Nonagenarians admission and prognosis in a tertiary center intensive coronary care unit - a prospective study.

BACKGROUND: With increasing life expectancy, the prevalence of nonagenarians with cardiovascular disease is steadily growing. However, this population is underrepresented in randomized trials and thus poorly defined, with little quality evidence to support and guide optimal management. The aim of the present study was to evaluate the clinical management, therapeutic approach, and outcomes of nonagenarians admitted to a tertiary care center intensive coronary care unit (ICCU). METHODS: We prospectively collected all patients admitted to a tertiary care center ICCU between July 2019 - July 2022 and compared nonagenarians to all other patients. The primary outcome was in-hospital mortality. RESULTS: A total of 3807 patients were included in the study. Of them 178 (4.7%) were nonagenarians and 93 (52%) females. Each year the prevalence of nonagenarians has increased from 4.0% to 2019, to 4.2% in 2020, 4.6% in 2021 and 5.3% in 2022. Admission causes differed between groups, including a lower rate of acute coronary syndromes (27% vs. 48.6%, p < 0.001) and a higher rate of septic shock (4.5% vs. 1.2%, p < 0.001) in nonagenarians. Nonagenarians had more comorbidities, such as hypertension, renal failure, and atrial fibrillation (82% vs. 59.6%, 23% vs. 12.9%, 30.3% vs. 14.4% p < 0.001, respectively). Coronary intervention was the main treatment approach, although an invasive strategy was less frequent in nonagenarians in comparison to younger subjects. In-hospital mortality rate was 2-fold higher in the nonagenarians (5.6% vs. 2.5%, p = 0.025). CONCLUSION: With increasing life expectancy, the prevalence of nonagenarians in ICCU's is expected to increase. Although nonagenarian patients had more comorbidities and higher in-hospital mortality, they generally have good outcomes after admission to the ICCU. Hence, further studies to create evidence-based practices and to support and guide optimal management in these patients are warranted.

Multimodal Machine Learning for Prediction of 30-Day Readmission Risk in Elderly Population.

BACKGROUND: Readmission within 30 days is a prevalent issue among elderly patients, linked to unfavorable health outcomes. Our objective was to develop and validate multimodal machine learning models for predicting 30-day readmission risk in elderly patients discharged from internal medicine departments. METHODS: This was a retrospective cohort study which included elderly patients aged 75 or older, who were hospitalized at the Hadassah Medical Center internal medicine departments between 2014 and 2020. Three machine learning algorithms were developed and employed to predict 30-day readmission risk. The primary measures were predictive model performance scores, specifically area under the receiver operator curve (AUROC), and average precision. RESULTS: This study included 19,569 admissions. Of them, 3258 (16.65%) resulted in 30-day readmission. Our 3 proposed models demonstrated high accuracy and precision on an unseen test set, with AUROC values of 0.87, 0.89, and 0.93, respectively, and average precision values of 0.76, 0.78, and 0.81. Feature importance analysis revealed that the number of admissions in the past year, history of 30-day readmission, Charlson score, and admission length were the most influential variables. Notably, the natural language processing score, representing the probability of readmission according to a textual-based model trained on social workers' assessment letters during hospitalization, ranked among the top 10 contributing factors. CONCLUSIONS: Leveraging multimodal machine learning offers a promising strategy for identifying elderly patients who are at high risk for 30-day readmission. By identifying these patients, machine learning models may facilitate the effective execution of preventive actions to reduce avoidable readmission incidents.

Immature Platelet Fraction and Clinical Outcomes in Patients Undergoing Transcatheter Aortic Valve Implantation.

INTRODUCTION: Immature platelets or reticulated platelets are newly released thrombocytes. They can be identified by their large size and high RNA cytoplasm concentration. Immature platelet fraction (IPF) represents the percentage of immature circulative platelets relative to the total number of platelets. The role of IPF in patients undergoing transcatheter aortic valve implantation (TAVI) is unknown. The aim of the current trial was to assess the levels of IPF in patients undergoing TAVI and correlation with clinical outcomes. MATERIAL AND METHODS: Immature platelet fraction levels were measured 3 times in all patients (preprocedure, 1-2 days post-procedure and 1-month post-procedure). Immature platelet fraction measurement was carried out using an autoanalyzer (Sysmex XE-2100). Patients were followed for 12 months. Primary outcomes were defined as complications during hospitalizations, rehospitalization, and mortality. RESULTS: Fifty-one patients were included in the study. Mean age was 79.8 (±9.6), and 28 (55%) were women. Twenty-one patients (41%) had complications: Of them, 6 of 21 (29%) occurred during hospitalizations (2-vascular complications; 2-sepsis, 2-implantation of a pacemaker), 9 of 21 (43%) patients were rehospitalized after the index admission, and 6 patients died during the follow-up period. Multivariate Cox regression analysis found that IPF < 7% in at least one of the 3 tests was associated with worse outcomes (hazard ratio 3.42; 95% CI 1.11-10.5, P = .032). CONCLUSION: Immature platelet fraction >7% in patients undergoing TAVI is associated with worse outcomes. Further studies are needed to better understand this phenomenon.

