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Natural and human-provoked disasters pose serious health risks to children, particularly children and youth with special healthcare needs, including many cardiology patients. The American Academy of Pediatrics (AAP) provides preparedness recommendations for families, but little is known about recommendation adherence. Caregivers of children seen in a pediatric cardiology clinic network were recruited to complete an electronic survey. Participants self-reported child medical history and their household's implementation of AAP recommended disaster preparedness items. Families received a link to AAP resources and a child ID card. Data were analyzed using descriptive statistics with Fisher's exact and Wilcoxon rank sum tests. 320 caregivers participated in the study, of whom 124 (38.8%) indicated that their child has a diagnosed cardiac condition, and 150 (46.9%) indicated that their child had special healthcare needs. The average preparedness item completion rate was 70.7% for household preparedness, 40.1% for reunification preparedness, and 26.3% for community preparedness. Households of children with medical needs had similar rates of preparedness compared to overall rates. Of all respondents, 27.8% previously received disaster preparedness resources, 67.7% would like resources on discussing disaster preparedness, and 93.0% intend to talk with their household about disaster preparedness after completing the survey. These results demonstrate a gap between AAP recommendations and household-level disaster preparedness, including patients with cardiac conditions and those with special healthcare needs. Families expressed that they were interested in getting resources for disaster preparedness. Pediatric cardiologists may consider asking about disaster preparedness and providing disaster preparedness resources tailored to the needs of their patients. [ABSTRACT FROM AUTHOR]

Background: Rapid access cardiology services have been proposed for assessment of acute cardiac conditions via an outpatient model-of-care that potentially could reduce hospitalisations. We describe a new Rapid Access Arrhythmia Clinic (RAAC) and compare major safety endpoints to usual care.Methods: We matched 312 adult patients with suspected arrhythmia in RAAC to historical age and sex-matched controls discharged from hospital within Western Sydney Local Health District with suspected arrhythmia. The primary endpoint was a composite of time to first unplanned cardiovascular hospitalisation or cardiac death over 12 months.Results: The average age of RAAC patients was 52.2±18.8 years and 51.6±18.8 years for controls, and 48.4% were female in both groups. Mean time from referral to first attended RAAC appointment was 10.5 days. Most were referred from emergency (177, 56.7%) and cardiologists at time of discharge (65, 20.8%). The most common reason for referral was palpitations (180, 57.7%). In total, 155 (49.7%) had a documented arrhythmia, with the most common being atrial fibrillation/flutter (88, 28.2%). The primary endpoint occurred in 35 (11.2%) patients in the RAAC pathway (97.1[95% CI 70-131.3] per 1,000 person-years), compared to 72 (23.1%) patients for usual care controls (229.5[95% CI 180.2-288.1] per 1,000 person-years). Using a propensity score analysis, RAAC pathway significantly reduced the primary endpoint by 59% compared to usual care (HR 0.41, 95% CI 0.27-0.62; p<0.001).Conclusions: RAACs for the early investigation and management of suspected arrhythmia is superior to usual care in terms of reduction in unplanned cardiovascular hospitalisation and death. [ABSTRACT FROM AUTHOR]

Objective: The study aimed to determine coronavirus disease-2019 (COVID-19) pandemic on the attitudes of cardiologists, the anxiety conditions and effect on clinical functioning. Material and Methods: The clinical activities scale developed by researchers and the Beck Anxiety Scale were used as data collection tools in the study. Descriptive statistical methods, materiality tests, correlation, and regression analysis were used in the analysis of the data. Results: In cardiology clinics, patient admission continued; elective interventional diagnosis and treatment work decreased. Cardiologists declared that there was a serious decline in acute coronary syndrome application (87.2% of cardiologists) and that the most common cause of patients not wanting to come to the hospital was the fear of being infected. The severity of pandemic and the improved treatment protocols for COVID- 19 were found to be effective in cardiac interventional treatment decisions. It was found that disruptions in clinical functioning increased anxiety (ß=0.128), whereas the fight and prevention against COVID-19 (ß=-0.304) decreased anxiety. Conclusion: The COVID-19 pandemic caused severe disruptions in the routine functioning of cardiology clinics and interventional procedures and an increase in anxiety among physicians. In a pandemic, preventions should be taken to minimize the treatment of non-infected patients and the anxiety of doctors besides the treatment of infected patients. [ABSTRACT FROM AUTHOR]

