Evaluation of Intermountain Risk Score for Short- and Long-Term Mortality in ST Elevation Myocardial Infarction Patients

The Simplified Thrombo-Inflammatory Score as a Novel Predictor of All-Cause Mortality in Patients with Heart Failure: A Retrospective Cohort Study

Background

The simplified thrombo-inflammatory score (sTIPS) has recently emerged as a novel prognostic score. Hence, we investigated the prognostic value of sTIPS for predicting long-term mortality in patients with heart failure (HF).

Methods

A total of 3741 patients were analyzed in this study. The sTIPS was calculated based on the white blood cell count (WBC) and the mean platelet volume to platelet count (MPV/PC) ratio at admission. The mean follow-up time was 22.75 months. Multivariable Cox regression analyses were used to investigate the associations between the sTIPS and all-cause mortality (ACM).

Results

In the whole study population, multivariate Cox regression analysis showed that patients in both the sTIPS 2 and sTIPS 1 groups had significantly increased risk of ACM as compared with patients in the sTIPS 0 group (hazard ratio [HR]=1.706, 95% confidence interval [CI]: 1.405-2.072, P<0.001 and HR = 1.431, 95% CI 1.270-1.612, P<0.001). The same significant trend was observed in heart failure with preserved ejection fraction (HFpEF) patients (sTIPS1 vs sTIPS0: HR = 1.366, 95% CI 1.100-1.697, P = 0.005; sTIPS2 vs sTIPS0: HR = 1.995, 95% CI 1.460-2.725, P<0.001). However, only sTIPS 1 group had a significantly increased the risk of ACM compared to the sTIPS 0 group among patients with HFmrEF (sTIPS1 vs sTIPS0: HR = 1.648, 95% CI 1.238-2.194, P = 0.001) and HFrEF (sTIPS1 vs sTIPS0: HR = 1.322, 95% CI 1.021-1.712, P = 0.035).

Conclusion

sTIPS is useful in predicting risk for long-term mortality in patients with HF.

Value of Absolute Eosinophil Count in Patients with Acute Coronary Syndrome Undergoing Percutaneous Coronary Intervention

Background and Aims

Elevated eosinophils typically indicate hypersensitive inflammation; however, their involvement in cardiovascular events remains incompletely understood. We investigated the association between the absolute eosinophil count (AEC) and major adverse cardiovascular and cerebrovascular events (MACCEs) in patients with acute coronary syndrome (ACS) undergoing percutaneous coronary intervention (PCI). Additionally, we determine whether the integration of AEC with the SYNTAX II score could improve predictive ability.

Methods and Results

The AECs of 1711 patients with ACS undergoing PCI from June 2016 to November 2017 were analyzed on admission. All recruitments were splitted into three groups based on AEC tertiles and 101 participants underwent one or more noteworthy outcomings. The association between AEC and MACCEs (defined as a composite of cardiovascular death, myocardial infarction [MI], and stroke) was tested by Cox proportional-hazards regression analysis. After adjusting for confounders, AEC was independently associated with MACCEs (HR 11.555, 95% CI: 3.318-40.239). Patients in the lowest AEC tertile (T1) as a reference, those in the higher tertiles had an incrementally higher risk of MACCEs (T3: HR 1.848 95% CI: 1.157-2.952; P for trend=0.008). Inclusion of AEC enhanced the predictive accuracy of the SYNTAX II score for MACCEs (AUC: from 0.701 [95% CI: 0.646-0.756] to 0.728 [95% CI: 0.677-0.780]; DeLong's test, P = 0.020).

Conclusion

AEC is independently linked to MACCEs in ACS patients who underwent PCI, and adds incremental predictive information to the SYNTAX II score.

Incidence and Predictors of Cardiac Arrest Among Patients Admitted to the Intensive Care Units of a Comprehensive Specialized Hospital in Central Ethiopia

Background

Cardiac arrest (CA) is a common public health problem. Worldwide, cardiac arrest ranks highly among hospitalised patients' public health concerns, particularly in low-income nations. Data on cardiac arrest in intensive care units in low-income countries are relatively scarce. Determining the incidence and predictors of cardiac arrest among ICU patients will be a very crucial and fruitful clinical practice in resource-limited areas like Ethiopia.

Methods

A retrospective cohort study was conducted by reviewing charts of 422 systematically selected patients admitted to the ICU from 2018 to 2022 in Wachemo University Comprehensive Specialized Hospital. The extraction tool was used for the data collection, Epi-data version 4.6.0 for data entry, and STATA version 14 for data cleaning and analysis. Kaplan-Meier, log rank test, and life table were used to describe the data. The Cox proportional hazard regression model was used for analysis.

