Sepsis and its sequelae are important causes of mortality and morbidity among all age groups (1–3). In the United States, more than one million hospitalizations for sepsis are observed annually (including 700,000 cases of severe sepsis), and the occurrence rate of sepsis in western countries is increasing (4 , 5). Rapid initiation of empiric antibiotics (ABs) is essential for the treatment of sepsis (6). However, as it can be challenging to determine when it is safe to discontinue ABs for septic patients, ABs are often prescribed for unnecessarily long periods of time in sepsis leading to potential adverse outcomes (7). Specifically, incorrect use and overuse of ABs is associated with an increase in antimicrobial resistance, mortality, and healthcare costs (8). Unnecessary exposure to ABs also increases the risks of allergic reactions and Clostridium difficile infections (9 , 10). The World Health Organization recently declared antimicrobial resistance “one of the biggest threats to global health, food security, and development today.” (11) In the United States, the Centers for Disease Control and Prevention estimate there are at least 37,000 deaths (including deaths related to C. difficile) directly related to AB use and resistance, costing as much as $55 billion in direct and indirect healthcare costs annually (12). Improved management of AB therapy in the treatment of suspected or confirmed sepsis could reduce the occurrence of antimicrobial-resistant pathogens, adverse drug reactions, and unnecessary health costs (13).
Procalcitonin (PCT) is produced in the C-cells of the thyroid gland under normal physiologic conditions and increases rapidly during severe infections. PCT has a sensitivity of 94% as an indicator for sepsis (8 , 14). Based on several research studies, PCT is recognized as an effective biomarker for infection (14). In a study by Meisner et al (15), PCT provided more information on the severity, course of sepsis, and multiple organ dysfunction syndromes than C-reactive protein (CRP). In a prospective study in a tertiary care setting, PCT levels were also found to be correlated with Sepsis-related Organ Failure Assessment scores (14). More importantly than observational research, however, are interventional studies investigating the effects of PCT-guided decision making on patient outcomes and use of ABs and other resources (16).
Herein, our aim was to summarize the current state of the clinical evidence around the effectiveness and safety of PCT guidance compared with standard of care in adults with confirmed or suspected sepsis. Effectiveness was measured through AB duration, and safety was measured through length of stay (LOS) in the ICU and all-cause mortality. This study was conducted as part of a regulatory submission to the U.S. Food and Drug Administration (FDA) (17).
MATERIALS AND METHODS
Search Strategy and Selection Criteria
A systematic review and meta-analysis of randomized controlled trials (RCTs) published in peer-reviewed journals was performed to assess PCT-guided therapy compared with standard of care among adult patients with suspected or confirmed sepsis. Articles published in English between 2004 and 2016 were retained. Studies published before 2004 were not included in this review because they preceded the commercialization of the first automated (Conformité Européene In Vitro Diagnostics [CE-IVD] marked) PCT immunoassay.
The search strategy used a prospectively defined algorithm in PubMed and the Cochrane Database of Systematic Reviews and was conducted on May 19th, 2016. The following keywords were used: [“procalcitonin” OR “PCT”] AND [“anti bacterial agents” OR “antibiotic” OR “antibiotics” OR “antibacterial agent” OR “antibacterial agents” OR “anti bacterial agent” OR “anti bacterial agents” OR “antimicrobial agent” OR “antimicrobial agents” OR “anti microbial agent” OR “antimicrobial agents”] AND [“sepsis” OR “septic shock”, OR “bacteremia” OR “bacterial infections”]. Additional details on the search strategy can be found in the Supplementary Table 1 (Supplemental Digital Content 1, http://links.lww.com/CCM/D135) and Supplementary Table 2 (Supplemental Digital Content 2, http://links.lww.com/CCM/D136).
A panel of six reviewers participated in the inclusion process. Each article retained after the initial search was screened by two of these independent reviewers. Articles were excluded if any of the following conditions was met, in this order of importance: 1) no original study data were presented; 2) the population of interest was not patients with suspected or confirmed sepsis; 3) PCT was not used to guide AB clinical decision making; 4) absence of control group; 5) PCT was not the focus of the trial; and 6) the article was not in English. Studies that were only available in abstract/poster formats or included pediatric patients were also excluded. Discrepancies between reviewers’ inclusion and exclusion decisions were resolved through discussion. In the event that an agreement between both reviewers could not be reached, the advice of independent adjudicators was used as a tie-breaker. If multiple exclusions were identified, the highest-ranked exclusion was selected.
