OBJECTIVES

Clinical pathways (CPs) ensure adherence to heart failure (HF) management guidelines. To optimize quality care in a low resource setting, an evidence-based care pathway for the management of acute HF was implemented at the emergency department (ED) of the Philippine General Hospital (PGH), the designated national tertiary hospital and referral center. This study aimed to describe the characteristics of adults with acute HF admitted at the ED and evaluate the quality of care they received, measured using physician adherence to the hospital’s acute heart failure CP.

METHODS

This was a retrospective, descriptive cohort study. We reviewed the inpatient charts of all adult patients with acute HF admitted to the ED of the PGH and referred to the Division of Cardiovascular Medicine between December 1, 2022 and May 31, 2023. Quality of care was assessed based on adherence to quality indicators adapted from routine and conditional order sets detailed in the pathway. Descriptive statistics was utilized to describe patient characteristics, quality of care, and outcomes.

RESULTS

Two hundred thirty-six (236) patients were included, with a mean age of 51.8 years. Majority were male (53.4%); hypertension (61.4%) and ischemic heart disease (53.8%) were the most common comorbidities, and infection the most common precipitant of decompensation (60.6%). There were optimal adherence rates to routine orders, which included referrals to Internal Medicine and Cardiology, baseline vital signs monitoring, fluid intake and output monitoring, chest radiograph, complete blood count, blood urea nitrogen, sodium, potassium, prothrombin time, partial thromboplastin time, arterial blood gas, urinalysis, and N-terminal pro b-type natriuretic peptide. Conditional orders, such as oxygen support, focused echocardiography, thyroid - stimulating hormone, and the use of vasopressors, diuretics, and venous thromboembolism prophylactic agents, were optimally performed when warranted. However, we noted suboptimal adherence to certain resource-intensive conditional orders, such as hourly monitoring of urine output (61.4%), hooking to cardiac monitor (53.8%), and performance of 12-lead ECG within 10 minutes (56.8%). Further, only 43.9% of patients were referred to the intensive care unit. Troponin I, calcium, magnesium, and albumin were ordered in excess.

CONCLUSION

Overall adherence rate of physicians to the hospital’s Acute Heart Failure Pathway was satisfactory. Work is needed to improve adherence to hourly urine output monitoring, consistent hooking to cardiac monitor, and timely performance of 12-lead ECG – an effort that begins with expanding in-hospital diagnostic equipment and human resource supply. We recommend continuous pathway implementation with periodic evaluation and stakeholder feedback to further improve quality of care.

Human ; Male ; Female ; Middle Aged: 45-64 Yrs Old ; Adult ; Albumins ; Blood ; Blood Urea Nitrogen ; Calcium ; Cardiology ; Chart ; Charts ; Cohort Studies ; Critical Care ; Critical Pathways ; Diagnostic Equipment ; Disease ; Diuretics ; Echocardiography ; Electrocardiography ; Emergencies ; Emergency Service, Hospital ; Equipment And Supplies ; Evaluation Studies As Topic ; Feedback ; Heart ; Heart Diseases ; Heart Failure ; Hormones ; Hospitals ; Hospitals, General ; Humans ; Hypertension ; Indicators And Reagents ; Infection ; Infections ; Inpatients ; Intensive Care Units ; Internal Medicine ; Lead ; Magnesium ; Male ; Medicine ; Myocardial Ischemia ; Natriuretic Peptide, Brain ; Natriuretic Peptides ; Nitrogen ; Overall ; Oxygen ; Partial Thromboplastin Time ; Patients ; Peptides ; Philippines ; Physicians ; Potassium ; Prothrombin ; Prothrombin Time ; Quality Of Health Care ; Referral And Consultation ; Sodium ; Statistics ; Tertiary Care Centers ; Thorax ; Thromboembolism ; Thromboplastin ; Thyroid Gland ; Time ; Troponin ; Troponin I ; Universities ; Urea ; Urinalysis ; Urine ; Venous Thromboembolism ; Vital Signs ; Work ; Workforce

BACKGROUND AND OBJECTIVE.

