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.

Frailty Syndrome in Patients with Carotid Disease: Simplifying How We Calculate Frailty.

BACKGROUND: Frailty syndrome is an established predictor of adverse outcomes after carotid surgery. Recently, a modified 5-factor National Surgical Quality Improvement Program frailty index has been used; however, its utility in vascular procedures is unclear. The aim of our study was to compare the 5-factor modified frailty index (mFI-5) with the 11-factor modified frailty index (mFI-11) regarding value and predictive ability for mortality, postoperative infection, and unplanned 30-day readmission. METHODS: The mFI was calculated by dividing the number of factors present for a patient by the number of available factors for which there were no missing data. Spearman rho test was used to assess the correlation between the mFI-5 and mFI-11. Predictive models, using both unadjusted and adjusted logistic regressions, were created for each outcome for carotid endarterectomy using 2005-2012 National Surgical Quality Improvement Program data, the last year all mFI-11 variables existed. RESULTS: A total of 36,000 patients were included with mean age of 74.6 ± 5.9 years, complication rate of 10.7%, mortality rate of 3.1%, and readmission rate of 6.2%. Correlation between mFI-5 and mFI-11 was above 0.9 across all outcomes for patients. mFI-5 had strong predictive ability for mortality, postoperative complications, and 30-day readmission. CONCLUSIONS: The mFI-5 and mFI-11 are equally effective predictors of postoperative outcomes in patients undergoing carotid endarterectomy. mFI-5 is a strong predictor of postoperative complications, mortality, and 30-day readmission.

Perceptions of students in different phases of medical education of the educational environment: Universiti Sultan Zainal Abidin.

BACKGROUND: The Dundee Ready Education Environment Measure (DREEM) was planned and designed to quantify the educational environment precisely for medical schools and health-related professional schools. DREEM is now considered a valid and reliable tool, which is globally accepted for measuring the medical educational environment. The educational environment encountered by students has an impact on satisfaction with the course of study, perceived sense of well-being, aspirations, and academic achievement. In addition to being measurable, the educational environment can also be changed, thus enhancing the quality of medical education and the environment, and the medical education process. The objective of this study was to assess the educational environment of the Universiti Sultan Zainal Abidin (UniSZA) undergraduate medical program from the students' perspective. The study expected to explore UniSZA medical students' overall perceptions, perceptions of learning, teachers, atmosphere, academic self-perception, and social self-perception using the DREEM questionnaire. METHODS: A cross-sectional survey was conducted to study the perceptions of the students toward the educational environment of UniSZA as a new medical school, using the DREEM questionnaire. All medical students of UniSZA from Years I-V enrolled in the Bachelor of Medicine and Bachelor of Surgery programs were the target population (n=270). Therefore, the universal sampling technique was used. The data were analyzed using the SPSS 20 software. This study obtained ethical clearance from the Faculty of Medicine and Health Sciences, UniSZA. RESULTS: A total of 195 out of 270 students responded. Respondents included 31% males and 69% females. The overall DREEM scores were significantly higher (P<0.001) for females than males. CONCLUSION: The medical students at UniSZA showed a positive perception of their educational environment. The new medical faculty, established for only a few years, has achieved an above-average, conducive educational environment for students. Most of the students showed a positive perception for the entire five domains tested in the DREEM survey. Females were consistently satisfied with UniSZA's educational environment, and self-perception was high, as compared to male undergraduates.