Patients with impaired kidney function have a significantly slower decrease of procalcitonin (PCT) levels during infection. Our aim was to study PCT-guided antibiotic stewardship and clinical outcomes in patients with impairments of kidney function as assessed by creatinine levels measured upon hospital admission.
We pooled and analyzed individual data from 15 randomized controlled trials who were randomly assigned to receive antibiotic therapy based on a PCT-algorithms or based on standard of care. We stratified patients on the initial glomerular filtration rate (GFR, ml/min/1.73 m2) in three groups (GFR >90 [chronic kidney disease; CKD 1], GFR 15–89 [CKD 2–4] and GFR<15 [CKD 5]). The main efficacy and safety endpoints were duration of antibiotic treatment and 30-day mortality.
Mean duration of antibiotic treatment was significantly shorter in PCT-guided (n=2,492) compared to control patients (n=2,510) (9.5–7.6 days; adjusted difference in days −2.01 [95% CI, −2.45 to −1.58]). CKD 5 patients had overall longer treatment durations, but a 2.5-day reduction in treatment duration was still found in patients receiving in PCT-guided care (11.3 vs. 8.6 days [95% CI −3.59 to −1.40]). There were 397 deaths in 2,492 PCT-group patients (15.9%) compared to 460 deaths in 2,510 control patients (18.3%) (adjusted odds ratio, 0.88 [95% CI 0.78 to 0.98)]. Effects of PCT-guidance on antibiotic treatment duration and mortality were similar in subgroups stratified by infection type and clinical setting (p interaction >0.05).
This individual patient data meta-analysis confirms that the use of PCT in patients with impaired kidney function, as assessed by admission creatinine levels, is associated with shorter antibiotic courses and lower mortality rates.
Antibiotic stewardship has become an international priority to reduce risk of multi-resistant organisms and potential drug-related side effects for patients . Among different other clinical and laboratory markers, procalcitonin (PCT) has emerged as an adjunct to clinical judgement to assess the risk for bacterial infection and treatment response to antibiotic therapy , , . PCT is released by different tissues in the body in response to systemic inflammation caused by bacterial infections through cytokine stimulation (e.g., interleukin [IL)-1β, IL-6 or tumor necrosis factor [TNF]-α) , , . The short time until PCT increases after bacterial infection and the kinetic profile, which differs in patients with and without response to infection , make PCT an interesting marker to monitor patients with infections and help to early reduce antibiotic treatment in case of a favorable clinical response , . Multiple randomized studies found that antibiotic stewardship based on clinical judgement and PCT levels results in reduced antibiotic exposure, lower risk for side-effects and improvements in clinical outcomes including overall survival , , . Two patient data meta-analyses published in 2018 with focus on patients with respiratory tract infections ,  and patients with sepsis  also confirmed associations of PCT-guidance and lower antibiotic consumption as well as improved health benefits. Still it is important to understand that PCT should be used only as an adjunct to clinical decision making since several factors and conditions may cause false positive and false negative results .
Kidney function has been identified as an important parameter that influences PCT kinetics. While PCT levels in healthy individuals are below the limit of detection, in patients with chronic kidney disease (CKD), elevated PCT levels and slower PCT kinetics have been reported , , . This may be due to different potential reasons including low grade inflammation in CKD patients with a constant stimulation of PCT ,  and a slower clearance of PCT due to impaired renal function. The importance of renal function on PCT kinetics, however, has been controversial with some reports not finding a significant effect of renal clearance on PCT , , , while a recently published case-control study reported a reduced PCT-clearance in patients with impaired kidney function . Importantly, there is a lack of data from clinical trials investigating whether the effectiveness and safety of antibiotic stewardship based on PCT levels is possibly reduced in CKD patients.
Herein, we conducted a secondary analysis using data from an individual-patient data meta-analysis to assess the safety and efficacy of using PCT to guide antibiotic treatment decisions according to kidney function based on admission creatinine levels in patients with respiratory infections or sepsis included in previous randomized trials.
We conducted a secondary analysis using our updated individual patient database , , , , , , . The study selection and data collection was based on the original protocol published in the Cochrane Library  and the report was prepared following the Preferred Reporting Items for Systematic Review and Meta-Analysis individual participant data guidelines , . Overall, we selected individual patient data from 15 randomized controlled trials including patients with a clinical diagnosis of infection treated in the ICU or in the medical ward and information about kidney function. Hence, in accordance with the initial protocol, we excluded trials lacking information regarding kidney function as well as pediatric trials and those not using PCT for guiding the initiation and duration of antibiotic therapy.
For this review the trial search was updated in February 2018 in collaboration with the Cochrane collaboration and conducted in all databases from the date of their inception to February 2018. In addition to the Cochrane Central Register for Controlled Trials (January 2017, Issue 1) databases searched included Medline Ovid (1966 to February 2017) and Embase (1980 to February 2017). All references were screened for eligibility and there were no language or publication restrictions. Two authors (Y.W. and M.A.M.) independently assessed trial eligibility based on titles, abstracts, and full-text reports, with further information being obtained directly from investigators as needed. Study protocols, case report forms, and unedited databases containing individual patient data were requested from investigators of all eligible trials. Data from each trial were first checked against reported results and queries were resolved with the principal investigator, trial data manager, or statistician. Data were assessed in a consistent manner across all trials, with standard definitions and parameters, and thus mortality rates differed slightly from previous reports. In accordance with the Cochrane methodology, we used the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE)  approach to assess risk of selection bias, performance bias, detection bias, attrition bias, reporting bias, and other types of bias. Initially, the grading was done by two authors (Y.W. and M.A.M.) and if conflicting, the grading was discussed with another author (P.S.) and within the meta-analysis group.
