Development of a predictive nomogram for 28-day mortality risk in non-traumatic or post-traumatic subarachnoid hemorrhage patients.

OBJECTIVE: Subarachnoid hemorrhage (SAH) is associated with high rates of mortality and permanent disability. At present, there are few definite clinical tools to predict prognosis in SAH patients. The current study aims to develop and assess a predictive nomogram model for estimating the 28-day mortality risk in both non-traumatic or post-traumatic SAH patients. METHODS: The MIMIC-III database was searched to select patients with SAH based on ICD-9 codes. Patients were separated into non-traumatic and post-traumatic SAH groups. Using LASSO regression analysis, we identified independent risk factors associated with 28-day mortality and incorporated them into nomogram models. The performance of each nomogram was assessed by calculating various metrics, including the area under the curve (AUC), net reclassification improvement (NRI), integrated discrimination improvement (IDI), and decision curve analysis (DCA). RESULTS: The study included 999 patients with SAH, with 631 in the non-traumatic group and 368 in the post-traumatic group. Logistic regression analysis revealed critical independent risk factors for 28-day mortality in non-traumatic SAH patients, including gender, age, glucose, platelet, sodium, BUN, WBC, PTT, urine output, SpO2, and heart rate and age, glucose, PTT, urine output, and body temperature for post-traumatic SAH patients. The prognostic nomograms outperformed the commonly used SAPSII and APSIII systems, as evidenced by superior AUC, NRI, IDI, and DCA results. CONCLUSION: The study identified independent risk factors associated with the 28-day mortality risk and developed predictive nomogram models for both non-traumatic and post-traumatic SAH patients. The nomogram holds promise in guiding prognosis improvement strategies for patients with SAH.

Independent effects of the triglyceride-glucose index on all-cause mortality in critically ill patients with coronary heart disease: analysis of the MIMIC-III database.

BACKGROUND: The triglyceride-glucose (TyG) index is a reliable alternative biomarker of insulin resistance (IR). However, whether the TyG index has prognostic value in critically ill patients with coronary heart disease (CHD) remains unclear. METHODS: Participants from the Medical Information Mart for Intensive Care III (MIMIC-III) were grouped into quartiles according to the TyG index. The primary outcome was in-hospital all-cause mortality. Cox proportional hazards models were constructed to examine the association between TyG index and all-cause mortality in critically ill patients with CHD. A restricted cubic splines model was used to examine the associations between the TyG index and outcomes. RESULTS: A total of 1,618 patients (65.14% men) were included. The hospital mortality and intensive care unit (ICU) mortality rate were 9.64% and 7.60%, respectively. Multivariable Cox proportional hazards analyses indicated that the TyG index was independently associated with an elevated risk of hospital mortality (HR, 1.71 [95% CI 1.25-2.33] P = 0.001) and ICU mortality (HR, 1.50 [95% CI 1.07-2.10] P = 0.019). The restricted cubic splines regression model revealed that the risk of hospital mortality and ICU mortality increased linearly with increasing TyG index (P for non-linearity = 0.467 and P for non-linearity = 0.764). CONCLUSIONS: The TyG index was a strong independent predictor of greater mortality in critically ill patients with CHD. Larger prospective studies are required to confirm these findings.

Establishment and validation of a prognosis nomogram for MIMIC-III patients with liver cirrhosis complicated with hepatic encephalopathy.

INTRODUCE: The purpose of this study was to establish a comprehensive prognosis nomogram for patients with liver cirrhosis complicated with hepatic encephalopathy (HE) in the intensive care unit (ICU) and to evaluate the predictive value of the nomogram. METHOD: This study analyzed 620 patients with liver cirrhosis complicated with HE from the Medical Information Mart for Intensive Care III(MIMIC-III) database. The patients were randomly divided into two groups in a 7-to-3 ratio to form a training cohort (n = 434) and a validation cohort (n = 176). Cox regression analyses were used to identify associated risk variables. Based on the multivariate Cox regression model results, a nomogram was established using associated risk predictor variables to predict the 90-day survival rate of patients with cirrhosis complicated with HE. The new model was compared with the Sequential organ failure assessment (SOFA) scoring model in terms of the concordance index (C-index), the area under the curve (AUC) of receiver operating characteristic (ROC) analysis, the net reclassification improvement (NRI), the integrated discrimination improvement (IDI), calibration curve, and decision curve analysis (DCA). RESULTS: This study showed that older age, higher mean heart rate, lower mean arterial pressure, lower mean temperature, higher SOFA score, higher RDW, and the use of albumin were risk factors for the prognosis of patients with liver cirrhosis complicated with HE. The use of proton pump inhibitors (PPI) was a protective factor. The performance of the nomogram was evaluated using the C-index, AUC, IDI value, NRI value, and DCA curve, showing that the nomogram was superior to that of the SOFA model alone. Calibration curve results showed that the nomogram had excellent calibration capability. The decision curve analysis confirmed the good clinical application ability of the nomogram. CONCLUSION: This study is the first study of the 90-day survival rate prediction of cirrhotic patients with HE in ICU through the data of the MIMIC-III database. It is confirmed that the eight-factor nomogram has good efficiency in predicting the 90-day survival rate of patients.