Results

The findings of this study revealed that the overall occurrence of cardiac arrest among critically ill ICU patients was 27% (95% CI: 23, 32). The incidence density rate of cardiac arrest among intensive care unit patients was 19.6 per 1000 person-days of observation. In a multivariable analysis, patients with chronic kidney disease, oxygen saturation <90%, delirium, intubation, and patients admitted to the ICU with cardiovascular disease were found to be independent predictors of cardiac arrest in the Intensive Care Unit.

Conclusion

The incidence density rate of cardiac arrest among intensive care unit patients was high. This study also revealed that chronic kidney disease, delirium, intubation, oxygen saturation level below 90% and patients admitted with cardiovascular disease were independent predictors of the occurrence of cardiac arrest among intensive care unit patients. Finally, we recommend that clinician pays attention to those identified as preventable risk factors for early interventions to improve the recovery process of patients in the ICU.

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.

The association between serum anion gap and acute kidney injury after coronary artery bypass grafting in patients with acute coronary syndrome

BACKGROUND

The purpose of this study was to explore the association between serum anion gap (SAG) and acute kidney injury (AKI) after coronary artery bypass grafting (CABG) in patients with acute coronary syndrome (ACS) in the Intensive Care Unit (ICU).

METHODS

We retrospectively analyzed the clinical data of 2,428 ACS patients who underwent CABG in the Medical Information Mart for Intensive Care IV (Mimic-IV) database. The endpoint of this study was AKI after CABG. The baseline data of the two groups (non-AKI group vs. AKI group) was compared, and the restricted cubic spline (RCS) plot, multivariable logistic regression model, and subgroup analysis were used to explore the relationship between SAG and the risk of AKI after CABG.

RESULTS

In the adjusted multivariate logistic regression model, SAG was an independent predictor of AKI after CABG (OR = 1.12, 95% CI: 1.02-1.23, P = 0.015). The RCS revealed that the relationship between SAG levels and risk of AKI was J-shaped. When the SAG was ≥ 11.58 mmol/L, the risk of AKI increased by 26% for each unit increase in SAG. Additionally, we further divided the SAG into quartiles. In the fully adjusted model, compared with the first quartile of SAG, the odds ratios (ORs) and 95% confidence intervals (CIs) for AKI risk across the SAG quartiles were 0.729 (0.311, 1.600), 1.308 (0.688-2.478), and 2.221 (1.072, 4.576).

CONCLUSIONS

The SAG level was associated with the risk of AKI after CABG in a J-shaped curve in the ICU. However, the underlying causes of the problem need to be investigated.

Prognostic Value of GRACE Risk Score Combined With Systemic Immune-Inflammation Index in Patients With ST-Segment Elevation Myocardial Infarction After Percutaneous Coronary Intervention

The Global Registry of Acute Coronary Events (GRACE) score and the systemic immune-inflammation index (SII) were used independently to predict adverse outcomes in patients with ST-elevation myocardial infarction (STEMI). In this study, 1041 patients with STEMI were divided into 4 groups based on GRACE scores and optimal cutoff values for SII. SII was positively correlated with GRACE score (r = 0.164; P < .001). SII (HR, hazard ratio: 2.051; 95%CI: 1.249-3.368; P = .005) and GRACE score (HR: 7.625; 95%CI: 3.692-15.746; P < .001) were independent risk predictors of short-term major adverse cardiovascular events (MACEs). Taking the low SII+low GRACE group as the reference group, the short-term MACE HR of the high SII+high GRACE group was 40.470 (95%CI: 5.547-295.253). Comparing the area under the curve, the combined use of SII and GRACE scores can significantly improve the prediction efficiency of short-term MACE compared with the single use of SII and GRACE scores. In conclusion, SII may be positively correlated with GRACE score, and the combination of the two accurately predicted the occurrence of short-term MACE in STEMI patients after percutaneous coronary intervention (PCI).

The Ratio of Contrast Volume/Glomerular Filtration Rate and Urine NGAL Predicts the Progression of Acute Kidney Injury to Chronic Kidney Disease in Patients After Planned Percutaneous Coronary Intervention

Objective

To evaluate the value of contrast volume/glomerular filtration ratio (Vc/eGFR ratio) and urine Neutrophil Gelatinase-Associated Lipocalin (uNGAL) in predicting the progression contract associated-acute kidney injury (CA-AKI) to chronic kidney disease (CKD) in planned percutaneous coronary intervention (PCI) patients.

