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).

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).

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.

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.

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.

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.

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.

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.

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.

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.

Development of nomogram to predict in-hospital death for patients with intracerebral hemorrhage: A retrospective cohort study.

Aim: This study aimed to investigate the association between systemic immune-inflammation (SII) and the risk of in-hospital death for patients with intracerebral hemorrhage (ICH) in the intensive care units (ICU) and to further develop a prediction model related to SII in predicting the risk of in-hospital death for patients with ICH. Methods: In this retrospective cohort study, we included 1,176 patients with ICH from the Medical Information Mart for Intensive Care III (MIMIC-III) database. All patients were randomly assigned to the training group for the construction of the nomogram and the testing group for the validation of the nomogram based on a ratio of 8:2. Predictors were screened by the least absolute shrinkage and selection operator (LASSO) regression analysis. A multivariate Cox regression analysis was used to investigate the association between SII and in-hospital death for patients with ICH in the ICU and develop a model for predicting the in-hospital death risk for ICU patients with ICH. The receiver operator characteristic curve was used to assess the predicting performance of the constructed nomogram. Results: In the training group, 232 patients with ICH died while 708 survived. LASSO regression showed some predictors, including white blood cell count, glucose, blood urea nitrogen, SII, the Glasgow Coma Scale, age, heart rate, mean artery pressure, red blood cell, bicarbonate, red blood cell distribution width, liver cirrhosis, respiratory failure, renal failure, malignant cancer, vasopressor, and mechanical ventilation. A prediction model integrating these predictors was established. The area under the curve (AUC) of the nomogram was 0.810 in the training group and 0.822 in the testing group, indicating that this nomogram might have a good performance. Conclusion: Systemic immune-inflammation was associated with an increased in-hospital death risk for patients with ICH in the ICU. A nomogram for in-hospital death risk for patients with ICH in the ICU was developed and validated.

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.

Dynamic nomogram for predicting acute kidney injury in patients with acute ischemic stroke: A retrospective study.

Background: This study sought to develop and validate a dynamic nomogram chart to assess the risk of acute kidney injury (AKI) in patients with acute ischemic stroke (AIS). Methods: These data were drawn from the Medical Information Mart for Intensive Care III (MIMIC-III) database, which collects 47 clinical indicators of patients after admission to the hospital. The primary outcome indicator was the occurrence of AKI within 48 h of intensive care unit (ICU) admission. Independent risk factors for AKI were screened from the training set using univariate and multifactorial logistic regression analyses. Multiple logistic regression models were developed, and nomograms were plotted and validated in an internal validation set. Based on the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) to estimate the performance of this nomogram. Results: Nomogram indicators include blood urea nitrogen (BUN), creatinine, red blood cell distribution width (RDW), heart rate (HR), Oxford Acute Severity of Illness Score (OASIS), the history of congestive heart failure (CHF), the use of vancomycin, contrast agent, and mannitol. The predictive model displayed well discrimination with the area under the ROC curve values of 0.8529 and 0.8598 for the training set and the validator, respectively. Calibration curves revealed favorable concordance between the actual and predicted incidence of AKI (p > 0.05). DCA indicates the excellent net clinical benefit of nomogram in predicting AKI. Conclusion: In summary, we explored the incidence of AKI in patients with AIS during ICU stay and developed a predictive model to help clinical decision-making.

Development and validation of a nomogram to predict the 30-day mortality risk of patients with intracerebral hemorrhage.

Background: Intracerebral hemorrhage (ICH) is a stroke syndrome with an unfavorable prognosis. Currently, there is no comprehensive clinical indicator for mortality prediction of ICH patients. The purpose of our study was to construct and evaluate a nomogram for predicting the 30-day mortality risk of ICH patients. Methods: ICH patients were extracted from the MIMIC-III database according to the ICD-9 code and randomly divided into training and verification cohorts. The least absolute shrinkage and selection operator (LASSO) method and multivariate logistic regression were applied to determine independent risk factors. These risk factors were used to construct a nomogram model for predicting the 30-day mortality risk of ICH patients. The nomogram was verified by the area under the receiver operating characteristic curve (AUC), integrated discrimination improvement (IDI), net reclassification improvement (NRI), and decision curve analysis (DCA). Results: A total of 890 ICH patients were included in the study. Logistic regression analysis revealed that age (OR = 1.05, P < 0.001), Glasgow Coma Scale score (OR = 0.91, P < 0.001), creatinine (OR = 1.30, P < 0.001), white blood cell count (OR = 1.10, P < 0.001), temperature (OR = 1.73, P < 0.001), glucose (OR = 1.01, P < 0.001), urine output (OR = 1.00, P = 0.020), and bleeding volume (OR = 1.02, P < 0.001) were independent risk factors for 30-day mortality of ICH patients. The calibration curve indicated that the nomogram was well calibrated. When predicting the 30-day mortality risk, the nomogram exhibited good discrimination in the training and validation cohorts (C-index: 0.782 and 0.778, respectively). The AUCs were 0.778, 0.733, and 0.728 for the nomogram, Simplified Acute Physiology Score II (SAPSII), and Oxford Acute Severity of Illness Score (OASIS), respectively, in the validation cohort. The IDI and NRI calculations and DCA analysis revealed that the nomogram model had a greater net benefit than the SAPSII and OASIS scoring systems. Conclusion: This study identified independent risk factors for 30-day mortality of ICH patients and constructed a predictive nomogram model, which may help to improve the prognosis of ICH patients.

Prevalence and Prognostic Impact of Malnutrition in Critical Patients With Acute Myocardial Infarction: Results From Chinese CIN Cohort and American MIMIC-III Database.

