Setting lactate kinetics at >30% might improve the clinical outcomes of patients with sepsis-associated hyperlactatemia. The aim of this study was to explore the outcome benefits of stepwise lactate kinetics vs central venous oxygen saturation (ScvO2)-oriented hemodynamic therapy at 6 h as the protocol goal during early resuscitation. The relevant parameters and adverse events after different targets in 360 randomly assigned patients with sepsis-associated hyperlactatemia were recorded and compared. Heart rate (HR) at 48 h in the ScvO2 group was higher than in the lactate kinetics group (105 ± 19 bpm vs 99 ± 20 bpm, P = 0.040). The liquid balance at 4 h, 12 h, and 24 h in the lactate kinetics group was larger than in the ScvO2 group (1535 (1271–1778) ml vs 826 (631–1219) ml, P < 0.001; 1688 (1173–1923) ml vs 1277 (962 − 1588) ml, P <0.001; and 1510 (904–2087) ml vs 1236 (740–1808) ml, P = 0.005), respectively. Mortality was higher in the ScvO2 group (27.9% vs 18.3%, P = 0.033), but there was no significant difference between the two groups in the length of stay in the ICU or mechanical ventilation. In terms of new onset organ dysfunction, there was a significant difference between the two groups in total bilirubin at 48 h and 72 h. Based on the 60-day survival curves, there was significantly more mortality in the ScvO2 group than in the lactate kinetics group (X 2 = 4.133, P = 0.042). In addition, fewer adverse events occurred in the lactate kinetics group. Stepwise lactate kinetics-oriented hemodynamic therapy can reduce mortality in patients with sepsis-associated hyperlactatemia compared with ScvO2-oriented therapy. National Institutes of Health Clinical Trials Registry, NCT02566460 . Registered on 26 September 2015.

Introduction: Critically ill immunocompromised (CIIC) patients with pulmonary infection are a population at high risk for invasive pulmonary aspergillosis (IPA). The host defenses are important factors to consider in determining the risk and outcome of infection. Quantification of changes in the status of host immunity could be valuable for clinical diagnosis and outcome prediction. Methods: We evaluated the quantitative changes in key humoral and cellular parameters in CIIC patients with pulmonary infection and their potential influence on the risk and prognosis of IPA. We monitored the evolution of these parameters in 150 CIIC patients with pulmonary infection on days 1, 3 and 10 (D1, D3 and D10) following ICU admission. The primary outcome was 28-day mortality. Follow-up included 60- and 90-day mortality. Results: Among the 150 CIIC patients included in this study, 62 (41.3%) had microbiological evidence of IPA. Compared with patients without IPA, CD3⁺, CD8⁺, CD28⁺CD4⁺ and CD28⁺CD8⁺ CD28⁺CD8⁺ T-cell counts (D1, D3 and D10) and B-cell counts (D1 and D3) were significantly reduced in patients with IPA (P < 0.05). Multivariate regression analysis revealed that CD8⁺ (D3 and D10) (odds ratio (OR) 0.34, 95% confidence interval (CI) 0.23 to 0.46; OR 0.68, 95% CI 0.56 to 0.80), CD28⁺CD8⁺ (D3) (OR 0.73, 95% CI 0.61 to 0.86) and CD3⁺ (D10) (OR 0.81, 95% CI 0.63 to 0.98) T-cell counts were independent predictors of IPA in CIIC patients. Receiver operating characteristic analysis of immune parameters predicting 28-day mortality revealed area under the curve values of 0.82 (95% CI 0.71 to 0.92), 0.94 (95% CI 0.87 to 0.99), and 0.94 (95% CI 0.85 to 0.99) for CD8⁺ T-cell counts (D1, D3 and D10, respectively) and 0.84 (95% CI 0.75 to 0.94), 0.92 (95% CI 0.85 to 0.99) and 0.90 (95% CI 0.79 to 0.99) for CD28⁺CD8⁺ T-cell counts (D1, D3 and D10, respectively). Kaplan-Meier survival analysis provided evidence that CD8⁺ and CD28⁺CD8⁺ T-cell counts (<149.5 cells/mm³ and <75 cells/mm³, respectively) were associated with early mortality in CIIC patients with IPA (logrank test; P < 0.001). Conclusions: CD8⁺ and CD28⁺CD8⁺ T-cell counts were significantly lower in CIIC patients with IPA than in non-IPA patients. Lower CD8⁺ and CD28⁺CD8⁺ T-cell counts in CIIC patients with pulmonary infection were associated with higher risk and early mortality in IPA and may be valuable for clinical diagnosis and outcome prediction.

