Association of Acidemia With Short-Term Mortality of Acute Myocardial Infarction: A Retrospective Study Base on MIMIC-III Database

Zhang, Tang; Guan, Yao-Zong; Líu, Hao

doi:10.1177/1076029620950837

Cited by 10 publications

(7 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In order to better study the treatment and prognosis of CCU patients, we chose to use the MIMIC database, which is difficult but with a large amount of data on critically ill patients, as the data source for the study, which ensures the reliability of the entire study results. All the C-indexes shown in the results are close to 1, which shows that the model constructed in this study has good judgment ability and is better than the model reported in the related literature [30][31][32]. The result of the calibration curve shows that this study still has a high consistency.…”

Section: Discussionsupporting

confidence: 58%

Deep-Learning-Based Survival Prediction of Patients in Coronary Care Units

Yang

Wang

Huang

et al. 2021

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

Background. A survival prediction model based on deep learning has higher accuracy than the CPH model in predicting the survival of CCU patients, and it also has a better discrimination ability. We collected information on patients with various diseases in coronary care units (CCUs) from the Medical Information Mart for Intensive Care III (MIMIC-III) database. The purpose of this study was to use this information to construct a neural-network model based on deep learning to predict the survival probabilities of patients with conditions that are common in CCUs. Method. We collected information on patients in the United States with five common diseases in CCUs from 2001 to 2012. We randomly divided the patients into a training cohort and a testing cohort at a ratio of 7 : 3 and applied a survival prediction method based on deep learning to predict their survival probability. We compared our model with the Cox proportional-hazards regression (CPH) model and used the concordance indexes (C-indexes), receiver operating characteristic (ROC) curve, and calibration plots to evaluate the predictive performance of the model. Results. The 3,388 CCU patients included in the study were randomly divided into 2,371 in the training cohort and 1,017 in the testing cohort. The stepwise regression results showed that the important factors affecting patient survival were the type of disease, age, race, anion gap, glucose, neutrophils, white blood cells, potassium, creatine kinase, and blood urea nitrogen ( P < 0.05 ). We used the training cohort to construct a deep-learning model, for which the C-index was 0.833, or about 5% higher than that for the CPH model (0.786). The C-index of the deep-learning model for the test cohort was 0.822, which was also higher than that for the CPH model (0.782). The areas under the ROC curve for the 28-day, 90-day, and 1-year survival probabilities were 0.875, 0.865, and 0.874, respectively, in the deep-learning model, respectively, and 0.830, 0.843, and 0.806 in the CPH model. These values indicate that the survival analysis model based on deep learning is better than the traditional CPH model in predicting the survival of CCU patients. Conclusion. A survival prediction model based on deep learning has higher accuracy than the CPH model in predicting the survival of CCU patients, and it also has a better discrimination ability.

show abstract

Section: Discussionsupporting

confidence: 58%

Deep-Learning-Based Survival Prediction of Patients in Coronary Care Units

Yang

Wang

Huang

et al. 2021

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

show abstract

“…Several studies have shown that BE is one of the most important tools for determining the severity of illness in acute care settings ( 9 , 10 , 13 , 14 ). Miñana et al revealed that admission pH, arterial PaO 2 , and PaCO 2 had no association with all-cause mortality in patients with HF ( 10 ).…”

Section: Discussionmentioning

confidence: 99%

“…For example, Xu et al found that the higher anion gap (AG) was significantly associated with an increased risk of mortality, and the measure of AG can be a robust and reliable predictor of AMI mortality during follow-up ( 7 ). In a recent study, Zhang et al concluded that acidemia may increase the 30-day survival rate and 90-day survival rate of patients with AMI admitted to the ICU, while acidemia is typically diagnosed based on the ABG with pH < 7.35 ( 9 ). However, Miñana et al found that arterial PaO 2 , PaCO 2 , and pH did not correlate with all-cause long-term mortality in patients diagnosed with AHF ( 10 ).…”

Section: Introductionmentioning

confidence: 99%

Base excess is associated with the risk of all-cause mortality in critically ill patients with acute myocardial infarction

Luo

Duan

Zheng

et al. 2022

Front. Cardiovasc. Med.