Influence of systolic blood pressure trajectory on in-hospital mortality in patients with sepsis.

BACKGROUND: Numerous studies have investigated the mean arterial pressure in patients with sepsis, and many meaningful results have been obtained. However, few studies have measured the systolic blood pressure (SBP) multiple times and established trajectory models for patients with sepsis with different SBP trajectories. METHODS: Data from patients with sepsis were extracted from the Medical Information Mart for Intensive Care-III database for inclusion in a retrospective cohort study. Ten SBP values within 10 h after hospitalization were extracted, and the interval between each SBP value was 1 h. The SBP measured ten times after admission was analyzed using latent growth mixture modeling to construct a trajectory model. The outcome was in-hospital mortality. The survival probability of different trajectory groups was investigated using Kaplan-Meier (K-M) analysis, and the relationship between different SBP trajectories and in-hospital mortality risk was investigated using Cox proportional-hazards regression model. RESULTS: This study included 3034 patients with sepsis. The median survival time was 67 years (interquartile range: 56-77 years). Seven different SBP trajectories were identified based on model-fit criteria. The in-hospital mortality rates of the patients in trajectory classes 1-7 were 25.5%, 40.5%, 11.8%, 18.3%, 23.5%, 13.8%, and 10.5%, respectively. The K-M analysis indicated that patients in class 2 had the lowest probability of survival. Univariate and multivariate Cox regression analysis indicated that, with class 1 as a reference, patients in class 2 had the highest in-hospital mortality risk (P < 0.001). Subgroup analysis indicated that a nominal interaction occurred between age group and blood pressure trajectory in the in-hospital mortality (P < 0.05). CONCLUSION: Maintaining a systolic blood pressure of approximately 140 mmHg in patients with sepsis within 10 h of admission was associated with a lower risk of in-hospital mortality. Analyzing data from multiple measurements and identifying different categories of patient populations with sepsis will help identify the risks among these categories.

Development and validation of a nomogram to predict the risk of renal replacement therapy among acute kidney injury patients in intensive care unit.

BACKGROUND: There are no universally accepted indications to initiate renal replacement therapy (RRT) among patients with acute kidney injury (AKI). This study aimed to develop a nomogram to predict the risk of RRT among AKI patients in intensive care unit (ICU). METHODS: In this retrospective cohort study, we extracted AKI patients from Medical Information Mart for Intensive Care III (MIMIC-III) database. Patients were randomly divided into a training cohort (70%) and a validation cohort (30%). Multivariable logistic regression based on Akaike information criterion was used to establish the nomogram. The discrimination and calibration of the nomogram were evaluated by Harrell's concordance index (C-index) and Hosmer-Lemeshow (HL) test. Decision curve analysis (DCA) was performed to evaluate clinical application. RESULTS: A total of 7413 critically ill patients with AKI were finally enrolled. 514 (6.9%) patients received RRT after ICU admission. 5194 (70%) patients were in the training cohort and 2219 (30%) patients were in the validation cohort. Nine variables, namely, age, hemoglobin, creatinine, blood urea nitrogen and lactate at AKI detection, comorbidity of congestive heart failure, AKI stage, and vasopressor use were included in the nomogram. The predictive model demonstrated satisfying discrimination and calibration with C-index of 0.938 (95% CI, 0.927-0.949; HL test, P = 0.430) in training set and 0.935 (95% CI, 0.919-0.951; HL test, P = 0.392) in validation set. DCA showed a positive net benefit of our nomogram. CONCLUSION: The nomogram developed in this study was highly accurate for RRT prediction with potential application value.

Association of estimated glomerular filtration rate and all-cause mortality in acute pancreatitis: a retrospective analysis.

