Ensemble machine learning for predicting in-hospital mortality in Asian women with ST-elevation myocardial infarction (STEMI).

The accurate prediction of in-hospital mortality in Asian women after ST-Elevation Myocardial Infarction (STEMI) remains a crucial issue in medical research. Existing models frequently neglect this demographic's particular attributes, resulting in poor treatment outcomes. This study aims to improve the prediction of in-hospital mortality in multi-ethnic Asian women with STEMI by employing both base and ensemble machine learning (ML) models. We centred on the development of demographic-specific models using data from the Malaysian National Cardiovascular Disease Database spanning 2006 to 2016. Through a careful iterative feature selection approach that included feature importance and sequential backward elimination, significant variables such as systolic blood pressure, Killip class, fasting blood glucose, beta-blockers, angiotensin-converting enzyme inhibitors (ACE), and oral hypoglycemic medications were identified. The findings of our study revealed that ML models with selected features outperformed the conventional Thrombolysis in Myocardial Infarction (TIMI) Risk score, with area under the curve (AUC) ranging from 0.60 to 0.93 versus TIMI's AUC of 0.81. Remarkably, our best-performing ensemble ML model was surpassed by the base ML model, support vector machine (SVM) Linear with SVM selected features (AUC: 0.93, CI: 0.89-0.98 versus AUC: 0.91, CI: 0.87-0.96). Furthermore, the women-specific model outperformed a non-gender-specific STEMI model (AUC: 0.92, CI: 0.87-0.97). Our findings demonstrate the value of women-specific ML models over standard approaches, emphasizing the importance of continued testing and validation to improve clinical care for women with STEMI.

Validity and measurement equivalence of EQ-5D-5L questionnaire among heart failure patients in Malaysia: a cohort study.

AIM: This study aimed to examine the validity of EQ-5D-5L among HFrEF patients in Malaysia, and to explore the measurement equivalence of three main language versions. METHODS: We surveyed HFrEF patients from two hospitals in Malaysia, using Malay, English or Chinese versions of EQ-5D-5L. EQ-5D-5L dimensional scores were converted to utility scores using the Malaysian value set. A confirmatory factor analysis longitudinal model was constructed. The utility and visual analog scale (VAS) scores were evaluated for validity (convergent, known-group, responsiveness), and measurement equivalence of the three language versions. RESULTS: 200 HFrEF patients (mean age = 61 years), predominantly male (74%) of Malay ethnicity (55%), completed the admission and discharge EQ-5D-5L questionnaire in Malay (49%), English (26%) or Chinese (25%) languages. 173 patients (86.5%) were followed up at 1-month post-discharge (1MPD). The standardized factor loadings and average variance extracted were ≥ 0.5 while composite reliability was ≥ 0.7, suggesting convergent validity. Patients with older age and higher New York Heart Association (NYHA) class reported significantly lower utility and VAS scores. The change in utility and VAS scores between admission and discharge was large, while the change between discharge and 1MPD was minimal. The minimal clinically important difference for utility and VAS scores was ±0.19 and ±11.01, respectively. Malay and English questionnaire were equivalent while the equivalence of Malay and Chinese questionnaire was inconclusive. LIMITATION: This study only sampled HFrEF patients from two teaching hospitals, thus limiting the generalizability of results to the entire heart failure population. CONCLUSION: EQ-5D-5L is a valid questionnaire to measure health-related quality of life and estimate utility values among HFrEF patients in Malaysia. The Malay and English versions of EQ-5D-5L appear equivalent for clinical and economic assessments.