Background: Heparin is one of the most commonly used medications in intensive care units. In clinical practice, the use of a weight-based heparin dosing nomogram is standard practice for the treatment of thrombosis. Recently, machine learning techniques have dramatically improved the ability of computers to provide clinical decision support and have allowed for the possibility of computer generated, algorithm-based heparin dosing recommendations. Objective: The objective of this study was to predict the effects of heparin treatment using machine learning methods to optimize heparin dosing in intensive care units based on the predictions. Patient state predictions were based upon activated partial thromboplastin time in 3 different ranges: subtherapeutic, normal therapeutic, and supratherapeutic, respectively. Methods: Retrospective data from 2 intensive care unit research databases (Multiparameter Intelligent Monitoring in Intensive Care III, MIMIC-III; e–Intensive Care Unit Collaborative Research Database, eICU) were used for the analysis. Candidate machine learning models (random forest, support vector machine, adaptive boosting, extreme gradient boosting, and shallow neural network) were compared in 3 patient groups to evaluate the classification performance for predicting the subtherapeutic, normal therapeutic, and supratherapeutic patient states. The model results were evaluated using precision, recall, F1 score, and accuracy. Results: Data from the MIMIC-III database (n=2789 patients) and from the eICU database (n=575 patients) were used. In 3-class classification, the shallow neural network algorithm performed the best (F1 scores of 87.26%, 85.98%, and 87.55% for data set 1, 2, and 3, respectively). The shallow neural network algorithm achieved the highest F1 scores within the patient therapeutic state groups: subtherapeutic (data set 1: 79.35%; data set 2: 83.67%; data set 3: 83.33%), normal therapeutic (data set 1: 93.15%; data set 2: 87.76%; data set 3: 84.62%), and supratherapeutic (data set 1: 88.00%; data set 2: 86.54%; data set 3: 95.45%) therapeutic ranges, respectively. Conclusions: The most appropriate model for predicting the effects of heparin treatment was found by comparing multiple machine learning models and can be used to further guide optimal heparin dosing. Using multicenter intensive care unit data, our study demonstrates the feasibility of predicting the outcomes of heparin treatment using data-driven methods, and thus, how machine learning–based models can be used to optimize and personalize heparin dosing to improve patient safety. Manual analysis and validation suggested that the model outperformed standard practice heparin treatment dosing.

Objectives: For patients with intra-abdominal infection (IAI), the rapid and accurate identification of pathogens remains a challenge. Metagenomic next-generation sequencing (mNGS) is a novel technique for infectious diseases, but its application in IAI is limited. In this study, we compared the microbiological diagnostic ability of plasma mNGS with that of conventional peritoneal drainage (PD) culture in critical care settings.Methods: From January 2018 to December 2020, a prospective observational study was performed at a tertiary teaching hospital in China and data on 109 abdominal sepsis patients were collected. The pathogen detection performance of plasma mNGS and PD culture method were compared.Measurements and Results: Ninety-two positive cases detected on PD culture, while plasma mNGS detected 61 positive cases. Forty-five patients (44.0%) had at least one matched pair of plasma mNGS and PD culture results. Compared with PD culture, the plasma mNGS was more rapid (27.1 ± 4.0 vs. 68.9 ± 22.3 h, p < 0.05). The patients received initial antibiotic treatment matched with mNGS detection showed better clinical outcomes.Conclusion: For abdominal sepsis patients, plasma mNGS can provide early, noninvasive, and rapid microbiological diagnosis. Compared with conventional PD smear, culture, and blood culture methods, plasma mNGS promote the rapid detection of pathogenic bacteria.

Autophagy dysfunction contributes to CD4 + T cell apoptosis during sepsis leading to impairment of adaptive immunity. However, the underlying mechanism is unclear. The mammalian target of rapamycin (mTOR) pathway modulates CD4 + T cell survival during sepsis through mechanisms that are not fully understood. We developed a mouse model of sepsis through cecal ligation and puncture (CLP) to investigate dynamic changes in autophagy in CD4 + T cells. We used T cell specific-mTOR/tuberous sclerosis complex 1 (TSC1)-knockout mice to explore the roles of the mTOR pathway in modulating autophagy during sepsis. We observed reduced fusion of autophagosomes with lysosomes in the CD4 + T cells of CLP mice, which may represent a characteristic feature of autophagy dysfunction. Deletion of mTOR relieved autophagosome-lysosome fusion dysfunction and ameliorated apoptosis of CD4 + T cells in CLP mice, but this rescued phenotype was abolished by treatment with bafilomycin A1, a specific A-L fusion inhibitor. We further explored the underlying molecular mechanism and found that phosphorylation levels of transcription factor EB were significant higher in CLP mice and that expression of A-L fusion protein SNAREs were restricted, both of which were ameliorated by mTOR deletion. Taken together, these results suggest that the mTOR pathway plays a critical role in regulation of CD4 + T-cell apoptosis during sepsis, partly through regulation of A-L fusion-related protein transcription.

Endoplasmic reticulum stress (ERS) has been well documented to participate in the pathophysiological processes of apoptosis in many diseases. Inhibition of ERS ameliorates pathological organ injury. However, the upstream signaling pathways and molecular regulatory mechanisms of which are still unknown. mTOR, an evolutionarily conserved protein kinase, is a key regulator of apoptosis. Hence, in this study, a classical cecal ligation and puncture (CLP) sepsis model was constructed by using the T cell-specific knockout mTOR and TSC1 (Tuberous Sclerosis Complex, the inhibitor of mTOR signaling pathway) mice to explore the underlying signaling pathway and molecular mechanism of host immune imbalance caused by apoptosis in sepsis. We found that mTOR may modulate septic T cell apoptosis by regulating Akt–IRE1–JNK pathway. To further clarify the possible mechanism, the specific inhibitors of PI3K-Akt and IRE1–JNK were used to intervene in mice before/after CLP, respectively. By analyzing the proteins of mTOR-ERS signaling pathway and the expression of apoptosis-related proteins and genes, we found that mTOR mediated the ER stress induced CD4⁺ T cell apoptosis in Septic mice by negatively regulating the Akt–IRE1–JNK-Caspase 3 signaling cascades. These results indicate that mTOR–Akt–IRE1α–JNK signaling pathway mediated the Endoplasmic reticulum stress induced CD4⁺ T cell apoptosis in Septic mice.