Early combination of albumin with crystalloids administration might be beneficial for the survival of septic patients: a retrospective analysis from MIMIC-IV database

Machine learning algorithm for predict the in-hospital mortality in critically ill patients with congestive heart failure combined with chronic kidney disease

BACKGROUND

The objective of this study was to develop and validate a machine learning (ML) model for predict in-hospital mortality among critically ill patients with congestive heart failure (CHF) combined with chronic kidney disease (CKD).

METHODS

After employing least absolute shrinkage and selection operator regression for feature selection, six distinct methodologies were employed in the construction of the model. The selection of the optimal model was based on the area under the curve (AUC). Furthermore, the interpretation of the chosen model was facilitated through the utilization of SHapley Additive exPlanation (SHAP) values and the Local Interpretable Model-Agnostic Explanations (LIME) algorithm.

RESULTS

This study collected data and enrolled 5041 patients on CHF combined with CKD from 2008 to 2019, utilizing the Medical Information Mart for Intensive Care Unit. After selection, 22 of the 47 variables collected post-intensive care unit admission were identified as mortality-associated and subsequently utilized in the development of ML models. Among the six models generated, the eXtreme Gradient Boosting (XGBoost) model demonstrated the highest AUC at 0.837. Notably, the SHAP values highlighted the sequential organ failure assessment score, age, simplified acute physiology score II, and urine output as the four most influential variables in the XGBoost model. In addition, the LIME algorithm explains the individualized predictions.

CONCLUSIONS

In conclusion, our study accomplished the successful development and validation of ML models for predicting in-hospital mortality in critically ill patients with CHF combined with CKD. Notably, the XGBoost model emerged as the most efficacious among all the ML models employed.

Association between the anion-gap and 28-day mortality in critically ill adult patients with sepsis: A retrospective cohort study

Metabolic acidosis is usually associated with the severity of the condition of patients with sepsis or septic shock. Serum anion gap (AG) is one of the indicators of response metabolism. This study was performed to investigate whether the initial serum AG is associated with the 28-day mortality in critically ill adult patients with sepsis. This retrospective cohort study, a total of 15,047 patients with confirmed Sepsis disease from 2008 to 2019 from the Medical Information Mart for Intensive Care IV (MIMIC-IV) v1.0 database. The MIMIC-IV database is a comprehensive, de-identified clinical dataset originating from the Beth Israel Deaconess Medical Center in Boston, it includes extensive data on intensive care unit (ICU) patients, such as vital signs, lab results, and medication orders, spanning multiple years, accessible to researchers through an application process. AG can be obtained by direct extraction in the MIMIC-IV database (itemid = 50,868 from the laboratory events table of mimic_hosp), inclusion of AG values for the first test on first day of ICU admission. The patients were grouped into quartiles according to the AG interquartile range. The primary outcome was the 28-day mortality. Multiple logistic regression analysis was used to calculate the odds ratio (OR), while accounting for potential confounders, and the robustness of the results were evaluated in subgroup analyses. Among the 15,047 patients included in this study, the average age was 65.9 ± 16.0 years, 42.5% were female, 66.1% were Caucasian, and the 28-day mortality rate was 17.9% (2686/15,047). Multiple logistic regression analysis revealed the 28-day mortality in every increase of AG (per SD mEq/L), there is an associated 1.2 times (OR 1.2, 95% CI 1.12-1.29, P < .001) increase. Increased 28-day mortality (OR 1.53, 95% confidence interval 1.29-1.81, P < .001) in the group with the AG (15-18 mEq/L), and (OR 1.69, 95% confidence interval 1.4-2.04, P < .001) in the group with the highest AG (≥18 mEq/L), AG (<12 mEq/L) as a reference group, in the fully adjusted model. In adult patients with sepsis, the early AG at the time of ICU admission is an independent risk factor for prognosis.

Development and application of a risk nomogram for the prediction of risk of carbapenem-resistant Acinetobacter baumannii infections in neuro-intensive care unit: a mixed method study

OBJECTIVE

This study aimed to develop and apply a nomogram with good accuracy to predict the risk of CRAB infections in neuro-critically ill patients. In addition, the difficulties and expectations of application such a tool in clinical practice was investigated.

METHODS

A mixed methods sequential explanatory study design was utilized. We first conducted a retrospective study to identify the risk factors for the development of CRAB infections in neuro-critically ill patients; and further develop and validate a nomogram predictive model. Then, based on the developed predictive tool, medical staff in the neuro-ICU were received an in-depth interview to investigate their opinions and barriers in using the prediction tool during clinical practice. The model development and validation is carried out by R. The transcripts of the interviews were analyzed by Maxqda.

RESULTS

In our cohort, the occurrence of CRAB infections was 8.63% (47/544). Multivariate regression analysis showed that the length of neuro-ICU stay, male, diabetes, low red blood cell (RBC) count, high levels of procalcitonin (PCT), and number of antibiotics ≥ 2 were independent risk factors for CRAB infections in neuro-ICU patients. Our nomogram model demonstrated a good calibration and discrimination in both training and validation sets, with AUC values of 0.816 and 0.875. Additionally, the model demonstrated good clinical utility. The significant barriers identified in the interview include "skepticism about the accuracy of the model", "delay in early prediction by the indicator of length of neuro-ICU stay", and "lack of a proper protocol for clinical application".

CONCLUSIONS

We established and validated a nomogram incorporating six easily accessed indicators during clinical practice (the length of neuro-ICU stay, male, diabetes, RBC, PCT level, and the number of antibiotics used) to predict the risk of CRAB infections in neuro-ICU patients. Medical staff are generally interested in using the tool to predict the risk of CRAB, however delivering clinical prediction tools in routine clinical practice remains challenging.

APASL clinical practice guidelines on the management of acute kidney injury in acute-on-chronic liver failure

Acute-on-chronic liver failure (ACLF) is a syndrome that is characterized by the rapid development of organ failures predisposing these patients to a high risk of short-term early death. The main causes of organ failure in these patients are bacterial infections and systemic inflammation, both of which can be severe. For the majority of these patients, a prompt liver transplant is still the only effective course of treatment. Kidneys are one of the most frequent extrahepatic organs that are affected in patients with ACLF, since acute kidney injury (AKI) is reported in 22.8-34% of patients with ACLF. Approach and management of kidney injury could improve overall outcomes in these patients. Importantly, patients with ACLF more frequently have stage 3 AKI with a low rate of response to the current treatment modalities. The objective of the present position paper is to critically review and analyze the published data on AKI in ACLF, evolve a consensus, and provide recommendations for early diagnosis, pathophysiology, prevention, and management of AKI in patients with ACLF. In the absence of direct evidence, we propose expert opinions for guidance in managing AKI in this very challenging group of patients and focus on areas of future research. This consensus will be of major importance to all hepatologists, liver transplant surgeons, and intensivists across the globe.

