Dataset Information

Practice Patterns and Outcomes Associated With Procalcitonin Use in Critically Ill Patients With Sepsis.

ABSTRACT:

Background.

Randomized trials support use of procalcitonin (PCT)-based algorithms to decrease duration of antibiotics for critically ill patients with sepsis. However, current use of PCT and associated outcomes in real-world clinical settings is unclear. We sought to determine PCT use in critically ill patients with sepsis in the United States and to examine associations between PCT use and clinical outcomes.

Methods.

This was a retrospective cohort study of approximately 20% of patients with sepsis hospitalized in US intensive care units. Hierarchical regression models were used to determine associations of PCT use with outcomes (antibiotic-days, incidence of Clostridium difficile infection, and in-hospital mortality). Sensitivity analyses were conducted to assess robustness of findings to different methods used to address unmeasured confounding (eg, instrumental variable, difference-in-differences analyses).

Results.

Among 20750 critically ill patients with sepsis in 107 hospitals with PCT available, 3769 (18%) patients had PCT levels checked; 1119 (29.7%) had serial PCT measurements. PCT use was associated with increased antibiotic-days (adjusted relative risk, 1.1; 95% confidence interval [CI], 1.15-1.18) and incidence of C. difficile (adjusted odds ratio, 1.42; 95% CI, 1.09-1.85) without a change in mortality (adjusted hazard ratio, 1.05; 95% CI, 0.93-1.19). Analysis of PCT use by instrumental variable and difference-in-difference analyses showed similar lack of antibiotic or outcome improvements associated with PCT use.

Conclusions.

PCT use was not associated with improved antibiotic use or other clinical outcomes in real-world settings. Programs to improve implementation of PCT-based strategies are warranted prior to widespread adoption.

SUBMITTER: Chu DC

PROVIDER: S-EPMC5434362 | biostudies-literature | 2017 Jun

REPOSITORIES: biostudies-literature

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Publications

Practice Patterns and Outcomes Associated With Procalcitonin Use in Critically Ill Patients With Sepsis.

Chu David C DC Mehta Anuj B AB Walkey Allan J AJ

Clinical infectious diseases : an official publication of the Infectious Diseases Society of America 20170601 11

<h4>Background.</h4>Randomized trials support use of procalcitonin (PCT)-based algorithms to decrease duration of antibiotics for critically ill patients with sepsis. However, current use of PCT and associated outcomes in real-world clinical settings is unclear. We sought to determine PCT use in critically ill patients with sepsis in the United States and to examine associations between PCT use and clinical outcomes.<h4>Methods.</h4>This was a retrospective cohort study of approximately 20% of p ...[more]

PMID: 28329238

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Project description:BackgroundProcalcitonin (PCT) has been widely investigated as an infection biomarker. The study aimed to prove that serum PCT, combining with other relevant variables, has an even better sepsis-detecting ability in critically ill patients.MethodsWe conducted a retrospective cohort study in a regional teaching hospital enrolling eligible patients admitted to intensive care units (ICU) between July 1, 2016, and December 31, 2016, and followed them until March 31, 2017. The primary outcome measurement was the occurrence of sepsis. We used multivariate logistic regression analysis to determine the independent factors for sepsis and constructed a novel PCT-based score containing these factors. The area under the receiver operating characteristics curve (AUROC) was applied to evaluate sepsis-detecting abilities. Finally, we validated the score using a validation cohort.ResultsA total of 258 critically ill patients (70.9±16.3 years; 55.4% man) were enrolled in the derivation cohort and further subgrouped into the sepsis group (n = 115) and the non-sepsis group (n = 143). By using the multivariate logistic regression analysis, we disclosed five independent factors for detecting sepsis, namely, "serum PCT level," "albumin level" and "neutrophil-lymphocyte ratio" at ICU admission, along with "diabetes mellitus," and "with vasopressor." We subsequently constructed a PCT-based score containing the five weighted factors. The PCT-based score performed well in detecting sepsis with the cut-points of 8 points (AUROC 0.80; 95% confidence interval (CI) 0.74-0.85; sensitivity 0.70; specificity 0.76), which was better than PCT alone, C-reactive protein and infection probability score. The findings were confirmed using an independent validation cohort (n = 72, 69.2±16.7 years, 62.5% men) (cut-point: 8 points; AUROC, 0.79; 95% CI 0.69-0.90; sensitivity 0.64; specificity 0.87).ConclusionsWe proposed a novel PCT-based score that performs better in detecting sepsis than serum PCT levels alone, C-reactive protein, and infection probability score.