EQ-5D is the most commonly used questionnaire to measure patients' health-related quality of life in clinical trials and health technology assessments. To increase confidence over clinical trial findings that heart failure interventions improve health-related quality of life and quality-adjusted life years (number of years alive with equivalence health-related quality of life), the questionnaire used to measure health-related quality of life needs to be validated in the specific population. Since EQ-5D-5L has not been validated in Malaysia's heart failure with reduced ejection fraction (HFrEF) population, this study evaluated the psychometric properties (validity) of EQ-5D-5L among HFrEF patients in Malaysia and the equivalence of different versions of languages (i.e. Malay, Chinese and English) of EQ-5D-5L in measuring the health-related quality of life. The findings suggested that EQ-5D-5L is a valid questionnaire to measure the health-related quality of life in HFrEF patients and estimate the quality-adjusted life years. The Malay and English versions of EQ-5D-5L appear to be equivalent for use in clinical trials and health technology assessments.

A case report of heterozygous familial hypercholesterolaemia with LDLR gene mutation complicated by premature coronary artery disease detected in primary care.

Background: Familial hypercholesterolaemia (FH) is an autosomal dominant genetic condition predominantly caused by the low-density lipoprotein receptor (LDLR) gene mutation. Case summary: This is the case of a 54-year-old Malay woman with genetically confirmed FH complicated by premature coronary artery disease (PCAD). She was clinically diagnosed in primary care at 52 years old, fulfilling the Simon Broome Criteria (possible FH), Dutch Lipid Clinic Criteria (score of 8: probable FH), and Familial Hypercholesterolaemia Case Ascertainment Tool (relative risk score of 9.51). Subsequently, she was confirmed to have a heterozygous LDLR c.190+4A>T intron 2 pathogenic variant at the age of 53 years. She was known to have hypercholesterolaemia and was treated with statin since the age of 25. However, the lipid-lowering agent was not intensified to achieve the recommended treatment target. The delayed FH diagnosis has caused this patient to have PCAD and percutaneous coronary intervention (PCI) at the age of 29 years and a second PCI at the age of 49 years. She also has a very strong family history of hypercholesterolaemia and PCAD, where seven out of eight of her siblings were affected. Despite this, FH was not diagnosed early, and cascade screening of family members was not conducted, resulting in a missed opportunity to prevent PCAD. Discussion: Familial hypercholesterolaemia can be clinically diagnosed in primary care to identify those who may require genetic testing. Multidisciplinary care focuses on improving identification, cascade screening, and management of FH, which is vital to improving prognosis and ultimately preventing PCAD.

Data analytics approach for short- and long-term mortality prediction following acute non-ST-elevation myocardial infarction (NSTEMI) and Unstable Angina (UA) in Asians.

BACKGROUND: Traditional risk assessment tools often lack accuracy when predicting the short- and long-term mortality following a non-ST-segment elevation myocardial infarction (NSTEMI) or Unstable Angina (UA) in specific population. OBJECTIVE: To employ machine learning (ML) and stacked ensemble learning (EL) methods in predicting short- and long-term mortality in Asian patients diagnosed with NSTEMI/UA and to identify the associated features, subsequently evaluating these findings against established risk scores. METHODS: We analyzed data from the National Cardiovascular Disease Database for Malaysia (2006-2019), representing a diverse NSTEMI/UA Asian cohort. Algorithm development utilized in-hospital records of 9,518 patients, 30-day data from 7,133 patients, and 1-year data from 7,031 patients. This study utilized 39 features, including demographic, cardiovascular risk, medication, and clinical features. In the development of the stacked EL model, four base learner algorithms were employed: eXtreme Gradient Boosting (XGB), Support Vector Machine (SVM), Naive Bayes (NB), and Random Forest (RF), with the Generalized Linear Model (GLM) serving as the meta learner. Significant features were chosen and ranked using ML feature importance with backward elimination. The predictive performance of the algorithms was assessed using the area under the curve (AUC) as a metric. Validation of the algorithms was conducted against the TIMI for NSTEMI/UA using a separate validation dataset, and the net reclassification index (NRI) was subsequently determined. RESULTS: Using both complete and reduced features, the algorithm performance achieved an AUC ranging from 0.73 to 0.89. The top-performing ML algorithm consistently surpassed the TIMI risk score for in-hospital, 30-day, and 1-year predictions (with AUC values of 0.88, 0.88, and 0.81, respectively, all p < 0.001), while the TIMI scores registered significantly lower at 0.55, 0.54, and 0.61. This suggests the TIMI score tends to underestimate patient mortality risk. The net reclassification index (NRI) of the best ML algorithm for NSTEMI/UA patients across these periods yielded an NRI between 40-60% (p < 0.001) relative to the TIMI NSTEMI/UA risk score. Key features identified for both short- and long-term mortality included age, Killip class, heart rate, and Low-Molecular-Weight Heparin (LMWH) administration. CONCLUSIONS: In a broad multi-ethnic population, ML approaches outperformed conventional TIMI scoring in classifying patients with NSTEMI and UA. ML allows for the precise identification of unique characteristics within individual Asian populations, improving the accuracy of mortality predictions. Continuous development, testing, and validation of these ML algorithms holds the promise of enhanced risk stratification, thereby revolutionizing future management strategies and patient outcomes.

