Influence of renal dysfunction on the differential behaviour of procalcitonin for the diagnosis of postoperative infection in cardiac surgery

Citation: de la Varga-Martínez, O.; Martín-Fernández, M.; Heredia-Rodríguez, M.; Ceballos, F.; Cubero-Gallego, H.; Priede-Vimbela, J.M.; Bardají-Carrillo, M.; Sánchez-de Prada, L.; López-Herrero, R.; Jorge-Monjas, P.; et al. Influence of Renal Dysfunction on the Differential Behaviour of Procalcitonin for the Diagnosis of Postoperative Infection in Cardiac Surgery. J. Clin. Med. 2022, 11, 7274. https://doi.org/10.3390/ jcm11247274 Academic Editors: Sandro Gelsomino and Andrea Dell’Amore Received: 19 August 2022 Accepted: 6 December 2022 Published: 7 December 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). Journal of Clinical Medicine Article Influence of Renal Dysfunction on the Differential Behaviour of Procalcitonin for the Diagnosis of Postoperative Infection in Cardiac Surgery Olga de la Varga-Martínez 1,2,* , Marta Martín-Fernández 2,3,4 , María Heredia-Rodríguez 2,3,5 , Francisco Ceballos 6, Hector Cubero-Gallego 7, Juan Manuel Priede-Vimbela 8, Miguel Bardají-Carrillo 8, Laura Sánchez-de Prada 2,9, Rocío López-Herrero 2,8 , Pablo Jorge-Monjas 2,3,8,10, Eduardo Tamayo 2,3,8,10,† and Esther Gómez-Sánchez 2,3,8,10,† 1Department of Anaesthesiology, Infanta Leonor University Hospital, Gran Via del Este 80, 28031 Madrid, Spain 2BioCritic, Group for Biomedical Research in Critical Care Medicine, Ramon y Cajal Ave. 7, 47005 Valladolid, Spain 3Center for Biomedical Research in Infectious Diseases Network (CIBERINFEC), Carlos III Health Institute, 28029 Madrid, Spain 4Department of Medicine, Faculty of Medicine, Universidad de Valladolid, 47005 Valladolid, Spain 5Department of Anaesthesiology, Clinical University Hospital of Salamanca, P.º de San Vicente, 58, 37007 Salamanca, Spain 6Viral Infection and Immunity Unit, National Center for Microbiology, Carlos III Health Institute, 28029 Madrid, Spain 7Interventional Cardiology Unit, Cardiology Department, Hospital del Mar, 08003 Barcelona, Spain 8Department of Anaesthesiology, Clinic University Hospital of Valladolid, Ramon y Cajal Ave. 3, 47003 Valladolid, Spain 9Microbiology and Immunology Department, Hospital Clínico Universitario de Valladolid, 47003 Valladolid, Spain 10 Department of Surgery, Faculty of Medicine, Universidad de Valladolid, 47005 Valladolid, Spain *Correspondence: olga.var[email protected]; Tel.: +34-911918000 † These authors contributed equally to this work. Abstract: Background: procalcitonin is a valuable marker in the diagnosis of bacterial infections; however, the impairment of renal function can influence its diagnostic precision. The objective of this study is to evaluate the differential behaviour of procalcitonin, as well as its usefulness in the diagnosis of postoperative pulmonary infection after cardiac surgery, depending on the presence or absence of impaired renal function. Materials and methods: A total of 805 adult patients undergoing cardiac surgery with extracorporeal circulation (CBP) were prospectively recruited, comparing the behaviour of biomarkers between the groups with and without postoperative pneumonia and according to the presence or absence of renal dysfunction. Results: Pulmonary infection was diagnosed in 42 patients (5.21%). In total, 228 patients (28.32%) presented postoperative renal dysfunction. Procalcitonin was significantly higher in infected patients, even in the presence of renal dysfunction. The optimal procalcitonin threshold differed markedly in patients with renal dysfunction compared to patients without renal dysfunction (1 vs. 0.78 ng/mL p< 0.05). The diagnostic accuracy