Quadratic function between arterial partial oxygen pressure and mortality risk in sepsis patients: an interaction with simplified acute physiology score

Oxygen therapy is widely used in emergency and critical care settings, while there is little evidence on its real therapeutic effect. The study aimed to explore the impact of arterial oxygen partial pressure (PaO2) on clinical outcomes in patients with sepsis. A large clinical database was employed for the study. Subjects meeting the diagnostic criteria of sepsis were eligible for the study. All measurements of PaO2 were extracted. The primary endpoint was death from any causes during hospital stay. Survey data analysis was performed by using individual ICU admission as the primary sampling unit. Quadratic function was assumed for PaO2 and its interaction with other covariates were explored. A total of 199,125 PaO2 samples were identified for 11,002 ICU admissions. Each ICU stay comprised 18 PaO2 samples in average. The fitted multivariable model supported our hypothesis that the effect of PaO2 on mortality risk was in quadratic form. There was significant interaction between PaO2 and SAPS-I (p = 0.007). Furthermore, the main effect of PaO2 on SOFA score was nonlinear. The study shows that the effect of PaO2 on mortality risk is in quadratic function form, and there is significant interaction between PaO2 and severity of illness.

Development and Validation of a Sepsis Mortality Risk Score for Sepsis-3 Patients in Intensive Care Unit

Background: Many severity scores are widely used for clinical outcome prediction for critically ill patients in the intensive care unit (ICU). However, for patients identified by sepsis-3 criteria, none of these have been developed. This study aimed to develop and validate a risk stratification score for mortality prediction in sepsis-3 patients. Methods: In this retrospective cohort study, we employed the Medical Information Mart for Intensive Care III (MIMIC III) database for model development and the eICU database for external validation. We identified septic patients by sepsis-3 criteria on day 1 of ICU entry. The Least Absolute Shrinkage and Selection Operator (LASSO) technique was performed to select predictive variables. We also developed a sepsis mortality prediction model and associated risk stratification score. We then compared model discrimination and calibration with other traditional severity scores. Results: For model development, we enrolled a total of 5,443 patients fulfilling the sepsis-3 criteria. The 30-day mortality was 16.7%. With 5,658 septic patients in the validation set, there were 1,135 deaths (mortality 20.1%). The score had good discrimination in development and validation sets (area under curve: 0.789 and 0.765). In the validation set, the calibration slope was 0.862, and the Brier value was 0.140. In the development dataset, the score divided patients according to mortality risk of low (3.2%), moderate (12.4%), high (30.7%), and very high (68.1%). The corresponding mortality in the validation dataset was 2.8, 10.5, 21.1, and 51.2%. As shown by the decision curve analysis, the score always had a positive net benefit. Conclusion: We observed moderate discrimination and calibration for the score termed Sepsis Mortality Risk Score (SMRS), allowing stratification of patients according to mortality risk. However, we still require further modification and external validation.

Prediction model for critically ill patients with acute respiratory distress syndrome

BACKGROUND AND OBJECTIVES

Acute respiratory distress syndrome (ARDS) is a major cause respiratory failure in intensive care unit (ICU). Early recognition of patients at high risk of death is of vital importance in managing them. The aim of the study was to establish a prediction model by using variables that were readily available in routine clinical practice.

METHODS

The study was a secondary analysis of data obtained from the NHLBI Biologic Specimen and Data Repository Information Coordinating Center. Patients were enrolled between August 2007 and July 2008 from 33 hospitals. Demographics and laboratory findings were extracted from dataset. Univariate analyses were performed to screen variables with p<0.3. Then these variables were subject to automatic stepwise forward selection with significance level of 0.1. Interaction terms and fractional polynomials were examined for variables in the main effect model. Multiple imputations and bootstraps procedures were used to obtain estimations of coefficients with better external validation. Overall model fit and logistic regression diagnostics were explored.