Development and validation of a nomogram for predicting in-hospital death in cirrhotic patients with acute kidney injury

BACKGROUND

The purpose of this study was to develop a nomogram for predicting in-hospital mortality in cirrhotic patients with acute kidney injury (AKI) in order to identify patients with a high risk of in-hospital death early.

METHODS

This study collected data on cirrhotic patients with AKI from 2008 to 2019 using the Medical Information Mart for Intensive Care IV. Multivariate logistic regression was used to identify confounding factors related to in-hospital mortality, which were then integrated into the nomogram. The concordance index (C-Index) was used to evaluate the accuracy of the model predictions. The area under the curve (AUC) and decision curve analysis (DCA) was used to assess the predictive performance and clinical utility of the nomogram.

RESULTS

The final study population included 886 cirrhotic patients with AKI, and 264 (29.8%) died in the hospital. After multivariate logistic regression, age, gender, cerebrovascular disease, heart rate, respiration rate, temperature, oxygen saturation, hemoglobin, blood urea nitrogen, serum creatinine, international normalized ratio, bilirubin, urine volume, and sequential organ failure assessment score were predictive factors of in-hospital mortality. In addition, the nomogram showed good accuracy in estimating the in-hospital mortality of patients. The calibration plots showed the best agreement with the actual presence of in-hospital mortality in patients. In addition, the AUC and DCA curves showed that the nomogram has good prediction accuracy and clinical value.

CONCLUSIONS

We have created a prognostic nomogram for predicting in-hospital death in cirrhotic patients with AKI, which may facilitate timely intervention to improve prognosis in these patients.

Association between glycemic variability and short-term mortality in patients with acute kidney injury: a retrospective cohort study of the MIMIC-IV database

Acute kidney injury (AKI) represents a significant challenge to global public health problem and is associated with poor outcomes. There is still considerable debate about the effect of mean blood glucose (MBG) and coefficient of variation (CV) of blood glucose on the short-term mortality of AKI patients. This retrospective cohort study aimed to explore the association between glycemic variability and short-term mortality in patients with AKI. Data from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database were analyzed, including 6,777 adult AKI patients. MBG and CV on the first day of ICU admission were calculated to represent the overall glycemic status and variability during the ICU stay in AKI patients. The primary outcome indicator was ICU 30-day mortality of AKI patients. Multivariate Cox regression analysis and smoothed curve fitting were used to assess the relationship between blood glucose levels and mortality. Eventually, the ICU 30-day mortality rate of AKI patients was 23.5%. The increased MBG and CV were significantly correlated with ICU 30-day mortality (hazards ratio (HR) = 1.20, 95% confidence interval (CI) 1.14-1.27; HR = 1.08, 95% CI 1.03-1.13). The smoothed curve fitting showed a U-shaped relationship between MBG on the first day of ICU admission and ICU 30-day mortality (inflection point = 111.3 mg/dl), while CV had a linear relationship with 30-day ICU mortality. Thus, we conclude that MBG and CV were significantly associated with short-term mortality in intensive care patients with AKI. Tighter glycemic control may be an effective measure to improve the prognosis of patients with AKI.

Predicting sepsis in-hospital mortality with machine learning: a multi-center study using clinical and inflammatory biomarkers

BACKGROUND

This study aimed to develop and validate an interpretable machine-learning model that utilizes clinical features and inflammatory biomarkers to predict the risk of in-hospital mortality in critically ill patients suffering from sepsis.

METHODS

We enrolled all patients diagnosed with sepsis in the Medical Information Mart for Intensive Care IV (MIMIC-IV, v.2.0), eICU Collaborative Research Care (eICU-CRD 2.0), and the Amsterdam University Medical Centers databases (AmsterdamUMCdb 1.0.2). LASSO regression was employed for feature selection. Seven machine-learning methods were applied to develop prognostic models. The optimal model was chosen based on its accuracy, F1 score and area under curve (AUC) in the validation cohort. Moreover, we utilized the SHapley Additive exPlanations (SHAP) method to elucidate the effects of the features attributed to the model and analyze how individual features affect the model's output. Finally, Spearman correlation analysis examined the associations among continuous predictor variables. Restricted cubic splines (RCS) explored potential non-linear relationships between continuous risk factors and in-hospital mortality.

RESULTS

3535 patients with sepsis were eligible for participation in this study. The median age of the participants was 66 years (IQR, 55-77 years), and 56% were male. After selection, 12 of the 45 clinical parameters collected on the first day after ICU admission remained associated with prognosis and were used to develop machine-learning models. Among seven constructed models, the eXtreme Gradient Boosting (XGBoost) model achieved the best performance, with an AUC of 0.94 and an F1 score of 0.937 in the validation cohort. Feature importance analysis revealed that Age, AST, invasive ventilation treatment, and serum urea nitrogen (BUN) were the top four features of the XGBoost model with the most significant impact. Inflammatory biomarkers may have prognostic value. Furthermore, SHAP force analysis illustrated how the constructed model visualized the prediction of the model.

CONCLUSIONS

This study demonstrated the potential of machine-learning approaches for early prediction of outcomes in patients with sepsis. The SHAP method could improve the interoperability of machine-learning models and help clinicians better understand the reasoning behind the outcome.

Association between acetaminophen administration and clinical outcomes in patients with sepsis admitted to the ICU: a retrospective cohort study

Background

Sepsis, affecting over 30 million people worldwide each year, is a key mortality risk factor in critically ill patients. There are significant regional discrepancies in its impact. Acetaminophen, a common over-the-counter drug, is often administered to control fever in suspected infection cases in intensive care units (ICUs). It is considered generally safe when used at therapeutic levels. Despite its widespread use, there's inconsistent research regarding its efficacy in sepsis management, which creates uncertainties for ICU doctors about its possible advantages or harm. To address this, we undertook a retrospective cohort study utilizing the MIMIC-IV database to examine the correlation between acetaminophen use and clinical outcomes in septic patients admitted to the ICU.

Methods

We gathered pertinent data on sepsis patients from the MIMIC-IV database. We used propensity score matching (PSM) to pair acetaminophen-treated patients with those who were not treated. We then used Cox Proportional Hazards models to examine the relationships between acetaminophen use and factors such as in-hospital mortality, 30-day mortality, hospital stay duration, and ICU stay length.

Results

The data analysis involved 22,633 sepsis patients. Post PSM, a total of 15,843 patients were matched; each patient not receiving acetaminophen treatment was paired with two patients who received it. There was a correlation between acetaminophen and a lower in-hospital mortality rate (HR 0.443; 95% CI 0.371-0.530; p < 0.001) along with 30-day mortality rate (HR 0.497; 95% CI 0.424-0.583; p < 0.001). Additionally, it correlated with a decrease in the duration of hospitalization [8.4 (5.0, 14.8) vs. 9.0 (5.1, 16.0), p < 0.001] and a shorter ICU stay [2.8 (1.5, 6.0) vs. 3.1 (1.7, 6.5); p < 0.05].