In the final analysis we enrolled all patients with a known kidney function and a suspected or proven infection who had been included in a previous trial and were randomized either to PCT-guided care or a control group. There were no exclusion criteria except for lack of information regarding kidney function. The main efficacy endpoint was duration of antibiotic treatment within 30 days. The main safety endpoint was all-cause mortality within 30 days of randomization. In case of a shorter follow-up period, the available information in the trials was used (e.g., mortality at the time of hospital discharge). Secondary outcomes comprised length of hospital stay, length of ICU stay, and the need for kidney replacement therapy. The definition of and indication for renal replacement therapy was based on the definition used in the different trials.
Patients were divided into three groups according to the glomerular filtration rate (GFR) (estimated using the MDRD [Modification of Diet in Renal Disease] study equation) and based on the CKD (Chronic Kidney Disease) classification from the KDIGO (Kidney Disease Improving Global Outcomes) . Patients with a GFR above or equal to 90 ml/min/1.73 m2 were assigned to the group with normal kidney function, patients with a GFR between 15 and 89 ml/min/1.73 m2 were assigned to the group with impaired renal function and patients with a GFR lower than 15 ml/min/1.73 m2 were assigned to the group with end stage renal disease.
The statistical approach was similar to the previously published Cochrane Library study protocol  with additional stratification according to kidney function. Using multivariate hierarchical logistic regression ,  we calculated odds ratios (ORs) and 95% confidence intervals (CIs) for the primary endpoint mortality. Variables in the multivariate analysis included treatment arm, age, treatment setting (ICU, medical ward) and type of infection. To control for within- and between-trial variability, a “trial” variable was added to the model as a random effect. For continuous and binary secondary endpoints, corresponding linear and logistic regression models were fitted, respectively. Patients were analyzed in groups to which they initially were randomly assigned and thus following the intention to treat principle. Predefined subgroup analyses were performed for type of infection, blood culture results, setting (ICU, medical ward) and the level of organ dysfunction (Sequential Organ Failure Assessment (SOFA)). All statistical analyses were done using STATA version 15.1 (StataCorp. College Station, Texas).
A total of 990 records were identified in the initial search, of which 71 were assessed for eligibility and a total of 32 trials were potentially eligible for analysis. Seventeen trials were excluded due to lack of clinical data or information regarding kidney function. Within the remaining 15 trials, we excluded 1,833 from 6,835 individual patients due to missing information regarding creatinine values (Figure 1). Our final analysis thus consists of 5,002 individual patient records included in fifteen randomized controlled trials. The trials were conducted in nine different countries, namely Australia, Belgium, Brazil, Denmark, France, Germany, Italy, The Netherlands and Switzerland (Table 1). Eleven of these trials were multicentric, nine were performed in the ICU and six in the emergency department (ED) and medical ward. The three largest trials were the SAPS trial (n=1,516) , the ProHOSP trial (n=1,359)  and the SISPICT trial (n=1,089) . The PCT algorithms used in the different trials were similar in concept and focused mainly on optimizing antibiotic tailoring by early stopping of therapy based on low PCT cutoff levels or a decrease in PCT from the peak by ≥80%. Adherence rates to PCT protocols were variable and ranged from 44 to 97%.
|First author, year||Country||Type of trial||Setting||Patients included in original trial, n||Patients excluded, missing information regarding kidney function, n||Clinical diagnosis||Type of PCT algorithm and PCT cutoffs used, µg/L||Adherence to PCT protocol, %|
|Annane (2013)||France||Multicenter||ICU||62||52||Severe sepsis without overt source of infection and negative blood culture||Initiation and duration; R against AB: <0.5 (<0.25); R for AB: >0.5 (>0.5)||63%|
|Bouadma (ProRATA, 2010)||France||Multicenter||ICU||621||593||Critically ill patients with assumed/proven bacterial infection||Initiation and duration; R against AB: <0.5 (<0.25); R for AB: >0.5 (>0.1)||46%|
|Layios (2012)||Belgium||Single center||ICU||379||3||Critically ill patients with suspected infection||Initiation; R against AB: <0.5 (<0.25); R for AB: >0.5 (>1.0)||46.3%|
|Krisstoffersen (2009)||Denmark||Multicenter||ED, medical ward||210||208||Lower ARI without radiographic confirmation||Initiation and duration; R against AB: <0.25; R for AB: >0.25 (>0.5)||59%|
|Oliveira (2013)||Brazil||Multicenter||ICU||94||75||Severe sepsis or septic shock (SOFA score>10 and/or bacteremia)||Discontinuation; initial<1.0: R against AB: 0.1 at day 4; initial>1.0: R against: >90% drop over peak value||87.8%|
|Christ-Crain (ProCAP, 2006)||Switzerland||Single center||ED, medical ward||302||5||CAP with radiographic confirmation||Initiation and duration; R against AB: <0.25 (<0.1); R for AB: >0.25 (>0.5)||87%|
|Stolz (ProCOLD, 2007)||Switzerland||Single center||ED, medical ward||208||174||COPD exacerbation according to the global initiative for chronic obstructive lung disease guidelines||Initiation and duration; R against AB: <0.25 (0.1); R for AB: > 0.25||Not reported|
|Christ-Crain (ProRESP, 2004)||Switzerland||Single center||ED||243||24||Lower ARI with radiographic confirmation||Initiation; R against AB: <0.25 (<0.1); R for AB: >0.25 (>0.5)||83%|
|De Jong (SAPS, 2016)||The Netherlands||Multicenter||ICU||1516||0||Critically ill patients with presumed infection||Duration; R against AB: <0.5 or >80% drop over peak value||44%|
|Bloss (SISPICT 2016)||Germany||Multicenter||ICU||1089||13||Severe sepsis and septic shock||Discontinuation at day 4, 7 and 10; R against AB: <1.0 or >50% drop to previous value||49.6%|
|Shehabi (2014)||Australia||Multicenter||ICU||394||355||Suspected sepsis, undifferentiated infections||Duration; R against AB: < 0.25 (<0.1) or > 90% drop||97%|
|Verduri (2015)||Italy||Multicenter||ED, medical ward||178||166||Acute COPD exacerbation||Initiation; R against AB: <0.1; R for AB: >0.25||Not reported|
|Nobre (2008)||Switzerland||Multicenter||ICU||79||71||Suspected severe sepsis or septic shock||Duration; R against AB: <0.5 (<0.25) or >80% drop; R for AB: >0.5 (>1.0)||81%|
|Schuetz (ProHOSP, 2009)||Switzerland||Multicenter||ED, medical ward||1359||50||Lower ARI with radiographic confirmation||Initiation and duration; R against AB: <0.25(<0.1); R for AB: >0.25 (>0.5)||91%|
|Stolz (ProVAP, 2009)||Switzerland||Multicenter||ICU||101||44||VAP when intubated>48 h||Duration; R against AB: <0.5 (0.25) or >80% drop; R for AB: >0.5 (>1.0)||Not reported|
ICU, intensive care unit; ED, emergency department; AB, antibiotic; ARI, acute respiratory infection; SOFA, Sequential Organ Failure Assessment, COPD, chronic obstructive pulmonary disease; VAP, ventilator-associated pneumonia; PCT, procalcitonin; R, recommendation.