Severe Acute Respiratory Syndrome - Coronavirus-2 (SARS-CoV-2) was initially known to affect the respiratory system and has been reported to also involve the cardiovascular system leading to myocardial damage. Remdesivir is one of the approved treatments for COVID-19, wherein viral replication is inhibited by terminating the RNA transcription prematurely. According to studies, the primary electrocardiographic effect of remdesivir in COVID-19 patients are sinus bradycardia and QT prolongation. The use of electrocardiogram (ECG) is an essential diagnostic tool in assessing the electrical conditions of the heart. The objective of this study is to describe the electrocardiographic profile of adult patients with COVID-19 who were given remdesivir and admitted in the University of the Philippines-Philippine General Hospital (UP-PGH). To this date, this is the only study done locally identifying the electrocardiographic profiles of adult patients with COVID-19 who were given remdesivir.

METHODS

This was a retrospective descriptive study involving adult patients with COVID-19 who were given remdesivir and admitted in UP-PGH from June 2021 to June 2022. Demographic profiles and 12-lead ECG done during the hospital admission were gathered. Descriptive statistics was used to summarize the clinical characteristics and the electrocardiographic findings of the patients.

RESULTS

There were 412 confirmed COVID-19 patients who were given remdesivir (mean age 56 years old; female 52%) included in this study. The most common comorbidities were hypertension, diabetes mellitus, and stroke. Majority of the patients had severe (58%) to critical (22%) COVID-19 infection. Most of the patients had sinus rhythm (94%), normal rate (72%), and normal axis (93%). The most common baseline ECG findings were non-specific ST-T wave changes (42%). Some patients had atrioventricular blocks (3.4%), bundle branch blocks (3.6%), prolonged QT interval (1.9%). Among those with repeat 12-L ECG (136 patients) during admission, ECG changes observed were sinus bradycardia (6%), prolonged QT interval (4%), and both (1.5%).

CONCLUSION

Based on this retrospective review, which to our knowledge is the only study done locally investigating the effects of remdesivir on ECG of adult Filipino patients with COVID-19 infection, majority of the patients had sinus rhythm, normal rate, and axis. The most common ECG finding was non-specific ST-T wave changes. This study demonstrated a low incidence of adverse ECG changes that would preclude the administration of remdesivir when indicated. These include sinus bradycardia and QT interval prolongation which did not require further interventions. ECG remains to be useful, low-cost noninvasive tool that can help monitor electrophysiologic adverse events of remdesivir.

Electrocardiography ; COVID-19 ;

Purulent pericarditis is a rare occurrence in the era of modern antibiotics. It is most often caused by organisms such as Staphylococcus aureus, Streptococcus pneumoniae, Viridans streptococci, Haemophilus influenzae and anaerobic bacteria with Streptococcus pyogenes (S.pyogenes) being a possible, though very uncommon etiology. This case represents an occurrence of S. pyogenes pericarditis in an apparently healthy female with no known immunocompromising condition. A 37-year-old female, married, real estate agent with no comorbidities came in for chest pain radiating to the upper back, relieved with leaning forward. Cardiac biomarker was normal, ECG demonstrated diffuse ST-segment elevation and PR segment depression, while imaging showed lobar pneumonia. Blood tests showed leukocytosis with neutrophilic predominance and workup for immunocompromised state was negative. The 2D echo showed large pericardial effusion with tamponade physiology. An urgent pericardiocentesis was done. Cultures grew Streptococcus pyogenes confirming the diagnosis of acute purulent pericarditis. Daily drainage of pericardial effusion, colchicine, ibuprofen was initiated together with antibiotics and the patient had resolution of pericardial effusion. Acute pyogenic pericarditis with cardiac tamponade is a rare but serious condition that requires prompt recognition and intervention. Early diagnosis, through a combination of clinical suspicion, ECG and echocardiography is crucial for initiating timely treatment.