Effects of time-restricted eating with different eating duration on anthropometrics and cardiometabolic health: A systematic review and meta-analysis.

BACKGROUND: Time-restricted eating (TRE) is a dietary approach that limits eating to a set number of hours per day. Human studies on the effects of TRE intervention on cardiometabolic health have been contradictory. Heterogeneity in subjects and TRE interventions have led to inconsistency in results. Furthermore, the impact of the duration of eating/fasting in the TRE approach has yet to be fully explored. AIM: To analyze the existing literature on the effects of TRE with different eating durations on anthropometrics and cardiometabolic health markers in adults with excessive weight and obesity-related metabolic diseases. METHODS: We reviewed a series of prominent scientific databases, including Medline, Scopus, Web of Science, Academic Search Complete, and Cochrane Library articles to identify published clinical trials on daily TRE in adults with excessive weight and obesity-related metabolic diseases. Randomized controlled trials were assessed for methodological rigor and risk of bias using version 2 of the Cochrane risk-of-bias tool for randomized trials (RoB-2). Outcomes of interest include body weight, waist circumference, fat mass, lean body mass, fasting glucose, insulin, HbA1c, homeostasis model assessment for insulin resistance (HOMA-IR), lipid profiles, C-reactive protein, blood pressure, and heart rate. RESULTS: Fifteen studies were included in our systematic review. TRE significantly reduces body weight, waist circumference, fat mass, lean body mass, blood glucose, insulin, and triglyceride. However, no significant changes were observed in HbA1c, HOMA-IR, total cholesterol, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, heart rate, systolic and diastolic blood pressure. Furthermore, subgroup analyses based on the duration of the eating window revealed significant variation in the effects of TRE intervention depending on the length of the eating window. CONCLUSION: TRE is a promising chrononutrition-based dietary approach for improving anthropometric and cardiometabolic health. However, further clinical trials are needed to determine the optimal eating duration in TRE intervention for cardiovascular disease prevention.

Validation of the general Framingham Risk Score (FRS), SCORE2, revised PCE and WHO CVD risk scores in an Asian population.

Background: Cardiovascular risk prediction models incorporate myriad CVD risk factors. Current prediction models are developed from non-Asian populations, and their utility in other parts of the world is unknown. We validated and compared the performance of CVD risk prediction models in an Asian population. Methods: Four validation groups were extracted from a longitudinal community-based study dataset of 12,573 participants aged ≥18 years to validate the Framingham Risk Score (FRS), Systematic COronary Risk Evaluation 2 (SCORE2), Revised Pooled Cohort Equations (RPCE), and World Health Organization cardiovascular disease (WHO CVD) models. Two measures of validation are examined: discrimination and calibration. Outcome of interest was 10-year risk of CVD events (fatal and non-fatal). SCORE2 and RPCE performances were compared to SCORE and PCE, respectively. Findings: FRS (AUC = 0.750) and RPCE (AUC = 0.752) showed good discrimination in CVD risk prediction. Although FRS and RPCE have poor calibration, FRS demonstrates smaller discordance for FRS vs. RPCE (298% vs. 733% in men, 146% vs. 391% in women). Other models had reasonable discrimination (AUC = 0.706-0.732). Only SCORE2-Low, -Moderate and -High (aged <50) had good calibration (X2 goodness-of-fit, P-value = 0.514, 0.189, 0.129, respectively). SCORE2 and RPCE showed improvements compared to SCORE (AUC = 0.755 vs. 0.747, P-value <0.001) and PCE (AUC = 0.752 vs. 0.546, P-value <0.001), respectively. Almost all risk models overestimated 10-year CVD risk by 3%-1430%. Interpretation: In Malaysians, RPCE are evaluated be the most clinically useful to predict CVD risk. Additionally, SCORE2 and RPCE outperformed SCORE and PCE, respectively. Funding: This work was supported by the Malaysian Ministry of Science, Technology, and Innovation (MOSTI) (Grant No: TDF03211036).

