Dataset Information

Sepsis in Intensive Care Unit Patients: Worldwide Data From the Intensive Care over Nations Audit.

ABSTRACT: Background:There is a need to better define the epidemiology of sepsis in intensive care units (ICUs) around the globe. Methods:The Intensive Care over Nations (ICON) audit prospectively collected data on all adult (>16 years) patients admitted to the ICU between May 8 and May 18, 2012, except those admitted for less than 24 hours for routine postoperative surveillance. Data were collected daily for a maximum of 28 days in the ICU, and patients were followed up for outcome data until death, hospital discharge, or for 60 days. Participation was entirely voluntary. Results:The audit included 10069 patients from Europe (54.1%), Asia (19.2%), America (17.1%), and other continents (9.6%). Sepsis, defined as infection with associated organ failure, was identified during the ICU stay in 2973 (29.5%) patients, including in 1808 (18.0%) already at ICU admission. Occurrence rates of sepsis varied from 13.6% to 39.3% in the different regions. Overall ICU and hospital mortality rates were 25.8% and 35.3%, respectively, in patients with sepsis, but it varied from 11.9% and 19.3% (Oceania) to 39.5% and 47.2% (Africa), respectively. After adjustment for possible confounders in a multilevel analysis, independent risk factors for in-hospital death included older age, higher simplified acute physiology II score, comorbid cancer, chronic heart failure (New York Heart Association Classification III/IV), cirrhosis, use of mechanical ventilation or renal replacement therapy, and infection with Acinetobacter spp. Conclusions:Sepsis remains a major health problem in ICU patients worldwide and is associated with high mortality rates. However, there is wide variability in the sepsis rate and outcomes in ICU patients around the globe.

SUBMITTER: Sakr Y

PROVIDER: S-EPMC6289022 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature

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Sepsis in Intensive Care Unit Patients: Worldwide Data From the Intensive Care over Nations Audit.

Sakr Yasser Y Jaschinski Ulrich U Wittebole Xavier X Szakmany Tamas T Lipman Jeffrey J Ñamendys-Silva Silvio A SA Martin-Loeches Ignacio I Leone Marc M Lupu Mary-Nicoleta MN Vincent Jean-Louis JL

Open forum infectious diseases 20181119 12

<h4>Background</h4>There is a need to better define the epidemiology of sepsis in intensive care units (ICUs) around the globe.<h4>Methods</h4>The Intensive Care over Nations (ICON) audit prospectively collected data on all adult (>16 years) patients admitted to the ICU between May 8 and May 18, 2012, except those admitted for less than 24 hours for routine postoperative surveillance. Data were collected daily for a maximum of 28 days in the ICU, and patients were followed up for outcome data un ...[more]

PMID: 30555852

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Project description:BackgroundThe protective cardiovascular effect of high-density lipoproteins (HDLs) is considered to chiefly rely on reverse cholesterol transport from peripheral tissues back to the liver. However, HDL particles display pleiotropic properties, including anti-inflammatory, anti-apoptotic or antioxidant functions. Some studies suggest that HDL concentration decreases during sepsis, and an association was reported between low HDL levels and a poor outcome. Like sepsis, trauma is also associated with a systemic inflammatory response syndrome. However, no study has yet explored changes in lipid profiles during trauma. We sought to compare lipid profiles between sepsis and trauma patients in intensive care unit (ICU). In septic patients, we analyzed the association between lipid profile, severity and prognosis.MethodsA prospective, observational, single-centered study was conducted in a surgical ICU. For each patient, total cholesterol, HDL, triglyceride and low-density lipoprotein cholesterol levels were assessed at admission. Short-term prognosis outcome was prospectively assessed.ResultsSeventy-five consecutive patients were admitted (50 sepsis and 25 trauma). There was no difference in SOFA and SAPSII scores between the two groups. Patients with sepsis had lower total cholesterol levels than patients with trauma. Regarding the lipoprotein profile, only HDLs differed significantly between the two groups (median [IQR] = 0.33 mmol/l [0.17-0.78] in sepsis patients versus median [IQR] = 0.99 mmol/l [0.74-1.28] in trauma patients; P < 0.0001). Whereas ICU mortality was not associated with lipid levels in the sepsis group, a significant negative correlation was found between HDL concentration and the length of ICU stay (r = -0.35; P = 0.03) in the group of survivor septic patients at ICU discharge. In addition, poor outcome defined as death or a SOFA score >6 at day 3 was associated with lower HDL levels (median [IQR] = 0.20 mmol/l [0.11-0.41] vs. 0.35 mmol/l [0.19-0.86] in patients with poor outcome versus others; P = 0.03).ConclusionsLipid profile was totally different between sepsis and trauma in ICU patients: HDL levels were low in septic patients, whereas their concentration was not altered in trauma patients. This major difference reinforces the necessity to explore the therapeutic potential of HDL in sepsis.