Patients and Methods

We examined 387 adult patients who had undergone planned percutaneous coronary intervention (PCI). We determined acute kidney injury (AKI) and chronic kidney disease (CKD) using the criteria set by the Kidney Disease: Improving Global Outcomes (KDIGO). We calculated the estimated glomerular filtration rate (eGFR) using the CKD-EPI formula based on serum creatinine levels. To determine the Vc/eGFR ratio, we considered the contrast medium volume and eGFR for each patient. Additionally, we measured urine NGAL levels using the ELISA method.

Results

The percentage of CA-AKI patients who developed CKD after planned PCI was 36.36%. Within the CA-AKI to CKD group, the Vc/eGFR ratio was 2.82, and uNGAL levels were significantly higher at 72.74 ng/mL compared to 1.93 ng/mL for Vc/eGFR ratio and 46.57 ng/mL for uNGAL in the recovery CA-AKI group. This difference was statistically significant (p<0.001). Diabetic mellitus, urine NGAL concentration, and Vc/eGFR ratio were found to be independent factors in the progression of CA-AKI to CKD. The Vc/eGFR ratio and uNGAL showed predictive capabilities for progressing CA-AKI to CKD with an AUC of 0.884 and 0.878, respectively. The sensitivity was 81.3% for both, while the specificity was 89.3% for Vc/eGFR ratio and 85.7% for uNGAL.

Conclusion

The Vc/eGFR ratio and uNGAL were good predictors for CA-AKI to CKD in planned PCI patients.

Comparative Evaluation of Intermountain Risk Score With Mehran Risk Score for Risk Estimation of Contrast-Induced Nephropathy and Short-Term Mortality in ST-Segment Elevation Myocardial Infarction Patients

Contrast-induced nephropathy (CIN) has become one of the most important causes of in-hospital acute renal failure with the increasing use of contrast-mediated imaging tools. This significantly increases the morbidity and mortality of the affected subjects and causes a financial burden on the health system. In this context, prediction of CIN is important and some risk scores have been developed to predict CIN. The most frequently used and popular among these is the Mehran Score (MS), which is based on a number of hemodynamic and metabolic parameters. The Intermountain Risk Score (IMRS) is a recently developed risk score that highly predicts short-term mortality based on common laboratory parameters, and many parameters of this risk score have been found to be closely associated with CIN. In this context, we aimed to compare MS and IMRS in terms of CIN and short-term mortality estimation. The study included 931 patients who underwent percutaneous coronary intervention. CIN developed in 21.5% of patients. Both MS and IMRS independently predicted CIN. In receiver operating characteristic analysis, IMRS was found to be non-inferior to MS in predicting CIN and IMRS was superior to MS in predicting short-term mortality. IMRS and MS were independently associated with short-term mortality.

Predictive Value of COPD History on In-Stent Restenosis in Coronary Arteries Following Percutaneous Coronary Intervention

Objective

Chronic obstructive pulmonary disease (COPD) is a prevalent chronic respiratory disease that poses a significant health risk to individuals. Patients with COPD are predisposed to a higher incidence of coronary artery disease (CAD) than the general population. This study aims to investigate the correlation between COPD and the incidence of in-stent restenosis (ISR) after percutaneous coronary intervention (PCI).

Methods

This study retrospectively analyzed the clinical data and laboratory test results of patients who underwent PCI at our hospital between January 2018 and December 2021 to investigate the relationship between COPD and drug-Eluting Stents (DES) postoperative ISR. We employed the best subset method to select the most suitable combination of predictive factors, utilizing the data, and verified the precision of the model by means of internal validation. We ultimately assessed the performance of the prediction model using an ROC curve.

Results

The research indicates that COPD is an independent risk factor for ISR after PCI (OR=2.437, 95% CI [1.336, 4.495], P=0.004). The analysis revealed an area under the receiver operating characteristic (ROC) curve of 0.783 for the training group and 0.705 for the testing group, indicating a model fitting for both groups (both > 0.5).

Conclusion

COPD history is a dependable predictor of stent restenosis post percutaneous coronary intervention.