MAIN RESULT

A total of 282 ARDS patients were included for model development. The final model included eight variables without interaction terms and non-linear functions. Because the variable coefficients changed substantially after exclusion of most poorly fitted and influential subjects, we estimated the coefficient after exclusion of these outliers. The equation for the fitted model was: g(Χ)=0.06×age(in years)+2.23(if on vasopressor)+1.37×potassium (mmol/l)-0.007×platelet count (×109)+0.03×heart rate (/min)-0.29×Hb(g/dl)-0.67×T(°C)+0.01×PaO_2+13, and the probability of death π(Χ)=eg(Χ)/(1+eg(Χ)).

CONCLUSION

The study established a prediction model for ARDS patients requiring mechanical ventilation. The model was examined with rigorous methodology and can be used for risk stratification in ARDS patients.

Use of the PiCCO system in critically ill patients with septic shock and acute respiratory distress syndrome: a study protocol for a randomized controlled trial

BACKGROUND

Hemodynamic monitoring is very important in critically ill patients with shock or acute respiratory distress syndrome(ARDS). The PiCCO (Pulse index Contour Continuous Cardiac Output, Pulsion Medical Systems, Germany) system has been developed and used in critical care settings for several years. However, its impact on clinical outcomes remains unknown.

METHODS/DESIGN

The study is a randomized controlled multi-center trial. A total of 708 patients with ARDS, septic shock or both will be included from January 2012 to January 2014. Subjects will be randomized to receive PiCCO monitoring or not. Our primary end point is 30-day mortality, and secondary outcome measures include ICU length of stay, days on mechanical ventilation, days of vasoactive agent support, ICU-free survival days during a 30-day period, mechanical-ventilation-free survival days during a 30-day period, and maximum SOFA score during the first 7 days.

DISCUSSION

We investigate whether the use of PiCCO monitoring will improve patient outcomes in critically ill patients with ARDS or septic shock. This will provide additional data on hemodynamic monitoring and help clinicians to make decisions on the use of PiCCO.

TRIAL REGISTRATION

http://www.clinicaltrials.gov NCT01526382.

Effectiveness of anisodamine for the treatment of critically ill patients with septic shock: a multicentre randomized controlled trial

BACKGROUND

Septic shock is characterized by an uncontrolled inflammatory response and microcirculatory dysfunction. There is currently no specific agent for treating septic shock. Anisodamine is an agent extracted from traditional Chinese medicine with potent anti-inflammatory effects. However, its clinical effectiveness remains largely unknown.

METHODS

In a multicentre, open-label trial, we randomly assigned adults with septic shock to receive either usual care or anisodamine (0.1-0.5 mg per kilogram of body weight per hour), with the anisodamine doses adjusted by clinicians in accordance with the patients' shock status. The primary end point was death on hospital discharge. The secondary end points were ventilator-free days at 28 days, vasopressor-free days at 28 days, serum lactate and sequential organ failure assessment (SOFA) score from days 0 to 6. The differences in the primary and secondary outcomes were compared between the treatment and usual care groups with the χ2 test, Student's t test or rank-sum test, as appropriate. The false discovery rate was controlled for multiple testing.

RESULTS

Of the 469 patients screened, 355 were assigned to receive the trial drug and were included in the analyses-181 patients received anisodamine, and 174 were in the usual care group. We found no difference between the usual care and anisodamine groups in hospital mortality (36% vs. 30%; p = 0.348), or ventilator-free days (median [Q1, Q3], 24.4 [5.9, 28] vs. 26.0 [8.5, 28]; p = 0.411). The serum lactate levels were significantly lower in the treated group than in the usual care group after day 3. Patients in the treated group were less likely to receive vasopressors than those in the usual care group (OR [95% CI] 0.84 [0.50, 0.93] for day 5 and 0.66 [0.37, 0.95] for day 6).