of procalcitonin increased significantly when the procalcitonin threshold was adapted to renal function. Conclusions: Procalcitonin is an accurate marker of postoperative infection in cardiac surgery, even in the presence of renal dysfunction. Renal function is an important determinant of procalcitonin levels and, therefore, its diagnostic thresholds must be adapted in the presence of renal dysfunction. Keywords: procalcitonin; marker; postoperative infection; renal dysfunction J. Clin. Med. 2022,11, 7274. https://doi.org/10.3390/jcm11247274 https://www.mdpi.com/journal/jcm J. Clin. Med. 2022,11, 7274 2 of 12 1. Introduction After a major surgery such as cardiac surgery with cardiopulmonary bypass (CPB), postoperative pulmonary infection has been reported to occur in 5.7–21.6% of patients [ 1 , 2 ], leading to death in up to 31.9% of cases [ 3 – 5 ]. Postoperative pulmonary infection is often diagnosed late. This is caused, on the one hand, by several confounding factors due to the activation of inflammatory cascades, which may lead to systemic inflammatory response syndrome (SIRS) [ 6 , 7 ], or, on the other hand, by the poor diagnostic performance of current infection biomarkers in the postoperative period [ 1 ]. This growing concern gives rise to an increase in research studies looking for new biomarkers, such as LIFTS or long non-coding RNAs (lncRNAs), with the aim of improving the diagnosis and prognosis of septic patients [7,8]. Following cardiac surgery, patients usually present an elevation of some biomarkers such as procalcitonin (PCT), C-reactive protein (CRP) and leukocytes due to an acute inflammatory response [ 9 , 10 ]. Furthermore, it has been reported that 34% of acute kidney injury (AKI) is related to major surgery [ 11 ]. In particular, cardiac surgery leads to a well-established risk of renal dysfunction [ 12 ]. A decreased renal function has been related to a higher cardiac morbidity and mortality in patients undergoing this type of surgery [ 13 , 14 ]. It has been described that renal function markedly influenced some biomarkers levels such as PCT in both infected and non-infected patients after major surgery [ 6 ]. Furthermore, high levels of C-Reactive Protein have been identified as a biomarker of AKI or mortality in several clinical settings [ 15 , 16 ]. In addition, leukocyte count is related to AKI in patients who underwent isolated coronary artery bypass grafting with cardiopulmonary bypass [ 17 ] and neutrophil count has been described as a marker of AKI in different diseases [ 18 – 23 ]. In line with this, the need to use different thresholds depending on the presence/absence of renal failure has been addressed in the case of PCT in postoperative infection. Amour J et al. [6] evaluated 276 patients to determine whether the accuracy of PCT in diagnosing postoperative infection is affected by renal function after vascular surgery [ 5 ]. Regarding cardiac surgery, Jebali et al. [ 6 ] concluded that PCT is a valuable marker of bacterial infections after cardiac surgery; however, to date, there are no studies that have evaluated the influence of impaired renal function on its diagnostic accuracy in the postoperative period of this type of surgery. In this sense, the objective of this study is to evaluate the differential behaviour of PCT as well as its usefulness in the diagnosis of postoperative pneumonia, based on the presence or absence of deterioration of renal function in patients after cardiac surgery. 