Subsequently, prospectively defined variables were extracted from the identified articles in an extraction grid. Two reviewers independently extracted data elements from the studies identified. Any discrepancies between the two extractions were identified in a reconciliation process by a third reviewer and were subsequently reconciled among the extractors. Authors of the original publications were not contacted.
In addition to the outcomes, other variables extracted from the articles included country of study, patient eligibility criteria, study setting, time to endpoint, and the PCT algorithm (i.e., the specific threshold of serum PCT that dictated whether or not to discontinue AB treatment). Physicians in the PCT-guided treatment arm were directed to consider their clinical judgment when making a decision about AB treatment, so the decision to discontinue AB was based on both PCT levels and clinical judgment. Adherence was defined as the congruence between the PCT algorithm and AB decision making. Aggregate-level demographic and clinical characteristics, such as age, race/ethnicity, previous use of AB medication, and primary diagnosis of interest were also extracted. The reviewers extracted data from the identified publications in a standardized Microsoft Excel form.
Mortality was expressed as a risk ratio (RR) and similarly, a weighted pooled RR was calculated among all studies that reported AB mortality. Continuous outcomes extracted included AB duration and ICU LOS (both measured in days) and were summarized using weighted mean differences (WMDs). All results were summarized in forest plots with both point estimates and 95% CI displayed. When the mean and SD were not reported in a study, the median, interquartile range (IQR), and/or range were extracted to estimate the mean and SD (18 , 19).
Bias was assessed using the Risk of Bias Assessment Tool for RCTs proposed by the Cochrane Collaboration (18). The Risk of Bias Assessment Tool provides an overview of the quality of the RCTs by scoring each publication on seven domains: random sequence generation (selection bias), allocation concealment (selection bias), blinding of participants and personnel (performance bias), blinding of outcome assessment (detection bias), incomplete outcome data (attrition bias), selective reporting (reporting bias), and other. Each domain is referred to as “low risk,” “high risk,” or “unclear risk.” Because of its ambiguity, the “other” category was not assessed in this study.
Both inverse-variance fixed and random effects (DerSimonian and Laird method) analyses were conducted (20). The I2 statistic was used to describe heterogeneity across studies (18 , 21 , 22). All analyses were conducted using Stata IC version 14.2, and package sbe24_3 was used to produce forest plots.
Figure 1 presents the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) diagram of the search strategy and study inclusion process (23). The PubMed literature search initially identified 354 potentially eligible articles, from which 79 were either not in English (n = 57) or published before 2004 (n = 22). The Cochrane Database of Systematic Reviews identified 94 potentially eligible publications. From both sources, 369 articles were identified for screening. After removing 36 duplicates, 333 articles remained. One expert of the U.S. FDA overseeing the search proposed an additional list of eight potentially relevant articles, one of which had already been identified and was removed (17). The seven remaining additional articles brought the total of articles to 340. After applying the exclusion criteria, 323 articles were excluded: 148 articles were excluded because the article did not present original data, 57 were excluded because the population was not patients with suspected or confirmed sepsis, 102 were excluded PCT because was not intended to guide clinical AB decision making, five article were excluded because there was no control group, 10 were excluded because PCT was not the focus of the publication, and one were excluded because it was not in English. A total of 17 articles were considered for final inclusion. After excluding posters and meeting abstracts (three), non-RCTs or RCTs with an irrelevant comparison arm (two), and pediatric studies (two), 10 studies remained and were included in the meta-analysis (24–33).
Table 1 presents key characteristics of the retained studies. Sample size of the RCTs included in the meta-analysis ranged between 27 and 1,546 subjects per study. Clinicians whose patients were in the PCT cohort were generally adherent to the PCT algorithm, (47–93%, among reported [24–26 , 31]). All 10 studies reported measures of AB duration, ICU LOS, and mortality. There was heterogeneity among the PCT algorithms used by the authors. Most studies used a cutoff of 0.5 ng/mL to recommend AB cessation among sepsis patients (24–27 , 29 , 30). In addition, studies included cessation rules when PCT levels had decreased by 80% of the peak PCT levels recorded (25 , 26), 90% of the peak (27), 90% of the baseline levels (31 , 33), or a reduction of 65–75% of the baseline levels (28 , 32).
