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 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: &lt;10, 10–15, 15–20, and &gt;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 &gt;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 &gt;20 mg/L with higher hospital mortality (1.585 [1.053–2.387]) than mean VTC of &lt;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 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 Diabetes mellitus is associated with lower mortality in intensive care unit: a multicenter historical cohort study

2020 ◽  
Author(s):  
Chang Hu ◽  
Bo Hu ◽  
Jing Wang ◽  
Zhiyong Peng ◽  
Kianoush B. Kashani ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Critically Ill ◽  
Old Age ◽  
Critically Ill Patients ◽  
Lower Mortality ◽  
Research Database ◽  
Icu Mortality ◽  
Historical Cohort ◽  
Basic Characteristics ◽  
Apache Iv

Abstract Background: The association of pre-existing diabetes mellitus and outcomes among critically ill patients remains unknown.Methods: This retrospective study enrolled patients who were covered by the eICU Collaborative Research Database from 2014 to 2015. DM was the exposure of interest, and diabetic individuals were adjudicated by the medical history, and blood glucose level (BGL). We abstracted basic characteristics, laboratory variables, and primary exposures. ICU mortality was the primary outcome.Results: In a cohort of 134,429 critically ill patients (male 54.4%, median age 66 [54-77] years, BMI 28[24-33] kg/m2), the prevalence of DM was 29%. In comparison with nondiabetics, DM patients were older, more obese, had higher Acute Physiology and Chronic Health Evaluation (APACHE)-IV score, and ICU admission BGL. In comparison with nondiabetics, pre-existing DM was associated with lower ICU mortality (OR: 0.846, 95%CI: 0.791-0.905). In multivariable logistic regression and Cox proportional hazard analyses, pre-existing DM was associated with decreased odds of ICU mortality in hyperglycemic patients (>163 mg/dL), higher APACHE IV score (>67), middle to old age (45-75 years), sepsis and morbid obesity (BMI>35 kg/m2). Also, in comparison with nondiabetics, pre-existing DM was associated with lower mortality among those with higher mean BGL (>128 mg/dL), and higher mortality in lower mean BGL (<107 mg/dL). Conclusions: In comparison with nondiabetics, pre-existing DM is associated with a lower adjusted ICU mortality. This association is stronger in DM patients with hyperglycemia, obesity, sepsis, middle to old age, and higher APACHE IV score.

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.

Relationship Between Initial Vancomycin Trough Levels and Early-Onset Vancomycin-Associated Nephrotoxicity in Critically Ill Patients

2017 ◽  
pp. 1 ◽  
Author(s):  
Masayuki Chuma ◽  
Makoto Makishima ◽  
Toru Imai ◽  
Naohiro Tochikura ◽  
Shinichiro Suzuki ◽  
...  
Keyword(s):  
Critically Ill ◽  
Critically Ill Patients ◽  
Early Onset ◽  
Vancomycin Trough ◽  
Trough Levels

scholarly journals Association Between Serum Osmolality and Acute Kidney Injury in Critically Ill Patients: A Retrospective Cohort Study

2021 ◽  
Vol 8 ◽  
Author(s):  
Jie Yang ◽  
Yisong Cheng ◽  
Ruoran Wang ◽  
Bo Wang
Keyword(s):  
Acute Kidney Injury ◽  
Logistic Regression ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Retrospective Cohort ◽  
Kidney Injury ◽  
High Serum ◽  
Serum Osmolality ◽  
Multivariate Logistic Regression ◽  
Low Serum

Purposes: Acute kidney injury (AKI) is a common complication in critically ill patients and is usually associated with poor outcomes. Serum osmolality has been validated in predicting critically ill patient mortality. However, data about the association between serum osmolality and AKI is still lacking in ICU. Therefore, the purpose of the present study was to investigate the association between early serum osmolality and the development of AKI in critically ill patients.Methods: The present study was a retrospective cohort analysis based on the medical information mart for intensive care III (MIMIC-III) database. 20,160 patients were involved in this study and divided into six subgroups according to causes for ICU admission. The primary outcome was the incidence of AKI after ICU admission. The association between early serum osmolality and AKI was explored using univariate and multivariate logistic regression analyses.Results: The normal range of serum osmolality was 285–300 mmol/L. High serum osmolality was defined as serum osmolality &gt;300 mmol/L and low serum osmolality was defined as serum osmolality &lt;285 mmol/L. Multivariate logistic regression indicated that high serum osmolality was independently associated with increased development of AKI with OR = 1.198 (95% CL = 1.199–1.479, P &lt; 0.001) and low serum osmolality was also independently associated with increased development of AKI with OR = 1.332 (95% CL = 1.199–1.479, P &lt; 0.001), compared with normal serum osmolality, respectively.Conclusions: In critically ill patients, early high serum osmolality and low serum osmolality were both independently associated with an increased risk of development of AKI.

Sign in / Sign up

Export Citation Format

Share Document