scholarly journals Relationship Between Mean Vancomycin Trough Concentration and Mortality in Critically Ill Patients: A Multicenter Retrospective Study

2021 ◽  
Vol 12 ◽  
Author(s):  
Yanli Hou ◽  
Jiajia Ren ◽  
Jiamei Li ◽  
Xuting Jin ◽  
Ya Gao ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Hospital Mortality ◽  
Critically Ill ◽  
Trough Concentration ◽  
Critically Ill Patients ◽  
Continuous Variable ◽  
Research Database ◽  
Categorical Variable ◽  
Vancomycin Trough ◽  
Relationship Of

Background: It remains unclear whether the mean vancomycin trough concentration (VTC) derived from the entire course of therapy is of potential benefit for critically ill patients. This study was conducted to explore the association between mean serum VTC and mortality in intensive care units (ICUs).Methods: 3,603 adult patients with two or more VTC records after receiving vancomycin treatment in the eICU Collaborative Research Database were included in this multicenter retrospective cohort study. Mean VTC was estimated using all measured VTCs and investigated as a continuous and categorical variable. Patients were categorised into four groups according to mean VTC: <10, 10–15, 15–20, and >20 mg/L. Multivariable logistic regression and subgroup analyses were performed to investigate the relationship of mean VTC with mortality.Results: After adjusting for a series of covariates, logistic regression analyses indicated that mean VTC, as a continuous variable, was positively correlated with ICU (odds ratio, 1.038, 95% confidence interval, [1.014–1.063]) and hospital (1.025 [1.005–1.046]) mortalities. As a categorical variable, mean VTC of 10–15 mg/L was not associated with reduced ICU (1.705 [0.975–2.981]) and hospital (1.235 [0.829–1.841]) mortalities. Mean VTC of 15–20 mg/L was not correlated with a lower risk of hospital mortality (1.370 [0.924–2.029]). Moreover, mean VTCs of 15–20 and >20 mg/L were significantly associated with higher ICU mortality (1.924 [1.111–3.332]; 2.428 [1.385–4.258]), and mean VTC of >20 mg/L with higher hospital mortality (1.585 [1.053–2.387]) than mean VTC of <10 mg/L. Similar results were observed in patients with different Acute Physiology and Chronic Health Evaluation IV score, creatinine clearance, age, and body mass index subgroups.Conclusion: Mean VTC was not associated with reduced ICU/hospital related mortality. Our results suggested that VTC monitoring might not guarantee vancomycin efficacy for ICU patients.

scholarly journals Relationship Between Mean Vancomycin Trough Concentration and Mortality in Critically Ill Patients: A Multicenter Retrospective Study

2021 ◽  
Author(s):  
Yanli Hou ◽  
Jiajia Ren ◽  
Jiamei Li ◽  
Xuting Jin ◽  
Ya Gao ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Critically Ill ◽  
Trough Concentration ◽  
Critically Ill Patients ◽  
Continuous Variable ◽  
Research Database ◽  
Categorical Variable ◽  
Icu Mortality ◽  
Vancomycin Trough ◽  
Relationship Of

Abstract Background: It remains unclear whether the mean vancomycin trough concentration (VTC) derived from the entire course of therapy is of potential benefit for critically ill patients. This study was conducted to explore the association between mean serum VTC and mortality in intensive care units (ICUs).Methods: 3,364 adult patients with two or more VTC records after receiving vancomycin therapy in the eICU Collaborative Research Database were included in this multicenter retrospective cohort study. Mean VTC was estimated using all measured VTCs and investigated as a continuous and categorical variable. Patients were categorized into four groups according to mean VTC: <10, 10–15, 15–20, and >20 mg/L. Multivariable logistic regression and subgroup analyses were performed to investigate the relationship of mean VTC with mortality.Results: After adjusting for a series of covariates, logistic regression analyses indicated that mean VTC, as a continuous variable, was positively correlated with ICU (odds ratio, 1.042, 95% confidence interval, [1.017–1.068]) and hospital (1.025 [1.004–1.046]) mortalities. As a categorical variable, mean VTC at 10–15 mg/L failed to reduce ICU mortality (1.512 [0.849–2.694]). Moreover, mean VTCs of 15–20 and >20 mg/L were significantly associated with higher ICU mortality (1.946 [1.106–3.424]; 2.314 [1.296–4.132]) than mean VTC <10 mg/L. Mean VTCs of 10–15, 15–20, and >20 mg/L were not associated with increased hospital mortality (1.154 [0.766–1.739]; 1.342 [0.896–2.011]; 1.496 [0.981–2.281]). Similar results were observed in different Acute Physiology and Chronic Health Evaluation IV score or creatinine clearance subgroups.Conclusions: Increasing mean VTC showed no benefit regarding ICU and hospital mortalities in critically ill patients. Our results suggested that continuous VTC monitoring might not guarantee vancomycin efficacy for ICU patients.

