Association of Dementia with Adverse Outcomes in Older Patients with Acute Myocardial Infarction in the ICU.

Dementia limits timely revascularization in individuals with acute myocardial infarction (AMI). However, it remains unclear whether dementia affects prognosis negatively in older individuals with AMI in the intensive care unit (ICU). This research aimed to evaluate the dementia effect on the outcomes in individuals with AMI in ICU.Data from 3,582 patients aged ≥ 65 years with AMI in ICU from the Medical Information Mart for Intensive Care IV (MIMIC IV) database were evaluated. The independent variable was dementia at baseline, and the primary finding was death from any cause during follow-up. A 1:1 propensity score matching (PSM) showed 208 participants with and without dementia. The correlation between dementia and poor prognosis of AMI was verified using a double-robust estimation method.In the PSM cohort, the 30-day all-cause mortality was 37.50% and 33.17% in the dementia and non-dementia groups (P = 0.356), respectively, and the 1-year all-cause mortality was 61.06% and 51.44%, respectively (P = 0.048). Cox regression analysis showed no association between dementia and elevated 30-day (hazard ratio [HR] 1.15, 95% confidence interval [CI] 0.84, 1.60) and 1-year (HR 1.28, 95% CI 0.99, 1.66) all-cause mortality after AMI. Similarly, dementia was not connected with in-hospital mortality, bleeding, or stroke after AMI. Interaction analysis showed that 1-year all-cause mortality was 48.00% higher in individuals with dementia and diabetic complications than in those without diabetic complications.Dementia is not an independent risk factor for adverse outcomes in AMI. Thus, it may be inappropriate to include dementia as a contraindication for invasive AMI therapy.

Red cell distribution width to total serum calcium ratio and in-hospital mortality risk in patients with acute ischemic stroke: A MIMIC-IV retrospective analysis.

We investigated the relationship among red cell distribution width (RDW), to total serum calcium (TSC) ratio (RCR), and in-hospital mortality in patients with acute ischemic stroke (AIS). This study was a retrospective analysis. The data of 2700 AIS patients was retrospectively analyzed from the Medical Information Mart for Intensive Care database (version IV). The main outcome of interest was in-hospital mortality. A Cox proportional hazards regression model was used to determine whether RCR was independently associated with in-hospital mortality. The Kaplan-Meier method was used to plot the survival curves for RCR. Subgroup analyses were performed to measure the mortality across various subgroups. The area under curve (AUC) of receiver operating characteristic curve (ROC) was calculated to ascertain the quality of RCR as a predictor of in-hospital mortality in patients with AIS. In the multivariate analysis, statistically significant differences were identified in age, ethnicity, length of ICU stay, mechanical ventilation, sequential organ failure assessment (SOFA) score, RDW, hemoglobin, RCR, whether taking anticoagulants, hyperlipidemia, and atrial fibrillation (P < .05). A threshold inflection point value of 1.83 was obtained through a two-piecewise regression model. There was a non-linear relationship between RCR and hospital mortality in patients with AIS. The hazard ratio (HR) and the 95% confidence intervals (CI) on the right and left of the inflection point were 0.93 (0.57-1.51; P = .7660) and 2.96 (1.37-6.42; P = .0060), respectively. The Kaplan-Meier curve indicated that survival rates were higher when RCR was ≤ 1.83 and lower when RDW was > 1.83 after adjustment for age, gender, BMI, ethnicity. The area under curve (AUC) of RCR was 0.715. A higher RCR was associated with an increased risk of in-hospital mortality in patients with AIS.

The relationship between red cell distribution width, serum calcium ratio, and in-hospital mortality among patients with acute respiratory failure: A retrospective cohort study of the MIMIC-IV database.

