Background 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).MethodsA 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).ResultsEighteen 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).ConclusionBased 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.
Emergency department (ED) length of stay (LOS) is associated with ED crowding and related complications. Previous studies either analyzed single patient disposition groups or combined different endpoints as a whole. The aim of this study is to evaluate different effects of relevant factors affecting ED LOS among different patient disposition groups.This is a retrospective electronic data analysis. The ED LOS and relevant covariates of all patients between January 2013 and December 2013 were collected. A competing risk accelerated failure time model was used to compute endpoint type-specific time ratios (TRs) for ED LOS.A total of 149,472 patients was included for analysis with an overall medium ED LOS of 2.15 [interquartile range (IQR) = 6.51] hours. The medium LOS for discharged, admission, and mortality patients was 1.46 (IQR = 2.07), 11.3 (IQR = 33.2), and 7.53 (IQR = 28.0) hours, respectively. In multivariate analysis, age (TR = 1.012, P < 0.0001], higher acuity (triage level I vs level V, TR = 2.371, P < 0.0001), pediatric nontrauma (compared with adult nontrauma, TR = 3.084, P < 0.0001), transferred patients (TR = 2.712, P < 0.0001), and day shift arrival (compared with night shift, TR = 1.451, P < 0.0001) were associated with prolonged ED LOS in the discharged patient group. However, opposite results were noted for higher acuity (triage level I vs level V, TR = 0.532, P < 0.0001), pediatric nontrauma (TR = 0.375, P < 0.0001), transferred patients (TR = 0.852, P < 0.0001), and day shift arrival (TR = 0.88, P < 0.0001) in the admission patient group.Common influential factors such as age, patient entity, triage acuity level, or arrival time may have varying effects on different disposition groups of patients. These findings and the suggested model could be used for EDs to develop individually tailored approaches to minimize ED LOS and further improve ED crowding status.
BackgroundEmergency department (ED) crowding continues to be an important health care issue in modern countries. Among the many crucial quality indicators for monitoring the throughput process, a patient’s length of stay (LOS) is considered the most important one since it is both the cause and the result of ED crowding. The aim of this study is to identify and quantify the influence of different patient-related or diagnostic activities-related factors on the ED LOS of discharged patients.MethodsThis is a retrospective electronic data analysis. All patients who were discharged from the ED of a tertiary teaching hospital in 2013 were included. A multivariate accelerated failure time model was used to analyze the influence of the collected covariates on patient LOS.ResultsA total of 106,206 patients were included for analysis with an overall medium ED LOS of 1.46 (interquartile range = 2.03) hours. Among them, 96% were discharged by a physician, 3.5% discharged against medical advice, 0.5% left without notice, and only 0.02% left without being seen by a physician. In the multivariate analysis, increased age (>80 vs <20, time ratio (TR) = 1.408, p<0.0001), higher acuity level (triage level I vs. level V, TR = 1.343, p<0.0001), transferred patients (TR = 1.350, p<0.0001), X-rays obtained (TR = 1.181, p<0.0001), CT scans obtained (TR = 1.515, p<0.0001), laboratory tests (TR = 2.654, p<0.0001), consultation provided (TR = 1.631, p<0.0001), observation provided (TR = 8.435, p<0.0001), critical condition declared (TR = 1.205, p<0.0001), day-shift arrival (TR = 1.223, p<0.0001), and an increased ED daily census (TR = 1.057, p<0.0001) lengthened the ED LOS with various effect sizes. On the other hand, male sex (TR = 0.982, p = 0.002), weekend arrival (TR = 0.928, p<0.0001), and adult non-trauma patients (compared with pediatric non-trauma, TR = 0.687, p<0.0001) were associated with shortened ED LOS. A prediction diagram was made accordingly and compared with the actual LOS.ConclusionsThe influential factors on the ED LOS in discharged patients were identified and quantified in the current study. The model’s predicted ED LOS may provide useful information for physicians or patients to better anticipate an individual’s LOS and to help the administrative level plan its staffing policy.
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