Neutrophil-to-Lymphocyte Ratio and Platelet-to-Lymphocyte Ratio are Important Indicators for Predicting in-Hospital Death in Elderly AMI Patients

Aim

To investigate the role of neutrophil-to-lymphocyte ratio(NLR) and platelet-to-lymphocyte(PLR) in predicting the risk of in-hospital mortality in elderly acute myocardial infarction(AMI) patients.

Methods

This study was a single-center, retrospective and observational study. From December 2015 to December 2021, a total of 1550 elderly patients (age ≥ 60 years) with AMI with complete clinical history data were enrolled in the Second Hospital of Dalian Medical University. Routine blood tests were performed on admission, and NLR and PLR were calculated based on neutrophil, platelet, and lymphocyte counts. Outcome was defined as all-cause mortality during hospitalization. Cox regression and restricted spline cubic(RCS) models were used to evaluate the association of NLR and in-hospital mortality risk and the association of PLR with in-hospital mortality risk, respectively.

Results

(1) A total of 132 (8.5%) patients died during hospitalization. From the results of blood routine, the white blood cell, neutrophil, NLR and PLR in the death group were higher than those in the non-death group, while the lymphocyte was lower than that in the non-death group, and the difference was statistically significant (P < 0.05). (2) The results of receiver operating characteristic(ROC) curves analysis showed that the predictive ability of NLR (AUC = 0.790) for in-hospital death was better than that of PLR (AUC = 0.637). (3) Multivariate Cox proportional regression hazard models showed that high NLR was associated with the risk of in-hospital mortality in elderly AMI patients (HR = 3.091, 95% CI 2.097-4.557, P < 0.001), while high PLR was not. (4) RCS models showed a nonlinear dose-response relationship between NLR and in-hospital death (P for nonlinear = 0.0007).

Conclusion

High NLR (> 6.69) is associated with the risk of in-hospital mortality in elderly patients with AMI and can be an independent predictor of poor short-term prognosis in elderly patients with AMI.

Stress hyperglycemia ratio: an independent predictor for in-hospital major adverse cardiovascular and cerebrovascular events in patients with st-segment elevation myocardial infarction

BACKGROUND

To assess the predictive accuracy of the stress hyperglycemia ratio (SHR) for in-hospital major adverse cardiovascular and cerebrovascular events (MACCE) in patients with ST-segment elevation myocardial infarction (STEMI).

METHODS

A total of 1,944 patients were enrolled within 24 h of a new STEMI diagnosis. The SHR was obtained by dividing the blood glucose level at admission by the estimated average glucose. MACCE were defined as acute cerebral infarction, mechanical complications of myocardial infarction, cardiogenic shock, and all-cause death. Patients were then categorized into the MACCE and non-MACCE groups according to the occurrence of in-hospital MACCE. Propensity score matching was used to balance confounding factors, and logistic regression was used to identify the potential predictive factors for MACCE.

RESULTS

A total of 276 patients were included after 1:1 matching, and the confounding factors were balanced between the two groups. The SHR was an independent predictor of in-hospital MACCE (odds ratio = 10.06, 95% confidence interval: 4.16-27.64, P < 0.001), while blood glucose at admission was not. The SHR was also an independent predictor for in-hospital MACCE in nondiabetic patients with STEMI (odds ratio = 11.26, 95% confidence interval: 3.05-55.21, P < 0.001).

CONCLUSION

SHR is an independent predictor of in-hospital MACCE in patients with acute STEMI, especially in nondiabetic patients.

Clinical characteristics and prognosis differences between isolated right and left ventricular myocardial infarction in the Chinese population: a retrospective study

Background and aims

Acute myocardial infarction (AMI) is divided into left ventricular myocardial infarction (LVMI) and right ventricular myocardial infarction (RVMI) according to the regions of myocardial ischemic necrosis. Clinical characteristics, treatment strategies, and prognosis differences between isolated RVMI and LVMI have not been well characterized. This study aimed to explore this difference of patients with isolated RVMI and LVMI.

Methods

This retrospective cohort study included 3,506 patients hospitalized with coronary angiography diagnosed type 1 myocardial infarction (MI). Characteristics of admission and treatment strategies were compared in patients with isolated RVMI and LVMI. COX proportional hazards models with and without inverse probability of treatment weighting (IPTW) adjustment were performed to estimate the difference in all-cause and cardiovascular mortality between the two groups.