BACKGROUND: Our primary objective was to explore the association between estimated glomerular filtration rate (eGFR) and all-cause mortality in acute pancreatitis (AP) admission to intensive care units. METHODS: This study is a retrospective cohort analysis based on the Medical Information Mart for Intensive Care III database. The eGFR was calculated based on Chronic Kidney Disease Epidemiology Collaboration equation. Cox models with restricted cubic spline functions were used to evaluated the association of eGFR with all-cause mortality. RESULTS: The mean eGFR was 65.93 ± 38.56 ml/min/1.73 m2 in 493 eligible patients. 28-day mortality was 11.97% (59/ 493), which decreased by 15% with every 10 ml/min/1.73 m2 increase in eGFR. The adjusted hazard ratio (95% confidence interval) was 0.85 (0.76-0.96). A non-linear association was proved between eGFR and all-cause mortality. When eGFR < 57 ml/min/1.73 m2, there was a negative correlation between eGFR and 28-day mortality, hazard ratio (95% CI) was 0.97 (0.95, 0.99). The eGFR was also negatively correlated with in-hospital and in-ICU mortality. Subgroup analysis confirmed that the association between eGFR and 28-day mortality in different characteristics was stable. CONCLUSIONS: The eGFR was negatively correlated with all-cause mortality in AP when eGFR is less than the threshold inflection point.

Admission Heart Rate and Mortality in Critically Ill Patients with Acute Aortic Dissection.

The association between admission heart rate (HR) and the mortality of critically ill patients with acute aortic dissection (AAD) remains unclear.The data were extracted from the Medical Information Mart for Intensive Care (MIMIC-III) database. Cox regression models and Kaplan-Meier (KM) survival curve were used to explore the association between admission HR and 90-day, 1-year, and 3-year mortality in patients with AAD. Sensitivity analyses were conducted to assess potential bias.A total of 374 eligible AAD patients were included and divided in 4 groups according to admission HR (HR ≤ 70, 71-80, 81-90, and > 90 beats per minute (bpm) ). The patients with AAD in the group with HR > 90 bpm had higher 90-day, 1-year, and 3-year mortality than those in the groups with HR ≤ 70, 71-80, and 81-90 bpm. After adjusting for age, sex, BMI, systolic blood pressure, diastolic blood pressure, SOFA score, SAPSII score, Stanford type, hypertension, coronary artery disease, liver disease, atrial fibrillation, valvular disease, intensive care unit mechanical ventilation, aortic surgery, and thoracic endovascular aortic repair, patients with admission HR > 90 bpm had a higher risk of 90-day, 1-year, and 3-year mortality [adjusted hazard ratio, 95% confidence interval, 5.14 (2.22-11.91) P < 0.001; 4.31 (2.10-8.84) P < 0.001; 3.01 (1.66-5.46) P < 0.001] than those with HR 81-90 bpm. The 90-day, 1-year, and 3-year mortality were similar among the groups with HR ≤ 70, 71-80, and 81-90 bpm.Admission HR > 90 bpm was independently associated with all-cause mortality in critically ill AAD patients, either type A or B aortic dissection.

Machine learning for the prediction of acute kidney injury in patients with sepsis.

BACKGROUND: Acute kidney injury (AKI) is the most common and serious complication of sepsis, accompanied by high mortality and disease burden. The early prediction of AKI is critical for timely intervention and ultimately improves prognosis. This study aims to establish and validate predictive models based on novel machine learning (ML) algorithms for AKI in critically ill patients with sepsis. METHODS: Data of patients with sepsis were extracted from the Medical Information Mart for Intensive Care III (MIMIC- III) database. Feature selection was performed using a Boruta algorithm. ML algorithms such as logistic regression (LR), k-nearest neighbors (KNN), support vector machine (SVM), decision tree, random forest, Extreme Gradient Boosting (XGBoost), and artificial neural network (ANN) were applied for model construction by utilizing tenfold cross-validation. The performances of these models were assessed in terms of discrimination, calibration, and clinical application. Moreover, the discrimination of ML-based models was compared with those of Sequential Organ Failure Assessment (SOFA) and the customized Simplified Acute Physiology Score (SAPS) II model. RESULTS: A total of 3176 critically ill patients with sepsis were included for analysis, of which 2397 cases (75.5%) developed AKI during hospitalization. A total of 36 variables were selected for model construction. The models of LR, KNN, SVM, decision tree, random forest, ANN, XGBoost, SOFA and SAPS II score were established and obtained area under the receiver operating characteristic curves of 0.7365, 0.6637, 0.7353, 0.7492, 0.7787, 0.7547, 0.821, 0.6457 and 0.7015, respectively. The XGBoost model had the best predictive performance in terms of discrimination, calibration, and clinical application among all models. CONCLUSION: The ML models can be reliable tools for predicting AKI in septic patients. The XGBoost model has the best predictive performance, which can be used to assist clinicians in identifying high-risk patients and implementing early interventions to reduce mortality.