Human ; Female ; Adult: 25-44 Yrs Old ; Viridans Streptococci ; Streptococcus Pyogenes ; Streptococcus Pneumoniae ; Staphylococcus Aureus ; Streptococcal Infections ; Electrocardiography ; Echocardiography ; Early Diagnosis

The surface electrocardiogram (ECG) can provide many clues to a patient’s underlying medical condition or tendency for arrhythmogenesis. An 80-year-old man with severe aortic stenosis and an implantable cardioverter-defibrillator (ICD) for advanced heart block presented with burping, chest discomfort and intermittent pounding sensations. His ECG showed atrial fibrillation with intermittent ventricular pacing at 60 bpm characterized by irregularly irregular rhythm, absent P waves, narrow intrinsic QRS complexes alternating with wide-paced beats (left bundle branch block [LBBB] morphology, superior axis) and visible pacing spikes. Device interrogation revealed ventricular tachycardia (VT) storm with multiple appropriate ICD shocks explaining his pounding sensations. This report highlights two key teaching points: recognizing atrial fibrillation during ventricular pacing—a frequently missed diagnosis affecting nearly half of patients with pacemakers—and managing VT storm to reduce shock burden. After device reprogramming and antiarrhythmic adjustment, the patient became asymptomatic.

Human ; Male ; Aged: 65-79 Yrs Old ; Thorax ; Teaching ; Tachycardia, Ventricular ; Electrocardiography ; Atrial Fibrillation ; Bundle-branch Block ; Constriction, Pathologic

BACKGROUND AND OBJECTIVE

COVID-19 has been associated with cardiac injury, often detectable through electrocardiographic (ECG) changes. This study seeks to characterize the cardiovascular and electrocardiographic profiles of adult patients diagnosed with COVID-19.

METHODS

This study included adult patients with confirmed COVID-19 from June 2021 to June 2022. Clinical profiles and 12-lead ECG tracings were obtained from electronic medical records and reviewed independently by three cardiologists. Descriptive analysis was performed to summarize the cardiovascular and electrocardiographic findings in this population.

RESULTS

The study included 998 COVID-19 patients (mean age: 50 years; 53.7% male). The most common comorbidities were hypertension, diabetes, and dyslipidemia. A majority (31.36%) presented with severe COVID-19 infection. The most frequent significant ECG abnormalities observed at admission were sinus tachycardia (22.8%), and atrial fibrillation (11.02%). Additional ischemic findings included ST segment depression (2.91%), T-wave inversion (1.70%), and ST segment elevation (2.71%).

CONCLUSION

The baseline ECG findings among COVID-19 patients were predominantly normal; however, significant abnormalities were also identified. The most frequent abnormalities included sinus tachycardia, atrial fibrillation, and ischemic changes, all of which may have clinical implications.

Human ; Coronavirus Disease 19 ; Covid-19 ; Electrocardiography ; Atrial Fibrillation