Overall, 1,420 patients (28.4%) had a normal kidney function (CKD 1), 2,512 patients (50.2%) had an impaired renal function (CKD 2–4) and 1,070 patients (21.4%) had end stage renal disease (CKD 5).
Baseline characteristics of patients were overall similar in the PCT and control groups as well as in the different CKD subgroups. Table 2 and Supplementary Material, Appendix Table 1 shows baseline characteristics stratified according to randomization and kidney function. The most common infection focus was the respiratory tract in 29%. The SOFA scores and PCT values on admission were markedly higher in patients with worse kidney function. The majority of patients was treated in the ICU.
|Parameter||Patients with normal kidney function CKD stage G1||Patients with impaired kidney function|
|Control (n=698)||PCT-group (n=722)||CKD stages G2-G4||CKD stage G5|
|Control (n=1260)||PCT-group (n=1252)||Control (n=552)||PCT-group (n=518)|
|Age, years (mean ± SD)||56.2 (16.3)||55.3 (17.1)||68.8 (14.4)||68.8 (13.8)||71.8 (13.4)||72.8 (12.7)|
|Male sex||422 (60.5%)||431 (59.7%)||745 (59.1%)||768 (61.3%)||318 (57.6%)||309 (59.7)|
|Infect focus, n (%)|
|Respiratory||273 (39.1%)||295 (40.9%)||373 (29.6%)||355 (28.4%)||88 (15.9%)||78 (15.1%)|
|Pneumoniae||139 (19.9%)||137 (19.0%)||332 (26.3%)||324 (25.9%)||184 (33.3%)||186 (35.9%)|
|COPD/bronchitis||64 (9.2%)||61 (8.4%)||134 (10.6%)||116 (9.3%)||78 (14.1%)||74 (14.3%)|
|URTI||5 (0.7%)||5 (0.7%)||4 (0.3%)||3 (0.2%)||–||–|
|Asthma||1 (0.1%)||3 (0.4%)||1 (0.1%)||4 (0.3%)||–||–|
|Urinary||16 (2.3%)||13 (1.8%)||35 (2.8%)||31 (2.5%)||25 (4.5%)||22 (4.2%)|
|Abdominal||56 (8.0%)||54 (7.5%)||201 (16.0%)||199 (15.9%)||77 (13.9%)||60 (11.6%)|
|Skin/soft tissue||10 (1.4%)||7 (1.0%)||15 (1.2%)||7 (0.6%)||8 (1.4%)||3 (0.6%)|
|Central nervous system||18 (2.6%)||14 (1.9%)||10 (0.8%)||16 (1.3%)||1 (0.2%)||2 (0.4%)|
|Genital/gynecologic||2 (0.3%)||0 (0.0%)||4 (0.3%)||3 (0.2%)||2 (0.4%)||0 (0.0%)|
|Catheter-related||5 (0.7%)||2 (0.3%)||5 (0.4%)||5 (0.4%)||0 (0.0%)||2 (0.4%)|
|Bloodstream||10 (1.4%)||6 (0.8%)||4 (0.3%)||4 (0.3%)||2 (0.4%)||4 (0.8%)|
|Others/Undifferentiated focusa||99 (14.2%)||125 (17.3%)||142 (11.3%)||185 (14.8%)||87 (15.8%)||87 (16.8%)|
|Setting, n (%)|
|ED/medical ward||215 (30.8%)||213 (29.5%)||485 (38.5%)||467 (37.3%)||269 (48.7%)||265 (51.2%)|
|ICU||483 (69.2%)||509 (70.5%)||775 (61.5%)||785 (62.7%)||283 (51.3%)||253 (48.8%)|
|Temperature, °C (mean ± SD)||37.9 (1.1)||38.0 (1.1)||37.8 (1.2)||37.9 (1.1)||37.8 (1.1)||37.9 (1.1)|
|SOFA score (points)||5.3 (3.6)||5.1 (3.3)||8.0 (3.7)||8.0 (3.9)||10.0 (3.8)||9.7 (4.0)|
|PCT day 0, µg/L (mean ± SD)||10.3 (41.7)||6.1 (25.4)||8.9 (23.2)||15.7 (45.0)||13.4 (31.6)||15.9 (41.7)|
|<0.1||113 (26.0%)||138 (19.5%)||116 (16.6%)||127 (10.8%)||35 (7.5%)||35 (7.2%)|
|0.1–0.25||78 (17.9%)||157 (22.1%)||147 (21.0%)||192 (16.3%)||72 (15.4%)||73 (15.1%)|
|0.25–0.5||41 (9.4%)||83 (11.7%)||72 (10.3%)||117 (9.9%)||51 (10.9%)||57 (11.8%)|
|0.5–2||75 (17.2%)||141 (19.9%)||106 (15.2%)||213 (18.1%)||84 (17.9%)||94 (19.4%)|
|>2||128 (29.4%)||190 (26.8%)||258 (36.9%)||530 (45.0%)||226 (48.3%)||226 (46.6%)|
|CRP day 0, mg/L (mean ± SD)||167.2 (123.0)||160.2 (120.1)||180.2 (126.5)||184.3 (130.7)||184.2 (131.6)||173.4 (133.6)|
|Creatinine, umol/L (mean ± SD)||47.7 (24.1)||45.3 (24.9)||121.0 (56.3)||121.5 (54.4)||232.5 (165.0)||238.7 (173.4)|
|Additional support, n (%)|
|Vasopressor use||352 (72.7%)||375 (73.4%)||734 (94.7%)||732 (93.2%)||237 (85.6%)||208 (86.0%)|
|Ventilator support||386 (79.9%)||407 (79.6%)||634 (82.7%)||669 (85.5%)||210 (76.1%)||187 (76.3%)|
|Renal replacement||50 (7.2%)||60 (8.3%)||133 (10.6%)||124 (9.9%)||118 (21.4%)||101 (19.5%)|
aMiscellaneous infections like endocarditis, mediastinitis, surgical wound infections or infection without a documented source.