scholarly journals Blood Urea Nitrogen and In-Hospital Mortality in Critically Ill Patients with Cardiogenic Shock: Analysis of the MIMIC-III Database

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
En-qian Liu ◽  
Chun-lai Zeng
Keyword(s):  
Logistic Regression ◽  
Hospital Mortality ◽  
Cardiogenic Shock ◽  
Critically Ill ◽  
Blood Urea Nitrogen ◽  
Critically Ill Patients ◽  
Medical Information ◽  
Multivariate Logistic Regression Model ◽  
Standard Deviation Increase ◽  
Urea Nitrogen

The association between blood urea nitrogen (BUN) and prognosis has been the focus of recent research. Therefore, the objective of this study was to investigate the association between BUN and hospital mortality in critically ill patients with cardiogenic shock (CS). This was a retrospective cohort study, in which data were obtained from the Medical Information Mart for Intensive Care III V1.4 database. Data from 697 patients with CS were analyzed. Logistic regression and subgroup analyses were used to assess the association between BUN and hospital mortality in patients with CS. The average age of the 697 participants was 71.14 years, and approximately 42.18% were men. In the multivariate logistic regression model, after adjusting for age, sex, diabetes, cardiac arrhythmias, urine output, simplified acute physiology score II, sequential organ failure assessment, creatinine, anion gap, and heart rate, high BUN demonstrated strong associations with increased in-hospital mortality (per standard deviation increase: odds ratio [OR] 1.47, 95% confidence interval [CI] 1.13–1.92). A similar result was observed in BUN tertile groups (BUN 23–37 mg/dL versus 6–22 mg/dL: OR [95% CI], 1.42 [0.86–2.34]; BUN 38–165 mg/dL versus 6–22 mg/dL: OR [95% CI], 1.99 [1.10–3.62]; P trend 0.0272). Subgroup analysis did not reveal any significant interactions among various subgroups, and higher BUN was associated with adverse clinical outcomes in patients with CS.

scholarly journals High-Normal Serum Magnesium and Hypermagnesemia Are Associated With Increased 30-Day In-Hospital Mortality: A Retrospective Cohort Study

2021 ◽  
Vol 8 ◽  
Author(s):  
Liao Tan ◽  
Qian Xu ◽  
Chan Li ◽  
Jie Liu ◽  
Ruizheng Shi
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Hospital Mortality ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Cox Regression ◽  
Serum Magnesium ◽  
Normal Serum ◽  
Magnesium Level ◽  
Serum Magnesium Level

Background: Magnesium, the fourth most abundant mineral nutrient in our body, plays a critical role in regulating ion channels and energy generation, intracardiac conduction, and myocardial contraction. In this study, we assessed the association of admission serum magnesium level with all-cause in-hospital mortality in critically ill patients with acute myocardial infarction (AMI).Methods: Clinical data were extracted from the eICU Collaborative Research Database (eICU-CRD). Only the data for the first intensive care unit (ICU) admission of each patient were used, and baseline data were extracted within 24 h after ICU admission. Logistic regression, Cox regression, and subgroup analyses were conducted to determine the relationship between admission serum magnesium level and 30-day in-hospital mortality in ICU patients with AMI.Results: A total of 9,005 eligible patients were included. In the logistic regression analysis, serum magnesium at 2.2 to ≤2.4 and &gt;2.4 mg/dl levels were both significant predictors of all-cause in-hospital mortality in AMI patients. Moreover, serum magnesium of 2.2 to ≤2.4 mg/dl showed higher risk of in-hospital mortality than magnesium of &gt;2.4 mg/dl (adjusted odds ratio, 1.63 vs. 1.39). The Cox regression analysis yielded similar results (adjusted hazard ratio, 1.36 vs. 1.25).Conclusions: High-normal serum magnesium and hypermagnesemia may be useful and easier predictors for 30-day in-hospital mortality in critically ill patients with AMI.

The Impact of Heart Rate Circadian Rhythm on In-hospital Mortality in Stroke and Critically Ill Patients: Insights from the eICU Collaborative Research Database

2022 ◽  
Author(s):  
Zhengning Yang ◽  
Zhe Li ◽  
Xu He ◽  
Zhen Yao ◽  
XiaoXia Xie ◽  
...  
Keyword(s):  
Heart Rate ◽  
Circadian Rhythm ◽  
Hospital Mortality ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Peak Time ◽  
Research Database ◽  
Discriminative Ability ◽  
The Impact ◽  
Apache Iv