CONCLUSIONS

There is no evidence that anisodamine can reduce hospital mortality among critically ill adults with septic shock treated in the intensive care unit. Trial registration ClinicalTrials.gov ( NCT02442440 ; Registered on 13 April 2015).

Case-crossover design and its implementation in R

Case-crossover design is a variation of case-control design that it employs persons' history periods as controls. Case-crossover design can be viewed as the hybrid of case-control study and crossover design. Characteristic confounding that is constant within one person can be well controlled with this method. The relative risk and odds ratio, as well as their 95% confidence intervals (CIs), can be estimated using Cochran-Mantel-Haenszel method. R codes for the calculation are provided in the main text. Readers may adapt these codes to their own task. Conditional logistic regression model is another way to estimate odds ratio of the exposure. Furthermore, it allows for incorporation of other time-varying covariates that are not constant within subjects. The model fitting per se is not technically difficult because there is well developed statistical package. However, it is challenging to convert original dataset obtained from case report form to that suitable to be passed to clogit() function. R code for this task is provided and explained in the text.

The top 2,000 cited articles in critical care medicine: a bibliometric analysis

BACKGROUND

The bibliometric analysis has been performed on several topics in critical care medicine (CCM) focusing on top 100 cited articles, but the analysis on CCM literature as a whole is missing. The present study aimed to perform a complete bibliometric analysis in the field of CCM.

METHODS

An electronic search of the Scopus database was performed on Feb 13, 2018. The search strategy involved core terms related to CCM. The top 2,000 most cited articles in the field of CCM were included in the analysis. Descriptive statistics on these top-cited articles, country distributions, and journals are reported. Individual author's productivity was assessed with the Lotka's law. Co-occurrence of keywords was visualized with the Fruchterman-Reingold layout. The Walktrap algorithm was employed for clustering analysis.

RESULTS

A total of 2,000 documents were included in the analysis with median citations of 386 times [interquartile range (IQR): 308-562 times]. The most cited article was the original paper that described the Acute Physiology and Chronic Health Evaluation (APACHE) II score. The included articles were published in 411 journals. The median number of documents published in one journal was 1, and the mean number was 4.9, indicating a skewed distribution. The maximum number of publications was 217 in CCM. Author's productivity profile was significantly different from the Lotka's law (P=0.001), with n and C values of 2.8 and 0.52, respectively. Fruchterman-Reingold network plot showed that studies involving human subject were the most common literature type. Sepsis was a major research topic that co-occurred with keywords such as disease severity, nonhuman, risk assessment and practice guideline.

CONCLUSIONS

The study performed bibliometric analyses of 2,000 top-cited articles in CCM. The most cited article was the one which developed the APACHE II score. Author's productivity was significantly different from the Lotka's law.

APACHE III Outcome Prediction in Patients Admitted to the Intensive Care Unit with Sepsis Associated Acute Lung Injury

BACKGROUND AND OBJECTIVE

Acute Physiology and Chronic Health Evaluation (APACHE) III score has been widely used for prediction of clinical outcomes in mixed critically ill patients. However, it has not been validated in patients with sepsis-associated acute lung injury (ALI). The aim of the study was to explore the calibration and predictive value of APACHE III in patients with sepsis-associated ALI.

METHOD

The study was a secondary analysis of a prospective randomized controlled trial investigating the efficacy of rosuvastatin in sepsis-associated ALI (Statins for Acutely Injured Lungs from Sepsis, SAILS). The study population was sepsis-related ALI patients. The primary outcome of the current study was the same as in the original trial, 60-day in-hospital mortality, defined as death before hospital discharge, censored 60 days after enrollment. Discrimination of APACHE III was assessed by calculating the area under the receiver operating characteristic (ROC) curve (AUC) with its 95% CI. Hosmer-Lemeshow goodness-of-fit statistic was used to assess the calibration of APACHE III. The Brier score was reported to represent the overall performance of APACHE III in predicting outcome.