2. Materials and Methods 2.1. Patient Selection A total of 805 adult patients who underwent heart valve surgery with cardiopulmonary bypass (CPB) and admitted at the “Hospital Clínico Universitario de Valladolid” (Valladolid, Spain) were prospectively recruited between June 2012 and January 2016. Patients were followed-up until June 2016. Patients <18 years, with a recent medical history of coronary artery disease, coronary artery bypass grafting, heart transplantation, with preoperative paced rhythms and those patients who required acute dialysis were excluded. As a second part of the initial study [ 24 ], patients were split into two groups depending on the presence of postoperative bacterial pneumonia. Centers for Disease Control and Prevention definitions for pulmonary infection were used [ 25 ]. The techniques and the treatment received by patients in the intensive care unit (ICU) did not differ from ordinary procedures. The study was approved by the Hospital’s Clinical Ethics Committee (CEIm) and informed consent was obtained from all study participants. This study followed the code of ethics of the World Medical Association (Declaration of Helsinki). 2.2. Definition of Impairment in Renal Function Creatinine was measured at ICU admission and at 8, 16, 24, 48 and 72 h after surgery. Creatinine clearance (CCr), which is a widely used test to estimate the glomerular filtration J. Clin. Med. 2022,11, 7274 3 of 12 rate and is defined as the volume of plasma which is completely cleared of creatinine within a unit of time, was estimated using the Cockcroft formula [ 12 ]. Thus, an impairment in renal function was defined as the presence of a postoperative CCr <50 mL/min at 16 h after surgery which was maintained for at least 48 h. 2.3. Procalcitonin and C-Reactive Protein Quantification Procalcitonin measurement in plasma was performed by electrochemiluminescence immunoassay on a chemistry analyser (Cobas 6000, Roche Diagnostics, Meylan, France) with a limit of detection of 0.02 ng/mL. Serum C-reactive protein was measured by particle enhanced immunoturbidimetric assay (e501 Module Analyzer, Roche Diagnostics) with a limit of detection of 0.15 mg/dL. The determinations were made on admission to the post-surgical critical care unit and during their stay at 8, 16, 24 and 72 h. 2.4. Statistical Analysis All data were analysed using the IBM SPSS 22.0 software (SPSS, Chicago, IL) and R version 3.0.1 (R Foundation for Statistical Computing, Vienna, Austria). Data are expressed as mean ±SD or median (95% confidence interval) in non-normally distributed variables. Differences between groups were assessed using the χ2 test for categorical variables and the Mann–Whitney U test for continuous variables. Assessment of diagnostic accuracy was performed by calculating the sensitivity, specificity, positive and negative predictive values and accuracy (defined as the sum of concordant cells divided by the sum of all cells in the two-by-two table) and their 95% confidence interval. We determined the receiver operating characteristic (ROC) curve and calculated the area under the ROC curve as well as its 95% confidence interval. The accuracy of the test depends on how well the test separates the group being tested and is reflected by the area under the ROC curve. Comparison of areas under the ROC curve was performed using a nonparametric technique. The ROC curve was used to determine the optimal threshold for PCT to diagnose infection. The optimal threshold was the one that minimised the distance to the ideal point (sensitivity = specificity = 1) on the ROC curve. Multivariate logistic regression analyses over time adjusted by age and sex were performed to evaluate the association between inflammatory biomarkers and an impairment of renal function. The generalised linear mixed model (GLMM), taking time into account and considering each individual as a random factor, was employed to explore the effect on creatinine clearance of all inflammatory biomarkers. We considered 2-sided p-values < 0.05 to indicate statistical significance. 