Economic Evaluations of Guideline-Directed Medical Therapies for Heart Failure With Reduced Ejection Fraction: A Systematic Review.

OBJECTIVES: Decision-analytic models (DAMs) with varying structures and assumptions have been applied in economic evaluations (EEs) to assist decision making for heart failure with reduced ejection fraction (HFrEF) therapeutics. This systematic review aimed to summarize and critically appraise the EEs of guideline-directed medical therapies (GDMTs) for HFrEF. METHODS: A systematic search of English articles and gray literature, published from January 2010, was performed on databases including MEDLINE, Embase, Scopus, NHSEED, health technology assessment, Cochrane Library, etc. The included studies were EEs with DAMs that compared the costs and outcomes of angiotensin-converting enzyme inhibitors, angiotensin-receptor blockers, angiotensin-receptor neprilysin inhibitors, beta-blockers, mineralocorticoid-receptor agonists, and sodium-glucose cotransporter-2 inhibitors. The study quality was evaluated using the Bias in Economic Evaluation (ECOBIAS) 2015 checklist and Consolidated Health Economic Evaluation Reporting Standards (CHEERS) 2022 checklists. RESULTS: A total of 59 EEs were included. Markov model, with a lifetime horizon and a monthly cycle length, was most commonly used in evaluating GDMTs for HFrEF. Most EEs conducted in the high-income countries demonstrated that novel GDMTs for HFrEF were cost-effective compared with the standard of care, with the standardized median incremental cost-effectiveness ratio (ICER) of $21 361/quality-adjusted life-year. The key factors influencing ICERs and study conclusions included model structures, input parameters, clinical heterogeneity, and country-specific willingness-to-pay threshold. CONCLUSIONS: Novel GDMTs were cost-effective compared with the standard of care. Given the heterogeneity of the DAMs and ICERs, alongside variations in willingness-to-pay thresholds across countries, there is a need to conduct country-specific EEs, particularly in low- and middle-income countries, using model structures that are coherent with the local decision context.

Current Insights on Dyslipidaemia Management for Prevention of Atherosclerotic Cardiovascular Disease: A Malaysian Perspective.

Dyslipidaemia is highly prevalent in the Malaysian population and is one of the main risk factors for atherosclerotic cardiovascular disease (ASCVD). Low-density lipoprotein cholesterol (LDL-C) is recognised as the primary target of lipid-lowering therapy to reduce the disease burden of ASCVD. Framingham General CV Risk Score has been validated in the Malaysian population for CV risk assessment. The Clinical Practice Guidelines (CPG) on the management of dyslipidaemia were last updated in 2017. Since its publication, several newer randomised clinical trials have been conducted with their results published in research articles and compared in meta-analysis. This underscores a need to update the previous guidelines to ensure good quality care and treatment for the patients. This review summarises the benefits of achieving LDL-C levels lower than the currently recommended target of < 1.8mmol/L without any safety concerns. In most high and very high-risk individuals, statins are the first line of therapy for dyslipidaemia management. However, certain high-risk individuals are not able to achieve the LDL-C goal as recommended in the guideline even with high-intensity statin therapy. In such individuals, lower LDL-C levels can be achieved by combining the statins with non-statin agents such as ezetimibe and PCSK9 inhibitors. Emerging non-statin lipid-lowering therapies and challenges in dyslipidaemia management are discussed in this article. The review also summarises the recent updates on local and international guidelines for dyslipidaemia management.