Conclusion

The use of acetaminophen may lower short-term mortality in critically ill patients with sepsis. To confirm this correlation, future research should involve multicenter randomized controlled trials.

Oral anticoagulants increased 30-day survival in sepsis patients complicated with atrial fibrillation: a retrospective analysis from MIMIC-IV database

Background

The severity of sepsis is associated with systemic clotting activation. Atrial fibrillation (AF) is the most commonly observed arrhythmia in patients with sepsis and can lead to a poor prognosis. The aim of this study is to elucidate the association between oral anticoagulants and survival from septic patients complicated with AF.

Methods

The data of 8,828 septic patients, including 2,955 AF and 5,873 without AF, were all originated from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. Patients with sepsis and AF are divided into OAC- group (n = 1,774) and OAC+ group (n = 1,181) based on OAC therapy. Septic patients with no AF were considered as the control group (n = 5,873, sepsis and no AF group). The main outcome endpoint was the survival rate of 30 day. The secondary outcome endpoint was the length of stay (LOS) from intensive care unit and hospital. Propensity score matching (PSM) was used to adjust the influence of superfluous factors, and a restricted mean survival time (RMST) analysis was used for calculating the benefit of survival time and survival rate. Analysis including univariate and multivariate logistic regression analysis was conducted to find prognosis-related predictors.

Results

After PSM, the OAC+group had a higher 30-day survival rate compared to the OAC- group (81.59% vs. 58.10%; P < 0.001) in the ICU. Despite the higher survival, the hospital LOS (14.65 days vs. 16.66 days; P = 0.15) and ICU LOS (6.93 days vs. 5.92 days; P = 0.02) were prolonged at OAC+ group than OAC- group. No difference was found in survival rate of 30 day between the sepsis patients using warfarin and patients using NOAC (85.60% vs. 79.84%, P = 0.12). The sepsis patients using warfarin had a prolonged LOS in ICU and hospital compared with the sepsis patients using NOAC. In the vasopressor subgroup, patients who received NOAC therapy were associated with a reduced 30-day survival rate (73.57% vs. 84.03%; P = 0.04) and reduced LOS in ICU and hospital than those on warfarin therapy.

Conclusion

This study demonstrated that oral anticoagulants may increase the 30-day survival rate of patients with sepsis and AF.

Construction and evaluation of neonatal respiratory failure risk prediction model for neonatal respiratory distress syndrome

BACKGROUND

Neonatal respiratory distress syndrome (NRDS) is a common respiratory disease in preterm infants, often accompanied by respiratory failure. The aim of this study was to establish and validate a nomogram model for predicting the probability of respiratory failure in NRDS patients.

METHODS

Patients diagnosed with NRDS were extracted from the MIMIC-iv database. The patients were randomly assigned to a training and a validation cohort. Univariate and stepwise Cox regression analyses were used to determine the prognostic factors of NRDS. A nomogram containing these factors was established to predict the incidence of respiratory failure in NRDS patients. The area under the receiver operating characteristic curve (AUC), receiver operating characteristic curve (ROC), calibration curves and decision curve analysis were used to determine the effectiveness of this model.

RESULTS

The study included 2,705 patients with NRDS. Univariate and multivariate stepwise Cox regression analysis showed that the independent risk factors for respiratory failure in NRDS patients were gestational age, pH, partial pressure of oxygen (PO2), partial pressure of carbon dioxide (PCO2), hemoglobin, blood culture, infection, neonatal intracranial hemorrhage, Pulmonary surfactant (PS), parenteral nutrition and respiratory support. Then, the nomogram was constructed and verified.

CONCLUSIONS

This study identified the independent risk factors of respiratory failure in NRDS patients and used them to construct and evaluate respiratory failure risk prediction model for NRDS. The present findings provide clinicians with the judgment of patients with respiratory failure in NRDS and help clinicians to identify and intervene in the early stage.

Cost-effectiveness of intravenous resuscitation fluids in sepsis patients: a patient-level data analysis in Jordan

AIM

Albumin role as fluid resuscitation in sepsis remains understudied in low- and middle-income countries. This study aimed to evaluate the cost-effectiveness of intravenous (IV) Albumin compared to Crystalloids in sepsis patients using patient-level data in Jordan.

METHODS

This was a retrospective cohort study of sepsis patients aged 18 or older admitted to intensive care units (ICU) at two major tertiary hospitals during the period 2018-2019. Patients information, type of IV fluid, and clinical outcomes were retrieved from medical records, and charges were retrieved from the billing system. A 90-day partitioned survival model with two health states (alive and dead) was constructed to estimate the survival of sepsis patients receiving either Albumin or Crystalloids as IV fluids for resuscitation. Overall survival was predicted by fitting a Weibull model on the patient-level data from the current study. To further validate the results, and to support the assessment of uncertainty, time-dependent transition probabilities of death at each cycle were estimated and used to construct a state-transition patient-level simulation model with 10,000 microsimulation trials. Adopting the healthcare system perspective, incremental cost-effectiveness ratios(ICERs) of Albumin versus Crystalloids were calculated in terms of the probability to be discharged alive from the ICU. Uncertainty was explored using probabilistic sensitivity analysis.

RESULTS

In the partitioned survival model, Albumin was associated with an incremental cost of $1,007 per incremental1% in the probability of being discharged alive from the ICU. In the state-transition patient-level simulation model, ICER was $1,268 per incremental 1% in the probability of being discharged alive. Probabilistic sensitivity analysis showed that Albumin was favored at thresholds >$800 per incremental 1%in the probability of being discharged alive from the ICU.

CONCLUSION

IV Albumin use in sepsis patients might not be cost-effective from the healthcare perspective of Jordan. This has important implications for policymakers to readdress Albumin prescribing practice in sepsis patients.

Clinical significance of hemoglobin level and blood transfusion therapy in elderly sepsis patients: A retrospective analysis

INTRODUCTION

The clinical significance of hemoglobin level and blood transfusion therapy in elderly sepsis patients remains controversial. The study investigated the relationship between mortality, hemoglobin levels, and blood transfusion in elderly sepsis patients.

METHODS

Elderly sepsis patients were included in the Marketplace for Medical Information in Intensive Care (MIMIC-IV) database. A multivariate regression model analyzed the relationship between the Hb level and the 28-day mortality risk. Logistic Multivariate analysis, Propensity Matching (PSM) analysis, an Inverse Probabilities Weighting (IPW) model and doubly robust estimation were applied to analyze the 28-day mortality risk between transfused and non-transfused patients in Hb at 7-8 g/dL, 8-9 g/dL, 9-10 g/dL, and 10-11 g/dL groups.