Abstract Background: The dysregulation of the heart rate circadian rhythm has been documented to be an independent risk factor in multiple diseases. However, data showing the impact of dysregulated heart rate circadian rhythm in stroke and critically ill patients are scarce.Methods: Stroke and critically ill patients in the ICU between 2014 and 2015 from the recorded eICU Collaborative Research Database were included in the current analyses. The impact of circadian rhythm of heart rate on in-hospital mortality was analyzed. Three variables, Mesor (rhythm-adjusted mean of heart rate), Amplitude (distance from the highest point of circadian rhythm of heart rate to Mesor), and Peak time (time when the circadian rhythm of heart rate reaches the highest point) were used to evaluate the heart rate circadian rhythm. The incremental value of circadian rhythm variables in addition to Acute Physiology and Chronic Health Evaluation (APACHE) IV score to predict in-hospital mortality was also explored.Results: A total of 6,201 eligible patients were included. The in-hospital mortality was 16.2% (1,002/6,201). The circadian rhythm variables of heart rate, Mesor, Amplitude, and Peak time, were identified to be independent risk factors of in-hospital mortality. After adjustments, Mesor per 10 beats per min (bpm) increase was associated with a 1.17-fold (95%CI: 1.11, 1.24, P<0.001) and Amplitude per 5 bpm was associated with a 1.14-fold (95%CI: 1.06, 1.24, P<0.001) increase in the risk of in-hospital mortality, respectively. The risk of in-hospital mortality was lower in patients who had Peak time reached between 18:00-24:00 or 00:00-06:00; whereas the risk was highest in patients who had Peak time reached between 12:00-18:00 (OR: 1.33, 95%CI: 1.05, 1.68, P=0.017). Compared with APACHE IV score only (c-index=0.757), combining APACHE IV score and circadian rhythm variables of heart rate (c-index=0.766) was associated with increased discriminative ability (P=0.003).Conclusion: Circadian rhythm of heart rate is an independent risk factor of the in-hospital mortality in stroke and critically ill patients. Including circadian rhythm variables regarding heart rate might increase the discriminative ability of the risk score to predict the short-term prognosis of patients.

scholarly journals A Machine-Learning Approach for Dynamic Prediction of Sepsis-Induced Coagulopathy in Critically Ill Patients With Sepsis

2021 ◽  
Vol 7 ◽  
Author(s):  
Qin-Yu Zhao ◽  
Le-Ping Liu ◽  
Jing-Chao Luo ◽  
Yan-Wei Luo ◽  
Huan Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Compact Model ◽  
Bayesian Optimization ◽  
Research Database ◽  
Learning Models ◽  
Dynamic Prediction ◽  
Machine Learning Models

Background: Sepsis-induced coagulopathy (SIC) denotes an increased mortality rate and poorer prognosis in septic patients.Objectives: Our study aimed to develop and validate machine-learning models to dynamically predict the risk of SIC in critically ill patients with sepsis.Methods: Machine-learning models were developed and validated based on two public databases named Medical Information Mart for Intensive Care (MIMIC)-IV and the eICU Collaborative Research Database (eICU-CRD). Dynamic prediction of SIC involved an evaluation of the risk of SIC each day after the diagnosis of sepsis using 15 predictive models. The best model was selected based on its accuracy and area under the receiver operating characteristic curve (AUC), followed by fine-grained hyperparameter adjustment using the Bayesian Optimization Algorithm. A compact model was developed, based on 15 features selected according to their importance and clinical availability. These two models were compared with Logistic Regression and SIC scores in terms of SIC prediction.Results: Of 11,362 patients in MIMIC-IV included in the final cohort, a total of 6,744 (59%) patients developed SIC during sepsis. The model named Categorical Boosting (CatBoost) had the greatest AUC in our study (0.869; 95% CI: 0.850–0.886). Coagulation profile and renal function indicators were the most important features for predicting SIC. A compact model was developed with an AUC of 0.854 (95% CI: 0.832–0.872), while the AUCs of Logistic Regression and SIC scores were 0.746 (95% CI: 0.735–0.755) and 0.709 (95% CI: 0.687–0.733), respectively. A cohort of 35,252 septic patients in eICU-CRD was analyzed. The AUCs of the full and the compact models in the external validation were 0.842 (95% CI: 0.837–0.846) and 0.803 (95% CI: 0.798–0.809), respectively, which were still larger than those of Logistic Regression (0.660; 95% CI: 0.653–0.667) and SIC scores (0.752; 95% CI: 0.747–0.757). Prediction results were illustrated by SHapley Additive exPlanations (SHAP) values, which made our models clinically interpretable.Conclusions: We developed two models which were able to dynamically predict the risk of SIC in septic patients better than conventional Logistic Regression and SIC scores.

scholarly journals Triglyceride-glucose index linked to hospital mortality in critically ill stroke patients: an observational multicentre study on eICU database