A case report of paraneoplastic bullous pemphigoid associated with mantle cell lymphoma: A rare presentation.

RATIONALE: We report a rare case of paraneoplastic bullous pemphigoid associated with mantle cell lymphoma. PATIENTS CONCERNS: The patient presented with 5 months' history of generalized skin itchiness, night sweat and loss of weight. The skin manifestations started over the foot and hand area. However, he started to developed tense blisters over the face, trunk and limbs 3 days prior to this admission. DIAGNOSES: The skin biopsy report showed subepidermal bullae, in which the immunofluorescence findings in keeping with bullous pemphigoid. The peripheral blood immunophenotyping was suggestive of mantle cell lymphoma. Hence, a diagnosis of paraneoplastic bullous pemphigoid associated with mantle cell lymphoma was made. INTERVENTIONS: The patient was initiated with a cytoreduction chemotherapy. OUTCOMES: Unfortunately, patient's condition deteriorated further due to neutropenic sepsis and he succumbed after 2 weeks of intensive care. LESSONS: Bullous pemphigoid associated with mantle cell lymphoma are very rare. The presentation of bullous pemphigoid led to the detection of mantle cell lymphoma. Early diagnosis and appropriate treatment is crucial in managing this aggressive type of the disease. Both, bullous pemphigoid and mantle cell lymphoma had a parallel clinical course which suggests a paraneoplastic phenomenon in this reported case.

Attainment of Low-Density Lipoprotein Cholesterol Targets and Prescribing Pattern of Lipid-Lowering Medications among Patients with Familial Hypercholesterolemia Attending Specialist Clinics.

AIMS: Patients with familial hypercholesterolemia (FH) are known to have higher exposure to coronary risk than those without FH with similar low-density lipoprotein cholesterol (LDL-C) level. Lipid-lowering medications (LLMs) are the mainstay treatments to lower the risk of premature coronary artery disease in patients with hypercholesterolemia. However, the LLM prescription pattern and its effectiveness among Malaysian patients with FH are not yet reported. The aim of this study was to report the LLM prescribing pattern and its effectiveness in lowering LDL-C level among Malaysian patients with FH treated in specialist hospitals. METHODS: Subjects were recruited from lipid and cardiac specialist hospitals. FH was clinically diagnosed using the Dutch Lipid Clinic Network Criteria. Patients' medical history was recorded using a standardized questionnaire. LLM prescription history and baseline LDL-C were acquired from the hospitals' database. Blood samples were acquired for the latest lipid profile assay. RESULTS: A total of 206 patients with FH were recruited. Almost all of them were on LLMs (97.6%). Only 2.9% and 7.8% of the patients achieved the target LDL-C of ＜1.4 and ＜1.8 mmol/L, respectively. The majority of patients who achieved the target LDL-C were prescribed with statin-ezetimibe combination medications and high-intensity or moderate-intensity statins. All patients who were prescribed with ezetimibe monotherapy did not achieve the target LDL-C. CONCLUSION: The majority of Malaysian patients with FH received LLMs, but only a small fraction achieved the therapeutic target LDL-C level. Further investigation has to be conducted to identify the cause of the suboptimal treatment target attainment, be it the factors of patients or the prescription practice.

An international, stepped wedge, cluster-randomized trial investigating the 0/1-h algorithm in suspected acute coronary syndrome in Asia: the rational of the DROP-Asian ACS study.