RESULTS

7473 elderly sepsis patients were enrolled in the study. The Hb level in the ICU and the 28-day mortality risk of patients with sepsis shared a non-linear relationship. The patients with Hb levels of <10 g/dL(p < 0.05) and > 15 g/dL(p < 0.05) within 24 h had a high mortality risk in multivariate analysis. In the Hb level 7-8 g/dL and 8-9 g/dL subgroup, the Multivariate analysis (p < 0.05), PSM (p < 0.05), IPW (p < 0.05) and doubly robust estimation (p < 0.05) suggested that blood transfusion could reduce the mortality risk. In the subgroup with a Hb level of 10-11 g/dL, IPW (p < 0.05) and doubly robust estimation (p < 0.05) suggested that blood transfusion could increase the mortality risk of elderly sepsis patients.

CONCLUSION

A non-linear relationship between the Hb level and the 28-day mortality risk and Hb levels of <10 g/dL and > 15 g/dL may increase the mortality risk, and blood transfusion with a Hb level of <9 g/dL may minimize mortality risk in elderly sepsis patients.

Fluid Resuscitation in Septic Patients

Sepsis is a life-threatening organ failure caused by a dysregulated response to infection. Fluid resuscitation and vasopressors are used to maintain systolic blood pressure and organ perfusion. Fluid resuscitation can be done with liberal or restricted fluids as well as colloids or crystalloid fluids. This review analyses the evidence for the use of liberal or restrictive fluids and colloids or crystalloids for the management of sepsis. A methodical search was conducted across PubMed, Cochrane Library, and ScienceDirect, and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines were followed for this study. Randomized controlled trials and retrospective observational studies were included in this study. Liberal and restrictive fluid strategies were found to be comparable in efficacy, but restrictive fluid regimens had the added benefit of a lower incidence of fluid overload. Balanced crystalloids were safer and more effective when compared to normal saline. Albumin replacement was found to be safe and showed efficacy in reducing mortality in patients with sepsis and septic shock.

Deep imputation of missing values in time series health data: A review with benchmarking

The imputation of missing values in multivariate time series (MTS) data is critical in ensuring data quality and producing reliable data-driven predictive models. Apart from many statistical approaches, a few recent studies have proposed state-of-the-art deep learning methods to impute missing values in MTS data. However, the evaluation of these deep methods is limited to one or two data sets, low missing rates, and completely random missing value types. This survey performs six data-centric experiments to benchmark state-of-the-art deep imputation methods on five time series health data sets. Our extensive analysis reveals that no single imputation method outperforms the others on all five data sets. The imputation performance depends on data types, individual variable statistics, missing value rates, and types. Deep learning methods that jointly perform cross-sectional (across variables) and longitudinal (across time) imputations of missing values in time series data yield statistically better data quality than traditional imputation methods. Although computationally expensive, deep learning methods are practical given the current availability of high-performance computing resources, especially when data quality and sample size are of paramount importance in healthcare informatics. Our findings highlight the importance of data-centric selection of imputation methods to optimize data-driven predictive models.

The predictive values of admission characteristics for 28-day all-cause mortality in septic patients with diabetes mellitus: a study from the MIMIC database

Background

Septic patients with diabetes mellitus (DM) are more venerable to subsequent complications and the resultant increase in associated mortality. Therefore, it is important to make tailored clinical decisions for this subpopulation at admission.

Method

Data from large-scale real-world databases named the Medical Information Mart for Intensive Care Database (MIMIC) were reviewed. The least absolute selection and shrinkage operator (LASSO) was performed with 10 times cross-validation methods to select the optimal prognostic factors. Multivariate COX regression analysis was conducted to identify the independent prognostic factors and nomogram construction. The nomogram was internally validated via the bootstrapping method and externally validated by the MIMIC III database with receiver operating characteristic (ROC), calibration curves, decision curve analysis (DCA), and Kaplan-Meier curves for robustness check.

Results

A total of 3,291 septic patients with DM were included in this study, 2,227 in the MIMIC IV database and 1,064 in the MIMIC III database, respectively. In the training cohort, the 28-day all-cause mortality rate is 23.9% septic patients with DM. The multivariate Cox regression analysis reveals age (hazard ratio (HR)=1.023, 95%CI: 1.016-1.031, p<0.001), respiratory failure (HR=1.872, 95%CI: 1.554-2.254, p<0.001), Sequential Organ Failure Assessment score (HR=1.056, 95%CI: 1.018-1.094, p=0.004); base excess (HR=0.980, 95%CI: 0.967-0.992, p=0.002), anion gap (HR=1.100, 95%CI: 1.080-1.120, p<0.001), albumin (HR=0.679, 95%CI: 0.574-0.802, p<0.001), international normalized ratio (HR=1.087, 95%CI: 1.027-1.150, p=0.004), red cell distribution width (HR=1.056, 95%CI: 1.021-1.092, p=0.001), temperature (HR=0.857, 95%CI: 0.789-0.932, p<0.001), and glycosylated hemoglobin (HR=1.358, 95%CI: 1.320-1.401, p<0.001) at admission are independent prognostic factors for 28-day all-cause mortality of septic patients with DM. The established nomogram shows satisfied accuracy and clinical utility with AUCs of 0.870 in the internal validation and 0.830 in the external validation cohort as well as 0.820 in the septic shock subpopulation, which is superior to the predictive value of the single SOFA score.

Conclusion

Our results suggest that admission characteristics show an optimal prediction value for short-term mortality in septic patients with DM. The established model can support intensive care unit physicians in making better initial clinical decisions for this subpopulation.

Clinical and economic outcomes associated with intravenous albumin fluid use in the intensive care unit: a retrospective cohort study

OBJECTIVES

This study was undertaken to evaluate the prescribing practice of albumin in the intensive care unit (ICU) and to compare the clinical and economic outcomes associated with intravenous (IV) albumin compared to crystalloids in the ICU.

METHODS

This was a retrospective cohort study of ICU adult patients admitted to King Abdullah University Hospital during 2018-2019. Patient demographics, clinical characteristics, and admission charges were retrieved from medical records and billing system. Survival analysis, multivariable regression models, and propensity score matching estimator were performed to evaluate the impact of IV resuscitation fluid types on the clinical and economic outcomes.

RESULTS

Albumin administration in the ICU was associated with significantly lower hazards of ICU death (HR = 0.57; P value <0.001), but without improving overall death probability compared to crystalloids. Albumin was associated with significant prolongation in the ICU length of stay (5.86 days; P value <0.001). Only 88 patients (24.3%) were prescribed albumin for Food and Drug Administration (FDA)-approved indications. Admission charges were significantly higher for patients treated with albumin (p value <0.001).

CONCLUSIONS

IV Albumin use in the ICU was not associated with significant improvement in clinical outcomes, but with a remarkable increase in economic burden. The majority of patients received albumin for non-FDA-approved indications.