The risk of bias assessment for the 10 studies retained is presented in Supplementary Figure 1 (Supplemental Digital Content 3, http://links.lww.com/CCM/D137) and Supplementary Figure 2 (Supplemental Digital Content 4, http://links.lww.com/CCM/D138). Because the data extracted came from publications instead of clinical study reports, it was not possible to assess whether some outcomes were omitted from the publication, and all studies had an unclear risk of bias for selective reporting (reporting bias). The type of bias with the most numerous high-risk studies was blinding of participants and personal (performance bias), which is due to the open-label nature of most of the trials.
Eight of the 10 retained studies were included in the meta-analysis of AB duration. Two studies (29 , 30) were excluded because AB duration was reported using measures that were inconsistent with those reported in the other studies. Layios et al (29) reported AB duration as consumption per 100 ICU days and did not find a statistically significant reduction in AB consumption (PCT patients, 62.6% treatment-days; control patients, 57.7% treatment days; p = 0.11). Najafi et al (30) reported AB duration as total number of treatment days by cohort (as opposed to mean per patient) and found a statistically significant reduction in exposure days (PCT patients, 128 d; control patients, 320 d; p = 0.003).
The overall mean AB duration from the random effects model was 7.35 days in the PCT arms and 8.85 days in the control arms. Both random effects and fixed effects models demonstrated a statistically significant reduction in AB duration (random effects WMD, –1.49 d; 95% CI, –2.27 to –0.71; p < 0.001; Fig. 2). Heterogeneity was considerable (I2 = 81.3%; p < 0.001).
The overall mean ICU LOS using the weights from the random effects model for all 10 included studies was 11.09 days in the treatment arms and 11.91 days in the control arms. There was no statistically significant difference in ICU LOS between the treatment arms (random effects WMD, –0.84 d; 95% CI, –2.52 to 0.84; p = 0.329; Fig. 3). Heterogeneity was considerable (I2 = 80.1%; p < 0.001). The time horizon for mortality varied between studies: while four studies reported mortality during the patient’s stay in the ICU (24 , 27 , 29 , 33), three studies reported mortality during the patient’s hospital stay (28 , 30 , 32), and three studies reported 28-day mortality (25 , 26 , 31). The pooled RR of mortality associated with PCT did not reach statistical significance (random effects RR, 0.90; 95% CI, 0.79–1.03; p = 0.114; Fig. 4). There was no indication of heterogeneity (I2 = 0.0%; p = 0.542). A funnel plot was produced to assess whether there was evidence of publication bias for mortality. No such evidence was found (data not shown).
The findings of this meta-analysis demonstrate that PCT is an effective biomarker in guiding AB discontinuation among patients with suspected or confirmed sepsis compared with the standard of care, with reductions in the treatment duration of AB and no observed adverse effects on ICU LOS and mortality. These findings are consistent with a 2013 systematic review by Prkno et al (34), that reviewed studies that use PCT in the ICU for patients with sepsis or septic shock. The review assessed 28-day mortality, hospital mortality, AB duration, ICU LOS, and hospital LOS (34). Seven studies were identified, of which five are included in the current review (24 , 25 , 28 , 31 , 32). The two other studies were not included because they did not present AB duration in a usable format for this review (35 , 36). The authors found similar conclusions to this study regarding mortality (both 28-day mortality and hospital mortality) and ICU LOS (hazard ratio [HR], 1.05; 95% CI, 0.82–1.29) (34). The authors also found longer AB duration among control patients compared with PCT patients (HR, 1.27; 95% CI, 1.01–1.53) (34). Our results complement these results with five additional studies, and add to the robustness of the findings.
