The soluble cluster of differentiation 14 (or presepsin) is a free fragment of glycoprotein expressed on monocytes and macrophages. Although many studies have been conducted recently, the diagnostic performance of presepsin for sepsis remains debated. We performed a systematic review and meta-analysis of the available literature to assess the accuracy of presepsin for the diagnosis of sepsis in adult patients and compared the performance between presepsin, C-reactive protein (CRP), and procalcitonin (PCT). A comprehensive systemic search was conducted in PubMed, EMBASE, and Google Scholar for studies that evaluated the diagnostic accuracy of presepsin for sepsis until January 2017. The hierarchical summary receiver operating characteristic method was used to pool individual sensitivity, specificity, diagnostic odds ratio (DOR), positive likelihood ratio (PLR), negative likelihood ratio (NLR), and area under the receiver operating characteristic curve (AUC). Eighteen studies, comprising 3470 patients, met our inclusion criteria. The pooled diagnosis sensitivity and specificity of presepsin for sepsis were 0.84 (95% CI 0.80–0.87) and 0.76 (95% CI 0.67–0.82), respectively. Furthermore, the pooled DOR, PLR, NLR, and AUC were 16 (95% CI 10–25), 3.4 (95% CI 2.5–4.6), 0.22 (95% CI 0.17–0.27), and 0.88 (95% CI 0.85–0.90), respectively. Significant heterogeneity was found in both sensitivities (Cochrane Q = 137.43, p < 0.001, I 2 = 87.63%) and specificities (Cochrane Q = 180.76, p < 0.001, I 2 = 90.60%). Additionally, we found no significant difference between presepsin and PCT (AUC 0.87 vs. 0.86) or CRP (AUC 0.85 vs. 0.85). However, for studies conducted in ICU, the pooled sensitivity of presepsin was found to be higher than PCT (0.88, 95% CI 0.82–0.92 vs. 0.75, 95% CI 0.68–0.81), while the pooled specificity of presepsin was lower than PCT (0.58, 95% CI 0.42–0.73 vs. 0.75, 95% CI 0.65–0.83). Based on the results of our meta-analysis, presepsin is a promising marker for diagnosis of sepsis as PCT or CRP, but its results should be interpreted more carefully and cautiously since too few studies were included and those studies had high heterogeneity between them. In addition, continuing re-evaluation during the course of sepsis is advisable.

Neutrophil CD64 is widely described as an accurate biomarker for the diagnosis of infection in patients with septic syndrome. We performed a systematic review and meta-analysis to evaluate the diagnostic accuracy of neutrophil CD64, comparing it with C-reactive protein (CRP) and procalcitonin (PCT) for the diagnosis of infection in adult patients with septic syndrome, based on sepsis-2 criteria. We searched the PubMed and Embase databases and Google Scholar. Original studies reporting the performance of neutrophil CD64 for sepsis diagnosis in adult patients were retained. The pooled sensitivity, specificity, diagnostic odds ratio (DOR), and hierarchical summary receiver operating characteristic (SROC) curve were calculated. We included 14 studies (2471 patients) from 2006 to 2017 in the meta-analysis. The pooled sensitivity and specificity of neutrophil CD64 for diagnosing infection in adult patients with septic syndrome were 0.87 (95% CI 0.80–0.92) and 0.89 (95% CI 0.82–0.93), respectively. The area under the SROC curve and the DOR were 0.94 (95% CI 0.92–0.96) and 53 (95% CI 22–128), respectively. There was significant heterogeneity between the studies included. Subgroup analyses showed that this heterogeneity was due to differences in sample size and the proportions of patients with sepsis included in the studies. Six studies (927 patients) compared neutrophil CD64 and CRP determinations, and six studies (744 patients) compared neutrophil CD64 and PCT determinations. The area under the SROC curve was larger for neutrophil CD64 than for CRP (0.89 [95% CI 0.87–0.92] vs. 0.84 [95% CI 0.80–0.88], P < 0.05) or PCT (0.89 [95% CI 0.84–0.95] vs. 0.84 [95% CI 0.79–0.89], P < 0.05). In adult patients with septic syndrome, neutrophil CD64 levels are an excellent biomarker with moderate accuracy outperforming both CRP and PCT determinations.