DECREASE IN THE PLATELET-TO-LYMPHOCYTE RATIO IN DAYS AFTER ADMISSION FOR SEPSIS CORRELATES WITH IN-HOSPITAL MORTALITY

ABSTRACT

Background: A previous study has linked an increase in platelet-to-lymphocyte ratio (PLR) to a poor prognosis; however, the relationship between early change in PLR and outcomes in sepsis patients is unclear. Methods : The Medical Information Mart for Intensive Care IV database was for this retrospective cohort analysis on patients meeting the Sepsis-3 criteria. All the patients meet the Sepsis-3 criteria. The platelet-to-lymphocyte ratio (PLR) was calculated by dividing the platelet count by the lymphocyte count. We collected all PLR measurements that were available within 3 days of admission for analysis of longitudinal changes over time. Multivariable logistic regression analysis was used to determine the relationship between the baseline PLR and in-hospital mortality. After correcting for possible confounders, the generalized additive mixed model was used to examine the trends in PLR over time among survivors and nonsurvivors. Results: Finally, 3,303 patients were enrolled, and both low and high PLR levels were significantly associated with higher in-hospital mortality in the multiple logistic regression analysis (tertile 1: odds ratio, 1.240; 95% confidence interval, 0.981-1.568 and tertile 3: odds ratio, 1.410; 95% confidence interval, 1.120-1.776, respectively). The generalized additive mixed model results revealed that the PLR of the nonsurvival group declined faster than that of the survival group within 3 days after intensive care unit admission. After controlling for confounders, the difference between the two groups steadily decreased and increased by an average of 37.38 daily. Conclusions : There was a U-shaped relationship between the baseline PLR and in-hospital mortality of sepsis patients, and there was a significant difference between the nonsurvival and survival groups in the change in PLR over time. The early decrease in PLR was related to an increase in in-hospital mortality.

Machine learning algorithm to predict mortality in critically ill patients with sepsis-associated acute kidney injury

This study aimed to establish and validate a machine learning (ML) model for predicting in-hospital mortality in patients with sepsis-associated acute kidney injury (SA-AKI). This study collected data on SA-AKI patients from 2008 to 2019 using the Medical Information Mart for Intensive Care IV. After employing Lasso regression for feature selection, six ML approaches were used to build the model. The optimal model was chosen based on precision and area under curve (AUC). In addition, the best model was interpreted using SHapley Additive exPlanations (SHAP) values and Local Interpretable Model-Agnostic Explanations (LIME) algorithms. There were 8129 sepsis patients eligible for participation; the median age was 68.7 (interquartile range: 57.2-79.6) years, and 57.9% (4708/8129) were male. After selection, 24 of the 44 clinical characteristics gathered after intensive care unit admission remained linked with prognosis and were utilized developing ML models. Among the six models developed, the eXtreme Gradient Boosting (XGBoost) model had the highest AUC, at 0.794. According to the SHAP values, the sequential organ failure assessment score, respiration, simplified acute physiology score II, and age were the four most influential variables in the XGBoost model. Individualized forecasts were clarified using the LIME algorithm. We built and verified ML models that excel in early mortality risk prediction in SA-AKI and the XGBoost model performed best.

Retrospective analysis of the use of canine-specific albumin in 125 critically ill dogs

OBJECTIVE

To describe the clinical use of canine-specific albumin (CSA) in critically ill dogs, report adverse events, and evaluate measurable clinical effects of CSA administration.

DESIGN

Retrospective case series from 2019 to 2020.

SETTING

Large, urban, private-practice referral and emergency center.

ANIMALS

Consecutive sample of 125 client-owned dogs administered CSA transfusions.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

The conditions most commonly associated with the use of CSA were surgical (32/125) and nonsurgical (20/125) gastrointestinal disease. Both serum albumin and total plasma protein concentrations were significantly increased posttransfusion (P < 0.001), and 16% albumin transfusions produced the greatest magnitude increase in serum albumin (P = 0.0015). Concurrent crystalloid administration did not affect change in albumin. While there was no significant improvement in blood pressure seen in those patients that received albumin, a significant improvement in shock index was identified (P = 0.02). Adverse events were uncommon; however, 8 critically ill dogs died during CSA administration.

CONCLUSIONS

CSA appears to be a relatively safe alternative to synthetic colloids and complementary to crystalloids in critically ill patients. More concentrated solutions may be more effective in raising serum albumin concentration. Further investigation into the indications for and efficacy of CSA will continue to improve our knowledge of this blood product.

Preventative Management of Sepsis-Induced Acute Respiratory Distress Syndrome in the Geriatric Population

Sepsis and its treatment are the most common etiologies of acute respiratory distress syndrome (ARDS), which has a disturbing mortality rate. Sepsis management relies heavily on the introduction of resuscitative fluids. However, when fluids are paired with the circulating inflammatory mediators of sepsis, patients are prone to lung damage. Survivors of sepsis-induced ARDS become plagued with functional and/or psychological sequelae such as impaired memory, difficulty in concentrating, and decreased mental processing speed. Specific techniques can be implemented when diagnosing and treating elderly patients with sepsis to prevent the onset of ARDS, including bed elevation and early antibiotics. Additionally, albumin infusion may be beneficial; however, more research must be conducted. Finally, inflammatory mediators, including serum mannose biomarkers and extracellular histone therapy, show a promising avenue for future treatment. Although there is limited research on osteopathic manipulative medicine (OMT) on ARDS or sepsis-induced ARDS, OMT that focuses on alleviating rib and thoracic somatic dysfunctions has been used as an adjunct therapy to treat other respiratory diseases, such as pneumonia and chronic obstructive pulmonary disease (COPD). The results of these studies may garner interest in whether the use of OMT as an adjunct therapy may be beneficial for patients with ARDS or sepsis-induced ARDS. This paper is intended to review the current guidelines for sepsis and ARDS management in elderly patients to identify measures to prevent sepsis-induced ARDS.

Increased ICU mortality in septic shock patients with hypo- or hyper- serum osmolarity: A retrospective study

Background

While many factors that are associated with increased mortality in septic shock patients have been identified, the effects of serum osmolarity on the outcomes of ICU patients with septic shock have not yet been studied.

Methods

The present study was designed to examine the association of serum osmolarity with ICU 28-day mortality in ICU patients with septic shock. Adult patients diagnosed with septic shock from the MIMIC-IV database were selected in this study. The serum osmolarity was calculated synchronously according to the serum concentrations of Na⁺, K⁺, glucose, and urea nitrogen.

Results

In the present study, a significant difference was observed between the 28-day mortality of septic shock patients with hypo-osmolarity, hyper-osmolarity, and normal osmolarity (30.8%, 34.9%, and 23.0%, respectively, p < 0.001), which were detected at ICU admission. After propensity score matching (PSM) for basic characteristics, the relatively higher mortality was still observed in the hypo-osmolarity and hyper-osmolarity groups, compared to normal osmolarity group (30.6%, 30.0% vs. 21.7%, p = 0.009). Furthermore, we found that transforming the hyper-osmolarity into normal osmolarity by fluid therapy on day 2 and 3 decreased this mortality.