ED, emergency department; ICU, intensive care unit; SD, standard deviation; SOFA, Sequential Organ Failure Assessment; PCT, procalcitonin; CRP, C-reactive protein; COPD, chronic obstructive pulmonary disease; URTI, upper respiratory tract infection; GFR, glomerular filtration rate; CKD, chronic kidney disease; ESRD, end stage renal disease.
Mean duration of antibiotic treatment was significantly shorter in PCT-guided patients (n=2,492) compared to control patients (n=2,510) (7.6 vs. 9.5 days, adjusted difference −2.01 days [95% CI −2.45 to 1.58]). CKD 5 patients had overall longer treatment durations, but a 2.5-day treatment duration reduction was still found in PCT-guided patients (8.6 vs. 11.3 days [95% CI −3.59 to −1.40]). Effects were similar in subgroups stratified based on type of infection, blood culture result, treatment location (ICU vs. ward) and SOFA score (Figures 2, 3, Supplementary Material, Appendix Tables 2–4 and Appendix Figures 1 and 2).
Within 30 days there were 397 deaths in 2,492 PCT-guided patients (15.9%) compared to 460 deaths in 2,510 control-group patients (18.3%), resulting in an adjusted odds ratio for overall mortality of 0.88 (95% CI, 0.78–0.98; p=0.023). There was no evidence for effect modification according to CKD group, but patients with CKD stages 2–4 had the highest mortality benefit from PCT-guidance (14.6 vs. 20%, adjusted OR 0.74 [95% CI 0.63–0.87, p<0.001). Mortality effects were similar in subgroups stratified based on type of infection, blood culture result, treatment location (ICU vs. ward) and SOFA score (Figures 4, 5, Supplementary Material, Appendix Table 2).
Overall, 285 of 2,492 (11.4%) patients in the PCT-guided group and 301 of 2,510 (12.0%) patients in the control group had acute renal failure and needed to undergo dialysis in the course of infection (adjusted OR 0.93 [95% CI 0.81–1.07], p=0.329). The risk for replacement therapy increased with CKD stage (7.8% in CKD 1, 10.2% in CKD 2–4, and 20.5% in CKD 5). Consistent with the overall finding, there were also no significant differences in renal replacement therapy rates between PCT-guided and control subjects throughout all subgroup analyses and no evidence of subgroup effect could be observed (p for interaction≥0.05, each) (Supplementary Material, Appendix Tables 3 and 4, Appendix Figures 1 and 2).
Length of hospital stay as well as length of ICU stay did not significantly differ in the PCT-guided and the control group (overall 22.7 ± 25.3 vs. 23.4 ± 24.1 days, adjusted difference 0.64 [95% CI -0.65 to 1.93], p=0.329 and 15.1 ± 16.5 vs. 15.4 ± 17.0 days, adjusted regression coefficient 0.15 [95% CI −1.03 to 1.33], p=0.801, respectively) (Supplementary Material, Appendix Tables 5 and 6, Appendix Figures 3–6). This finding was similar across all subgroups without evidence for a subgroup effect (p for interaction≥0.05, each).
The main findings of this meta-analysis including individual patient data from 5,002 participants from 15 randomized controlled trials are twofold. First, regarding efficacy, we found a significant reduction of antibiotic exposure due to shorter antibiotic treatment durations in PCT-guided patients in all kidney function subgroups. Although, CKD 5 patients had overall longer antibiotic courses, there was still a significant reduction observed in PCT-guided patients compared to the control group. Further, effects were similar in subgroups stratified by type of infection, blood culture results, site of treatment (medical ward vs. ICU), and disease severity according to SOFA score. Second, regarding safety, our overall analysis showed a significant reduction in mortality in PCT-guided patients compared to control group patients with different subgroup analyses showing similar results. Particularly, mortality and other safety outcomes were robust when stratified according to kidney function, by type of infection and by severity of illness.
