Risk factors associated with major adverse cardiac and cerebrovascular events following percutaneous coronary intervention: a 10-year follow-up comparing random survival forest and Cox proportional-hazards model

Machine Learning Applications in Acute Coronary Syndrome: Diagnosis, Outcomes and Management

Acute coronary syndrome (ACS) is a leading cause of death worldwide. Prompt and accurate diagnosis of acute myocardial infarction (AMI) or ACS is crucial for improved management and prognosis of patients. The rapid growth of machine learning (ML) research has significantly enhanced our understanding of ACS. Most studies have focused on applying ML to detect ACS, predict prognosis, manage treatment, identify risk factors, and discover potential biomarkers, particularly using data from electrocardiograms (ECGs), electronic medical records (EMRs), imaging, and omics as the main data modality. Additionally, integrating ML with smart devices such as wearables, smartphones, and sensor technology enables real-time dynamic assessments, enhancing clinical care for patients with ACS. This review provided an overview of the workflow and key concepts of ML as they relate to ACS. It then provides an overview of current ML algorithms used for ACS diagnosis, prognosis, identification of potential risk biomarkers, and management. Furthermore, we discuss the current challenges faced by ML algorithms in this field and how they might be addressed in the future, especially in the context of medicine.

Consequences of low estimated glomerular filtration rate either before or early after kidney donation

In the general population, decreases in glomerular filtration rate (GFR) are associated with subsequent development of chronic kidney disease (CKD), cardiovascular disease (CVD), and death. It is unknown if low estimated GFR (eGFR) before or early after kidney donation was also associated with these risks. One thousand six hundred ninety-nine living donors who had both predonation and early (4-10 weeks) postdonation eGFR were included. We studied the relationships between eGFR, age at donation, and the time to sustained eGFR<45 (CKD stage 3b) and <30 mL/min/1.73m2 (CKD stage 4), hypertension, diabetes mellitus (DM), CVD, and death. Median follow-up was 12 (interquartile range, 6-21) years. Twenty-year event rates were 5.8% eGFR<45 mL/min/1.73m2; 1.2% eGFR<30 mL/min/1.73m2; 29.0% hypertension; 7.8% DM; 8.0% CVD; and 5.2% death. The median time to eGFR<45 mL/min/1.73m2 (N = 79) was 17 years, and eGFR<30 mL/min/1.73m2 (N = 22) was 25 years. Both low predonation and early postdonation eGFR were associated with eGFR<45 mL/min/1.73m2 (P < .0001) and eGFR<30 mL/min/1.73m2 (P < .006); however, the primary driver of risk for all ages was low postdonation (rather than predonation) eGFR. Predonation and postdonation eGFR were not associated with hypertension, DM, CVD, or death. Low predonation and early postdonation eGFR are risk factors for developing eGFR<45 mL/min/1.73m2 (CKD stage 3b) and <30 mL/min/1.73m2 (CKD stage 4), but not CVD, hypertension, DM, or death.

Triglyceride-glucose index predicts major adverse cardiovascular events in patients with chronic kidney disease

BACKGROUND AND PURPOSE

Triglyceride-glucose (TyG) index has been regarded as a reliable surrogate marker of insulin resistance for predicting cardiovascular outcomes. The current study aimed to explore the associations between TyG index with major adverse cardiovascular events (MACE) in patients with chronic kidney disease (CKD).

METHODS/PATIENTS

13,517 patients with chronic kidney disease (CKD) from the Kailuan study were included. Patients were divided into quartiles according to the TyG index. The outcomes were MACE, including acute myocardial infarction (AMI) and ischemic stroke (IS). The association between TyG index and the risk of MACE was analyzed by Cox regression models.

RESULTS

During 13.87-year follow-up, a total 1356 MACEs occurred. Multivariable Cox proportional-hazards analyses showed that a higher TyG index quartile was associated with an elevated risk of MACE.

CONCLUSIONS

TyG index is significantly related to MACE in patients with CKD. TyG index can be regarded as a novel predictor of MACE for patients with CKD.

