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.
METHODS: ACE inhibitory proteins were isolated from P. cystidiosus based on the bioassay guided purification steps, i.e. ammonium sulphate precipitation, reverse phase high performance liquid chromatography and size exclusion chromatography. Active fraction was then analysed by LC-MS/MS and potential ACE inhibitory peptides identified were chemically synthesized. Effect of in vitro gastrointestinal digestions on the ACE inhibitory activity of the peptides and their inhibition patterns were evaluated.
RESULTS: Two potential ACE inhibitory peptides, AHEPVK and GPSMR were identified from P. cystidiosus with molecular masses of 679.53 and 546.36 Da, respectively. Both peptides exhibited potentially high ACE inhibitory activity with IC50 values of 62.8 and 277.5 μM, respectively. SEC chromatograms and BIOPEP analysis of these peptides revealed that the peptide sequence of the hexapeptide, AHEPVK, was stable throughout gastrointestinal digestion. The pentapeptide, GPSMR, was hydrolysed after digestion and it was predicted to release a dipeptide ACE inhibitor, GP, from its precursor. The Lineweaver-Burk plot of AHEPVK showed that this potent and stable ACE inhibitor has a competitive inhibitory effect against ACE.
CONCLUSION: The present study indicated that the peptides from P. cystidiosus could be potential ACE inhibitors. Although these peptides had lower ACE inhibitory activity compared to commercial antihypertensive drugs, they are derived from mushroom which could be easily obtained and should have no side effects. Further in vivo studies can be carried out to reveal the clear mechanism of ACE inhibition by these peptides.