INTRODUCTION: Out-of-hospital-cardiac-arrest (OHCA) is a major public health challenge and post-return-of-spontaneous-circulation (ROSC) goals have shifted from just survival to survival with intact neurology. Although post-ROSC care is crucial for survival with intact neurology, there are insufficient well-established protocols for post-resuscitation care. We aimed to evaluate post-resuscitation care in the emergency department (ED) of adult (aged ≥16 years) OHCA patients with sustained ROSC and its associated neurologically intact survival. METHODS: A retrospective review of electronic medical records was conducted for OHCA patients with sustained ROSC at the ED. Data including demographics, pre-hospital resuscitation, ED resuscitation, post-resuscitation care and eventual outcomes were analysed. RESULTS: Among 921 OHCA patients, 85 (9.2%) had sustained ROSC at the ED. Nineteen patients (19/85, 22.4%) survived, with 13 (13/85, 15.3%) having intact neurology at discharge. Electrocardiogram and chest X-ray were performed in all OHCA patients, whereas computed tomography (CT) was performed inconsistently, with CT brain being most common (74/85, 87.1%), while CT pulmonary angiogram (6/85, 7.1%), abdomen and pelvis (4/85, 4.7%) and aortogram (2/85, 2.4%) were done infrequently. Only four patients (4.7%) had all five neuroprotective goals of normoxia, normocarbia, normotension, normothermia and normoglycaemia achieved in the ED. The proportion of all five neuroprotective goals being met was significantly higher ( P = 0.01) among those with neurologically intact survival (3/13, 23.1%) than those without (1/72, 1.4%). CONCLUSION Post-resuscitation care at the ED showed great variability, indicating gaps between recommended guidelines and clinical practice. Good quality post-resuscitation care, centred around neuroprotection goals, must be initiated promptly to achieve meaningful survival with intact neurology.
Humans ; Out-of-Hospital Cardiac Arrest/mortality* ; Retrospective Studies ; Male ; Female ; Middle Aged ; Emergency Service, Hospital ; Cardiopulmonary Resuscitation/methods* ; Return of Spontaneous Circulation ; Aged ; Adult ; Treatment Outcome ; Electrocardiography ; Tomography, X-Ray Computed ; Aged, 80 and over

INTRODUCTION: Arrhythmias, especially atrial fibrillation (AF) and ventricular arrhythmias, are independent risk factors of mortality in patients with ischaemic heart disease (IHD). While there is a growing body of evidence that suggests an association between obstructive sleep apnoea (OSA) and cardiac arrhythmias, evidence on this relationship in patients with IHD has been scant and inconsistent. We hypothesised that in patients with IHD, severe OSA is associated with an increased risk of nocturnal arrhythmias. METHODS: We studied 103 consecutive patients with IHD who underwent an overnight polysomnography. Exposed subjects were defined as patients who had an apnoea-hypopnoea index (AHI) ≥30/h (severe OSA), and nonexposed subjects were defined as patients who had an AHI <30/h (nonsevere OSA). All electrocardiograms (ECGs) were interpreted by the Somte ECG analysis software and confirmed by a physician blinded to the presence or absence of exposure. Arrhythmias were categorised as supraventricular and ventricular. Arrhythmia subtypes (ventricular, atrial and conduction delay) were analysed as dichotomous outcomes using multiple logistic regression models. RESULTS: Atrial fibrillation and AF/flutter (odds ratio 13.5, 95% confidence interval 1.66-109.83; P = 0.003) were found to be more common in the severe OSA group than in the nonsevere OSA group. This association remained significant after adjustment for potential confounders. There was no significant difference in the prevalence of ventricular and conduction delay arrhythmias between the two groups. CONCLUSION In patients with IHD, there was a significant association between severe OSA and nocturnal AF/flutter. This underscores the need to evaluate for OSA in patients with IHD, as it may have important implications on clinical outcomes.
Humans ; Sleep Apnea, Obstructive/diagnosis* ; Atrial Fibrillation/diagnosis* ; Male ; Female ; Middle Aged ; Polysomnography ; Electrocardiography ; Myocardial Ischemia/complications* ; Aged ; Risk Factors ; Logistic Models