MAIN RESULTS

A total of 745 patients were included in the study, including 540 survivors and 205 non-survivors. Non-survivors were significantly older than survivors (59.71 ± 16.17 vs 52.00 ± 15.92 years, p < 0.001). The primary causes of ALI were also different between survivors and non-survivors (p = 0.017). Survivors were more likely to have the cause of sepsis than non-survivors (21.2% vs. 15.1%). APACHE III score was higher in non-survivors than in survivors (106.72 ± 27.30 vs. 88.42 ± 26.86; p < 0.001). Discrimination of APACHE III to predict mortality in ALI patients was moderate with an AUC of 0.68 (95% confidence interval: 0.64-0.73).

CONCLUSION

this study for the first time validated the discrimination of APACHE III in sepsis associated ALI patients. The result shows that APACHE III score has moderate predictive value for in-hospital mortality among adults with sepsis-associated acute lung injury.

Individualized Mechanical power-based ventilation strategy for acute respiratory failure formalized by finite mixture modeling and dynamic treatment regimen

BACKGROUND

Mechanical ventilation (MV) is the key to the successful treatment of acute respiratory failure (ARF) in the intensive care unit (ICU). The study aims to formalize the concept of individualized MV strategy with finite mixture modeling (FMM) and dynamic treatment regime (DTR).

METHODS

ARF patients requiring MV for over 48 h from 2008 to 2019 were included. FMM was conducted to identify classes of ARF. Static and dynamic mechanical power (MP_static and MP_dynamic) and relevant clinical variables were calculated/collected from hours 0 to 48 at an interval of 8 h.Δ M P was calculated as the difference between actual and optimal MP.

FINDINGS

A total of 8768 patients were included for analysis with a mortality rate of 27%. FFM identified three classes of ARF, namely, the class 1 (baseline), class 2 (critical) and class 3 (refractory respiratory failure). The effect size of MP_static on mortality is the smallest in class 1 (HR for every 5 Joules/min increase: 1.29; 95% CI: 1.15 to 1.45; p < 0.001) and the largest in class 3 (HR for every 5 Joules/min increase: 1.83; 95% CI: 1.52 to 2.20; p < 0.001).

INTERPRETATION

MP has differing therapeutic effects for subtypes of ARF. Optimal MP estimated by DTR model may help to improve survival outcome.

FUNDING

The study was funded by Health Science and Technology Plan of Zhejiang Province (2021KY745), Key Research & Development project of Zhejiang Province (2021C03071) and Yilu "Gexin" - Fluid Therapy Research Fund Project (YLGX-ZZ-2,020,005).

Structural equation modeling in the context of clinical research

Structural equation modeling (SEM) has been widely used in economics, sociology and behavioral science. However, its use in clinical medicine is quite limited, probably due to technical difficulties. Because SEM is particularly suitable for analysis of complex relationships among observed variables, it must have potential applications to clinical medicine. The article introduces basic ideas of SEM in the context of clinical medicine. A simulated dataset is employed to show how to do model specification, model fit, visualization and assessment of goodness-of-fit. The first example fits a SEM with continuous outcome variable using sem() function, and the second explores the binary outcome variable using lavaan() function.

Missing data exploration: highlighting graphical presentation of missing pattern

Functions shipped with R base can fulfill many tasks of missing data handling. However, because the data volume of electronic medical record (EMR) system is always very large, more sophisticated methods may be helpful in data management. The article focuses on missing data handling by using advanced techniques. There are three types of missing data, that is, missing completely at random (MCAR), missing at random (MAR) and not missing at random (NMAR). This classification system depends on how missing values are generated. Two packages, Multivariate Imputation by Chained Equations (MICE) and Visualization and Imputation of Missing Values (VIM), provide sophisticated functions to explore missing data pattern. In particular, the VIM package is especially helpful in visual inspection of missing data. Finally, correlation analysis provides information on the dependence of missing data on other variables. Such information is useful in subsequent imputations.