3. Results 3.1. Clinical Characteristics A total of 42 patients presented pulmonary infection, while 761 patients did not. Baseline characteristics of patients are reported in Table 1. Patients were similar in terms of age and sex. Patients in the infection group more frequently presented a clinical history of diabetes mellitus (33.3 (14) vs. 19.4 (148), p= 0.029), stroke ( 14.3 (6) vs. 4.2 (32) , p= 0.003 ) and atrial fibrillation (64.3 (27) vs. 35 (266), p< 0.001). As expected, higher organ dysfunction evaluated by the SOFA score was present in infected patients ( 10 (3.50) vs. 3 (3) ,p< 0.001). This group of patients presented higher levels of Troponin T ( 1129.50 (2640.10) vs. 520.90 (449.30) , p< 0.001 ), PCT (0.51 (1.21) vs. 0.34 (0.47), p= 0.014), creatinine (1.34 (0.94) vs. 0.95 (0.45), p< 0.001 ), glucose (188.35 (79.2) vs. 167.2 (44), p= 0.002), GOT ( 92.95 (139.6) vs. 51.55 (34.4) , p= 0.002), LDH (490 (390) vs. 355.5 (141), p< 0.001), CK-MB ( 39.25 (62.08) vs. 22.24 (26.77) , p< 0.001) and total bilirubin (1.20 (1.70) vs. 0.80 (0.77), p< 0.001), as well as higher levels of white blood cells (13925 (7717.5) vs. 11565 (4857.5), p= 0.003) and neutrophils ( 11535 (7124.14) vs. 9822.19 (4404.18) ,p= 0.002) in comparison with the no-infection group. Patients with infection also showed higher hospital mortality (61.9 (26) vs. 3.2 (24), p< 0.001 ) just as prolonged hospital stay (20.5 (21) vs. 13 (8), p< 0.001), ICU stay (11 (13) vs. 3 (3), p< 0.001) and mechanical ventilation length (120 (171) vs. 6 (3), p< 0.001). J. Clin. Med. 2022,11, 7274 4 of 12 Table 1. Characteristics of the patients in the infection and non-infection groups. Continuous variables are represented as median and interquartile range (IQR); categorical variables are represented as number (n) and percentages (%). ICU: intensive care unit; SOFA: Sequential Organ Failure Assessment Score; INR: international normalised ratio. pvalues < 0.05 are highlighted in bold. No Infection (1) (n= 761) Infection (2) (n= 42) pValue (1 vs. 2) Characteristics Age [years, median (IQR)] 14 (70) 11 (72.50) 0.25 Male [n, (%)] 364 (47.8) 23 (54.8) 0.38 Functional classification (NYHA) I 1 (2) 1 (2) 0.07 II 70.4 (536) 59.5 (25) 0.13 III-IV 29.6 (225) 40.5 (17) 0.13 Comorbidities, (n, (%)) Peripheral arterial disease 20 (2.6) 2 (4.8) 0.41 Chronic respiratory disease 61 (8) 3 (7.1) 0.84 High Blood Pressure 669 (87.9) 39 (92.9) 0.33 Chronic renal failure 42 (5.5) 4 (9.5) 0.28 Diabetes mellitus 148 (19.4) 14 (33.3) 0.029 Dyslipidaemia 557 (73.2) 35 (83.3) 0.15 Stroke 32 (4.2) 6 (14.3) 0.003 Chronic ischemic cardiac disease 54 (7.1) 4 (9.5) 0.55 Previous valve intervention 88 (11.6) 8 (19) 0.15 Atrial fibrillation 266 (35) 27 (64.3) <0.001 EuroSCORE II, % 0.8 (1.65) 1.1 (2.06) 0.001 Intraoperative characteristics Time of CPB, min 95 (38) 122.50 (67) <0.001 Time of aortic cross-clamp, min 69 (30) 91 (47) <0.001 Defibrillation 165 (21.7) 10 (23.8) 0.75 Laboratory assessments, (median (IQR)) Weight (kg) 74 (15) 70 (16) 0.42 Height (cm) 161 (15) 156 (19) 0.020 Troponin T hs (pg/mL) 520.90 (449.3) 1129.50 (2640.1) <0.001 Total bilirubin (mg/dL) 0.8 (0.7) 1.20 (1.7) <0.001 GOT (U/L) 51.55 (34.4) 92.95 (139.6) 0.002 Glucose (mg/dL) 167.2 (44) 188.35 (79.2) 0.002 Creatinine (mg/dL) 0.95 (0.45) 1.34 (0.9) <0.001 Na (mmol/L) 138.91 (3.5) 138.48 (6.9) 0.52 K (mmol/L) 4 (0.62) 4.43 (0.7) <0.001 Ca (mg/dL) 8.01 (0.5) 8 (0.7) 0.83 Mg (mg/dL) 1.66 (0.3) 1.62 (0.3) 0.43 Cl (mmol/L) 101 (4) 102 (7.6) 0.40 