Project description:Background: The gut is thought to play an important role in the pathogenesis of sepsis. Changes in the gut microbiota are closely related to the occurrence and development of human diseases, but few studies have focused on taxonomic composition of gut microbiota in septic patients. Knowledge of changes in the gut microbiota is a key issue in intensive care. Clinicians must understand how an altered gut microbiota affects the susceptibility and prognosis of septic patients. Measurements and Main Results: In the single-center case control study, 20 septic patients and 20 healthy children were recruited. The taxonomic composition of gut microbiota was determined via 16S rRNA gene sequencing. Gut microbiota diversity in children with sepsis was significantly reduced compared with that in healthy children. The taxonomic composition of gut microbiota can effectively distinguish children with sepsis from healthy children. Thirteen taxa of gut microbiota were significantly increased in the guts of children with sepsis compared with those of healthy children. The increased abundances of Enterococcaceae, Enterococcus, and Enterococcus durans in gut of septic patients were significantly positively correlated with blood inflammation indicators CRP and WBC. The abundances of seven bacteria were significantly decreased in the guts of septic children compared with those of healthy children. The decreased abundance of Bifidobacteriales in gut of septic patients is significantly negatively correlated with blood inflammation index WBC. A machine-learning classifier was built for distinguishing sepsis and achieved the AUC value of 81.25%. It shows that the composition of gut microbiota has certain potential for diagnosis of sepsis. Conclusions: Gut microbiota alterations in septic patients exhibit proliferation of opportunistic pathogenic bacteria, the massive reduction of the commensal flora, and the significant decrease in the diversity of the gut microbiota. Dysbiosis may also account for some changes in the inflammation indexes.

Project description:BACKGROUND: In the neonatal intensive care unit (NICU), predefined acuity-based team care models are restricted to core roles and neglect interactions with providers outside of the team, such as interactions that transpire via electronic health record (EHR) systems. These unaccounted interactions may be related to the efficiency of resource allocation, information flow, communication, and thus impact patient outcomes. This study applied network analysis methods to EHR audit logs to model the interactions of providers beyond their core roles to better understand the interaction network patterns of acuity-based teams and relationships of the network structures with postsurgical length of stay (PSLOS). METHODS: The study used the EHR log data of surgical neonates from a large academic medical center. The study included 104 surgical neonates, for whom 9,206 unique actions were performed by 457 providers in their EHRs. We applied network analysis methods to model EHR provider interaction networks of acuity-based teams in NICU postoperative care. We partitioned each EHR network into three subnetworks based on interaction types: (1) interactions between known core providers who were documented in scheduling records (core subnetwork); (2) interactions between core and noncore providers (extended subnetwork); and (3) interactions between noncore providers (extended subnetwork). For each core subnetwork, we assessed its capability to replicate predefined core-provider relations as documented in scheduling records. We further compared each EHR network, as well as its subnetworks, using standard network measures to determine its differences in network topologies. We conducted a case study to learn provider interaction networks taking care of 15 neonates who underwent gastrostomy tube placement surgery from EHR log data and measure the effectiveness of the interaction networks on PSLOS by the proportional-odds model. RESULTS: The provider networks of four acuity-based teams (two high and two low acuity), along with their subnetworks, were discovered. We found that beyond capturing the predefined core-provider relations, EHR audit logs can also learn a large number of relations between core and noncore providers or among noncore providers. Providers in the core subnetwork exhibited a greater number of connections with each other than with providers in the extended subnetworks. Many more providers in the core subnetwork serve as a hub than those in the other types of subnetworks. We also found that high-acuity teams exhibited more complex network structures than low-acuity teams, with high-acuity team generating 6,416 interactions between 407 providers compared with 931 interactions between 124 providers, respectively. In addition, we discovered that high-acuity and low-acuity teams shared more than 33 and 25% of providers with each other, respectively, but exhibited different collaborative structures demonstrating that NICU providers shift across different acuity teams and exhibit different network characteristics. Results of case study show that providers, whose patients had lower PSLOS, tended to disperse patient-related information to more colleagues within their network than those who treated higher PSLOS patients (p = 0.03). CONCLUSION: Network analysis can be applied to EHR log data to model acuity-based NICU teams capturing interactions between providers within the predesigned core team as well as those outside of the core team. In the NICU, dissemination of information may be linked to reduced PSLOS. EHR log data provide an efficient, accessible, and research-friendly way to study provider interaction networks. Findings should guide improvements in the EHR system design to facilitate effective interactions between providers.

Dataset Information

Sepsis in Intensive Care Unit Patients: Worldwide Data From the Intensive Care over Nations Audit.

Publications

Sepsis in Intensive Care Unit Patients: Worldwide Data From the Intensive Care over Nations Audit.

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