To investigate the impact of RDW/CA (the ratio of red cell distribution width to calcium) on in-hospital mortality in patients with acute respiratory failure (ARF). This retrospective cohort study analyzed the data of 6981 ARF patients from the Medical Information Mart for Intensive Care (MIMIC-IV) database 2.0. Critically ill participants between 2008 and 2019 at the Beth Israel Deaconess Medical Center in Boston. The primary outcome of interest was in-hospital mortality. A Cox proportional hazards regression model was used to determine whether the RDW/CA ratio independently correlated with in-hospital mortality. The Kaplan-Meier method was used to plot the survival curves of the RDW/CA. Subgroup analyses were performed to measure the mortality across various subgroups. After adjusting for potential covariates, we found that a higher RDW/CA was associated with an increased risk of in-hospital mortality (HR = 1.17, 95% CI: 1.01-1.35, P = .0365) in ARF patients. A nonlinear relationship was observed between RDW/CA and in-hospital mortality, with an inflection point of 1.97. When RDW/CA ≥ 1.97 was positively correlated with in-hospital mortality in patients with ARF (HR = 1.554, 95% CI: 1.183-2.042, P = .0015). The Kaplan-Meier curve indicated the higher survival rates for RDW/CA < 1.97 and the lower for RDW/CA ≥ 1.97 after adjustment for age, gender, body mass index, and ethnicity. RDW/CA is an independent predictor of in-hospital mortality in patients with ARF. Furthermore, a nonlinear relationship was observed between RDW/CA and in-hospital mortality in patients with ARF.

Association of magnesium sulfate use with mortality in critically ill patients with sepsis: a retrospective propensity score-matched cohort study.

BACKGROUND: Trials have demonstrated lower rates of acute kidney injury in critically ill patients receiving magnesium supplementation, but they have yielded conflicting results regarding mortality. METHODS: This is a retrospective cohort study based on the MIMIC-IV (Medical Information Mart in Intensive Care-IV) database. Adult critically ill patients with sepsis were included in the analysis. The exposure was magnesium sulfate use during ICU stay. The primary outcome was 28-day all-cause mortality. Propensity score matching (PSM) was conducted at a 1:1 ratio. Multivariable analyses were used to adjust for confounders. RESULTS: The pre-matched and propensity score-matched cohorts included 10 999 and 6052 patients, respectively. In the PSM analysis, 28-day all-cause mortality rate was 20.2% (611/3026) in the magnesium sulfate use group and 25.0% (757/3026) in the no use group. Magnesium sulfate use was associated with lower 28-day all-cause mortality (hazard ratio [HR], 0.70; 95% CI, 0.61-0.79; P<0.001). Lower mortality was observed regardless of baseline serum magnesium status: for hypomagnesaemia, HR, 0.64; 95% confidence interval (CI), 0.45-0.93; P=0.020; for normomagnesaemia, HR, 0.70; 95% CI, 0.61-0.80; P<0.001. Magnesium sulfate use was also associated with lower ICU mortality (odds ratio [OR], 0.52; 95% CI, 0.42-0.64; P<0.001), lower in-hospital mortality (OR, 0.65; 95% CI, 0.55-0.77; P<0.001), and renal replacement therapy (OR, 0.67; 95% CI, 0.52-0.87; P=0.002). A sensitivity analysis using the entire cohort also demonstrated lower 28-day all-cause mortality (HR, 0.62; 95% CI, 0.56-0.69; P<0.001). CONCLUSIONS: Magnesium sulfate use was associated with lower mortality in critically ill patients with sepsis. Prospective studies are needed to verify this finding.

Red Cell Distribution Width-to-Platelet Count Ratio: A Promising Predictor of In-Hospital All-Cause Mortality in Critically Ill Patients with Acute Ischemic Stroke.

INTRODUCTION: The red blood cell distribution width-to-platelet ratio (RPR), a novel inflammatory index, has already been proven as a prognostic factor in some other diseases, but its prognostic effect on critically ill patients with acute ischemic stroke (AIS) has been rarely investigated. This study aimed to investigate the association between RPR and in-hospital mortality in these patients. METHODS: We extracted clinical data from the Medical Information Mart for Intensive Care IV 1.0 database. The primary outcome was in-hospital all-cause mortality of patients with critical AIS. The main independent variable was RPR. To investigate the association between RPR and in-hospital all-cause mortality in patients with critical AIS, multivariable logistic analyses, smooth curve fitting, and stratified analyses were conducted. RESULTS: In total, 2,673 patients with AIS who were admitted to the intensive care unit were included in the study. In the multivariable analysis, in-hospital mortality was positively related to RPR (odds ratio [OR] 1.28, 95% confidence interval [CI] 1.02-1.59). According to the two-piecewise logistic regression model, we found that the inflection point of RPR was 1.89%. To the left of the inflection point (RPR ≤1.89%), we did not detect any relationship between RPR and in-hospital all-cause mortality (OR [95% CI]: 0.73 [0.41, 1.31], p = 0.2884). In contrast, to the right of the inflection point (RPR >1.89%), RPR was positively related to in-hospital all-cause mortality (OR [95% CI]: 1.61 [1.18, 2.19], p = 0.0027). CONCLUSIONS: RPR showed a nonlinear relationship with in-hospital all-cause mortality in patients with critical AIS.