Troponin level at presentation as a prognostic factor among patients presenting with non-ST-segment elevation myocardial infarction.

BACKGROUND: Timely reperfusion within 120 min is strongly recommended in patients presenting with non-ST-segment myocardial infarction (NSTEMI) with very high-risk features. Evidence regarding the use of high-sensitivity cardiac troponin (hs-cTn) concentration upon admission for the risk-stratification of patients presenting with NSTEMI to expedite percutaneous coronary intervention (PCI) and thus potentially improve outcomes is limited. METHODS: All patients admitted to a tertiary care center ICCU between July 2019 and July 2022 were included. Hs-cTnI levels on presentaion were recorded, dividing patients into quartiles based on baseline hs-cTnI. Association between initial hs-cTnI and all-cause mortality during up to 3 years of follow-up was studied. RESULTS: A total of 544 NSTEMI patients with a median age of 67 were included. Hs-cTnI levels in each quartile were: (a) ≤122, (b) 123-680, (c) 681-2877, and (d) ≥2878 ng/L. There was no difference between the initial hs-cTnI level groups regarding age and comorbidities. A higher mortality rate was observed in the highest hs-cTnI quartile as compared with the lowest hs-cTnI quartile (16.2% vs. 7.35%, p = .03) with hazard ratio (HR) for mortality of 2.6 (95% confidence interval [CI]: 1.23-5.4; p = .012) in the unadjusted model, and HR of 2.06 (95% CI: 1.01-4.79; p = .047) with adjustment for age, gender, serum creatinine, and significant comorbidities. CONCLUSIONS: Patients with NSTEMI and higher hs-cTnI levels upon admission faced elevated mortality risk. This underscores the need for further prospective investigations into early reperfusion strategies' impact on NSTEMI patients' mortality, based on admission troponin elevation.

Artificial Intelligence Assessment of Biological Age From Transthoracic Echocardiography: Discrepancies with Chronologic Age Predict Significant Excess Mortality.

BACKGROUND: Age and sex can be estimated using artificial intelligence on the basis of various sources. The aims of this study were to test whether convolutional neural networks could be trained to estimate age and predict sex using standard transthoracic echocardiography and to evaluate the prognostic implications. METHODS: The algorithm was trained on 76,342 patients, validated in 22,825 patients, and tested in 20,960 patients. It was then externally validated using data from a different hospital (n = 556). Finally, a prospective cohort of handheld point-of-care ultrasound devices (n = 319; ClinicalTrials.gov identifier NCT05455541) was used to confirm the findings. A multivariate Cox regression model was used to investigate the association between age estimation and chronologic age with overall survival. RESULTS: The mean absolute error in age estimation was 4.9 years, with a Pearson correlation coefficient of 0.922. The probabilistic value of sex had an overall accuracy of 96.1% and an area under the curve of 0.993. External validation and prospective study cohorts yielded consistent results. Finally, survival analysis demonstrated that age prediction ≥5 years vs chronologic age was associated with an independent 34% increased risk for death during follow-up (P < .001). CONCLUSIONS: Applying artificial intelligence to standard transthoracic echocardiography allows the prediction of sex and the estimation of age. Machine-based estimation is an independent predictor of overall survival and, with further evaluation, can be used for risk stratification and estimation of biological age.

The Effect of War on STEMI Incidence: Insights from Intensive Cardiovascular Care Unit Admissions.