Background: A number of Australian hospitals are piloting outpatient rapid access chest pain clinics (RACC) as a new clinical pathway. These clinics aim to provide an alternative to inpatient care for timely access to specialist cardiology assessment whilst improving quality of care and experience. An understanding of overall patient satisfaction can be considered a marker of overall clinical success, contributes to cost-effectiveness, and is needed to develop improvement strategies. We aimed to evaluate uncertainty, satisfaction and experience among patients treated in a RACC.Methods: All consecutive patients presenting with troponin negative intermediate risk chest pain treated in a new RACC in a public quaternary teaching hospital over a 12-month period, were invited to participate in two core components: A paper-based questionnaire, and a one-month follow-up telephone structured interview. Patient satisfaction and uncertainty were assessed by the short form of the Mishel Uncertainty in Illness Scale (MUIS) and Picker Dimensions of care.Results: There was a 51.2% response rate (190/371). Patients ranged in age from 20 to 89 years (56.4±13.6 years) and 47% were female. Findings illustrated a global high level of satisfaction in all areas: care, comfort, communication and engagement, with low levels of uncertainty in illness experienced by patients.Conclusion: There were high levels of satisfaction and minimal uncertainty for patients related to their management and care in the RACC during the study period. These results have formed part of the formal cost-effectiveness assessment of the RACC used to support its transition from a pilot implementation to standard of care at the study centre. [ABSTRACT FROM AUTHOR]

Abstract Background The risk factors for abdominal aortic aneurysm (AAA) are present in many of the patients that attend our cardiology service. The aim of this study was the evaluation of the prospects of examining the abdominal aorta during our consultations and the relationship of AAA with risk factors and ischemic cardiopathy. Methods A descriptive transversal observational study was designed including 274 male patients aged ≥60 years, attended consecutively in the cardiology service, in which we studied the abdominal aorta and adjusted a logistic regression model to determine the risk factors associated with AAA. Results We were able to visualize and measure the abdominal aorta in 95.4% of cases in a fast and reliable way. The prevalence of AAA was 8.76%. 75% of patients with AAA presented ischemic heart disease. Patients with AAA were characterized by the presence of ischemic cardiopathy (Odds Ratio (OR): 4.27, 95% Confidence Interval (CI): 1.37–13.31, p = 0.012), dyslipidemia (OR: 4.99, 95% CI: 1, 07–23.31; p = 0.041), arterial hypertension (OR: 4.14, 95% CI: 1.07–15.98, p = 0.039), and a longer history of smoking (OR: 1.03; 95% CI: 1002–1.054; p = 0.037). Conclusions The evaluation of the abdominal aorta during cardiology consultations is feasible with the standard resources. Patients treated in the cardiology service present a high prevalence of AAA. We have adjusted and validated a clinical prediction model based on risk factors that allows the identification, in the cardiology consult, of patients with the highest risk of suffering from AAA. Highlights • The prevalence of abdominal aortic aneurysm in the cardiology clinic is high. • Its diagnosis is easy and fast with the existing resources in the clinic. • We validate a clinical prediction model for aneurysms based on risk factors. [ABSTRACT FROM AUTHOR]

Background: Rapid Access Cardiology (RAC) services are hospital co-located cardiologist-led outpatient clinics providing prompt assessment and management of chest pain. This service model is part of chest pain management in the United Kingdom. However, little data exists on RAC services in Australia. Our aim was to describe the introduction of RAC services to an Australian tertiary centre (utility, safety, and acceptability).Methods: Referrals were accepted for low-intermediate risk chest pain. Referrer and patient clinical data was collected prospectively in the first year of RAC - 4 February 2015 to 4 February 2016. Data was linked to hospital presentations/admissions to identify readmissions/mortality data.Results: Among 520 patients (55.0% male, mean age 55.2 years), 87.6% were referred from emergency and 68.4% assessed within 5 days. The final diagnosis was new coronary artery disease (CAD) in 7.9%, and 81.3% had  ≥2 cardiovascular risk factors (diabetes, hyperlipidaemia, hypertension, overweight/obesity, smoker, pre-existing CAD, and chronic renal failure). On average, 0.8 cardiac tests were ordered per person. In total, 35 (6.7%) had invasive coronary angiograms, with 51.4% having obstructive CAD. Patients reported in surveys (82.8% response rate) that 93.0% "strongly agreed" RAC services were useful to the community. Referrers were also "very satisfied" with RAC (7/17) or "satisfied" (9/17). Furthermore, of 336 referrals, referrers reported without RAC they would admit the patient in 11.3% of cases. There were 4.8% (25/520) unplanned cardiovascular readmissions and 0.6% (3/520) of these were for acute coronary syndromes and no deaths.Conclusions: Outpatient RAC services are an accepted, effective and safe pathway for management of low-intermediate risk chest pain. [ABSTRACT FROM AUTHOR]