Identification of risk factors for mortality associated with COVID-19

OBJECTIVES

Coronavirus Disease 2019 (COVID-19) has become a pandemic outbreak. Risk stratification at hospital admission is of vital importance for medical decision making and resource allocation. There is no sophisticated tool for this purpose. This study aimed to develop neural network models with predictors selected by genetic algorithms (GA).

METHODS

This study was conducted in Wuhan Third Hospital from January 2020 to March 2020. Predictors were collected on day 1 of hospital admission. The primary outcome was the vital status at hospital discharge. Predictors were selected by using GA, and neural network models were built with the cross-validation method. The final neural network models were compared with conventional logistic regression models.

RESULTS

A total of 246 patients with COVID-19 were included for analysis. The mortality rate was 17.1% (42/246). Non-survivors were significantly older (median (IQR): 69 (57, 77) vs. 55 (41, 63) years; p < 0.001), had higher high-sensitive troponin I (0.03 (0, 0.06) vs. 0 (0, 0.01) ng/L; p < 0.001), C-reactive protein (85.75 (57.39, 164.65) vs. 23.49 (10.1, 53.59) mg/L; p < 0.001), D-dimer (0.99 (0.44, 2.96) vs. 0.52 (0.26, 0.96) mg/L; p < 0.001), and α-hydroxybutyrate dehydrogenase (306.5 (268.75, 377.25) vs. 194.5 (160.75, 247.5); p < 0.001) and a lower level of lymphocyte count (0.74 (0.41, 0.96) vs. 0.98 (0.77, 1.26) × 109/L; p < 0.001) than survivors. The GA identified a 9-variable (NNet1) and a 32-variable model (NNet2). The NNet1 model was parsimonious with a cost on accuracy; the NNet2 model had the maximum accuracy. NNet1 (AUC: 0.806; 95% CI [0.693-0.919]) and NNet2 (AUC: 0.922; 95% CI [0.859-0.985]) outperformed the linear regression models.

CONCLUSIONS

Our study included a cohort of COVID-19 patients. Several risk factors were identified considering both clinical and statistical significance. We further developed two neural network models, with the variables selected by using GA. The model performs much better than the conventional generalized linear models.

Calcium supplementation improves clinical outcome in intensive care unit patients: a propensity score matched analysis of a large clinical database MIMIC-II

Observational studies have linked hypocalcemia with adverse clinical outcome in critically ill patients. However, calcium supplementation has never been formally investigated for its beneficial effect in critically ill patients. To investigate whether calcium supplementation can improve 28-day survival in adult critically ill patients. Secondary analysis of a large clinical database consisting over 30,000 critical ill patients was performed. Multivariable analysis was performed to examine the independent association of calcium supplementation and 28-day morality. Furthermore, propensity score matching technique was employed to investigate the role of calcium supplementation in improving survival.

INTERVENTION

none. Primary outcome was the 28-day mortality. 90-day mortality was used as secondary outcome. A total of 32,551 adult patients, including 28,062 survivors and 4489 non-survivors (28-day mortality rate: 13.8 %) were included. Calcium supplementation was independently associated with improved 28-day mortality after adjusting for confounding variables (hazard ratio: 0.51; 95 % CI 0.47-0.56). Propensity score matching was performed and the after-matching cohort showed well balanced covariates. The results showed that calcium supplementation was associated with improved 28- and 90-day mortality (p < 0.05 for both Log-rank test). In adult critically ill patients, calcium supplementation during their ICU stay improved 28-day survival. This finding supports the use of calcium supplementation in critically ill patients.