INTRODUCTION: Elevated troponin, while essential for diagnosing myocardial infarction, can also be present in non-myocardial infarction conditions. The myocardial-ischaemic-injury-index (MI3) algorithm is a machine learning algorithm that considers age, sex and cardiac troponin I (TnI) results to risk-stratify patients for type 1 myocardial infarction. METHOD: Patients aged ≥25 years who presented to the emergency department (ED) of Singapore General Hospital with symptoms suggestive of acute coronary syndrome with no diagnostic 12-lead electrocardiogram (ECG) changes were included. Participants had serial ECGs and high-sensitivity troponin assays performed at 0, 2 and 7 hours. The primary outcome was the adjudicated diagnosis of type 1 myocardial infarction at 30 days. We compared the performance of MI3 in predicting the primary outcome with the European Society of Cardiology (ESC) 0/2-hour algorithm as well as the 99th percentile upper reference limit (URL) for TnI. RESULTS: There were 1351 patients included (66.7% male, mean age 56 years), 902 (66.8%) of whom had only 0-hour troponin results and 449 (33.2%) with serial (both 0 and 2-hour) troponin results available. MI3 ruled out type 1 myocardial infarction with a higher sensitivity (98.9, 95% confidence interval [CI] 93.4-99.9%) and similar negative predictive value (NPV) 99.8% (95% CI 98.6-100%) as compared to the ESC strategy. The 99th percentile cut-off strategy had the lowest sensitivity, specificity, positive predictive value and NPV. CONCLUSION The MI3 algorithm was accurate in risk stratifying ED patients for myocardial infarction. The 99th percentile URL cut-off was the least accurate in ruling in and out myocardial infarction compared to the other strategies.
Humans ; Male ; Female ; Emergency Service, Hospital ; Middle Aged ; Electrocardiography ; Machine Learning ; Singapore ; Chest Pain/blood* ; Troponin I/blood* ; Myocardial Infarction/blood* ; Risk Assessment/methods* ; Aged ; Algorithms ; Acute Coronary Syndrome/blood* ; Adult ; Sensitivity and Specificity

Cardiovascular diseases and psychological disorders represent two major threats to human physical and mental health. Research on electrocardiogram (ECG) signals offers valuable opportunities to address these issues. However, existing methods are constrained by limitations in understanding ECG features and transferring knowledge across tasks. To address these challenges, this study developed a multi-resolution feature encoding network based on residual networks, which effectively extracted local morphological features and global rhythm features of ECG signals, thereby enhancing feature representation. Furthermore, a model compression-based continual learning method was proposed, enabling the structured transfer of knowledge from simpler tasks to more complex ones, resulting in improved performance in downstream tasks. The multi-resolution learning model demonstrated superior or comparable performance to state-of-the-art algorithms across five datasets, including tasks such as ECG QRS complex detection, arrhythmia classification, and emotion classification. The continual learning method achieved significant improvements over conventional training approaches in cross-domain, cross-task, and incremental data scenarios. These results highlight the potential of the proposed method for effective cross-task knowledge transfer in ECG analysis and offer a new perspective for multi-task learning using ECG signals.
Humans ; Electrocardiography/methods* ; Mental Health ; Algorithms ; Signal Processing, Computer-Assisted ; Machine Learning ; Arrhythmias, Cardiac/diagnosis* ; Cardiovascular Diseases ; Neural Networks, Computer ; Mental Disorders

Deep learning method can be used to automatically analyze electrocardiogram (ECG) data and rapidly implement arrhythmia classification, which provides significant clinical value for the early screening of arrhythmias. How to select arrhythmia features effectively under limited abnormal sample supervision is an urgent issue to address. This paper proposed an arrhythmia classification algorithm based on an adaptive multi-feature fusion network. The algorithm extracted RR interval features from ECG signals, employed one-dimensional convolutional neural network (1D-CNN) to extract time-domain deep features, employed Mel frequency cepstral coefficients (MFCC) and two-dimensional convolutional neural network (2D-CNN) to extract frequency-domain deep features. The features were fused using adaptive weighting strategy for arrhythmia classification. The paper used the arrhythmia database jointly developed by the Massachusetts Institute of Technology and Beth Israel Hospital (MIT-BIH) and evaluated the algorithm under the inter-patient paradigm. Experimental results demonstrated that the proposed algorithm achieved an average precision of 75.2%, an average recall of 70.1% and an average F ₁-score of 71.3%, demonstrating high classification accuracy and being able to provide algorithmic support for arrhythmia classification in wearable devices.
Humans ; Arrhythmias, Cardiac/diagnosis* ; Algorithms ; Electrocardiography/methods* ; Neural Networks, Computer ; Signal Processing, Computer-Assisted ; Deep Learning ; Classification Algorithms