A Systematic Review of Artificial Intelligence Models for Time-to-Event Outcome Applied in Cardiovascular Disease Risk Prediction

Artificial intelligence (AI) based predictive models for early detection of cardiovascular disease (CVD) risk are increasingly being utilised. However, AI based risk prediction models that account for right-censored data have been overlooked. This systematic review (PROSPERO protocol CRD42023492655) includes 33 studies that utilised machine learning (ML) and deep learning (DL) models for survival outcome in CVD prediction. We provided details on the employed ML and DL models, eXplainable AI (XAI) techniques, and type of included variables, with a focus on social determinants of health (SDoH) and gender-stratification. Approximately half of the studies were published in 2023 with the majority from the United States. Random Survival Forest (RSF), Survival Gradient Boosting models, and Penalised Cox models were the most frequently employed ML models. DeepSurv was the most frequently employed DL model. DL models were better at predicting CVD outcomes than ML models. Permutation-based feature importance and Shapley values were the most utilised XAI methods for explaining AI models. Moreover, only one in five studies performed gender-stratification analysis and very few incorporate the wide range of SDoH factors in their prediction model. In conclusion, the evidence indicates that RSF and DeepSurv models are currently the optimal models for predicting CVD outcomes. This study also highlights the better predictive ability of DL survival models, compared to ML models. Future research should ensure the appropriate interpretation of AI models, accounting for SDoH, and gender stratification, as gender plays a significant role in CVD occurrence.

Mortality prediction and influencing factors for intensive care unit patients with acute tubular necrosis: random survival forest and cox regression analysis

Background: Patients with acute tubular necrosis (ATN) not only have severe renal failure, but also have many comorbidities, which can be life-threatening and require timely treatment. Identifying the influencing factors of ATN and taking appropriate interventions can effectively shorten the duration of the disease to reduce mortality and improve patient prognosis. Methods: Mortality prediction models were constructed by using the random survival forest (RSF) algorithm and the Cox regression. Next, the performance of both models was assessed by the out-of-bag (OOB) error rate, the integrated brier score, the prediction error curve, and area under the curve (AUC) at 30, 60 and 90 days. Finally, the optimal prediction model was selected and the decision curve analysis and nomogram were established. Results: RSF model was constructed under the optimal combination of parameters (mtry = 10, nodesize = 88). Vasopressors, international normalized ratio (INR)_min, chloride_max, base excess_min, bicarbonate_max, anion gap_min, and metastatic solid tumor were identified as risk factors that had strong influence on mortality in ATN patients. Uni-variate and multivariate regression analyses were used to establish the Cox regression model. Nor-epinephrine, vasopressors, INR_min, severe liver disease, and metastatic solid tumor were identified as important risk factors. The discrimination and calibration ability of both predictive models were demonstrated by the OOB error rate and the integrated brier score. However, the prediction error curve of Cox regression model was consistently lower than that of RSF model, indicating that Cox regression model was more stable and reliable. Then, Cox regression model was also more accurate in predicting mortality of ATN patients based on the AUC at different time points (30, 60 and 90 days). The analysis of decision curve analysis shows that the net benefit range of Cox regression model at different time points is large, indicating that the model has good clinical effectiveness. Finally, a nomogram predicting the risk of death was created based on Cox model. Conclusion: The Cox regression model is superior to the RSF algorithm model in predicting mortality of patients with ATN. Moreover, the model has certain clinical utility, which can provide clinicians with some reference basis in the treatment of ATN and contribute to improve patient prognosis.

Risk factors affecting patients survival with colorectal cancer in Morocco: survival analysis using an interpretable machine learning approach

The aim of our study was to assess the overall survival rates for colorectal cancer at 3 years and to identify associated strong prognostic factors among patients in Morocco through an interpretable machine learning approach. This approach is based on a fully non-parametric survival random forest (RSF), incorporating variable importance and partial dependence effects. The data was povided from a retrospective study of 343 patients diagnosed and followed at Hassan II University Hospital. Covariate selection was performed using the variable importance based on permutation and partial dependence plots were displayed to explore in depth the relationship between the estimated partial effect of a given predictor and survival rates. The predictive performance was measured by two metrics, the Concordance Index (C-index) and the Brier Score (BS). Overall survival rates at 1, 2 and 3 years were, respectively, 87% (SE = 0.02; CI-95% 0.84-0.91), 77% (SE = 0.02; CI-95% 0.73-0.82) and 60% (SE = 0.03; CI-95% 0.54-0.66). In the Cox model after adjustment for all covariates, sex, tumor differentiation had no significant effect on prognosis, but rather tumor site had a significant effect. The variable importance obtained from RSF strengthens that surgery, stage, insurance, residency, and age were the most important prognostic factors. The discriminative capacity of the Cox PH and RSF was, respectively, 0.771 and 0.798 for the C-index while the accuracy of the Cox PH and RSF was, respectively, 0.257 and 0.207 for the BS. This shows that RSF had both better discriminative capacity and predictive accuracy. Our results show that patients who are older than 70, living in rural areas, without health insurance, at a distant stage and who have not had surgery constitute a subgroup of patients with poor prognosis.