Chest pain is common and places a significant burden on hospital resources. Many patients with undifferentiated low- to intermediate-risk chest pain are admitted to hospital. Rapid-access cardiology ( RAC) services are hospital co-located, cardiologist-led outpatient clinics that provide rapid assessment and immediate management but not long-term management. This service model is described as part of chest pain management and the National Service Framework for coronary heart disease in the United Kingdom ( UK). We review the evidence on the effectiveness, safety and acceptability of RAC services. Our review finds that early assessment in RAC outpatient services of patients with suspected angina, without high-risk features suspicious of an acute coronary syndrome, is safe, can reduce hospitalisations, is cost effective and has good medical practitioner and patient acceptability. However, the literature is limited in that the evaluation of this model of care has been only in the UK. It is potentially suited to other settings and needs further evaluation in other settings to assess its utility. [ABSTRACT FROM AUTHOR]

Purpose: To assess modifiable clusters of cardiovascular risk factors and patterns of antihypertensive drugs use as well as identify clinical characteristics associated with blood pressure control in Jordanians. Methods: A cross-sectional observational study was conducted in cardiology outpatient clinics at two hospitals in Amman, Jordan. Outcomes studied were prevalence of cardiovascular risk factors, patterns of antihypertensive medication use, rate of blood pressure control and factors associated with such control. Results: The number of concomitant medical conditions was high: diabetes mellitus (51 %), dyslipidemia (82 %), coronary artery disease (71 %), history of acute coronary syndrome (37 %) or coronary revascularization (64 %). Hypertension was controlled in 44 % of patients. Average number of antihypertensive medications was 2.38 ± 1.21. The most commonly prescribed monotherapy medications were beta-blockers (48 %), followed by angiotensin-converting enzyme inhibitors (ACEIs) with 28 % and angiotensin II receptor blockers (ARBs) with 23 %. Among all patients, beta-blockers (67 %) were also the most prescribed, followed by ACEIs (47 %) and ARBs (41%). Multivariate logistic regression analysis revealed inverse association between BP control and the presence of diabetes mellitus. Conclusion: There is inadequate cardiovascular risk assessment and control of blood pressure in hypertensive patients in Jordan. Several practical measures need to be taken urgently to mitigate these deficiencies. [ABSTRACT FROM AUTHOR]

In distant 1972, within framework of the Internal Clinic, a cardiologic department was organized which was soon, on 29.XII.1974, transformed into the Cardiology Clinic, later the Institute for Heart Diseases, and in 2008 was renamed the University Cardiology Clinic. The greater part of its foundation was possible owing to Prof. Dimitar Arsov and Prof. Radovan Percinkovski, who was the clinic’s first director in the period from 1974 to 1984. In 1985, the Clinic moved into its own new building, and in that way was physically detached from the Internal Clinics. Until its move to the new building, the Clinic functioned in the Internal Clinics building, organized as an outpatient polyclinic and inpatient infirmary department with clinical beds, a coronary intensive care unit and a haemodynamics laboratory equipped with the most modern equipment of that time. Today the Clinic functions through two integral divisions: an inpatient infirmary department which comprises an intensive coronary care unit and fourteen wards which altogether have 139 clinical beds, and the diagnostic centre which comprises an emergency clinic and day hospital, a communal and consultative outpatients’ clinic functioning on a daily basis, through which some 300–350 patients pass every day, and diagnostic laboratories with a capacity of nearly 100 non-invasive and 20–30 invasive diagnostic procedures daily. The Clinic is a teaching base, and its doctors are educators of students at the Medical, Dental and Pharmacy Faculties, and also of students at the High School for Nurses and X-ray technicians, but also for those in Internal Medicine and especially Cardiology. The Clinic is also a base for scientific Masters’ and post-doctoral studies, and such higher degrees are achieved not only by doctors who work here, but also by doctors from Medical Centres both in the country and abroad. Doctors working in this institution publish widely, not only a great number of books and monographs, but also original scientific papers published in indexed medical journals. [ABSTRACT FROM AUTHOR]