Consistent with findings of previous studies, we found higher PCT levels in patients with impaired kidney function , , , . There are different explanations for the higher PCT levels in patients with CKD. First, persistent low-grade inflammation could contribute to higher PCT baseline levels in CKD patients and impairment in kidney function itself trigger an inflammatory response that elevates PCT levels. Gupta et al. showed that plasma levels of several cytokines, especially IL-1β, IL-6 and TNF-α were increased in patients with impaired kidney function . Hence, these cytokines may also induce expression of PCT by human epithelial cells throughout several tissues in the body. Second, a reduced renal clearance of both, stimulating inflammatory markers and PCT, may increase baseline as well as follow-up PCT levels too. Still, clinical data regarding the effects of renal function on PCT levels have been inconsistent. Meisner and colleagues examined the mechanisms of PCT elimination and revealed that even though the plasma clearance rate seems to be prolonged up to 30–50% in patients with kidney dysfunction, removal of PCT through the kidneys is not a major component . In contrast, a recently published case-control study, conducted by Wu and colleagues, found an association between diminished kidney function and insufficient renal PCT-clearance . They also observed a correlation between cystatin C, a more precise marker of kidney function compared to creatinine, and PCT and argued for similar clearance mechanisms due to similar molecular properties of these two molecules. Our analysis does not provide any new insights regarding mechanisms of PCT-elimination. However, we also confirm higher PCT-levels and longer antibiotic treatment durations in patients with impaired kidney function independent of randomization arm, suggesting that kidney function influences levels of PCT at baseline and during follow-up. Third, patients with CKD may have a functionally declined immune system, resulting in more severe infections. As we know from multiple trials regarding septic patients, early and accurate diagnosis and differentiation from noninfectious causes are crucial for rapidly starting the appropriate initial treatment , . This holds also true for patients with CKD and associated comorbidities who are prone to severe infections, sepsis and septic shock. However, overuse of antibiotics in patients with viral instead of bacterial infection as well as unnecessarily long antibiotic duration are jointly responsible for the development of multidrug-resistant bacterial pathogens , . Importantly, unnecessary administration and prolonged duration of antibiotic treatment contributes to antibiotic side effects and to a possible further impairment of kidney function and thus, worsening clinical outcomes. Hence, once antibiotic treatment is started, daily assessment of patients using clinical and objective parameters such as biomarkers is important. In recent years, PCT has been demonstrated to be a helpful adjunct to clinical judgement and traditional clinical parameters to decide whether to start antibiotic treatment and how long treatment should be continued. PCT levels increase within 6–12 h after bacterial invasion, are highest in patients with bacteremia, correlate with disease severity ,  and rapidly fall by about 50% each day during resolution of infection. In contrast, PCT expression is blocked by cytokines, which are typically released in response to viral infection, making it a more specific parameter to differentiate between bacterial and viral infection , . Several studies investigating the diagnostic accuracy of PCT yielded diverging results , . However, observational studies found sensitivities and specificities of around 80%, which could be increased using algorithms containing a variety of PCT-cutoff points in combination with clinical criteria , . Regarding safety of usage of such a PCT-algorithm, two different patient-level meta-analyses of high quality randomized controlled trials on patients with acute respiratory infections and critical illness showed improved survival, when antibiotic treatment was based on PCT-values , . In the safety assessment, our study showed no difference in mortality between the PCT-guided and the control group. But the reduced antibiotic treatment durations as well as no obvious difference in need for renal replacement therapy, length of hospital and length of ICU stay, did not reveal any associated harm. Therefore, it is helpful to use PCT to guide antibiotic stewardship in patients with CKD, but due to the finding of significant increased levels of PCT in patients with CKD even without infection, higher cutoff values seem reasonable. The earlier mentioned case-control study by Wu et colleagues revealed a 94.7% sensitivity and 90.8% specificity with the use of a PCT cutoff value of 0.075 ng/mL.
Our analysis supports the use of PCT in patients with impaired kidney function and is in line with several previous studies , , . In PCT-guided patients, antibiotic therapy was shortened by an average of 2 days. This effect tended to be more pronounced in patients with end stage renal disease and those suffering from respiratory tract infections. These effects were not significant in all subgroups due to small sample sizes, but the negative interaction test points to robust results. Clearly, more data is needed to look at specific patient populations, which were underrepresented in our analysis despite the individual data meta-analysis approach.
The strength of this meta-analysis includes a predefined study protocol, a comprehensive search and retrieval of all relevant trials, and a network that permitted inclusion of individual patient data from most eligible trials. We also standardized outcome definitions across trials and performed appropriate subgroup and sensitivity analyses, thereby overcoming the limitations of previous meta-analyses with aggregated data to allow more definitive conclusions. To our knowledge, this is the first analysis addressing the effects of PCT-guidance in patients with CKD.
However, there are some limitations of our study. First, we limited our data to immunocompetent adults and patients not being on hemodialysis before inclusion, thereby reducing generalisability of our conclusions to other patient populations. Second, the heterogeneity of our patient population with regard to focus of infection, clinical setting and disease severity, also limits generalisability of results, in particular with regard to the primary endpoint mortality. Third, the adherence to the PCT-protocols among the studies varied widely from 44 to 97%. Overall, adherence rates were better in low-risk populations, whereas the adherence in high-risk patients was lower. This can be explained by the fact, that in case of clinically seriously ill patients, physicians tend to decide rather on clinical findings than on laboratory results. Because of the fact, that low adherence may interfere with the effects of PCT-protocols, it is important that physicians are educated regarding correct and beneficial use of PCT . Finally, we had very limited data on kidney function for individual patients due to the heterogeneity of trials and thus based our analysis on the initial creatinine level. Particularly, we were not able strictly separate acute from chronic conditions and also do not know how kidney function changed over the course of the hospital stay. This data would also provide important information to better understand the influence of kidney function and its recovery on levels of PCT and associated clinical effects regarding antibiotic use and outcome.
In conclusion, this individual patient data meta-analysis confirms that in patients with impaired kidney function, estimated based on admission creatinine levels, the use of PCT is associated with shorter antibiotic treatment durations without any apparent harm.
