovative scientific approach. Consequently, we conducted a detailed search on AI applications in AH: the articles (quantitative and qualitative) reviewed in this paper were obtained by searching journal databases such as PubMed and subject-specific professional websites, including Google Scholar. The search terms included artificial intelligence, artificial neural network, deep learning, machine learning, big data, arterial hypertension, blood pressure, blood pressure measurement, cardiovascular disease, and personalized medicine. Specifically, AI-based systems could help continuously monitor BP using wearable technologies; in particular, BP can be estimated from a photoplethysmograph (PPG) signal obtained from a smartphone or a smartwatch using DL. Furthermore, thanks to ML algorithms, it is possible to identify new hypertension genes for the early diagnosis of AH and the prevention of complications. Moreover, integrating AI with omics-based technologies will lead to the definition of the trajectory of the hypertensive patient and the use of the most appropriate drug. However, AI is not free from technical issues and biases, such as over/underfitting, the "black-box" nature of many ML algorithms, and patient data privacy. In conclusion, AI-based systems will change clinical practice for AH by identifying patient trajectories for new, personalized care plans and predicting patients' risks and necessary therapy adjustments due to changes in disease progression and/or therapy response.

Implantable defibrillator-detected heart failure status predicts atrial fibrillation occurrence.

BACKGROUND: In heart failure (HF) patients, atrial fibrillation (AF) is associated with a worse prognosis. Implantable cardioverter-defibrillator (ICD) diagnostics allow continuous monitoring of AF and are equipped with algorithms for HF monitoring. OBJECTIVE: We evaluated the association between the values of the multisensor HF HeartLogic index and the incidence of AF, and assessed the performance of the index in detecting follow-up periods of significantly increased AF risk. METHODS: The HeartLogic feature was activated in 568 ICD patients. Median follow-up was 25 months [25th-75th percentile (15-35)]. The HeartLogic algorithm calculates a daily HF index and identifies periods of IN-alert state on the basis of a configurable threshold. The endpoints were daily AF burden ≥5 minutes, ≥6 hours, and ≥23 hours. RESULTS: The HeartLogic index crossed the threshold value 1200 times. AF burden ≥5 minutes/day was documented in 183 patients (32%), ≥6 hours/day in 118 patients (21%), and ≥23 hours/day in 89 patients (16%). The weekly time of IN-alert state was independently associated with AF burden ≥5 minutes/day (hazard ratio [HR] 1.95; 95% confidence interval [CI] 1.22-3.13; P = .005), ≥6 hours/day (HR 2.66; 95% CI 1.60-4.44; P <.001), and ≥23 hours/day (HR 3.32; 95% CI 1.83-6.02; P <.001), after correction for baseline confounders. Comparison of the episode rates in the IN-alert state with those in the OUT-of-alert state yielded HR ranging from 1.57 to 3.11 for AF burden from ≥5 minutes to ≥23 hours. CONCLUSIONS: The HeartLogic alert state was independently associated with AF occurrence. The intervals of time defined by the algorithm as periods of increased risk of HF allow risk stratification of AF according to various thresholds of daily burden.