Conclusion

The serum osmolarity disorder is markedly associated with increased 28-day mortality in septic shock patients.

Impact on outcomes of measuring lactates prior to ICU in unselected heterogeneous critically ill patients: A propensity score analysis

BACKGROUND

Elevated blood lactate levels were reported as effective predictors of clinical outcome and mortality in ICU. However, there have been no studies simply comparing the timing of measuring lactates before vs. after ICU admission.

METHODS

A total of 19,226 patients with transfer time ≤ 24 hr were extracted from the Medical Information Mart for Intensive Care IV database (MIMIC-IV). After 1:1 propensity score matching, the patients were divided into two groups: measuring lactates within 3 hr before (BICU group, n = 4,755) and measuring lactate within 3 hr after ICU admission(AICU group, n = 4,755). The primary and secondary outcomes were hospital mortality, hospital 28-day mortality, ICU mortality, ICU length of stay (LOS), hospital LOS, and restricted mean survival time (RMST).

RESULTS

Hospital, hospital 28-day, and ICU mortality were significantly higher in AICU group (7.0% vs.9.8%, 6.7% vs. 9.4%, and 4.6% vs.6.7%, respectively, p<0.001 for all) Hospital LOS and ICU LOS were significantly longer in AICU group (8.4 days vs. 9.0 days and 3.0 days vs. 3.5 days, respectively, p<0.001 for both). After adjustment for predefined covariates, a significant association between the timing of measuring lactate and hospital mortality was observed in inverse probability treatment weight (IPTW) multivariate regression, doubly robust multivariate regression, and multivariate regression models (OR, 0.96 [95%CI, 0.95-0.97], OR 0.52 [95%CI, 0.46-0.60], OR 0.66 [95%CI, 0.56-0.78], respectively, p<0.001 for all), indicating the timing as a significant risk-adjusted factor for lower hospital mortality. The difference (BICU-AICU) of RMST at 28- days after ICU admission was 0.531 days (95%CI, 0.002-1.059, p<0.05). Placement of A-line and PA-catheter, administration of intravenous antibiotics, and bolus fluid infusion during the first 24-hr in ICU were significantly more frequent and faster in the BICU vs AICU group (67.6% vs. 51.3% and 126min vs.197min for A-line, 19.6% vs.13.2% and 182min vs. 274min for PA-catheter, 77.5% vs.67.6% and 109min vs.168min for antibiotics, and 57.6% vs.51.6% and 224min vs.278min for bolus fluid infusion, respectively, p<0.001 for all). Additionally, a significant indirect effect was observed in frequency (0.19879 [95% CI, 0.14061-0.25697] p<0.001) and time (0.07714 [95% CI, 0.22600-0.13168], p<0.01) of A-line replacement, frequency of placement of PA-catheter (0.05614 [95% CI, 0.04088-0.07140], p<0.001) and frequency of bolus fluid infusion (0.02193 [95%CI, 0.00303-0.04083], p<0.05).

CONCLUSIONS

Measuring lactates within 3 hr prior to ICU might be associated with lower hospital mortality in unselected heterogeneous critically ill patients with transfer time to ICU ≤ 24hr, presumably due to more frequent and faster therapeutic interventions.

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.

Effects of growth trajectory of shock index within 24 h on the prognosis of patients with sepsis

Background

Sepsis is a serious disease with high clinical morbidity and mortality. Despite the tremendous advances in medicine and nursing, treatment of sepsis remains a huge challenge. Our purpose was to explore the effects of shock index (SI) trajectory changes on the prognosis of patients within 24 h after the diagnosis of sepsis.

Methods

This study was based on Medical Information Mart for Intensive Care IV (MIMIC- IV). The effects of SI on the prognosis of patients with sepsis were investigated using C-index and restricted cubic spline (RCS). The trajectory of SI in 24 h after sepsis diagnosis was classified by latent growth mixture modeling (LGMM). Cox proportional hazard model, double robust analysis, and subgroup analysis were conducted to investigate the influence of SI trajectory on in-hospital death and secondary outcomes.

Results

A total of 19,869 patients were eventually enrolled in this study. C-index showed that SI had a prognostic value independent of Sequential Organ Failure Assessment for patients with sepsis. Moreover, the results of RCS showed that SI was a prognostic risk factor. LGMM divided SI trajectory into seven classes, and patients with sepsis in different classes had notable differences in prognosis. Compared with the SI continuously at a low level of 0.6, the SI continued to be at a level higher than 1.0, and the patients in the class whose initial SI was at a high level of 1.2 and then declined had a worse prognosis. Furthermore, the trajectory of SI had a higher prognostic value than the initial SI.

Conclusion

Both initial SI and trajectory of SI were found to be independent factors that affect the prognosis of patients with sepsis. Therefore, in clinical treatment, we should closely monitor the basic vital signs of patients and arrive at appropriate clinical decisions on basis of their change trajectory.

Early pulmonary artery catheterization is not associated with survival benefits in critically ill patients with cardiac disease: An analysis of the MIMIC-IV database

BACKGROUND

Many studies demonstrated no improved survival in patients with pulmonary artery catheter placement. However, no consistent conclusions have been drawn regarding the impact of pulmonary artery catheter in critically ill patients with heart disease. This study aimed to investigate the association of early pulmonary artery catheter use with 28-day mortality in that population.

METHODS

The Multiparameter Intelligent Monitoring in Intensive Care IV (MIMIC-IV) database, a single-center critical care database, was employed to investigate this issue. This study enrolled a total of 11,887 critically ill patients with cardiac disease with or without pulmonary artery catheter insertion. The primary outcome was 28-day mortality. The multivariate regression was modeled to examine the association between pulmonary artery catheter and outcomes. Additionally, we examined the effect modification by cardiac surgeries. Propensity score matching was conducted to validate our findings.

RESULTS

No improvement in 28-day mortality was observed among the pulmonary artery catheter group compared to the non-pulmonary artery catheter group (odds ratio 95% confidence interval: 1.18 [1.00-1.38], P = .049). When stratified by cardiac surgeries, the results were consistent. The patients in the pulmonary artery catheter group had fewer ventilation-free days and vasopressor-free days than those in the nonpulmonary artery catheter group after surgery stratification. In the surgical patients, pulmonary artery catheter insertion was not associated with the occurrence of acute kidney injury, and it was associated with a higher daily fluid input (mean difference 95% confidence interval: 0.13 [0.05-0.20], P = .001). In nonsurgical patients, the pulmonary artery catheter group had a higher risk of acute kidney injury occurrence (odds ratio 95% confidence interval: 1.94 [1.32-2.84], P = .001).

CONCLUSION

Early pulmonary artery catheter placement is not associated with survival benefits in critically ill patients with cardiac diseases, either in surgical or nonsurgical patients.

Infusion of Human Albumin on Acute Pancreatitis Therapy: New Tricks for Old Dog?