Urea (mg/dL) 37.9 (19.7) 56.9 (20.6) <0.001 CPK (U/mL) 544 (412) 632.5 (904) 0.12 J. Clin. Med. 2022,11, 7274 5 of 12 Table 1. Cont. No Infection (1) (n= 761) Infection (2) (n= 42) pValue (1 vs. 2) CK-MB (U/L) 22.24 (26.7) 39.25 (62.1) <0.001 LDH (U/L) 355.5 (141) 490 (390) <0.001 Lactate (mmol/L) 0.91 (1.5) 0.44 (1.83) 0.09 Procalcitonin (ng/mL) 0.34 (0.4) 0.51 (1.2) 0.014 C-Reactive Protein (mg/L) 79.79 (49.7) 82.49 (98.6) 0.72 Erythrocytes (cells/mm3)3710 (620) 3675 (490) 0.52 Haematocrit (%) 32.7 (5.3) 32 (4.9) 0.37 Haemoglobin (g/dL) 10.9 (2) 10.85 (1.7) 0.24 Platelet count (cells/mm3)136,000 (57000) 135,000 (59000) 0.30 White Blood cells (cells/mm3)11,565 (4857.5) 13,925 (7717.5) 0.003 Neutrophils (cells/mm3)9822.19 (4404.1) 11,535 (7124.1) 0.002 SOFA score 3 (3) 10 (3.50) <0.001 Outcome Length of hospital stay, [days, median (IQR)] 13 (8) 20.5 (21) <0.001 Length of ICU stay, [days, median (IQR)] 3 (3) 11 (13) <0.001 Length of mechanical ventilation, [hours, median (IQR)] 6 (3) 120 (171) <0.001 Mortality, [n(%)] 24 (3.2) 61.9 (26) <0.001 3.2. Biomarker Levels Based on Presence/Absence of Renal Failure and over Time in Patients with Infection As shown in Table 2, as a result of the comparison between the groups of patients with and without renal insufficiency within the infected patients, it was observed that there were only statistically significant differences in PCT levels upon admission to the ICU, being significantly higher in patients with renal insufficiency (0.30 (0.25) vs. 0.15 (0.13), p= 0.021). In the infected patients, the rest of the PCT measurements throughout their stay in the ICU were not influenced by the presence or absence of impaired renal function. Table 2. Biomarker levels based on the presence/absence of renal function impairment over time in patients with lung infection. Biomarkers are represented as median and interquartile range (IQR). pvalues < 0.05 are highlighted in bold. No Renal Function Impairment (n= 13) Renal Function Impairment (n= 29) pValue Procalcitonin 0 h (ng/mL) 0.15 (0.13) 0.30 (0.25) 0.021 C-Reactive Protein 0 h (mg/L) 2.98 (21.80) 4.95 (53.29) 0.06 White Blood cells 0 h (cells/mm3)11640 (8150) 11,860 (8745) 0.47 Neutrophils 0 h (cells/mm3)9463.32 (7378.21) 10,223.32 (5991.08) 0.52 Procalcitonin 8 h (ng/mL) 0.20 (0.85) 0.30 (0.86) 0.40 C-Reactive Protein 8 h (mg/L) 10.62 (39.47) 15.19 (95.39) 0.19 White Blood cells 8 h (cells/mm3)11,990 (6365) 11,280 (7530) 0.42 Neutrophils 8 h (cells/mm3)10,611.15 (5698.08) 9956.25 (7014.15) 0.36 Procalcitonin 16 h (ng/mL) 0.40 (0.96) 0.90 (1.65) 0.63 C-Reactive Protein 16 h (mg/L) 89.55 (80.04) 75.43 (136.98) 0.63 J. Clin. Med. 2022,11, 7274 6 of 12 Table 2. Cont. No Renal Function Impairment (n= 13) Renal Function Impairment (n= 29) pValue White Blood cells 16 h (cells/mm3)14,060 (8625) 13,820 (7565) 0.82 Neutrophils 16 h (cells/mm3)11,838.52 (8051.52) 11,233.25 (6893) 0.73 Procalcitonin 24 h (ng/mL) 0.25 (2.98) 1.50 (4.40) 0.09 C-Reactive Protein 24 h (mg/L) 179.33 (148.41) 160.78 (132.95) 0.73 White Blood cells 24 h (cells/mm3)13,970 (9360) 15850 (8295) 0.84 Neutrophils 24 h (cells/mm3)12,838.43 (8834.61) 13,730.84 (6143.56) 0.99 Procalcitonin 48 h (ng/mL) 0.81 (2.55) 0.84 (2.42) 0.88 C-Reactive Protein 48 h (mg/L) 265.09 (127.48) 221.95 (134.19) 0.78 White Blood cells 48 h (cells/mm3)14,120 (11855) 16,140 (7055) 0.48 Neutrophils 48 h (cells/mm3)12,623.28 (10,918.98) 13,769.67 (5675.79) 0.67 Procalcitonin 72 h (ng/mL) 0.79 (6.17) 1.90 (3.82) 0.34 C-Reactive Protein 72 h (mg/L) 195.10 (89.37) 243.78 (104.55) 0.35 White Blood cells 72 h (cells/mm3)11820 (3710) 15410 (9465) 0.12 Neutrophils 72 h (cells/mm3)10,160.29 (4708.34) 13,851 (8306.30) 