(1) Background: The impact of armed conflicts on public health is undeniable, with psychological stress emerging as a significant risk factor for cardiovascular disease (CVD). Nevertheless, contemporary data regarding the influence of war on CVD, and especially on acute coronary syndrome (ACS), are scarce. Hence, the aim of the current study was to assess the repercussions of war on the admission and prognosis of patients admitted to a tertiary care center intensive cardiovascular care unit (ICCU). (2) Methods: All patients admitted to the ICCU during the first three months of the Israel-Hamas war (2023) were included and compared with all patients admitted during the same period in 2022. The primary outcome was in-hospital mortality. (3) Results: A total of 556 patients (184 females [33.1%]) with a median age of 70 (IQR 59-80) were included. Of them, 295 (53%) were admitted to the ICCU during the first three months of the war. Fewer Arab patients and more patients with ST-segment elevation myocardial infraction (STEMI) were admitted during the war period (21.8% vs. 13.2%, p < 0.001, and 31.9% vs. 24.1%, p = 0.04, respectively), whereas non-STEMI (NSTEMI) patients were admitted more frequently in the pre-war year (19.3% vs. 25.7%, p = 0.09). In-hospital mortality was similar in both groups (4.4% vs. 3.4%, p = 0.71; HR 1.42; 95% CI 0.6-3.32, p = 0.4). (4) Conclusions: During the first three months of the war, fewer Arab patients and more STEMI patients were admitted to the ICCU. Nevertheless, in-hospital mortality was similar in both groups.

Incidence of Ventricular Fibrillation and Sustained Ventricular Tachycardia Complicating Non-ST Segment Elevation Myocardial Infarction.

Background: Primary ventricular fibrillation (VF) and sustained ventricular tachycardia (VT) are potentially lethal complications in patients suffering from acute myocardial infarction (MI). In contrast with the profound data regarding the incidence and prognostic value of ventricular arrhythmias in ST elevation myocardial infarction (STEMI) patients, data regarding contemporary non-ST elevation myocardial infarction (NSTEMI) patients with ventricular arrhythmias is scarce. The aim of the current study was to investigate the incidence of VF/VT complicating NSTEMI among patients admitted to an intensive coronary care unit (ICCU). Methods: Prospective, single-center study of patients diagnosed with NSTEMI admitted to ICCU between June 2019 and December 2022. Data including demographics, presenting symptoms, comorbid conditions, and physical examination, as well as laboratory and imaging data, were analyzed. Patients were continuously monitored for arrhythmias during their admission. The study endpoint was the development of VF/sustained VT during admission. Results: A total of 732 patients were admitted to ICCU with a diagnosis of NSTEMI. Of them, six (0.8%) patients developed VF/VT during their admission. Nevertheless, three were excluded after they were misdiagnosed with NSTEMI instead of posterior ST elevation myocardial infarction (STEMI). Hence, only three (0.4%) NSTEMI patients had VF/VT during admission. None of the patients died during 1-year follow-up. Conclusions: VF/VT in NSTEMI patients treated according to contemporary guidelines including early invasive strategy is rare, suggesting these patients may not need routine monitoring and ICCU setup.

Artificial intelligence based prediction model of in-hospital mortality among females with acute coronary syndrome: for the Jerusalem Platelets Thrombosis and Intervention in Cardiology (JUPITER-12) Study Group.

Introduction: Despite ongoing efforts to minimize sex bias in diagnosis and treatment of acute coronary syndrome (ACS), data still shows outcomes differences between sexes including higher risk of all-cause mortality rate among females. Hence, the aim of the current study was to examine sex differences in ACS in-hospital mortality, and to implement artificial intelligence (AI) models for prediction of in-hospital mortality among females with ACS. Methods: All ACS patients admitted to a tertiary care center intensive cardiac care unit (ICCU) between July 2019 and July 2023 were prospectively enrolled. The primary outcome was in-hospital mortality. Three prediction algorithms, including gradient boosting classifier (GBC) random forest classifier (RFC), and logistic regression (LR) were used to develop and validate prediction models for in-hospital mortality among females with ACS, using only available features at presentation. Results: A total of 2,346 ACS patients with a median age of 64 (IQR: 56-74) were included. Of them, 453 (19.3%) were female. Female patients had higher prevalence of NSTEMI (49.2% vs. 39.8%, p < 0.001), less urgent PCI (<2 h) rates (40.2% vs. 50.6%, p < 0.001), and more complications during admission (17.7% vs. 12.3%, p = 0.01). In-hospital mortality occurred in 58 (2.5%) patients [21/453 (5%) females vs. 37/1,893 (2%) males, HR = 2.28, 95% CI: 1.33-3.91, p = 0.003]. GBC algorithm outscored the RFC and LR models, with area under receiver operating characteristic curve (AUROC) of 0.91 with proposed working point of 83.3% sensitivity and 82.4% specificity, and area under precision recall curve (AUPRC) of 0.92. Analysis of feature importance indicated that older age, STEMI, and inflammatory markers were the most important contributing variables. Conclusions: Mortality and complications rates among females with ACS are significantly higher than in males. Machine learning algorithms for prediction of ACS outcomes among females can be used to help mitigate sex bias.