BACKGROUND: More than half of the world's population lives in Asia. With current life expectancies in Asian countries, the burden of cardiovascular disease is increasing exponentially. Overcrowding in the emergency departments (ED) has become a public health problem. Since 2015, the European Society of Cardiology recommends the use of a 0/1-h algorithm based on high-sensitivity cardiac troponin (hs-cTn) for rapid triage of patients with suspected non-ST elevation acute coronary syndrome (NSTE-ACS). However, these algorithms are currently not recommended by Asian guidelines due to the lack of suitable data. METHODS: The DROP-Asian ACS is a prospective, stepped wedge, cluster-randomized trial enrolling 4260 participants presenting with chest pain to the ED of 12 acute care hospitals in five Asian countries (UMIN; 000042461). Consecutive patients presenting with suspected acute coronary syndrome between July 2022 and Apr 2024 were included. Initially, all clusters will apply "usual care" according to local standard operating procedures including hs-cTnT but not the 0/1-h algorithm. The primary outcome is the incidence of major adverse cardiac events (MACE), the composite of all-cause death, myocardial infarction, unstable angina, or unplanned revascularization within 30 days. The difference in MACE (with one-sided 95% CI) was estimated to evaluate non-inferiority. The non-inferiority margin was prespecified at 1.5%. Secondary efficacy outcomes include costs for healthcare resources and duration of stay in ED. CONCLUSIONS: This study provides important evidence concerning the safety and efficacy of the 0/1-h algorithm in Asian countries and may help to reduce congestion of the ED as well as medical costs.

In-hospital mortality risk stratification of Asian ACS patients with artificial intelligence algorithm.

BACKGROUND: Conventional risk score for predicting in-hospital mortality following Acute Coronary Syndrome (ACS) is not catered for Asian patients and requires different types of scoring algorithms for STEMI and NSTEMI patients. OBJECTIVE: To derive a single algorithm using deep learning and machine learning for the prediction and identification of factors associated with in-hospital mortality in Asian patients with ACS and to compare performance to a conventional risk score. METHODS: The Malaysian National Cardiovascular Disease Database (NCVD) registry, is a multi-ethnic, heterogeneous database spanning from 2006-2017. It was used for in-hospital mortality model development with 54 variables considered for patients with STEMI and Non-STEMI (NSTEMI). Mortality prediction was analyzed using feature selection methods with machine learning algorithms. Deep learning algorithm using features selected from machine learning was compared to Thrombolysis in Myocardial Infarction (TIMI) score. RESULTS: A total of 68528 patients were included in the analysis. Deep learning models constructed using all features and selected features from machine learning resulted in higher performance than machine learning and TIMI risk score (p < 0.0001 for all). The best model in this study is the combination of features selected from the SVM algorithm with a deep learning classifier. The DL (SVM selected var) algorithm demonstrated the highest predictive performance with the least number of predictors (14 predictors) for in-hospital prediction of STEMI patients (AUC = 0.96, 95% CI: 0.95-0.96). In NSTEMI in-hospital prediction, DL (RF selected var) (AUC = 0.96, 95% CI: 0.95-0.96, reported slightly higher AUC compared to DL (SVM selected var) (AUC = 0.95, 95% CI: 0.94-0.95). There was no significant difference between DL (SVM selected var) algorithm and DL (RF selected var) algorithm (p = 0.5). When compared to the DL (SVM selected var) model, the TIMI score underestimates patients' risk of mortality. TIMI risk score correctly identified 13.08% of the high-risk patient's non-survival vs 24.7% for the DL model and 4.65% vs 19.7% of the high-risk patient's non-survival for NSTEMI. Age, heart rate, Killip class, cardiac catheterization, oral hypoglycemia use and antiarrhythmic agent were found to be common predictors of in-hospital mortality across all ML feature selection models in this study. The final algorithm was converted into an online tool with a database for continuous data archiving for prospective validation. CONCLUSIONS: ACS patients were better classified using a combination of machine learning and deep learning in a multi-ethnic Asian population when compared to TIMI scoring. Machine learning enables the identification of distinct factors in individual Asian populations to improve mortality prediction. Continuous testing and validation will allow for better risk stratification in the future, potentially altering management and outcomes.