Early management of sepsis with emphasis on early goal directed therapy: AME evidence series 002

Severe sepsis and septic shock are major causes of morbidity and mortality in patients entering the emergency department (ED) or intensive care unit (ICU). Despite substantial efforts to improve patient outcome, treatment of sepsis remains challenging to clinicians. In this context, early goal directed therapy (EGDT) represents an important concept emphasizing both early recognition of sepsis and prompt initiation of a structured treatment algorithm. As part of the AME evidence series on sepsis, we conducted a systematic review of all randomized controlled EGDT trials. Focus was laid on the setting (emergency department versus ICU) where EGDT was carried out. Early recognition of sepsis, through clinical or automated systems for early alert, together with well-timed initiation of the recommended therapy bundles may improve patients' outcome. However, the original "EGDT" protocol by Rivers and coworkers has been largely modified in subsequent trials. Currently, many investigators opt for an "expanded" EGDT (as suggested by the Surviving Sepsis Campaign). Evidence is also presented on the effectiveness of automated systems for early sepsis alert. Early recognition of sepsis and well-timed initiation of the SSC bundle may improve patient outcome.

Identification of three classes of acute respiratory distress syndrome using latent class analysis

Acute respiratory distress syndrome (ARDS) is a highly heterogeneous syndrome that can exhibit significant differences in the underlying causes, leading to different responses to treatment. It is required to identify subtypes of ARDS to guideline clinical treatment and trial design. The study aimed to identify subtypes of ARDS using latent class analysis (LCA). The study was a secondary analysis of the EDEN study, which was a randomized, controlled, multicenter trial conducted from January 2, 2008 to April 12, 2011. The primary study endpoint was death through 90-day follow up. LCA was performed incorporating variables on day 0 before randomization. The number of classes was chosen by a bootstrapped likelihood ratio test, Bayesian information criterion and the number of patients in each class. A total of 943 patients were enrolled in the study, including 219 (23.2%) non-survivors and 724 (76.8%) survivors. The LCA identified three classes of ARDS. Class 1 (hemodynamically unstable type) had significantly higher mortality rate (p = 0.003) than class 2 (intermediate type) and 3 (stable type) through 90 days follow up. There was significant interaction between cumulative fluid balance and the class (p = 0.02). While more fluid balance was beneficial for class 1, it was harmful for class 2 and 3. In conclusion, the study identified three classes of ARDS, which showed different clinical presentations, responses to fluid therapy and prognosis. The classification system used simple clinical variables and could help to design ARDS trials in the future.

Echocardiography for patients undergoing extracorporeal cardiopulmonary resuscitation: a primer for intensive care physicians

Echocardiography is an invaluable tool in the management of patients with extracorporeal cardiopulmonary resuscitation (ECPR) and subsequent extracorporeal membrane oxygenation (ECMO) support and weaning. At the very beginning, echocardiography can identify the etiology of cardiac arrest, such as massive pulmonary embolism and cardiac tamponade. Eliminating these culprits saves life and may avoid the initiation of extracorporeal cardiopulmonary resuscitation. If the underlying causes are not identified or intrinsic to the heart (e.g., such as those caused by cardiomyopathy and myocarditis), conventional cardiopulmonary resuscitation (CCPR) will continue to maintain cardiac output. The quality of CCPR can be monitored, and if cardiac output cannot be maintained, early institution of extracorporeal cardiopulmonary resuscitation may be reasonable. Cannulation is sometimes challenging for extracorporeal cardiopulmonary resuscitation patients. Fortunately, with the help of ultrasonography procedures including localization of vessels, selecting a cannula of appropriate size and confirmation of catheter tip may become easy under sophisticated hand. Monitoring of cardiac function and complications during extracorporeal membrane oxygenation support can be done with echocardiography. However, the cardiac parameters should be interpreted with understanding of hemodynamic configuration of extracorporeal membrane oxygenation. Thrombus and blood stasis can be identified with ultrasound, which may prompt mechanical and pharmacological interventions. The final step is extracorporeal membrane oxygenation weaning. A number of studies investigated the accuracy of some echocardiographic parameters in predicting success rate and demonstrated promising results. Parameters and threshold for successful weaning include aortic VTI ≥ 10 cm, LVEF > 20-25%, and lateral mitral annulus peak systolic velocity >6 cm/s. However, the effectiveness of echocardiography in ECPR patients cannot be determined in observational studies and requires randomized controlled trials in the future. The contents in this review are well known to echocardiography specialists; thus, it should be used as an educational material for emergency or intensive care physicians. There is a trend that focused echocardiography is performed by intensivists and emergency physicians.