View full text Add to dashboard Cite

BackgroundBase excess (BE) represents an increase or decrease of alkali reserves in plasma to diagnose acid-base disorders, independent of respiratory factors. Current findings about the prognostic value of BE on mortality of patients with acute myocardial infarction (AMI) are still unclear. The purpose of this study was to explore the prognostic significance of BE for short-term all-cause mortality in patients with AMI.MethodsA total of 2,465 patients diagnosed with AMI in the intensive care unit from the Medical Information Mart for Intensive Care III (MIMIC-III) database were included in our study, and we explored the association of BE with 28-day and 90-day all-cause mortality using Cox regression analysis. We also used restricted cubic splines (RCS) to evaluate the relationship between BE and hazard ratio (HR). The primary outcomes were 28-day and 90-day all-cause mortality.ResultsWhen stratified according to quantiles, low BE levels at admission were strongly associated with higher 28-day and 90-day all-cause mortality. Multivariable Cox proportional hazard models revealed that low BE was an independent risk factor of 28-day all-cause mortality [HR 4.158, 95% CI 3.203–5.398 (low vs. normal BE) and HR 1.354, 95% CI 0.896–2.049 (high vs. normal BE)] and 90-day all-cause mortality [HR 4.078, 95% CI 3.160–5.263 (low vs. normal BE) and HR 1.369, 95% CI 0.917–2.045 (high vs. normal BE)], even after adjustment for significant prognostic covariates. The results were also consistent in subgroup analysis. RCS revealed an “L-type” relationship between BE and 28-day and 90-day all-cause mortality, as well as adjusting for confounding variables. Meanwhile, Kaplan–Meier survival curves were stratified by combining BE with carbon dioxide partial pressure (PaCO2), and patients had the highest mortality in the group which had low BE (< 3.5 mEq/L) and high PaCO2 (> 45 mmHg) compared with other groups.ConclusionOur study revealed that low BE was significantly associated with 28-day and 90-day mortality in patients with AMI and indicated the value of stratifying the mortality risk of patients with AMI by BE.

show abstract

“…13 Another research presented that 1 of the coagulation indicators, PT, is regarded as an independent predictor for 2-year mortality in acute myocardial infarction patients. 14 McClintock compared biomarkers of coagulation and fibrinolysis between survivors and non-survivors, indicating that coagulation and fibrinolysis predict mortality in patients with lung-protective ventilation. Moreover, the levels of thrombomodulin and plasminogen activator inhibitor 1 (PAI) are significantly higher in non-survivors.…”

Section: Discussionmentioning

confidence: 99%

The Association of Coagulation Indicators and Coagulant Agents With 30-Day Mortality of Critical Diabetics

Huang

Zhong

Liu

2021

Clin Appl Thromb Hemost

View full text Add to dashboard Cite

Diabetes, regarded as a global health concerned disease, was focused by the World Health Organization (WHO). Patients with diabetes may have a hypercoagulable and hypo-fibrinolysis state. There is lots of research about cardiovascular effects on diabetes patients, but less about the coagulation system. This study is designed to investigate the relationship between coagulation indicators and 30-day mortality of critical diabetes patients. In this retrospective, single-center study, we included adult patients diagnosed with diabetes. Data, including demographic, complication, laboratory tests, scoring system, and anticoagulant treatment, were extracted from Medical Information Mart for Intensive Care (MIMIC-III). The receiver operating characteristic (ROC) curve and Kaplan-Meier curve were applied to predict the association of mortality and coagulation indicators. Cox hazard regression model and subgroup analysis were used to analyze the risk factors associated with 30-day mortality. A total of 4026 patients with diabetes mellitus were included in our study, of whom 3312 survived after admitted to the hospital and 714 died. Cox hazard regression showed anticoagulant therapy might decrease the risk of 30-day mortality after adjusted. In age <70 subgroup analysis, we found that patients with PTT <26.8 s or lightly increased PT may increase odds of 30-day hospital death (HR, 95%CI, 2.044 (1.376, 3.034), 1.562 (1.042, 2.343)). When age >70, lightly increased PTT may reduce the risk of mortality, but PT >16.3 s, a high level of hypo-coagulation state, increase risk of mortality (HR, 95%CI, 0.756 (0.574, 0.996), 1.756 (1.129, 2.729)). Critical diabetes patients may benefit from anticoagulant agents. The abnormal coagulant function is related to the risk of 30-day mortality.

show abstract

Association of Acidemia With Short-Term Mortality of Acute Myocardial Infarction: A Retrospective Study Base on MIMIC-III Database

Cited by 10 publications

References 42 publications

Deep-Learning-Based Survival Prediction of Patients in Coronary Care Units

Deep-Learning-Based Survival Prediction of Patients in Coronary Care Units

Base excess is associated with the risk of all-cause mortality in critically ill patients with acute myocardial infarction

The Association of Coagulation Indicators and Coagulant Agents With 30-Day Mortality of Critical Diabetics

Contact Info

Product

Resources

About