Funding source: Roche Diagnostics
Award Identifier / Grant number: KSA 2019 001
We thank all researchers and patients involved in the individual trials for sharing their data.
Research funding: This research was supported by the Roche Diagnostics (KSA 2019 001).
Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
Competing interests: PS and BM report receiving grants from Thermofisher, Roche Diagnostics and BioMerieux. CEL received lecture fees from Thermofisher Brahms, BioMérieux. YS has received unrestricted research grants from Thermo-fisher, BioMérieux, Orion Pharma, and Pfizer. MCC, DS and MT have received research support from Thermo-Fisher. All other authors declare that they have no conflicts of interest. The National Institute for Health Research provided a grant for the initial Cochrane analysis. Roche Diagnostics provided an unrestricted research grant for this analysis. The funders of the study had no bearing on study design, data collection, data analysis, data interpretation, or writing of the report. The corresponding author had full access to all the data in the study and had final responsibility for the decision to submit for publication.
Informed consent: Informed consent was obtained from all individuals included in this study.
Ethical approval: All trial included in the meta-analysis enrolled patients providing informed consent and had approval from their Institutional review boards.
Availability of data and material: Data of the individual trials are available upon reasonable request based on the authorization of principal investigators of the trials.
2. Lee, CC, Kwa, ALH, Apisarnthanarak, A, Feng, JY, Gluck, EH, Ito, A, et al. Procalcitonin (PCT)-guided antibiotic stewardship in Asia-Pacific countries: adaptation based on an expert consensus meeting. Clin Chem Lab Med 2020;58:1983–91. https://doi.org/10.1515/cclm-2019-1122. Search in Google Scholar
3. Schuetz, P, Beishuizen, A, Broyles, M, Ferrer, R, Gavazzi, G, Gluck, EH, et al. Procalcitonin (PCT)-guided antibiotic stewardship: an international experts consensus on optimized clinical use. Clin Chem Lab Med 2019;57:1308–18. https://doi.org/10.1515/cclm-2018-1181. Search in Google Scholar
4. Neeser, O, Branche, A, Mueller, B, Schuetz, P. How to: implement procalcitonin testing in my practice. Clin Microbiol Infect 2019;25:1226–30. https://doi.org/10.1016/j.cmi.2018.12.028. Search in Google Scholar
5. Linscheid, P, Seboek, D, Schaer, DJ, Zulewski, H, Keller, U, Muller, B. Expression and secretion of procalcitonin and calcitonin gene-related peptide by adherent monocytes and by macrophage-activated adipocytes. Crit Care Med 2004;32:1715–21. https://doi.org/10.1097/01.ccm.0000134404.63292.71. Search in Google Scholar
6. Heilmann, E, Gregoriano, C, Schuetz, P. Biomarkers of infection: are they useful in the ICU? Semin Respir Crit Care Med 2019;40:465–75. https://doi.org/10.1055/s-0039-1696689. Search in Google Scholar
7. Schuetz, P, Aujesky, D, Muller, C, Muller, B. Biomarker-guided personalised emergency medicine for all - hope for another hype? Swiss Med Wkly 2015;145:w14079. https://doi.org/10.4414/smw.2015.14079. Search in Google Scholar
8. Schuetz, P, Birkhahn, R, Sherwin, R, Jones, AE, Singer, A, Kline, JA, et al. Serial procalcitonin predicts mortality in severe sepsis patients: results from the multicenter procalcitonin MOnitoring SEpsis (MOSES) study. Crit Care Med 2017;45:781–9. https://doi.org/10.1097/ccm.0000000000002321. Search in Google Scholar
9. Schuetz, P, Koller, M, Christ-Crain, M, Steyerberg, E, Stolz, D, Muller, C, et al. Predicting mortality with pneumonia severity scores: importance of model recalibration to local settings. Epidemiol Infect 2008;136:1628–37. https://doi.org/10.1017/s0950268808000435. Search in Google Scholar
10. Dandona, P, Nix, D, Wilson, MF, Aljada, A, Love, J, Assicot, M, et al. Procalcitonin increase after endotoxin injection in normal subjects. J Clin Endocrinol Metab 1994;79:1605–8. https://doi.org/10.1210/jc.79.6.1605. Search in Google Scholar
11. Schuetz, P, Wirz, Y, Sager, R, Christ-Crain, M, Stolz, D, Tamm, M, et al. Effect of procalcitonin-guided antibiotic treatment on mortality in acute respiratory infections: a patient level meta-analysis. Lancet Infect Dis 2018;18:95–107. https://doi.org/10.1016/s1473-3099(17)30592-3. Search in Google Scholar
12. de Jong, E, van Oers, JA, Beishuizen, A, Vos, P, Vermeijden, WJ, Haas, LE, et al. Efficacy 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;16:819–27. https://doi.org/10.1016/s1473-3099(16)00053-0. Search in Google Scholar
13. Schuetz, P, Balk, R, Briel, M, Kutz, A, Christ-Crain, M, Stolz, D, et al. Economic evaluation of procalcitonin-guided antibiotic therapy in acute respiratory infections: a US health system perspective. Clin Chem Lab Med 2015;53:583–92. https://doi.org/10.1515/cclm-2014-1015. Search in Google Scholar
14. Schuetz, P, Wirz, Y, Sager, R, Christ-Crain, M, Stolz, D, Tamm, M, et al. Procalcitonin to initiate or discontinue antibiotics in acute respiratory tract infections. Cochrane Database Syst Rev 2017;10:CD007498. https://doi.org/10.1002/14651858.CD007498.pub3. Search in Google Scholar