Use of survival support vector machine combined with random survival forest to predict the survival of nasopharyngeal carcinoma patients

Background

The Cox regression model is not sufficiently accurate to predict the survival prognosis of nasopharyngeal carcinoma (NPC) patients. It is impossible to calculate and rank the importance of impact factors due to the low predictive accuracy of the Cox regression model. So, we developed a system. Using the SEER (The Surveillance, Epidemiology, and End Results) database data on NPC patients, we proposed the use of random survival forest (RSF) and survival-support vector machine (SVM) from the machine learning methods to develop a survival prediction system specifically for NPC patients. This approach aimed to make up for the insufficiency of the Cox regression model. We also used the Cox regression model to validate the development of the nomogram and compared it with machine learning methods.

Methods

A total of 1,683 NPC patients were extracted from the SEER database from January 2010 to December 2015. We used R language for modeling work, established the nomogram of survival prognosis of NPC patients by Cox regression model, ranked the correlation of influencing factors by RSF model VIMP (variable important) method, developed a survival prognosis system for NPC patients based on survival-SVM, and used C-index for model evaluation and performance comparison.

Results

Although the Cox regression models can be developed to predict the prognosis of NPC patients, their accuracy was lower than that of machine learning methods. When we substituted the data for the Cox model, the C-index for the training set was only 0.740, and the C-index for the test set was 0.721. In contrast, the C index of the survival-SVM model was 0.785. The C-index of the RSF model was 0.729. The importance ranking of each variable could be obtained according to the VIMP method.

Conclusions

The prediction results from the Cox model are not as good as those of the RSF method and survival-SVM based on the machine learning method. For the survival prognosis of NPC patients, the machine learning method can be considered for clinical application.

Intelligent prediction of major adverse cardiovascular events (MACCE) following percutaneous coronary intervention using ANFIS-PSO model

Background

This study aimed to use the hybrid method based on an adaptive neuro-fuzzy inference system (ANFIS) and particle swarm optimization (PSO) to predict the long term occurrence of major adverse cardiac and cerebrovascular events (MACCE) of patients underwent percutaneous coronary intervention (PCI) with stent implantation.

Method

This retrospective cohort study included a total of 220 patients (69 women and 151 men) who underwent PCI in Ekbatan medical center in Hamadan city, Iran, from March 2009 to March 2012. The occurrence and non-occurrence of MACCE, (including death, CABG, stroke, repeat revascularization) were considered as a binary outcome. The predictive performance of ANFIS model for predicting MACCE was compared with ANFIS-PSO and logistic regression.

Results

During ten years of follow-up, ninety-six patients (43.6%) experienced the MACCE event. By applying multivariate logistic regression, the traditional predictors such as age (OR = 1.05, 95%CI: 1.02–1.09), smoking (OR = 3.53, 95%CI: 1.61–7.75), diabetes (OR = 2.17, 95%CI: 2.05–16.20) and stent length (OR = 3.12, 95%CI: 1.48–6.57) was significantly predicable to MACCE. The ANFIS-PSO model had higher accuracy (89%) compared to the ANFIS (81%) and logistic regression (72%) in the prediction of MACCE.

Conclusion

The predictive performance of ANFIS-PSO is more efficient than the other models in the prediction of MACCE. It is recommended to use this model for intelligent monitoring, classification of high-risk patients and allocation of necessary medical and health resources based on the needs of these patients. However, the clinical value of these findings should be tested in a larger dataset.

Artificial intelligence predictive system of individual survival rate for lung adenocarcinoma

Background

The current research aimed to develop an artificial intelligence predictive system for individual survival rate of lung adenocarcinoma (LUAD).

Methods

Independent risk variables were identified by multivariate Cox regression. Artificial intelligence predictive system was constructed using three different data mining algorithms.

Results

Stage, PM, chemotherapy, PN, age, PT, sex, and radiation_surgery were determined as risk factors for LUAD patients. For 12-month survival rate in model cohort, concordance indexes of RFS, MTLR, and Cox models were 0.852, 0.821, and 0.835, respectively. For 36-month survival rate in model cohort, concordance indexes of RFS, MTLR, and Cox models were 0.901, 0.864, and 0.862, respectively. For 60-month survival rate in model cohort, concordance indexes of RFS, MTLR, and Cox models were 0.899, 0.874, and 0.866, respectively. The concordance indexes in validation dataset were similar to those in model dataset.

Conclusions

The current study designed an individualized survival predictive system, which could provide individual survival curves using three different artificial intelligence algorithms. This artificial intelligence predictive system could directly convey treatment benefits by comparing individual mortality risk curves under different treatments. This artificial intelligence predictive tool is available at https://zhangzhiqiao11.shinyapps.io/Artificial_Intelligence_Survival_Prediction_System_AI_E1001/.