Predictive value of lymphocyte-to-monocyte ratio in critically Ill patients with atrial fibrillation: A propensity score matching analysis.

BACKGROUND: Inflammation plays a key role in the initiation and progression of atrial fibrillation (AF). Lymphocyte-to-monocyte ratio (LMR) has been proved to be a reliable predictor of many inflammation-associated diseases, but little data are available on the relationship between LMR and AF. We aimed to evaluate the predictive value of LMR in predicting all-cause mortality among AF patients. METHODS: Data of patients diagnosed with AF were retrieved from the Medical Information Mart for Intensive Care-III (MIMIC-III) database. X-tile analysis was used to calculate the optimal cutoff value for LMR. The Cox regression model was used to assess the association of LMR and 28-day, 90-day, and 1-year mortality. Additionally, a propensity score matching (PSM) method was performed to minimize the impact of potential confounders. RESULTS: A total of 3567 patients hospitalized with AF were enrolled in this study. The X-tile software indicated that the optimal cutoff value of LMR was 2.67. A total of 1127 pairs were generated, and all the covariates were well balanced after PSM. The Cox proportional-hazards model showed that patients with the low LMR (≤2.67) had a higher 1-year all-cause mortality than those with the high LMR (>2.67) in the study cohort before PSM (HR = 1.640, 95% CI: 1.437-1.872, p < 0.001) and after PSM (HR = 1.279, 95% CI: 1.094-1.495, p = 0.002). The multivariable Cox regression analysis for 28-day and 90-day mortality yielded similar results. CONCLUSIONS: The lower LMR (≤2.67) was associated with a higher risk of 28-day, 90-day, and 1-year all-cause mortality, which might serve as an independent predictor in AF patients.

Association of serum anion gap and risk of long-term mortality in patients following coronary artery bypass grafting: A propensity score matching study.

BACKGROUND: The present study aimed to explore the relationship between serum anion gap (AG) and long-term mortality in patients undergoing coronary artery bypass grafting (CABG). METHODS: Clinical variables were extracted among patients undergoing CABG from Medical Information Mart for Intensive Care III (MIMIC III) database. The primary outcome was 4-year mortality following CABG. An optimal cut-off value of AG was determined by the receiver operating characteristic (ROC) curve. The Kaplan-Meier (K-M) analysis and multivariate Cox hazard analysis were performed to investigate the prognostic value of AG in long-term mortality after CABG. To eliminate the bias between different groups, propensity score matching (PSM) was conducted to validate the findings. RESULTS: The optimal cut-off value of AG was 17.00 mmol/L. Then a total of 3162 eligible patients enrolled in this study were divided into a high AG group (≥17.00, n = 1022) and a low AG group (<17.00, n = 2,140). A lower survival rate was identified in the high AG group based on the K-M curve (p < .001). Compared with patients in the low AG group, patients in the high AG group had an increased risk of long-term mortality [1-year mortality: hazard ratio, HR: 2.309, 95% CI (1.672-3.187), p < .001; 2-year mortality: HR: 1.813, 95% CI (1.401-2.346), p < .001; 3- year mortality: HR: 1.667, 95% CI (1.341-2.097), p < .001; 4-year mortality: HR: 1.710, 95% CI (1.401-2.087), p < .001] according to multivariate Cox hazard analysis. And further validation of above results was consistent in the matched cohort after PSM. CONCLUSIONS: The AG is an independent predictive factor for long-term all-cause mortality in patients following CABG, where a high AG value is associated with an increased mortality.

Estimated plasma volume status (ePVS) is a predictor for acute myocardial infarction in-hospital mortality: analysis based on MIMIC-III database.