Objective: Human serum albumin (HSA) infusion is a common administration on acute pancreatitis therapy in the Intensive Care Unit (ICU), but its actual association with patients’ outcomes has not been confirmed. The study is aimed to determine whether the in-hospital prognosis of ICU patients with acute pancreatitis could benefit from HSA.

Methods: 950 acute pancreatitis patients diagnosed in 2008–2019 were extracted from the MIMIC-IV database as our primary study cohort. The primary outcome was in-hospital mortality. We also performed an external validation with a cohort of 104 acute pancreatitis patients after PSM matching from the eICU database.

Results: In MIMIC-IV, 228 acute pancreatitis patients received HSA infusion (Alb group) during their hospitalization, while 722 patients did not (non-Alb group). Patients in the Alb group presented a poorer survival curve than the non-Alb group, while this difference disappeared after PSM or IPTW matching (log-rank test: PSM: p = 0.660, IPTW: p = 0.760). After including covariates, no association was found between HSA infusion and patients’ in-hospital mortality before and after matching (original cohort: HR: 1.00, 95% CI: 0.66–1.52, p = 0.998). HSA infusion also did not benefit patients’ 28-days or ICU mortality, while it was significantly associated with a longer duration of hospital and ICU. In addition, the initial serum albumin levels, infections, the total amount, or the initial timing of infusion did not affect the conclusion. Similarly, in the eICU cohort, HSA infusion was still not a beneficial prognostic factor for patients’ in-hospital prognosis (p = 0.087).

Conclusion: Intravenous human serum albumin infusion could not benefit acute pancreatitis patients’ in-hospital prognosis and was associated with prolonged hospital and ICU duration.

Interpretable Machine Learning for Early Prediction of Prognosis in Sepsis: A Discovery and Validation Study

Introduction

This study aimed to develop and validate an interpretable machine-learning model based on clinical features for early predicting in-hospital mortality in critically ill patients with sepsis.

Methods

We enrolled all patients with sepsis in the Medical Information Mart for Intensive Care IV (MIMIC-IV, v.1.0) database from 2008 to 2019. Lasso regression was used for feature selection. Seven machine-learning methods were applied to develop the models. The best model was selected based on its accuracy and area under curve (AUC) in the validation cohort. Furthermore, we employed the SHapley Additive exPlanations (SHAP) method to illustrate the effects of the features attributed to the model, and to analyze how the individual features affect the output of the model, and to visualize the Shapley value for a single individual.

Results

In total, 8,817 patients with sepsis were eligible for participation, the median age was 66.8 years (IQR, 55.9–77.1 years), and 3361 of 8817 participants (38.1%) were women. After selection, 25 of a total 57 clinical parameters collected on day 1 after ICU admission remained associated with prognosis and were used for developing the machine-learning models. Among seven constructed models, the eXtreme Gradient Boosting (XGBoost) model achieved the best performance with an AUC of 0.884 and an accuracy of 89.5% in the validation cohort. Feature importance analysis showed that Glasgow Coma Scale (GCS) score, blood urea nitrogen, respiratory rate, urine output, and age were the top 5 features of the XGBoost model with the greatest impact. Furthermore, SHAP force analysis illustrated how the constructed model visualized the individualized prediction of death.

Conclusions

We have demonstrated the potential of machine-learning approaches for predicting outcome early in patients with sepsis. The SHAP method could improve the interpretability of machine-learning models and help clinicians better understand the reasoning behind the outcome.

Graphical abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s40121-022-00628-6.

[Fluid therapy in sepsis and septic shock]

Fluid resuscitation is a crucial part of the treatment of hypotension and shock of any etiology. Particularly in septic shock, it is an essential element of the initial care bundle. Like all treatments in sepsis, it is also subject to multiple controversies: what type of fluid, how much, how long to administer it, potential risks, toxicity? The main guideline, the Surviving Sepsis Campaign, continues to indicate crystalloids as the main fluid in resuscitation. But the possibility of crystalloids balanced on 0.9% saline or combined use with albumin in the resuscitation of the septic patient is still under debate. This is probably another point where we should always consider individualizing both the type and amount of fluids to be administered in both the initial and maintenance phases of the management of sepsis and septic shock.

Acute kidney injury-attributable mortality in critically ill patients with sepsis

Background

To assess whether acute kidney injury (AKI) is independently associated with hospital mortality in ICU patients with sepsis, and estimate the excess AKI-related mortality attributable to AKI.

Methods

We analyzed adult patients from two distinct retrospective critically ill cohorts: (1) Medical Information Mart for Intensive Care IV (MIMIC IV; n = 15,610) cohort and (2) Wenzhou (n = 1,341) cohort. AKI was defined by Kidney Disease: Improving Global Outcomes (KDIGO) criteria. We applied multivariate logistic and linear regression models to assess the hospital and ICU mortality, hospital length-of-stay (LOS), and ICU LOS. The excess attributable mortality for AKI in ICU patients with sepsis was further evaluated.

Results

AKI occurred in 5,225 subjects in the MIMIC IV cohort (33.5%) and 494 in the Wenzhou cohort (36.8%). Each stage of AKI was an independent risk factor for hospital mortality in multivariate logistic regression after adjusting for baseline illness severity. The excess attributable mortality for AKI was 58.6% (95% CI [46.8%-70.3%]) in MIMIC IV and 44.6% (95% CI [12.7%-76.4%]) in Wenzhou. Additionally, AKI was independently associated with increased ICU mortality, hospital LOS, and ICU LOS.

Conclusion

Acute kidney injury is an independent risk factor for hospital and ICU mortality, as well as hospital and ICU LOS in critically ill patients with sepsis. Thus, AKI is associated with excess attributable mortality.

Early Combination of Albumin With Crystalloid Administration Might Reduce Mortality in Patients With Cardiogenic Shock: An Over 10-Year Intensive Care Survey

BACKGROUND

In updated international guidelines, combined albumin resuscitation is recommended for septic shock patients who receive large volumes of crystalloids, but minimal data exist on albumin use and the optimal timing in those with cardiogenic shock (CS). The objective of this study was to evaluate the relationship between resuscitation with a combination of albumin within 24 h and 30-day mortality in CS patients.

METHODS

We screened patients with CS from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. Multivariable Cox proportional hazards models and propensity score matching (PSM) were employed to explore associations between combined albumin resuscitation within 24 h and 30-day mortality in CS. Models adjusted for CS considered potential confounders. E-value analysis suggested for unmeasured confounding.