Project description:Objective: It is unclear whether the host response of gram-positive sepsis differs from gram-negative sepsis at a transcriptome level. Using microarray technology, we compared the gene-expression profiles of gram-positive sepsis and gram-negative sepsis in critically ill patients. Design: A prospective cross-sectional study. Setting: A 20-bed general intensive care unit of a tertiary referral hospital. Patients: Seventy-two patients admitted to the intensive care unit. Interventions: Intravenous blood was collected for leukocyte separation and RNA extraction. Microarray experiements were then performed examing the expression level of 19,232 genes in each sample. Measurements and Main Results: There was no difference in the expression profile between gram-positive and gram-negative sepsis. The finding remained unchanged even when genes with lower expression level were included or after statistical stringency was lowered. There were, however, ninety-four genes differentially expressed between sepsis and control patients. These genes included those involved in immune regulation, inflammation and mitochondrial function. Hierarchical cluster analysis confirmed that the difference in gene expression profile existed between sepsis and control patients, but not between gram-positive and gram-negative patients. Conclusion: Gram-positive and gram-negative sepsis share a common host response at a transcriptome level. These findings support the hypothesis that the septic response is non-specific and is designed to provide a more general response that can be elicited by a wide range of different micro-organisms. The study included seventy-two critically ill patients admitted to the intensive care unit (ICU) of Nepean Hospital, Sydney, Australia. Of these, fifty-five patients were diagnosed to have sepsis, as confirmed by microbiological culture. The remaining seventeen patients did not have sepsis and were therefore used as controls. The study was approved by the hospital ethics committee and informed consent was obtained from all patients or their relatives. Patient Samples. Whole blood was taken from each patient on admission to ICU. Neutrophils were separated from whole blood using density-gradient separation with Ficoll-PaqueP P(Amersham). Subsequent neutrophil RNA extraction was performed using guanidinium thiocyanate (Ambion). Microarray Experiment. The neutrophil RNA was converted to cDNA, fluorescently labeled and hybridized to its complimentary sequences on the microarray (Invitrogen). The fluorescent signals on each micrroarray were captured using the GenePix 4000B laser scanner (Axon Instruments). Expression level of each gene was represented by the intensity of its fluorescent signal. Data Extraction. All signal intensity values were processed using background-subtraction method. Prior to analysis, all values were log-transformed and normalized by fitting a print-tip group Lowess curve. Normalization minimizes bias due to dye chemistry, signal intensity or location of a gene on the array. It ensures the detection of genes that are truly differentially expressed, instead of those caused by experimental artifacts or variation in the hybridization process. After normalization, genes that had more than 50% of data missing were removed. We then selected genes that had at least 80% of the data showing two-fold changes from the geneâs median values. After filtering, 1617 genes were available for further analysis.