Association between albumin corrected anion gap and 30-day all-cause mortality of critically ill patients with acute myocardial infarction: a retrospective analysis based on the MIMIC-IV database.

BACKGROUND: The anion gap (AG) has been linked to the prognosis of many cardiovascular disorders. However, the correlation between albumin-corrected anion gap (ACAG) and 30 d all-cause mortality of intensive care patients with acute myocardial infarction (AMI) is unclear. Furthermore, owing to the lack of studies, it is also unknown whether ACAG is more accurate than AG in predicting the mortality of AMI. METHODS: The Medical Information Mart for Intensive Care IV (MIMIC IV) dataset was used to provide patient data in this retrospective cohort study. ACAG is computed using the formulae: [4.4-{albumin (g/dl)}] × 2.5 + AG. The primary outcome was 30 d all-cause mortality intensive care patients with AMI. To explore the prognostic worthiness of ACAG, the receiver operating characteristic curve, smooth curve fitting, Cox regression model, and Kaplan survival analysis was performed. RESULTS: We enrolled 2,160 patients in this study. ACAG had a better predictive value for 30 d all-cause mortality than AG, with an area under the curve of 0.66. The association between ACAG levels and overall mortality was nonlinear. In our model, after correcting for confounding factors, the ACAG was the independent predictor for 30 d all-cause mortality (HR 1.75, 95%CI 1.24, 2.47). ACAG K-M estimator curve analyses revealed that the group with ACAG ≥ 21.75 mmol/l had poor survival rate than the other group. CONCLUSIONS: High serum ACAG levels were a significant risk factor for 30 d all-cause mortality in critically ill patients with AMI. ACAG concentration and 30 d all-cause mortality had a nonlinear relationship. ACAG had better predictive value in identifying 30 d all-cause mortality of patients with AMI in ICU than the AG.

Red Cell Distribution Width/Albumin Ratio: A Predictor of In-Hospital All-Cause Mortality in Patients with Acute Myocardial Infarction in the ICU.

Purpose: Red cell distribution width (RDW) and albumin level are linked to adverse outcomes in patients with acute myocardial infarction (AMI). Nonetheless, it remains unknown whether the RDW/albumin ratio (RAR) is associated with the short-term prognosis of AMI. Using a large cohort, we aimed to explore the association between RAR and in-hospital all-cause mortality in intensive care unit (ICU) patients with AMI. Patients and Methods: The patients' data analyzed in this retrospective cohort investigation were obtained from the eICU Collaborative Research Data Resource. RAR was calculated based on the serum albumin level and RDW. The primary outcome was in-hospital all-cause mortality. Receiver operating characteristic curve, multiple logistic regression model, and Kaplan-Meier survival analysis were performed to explore the prognostic value of RAR. Results: We enrolled 2594 patients in this study. After correcting for confounding factors, the RAR was an independent predictor for in-hospital mortality in our model (odds ratio [OR] 1.27, 95% confidence interval [CI] 1.12, 1.43). A similar relationship was observed with mechanical ventilation use. RAR showed a better predictive value with an area under the curve (AUC) of 0.738 (cutoff, 4.776) for in-hospital all-cause mortality compared to RDW or albumin alone. Kaplan-Meier estimator curve analyses for RAR demonstrated that the group with RAR ≥4.776%/g/dL had poorer survival than the group with RAR <4.776%/g/dL (p< 0.0001). The subgroup analysis revealed no significant interaction between RAR and in-hospital all-cause mortality in all strata. Conclusion: RAR was an independent risk factor for in-hospital all-cause mortality in ICU patients with AMI. Higher RAR values corresponded to higher mortality rates. RAR is a more accurate predictor of in-hospital all-cause mortality in patients with AMI in the ICU than albumin or RDW. Thus, RAR may be a potential biomarker of AMI.

Development and validation of a prediction model for the early occurrence of acute kidney injury in patients with acute pancreatitis.