In-hospital risk stratification algorithm of Asian elderly patients.

Limited research has been conducted in Asian elderly patients (aged 65 years and above) for in-hospital mortality prediction after an ST-segment elevation myocardial infarction (STEMI) using Deep Learning (DL) and Machine Learning (ML). We used DL and ML to predict in-hospital mortality in Asian elderly STEMI patients and compared it to a conventional risk score for myocardial infraction outcomes. Malaysia's National Cardiovascular Disease Registry comprises an ethnically diverse Asian elderly population (3991 patients). 50 variables helped in establishing the in-hospital death prediction model. The TIMI score was used to predict mortality using DL and feature selection methods from ML algorithms. The main performance metric was the area under the receiver operating characteristic curve (AUC). The DL and ML model constructed using ML feature selection outperforms the conventional risk scoring score, TIMI (AUC 0.75). DL built from ML features (AUC ranging from 0.93 to 0.95) outscored DL built from all features (AUC 0.93). The TIMI score underestimates mortality in the elderly. TIMI predicts 18.4% higher mortality than the DL algorithm (44.7%). All ML feature selection algorithms identify age, fasting blood glucose, heart rate, Killip class, oral hypoglycemic agent, systolic blood pressure, and total cholesterol as common predictors of mortality in the elderly. In a multi-ethnic population, DL outperformed the TIMI risk score in classifying elderly STEMI patients. ML improves death prediction by identifying separate characteristics in older Asian populations. Continuous testing and validation will improve future risk classification, management, and results.

Familial hypercholesterolaemia and coronary risk factors among patients with angiogram-proven premature coronary artery disease in an Asian cohort.

BACKGROUND: Familial hypercholesterolaemia (FH) patients have elevated levels of low-density lipoprotein cholesterol, rendering them at high risk of premature coronary artery disease (PCAD). However, the FH prevalence among angiogram-proven PCAD (AP-PCAD) patients and their status of coronary risk factors (CRFs) have not been reported in the Asian population. OBJECTIVES: This study aimed to (1) determine the prevalence of clinically diagnosed FH among AP-PCAD patients, (2) compare CRFs between AP-PCAD patients with control groups, and (3) identify the independent predictors of PCAD. METHODS: AP-PCAD patients and FH patients without PCAD were recruited from Cardiology and Specialist Lipid Clinics. Subjects were divided into AP-PCAD with FH (G1), AP-PCAD without FH (G2), FH without PCAD (G3) and normal controls (G4). Medical records were collected from the clinic database and standardised questionnaires. FH was clinically diagnosed using Dutch Lipid Clinic Network Criteria. RESULTS: A total of 572 subjects were recruited (males:86.4%; mean±SD age: 55.6±8.5years). The prevalence of Definite, Potential and All FH among AP-PCAD patients were 6%(19/319), 16% (51/319) and 45.5% (145/319) respectively. G1 had higher central obesity, family history of PCAD and family history of hypercholesterolaemia compared to other groups. Among all subjects, diabetes [OR(95% CI): 4.7(2.9,7.7)], hypertension [OR(95% CI): 14.1(7.8,25.6)], FH [OR(95% CI): 2.9(1.5,5.5)] and Potential (Definite and Probable) FH [OR(95% CI): 4.5(2.1,9.6)] were independent predictors for PCAD. Among FH patients, family history of PCAD [OR(95% CI): 3.0(1.4,6.3)] and Definite FH [OR(95% CI): 7.1(1.9,27.4)] were independent predictors for PCAD. CONCLUSION: Potential FH is common among AP-PCAD patients and contributes greatly to the AP-PCAD. FH-PCAD subjects have greater proportions of various risk factors compared to other groups. Presence of FH, diabetes, hypertension, obesity and family history of PCAD are independent predictors of PCAD. FH with PCAD is in very-high-risk category, hence, early management of modifiable CRFs in these patients are warranted.

