METHODS: This retrospective cohort study was conducted on 866 patients from the Gulf Left Main Registry who presented between 2015 and 2019. The study outcome was hospital all-cause mortality. Various machine learning models [logistic regression, random forest (RF), k-nearest neighbor, support vector machine, naïve Bayes, multilayer perception, boosting] were used to predict mortality, and their performance was measured using accuracy, precision, recall, F1 score, and area under the receiver operator characteristic curve (AUC).
RESULTS: Nonsurvivors had significantly greater EuroSCORE II values (1.84 (10.08-3.67) vs. 4.75 (2.54-9.53) %, P<0.001 for survivors and nonsurvivors, respectively). The EuroSCORE II score significantly predicted hospital mortality (OR: 1.13 (95% confidence interval: 1.09-1.18), P<0.001), with an AUC of 0.736. RF achieved the best ML performance (accuracy=98, precision=100, recall=97 and F1 score=98). Explainable artificial intelligence using SHAP demonstrated the most important features as follows: preoperative lactate level, emergency surgery, chronic kidney disease (CKD), NSTEMI, nonsmoking status, and sex. QLattice identified lactate and CKD as the most important factors for predicting hospital mortality this patient group.
CONCLUSION: This study demonstrates the potential of ML, particularly the Random Forest, to accurately predict hospital mortality in patients undergoing CABG for LMCA disease and its superiority over traditional methods. The key risk factors identified, including preoperative lactate levels, emergency surgery, chronic kidney disease, NSTEMI, nonsmoking status, and sex, provide valuable insights for risk stratification and informed decision-making in this high-risk patient population. Additionally, incorporating newly identified risk factors into future risk scoring systems can further improve mortality prediction accuracy.
METHODS: The development data set comprised 138,309 women from 17 case-control studies. PRSs were generated using a clumping and thresholding method, lasso penalized regression, an Empirical Bayes approach, a Bayesian polygenic prediction approach, or linear combinations of multiple PRSs. These PRSs were evaluated in 89,898 women from 3 prospective studies (1592 incident cases).
RESULTS: The best performing PRS (genome-wide set of single-nucleotide variations [formerly single-nucleotide polymorphism]) had a hazard ratio per unit SD of 1.62 (95% CI = 1.46-1.80) and an area under the receiver operating curve of 0.635 (95% CI = 0.622-0.649). Combined Asian and European PRSs (333 single-nucleotide variations) had a hazard ratio per SD of 1.53 (95% CI = 1.37-1.71) and an area under the receiver operating curve of 0.621 (95% CI = 0.608-0.635). The distribution of the latter PRS was different across ethnic subgroups, confirming the importance of population-specific calibration for valid estimation of breast cancer risk.
CONCLUSION: PRSs developed in this study, from association data from multiple ancestries, can enhance risk stratification for women of Asian ancestry.
METHODS: The Gulf-CS registry included 1,513 patients with AMI-CS diagnosed between January 2020 and December 2022.
RESULTS: The incidence of AMI-CS was 4.1% (1513/37379). The median age was 60 years. The most common presentation was ST-elevation MI (73.83%). In-hospital mortality was 45.5%. Majority of patients were in SCAI stage D and E (68.94%). Factors associated with hospital mortality were previous coronary artery bypass graft (OR:2.49; 95%CI: 1.321-4.693), cerebrovascular accident (OR:1.621, 95%CI: 1.032-2.547), chronic kidney disease (OR:1.572; 95%CI1.158-2.136), non-ST-elevation MI (OR:1.744; 95%CI: 1.058-2.873), cardiac arrest (OR:5.702; 95%CI: 3.640-8.933), SCAI stage D and E (OR:19.146; 95CI%: 9.902-37.017), prolonged QRS (OR:10.012; 95%CI: 1.006-1.019), right ventricular dysfunction (OR:1.679; 95%CI: 1.267-2.226) and ventricular septal rupture (OR:6.008; 95%CI: 2.256-15.998). Forty percent had invasive hemodynamic monitoring, 90.02% underwent revascularization, and 45.80% received mechanical circulatory support (41.31% had Intra-Aortic Balloon Pump and 14.21% had Extracorporeal Membrane Oxygenation/Impella devices). Survival at 12 months was 51.49% (95% CI: 46.44- 56.29%).
CONCLUSIONS: The study highlighted the significant burden of AMI-CS in this region, with high in-hospital mortality. The study identified several key risk factors associated with increased hospital mortality. Despite the utilization of invasive hemodynamic monitoring, revascularization, and mechanical circulatory support in a substantial proportion of patients, the 12-month survival rate remained relatively low.
METHODS: We used data from 3,184 BRCA1 and 2,157 BRCA2 families in the Consortium of Investigators of Modifiers of BRCA1/2 to estimate age-specific relative (RR) and absolute risks for 22 first primary cancer types adjusting for family ascertainment.
RESULTS: BRCA1 PVs were associated with risks of male breast (RR = 4.30; 95% CI, 1.09 to 16.96), pancreatic (RR = 2.36; 95% CI, 1.51 to 3.68), and stomach (RR = 2.17; 95% CI, 1.25 to 3.77) cancers. Associations with colorectal and gallbladder cancers were also suggested. BRCA2 PVs were associated with risks of male breast (RR = 44.0; 95% CI, 21.3 to 90.9), stomach (RR = 3.69; 95% CI, 2.40 to 5.67), pancreatic (RR = 3.34; 95% CI, 2.21 to 5.06), and prostate (RR = 2.22; 95% CI, 1.63 to 3.03) cancers. The stomach cancer RR was higher for females than males (6.89 v 2.76; P = .04). The absolute risks to age 80 years ranged from 0.4% for male breast cancer to approximately 2.5% for pancreatic cancer for BRCA1 carriers and from approximately 2.5% for pancreatic cancer to 27% for prostate cancer for BRCA2 carriers.
CONCLUSION: In addition to female breast and ovarian cancers, BRCA1 and BRCA2 PVs are associated with increased risks of male breast, pancreatic, stomach, and prostate (only BRCA2 PVs) cancers, but not with the risks of other previously suggested cancers. The estimated age-specific risks will refine cancer risk management in men and women with BRCA1/2 PVs.