BACKGROUND: Acute pancreatitis (AP) is associated with a high incidence of acute kidney injury (AKI). This study aimed to develop a nomogram for predicting the early onset of AKI in AP patients admitted to the intensive care unit. METHOD: Clinical data for 799 patients diagnosed with AP were extracted from the Medical Information Mart for Intensive Care IV database. Eligible AP patients were randomly divided into training and validation cohorts. The independent prognostic factors for the early development of AKI in AP patients were determined using the all-subsets regression method and multivariate logistic regression. A nomogram was constructed for predicting the early occurrence of AKI in AP patients. The performance of the nomogram was evaluated based on the area under the receiver operating characteristic curve (AUC), calibration curves and decision curve analysis (DCA). RESULTS: Seven independent prognostic factors were identified as predictive factors for early onset AKI in AP patients. The AUC of the nomogram in the training and validation cohorts were 0.795 (95% CI, 0.758-0.832) and 0.772 (95% CI, 0.711-0.832), respectively. The AUC of the nomogram was higher compared with that of the BISAP, Ranson, APACHE II scores. Further, the calibration curve revealed that the predicted outcome was in agreement with the actual observations. Finally, the DCA curves showed that the nomogram had a good clinical applicability value. CONCLUSION: The constructed nomogram showed a good predictive ability for the early occurrence of AKI in AP patients.

Association between the blood urea nitrogen to creatinine ratio and in­hospital mortality among patients with acute myocardial infarction: A retrospective cohort study.

The present study aimed to determine the association between the blood urea nitrogen (BUN) and creatinine (Cr) ratio and in-hospital mortality in patients with acute myocardial infarction (AMI). The present retrospective cohort study included adult patients (≥18 years of age) who were admitted to the intensive care unit (ICU) with a primary diagnosis of AMI. Medical records were obtained from the electronic ICU collaborative research database, which includes data from throughout continental USA. Data included demographic characteristics, vital signs, laboratory tests and comorbidities. The clinical endpoint was in-hospital mortality. The Cox proportional hazards model was used to evaluate the prognostic values of the basic BUN/Cr ratio and the Kaplan-Meier method was used to plot survival curves. Subgroup analyses were performed to measure mortality across various subgroups. In total, 5,965 eligible patients were included. In the Cox regression analysis, after being adjusted for age, sex, ethnicity and other confounding factors, the BUN/Cr ratio was found to be a significant risk predictor of in-hospital mortality. There was a non-linear relationship between the BUN/Cr ratio and in-hospital mortality after adjusting for potential confounders. A two-piecewise regression model was used to obtain a threshold inflection point value of 18. Furthermore, after adjusting for additional confounding factors (age, sex, ethnicity, BMI, heart rate, oxygen saturation, platelets, total protein, AMI category, heart failure, history of diabetes, history of hypertension, percutaneous coronary intervention, and administration of norepinephrine, dopamine and epinephrine), the BUN/Cr ratio remained a significant predictor of in-hospital mortality (third vs. first tertile: Hazard ratio, 1.50; 95% CI, 1.08-2.09; P<0.05). The Kaplan-Meier curve for tertiles of the BUN/Cr ratio indicated that in-hospital mortality rates were highest when the BUN/Cr ratio was ≥18.34 after adjustment for age, sex and ethnicity (P<0.05). The present findings demonstrated that a higher BUN/Cr ratio was associated with an increased risk of in-hospital mortality in patients with non-ST-segment elevation myocardial infarction. These results support a revision of how the prognosis of patients with AMI is predicted.

Propensity score analysis of red cell distribution width to serum calcium ratio in acute myocardial infarction as a predictor of in-hospital mortality.

Objective: Red cell distribution width (RDW) and serum calcium (Ca) levels are predictors of in-hospital mortality in acute myocardial infarction (AMI) patients. However, their sensitivity and specificity are limited. Therefore, this study aimed to determine whether the RDW to Ca ratio (RCR) acquired on admission can be used to predict the in-hospital mortality of AMI patients. Methods: This retrospective cohort study extracted clinical information from the Medical Information Market for Intensive IV (MIMIC-IV) database on 2,910 AMI patients enrolled via propensity score matching (PSM). Prognostic values were assessed using a multivariate logistic model and three PSM approaches. Analysis was performed based on stratified variables and interactions among sex, age, ethnicity, anemia, renal disease, percutaneous transluminal coronary intervention (PCI), coronary artery bypass grafting (CABG), atrial fibrillation, congestive heart failure, dementia, diabetes, paraplegia, hypertension, cerebrovascular disease, and Sequential Organ Failure Assessment (SOFA) score. Results: A total of 4,105 ICU-admitted AMI patients were analyzed. The optimal cut-off value of the RCR for in-hospital mortality was 1.685. The PSM was performed to identify 1,455 pairs (2,910) of score-matched patients, with balanced differences exhibited for nearly all variables.The patients' median age was 72 years (range, 63-82 years) and 60.9% were male. The risk of in-hospital mortality incidence increased with increasing RCR levels. After adjusting for confounders, the risk ratio for the incidence of in-hospital mortality for high RCR was 1.75 [95% confidence interval (CI): 1.60-1.94, P = 0.0113] compared to that associated with low RCR in the PSM cohort. High RCR was also substantially implicated in in-hospital mortality incidence in the weighted cohorts [odds ratio (OR) = 1.76, 95% CI: 1.62-1.94, P = 0.0129]. Assessment of RCR in three groups showed that patients with high RCR also had a higher risk of in-hospital mortality (OR = 3.04; 95% CI, 2.22-4.16; P < 0.0001) than in patients with RCR in the adjusted model. In the sensitivity analysis, both the original and weighted groups showed similar results. Conclusion: The RCR at admission may be useful for predicting in-hospital mortality in ICU-admitted AMI patients.