Project description:IMPORTANCE:Atrial fibrillation (AF) during sepsis is associated with an increased risk of ischemic stroke during hospitalization, but risks and benefits associated with anticoagulation for AF during sepsis are unclear. OBJECTIVE:To determine clinician practice patterns and patient risk of stroke and bleeding associated with use of anticoagulation for AF during sepsis. DESIGN, SETTING, AND PARTICIPANTS:A retrospective cohort study using enhanced administrative claims data from approximately 20% of patients hospitalized in the United States July 1, 2010, to June 30, 2013, examined patients with AF during sepsis who did not have additional indications for therapeutic anticoagulation. Propensity score and instrumental variable analyses were used to evaluate risks of in-hospital stroke and bleeding associated with anticoagulation during sepsis. EXPOSURES:Parenteral anticoagulants administered in doses greater than those used for prophylaxis of venous thromboembolism. MAIN OUTCOMES AND MEASURES:Ischemic stroke and clinically significant bleeding events during hospitalization. RESULTS:Of 113?511 patients hospitalized with AF and sepsis, 38?582 were included in our primary analysis (18?976 men and 19?606 women; mean [SD] age, 74.9 [11.7] years). A total of 13?611 patients (35.3%) received parenteral anticoagulants, while 24?971 (64.7%) did not. Hospital utilization rates of parenteral anticoagulants for AF during sepsis varied (median, 33%; 25th-75th percentile, 25%-43%). CHA2DS2VASc scores (congestive heart failure, hypertension, age ?75 years [doubled], type 1 or type 2 diabetes, stroke or transient ischemic attack or thromboembolism [doubled], vascular disease [prior myocardial infarction, peripheral artery disease, or aortic plaque], age 65-75 years, sex category [female]) poorly discriminated the risk of ischemic stroke during sepsis (C statistic, 0.526). Among 27?010 propensity score-matched patients, rates of in-hospital ischemic stroke events did not differ significantly between patients who did (174 of 13?505 [1.3%]) and did not (185 of 13?505 [1.4%]) receive parenteral anticoagulation (relative risk [RR], 0.94; 95% CI, 0.77-1.15). Clinically significant bleeding occurred more often among patients who received parenteral anticoagulation (1163 of 13?505 [8.6%]) than patients who did not receive parenteral anticoagulation (979 of 13?505 [7.2%]; RR, 1.21; 95% CI, 1.10-1.32). Risk of ischemic stroke associated with parenteral anticoagulation did not differ significantly between patients with preexisting (RR, 1.12; 95% CI, 0.86-1.44) or newly diagnosed AF (RR, 0.85; 95% CI 0.57-1.27; P?=?.31 for interaction). Results were robust to multiple sensitivity analyses, including hospital utilization rates of parenteral anticoagulation for AF as an instrument for anticoagulation exposure (RR for stroke, 1.08; 95% CI, 0.62-1.90; RR for bleeding, 1.23; 95% CI, 0.88-1.72). CONCLUSIONS AND RELEVANCE:Among patients with AF during sepsis, parenteral anticoagulation was not associated with reduced risk of ischemic stroke and was associated with higher bleeding rates.

Project description:Background: Systemic inflammation is a whole body reaction that can have an infection-positive (i.e. sepsis) or infection-negative origin. It is important to distinguish between septic and non-septic presentations early and reliably, because this has significant therapeutic implications for critically ill patients. We hypothesized that a molecular classifier based on a small number of RNAs expressed in peripheral blood could be discovered that would: 1) determine which patients with systemic inflammation had sepsis; 2) be robust across independent patient cohorts; 3) be insensitive to disease severity; and 4) provide diagnostic utility. The overall goal of this study was to identify and validate such a molecular classifier. Methods and Findings: We conducted an observational, non-interventional study of adult patients recruited from tertiary intensive care units (ICU). Biomarker discovery was conducted with an Australian cohort (n = 105) consisting of sepsis patients and post -surgical patients with infection-negative systemic inflammation. Using this cohort, a four-gene classifier consisting of a combination of CEACAM4, LAMP1, PLA2G7 and PLAC8 RNA biomarkers was identified. This classifier, designated SeptiCyteÂ® Lab, was externally validated using RT-qPCR and receiver operating characteristic (ROC) curve analysis in five cohorts (n = 345) from the Netherlands. Cohort 1 (n=59) consisted of unambiguous septic cases and infection-negative systemic inflammation controls; SeptiCyteÂ® Lab gave an area under curve (AUC) of 0.96 (95% CI: 0.91-1.00). ROC analysis of a more heterogeneous group of patients (Cohorts 2-5; 249 patients after excluding 37 patients with infection likelihood possible) gave an AUC of 0.89 (95% CI: 0.85-0.93). Disease severity, as measured by Sequential Organ Failure Assessment (SOFA) score or the Acute Physiology and Chronic Health Evaluation (APACHE) IV score, was not a significant confounding variable. The diagnostic utility o f SeptiCyteÂ® Lab was evaluated by comparison to various clinical and laboratory parameters that would be available to a clinician within 24 hours of ICU admission. SeptiCyteÂ® Lab was significantly better at differentiating sepsis from infection-negative systemic inflammation than all tested parameters, both singly and in various logistic combinations. SeptiCyteÂ® Lab more than halved the diagnostic error rate compared to PCT in all tested cohorts or cohort combinations. Conclusions: SeptiCyteÂ® Lab is a rapid molecular assay that may be clinically useful in the management of ICU patients with systemic inflammation. SIRS and Sepsis ICU patients, admission samples Retrospective, mutli-site sutdy using retrospective physician adjudication as a comparator