An important feature of the present study’s findings was the high level of heterogeneity for AB duration and ICU LOS. In the case of AB duration, heterogeneity was caused by the study by Annane et al (24) (omitting this study results in heterogeneity of 0.0%). Although not statistically significant, the study by Annane et al (24) yielded slightly shorter AB duration among controls. This is due to the approximation of the variance in AB duration in each cohort. The median number of days on AB was 5 days in both the PCT and the control cohorts, but the approximated mean was higher in the PCT group due to the IQRs reported (control, = 2–5 d; PCT = 4–5 d). This is in contrast with the approach used by Prkno et al (34) (heterogeneity for AB duration was 0.0%), who did not approximate the mean number of days per cohort and used an exponential distribution to generate HR’s and variances (34). For ICU LOS, heterogeneity can be explained by the study by Nobre et al (31), which shows a 19-day reduction in ICU LOS. This is due to a patient with an unusually long stay in the control group (91 d). This outlier created an imbalance in the imputed means of the cohorts and is the reason for this large reduction in ICU LOS. The study by Deliberato et al (27) showed a statistically significant higher ICU LOS among PCT patients, which was also due to a patient whose ICU stay was long (57 d, in the PCT arm). A sensitivity analysis excluding the studies by Nobre et al (31) and Deliberato et al (27) was consistent with the findings from the main analysis (random effects WMD, –0.35 d; 95% CI, –1.06 to 0.37; I2 = 17.7%). Therefore, the high level of heterogeneity observed for AB duration and ICU LOS is not due to the comparison of incompatible studies (qualitative interaction) but rather because of the choice of methodology.
During sepsis, pathogens and their antigens stimulate pro- and anti-inflammatory mediators that constitute the host defense. These host-response markers are potential biomarkers that provide information about severity, causative organisms of infection, and risk for adverse patient outcomes. Recent studies have provided strong evidence that host-response markers facilitate early recognition of sepsis, enable assessment of its severity, and provide guidance regarding therapeutic decisions in individual patients (37–39). This may allow for a transition from bundled, nonspecific infection management involving protocols to more individualized management based on the clinical profile of each patient. Among different host-response markers, PCT has been found to be helpful in different studies to improved patient management (16 , 40). A meta-analysis from 2013 that included 3,244 critically ill patients classified as having sepsis or a systemic inflammatory response syndrome with no infection showed a good high discriminatory ability of PCT (area under the curve of 0.85), with pooled sensitivity and specificity of 0.77 and 0.79, respectively (41). Observational data for pneumonia and general emergency patients with positive blood culture as the gold standard have shown similar results with regard to discrimination (42 , 43).
Based on the presented meta-analysis and a 2012 individual patient data analysis focusing on respiratory infection patients (40 , 44), the U.S. FDA has recently cleared the expanded use of PCT (i.e., the Vidas B·R·A·H·M·S PCT Assay) to help healthcare providers determine if antibiotic treatment should be started or discontinued in patients with lower respiratory tract infections and discontinued in patients with sepsis (17).
Strengths and Limitations
The large number of subjects included and diversity in study characteristics are key strengths of the present meta-analysis. However, the study was also subject to limitations. First, the relatively small number of studies made it challenging to use additional meta-analytical techniques, such as stratifications and meta-regressions, to appraise the effect of independent variables on pooled estimates. Second, imputation of missing values was necessary for continuous outcomes when means or SDs were not available. Although the methods employed to transform those variables were validated approaches and are expected to have affected both treatment arms similarly (18 , 19), these methods created outliers for AB duration and ICU LOS and increased heterogeneity for these outcomes. Third, the definition of sepsis varied between studies. For example, while the study by Annane et al (24) included patients presenting with a “phenotype of severe sepsis or septic shock”, the study by Deliberato et al (27) only included patients with microbiologically confirmed infections. Because of these variations in the inclusion criteria, and because “sepsis” is not a consistently defined term (45), there could be discrepancies in the severity of the patients enrolled in each study and these results should be interpreted accordingly.
In light of the positive effect of PCT on reducing AB duration with no observed adverse impact on key safety outcomes, the use of PCT as a biomarker to guide AB treatment decision-making has the potential to improve the quality of care for adults with confirmed or suspected sepsis. Further research is necessary to assess the impact of PCT on decreasing the incidence of AB-related adverse events. Given the growing threat of antimicrobial resistance, the reduction in duration of AB treatments due to better-targeted treatment under PCT guidance could also have important societal implications.
The authors thank Nathalie Picot for her help in designing the literature search equation. They also thank Louise Zimmer and Dana Wilkins for adjudicating discrepant exclusion responses and editing the manuscript.