RDW-to-ALB Ratio Is an Independent Predictor for 30-Day All-Cause Mortality in Patients with Acute Ischemic Stroke: A Retrospective Analysis from the MIMIC-IV Database.

Purpose: Previous studies have shown that the peripheral red blood cell distribution width (RDW) and human serum albumin (ALB) were both predictors of the risk and mortality of cerebrovascular diseases, and the ratio of RDW to ALB (RAR) was a combined new index that can predict the prognosis of the cardiovascular and respiration systemic diseases, but its role in cerebrovascular diseases had not been effectively evaluated. This study is aimed at exploring whether RAR can effectively predict the 30-day all-cause mortality of acute ischemic stroke (AIS) patients. Methods: This retrospective cohort study was conducted on AIS patients (age > 18 years) in the intensive care database MIMIC-IV. The RAR was measured based on the red blood cell distribution width and albumin. The main result was 30-day all-cause mortality, and the secondary results were ICU mortality and hospital mortality. Obtain the odds ratio (OR) estimate from the logistic regression model of log-transformed RAR values and mortality. We had used another database for external validation. Results: A total of 1412 patients were enrolled, with an average age of 68.8 ± 15.9, including 708 (50.1%) males. When log-transformed RAR values were used as a continuous variable, as the values increases, the risk of death increases (30-day all-cause mortality OR = 4.02 (2.21, 7.32) P < 0.0001, ICU mortality OR = 3.81 (1.92, 7.54) P = 0.0001, and hospital mortality OR = 3.31 (1.83, 6.00) P < 0.0001), when the values were used as three-category variables and as a trend variable was also positively correlated with each mortality rate. Especially as the categorical variables, a dose-response relationship was clearly observed, that was, as the category of RAR increased (Q1 to Q3), the HR value of the risk of death gradually steadily increased. Such a relationship can also be observed in the external validation database. In the subgroup analysis, we observed an increased risk of death in the patient with hyperlipidemia and low HAS-BLED scores; however, no significant interaction was found in other subgroup analyses (including the diagnostic sequence of AIS). Conclusion: RAR was a predictor of mortality in AIS patients. However, more in-depth research is needed to further analyze and confirm the role of RAR in AIS patients.

The baseline and repeated measurements of DBP to assess in-hospital mortality risk among critically ill patients with acute myocardial infarction: A retrospective cohort study.

Changes in diastolic blood pressure (DBP) are common in patients with acute myocardial infarction (AMI). The relationship between the dynamic change of DBP and in-hospital mortality among patients with AMI remains unclear. This study aimed to explore the importance of DBP during disease development among patients with AMI. We performed a retrospective cohort study involving patients from the Medical Information Mart for Intensive Care III database, which included > 40,000 patients admitted to the intensive care unit (ICU). Overall, 3209 adult AMI admissions were identified. We extracted the clinical and laboratory information in the patients with AMI. Cox proportional hazards models were used to evaluate the prognostic values of baseline DBP. We used the generalized additive mixed model (GAMM) to compare trends in DBP over time among survivors and non-survivors, after adjusting for potential confounders. During the ICU stay, 189 patients died (mortality rate, 6.36%). The age of each non-survivor together with the variations in DBP over time from admission to the time of death is of great importance to the scientific community. Cox multivariable regression analysis displayed that after adjusting for confounding factors, ascended baseline DBP was an important hazard factor for hospital deaths (hazard ratio, 1.02; 95% confidence interval, 1.01-1.03; P = .003). Based on GAMM, DBP in the death group was markedly lower than that of the surviving group. Moreover, the difference between the two groups showed an increasing trend within 3 days after ICU admission. After adjusting for various variables, the results were stable. DBP significantly contributed to in-hospital mortality among patients with AMI. There was a nonlinear correlation between baseline DBP and in-hospital mortality among patients with AMI, and the DBP of the non-survivors decreased within the first 3 days after ICU admission. However, the causality cannot be deduced from our data.