2020 ◽  
Author(s):  
Bingjun Zhang ◽  
Lingling Liu ◽  
Hengfang Ruan ◽  
Qiang Zhu ◽  
Dafan Yu ◽  
...  
Keyword(s):  
Hospital Mortality ◽  
Critically Ill ◽  
Univariate Analysis ◽  
Continuous Variable ◽  
Research Database ◽  
Stroke Patients ◽  
Icu Mortality ◽  
Tyg Index ◽  
Increased Risk ◽  
Adjusted Model

Abstract Background: The triglyceride‑glucose (TyG) index is a reliable surrogate of insulin resistance and a marker for ischemic stroke (IS) incident. Whether the TyG index predicts stroke outcome remains uncertain. This study investigated the prognostic value of the TyG index in critically ill stroke patients.Methods: This was a retrospective observational study that included stroke patients, and all data were extracted from the eICU Collaborative Research Database. The TyG index was calculated as the ln (fasting glucose level [mg/dL] × triglyceride level [mg/dL]/2). The outcomes included the hospital and intensive care unit (ICU) death. Multivariate logistic regression was used to determine independent risk factors. The smoothing curves and forest plots were illustrated.Results: A total of 4570 eligible subjects were enrolled. The mean level of TyG index was 9.1 ± 0.7. The hospital and ICU mortality rate were 10.3% and 5.0% respectively. TyG index as a continuous variable was associated hospital mortality in univariate analysis (OR 1.723, 95% CI 1.524-1.948, P < 0.001), adjusted model 1 (OR 1.861, 95% CI 1637-2.116, P < 0.001) and adjusted model 2 (OR 2.543, 95% CI 1.588-4.073, P < 0.001). TyG was also associated ICU mortality in univariate analysis (OR 2.146, 95% CI 1.826-2.523, P < 0.001), adjusted model 1 (OR 2.183, 95% CI 1.847-2.580, P < 0.001), and adjusted model 2 (OR 2.672, 95% CI 1.376-5.188, P < 0.001). The smoothing curves observed a continuous linear association after adjusting all covariates both in hospital and ICU mortality. Subgroup analysis demonstrated TyG index was associated with increased risk of hospital and ICU death in critically ill IS (P < 0.05), but not in hemorrhage stroke (P > 0.05).Conclusion: The TyG index is a potential predictor for hospital and ICU mortality in critically ill stroke patients, especially in IS patients.

scholarly journals Association between Latent Trajectories of Fluid Balance and Clinical Outcomes in Critically Ill Patients with Acute Kidney Injury: A Prospective Multicenter Observational Study

2021 ◽  
pp. 1-11
Author(s):  
Meiping Wang ◽  
Bo Zhu ◽  
Li Jiang ◽  
Xuying Luo ◽  
Na Wang ◽  
...  
Keyword(s):  
Acute Kidney Injury ◽  
Logistic Regression ◽  
Hospital Mortality ◽  
Clinical Outcomes ◽  
Critically Ill ◽  
Fluid Balance ◽  
Critically Ill Patients ◽  
Kidney Injury ◽  
Icu Mortality ◽  
Increased Risk

<b><i>Introduction:</i></b> We aimed to identify different trajectories of fluid balance (FB) and investigate the effect of FB trajectories on clinical outcomes in intensive care unit (ICU) patients with acute kidney injury (AKI) and the dose-response association between fluid overload (FO) and mortality. <b><i>Methods:</i></b> We derived data from the Beijing Acute Kidney Injury Trial (BAKIT). A total of 1,529 critically ill patients with AKI were included. The primary outcome was 28-day mortality, and hospital mortality, ICU mortality and AKI stage were the secondary outcomes. A group-based trajectory model was used to identify the trajectory of FB during the first 7 days. Multivariable logistic regression was performed to examine the relationship between FB trajectories and clinical outcomes. A logistic regression model with restricted cubic splines was used to examine the dose relationship between FO and 28-day mortality. <b><i>Results:</i></b> Three distinct trajectories of FB were identified: low FB (1,316, 86.1%), decreasing FB (120, 7.8%), and high FB (93, 6.1%). Compared with low FB, high FB was associated with increased 28-day mortality (odds ratio [OR] 1.94, 95% confidence interval [CI] 1.17–3.19) and AKI stage (OR 2.04, 95% CI 1.23–3.37), whereas decreasing FB was associated with a reduction in 28-day mortality by approximately half (OR 0.53, 95% CI 0.32–0.87). Similar results were found for the outcomes of ICU mortality and hospital mortality. We observed a J-shaped relationship between maximum FO and 28-day mortality, with the lowest risk at a maximum FO of 2.8% L/kg. <b><i>Conclusion:</i></b> Different trajectories of FB in critically ill patients with AKI were associated with clinical outcomes. An FB above or below a certain range was associated with an increased risk of mortality. Further studies should explore this relationship and search for the optimal fluid management strategies for critically ill patients with AKI.

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