15. Wirz, Y, Meier, MA, Bouadma, L, Luyt, CE, Wolff, M, Chastre, J, et al. Effect of procalcitonin-guided antibiotic treatment on clinical outcomes in intensive care unit patients with infection and sepsis patients: a patient-level meta-analysis of randomized trials. Crit Care 2018;22:191. https://doi.org/10.1186/s13054-018-2125-7. Search in Google Scholar
16. Rodriguez, AH, Aviles-Jurado, FX, Diaz, E, Schuetz, P, Trefler, SI, Sole-Violan, J, et al. Procalcitonin (PCT) levels for ruling-out bacterial coinfection in ICU patients with influenza: a CHAID decision-tree analysis. J Infect 2016;72:143–51. https://doi.org/10.1016/j.jinf.2015.11.007. Search in Google Scholar
17. Meisner, M, Schmidt, J, Huttner, H, Tschaikowsky, K. The natural elimination rate of procalcitonin in patients with normal and impaired renal function. Intensive Care Med 2000;26(Suppl 2):S212–6. https://doi.org/10.1007/s001340051146. Search in Google Scholar
18. Level, C, Chauveau, P, Delmas, Y, Lasseur, C, Pelle, G, Peuchant, E, et al. Procalcitonin: a new marker of inflammation in haemodialysis patients?. Nephrol Dial Transplant 2001;16:980–6. https://doi.org/10.1093/ndt/16.5.980. Search in Google Scholar
19. Grace, E, Turner, RM. Use of procalcitonin in patients with various degrees of chronic kidney disease including renal replacement therapy. Clin Infect Dis 2014;59:1761–7. https://doi.org/10.1093/cid/ciu732. Search in Google Scholar
20. Wu, SC, Liang, CX, Zhang, YL, Hu, WP. Elevated serum procalcitonin level in patients with chronic kidney disease without infection: a case-control study. J Clin Lab Anal 2020;34:e23065. https://doi.org/10.1002/jcla.23065. Search in Google Scholar
21. Dumea, R, Siriopol, D, Hogas, S, Mititiuc, I, Covic, A. Procalcitonin: diagnostic value in systemic infections in chronic kidney disease or renal transplant patients. Int Urol Nephrol 2014;46:461–8. https://doi.org/10.1007/s11255-013-0542-8. Search in Google Scholar
22. Dahaba, AA, Rehak, PH, List, WF. Procalcitonin and C-reactive protein plasma concentrations in nonseptic uremic patients undergoing hemodialysis. Intensive Care Med 2003;29:579–83. https://doi.org/10.1007/s00134-003-1664-8. Search in Google Scholar
23. Herget-Rosenthal, S, Klein, T, Marggraf, G, Hirsch, T, Jakob, HG, Philipp, T, et al. Modulation and source of procalcitonin in reduced renal function and renal replacement therapy. Scand J Immunol 2005;61:180–6. https://doi.org/10.1111/j.0300-9475.2005.01545.x. Search in Google Scholar
24. Kutz, A, Briel, M, Christ-Crain, M, Stolz, D, Bouadma, L, Wolff, M, et al. Prognostic value of procalcitonin in respiratory tract infections across clinical settings. Crit Care 2015;19:74. https://doi.org/10.1186/s13054-015-0792-1. Search in Google Scholar
25. Meier, MA, Branche, A, Neeser, OL, Wirz, Y, Haubitz, S, Bouadma, L, et al. Procalcitonin-guided antibiotic treatment in patients with positive blood cultures: a patient-level meta-analysis of randomized trials. Clin Infect Dis 2019;69:388–96. https://doi.org/10.1093/cid/ciy917. Search in Google Scholar
26. Schuetz, P, Briel, M, Christ-Crain, M, Stolz, D, Bouadma, L, Wolff, M, et al. Procalcitonin to guide initiation and duration of antibiotic treatment in acute respiratory infections: an individual patient data meta-analysis. Clin Infect Dis 2012;55:651–62. https://doi.org/10.1093/cid/cis464. Search in Google Scholar
27. Schuetz, P, Muller, B, Christ-Crain, M, Stolz, D, Tamm, M, Bouadma, L, et al. Procalcitonin to initiate or discontinue antibiotics in acute respiratory tract infections. Cochrane Database Syst Rev 2012;10:Cd007498. https://doi.org/10.1002/14651858.cd007498.pub2. Search in Google Scholar
28. Schuetz, P, Briel, M, Christ‐Crain, M, Wolbers, M, Stolz, D, Tamm, M, et al. Procalcitonin to initiate or withhold antibiotics in acute respiratory tract infections. Cochrane Database Syst Rev 2008. https://doi.org/10.1002/14651858.cd007498. Search in Google Scholar
29. Stewart, LA, Clarke, M, Rovers, M, Riley, RD, Simmonds, M, Stewart, G, et al. Preferred reporting Items for systematic review and meta-analyses of individual participant data: the PRISMA-IPD statement. J Am Med Assoc 2015;313:1657–65. https://doi.org/10.1001/jama.2015.3656. Search in Google Scholar
30. Liberati, A, Altman, DG, Tetzlaff, J, Mulrow, C, Gotzsche, PC, Ioannidis, JP, et al. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate healthcare interventions: explanation and elaboration. BMJ 2009;339:b2700. https://doi.org/10.1136/bmj.b2700. Search in Google Scholar
31. Guyatt, GH, Oxman, AD, Vist, GE, Kunz, R, Falck-Ytter, Y, Alonso-Coello, P, et al. GRADE: an emerging consensus on rating quality of evidence and strength of recommendations. BMJ 2008;336:924–6. https://doi.org/10.1136/bmj.39489.470347.ad. Search in Google Scholar
32. Levey, AS, Eckardt, KU, Tsukamoto, Y, Levin, A, Coresh, J, Rossert, J, et al. Definition and classification of chronic Kidney disease: a position statement from Kidney disease: improving global outcomes (KDIGO). Kidney Int 2005;67:2089–100. https://doi.org/10.1111/j.1523-1755.2005.00365.x. Search in Google Scholar