Interpretable machine learning for 28-day all-cause in-hospital mortality prediction in critically ill patients with heart failure combined with hypertension: A retrospective cohort study based on medical information mart for intensive care database-IV and eICU databases.

Background: Heart failure (HF) combined with hypertension is an extremely important cause of in-hospital mortality, especially for the intensive care unit (ICU) patients. However, under intense working pressure, the medical staff are easily overwhelmed by the large number of clinical signals generated in the ICU, which may lead to treatment delay, sub-optimal care, or even wrong clinical decisions. Individual risk stratification is an essential strategy for managing ICU patients with HF combined with hypertension. Artificial intelligence, especially machine learning (ML), can develop superior models to predict the prognosis of these patients. This study aimed to develop a machine learning method to predict the 28-day mortality for ICU patients with HF combined with hypertension. Methods: We enrolled all critically ill patients with HF combined with hypertension in the Medical Information Mart for IntensiveCare Database-IV (MIMIC-IV, v.1.4) and the eICU Collaborative Research Database (eICU-CRD) from 2008 to 2019. Subsequently, MIMIC-IV was divided into training cohort and testing cohort in an 8:2 ratio, and eICU-CRD was designated as the external validation cohort. The least absolute shrinkage and selection operator (LASSO) Cox regression with internal tenfold cross-validation was used for data dimension reduction and identifying the most valuable predictive features for 28-day mortality. Based on its accuracy and area under the curve (AUC), the best model in the validation cohort was selected. In addition, we utilized the Shapley Additive Explanations (SHAP) method to highlight the importance of model features, analyze the impact of individual features on model output, and visualize an individual's Shapley values. Results: A total of 3,458 and 6582 patients with HF combined with hypertension in MIMIC-IV and eICU-CRD were included. The patients, including 1,756 males, had a median (Q1, Q3) age of 75 (65, 84) years. After selection, 22 out of a total of 58 clinical parameters were extracted to develop the machine-learning models. Among four constructed models, the Neural Networks (NN) model performed the best predictive performance with an AUC of 0.764 and 0.674 in the test cohort and external validation cohort, respectively. In addition, a simplified model including seven variables was built based on NN, which also had good predictive performance (AUC: 0.741). Feature importance analysis showed that age, mechanical ventilation (MECHVENT), chloride, bun, anion gap, paraplegia, rdw (RDW), hyperlipidemia, peripheral capillary oxygen saturation (SpO2), respiratory rate, cerebrovascular disease, heart rate, white blood cell (WBC), international normalized ratio (INR), mean corpuscular hemoglobin concentration (MCHC), glucose, AIDS, mean corpuscular volume (MCV), N-terminal pro-brain natriuretic peptide (Npro. BNP), calcium, renal replacement therapy (RRT), and partial thromboplastin time (PTT) were the top 22 features of the NN model with the greatest impact. Finally, after hyperparameter optimization, SHAP plots were employed to make the NN-based model interpretable with an analytical description of how the constructed model visualizes the prediction of death. Conclusion: We developed a predictive model to predict the 28-day mortality for ICU patients with HF combined with hypertension, which proved superior to the traditional logistic regression analysis. The SHAP method enables machine learning models to be more interpretable, thereby helping clinicians to better understand the reasoning behind the outcome and assess in-hospital outcomes for critically ill patients.

The Association between Systemic Immune-Inflammation Index and All-Cause Mortality in Acute Ischemic Stroke Patients: Analysis from the MIMIC-IV Database.