Single-cell transcriptome profiling reveals heterogeneous neutrophils with prognostic values in sepsis

Neutrophils constitute the largest proportion of nucleated peripheral blood cells, and neutrophils have substantial heterogeneity. We profiled nearly 300,000 human peripheral blood cells in this study using single-cell RNA sequencing. A large proportion (>50%) of these cells were annotated as neutrophils. Neutrophils were further clustered into four subtypes, including Neu1, Neu2, Neu3, and Neu4. Neu1 is characterized by high expression of MMP9, HP, and RGL4. Neu1 was associated with septic shock and significantly correlated with the sequential organ failure assessment (SOFA) score. A gene expression module in Neu1 named Neu1_C (characterized by expression of NFKBIA, CXCL8, G0S2, and FTH1) was highly predictive of septic shock with an area under the curve of 0.81. The results were extensively validated in external bulk datasets by using single-cell deconvolution methods. In summary, our study establishes a general framework for studying neutrophil-related mechanisms, prognostic biomarkers, and potential therapeutic targets for septic shock.

Prediction model for patients with acute respiratory distress syndrome: use of a genetic algorithm to develop a neural network model

BACKGROUND

Acute respiratory distress syndrome (ARDS) is associated with significantly increased risk of death, and early risk stratification may help to choose the appropriate treatment. The study aimed to develop a neural network model by using a genetic algorithm (GA) for the prediction of mortality in patients with ARDS.

METHODS

This was a secondary analysis of two multicenter randomized controlled trials conducted in forty-four hospitals that are members of the National Heart, Lung, and Blood Institute, founded to create an acute respiratory distress syndrome Clinical Trials Network. Model training and validation were performed using the SAILS and OMEGA studies, respectively. A GA was employed to screen variables in order to predict 90-day mortality, and a neural network model was trained for the prediction. This machine learning model was compared to the logistic regression model and APACHE III score in the validation cohort.

RESULTS

A total number of 1,071 ARDS patients were included for analysis. The GA search identified seven important variables, which were age, AIDS, leukemia, metastatic tumor, hepatic failure, lowest albumin, and FiO2. A representative neural network model was constructed using the forward selection procedure. The area under the curve (AUC) of the neural network model evaluated with the validation cohort was 0.821 (95% CI [0.753-0.888]), which was greater than the APACHE III score (0.665; 95% CI [0.590-0.739]; p = 0.002 by Delong's test) and logistic regression model, albeit not statistically significant (0.743; 95% CI [0.669-0.817], p = 0.130 by Delong's test).

CONCLUSIONS

The study developed a neural network model using a GA, which outperformed conventional scoring systems for the prediction of mortality in ARDS patients.

Gene signature for the prediction of the trajectories of sepsis-induced acute kidney injury

BACKGROUND

Acute kidney injury (AKI) is a common complication in sepsis. However, the trajectories of sepsis-induced AKI and their transcriptional profiles are not well characterized.

METHODS

Sepsis patients admitted to centres participating in Chinese Multi-omics Advances In Sepsis (CMAISE) from November 2020 to December 2021 were enrolled, and gene expression in peripheral blood mononuclear cells was measured on Day 1. The renal function trajectory was measured by the renal component of the SOFA score (SOFArenal) on Days 1 and 3. Transcriptional profiles on Day 1 were compared between these renal function trajectories, and a support vector machine (SVM) was developed to distinguish transient from persistent AKI.