RESULTS

We categorized 1,332 and 254 patients into crystalloid-only and early albumin combination groups, respectively. Patients who received the albumin combination had decreased 30-day and 60-day mortality (21.7 vs. 32.4% and 25.2 vs. 34.2%, respectively, P < 0.001), and the results were robust after PSM (21.3 vs. 44.7% and 24.9 vs. 47.0%, respectively, P < 0.001) and following E-value. Stratified analysis showed that only ≥ 60 years old patients benefited from administration early albumin. In the early albumin combination group, the hazard ratios (HRs) of different adjusted covariates remained significant (HRs of 0.45-0.64, P < 0.05). Subgroup analysis showed that resuscitation with combination albumin was significantly associated with reduced 30-day mortality in patients with maximum sequential organ failure assessment score≥10, with acute myocardial infarction, without an Impella or intra-aortic balloon pump, and with or without furosemide and mechanical ventilation (HRs of 0.49, 0.58, 0.65, 0.40, 0.65 and 0.48, respectively; P < 0.001).

CONCLUSION

This study found, compared with those given crystalloid-only, resuscitation with combination albumin within 24 h is associated with lower 30-day mortality of CS patients aged≥60. The results should be conducted to further assess in randomized controlled trials.

Influence of ambulatory blood pressure-related indicators within 24 h on in-hospital death in sepsis patients

Background: Sepsis is a serious public health problem worldwide. Blood pressure is one of the indicators that is closely monitored in intensive-care units, and it reflects complex interactions between the internal cardiovascular control mechanism and the external environment. We aimed to determine the impact of indicators related to the ambulatory blood pressure on the prognosis of sepsis patients. Methods: This retrospective study was based on the Medical Information Mart for Intensive Care IV database. Relevant information about sepsis patients was extracted according to specific inclusion and exclusion criteria. Examined parameters included the average blood pressure, blood pressure variability (BPV), and circadian rhythm, and the study outcome was in-hospital death. We investigated the effects of these indicators on the risk of in-hospital death among sepsis patients using Cox proportional-hazards models, restricted cubic splines analysis, and subgroup analysis. Results: This study enrolled 10,316 sepsis patients, among whom 2,117 died during hospitalization. All parameters except the nighttime variation coefficient of the diastolic blood pressure (DBP) were associated with in-hospital death of sepsis patients. All parameters except for fluctuations in DBP exhibited nonlinear correlations with the outcome. The subgroup analysis revealed that some of the examined parameters were associated with in-hospital death only in certain subgroups. Conclusion: Indicators related to the ambulatory blood pressure within 24 h are related to the prognosis of sepsis patients. When treating sepsis, in addition to blood pressure, attention should also be paid to BPV and the circadian rhythm in order to improve the prognosis and the survival rate.

Analysis of the correlation between the longitudinal trajectory of SOFA scores and prognosis in patients with sepsis at 72 hour after admission based on group trajectory modeling

Background

To identify the distinct trajectories of the Sequential Organ Failure Assessment (SOFA) scores at 72 h for patients with sepsis in the Medical Information Mart for Intensive Care (MIMIC)-IV database and determine their effects on mortality and adverse clinical outcomes.

Methods

A retrospective cohort study was carried out involving patients with sepsis from the MIMIC-IV database. Group-based trajectory modeling (GBTM) was used to identify the distinct trajectory groups for the SOFA scores in patients with sepsis in the intensive care unit (ICU). The Cox proportional hazards regression model was used to investigate the relationship between the longitudinal change trajectory of the SOFA score and mortality and adverse clinical outcomes.

Results

A total of 16,743 patients with sepsis were included in the cohort. The median survival age was 66 years (interquartile range: 54-76 years). The 7-day and 28-day in-hospital mortality were 6.0% and 17.6%, respectively. Five different trajectories of SOFA scores according to the model fitting standard were determined: group 1 (32.8%), group 2 (30.0%), group 3 (17.6%), group 4 (14.0%) and group 5 (5.7%). Univariate and multivariate Cox regression analyses showed that, for different clinical outcomes, trajectory group 1 was used as the reference, while trajectory groups 2-5 were all risk factors associated with the outcome (P < 0.001). Subgroup analysis revealed an interaction between the two covariates of age and mechanical ventilation and the different trajectory groups of patients' SOFA scores (P < 0.05).

Conclusion

This approach may help identify various groups of patients with sepsis, who may be at different levels of risk for adverse health outcomes, and provide subgroups with clinical importance.

Influence of the trajectory of the urine output for 24 h on the occurrence of AKI in patients with sepsis in intensive care unit

BACKGROUND

Sepsis-associated acute kidney injury (S-AKI) is a common and life-threatening complication in hospitalized and critically ill patients. This condition is an independent cause of death. This study was performed to investigate the correlation between the trajectory of urine output within 24 h and S-AKI.

METHODS

Patients with sepsis were studied retrospectively based on the Medical Information Mart for Intensive Care IV. Latent growth mixture modeling was used to classify the trajectory of urine output changes within 24 h of sepsis diagnosis. The outcome of this study is AKI that occurs 24 h after sepsis. Cox proportional hazard model, Fine-Gray subdistribution proportional hazard model, and doubly robust estimation method were used to explore the risk of AKI in patients with different trajectory classes.

RESULTS

A total of 9869 sepsis patients were included in this study, and their 24-h urine output trajectories were divided into five classes. The Cox proportional hazard model showed that compared with class 1, the HR (95% CI) values for classes 3, 4, and 5 were 1.460 (1.137-1.875), 1.532 (1.197-1.961), and 2.232 (1.795-2.774), respectively. Competing risk model and doubly robust estimation methods reached similar results.

CONCLUSIONS

The trajectory of urine output within 24 h of sepsis patients has a certain impact on the occurrence of AKI. Therefore, in the early treatment of sepsis, close attention should be paid to changes in the patient's urine output to prevent the occurrence of S-AKI.

Preoperative Glucose-to-Lymphocyte Ratio is an Independent Predictor for Acute Kidney Injury After Cardiac Surgery in Patients in Intensive Care Unit

Background

We aimed to investigate the association between preoperative glucose-to-lymphocyte ratio (GLR) and cardiac surgery associated with acute kidney injury (CSA-AKI) in patients in the intensive care unit (ICU).

Methods

The Medical Information Mart for Intensive Care IV (MIMIC-IV version 1.0) database was used to identify adults' patients who performed cardiac surgery during ICU stay. The primary outcome was the development of AKI based on the KDIGO criteria. Multivariable logistic regression was applied to investigate the association between GLR and clinical outcomes, and propensity score matching (PSM) and inverse probability of treatment weighting (IPTW) were also used to validate our findings.

Results

The optimal cut-off value for GLR was 1.28. Among the 7181 patients who conducted cardiac surgery, 2072 high-GLR group (≥1.28) patients and 2072 low-GLR group (<1.28) patients, had similar propensity scores were included in this study. After matching, the high-GLR group had a significantly higher incidence of AKI (odds ratio, OR, 3.28, 95% confidence index, 95% CI, 2.81-3.84, P <0.001) even after adjustment for confounding factors. Moreover, the performance of GLR was superior to that of SOFA scores and GLR plus clinical model could add more net benefit for CSA-AKI than clinical model alone.

Conclusion

Preoperative GLR could serve as a good predictor for CSA-AKI in patients in ICU.