Traumatic rib fractures can cause chest complications that need further treatment and hospitalization. We hypothesized that an increase in the number of displaced rib fractures will be accompanied by an increase in chest complications. We retrospectively reviewed the trauma registry between January 2013 and May 2015 in a teaching hospital in northeastern Taiwan. Patients admitted with chest trauma and rib fractures without concomitant severe brain, splenic, pelvic or liver injuries were included. The demographic data, such as gender, age, the index of coexistence disease, alcohol consumption, trauma mechanisms were analyzed as potential predictors of pulmonary complications. Pulmonary complications were defined as pneumothorax, hemothorax, flail chest, pulmonary contusion, and pneumonia. In the 29 months of the study period, a total of 3151 trauma patients were admitted to our hospital. Among them, 174 patients were enrolled for final analysis. The most common trauma mechanism was road traffic accidents (58.6%), mainly motorbike accidents (n = 70, 40.2%). Three or more displaced rib fractures had higher specificity for predicting complications, compared to three or more total rib fractures (95.5% vs 59.1%). Adjusting the severity of chest trauma using TTSS and Ribscore by multivariable logistic regression analysis, we found that three or more rib fractures or any displaced rib fracture was the most significant predictor for developing pulmonary complication (aOR: 5.49 95% CI: 1.82–16.55). Furthermore, there were 18/57 (31.6%) patients with fewer than three ribs fractures developed pulmonary complications. In these 18 patients, only five patients had delayed onset complications and four of them had at least one displaced rib fracture. In this retrospective cohort study, we found that the number of displaced or total rib fractures, bilateral rib fractures, and rib fractures in more than two areas were associated with the more chest complications. Furthermore, three or more rib fracture or any displacement were found to be the most sensitive risk factor for chest complications, independent of other risk factors or severity index. The number of displaced rib fractures could be a strong predictor for developing pulmonary complications. For patients with fewer than three rib fractures without rib displacement and initial lung or other organ injuries, outpatient management could be safe and efficient.

Sepsis was recently redefined as a life-threatening disease involving organ dysfunction caused by a dysregulated host response to infection. Biomarkers play an important role in early detection, diagnosis, and prognostication. We reviewed six promising biomarkers for detecting sepsis and systemic infection, including C-reactive protein (CRP), procalcitonin (PCT), interleukin-6 (IL-6), CD64, presepsin, and sTREM-1. Among the recent studies, we found the following risks of bias: only a few studies adopted the random or consecutive sampling strategy; extensive case-control analysis, which worsened the over-estimated performance; most of the studies used post hoc cutoff values; and heterogeneity with respect to the inclusion criteria, small sample sizes, and different quantitative synthesis methods applied in meta-analyses. We recommend that CD64 and presepsin should be considered as the most promising biomarkers for diagnosing sepsis. Future studies should enroll a larger sample size with a cohort rather than a case-control study design. A random or consecutive study design with a pre-specified laboratory threshold, consistent sampling timing, and an updated definition of sepsis will also increase the reliability of the studies. Further investigations of appropriate specimens, testing assays, and cutoff levels for specific biomarkers are also warranted.

Electronic health records (EHR) have revolutionized cardiovascular disease (CVD) research by enabling comprehensive, large‐scale, and dynamic data collection. Integrating EHR data with advanced analytical methods, including artificial intelligence (AI), transforms CVD risk prediction and management methodologies. This review examines the advancements and challenges of using EHR in developing CVD prediction models, covering traditional and AI‐based approaches. While EHR‐based CVD risk prediction has greatly improved, moving from models that integrate real‐world data on medication use and imaging, challenges persist regarding data quality, standardization across health care systems, and geographic variability. The complexity of EHR data requires sophisticated computational methods and multidisciplinary approaches for effective CVD risk modeling. AI's deep learning enhances prediction performance but faces limitations in interpretability and the need for validation and recalibration for diverse populations. The future of CVD risk prediction and management increasingly depends on using EHR and AI technologies effectively. Addressing data quality issues and overcoming limitations from retrospective data analysis are critical for improving the reliability and applicability of risk prediction models. Integrating multidimensional data, including environmental, lifestyle, social, and genomic factors, could significantly enhance risk assessment. These models require continuous validation and recalibration to ensure their adaptability to diverse populations and evolving health care environments, providing reassurance about their reliability.

The associations between dysglycemia and mortality in septic patients with and without diabetes are yet to be confirmed. Our aim was to analyze the association of diabetes and sepsis mortality, and to examine how dysglycemia (hyperglycemia, hypoglycemia and glucose variability) affects in-hospital mortality of patients with suspected sepsis in emergency department (ED) and intensive care units. Clinically suspected septic patients admitted to ED were included, and stratified into subgroups according to in-hospital mortality and the presence of diabetes. We analyzed patients’ demographics, comorbidities, clinical and laboratory parameters, admission glucose levels and severity of sepsis. Odds ratio of mortality was assessed after adjusting for possible confounders. The correlations of admission glucose and CoV (blood glucose coefficients of variation) and mortality in diabetes and non-diabetes were also tested. Diabetes was present in 58.3% of the patients. Diabetic patients were older, more likely to have end-stage renal disease and undergoing hemodialysis, but had fewer malignancies, less sepsis severity (lower Mortality in Emergency Department Sepsis Score), less steroid usage in emergency department, and lower in-hospital mortality rate (aOR:0.83, 95% CI 0.65–0.99, p = 0.044). Hyperglycemia at admission (glucose≥200 mg/dL) was associated with higher risks of in-hospital mortality among the non-diabetes patients (OR:1.83 vs. diabetes, 95% CI 1.20–2.80, p = 0.005) with the same elevated glucose levels at admission. In addition, CoV>30% resulted in higher risk of death as well (aOR:1.88 vs. CoV between 10 and 30, 95%CI 1.24–2.86 p = 0.003). This study indicates that while diabetes mellitus seems to be a protective factor in sepsis patients, hyper- or hypoglycemia status on admission, and increased blood glucose variation during hospital stays, were independently associated with increased odds ratio of mortality.