Purpose: Acute ischemic stroke (AIS) is a devastating disease and remains the leading cause of death and disability. This retrospective study aims to investigate associations between systemic immune-inflammation index (SII) and all-cause mortality in patients with AIS. Patients and Methods. We used the data from Medical Information Mart for Intensive Care IV. A total of 1,181 patients with acute ischemic stroke (AIS) were included. Systemic immune-inflammation index (SII) was calculated as platelet count (/L) × neutrophil count (/L)/lymphocyte count (/L). The main outcomes were 30-day all-cause mortality. The association between SII with mortality was evaluated using the Cox proportional hazards regression model. Results: After adjusting for potential covariates, the highest quartiles of SII versus the lowest quartiles of SII, the HR was 2.74 (CI 1.79-4.19, P < 0.001). Log-transformed SII was significantly associated with 30-day all-cause mortality (HR 2.44; CI 1.72-3.46, P < 0.001). Furthermore, we found that there is a nearly linear relationship (P=0.265) between logarithmic transformed SII with all-cause mortality. Conclusion: Elevated SII of patients with acute ischemic stroke increased the risk of 30-day all-cause mortality. SII may serve as a useful marker to elucidate the role of thrombocytosis, inflammation, and immunity interaction in the development of AIS.

Effect of Phase Composition on Leaching Behavior and Mechanical Properties of Ceramics from Ferrochrome Slag and Tundish Slag.

Ferrochrome slag (FS) and tundish slag (TS) are two typical slags containing high contents of Cr2O3 (3.88 wt.%) and MnO (18.69 wt.%), respectively. In this study, batches of ceramics were prepared from FS and TS, and their Cr/Mn leaching behaviors, mechanical properties and microstructures were investigated. Results showed that ceramics with 80 wt.% FS or 85 wt.% TS had acceptable properties. By controlling its composition and sintering temperature, pyroxene or spinel phases could become the main crystalline phases of the fired ceramics containing either of the two slags. For both slag series, pyroxene phases contributed to higher bending strengths, whereas spinel phases led to lower Cr/Mn leaching rates. Both ceramic containing 20 wt.% FS and ceramic containing 85 wt.% TS had the main crystals of pyroxene phases and possessed the highest bending strengths (FS20: 114.52 MPa and TS85: 124.61 MPa). However, both ceramic containing 80 wt.% FS and ceramic containing 25 wt.% TS with main crystals from the spinel phases had the lowest Cr/Mn leaching rates (FS80: Cr 0.05% and TS25: Mn 0.43%). Therefore, optimum designs for the compositions of ceramics from different slags were achieved by changing the proportions of pyroxene and spinel phases to obtain a balance between the high strengths of materials and the stable retention of heavy metal ions. This study provides an important basis for long-term research on the large-scale reuse of heavy metal-containing slags in the ceramic industry.

Relationship Between the Hemoglobin-to-Red Cell Distribution Width Ratio and All-Cause Mortality in Ischemic Stroke Patients with Atrial Fibrillation: An Analysis from the MIMIC-IV Database.

AIM: To investigate the association between the hemoglobin-to-red cell distribution width (RDW) ratio (HRR) and all-cause mortality in ischemic stroke patients with atrial fibrillation (AF). DESIGN: This study was a retrospective cohort analysis. In total, 1018 ischemic stroke patients with AF were enrolled using the Medical Information Mart for Intensive Care database, (MIMIC)-IV. The patients were divided into four groups according to the HRR values. The primary outcome was 180-day all-cause mortality. METHODS: Multivariate Cox proportional risk regression models were used to examine the association between HRR and all-cause mortality. The non-linear relationship between HRR and all-cause mortality was confirmed using a Cox proportional risk regression model fitted by cubic spline function and smooth curve fitting. RESULTS: A total of 246/1018 patients (24.17%) died. The serum HRR values were negatively associated with 180-day all-cause mortality (hazard ratio (HR) 0.80, 95% confidence interval (CI) 0.68-0.94). A two-piecewise regression model was used to obtain a threshold inflection point value of 9.74. The HR and the 95% CI on the left inflection point were 0.73 and 0.61-0.87 (p = 0.0005); on the right inflection point they were 1.06 and 0.82-1.38 (p = 0.6383). CONCLUSION: The relationship between all-cause mortality and the HRR values was non-linear in ischemic stroke patients with AF. All-cause mortality and HRR values were negatively correlated when the HRR value was ≤9.74.

Relationship between the red cell distribution width-to-platelet ratio and in-hospital mortality among critically ill patients with acute myocardial infarction: a retrospective analysis of the MIMIC-IV database.