1. Vincent JL, Marshall JC, Namendys-Silva SA, et alICON investigators: Assessment of the worldwide burden of critical illness: The intensive care over nations (ICON) audit. Lancet Respir Med 2014; 2:380–386
2. Mayr FB, Yende S, Linde-Zwirble WT, et alInfection rate and acute organ dysfunction risk as explanations for racial differences in severe sepsis
. JAMA 2010; 303:2495–2503
3. Fleischmann C, Thomas-Rueddel DO, Hartmann M, et alHospital incidence and mortality rates of sepsis
. Dtsch Arztebl Int 2016; 113:159–166
4. Lagu T, Rothberg MB, Shieh MS, et alHospitalizations, costs, and outcomes of severe sepsis
in the United States 2003 to 2007. Crit Care Med 2012; 40:754–761
5. Suarez De La Rica A, Gilsanz F, Maseda EEpidemiologic trends of sepsis
in western countries. Ann Transl Med 2016; 4:325
6. Dellinger RP, Levy MM, Rhodes A, et alSurviving Sepsis
Campaign Guidelines Committee including The Pediatric Subgroup: Surviving sepsis
campaign: International guidelines for management of severe sepsis
and septic shock, 2012. Intensive Care Med 2013; 39:165–228
7. Zilahi G, McMahon MA, Povoa P, et alDuration of antibiotic therapy in the intensive care unit. J Thorac Dis 2016; 8:3774–3780
8. Kibe S AK, Barlow GDiagnostic and prognostic biomarkers
in critical care. J Antimicrob Chemother 2011; 66:ii33–40
9. Barlam TF, Soria-Saucedo R, Cabral HJ, et alUnnecessary antibiotics for acute respiratory tract infections: Association with care setting and patient demographics. Open Forum Infect Dis 2016; 3:ofw045
10. Srigley JA, Brooks A, Sung M, et alInappropriate use of antibiotics and Clostridium difficile infection. Am J Infect Control 2013; 41:1116–1118
13. Lawrence KL, Kollef MHAntimicrobial stewardship in the intensive care unit: Advances and obstacles. Am J Respir Crit Care Med 2009; 179:434–438
14. Sudhir U, Venkatachalaiah RK, Kumar TA, et alSignificance of serum procalcitonin in sepsis
. Indian J Crit Care Med 2011; 15:1–5
15. Meisner M, Tschaikowsky K, Palmaers T, et alComparison of procalcitonin (PCT) and C-reactive protein (CRP) plasma concentrations at different SOFA scores during the course of sepsis
and MODS. Crit Care 1999; 3:45–50
16. Sager R, Kutz A, Mueller B, et alProcalcitonin-guided diagnosis and antibiotic stewardship revisited. BMC Med 2017; 15:15
17. U.S. Food and Drug Administration: 2016 Meeting materials of the microbiology devices panel. 2016. Available at: goo.gl/JXEti5. Accessed April 7, 2017
18. Higgins JP GSCochrane Handbook for Systematic Review of Interventions. The Cochrane Collaboration, 2011; 5.1.0
19. Wan X, Wang W, Liu J, et alEstimating the sample mean and standard deviation from the sample size, median, range and/or interquartile range. BMC Med Res Methodol 2014; 14:135
20. DerSimonian R, Laird NMeta-analysis in clinical trials. Control Clin Trials 1986; 7:177–188
21. Higgins JP, Thompson SGQuantifying heterogeneity in a meta-analysis. Stat Med 2002; 21:1539–1558
22. Higgins JP, Thompson SG, Deeks JJ, et alMeasuring inconsistency in meta-analyses. BMJ 2003; 327:557–560
23. Moher D, Shamseer L, Clarke M, et alPRISMA-P Group: Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement. Syst Rev 2015; 4:1
24. Annane D, Maxime V, Faller JP, et alProcalcitonin levels to guide antibiotic therapy in adults with non-microbiologically proven apparent severe sepsis
: A randomised controlled trial. BMJ Open 2013; 3:e002186
25. Bouadma L, Luyt CE, Tubach F, et alPRORATA trial group: Use of procalcitonin to reduce patients’ exposure to antibiotics in intensive care units (PRORATA trial): A multicentre randomised controlled trial. Lancet 2010; 375:463–474
26. de Jong E, van Oers JA, Beishuizen A, et alEfficacy and safety of procalcitonin guidance in reducing the duration of antibiotic treatment in critically ill patients: A randomised, controlled, open-label trial. Lancet Infect Dis 2016; 16:819–827