Project description:BackgroundEarly and accurate diagnosis of sepsis is challenging. Although procalcitonin and presepsin have been identified as potential biomarkers to differentiate between sepsis and other non-infectious causes of systemic inflammation, the diagnostic accuracy of these biomarkers remains controversial. Herein, we performed a comprehensive meta-analysis to assess the overall diagnostic value of procalcitonin and presepsin for the diagnosis of sepsis.MethodsWe searched three electronic databases (MEDLINE, EMBASE, and the Cochrane Central Register of Controlled Trials) for relevant studies. Two authors independently screened articles on the basis of inclusion and exclusion criteria. The pooled sensitivity, specificity, and summary receiver operating characteristic curves were estimated. The quality of evidence for diagnostic accuracy in absolute effects, i.e., the number of true or false positives and true or false negatives, gave a particular pre-test probability.ResultsWe included 19 studies (19 observational studies and no randomized controlled trials) that had enrolled 3012 patients. Analyses of summary receiver operating characteristic curves revealed areas under the receiver operating characteristic curves of 0.84 for procalcitonin and 0.87 for presepsin. The pooled sensitivities and specificities were 0.80 (95% confidence interval 0.75 to 0.84) and 0.75 (95% confidence interval 0.67 to 0.81) for procalcitonin. For presepsin, these values were 0.84 (95% confidence interval 0.80 to 0.88) and 0.73 (95% confidence interval 0.61 to 0.82), respectively. There were no statistically significant differences in both pooled sensitivities (p = 0.48) and specificities (p = 0.57) between procalcitonin and presepsin.ConclusionOur meta-analysis provided evidence that the diagnostic accuracy of procalcitonin and presepsin in detecting infection was similar and that both are useful for early diagnosis of sepsis and subsequent reduction of mortality in critically ill adult patients.Systematic review registrationThe study was registered in PROSPERO under the registration number CRD42016035784.

Project description:Sepsis is defined by life-threatening organ dysfunction during infection and is the leading cause of death in hospitals. During sepsis, there is a high risk that new onset of atrial fibrillation (AF) can occur, which is associated with significant morbidity and mortality. Consequently, early prediction of AF during sepsis would allow testing of interventions in the intensive care unit (ICU) to prevent AF and its severe complications. In this paper, we present a novel automated AF prediction algorithm for critically ill sepsis patients using electrocardiogram (ECG) signals. From the heart rate signal collected from 5-min ECG, feature extraction is performed using the traditional time, frequency, and nonlinear domain methods. Moreover, variable frequency complex demodulation and tunable Q-factor wavelet-transform-based time-frequency methods are applied to extract novel features from the heart rate signal. Using a selected feature subset, several machine learning classifiers, including support vector machine (SVM) and random forest (RF), were trained using only the 2001 Computers in Cardiology data set. For testing the proposed method, 50 critically ill ICU subjects from the Medical Information Mart for Intensive Care (MIMIC) III database were used in this study. Using distinct and independent testing data from MIMIC III, the SVM achieved 80% sensitivity, 100% specificity, 90% accuracy, 100% positive predictive value, and 83.33% negative predictive value for predicting AF immediately prior to the onset of AF, while the RF achieved 88% AF prediction accuracy. When we analyzed how much in advance we can predict AF events in critically ill sepsis patients, the algorithm achieved 80% accuracy for predicting AF events 10 min early. Our algorithm outperformed a state-of-the-art method for predicting AF in ICU patients, further demonstrating the efficacy of our proposed method. The annotations of patients' AF transition information will be made publicly available for other investigators. Our algorithm to predict AF onset is applicable for any ECG modality including patch electrodes and wearables, including Holter, loop recorder, and implantable devices.

Dataset Information

Practice Patterns and Outcomes Associated With Procalcitonin Use in Critically Ill Patients With Sepsis.

Background.

Methods.

Results.

Conclusions.

Publications

Practice Patterns and Outcomes Associated With Procalcitonin Use in Critically Ill Patients With Sepsis.

Similar Datasets

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