OBJECTIVES: We aimed to investigate the association between red cell distribution width-to-platelet ratio (RPR), and in-hospital mortality in critically ill patients with acute myocardial infarction (AMI). DESIGN: A retrospective cohort study. SETTING: Data were collected from the Medical Information Mart for Intensive Care database (MIMIC-IV) consisting of critically ill participants between 2008 and 2019 at the Beth Israel Deaconess Medical Centre in Boston. PARTICIPANTS: A total of 5067 patients with AMI were enrolled from the MIMIC-IV database. PRIMARY AND SECONDARY OUTCOME: In-hospital mortality. RESULTS: A total of 4034 patients survived, while 1033 died. In a multiple regression analysis adjusted for age, weight and ethnicity, RPR also showed a positive correlation with in-hospital mortality (HR 1.91, 95% CI 1.42 to 2.56, p<0.0001). Moreover, after adjusting for additional confounding factors, obvious changes were observed (HR 1.63, 95% CI 1.03 to 2.57, p=0.0357). In model 2, the high ratio quartile remained positively associated with hospital mortality compared with the low ratio quartile (HR 1.20, 95% CI 1.01 to 1. 43), with a p-value trend of 0.0177. Subgroup analyses showed no significant effect modifications on the association between RPR and in-hospital mortality in the different AMI groups (p>0.05). CONCLUSION: RPR is an independent predictor of in-hospital mortality in critically ill patients with AMI.

Association of hypocalcemia with in-hospital mortality in critically ill patients with intracerebral hemorrhage: A retrospective cohort study.

Background and purpose: There was little evidence to study the relationship between hypocalcemia and mortality among critically ill patients with intracerebral hemorrhage (ICH) aged ≥16 years. This study aimed to determine the potential association between hypocalcemia and in-hospital and ICU mortality in patients with ICH in the United States. Methods: We analyzed 1,954 patients with ICH from the e-Intensive Care Unit Collaborative Research Database and divided them into hypocalcemia and non-hypocalcemia groups. Hypocalcemia was defined as albumin-adjusted total calcium below 8.4 mg/dl. The primary and secondary outcomes were hospital and ICU mortality, respectively. We performed multivariable regression and subgroup analyses to evaluate the association of hypocalcemia with hospital and ICU mortality. Cumulative survival rate analysis was performed using Kaplan-Meier curves with log-rank statistics. Results: We enrolled 1,954 patients with ICH who had been hospitalized in ICU for >24 h and were older than 16 years (average age, 61.8 years; men, 56.7%). We noted that 373 (19%) hospital mortality occurred, including 235 (12%) ICU mortality. In this sample, 195 patients had hypocalcemia. Multivariable logistic regression analyses showed that hypocalcemia was associated with a 67% increased risk of in-hospital and a 72% increased risk of ICU mortality. This association was consistent across subgroup analyses. Conclusions: Hypocalcemia was associated with a high risk of hospital and ICU mortality among critically ill patients with ICH. Future prospective, randomized, controlled studies are needed to confirm our results.

Twenty-eight-day in-hospital mortality prediction for elderly patients with ischemic stroke in the intensive care unit: Interpretable machine learning models.

Background: Risk stratification of elderly patients with ischemic stroke (IS) who are admitted to the intensive care unit (ICU) remains a challenging task. This study aims to establish and validate predictive models that are based on novel machine learning (ML) algorithms for 28-day in-hospital mortality in elderly patients with IS who were admitted to the ICU. Methods: Data of elderly patients with IS were extracted from the electronic intensive care unit (eICU) Collaborative Research Database (eICU-CRD) records of those elderly patients admitted between 2014 and 2015. All selected participants were randomly divided into two sets: a training set and a validation set in the ratio of 8:2. ML algorithms, such as Naïve Bayes (NB), eXtreme Gradient Boosting (xgboost), and logistic regression (LR), were applied for model construction utilizing 10-fold cross-validation. The performance of models was measured by the area under the receiver operating characteristic curve (AUC) analysis and accuracy. The present study uses interpretable ML methods to provide insight into the model's prediction and outcome using the SHapley Additive exPlanations (SHAP) method. Results: As regards the population demographics and clinical characteristics, the analysis in the present study included 1,236 elderly patients with IS in the ICU, of whom 164 (13.3%) died during hospitalization. As regards feature selection, a total of eight features were selected for model construction. In the training set, both the xgboost and NB models showed specificity values of 0.989 and 0.767, respectively. In the internal validation set, the xgboost model identified patients who died with an AUC value of 0.733 better than the LR model which identified patients who died with an AUC value of 0.627 or the NB model 0.672. Conclusion: The xgboost model shows the best predictive performance that predicts mortality in elderly patients with IS in the ICU. By making the ML model explainable, physicians would be able to understand better the reasoning behind the outcome.