27. Deliberato RO, Marra AR, Sanches PR, et alClinical and economic impact of procalcitonin to shorten antimicrobial therapy in septic patients with proven bacterial infection in an intensive care setting. Diagn Microbiol Infect Dis 2013; 76:266–271
28. Hochreiter M, Köhler T, Schweiger AM, et alProcalcitonin to guide duration of antibiotic therapy in intensive care patients: A randomized prospective controlled trial. Crit Care 2009; 13:R83
29. Layios N, Lambermont B, Canivet JL, et alProcalcitonin usefulness for the initiation of antibiotic treatment in intensive care unit patients. Crit Care Med 2012; 40:2304–2309
30. Najafi A, Khodadadian A, Sanatkar M, et alThe comparison of procalcitonin guidance administer antibiotics with empiric antibiotic therapy in critically ill patients admitted in intensive care unit. Acta Med Iran 2015; 53:562–567
31. Nobre V, Harbarth S, Graf JD, et alUse of procalcitonin to shorten antibiotic treatment duration in septic patients: A randomized trial. Am J Respir Crit Care Med 2008; 177:498–505
32. Schroeder S, Hochreiter M, Koehler T, et alProcalcitonin (PCT)-guided algorithm reduces length of antibiotic treatment in surgical intensive care patients with severe sepsis
: Results of a prospective randomized study. Langenbecks Arch Surg 2009; 394:221–226
33. Shehabi Y, Sterba M, Garrett PM, et alProGUARD Study Investigators; ANZICS Clinical Trials Group: Procalcitonin algorithm in critically ill adults with undifferentiated infection or suspected sepsis
. A randomized controlled trial. Am J Respir Crit Care Med 2014; 190:1102–1110
34. Prkno A WC, Brunkhorst F, Schlattmann PProcalcitonin-guided therapy in intensive care unit patients with severe sepsis
and septic shock—a systematic review and meta-analysis. Crit Care 2013; 17:1–11
35. Jensen JU, Hein L, Lundgren B, et alProcalcitonin And Survival Study (PASS) Group: Procalcitonin-guided interventions against infections to increase early appropriate antibiotics and improve survival in the intensive care unit: A randomized trial. Crit Care Med 2011; 39:2048–2058
36. Svoboda P, Kantorová I, Scheer P, et alCan procalcitonin help us in timing of re-intervention in septic patients after multiple trauma or major surgery? Hepatogastroenterology 2007; 54:359–363
37. Schuetz P, Albrich W, Christ-Crain M, et alProcalcitonin for guidance of antibiotic therapy. Expert Rev Anti Infect Ther 2010; 8:575–587
38. Schuetz P, Aujesky D, Müller C, et alBiomarker-guided personalised emergency medicine for all—hope for another hype? Swiss Med Wkly 2015; 145:w14079
39. Schuetz P, Christ-Crain M, Müller BProcalcitonin and other biomarkers
to improve assessment and antibiotic stewardship in infections—hope for hype? Swiss Med Wkly 2009; 139:318–326
40. Schuetz P, Briel M, Mueller BClinical outcomes associated with procalcitonin algorithms to guide antibiotic therapy in respiratory tract infections. JAMA 2013; 309:717–718
41. Wacker C, Prkno A, Brunkhorst FM, et alProcalcitonin as a diagnostic marker for sepsis
: A systematic review and meta-analysis. Lancet Infect Dis 2013; 13:426–435
42. Müller F, Christ-Crain M, Bregenzer T, et alProHOSP Study Group: Procalcitonin levels predict bacteremia in patients with community-acquired pneumonia: A prospective cohort trial. Chest 2010; 138:121–129
43. Laukemann S, Kasper N, Kulkarni P, et alCan we reduce negative blood cultures with clinical scores and blood markers? Results from an observational cohort study. Medicine (Baltimore) 2015; 94:e2264
44. Schuetz P, Muller B, Christ-Crain M, et alProcalcitonin to initiate or discontinue antibiotics in acute respiratory tract infections. Cochrane Database Syst Rev 2012; 9:CD007498
45. Singer M, Deutschman CS, Seymour CW, et alThe third international consensus definitions for sepsis
and septic shock (Sepsis
-3). JAMA 2016; 315:801–810
anti-bacterial agents; biomarkers; calcitonin; sepsis; systemic inflammatory response syndrome
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