Results

In this retrospective study, we found the frequency of isolated RVMI was significantly lower in the population than that of isolated LVMI (406 (11.6%) vs 3,100 (88.4%)). Patients with isolated RVMI have similar age, sex, and comorbidities to the patients with isolated LVMI. However, patients with isolated RVMI have lower heart rate and blood pressure, but higher rates of cardiogenic shock and atrioventricular block. It is noteworthy that patients with isolated RVMI are more likely to be complicated with the multivessel lesion. Patients with isolated RVMI have lower risk of all-cause mortality (HR 0.36; 95% CI [0.24-0.54], p < 0.001) and cardiovascular mortality (HR 0.37; 95% CI [0.22-0.62], p < 0.001) than patients with isolated LVMI.

Conclusions

This study showed that patients with isolated RVMI and LVMI have similar baseline characteristics. However, the clinical manifestations were different in the isolated RVMI and LVMI patients. This study revealed a better prognosis of isolated RVMI patients compared to isolated LVMI, which indicates the ischemic region could be considered in AMI risk stratification models for better assessment of risk for adverse clinical events.

Association between serum calcium level and in-hospital mortality in patients with acute myocardial infarction: a retrospective cohort study

The association between serum calcium levels and the prognosis of patients with acute myocardial infarction (AMI) remains controversial. This study aimed to explore the association between serum calcium and in-hospital mortality in patients with AMI. The data of this study were extracted from the Philips eICU Collaborative Research Database. A total of 7284 patients were eventually enrolled in this study, of which 799 (10.97%) died during hospitalization. For each patient, serum calcium, corrected to albumin, was calculated and categorized into four groups: Q1 ≤ 8.5, Q2 8.5-9.5, Q3 9.5-10.5, and Q4 > 10.5 mg/dL. Multivariate analysis demonstrated that corrected sCa was an independent predictor of in-hospital death (Q2 vs. Q1, OR 0.5, 95% CI 0.4-0.7, P < 0.001; Q3 vs. Q1, OR 0.8, 95% CI 0.6-1.0, P = 0.035; Q4 vs. Q1, OR 1.6, 95% CI 1.1-2.3, P = 0.008). The association remained stable in the fully adjusted model. A significant U-shaped association between corrected serum calcium and in-hospital mortality was observed in piecewise linear regression model (Corrected sCa < 9.4 mg/dL, OR 0.8, 95% CI 0.7-0.9, P < 0.001; corrected sCa > 9.4 mg/dL, OR 1.5, 95% CI 1.3-1.8, P < 0.001). In conclusion, both decreased and increased corrected serum calcium is associated with increased in-hospital mortality in patients with AMI, and patients may have the lowest risk of in-hospital death when corrected serum calcium is 9.4 mg/dL (2.35 mmol/L).

Risk factors for sudden cardiac arrest in patients with ST-segment elevation myocardial infarction: a retrospective cohort study

Background

Sudden cardiac arrest (SCA) is a critical complication of acute myocardial infarction, especially ST-segment elevation myocardial infarction (STEMI). This study identified the risk factors for SCA in patients with STEMI before receiving catheterization.

Methods

We retrospectively analyzed the data of patients with STEMI and cardiac arrest who presented to a tertiary care center in Taiwan between January 1, 2016, and December 31, 2019. Only patients with coronary artery disease (CAD) confirmed by coronary angiography were included in this study. We collected the patients’ demographic and clinical data, such as age, sex, medical history, estimated glomerular filtration rate (eGFR), and coronary angiographic findings. The primary outcome of this study was SCA in patients with STEMI. Continuous and nominal variables were compared using the two-sample Student's t-test and chi-squared test, respectively. The results of logistic regression were subjected to multivariate analysis with adjustment for possible confounders.

Results

A total of 920 patients with STEMI and coronary angiography–documented CAD and 108 patients with SCA who presented between January 1, 2016, and December 31, 2019, were included. The bivariate logistic regression analysis of patients’ demographic data revealed that patients with STEMI and SCA were slightly younger, were more likely to have diabetes mellitus, and had a lower eGFR than did the patients without SCA. The coronary angiographic findings indicated a higher prevalence of left main CAD and three-vessel disease in patients with SCA than in patients without SCA. Multivariate logistic regression revealed that left main CAD (odds ratio [OR]: 3.77; 95% confidence interval [CI], 1.84 to 7.72), a lower eGFR (OR: 0.97; 95% CI, 0.96 to 0.98), and younger age (OR: 0.98; 95% CI, 0.96 to 0.99) were the risk factors for SCA in patients with STEMI.

Conclusions

Left main CAD, lower eGFR, and younger age are the risk factors for cardiac arrest in patients with acute myocardial infarction.