BACKGROUND: Estimated plasma volume status (ePVS) has been reported that associated with poor prognosis in heart failure patients. However, no researchinvestigated the association of ePVS and prognosis in patients with acute myocardial infarction (AMI). Therefore, we aimed to determine the association between ePVS and in-hospital mortality in AMI patients. METHODS AND RESULTS: We extracted AMI patients data from MIMIC-III database. A generalized additive model and logistic regression model were used to demonstrate the association between ePVS levels and in-hospital mortality in AMI patients. Kaplan-Meier survival analysis was used to pooled the in-hospital mortality between the various group. ROC curve analysis were used to assessed the discrimination of ePVS for predicting in-hospital mortality. 1534 eligible subjects (1004 males and 530 females) with an average age of 67.36 ± 0.36 years old were included in our study finally. 136 patients (73 males and 63 females) died in hospital, with the prevalence of in-hospital mortality was 8.9%. The result of the Kaplan-Meier analysis showed that the high-ePVS group (ePVS ≥ 5.28 mL/g) had significant lower survival possibility in-hospital admission compared with the low-ePVS group (ePVS < 5.28 mL/g). In the unadjusted model, high-level of ePVS was associated with higher OR (1.09; 95% CI 1.06-1.12; P < 0.001) compared with low-level of ePVS. After adjusted the vital signs data, laboratory data, and treatment, high-level of ePVS were also associated with increased OR of in-hospital mortality, 1.06 (95% CI 1.03-1.09; P < 0.001), 1.05 (95% CI 1.01-1.08; P = 0.009), 1.04 (95% CI 1.01-1.07; P = 0.023), respectively. The ROC curve indicated that ePVS has acceptable discrimination for predicting in-hospital mortality. The AUC value was found to be 0.667 (95% CI 0.653-0.681). CONCLUSION: Higher ePVS values, calculated simply from Duarte's formula (based on hemoglobin/hematocrit) was associated with poor prognosis in AMI patients. EPVS is a predictor for predicting in-hospital mortality of AMI, and could help refine risk stratification.

Admission white blood cell count predicts post-discharge mortality in patients with acute aortic dissection: data from the MIMIC-III database.

BACKGROUND: Inflammation underlies both the pathogenesis and prognosis in patients with acute aortic dissection (AAD). This study aimed to assess the association of ICU admission of white blood cell count (WBCc) with post-discharge mortality in these patients. METHODS: Clinical data were extracted from the MIMIC-III V1.4 database. After adjusted to covariables, Cox regression analysis and Kaplan-Meier survival curve were performed to determine the relationship between WBCc on admission and post-discharge mortality (30-day, 90-day, 1-year and 5-year) in AAD patients. Subgroup analysis and receiver operating characteristic (ROC) curve analysis were used to test the performance of WBCc in predicting mortality in AAD patients. RESULTS: A total of 325 eligible patients were divided into 2 groups: normal-WBCc group (≤ 11 k/uL) and high-WBCc group (> 11 K/uL). In univariate Cox regression analysis, high WBCc was significant risk predictor of 30-day, 90-day, 1-year and 5-year mortality [hazard ratio (HR), 95% CI, P 2.58 1.36-4.91 0.004; 3.16 1.76-5.70 0.000; 2.74 1.57-4.79 0.000; 2.10 1.23-3.54 0.006]. After adjusting for age and other risks, high WBCc remained a significant predictor of 30-day, 90-day and 1-year mortality in AAD patients (HR, 95% CI, P 1.994 1.058-3.76 0.033; 2.118 1.175-3.819 0.013; 2.37 1.343-4.181 0.003). The area under ROC curve of WBCc for predicting 30-day, 90-day, 1-year and 5-year mortality were 0.69, 0.70, 0.66 and 0.61, respectively. The results from subgroups analysis showed that there was no interaction in most strata and patients who were younger than 69 years of age or had history of respiratory disease with an elevated WBCc had an excess risk of 30-day mortality (HR, 95% CI, P 3.18 1.41-7.14 0.005; 3.84 1.05-14.13 0.043). CONCLUSIONS: Higher than normal WBCc on admission may predict post-discharge mortality in patients with AAD.

Improvement of APACHE II score system for disease severity based on XGBoost algorithm.

BACKGROUND: Prognostication is an essential tool for risk adjustment and decision making in the intensive care units (ICUs). In order to improve patient outcomes, we have been trying to develop a more effective model than Acute Physiology and Chronic Health Evaluation (APACHE) II to measure the severity of the patients in ICUs. The aim of the present study was to provide a mortality prediction model for ICUs patients, and to assess its performance relative to prediction based on the APACHE II scoring system. METHODS: We used the Medical Information Mart for Intensive Care version III (MIMIC-III) database to build our model. After comparing the APACHE II with 6 typical machine learning (ML) methods, the best performing model was screened for external validation on anther independent dataset. Performance measures were calculated using cross-validation to avoid making biased assessments. The primary outcome was hospital mortality. Finally, we used TreeSHAP algorithm to explain the variable relationships in the extreme gradient boosting algorithm (XGBoost) model. RESULTS: We picked out 14 variables with 24,777 cases to form our basic data set. When the variables were the same as those contained in the APACHE II, the accuracy of XGBoost (accuracy: 0.858) was higher than that of APACHE II (accuracy: 0.742) and other algorithms. In addition, it exhibited better calibration properties than other methods, the result in the area under the ROC curve (AUC: 0.76). we then expand the variable set by adding five new variables to improve the performance of our model. The accuracy, precision, recall, F1, and AUC of the XGBoost model increased, and were still higher than other models (0.866, 0.853, 0.870, 0.845, and 0.81, respectively). On the external validation dataset, the AUC was 0.79 and calibration properties were good. CONCLUSIONS: As compared to conventional severity scores APACHE II, our XGBoost proposal offers improved performance for predicting hospital mortality in ICUs patients. Furthermore, the TreeSHAP can help to enhance the understanding of our model by providing detailed insights into the impact of different features on the disease risk. In sum, our model could help clinicians determine prognosis and improve patient outcomes.