RESULTS

A total of 172 sepsis patients were enrolled during the study period. The renal function trajectory was classified into four types: non-AKI (SOFArenal = 0 on Days 1 and 3, n = 50), persistent AKI (SOFArenal > 0 on Days 1 and 3, n = 62), transient AKI (SOFArenal > 0 on Day 1 and SOFArenal = 0 on Day 3, n = 50) and worsening AKI (SOFArenal = 0 on Days 1 and SOFArenal > 0 on Day 3, n = 10). The persistent AKI group showed severe organ dysfunction and prolonged requirements for organ support. The worsening AKI group showed the least organ dysfunction on day 1 but had higher serum lactate and prolonged use of vasopressors than the non-AKI and transient AKI groups. There were 2091 upregulated and 1,902 downregulated genes (adjusted p < 0.05) between the persistent and transient AKI groups, with enrichment in the plasma membrane complex, receptor complex, and T-cell receptor complex. A 43-gene SVM model was developed using the genetic algorithm, which showed significantly greater performance predicting persistent AKI than the model based on clinical variables in a holdout subset (AUC: 0.948 [0.912, 0.984] vs. 0.739 [0.648, 0.830]; p < 0.01 for Delong's test).

CONCLUSIONS

Our study identified four subtypes of sepsis-induced AKI based on kidney injury trajectories. The landscape of host response aberrations across these subtypes was characterized. An SVM model based on a gene signature was developed to predict renal function trajectories, and showed better performance than the clinical variable-based model. Future studies are warranted to validate the gene model in distinguishing persistent from transient AKI.

Critical Care Database Comprising Patients With Infection

Patients treated in the intensive care unit (ICU) are closely monitored and receive intensive treatment. Such aggressive monitoring and treatment will generate high-granularity data from both electronic healthcare records and nursing charts. These data not only provide infrastructure for daily clinical practice but also can help to inform clinical studies. It is technically challenging to integrate and cleanse medical data from a variety of sources. Although there are several open-access critical care databases from western countries, there is a lack of this kind of database for Chinese adult patients. We established a critical care database involving patients with infection. A large proportion of these patients have sepsis and/or septic shock. High-granularity data comprising laboratory findings, baseline characteristics, medications, international statistical classification of diseases (ICD) code, nursing charts, and follow-up results were integrated to generate a comprehensive database. The database can be utilized for a variety of clinical studies. The dataset is fully accessible at PhysioNet(https://physionet.org/content/icu-infection-zigong-fourth/1.0/).

Antipyretic therapy in critically ill patients with established sepsis: a trial sequential analysis

BACKGROUND AND OBJECTIVE

antipyretic therapy for patients with sepsis has long been debated. The present study aimed to explore the beneficial effect of antipyretic therapy for ICU patients with sepsis.

DESIGN

systematic review and trial sequential analysis of randomized controlled trials.

DATABASE

Pubmed, Scopus, EBSCO and EMBASE were searched from inception to August 5, 2014.

METHODS

Mortality was dichotomized as binary outcome variable and odds ratio (OR) was chosen to be the summary statistic. Pooled OR was calculated by using DerSimonian and Laird method. Statistical heterogeneity was assessed by using the statistic I2. Trial sequential analysis was performed to account for the small number of trials and patients.

MAIN RESULTS

A total of 6 randomized controlled trials including 819 patients were included into final analysis. Overall, there was no beneficial effect of antipyretic therapy on mortality risk in patients with established sepsis (OR: 1.02, 95% CI: 0.50-2.05). The required information size (IS) was 2582 and our analysis has not yet reached half of the IS. The Z-curve did not cross the O'Brien-Fleming α-spending boundary or reach the futility, indicating that the non-significant result was probably due to lack of statistical power.

CONCLUSION

our study fails to identify any beneficial effect of antipyretic therapy on ICU patients with established diagnosis of sepsis. Due to limited number of total participants, more studies are needed to make a conclusive and reliable analysis.