Complete Heart Block as a Clinical Feature in Critically Ill Coronavirus Disease 2019 (COVID-19) Patients: A Case Series of Three Cases.

Background. Novel coronavirus-19 disease (COVID-19) is associated with significant cardiovascular morbidity and mortality. However, there have been very few reports on complete heart block (CHB) associated with COVID-19. This case series describes clinical characteristics, potential mechanisms, and short-term outcomes of critically ill COVID-19 patients complicated by CHB. Case Summary. We present three cases of new-onset CHB in critically ill COVID-19 patients. Patient 1 is a 41-year-old male with well-documented history of Familial Mediterranean Fever (FMF) who required mechanical ventilator support for acute hypoxic respiratory failure from severe COVID-19 pneumonia. He developed new-onset CHB without a hemodynamic derangement but subsequently had acute coronary syndrome complicated by cardiogenic shock. Patient 2 is a 77-year-old male with no past medical history who required intubation for severe COVID-19 pneumonia acute hypoxic respiratory failure. He developed CHB with sinus pause requiring temporary pacing but subsequently developed multiorgan failure. Patient 3 is 36-year-old lady 38 + 2 weeks pregnant, gravida 2 para 1 with no other medical history, who had an emergency Lower Section Caesarean Section (LSCS) as she required intubation for acute hypoxic respiratory failure. She exhibited new-onset CHB without hemodynamic compromise. The CHB resolved spontaneously after 24 hours. Discussion. COVID-19-associated CHB is a very rare clinical manifestation. The potential mechanisms for CHB in patients with COVID-19 include myocardial inflammation or direct viral infiltration as well as other causes such as metabolic derangements or use of sedatives. Patients diagnosed with COVID-19 should be monitored closely for the development of bradyarrhythmia and hemodynamic instability.

Short- and long-term mortality prediction after an acute ST-elevation myocardial infarction (STEMI) in Asians: A machine learning approach.

BACKGROUND: Conventional risk score for predicting short and long-term mortality following an ST-segment elevation myocardial infarction (STEMI) is often not population specific. OBJECTIVE: Apply machine learning for the prediction and identification of factors associated with short and long-term mortality in Asian STEMI patients and compare with a conventional risk score. METHODS: The National Cardiovascular Disease Database for Malaysia registry, of a multi-ethnic, heterogeneous Asian population was used for in-hospital (6299 patients), 30-days (3130 patients), and 1-year (2939 patients) model development. 50 variables were considered. Mortality prediction was analysed using feature selection methods with machine learning algorithms and compared to Thrombolysis in Myocardial Infarction (TIMI) score. Invasive management of varying degrees was selected as important variables that improved mortality prediction. RESULTS: Model performance using a complete and reduced variable produced an area under the receiver operating characteristic curve (AUC) from 0.73 to 0.90. The best machine learning model for in-hospital, 30 days, and 1-year outperformed TIMI risk score (AUC = 0.88, 95% CI: 0.846-0.910; vs AUC = 0.81, 95% CI:0.772-0.845, AUC = 0.90, 95% CI: 0.870-0.935; vs AUC = 0.80, 95% CI: 0.746-0.838, AUC = 0.84, 95% CI: 0.798-0.872; vs AUC = 0.76, 95% CI: 0.715-0.802, p < 0.0001 for all). TIMI score underestimates patients' risk of mortality. 90% of non-survival patients are classified as high risk (>50%) by machine learning algorithm compared to 10-30% non-survival patients by TIMI. Common predictors identified for short- and long-term mortality were age, heart rate, Killip class, fasting blood glucose, prior primary PCI or pharmaco-invasive therapy and diuretics. The final algorithm was converted into an online tool with a database for continuous data archiving for algorithm validation. CONCLUSIONS: In a multi-ethnic population, patients with STEMI were better classified using the machine learning method compared to TIMI scoring. Machine learning allows for the identification of distinct factors in individual Asian populations for better mortality prediction. Ongoing continuous testing and validation will allow for better risk stratification and potentially alter management and outcomes in the future.