Inhaled Beta2-Agonists Increase In-Hospital Mortality in ICU Patients with Heart Failure.

The impact of beta2-agonists (B2As) on heart failure (HF) remains controversial. This study aimed to investigate whether inhaled B2As increased in-hospital mortality in ICU patients with HF.The Multiparameter Intelligent Monitoring in Intensive Care III database was initially searched to identify adult patients (≥ 18 years old) with HF in ICU. Then, patients using or not using inhaled B2As were matched using propensity score matching on a 1:1 basis to control for baseline confounders. In-hospital mortality was compared between the two groups, and logistic regression analysis was performed to assess the association between B2As and in-hospital mortality.The initial search retrieved 2345 eligible patients with HF from the database. After propensity score matching, 705 pairs of patients were included in the final analysis. Patients using B2As had markedly higher in-hospital mortality than those not using B2As (4.68% versus 2.27%; P = 0.013). In the multivariate logistic regression analysis, B2A use (odd ratios (OR), 2.471; 95% confidence interval (CI), 1.289-4.734; P = 0.006), stroke (OR, 4.581; 95% CI, 1.621-12.948; P = 0.004), and simplified acute physiology score II (SAPS-II) scores (OR, 1.090; 95% CI, 1.064-1.116; P < 0.001) were significantly associated with increased risk of in-hospital mortality, whereas renin angiotensin system inhibitor use (OR, 0.396; 95% CI, 0.202-0.778; P = 0.007) was significantly associated with decreased risk of in-hospital mortality. Subgroup analysis further indicated that the association between B2A use and mortality was significant only in patients with HF without chronic pulmonary disease (OR, 2.427; 95% CI, 1.351-4.362; P = 0.003), but not in those with chronic pulmonary disease (OR, 2.094; 95% CI, 0.582-7.537; P = 0.258).In ICU patients with HF but without chronic pulmonary disease, the use of inhaled B2As is associated with increased in-hospital mortality.

Predictive nomogram for 28-day mortality risk in mitral valve disorder patients in the intensive care unit: A comprehensive assessment from the MIMIC-III database.

BACKGROUND: Mitral valve disorder (MVD) stands as the most prevalent valvular heart disease. Presently, a comprehensive clinical index to predict mortality in MVD remains elusive. The aim of our study is to construct and assess a nomogram for predicting the 28-day mortality risk of MVD patients. METHODS: Patients diagnosed with MVD were identified via ICD-9 code from the MIMIC-III database. Independent risk factors were identified utilizing the LASSO method and multivariate logistic regression to construct a nomogram model aimed at predicting the 28-day mortality risk. The nomogram's performance was assessed through various metrics including the area under the curve (AUC), calibration curves, Hosmer-Lemeshow test, integrated discriminant improvement (IDI), net reclassification improvement (NRI), and decision curve analysis (DCA). RESULTS: The study encompassed a total of 2771 patients diagnosed with MVD. Logistic regression analysis identified several independent risk factors: age, anion gap, creatinine, glucose, blood urea nitrogen level (BUN), urine output, systolic blood pressure (SBP), respiratory rate, saturation of peripheral oxygen (SpO2), Glasgow Coma Scale score (GCS), and metastatic cancer. These factors were found to independently influence the 28-day mortality risk among patients with MVD. The calibration curve demonstrated adequate calibration of the nomogram. Furthermore, the nomogram exhibited favorable discrimination in both the training and validation cohorts. The calculations of IDI, NRI, and DCA analyses demonstrate that the nomogram model provides a greater net benefit compared to the Simplified Acute Physiology Score II (SAPSII), Acute Physiology Score III (APSIII), and Sequential Organ Failure Assessment (SOFA) scoring systems. CONCLUSION: This study successfully identified independent risk factors for 28-day mortality in patients with MVD. Additionally, a nomogram model was developed to predict mortality, offering potential assistance in enhancing the prognosis for MVD patients. It's helpful in persuading patients to receive early interventional catheterization treatment, for example, transcatheter mitral valve replacement (TMVR), transcatheter mitral valve implantation (TMVI).