• Predicted consultation length by accounting for its semicontinuous nature. • Developed two-part machine learning approach to predict no-shows and visit length. • Consultation length is influenced by patient-, visit-, and doctor-related variables. • Integrating machine learning-based predictions improves schedule outcomes. • Illustrated model deployment procedure to automate prediction tasks. The observed consultation length at specialty clinics, such as cardiology care, is represented by two underlying groups - one with zero service time due to patient no-shows, and the other characterized by positive values with high variance. This inconstancy affects the scheduler's ability to accurately estimate consultation length, which, in turn, hinders effective utilization of the clinic's resources and timely access to care. The objectives of this study were to: (i) predict the consultation length by accounting for its semicontinuous nature (i.e., zero in case of no-shows and positive otherwise), using machine learning (ML) algorithms, (ii) identify important features for predicting no-shows and non-zero consultation length, and (iii) assess the impact of integrating the ML-based prediction with the appointment scheduling system. We used two-years of data extracted from the electronic medical records of a cardiology clinic. By leveraging 16 predictors pertaining to the patient, appointment, and doctor, a two-part ML-based approach was developed to handle the semicontinuous consultation length. Supervised classification models were employed to predict no-shows (i.e., categorize the consultation length as zero or positive), and regression algorithms were developed for estimating non-zero consultation lengths. Three algorithms, namely, random forests, stochastic gradient boosting, and deep neural networks, were individually employed for both no-show classification and positive consultation length prediction. Finally, the best performing classification and regression models were combined to establish the complete two-part model, and its prediction error on new data is benchmarked against the clinic's current performance. The evaluation metrics for classification models were area under the receiver operating characteristic curve (AUC-ROC) and area under the precision-recall curve (AUC-PR). The prediction performance of regression algorithms was evaluated by mean absolute error (MAE), root mean squared error (RMSE), and mean absolute percentage error (MAPE). A simulation modeling approach was adopted to ascertain the effectiveness of using ML-based prediction for scheduling decisions as opposed to the clinic's current strategy. Among the classification models tested, stochastic gradient boosted classification tree (SGBCT) demonstrated best performance (AUC-ROC = 0.85, AUC-PR = 0.64). For positive consultation length prediction, deep neural network regressor (DNNR) resulted in lowest prediction error (MAE = 8.55, RMSE = 6.88, MAPE = 12.24). The complete two-part model (SGBCT + DNNR) outperformed the clinic's approach to consultation length estimation by achieving 50 % and 52 % reduction in RMSE and MAE, respectively. Further adopting it for appointment scheduling could reduce the patient waiting time and doctor idle time by 56 % and 52 %, respectively. Besides, several clinical insights, along with critical features for no-show and consultation length prediction, were also identified from our proof-of-concept study. This study demonstrates that routine clinical tasks such as estimation of consultation length and no-shows can be accurately predicted using ML algorithms, and subsequently integrated into the clinical scheduling system to improve resource utilization and reduce patient waiting time. [ABSTRACT FROM AUTHOR]

Background: The observed consultation length at specialty clinics, such as cardiology care, is represented by two underlying groups - one with zero service time due to patient no-shows, and the other characterized by positive values with high variance. This inconstancy affects the scheduler's ability to accurately estimate consultation length, which, in turn, hinders effective utilization of the clinic's resources and timely access to care. The objectives of this study were to: (i) predict the consultation length by accounting for its semicontinuous nature (i.e., zero in case of no-shows and positive otherwise), using machine learning (ML) algorithms, (ii) identify important features for predicting no-shows and non-zero consultation length, and (iii) assess the impact of integrating the ML-based prediction with the appointment scheduling system.Methods: We used two-years of data extracted from the electronic medical records of a cardiology clinic. By leveraging 16 predictors pertaining to the patient, appointment, and doctor, a two-part ML-based approach was developed to handle the semicontinuous consultation length. Supervised classification models were employed to predict no-shows (i.e., categorize the consultation length as zero or positive), and regression algorithms were developed for estimating non-zero consultation lengths. Three algorithms, namely, random forests, stochastic gradient boosting, and deep neural networks, were individually employed for both no-show classification and positive consultation length prediction. Finally, the best performing classification and regression models were combined to establish the complete two-part model, and its prediction error on new data is benchmarked against the clinic's current performance. The evaluation metrics for classification models were area under the receiver operating characteristic curve (AUC-ROC) and area under the precision-recall curve (AUC-PR). The prediction performance of regression algorithms was evaluated by mean absolute error (MAE), root mean squared error (RMSE), and mean absolute percentage error (MAPE). A simulation modeling approach was adopted to ascertain the effectiveness of using ML-based prediction for scheduling decisions as opposed to the clinic's current strategy.Results: Among the classification models tested, stochastic gradient boosted classification tree (SGBCT) demonstrated best performance (AUC-ROC = 0.85, AUC-PR = 0.64). For positive consultation length prediction, deep neural network regressor (DNNR) resulted in lowest prediction error (MAE = 8.55, RMSE = 6.88, MAPE = 12.24). The complete two-part model (SGBCT + DNNR) outperformed the clinic's approach to consultation length estimation by achieving 50 % and 52 % reduction in RMSE and MAE, respectively. Further adopting it for appointment scheduling could reduce the patient waiting time and doctor idle time by 56 % and 52 %, respectively. Besides, several clinical insights, along with critical features for no-show and consultation length prediction, were also identified from our proof-of-concept study.Conclusion: This study demonstrates that routine clinical tasks such as estimation of consultation length and no-shows can be accurately predicted using ML algorithms, and subsequently integrated into the clinical scheduling system to improve resource utilization and reduce patient waiting time. [ABSTRACT FROM AUTHOR]