33. Thompson, SG, Turner, RM, Warn, DE. Multilevel models for meta-analysis, and their application to absolute risk differences. Stat Methods Med Res 2001;10:375–92. https://doi.org/10.1191/096228001682157616. Search in Google Scholar
34. Turner, RM, Omar, RZ, Yang, M, Goldstein, H, Thompson, SG. A multilevel model framework for meta-analysis of clinical trials with binary outcomes. Stat Med 2000;19:3417–32. https://doi.org/10.1002/1097-0258(20001230)19:24<3417::aid-sim614>3.0.co;2-l. Search in Google Scholar
35. Schuetz, P, Christ-Crain, M, Thomann, R, Falconnier, C, Wolbers, M, Widmer, I, et al. Effect of procalcitonin-based guidelines vs standard guidelines on antibiotic use in lower respiratory tract infections: the ProHOSP randomized controlled trial. J Am Med Assoc 2009;302:1059–66. https://doi.org/10.1001/jama.2009.1297. Search in Google Scholar
36. Bloos, F, Trips, E, Nierhaus, A, Briegel, J, Heyland, DK, Jaschinski, U, et al. Effect of sodium selenite administration and procalcitonin-guided therapy on mortality in patients with severe sepsis or septic shock: a randomized clinical trial. JAMA Intern Med 2016;176:1266–76. https://doi.org/10.1001/jamainternmed.2016.2514. Search in Google Scholar
37. Sun, Y, Jiang, L, Shao, X. Predictive value of procalcitonin for diagnosis of infections in patients with chronic kidney disease: a comparison with traditional inflammatory markers C-reactive protein, white blood cell count, and neutrophil percentage. Int Urol Nephrol 2017;49:2205–16. https://doi.org/10.1007/s11255-017-1710-z. Search in Google Scholar
38. Gupta, J, Mitra, N, Kanetsky, PA, Devaney, J, Wing, MR, Reilly, M, et al. Association between albuminuria, kidney function, and inflammatory biomarker profile in CKD in CRIC. Clin J Am Soc Nephrol 2012;7:1938–46. https://doi.org/10.2215/cjn.03500412. Search in Google Scholar
39. Meisner, M, Lohs, T, Huettemann, E, Schmidt, J, Hueller, M, Reinhart, K. The plasma elimination rate and urinary secretion of procalcitonin in patients with normal and impaired renal function. Eur J Anaesthesiol 2001;18:79–87. https://doi.org/10.1097/00003643-200102000-00004. Search in Google Scholar
40. Rhodes, A, Evans, LE, Alhazzani, W, Levy, MM, Antonelli, M, Ferrer, R, et al. Surviving sepsis Campaign: international guidelines for management of sepsis and septic shock: 2016. Intensive Care Med 2017;43:304–77. https://doi.org/10.1007/s00134-017-4683-6. Search in Google Scholar
41. Kumar, A, Ellis, P, Arabi, Y, Roberts, D, Light, B, Parrillo, JE, et al. Initiation of inappropriate antimicrobial therapy results in a fivefold reduction of survival in human septic shock. Chest 2009;136:1237–48. https://doi.org/10.1378/chest.09-0087. Search in Google Scholar
42. Gonzales, R, Steiner, JF, Sande, MA. Antibiotic prescribing for adults with colds, upper respiratory tract infections, and bronchitis by ambulatory care physicians. J Am Med Assoc 1997;278:901–4. https://doi.org/10.1001/jama.278.11.901. Search in Google Scholar
43. Lawrence, KL, Kollef, MH. Antimicrobial stewardship in the intensive care unit: advances and obstacles. Am J Respir Crit Care Med 2009;179:434–8. https://doi.org/10.1164/rccm.200809-1394cp. Search in Google Scholar
44. Schuetz, P, Hausfater, P, Amin, D, Amin, A, Haubitz, S, Faessler, L, et al. Biomarkers from distinct biological pathways improve early risk stratification in medical emergency patients: the multinational, prospective, observational TRIAGE study. Crit Care 2015;19:377. https://doi.org/10.1186/s13054-015-1098-z. Search in Google Scholar
45. Christ-Crain, M, Muller, B. Procalcitonin in bacterial infections--hype, hope, more or less? Swiss Med Wkly 2005;135:451–60. Search in Google Scholar
46. Wacker, C, Prkno, A, Brunkhorst, FM, Schlattmann, P. Procalcitonin as a diagnostic marker for sepsis: a systematic review and meta-analysis. Lancet Infect Dis 2013;13:426–35. https://doi.org/10.1016/s1473-3099(12)70323-7. Search in Google Scholar
47. Tang, BM, Eslick, GD, Craig, JC, McLean, AS. Accuracy of procalcitonin for sepsis diagnosis in critically ill patients: systematic review and meta-analysis. Lancet Infect Dis 2007;7:210–7. https://doi.org/10.1016/s1473-3099(07)70052-x. Search in Google Scholar
48. Schuetz, P, Chiappa, V, Briel, M, Greenwald, JL. Procalcitonin algorithms for antibiotic therapy decisions: a systematic review of randomized controlled trials and recommendations for clinical algorithms. Arch Intern Med 2011;171:1322–31. https://doi.org/10.1001/archinternmed.2011.318. Search in Google Scholar
49. Huang, DT, Yealy, DM, Filbin, MR, Brown, AM, Chang, CH, Doi, Y, et al. Procalcitonin-guided use of antibiotics for lower respiratory tract infection. N Engl J Med 2018;379:236–49. https://doi.org/10.1056/nejmoa1802670. Search in Google Scholar
The online version of this article offers supplementary material (https://doi.org/10.1515/cclm-2020-0931).
© 2020 Eva Heilmann et al., published by De Gruyter, Berlin/Boston
This work is licensed under the Creative Commons Attribution 4.0 International License.