Prognostic significance of the aspartate aminotransferase to lymphocyte ratio index in patients with acute myocardial infarction.

BACKGROUND: This study aimed to investigate the clinical value and prognostic significance of the alanine aspartate aminotransferase-to-lymphocyte ratio index (ALRI) in patients diagnosed with acute myocardial infarction (AMI). METHODS: Clinical indices of patients with AMI were collected from the Medical Information Mark for Intensive Care (MIMIC) III database and Wuhan Sixth Hospital. Cox regression analysis was used to explore whether ALRI was a risk factor for a worse prognosis in patients with AMI, and a nomogram including ALRI was created to estimate its predictive performance for 28-day mortality. RESULTS: Based on clinical data from the MIMIC-III database, we found that a high ALRI was closely associated with a variety of clinical parameters. It was an important risk factor for 28-day survival in patients with AMI (HR = 5.816). ALRI had a high predictive power for worse 28-day survival in patients with AMI (area under the curve [AUC] = 0.754). Additionally, we used clinical data from the Wuhan Sixth Hospital to verify the predictive power of ALRI in patients with AMI, and a high level of ALRI remained an independent risk factor for worse survival in patients with AMI (HR = 4.969). The AMI nomogram, including ALRI, displayed a good predictive performance for 28-day mortality in both the MIMIC-III (AUC = 0.826) and Wuhan Sixth Hospital cohorts (AUC = 0.795). CONCLUSION: The ALRI is closely related to the survival outcomes of patients with newly diagnosed AMI, indicating that it could serve as a novel biomarker for risk stratification such patients.

Association between anion gap and the 30-day mortality of patients with ventilator-associated pneumonia: a study of the MIMIC-III database.

Background: Serum anion gap (AG) can potentially be applied to the diagnosis of various metabolic acidosis, and a recent study has reported the association of AG with the mortality of patients with coronavirus disease 2019 (COVID-19). However, the relationship of AG with the short-term mortality of patients with ventilator-associated pneumonia (VAP) is still unclear. Herein, we aimed to investigate the association between AG and the 30-day mortality of VAP patients, and construct and assess a multivariate predictive model for the 30-day mortality risk of VAP. Methods: This retrospective cohort study extracted data of 477 patients with VAP from the Medical Information Mart for Intensive Care III (MIMIC-III) database. Data of patients were divided into a training set and a testing set with a ratio of 7:3. In the training set, variables significantly associated with the 30-day mortality of VAP patients were included in the multivariate predictive model through univariate Cox regression and stepwise regression analyses. Then, the predictive performance of the multivariate predictive model was assessed in both training set and testing set, and compared with the single AG and other scoring systems including the Sequential Organ Failure Assessment (SOFA) score, the confusion, urea, respiratory rate (RR), blood pressure, and age (≥65 years old) (CURB-65) score, and the blood urea nitrogen (BUN), altered mental status, pulse, and age (>65 years old) (BAP-65) score. In addition, the association of AG with the 30-day mortality of VAP patients was explored in subgroups of gender, age, and infection status. The evaluation indexes were hazard ratios (HRs), C-index, and 95% confidence intervals (CIs). Results: A total of 70 patients died within 30 days. The multivariate predictive model consisted of AG (HR =1.052, 95% CI: 1.008-1.098), age (HR =1.037, 95% CI: 1.019-1.055), duration of mechanical ventilation (HR =0.998, 95% CI: 0.996-0.999), and vasopressors use (HR =1.795, 95% CI: 1.066-3.023). In both training set (C-index =0.725, 95% CI: 0.670-0.780) and testing set (C-index =0.717, 95% CI: 0.637-0.797), the multivariate model had a relatively superior predictive performance to the single AG value. Moreover, the association of AG with the 30-day mortality was also found in patients who were male (HR =1.088, 95% CI: 1.029-1.150), and whatever the pathogens they infected (bacterial infection: HR =1.059, 95% CI: 1.011-1.109; fungal infection: HR =1.057, 95% CI: 1.002-1.115). Conclusions: The AG-related multivariate model had a potential predictive value for the 30-day mortality of patients with VAP. These findings may provide some references for further exploration on simple and robust predictors of the short-term mortality risk of VAP, which may further help clinicians to identify patients with high risk of mortality in an early stage in the intensive care units (ICUs).

Associations of systolic blood pressure and in-hospital mortality in critically ill patients with acute kidney injury.

