Background and aim: The sensitivity and specificity of biomarkers and scoring systems used for predicting fatality of severe sepsis patients remain unsatisfactory. This study aimed to determine the prognostic value of circulating plasma DNA levels in severe septic patients presenting at the Emergency Department (ED). Methods: Sixty-seven consecutive patients with severe sepsis and 33 controls were evaluated. Plasma DNA levels were estimated by real-time quantitative polymerase chain reaction assay using primers for the human β-hemoglobin and ND2 gene. The patients’ clinical and laboratory data on admission were analyzed. Results: The median plasma nuclear and mitochondria DNA levels for severe septic patients on admission were significantly higher than those of the controls. The mean plasma nuclear DNA level on admission correlated with lactate concentration (γ = 0.36, p = 0.003) and plasma mitochondrial DNA on admission (γ = 0.708, p < 0.001). Significant prognostic factors for fatality included mechanical ventilation within the first 24 hours (p = 0.013), mean sequential organ failure assessment (SOFA) score on admission (p = 0.04), serum lactate (p < 0.001), and both plasma nuclear and mitochondrial DNA on admission (p < 0.001). Plasma mitochondrial DNA was an independent predictor of fatality by stepwise logistic regression such that an increase by one ng/mL in level would increase fatality rate by 0.7%. Conclusion: Plasma DNA has potential use for predicting outcome in septic patients arriving at the emergency room. Plasma mitochondrial DNA level on admission is a more powerful predictor than lactate concentration or SOFA scores on admission.

In emergency departments, the most common cause of death associated with suspected infected patients is sepsis. In this study, deep learning algorithms were used to predict the mortality of suspected infected patients in a hospital emergency department. During January 2007 and December 2013, 42,220 patients considered in this study were admitted to the emergency department due to suspected infection. In the present study, a deep learning structure for mortality prediction of septic patients was developed and compared with several machine learning methods as well as two sepsis screening tools: the systemic inflammatory response syndrome (SIRS) and quick sepsis-related organ failure assessment (qSOFA). The mortality predictions were explored for septic patients who died within 72 h and 28 days. Results demonstrated that the accuracy rate of deep learning methods, especially Convolutional Neural Network plus SoftMax (87.01% in 72 h and 81.59% in 28 d), exceeds that of the other machine learning methods, SIRS, and qSOFA. We expect that deep learning can effectively assist medical staff in early identification of critical patients.

Purpose: To build machine learning models for predicting the risk of in-hospital death in patients with sepsis within 48 h, using only dynamic changes in the patient's vital signs.Methods: This retrospective observational cohort study enrolled septic patients from five emergency departments (ED) in Taiwan. We adopted seven variables, i.e., age, sex, systolic blood pressure, diastolic blood pressure, heart rate, respiratory rate, and body temperature.Results: Among all 353,253 visits, after excluding 159,607 visits (45%), the study group consisted of 193,646 ED visits. With a leading time of 6 h, the convolutional neural networks (CNNs), long short-term memory (LSTM), and random forest (RF) had accuracy rates of 0.905, 0.817, and 0.835, respectively, and the area under the receiver operating characteristic curve (AUC) was 0.840, 0.761, and 0.770, respectively. With a leading time of 48 h, the CNN, LSTM, and RF achieved accuracy rates of 0.828, 0759, and 0.805, respectively, and an AUC of 0.811, 0.734, and 0.776, respectively.Conclusion: By analyzing dynamic vital sign data, machine learning models can predict mortality in septic patients within 6 to 48 h of admission. The performance of the testing models is more accurate if the lead time is closer to the event.

Introduction: Statins are reported to have anti-inflammatory and anti-oxidative effects aside from cholesterol-lowering effects. This study aimed to evaluate the effects of statin therapy on oxidized LDL (Ox-LDL) and the clinical outcome of patients with acute ischemic stroke (AIS). Methods: This prospective study enrolled 120 patients with AIS divided in the statin (n = 55) and non-statin (n = 65) groups. Eighty sex- and age- matched participants were recruited as risk controls. Ox-LDL was measured using a monoclonal antibody-based enzyme-linked immune-sorbent assay at different time points after AIS. The clinical outcomes were analyzed between the statin and non-statin groups. Results: Plasma Ox-LDL was significantly higher in stroke patients than in the controls (P < 0.001). Plasma Ox-LDL level was significantly reduced in the statin group on day 7 and day 30 compared to the non-statin group (P < 0.01). The plasma Ox-LDL positively correlated with serum total cholesterol, LDL-cholesterol, and hemoglobin A1c (HbA1c). Among the potential risk factors, only National Institutes of Health stroke scale (NIHSS) score and Ox-LDL level on admission were independently associated with 3-month outcome. Conclusions: Our study demonstrates that statin therapy reduces plasma Ox-LDL level after AIS. Plasma Ox-LDL may be a more powerful predictor than serum LDL, high-sensitivity C-reactive protein or white blood cell counts for stroke outcome. Therefore, assay of plasma Ox-LDL should be added as a predictor among the panel of conventional biomarkers in stroke outcome.

Objective: The aim of this study was to compare quick Sepsis-related Organ Failure Assessment (qSOFA) and Systemic Inflammatory Response Syndrome (SIRS) scores for predicting mortality. Patients and methods: A single-center, retrospective study of adult patients with suspected infection was conducted. Area under the curve (AUC) and multivariate analyses were used to explore associations between the qSOFA and SIRS scores and mortality. Results: Of the 69 115 patients enrolled, 1798 died within 72 h and 5640 within 28 days. The qSOFA scores were better than SIRS scores at predicting 72-h mortality (AUC: 0.77 vs. 0.64). However, the discriminatory power of both scores was low in terms of 28-day mortality (AUC: 0.69 vs. 0.60). Patients with qSOFA score of at least 2 had a higher hazard ratio for 72-h mortality than for 28-day mortality (2.64 vs. 1.91). Conclusion: The qSOFA scores are more accurate than SIRS scores for predicting 72-h mortality and are better at predicting 72-h mortality than 28-day mortality.

This study aimed to explore the role of apoptosis initiators, caspase-9, caspase-10, mitochondrial anti-viral signaling protein (MAVS), and interferon regulatory factor 7 (pIRF7), in patients with systemic lupus erythematosus (SLE). Leukocyte apoptosis was determined by flow cytometry, including annexin V, APO2.7, and 7-amino-actinomycin D (7-AAD) on each subtype of leukocyte in 35 patients with SLE, 15 disease controls, and 17 volunteer normal controls. Levels of caspase-9, caspase-10, MAVS, and pIRF7 in mononuclear cells and the disease activity index (SLEDAI) in the SLE patients were determined. Correlation among intracellular adaptor proteins and caspase levels were calculated. The SLE patients had higher APO2.7 in total leukocyte, lymphocyte, and monocytes, and higher late apoptosis markers in total leukocytes and neutrophils than normal controls (all p < 0.05). Disease activity was positively associated with the APO2.7 of CD19+ cells in SLE, but negatively associated with MAVS and caspase-9 levels (all p < 0.05). Markers of viral infection and anti-virus transcription factors like MDA5, MAVS, and pIRF7 were significantly higher in SLE patients than in disease controls (p < 0.05). Caspase-9 and caspase-10 levels positively correlated with MAVS and pIRF7 in SLE patients (p < 0.05). The disease activity of SLE is positively associated with APO2.7 level of CD19+ cells but negatively associated with MAVS and caspase-9 levels, which all point to a mitochondrial pathway.