Persistence with Antiplatelet and Risk of Major Adverse Cardiac and Cerebrovascular Events in Acute Coronary Syndrome Patients after Percutaneous Coronary Intervention in Indonesia: A Retrospective Cohort Study

BACKGROUND: Acute coronary syndrome (ACS) is a life-threatening condition that carries high risk of recurrent cardiovascular events and death. Persistence with treatment is known to reduce morbidity and mortality in patients with ACS. AIM: This study focuses on ACS patients undergoing their first percutaneous coronary intervention (PCI) to investigate the association between persistence with antiplatelet therapy and clinical outcomes. MATERIALS AND METHODS: A retrospective cohort study with 2 years of follow-up was conducted with 367 patients recruited. Patients were deemed as having persistence with antiplatelet therapy (WHO ATC code: B0A1C), if the gap between prescriptions was ≤30 days. The clinical outcomes were defined as a composite of major adverse cardiac event (MACE), major adverse cardiovascular and cerebrovascular events (MACCE), myocardial infarction, recurrent PCI, stroke, all-cause death, cardiovascular death, and hospitalization. RESULTS: Cumulative persistence with antiplatelet showed that 72.3% of all ACS patients were still taking antiplatelet 1 year after PCI. Persistence to treatment with antiplatelet therapy can be used as a predictor of MACE or MACCE, because it was associated with recurrent PCI (RR 3.09, 95% CI = 1.18−8.05). History of cardiovascular disease in non-persistence patients was associated with increased risk of MACE (RR 4.90 95% CI = 1.37−17.48) and MACCE (RR 3.67 95% CI = 1.12−11.98) events. CONCLUSION: After PCI, not all ACS patients continued taking their drug exactly as prescribed. Our study indicates that among ACS patients who underwent their first PCI, non-persistence with antiplatelet therapy might lead to worse clinical outcomes. This data will help promote secondary prevention among ACS patients after PCI.

Prediction of prognosis in elderly patients with sepsis based on machine learning (random survival forest)

Background

Elderly patients with sepsis have many comorbidities, and the clinical reaction is not obvious. Thus, clinical treatment is difficult. We planned to use the laboratory test results and comorbidities of elderly patients with sepsis from a large-scale public database Medical Information Mart for Intensive Care (MIMIC) IV to build a random survival forest (RSF) model and to evaluate the model’s predictive value for these patients.

Methods

Clinical information of elderly patients with sepsis in MIMIC IV database was collected retrospectively. Machine learning (RSF) was used to select the top 30 variables in the training cohort to build the final RSF model. The model was compared with the traditional scoring systems SOFA, SAPSII, and APSIII. The performance of the model was evaluated by C index and calibration curve.

Results

A total of 6,503 patients were enrolled in the study. The top 30 important variables screened by RSF were used to construct the final RSF model. The new model provided a better C-index (0.731 in the validation cohort). The calibration curve described the agreement between the predicted probability of RSF model and the observed 30-day survival.

Conclusions

We constructed a prognostic model to predict a 30-day mortality risk in elderly patients with sepsis based on machine learning (RSF algorithm), and it proved superior to the traditional scoring systems. The risk factors affecting the patients were also ranked. In addition to the common risk factors of vasopressors, ventilator use, and urine output. Newly added factors such as RDW, type of ICU unit, malignant cancer, and metastatic solid tumor also significantly influence prognosis.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12873-022-00582-z.

Current and Future Applications of Artificial Intelligence in Coronary Artery Disease

Cardiovascular diseases (CVDs) carry significant morbidity and mortality and are associated with substantial economic burden on healthcare systems around the world. Coronary artery disease, as one disease entity under the CVDs umbrella, had a prevalence of 7.2% among adults in the United States and incurred a financial burden of 360 billion US dollars in the years 2016–2017. The introduction of artificial intelligence (AI) and machine learning over the last two decades has unlocked new dimensions in the field of cardiovascular medicine. From automatic interpretations of heart rhythm disorders via smartwatches, to assisting in complex decision-making, AI has quickly expanded its realms in medicine and has demonstrated itself as a promising tool in helping clinicians guide treatment decisions. Understanding complex genetic interactions and developing clinical risk prediction models, advanced cardiac imaging, and improving mortality outcomes are just a few areas where AI has been applied in the domain of coronary artery disease. Through this review, we sought to summarize the advances in AI relating to coronary artery disease, current limitations, and future perspectives.