PURPOSE: Although systolic blood pressure (SBP) is associated with acute renal injury (AKI), the relationship between baseline SBP and prognosis in critically ill patients with AKI is unclear. We aimed to assess the linearity and profile of the relationship between SBP at intensive care unit (ICU) admission and in-hospital mortality in these patients. METHODS: Data of AKI patients in the ICU settings were extracted from the Medical Information Mart for Intensive Care III database. The association between seven SBP categories (< 100, 100-109, 110-119, 120-129, 130-139, 140-149, and ≥ 150 mmHg) and all-cause in-hospital mortality was assessed by Cox proportional hazard models. Restricted cubic spline analysis for the multivariate Cox model was performed to explore the shape of the relationship between SBP and mortality. RESULTS: A total of 24,202 patients with AKI were included in this study. A typically U-shaped relationship was found between SBP at admission and in-hospital mortality. Among all SBP categories, the lowest risk of death was observed in patients with SBP around 110-119 mmHg, whereas the highest was noted in patients with extremely low SBP (< 100 mmHg), followed by those with extremely high SBP (≥ 150 mmHg). SBP showed a significant interaction with vasopressor use and AKI stage in relation to the risk of in-hospital mortality. CONCLUSIONS: SBP upon admission showed a non-linear association with all-cause in-hospital mortality in critically ill patients with AKI. Patients with low or high SBP show an increased risk of mortality compared to patients with normal SBP.

Association between glucocorticoid use and all-cause mortality in critically ill patients with heart failure: A cohort study based on the MIMIC-III database.

Background: Heart failure (HF) is the terminal stage of various heart diseases. Conventional treatments have poor efficacy, and diuretic resistance can present. Previous studies have found that the use of glucocorticoids can enhance the diuretic effect of patients with heart failure and reduce heart failure symptoms. However, the relationship between glucocorticoid use and mortality in patients with heart failure in intensive care units is unclear. Objectives: The aim of this study was to determine the association between glucocorticoid use and all-cause mortality in critically ill patients with heart failure. Methods: The information on patients with heart failure in this study was extracted from the MIMIC-III (Medical Information Mart for Intensive Care-III) database. Patients in the glucocorticoid and non-glucocorticoid groups were matched using propensity scores. The Kaplan-Meier method was used to explore the difference in survival probability between the two groups. A Cox proportional-hazards regression model was used to analyze the hazard ratios (HRs) for the two patient groups. Subgroup analyses were performed with prespecified stratification variables to demonstrate the robustness of the results. Results: The study included 9,482 patients: 2,099 in the glucocorticoid group and 7,383 in the non-glucocorticoid group. There were 2,055 patients in each group after propensity-score matching. The results indicated that the non-glucocorticoid group was not significantly associated with reduced mortality in patients with heart failure during the 14-day follow-up period [HRs = .901, 95% confidence interval (CI) = .767-1.059]. During the follow-up periods of 15-30 and 15-90 days, the mortality risk was significantly lower in the non-glucocorticoid group than in the glucocorticoid group (HRs = .497 and 95% CI = .370-.668, and HRs = .400 and 95% CI = .310-.517, respectively). Subgroup analyses indicated no interaction among each stratification variable and glucocorticoid use. Conclusion: Glucocorticoid use was associated with an increased mortality risk in critically ill patients with heart failure.

Comparative effectiveness and safety of bolus vs. continuous infusion of loop diuretics: Results from the MIMIC-III Database.

BACKGROUND: It is unclear whether fluid management goals are best achieved by bolus injection or continuous infusion of loop diuretics. In this study, we compared the effectiveness and safety of a continuous infusion with that of a bolus injection when an increased loop diuretic dosage is required in intensive care unit (ICU) patients. METHODS: We obtained data from the MIMIC-III database for patients who were first-time ICU admissions and required an increased diuretic dosage. Patients were excluded if they had an estimated glomerular filtration rate <15 ml/min/1.73 m2, were receiving renal replacement therapy, had a baseline systolic blood pressure <80 mmHg, or required a furosemide dose <120 mg. The patients were divided into a continuous group and a bolus group. Propensity score matching was used to balance patients' background characteristics. RESULTS: The final dataset included 807 patients (continuous group, n = 409; bolus group, n = 398). After propensity score matching, there were 253 patients in the bolus group and 231 in the continuous group. The 24 h urine output per 40 mg of furosemide was significantly greater in the continuous group than in the bolus group (234.66 ml [95% confidence interval (CI) 152.13-317.18, p < 0.01]). There was no significant between-group difference in the incidence of acute kidney injury (odds ratio 0.96, 95% CI 0.66-1.41, p = 0.85). CONCLUSIONS: Our results indicate that a continuous infusion of loop diuretics may be more effective than a bolus injection and does not increase the risk of acute kidney injury in patients who need an increased diuretic dosage in the ICU.