Background: Malnutrition is associated with poor prognosis in patients with acute myocardial infarction (AMI). However, the prognostic impact of malnutrition in critical patients with AMI has not been well addressed. Methods: We analyzed two critical AMI cohorts from Cardiorenal ImprovemeNt (CIN) in China and Medical Information Mark for Intensive Care-III (MIMIC-III) in the United States. The primary outcome was all-cause mortality. Cox proportional hazards models were constructed to examine the risk of malnutrition for mortality in critical patients with AMI. Results: There were 2,075 critical patients with AMI (mean age, 62.5 ± 12.3 years, 20.00% were female) from the CIN cohort and 887 critical patients with AMI (mean age, 70.1 ± 12.9 years, 37.43% were female) from MIMIC-III included in this study. Based on the Controlling Nutritional Status (CONUT) score, of the Chinese patients with AMI, the prevalence was 47.5, 28.3, and 3.5% for mild, moderate, and severe malnutrition, respectively. The percentage of mild, moderate, and severe malnutrition was 41.60, 30.55, and 7.32% in the MIMIC-III cohort, respectively. Controlling for confounders, worse nutritional state was significantly associated with increased risk for all-cause mortality [an adjusted hazard ratio for mild, moderate, and severe malnutrition, respectively, 1.10 (95% confidence interval (CI): 0.76-1.59), 1.49 (95% CI: 1.02-2.19), and 1.70 (95% CI: 1.00-2.88) in the CIN cohort and 1.41 (95% CI: 0.95-2.09), 1.97 (95% CI: 1.32-2.95), and 2.70 (95% CI: 1.67-4.37) in the MIMIC-III cohort]. Conclusion: Malnutrition was independently associated with an increased risk of all-cause mortality in critical patients with AMI after full adjustments. Further trials are needed to prospectively evaluate the efficacy of nutritional interventions in critical patients with AMI.

Development and validation of a survival prediction model for patients received mechanical ventilation in the intensive care unit: a large sample size cohort from the MIMIC database.

BACKGROUND: Mechanical ventilation remains one of the primary management measures for critically ill patients in intensive care units (ICUs). However, previous studies on the prognosis prediction of ICU patients received mechanical ventilation were limited. This study was to develop and validate a nomogram for predicting short- and long-term survival among patients who received mechanical ventilation in the ICU. METHODS: This was a retrospective cohort study with a 3-year follow-up. Demographic, laboratory, clinical data of 16,775 participants aged ≥18 years who received mechanical ventilation in the ICU were extracted from the Medical Information Mart for Intensive Care III (MIMIC-III) database. The outcomes of this study were 1-month, 3-month, 1-year, and 3-year survival. All eligible patients were randomly classified into the training and testing groups with a ratio of 7:3. A multivariate Cox regression in the training group was used to explore the predictors and develop the predictive nomogram. Internal and subgroup validations were performed, and the C-index was calculated to estimate the predictive performance of the nomogram. The time-dependent receiver operating characteristic curves were drawn, and corresponding areas under the curve (AUC) were calculated. RESULTS: Totally 6,291 patients died during the follow-up duration. Age, gender, ethnicity, ICU type, comorbidity, days of mechanical ventilation, white blood cell count, blood urea nitrogen, the fraction of inspiration O2, Sequential Organ Failure Assessment scores, and the Glasgow coma score were predictors of the survival of ICU patients who received mechanical ventilation (P<0.05). The C-index of the nomogram was 0.819 and was validated in the testing group at 0.816. The AUCs for the prognostic nomogram for 1-month, 3-month, 1-year, and 3-year survival were 0.889, 0.892, 0.882, and 0.866, respectively. CONCLUSIONS: This nomogram showed good predictive performance for short- and long-term survival in ICU patients treated with mechanical ventilation, which may be a useful tool for clinicians to assess the prognosis of patients and to adjust treatment strategies in time.

Machine Learning Methods for Predicting Long-Term Mortality in Patients After Cardiac Surgery.

Objective: This study aims to construct and validate several machine learning (ML) algorithms to predict long-term mortality and identify risk factors in unselected patients post-cardiac surgery. Methods: The Medical Information Mart for Intensive Care (MIMIC-III) database was used to perform a retrospective administrative database study. Candidate predictors consisted of the demographics, comorbidity, vital signs, laboratory test results, scoring systems, and treatment information on the first day of ICU admission. Four-year mortality was set as the study outcome. We used the ML methods of logistic regression (LR), artificial neural network (NNET), naïve bayes (NB), gradient boosting machine (GBM), adapting boosting (Ada), random forest (RF), bagged trees (BT), and eXtreme Gradient Boosting (XGB). The prognostic capacity and clinical utility of these ML models were compared using the area under the receiver operating characteristic curves (AUC), calibration curves, and decision curve analysis (DCA). Results: Of 7,368 patients in MIMIC-III included in the final cohort, a total of 1,337 (18.15%) patients died during a 4-year follow-up. Among 65 variables extracted from the database, a total of 25 predictors were selected using recursive feature elimination and included in the subsequent analysis. The Ada model performed best among eight models in both discriminatory ability with the highest AUC of 0.801 and goodness of fit (visualized by calibration curve). Moreover, the DCA shows that the net benefit of the RF, Ada, and BT models surpassed that of other ML models for almost all threshold probability values. Additionally, through the Ada technique, we determined that red blood cell distribution width (RDW), blood urea nitrogen (BUN), SAPS II, anion gap (AG), age, urine output, chloride, creatinine, congestive heart failure, and SOFA were the Top 10 predictors in the feature importance rankings. Conclusions: The Ada model performs best in predicting 4-year mortality after cardiac surgery among the eight ML models, which might have significant application in the development of early warning systems for patients following operations.