0.08 In Figure 1, box plots show biomarker levels over time in infected patients based on presence/absence of renal failure. They show the progressive increase in CRP levels in infected patients, both with and without kidney failure, as well as PCT, reaching their highest value at 72 h. J. Clin. Med. 2022, 11, x FOR PEER REVIEW 6 of 13 C-Reactive Protein 16 h (mg/L) 89.55 (80.04) 75.43 (136.98) 0.63 White Blood cells 16 h (cells/mm 3 ) 14,060 (8625) 13,820 (7565) 0.82 Neutrophils 16 h (cells/mm 3 ) 11,838.52 (8051.52) 11,233.25 (6893) 0.73 Procalcitonin 24 h (ng/mL) 0.25 (2.98) 1.50 (4.40) 0.09 C-Reactive Protein 24 h (mg/L) 179.33 (148.41) 160.78 (132.95) 0.73 White Blood cells 24 h (cells/mm 3 ) 13,970 (9360) 15850 (8295) 0.84 Neutrophils 24 h (cells/mm 3 ) 12,838.43 (8834.61) 13,730.84 (6143.56) 0.99 Procalcitonin 48 h (ng/ml) 0.81 (2.55) 0.84 (2.42) 0.88 C-Reactive Protein 48 h (mg/L) 265.09 (127.48) 221.95 (134.19) 0.78 White Blood cells 48 h (cells/mm 3 ) 14120 (11855) 16140 (7055) 0.48 Neutrophils 48 h (cells/mm 3 ) 12,623.28 (10,918.98) 13,769.67 (5675.79) 0.67 Procalcitonin 72 h (ng/ml) 0.79 (6.17) 1.90 (3.82) 0.34 C-Reactive Protein 72 h (mg/L) 195.10 (89.37) 243.78 (104.55) 0.35 White Blood cells 72 h (cells/mm 3 ) 11820 (3710) 15410 (9465) 0.12 Neutrophils 72 h (cells/mm 3 ) 10,160.29 (4708.34) 13,851 (8306.30) 0.08 Figure 1. Box plots showing biomarker levels over time based on the presence/absence of renal failure in patients with infection. 3.3. Biomarker Levels Based on the Presence/Absence of Renal Failure and over Time in Patients without Infection This comparison revealed that PCT levels, white blood cells and neutrophil counts at 16 h and 24 h were significantly higher in non-infected patients with renal failure. Likewise, PCT and C-Reactive protein levels were significantly higher in this group of patients at 48 h and 72 h (Table 3). In Figure 2, box plots show biomarker levels over time in non-infected patients based on the presence/absence of renal failure. Figure 1. Box plots showing biomarker levels over time based on the presence/absence of renal failure in patients with infection. J. Clin. Med. 2022,11, 7274 7 of 12 3.3. Biomarker Levels Based on the Presence/Absence of Renal Failure and over Time in Patients without Infection This comparison revealed that PCT levels, white blood cells and neutrophil counts at 16 h and 24 h were significantly higher in non-infected patients with renal failure. Likewise, PCT and C-Reactive protein levels were significantly higher in this group of patients at 48 h and 72 h (Table 3). In Figure 2, box plots show biomarker levels over time in non-infected patients based on the presence/absence of renal failure. Table 3. Biomarker levels based on the presence/absence of renal function impairment over time in patients without lung infection. Biomarkers are represented as median and interquartile range (IQR). pvalues < 0.05 are highlighted in bold. No Renal Function Impairment (n= 562) Renal Function Impairment (n= 199) pValue Procalcitonin 0 h (ng/mL) 0.20 (0.13) 0.19 (0.13) 0.847 C-Reactive Protein 0 h (mg/L) 1.84 (4.39) 2.37 (6.16) 0.091 White Blood cells 0 h (cells/mm3)10,810 (4945) 10,360 (5550) 0.782 Neutrophils 0 h (cells/mm3)8969.94 (4343.85) 8675.04 () 0.893 Procalcitonin 8 h (ng/mL) 0.20 (0.15) 0.20 (0.20) 0.143 C-Reactive Protein 8 h (mg/L) 11.21 (14.55) 12.22 (18.71) 0.383 White Blood cells 8 h (cells/mm3)11,380 (4755) 11,210 (5090) 0.824 Neutrophils 8 h (cells/mm3)9624.36 (4430.27) 9459.84 (4881.14) 0.809 Procalcitonin 16 h (ng/mL) 0.30 (0.36) 0.59 (0.89) <0.001 C-Reactive Protein 16 h (mg/L) 79.64 (48.31) 80.82 (57.13) 0.885 White Blood cells 16 h (cells/mm3)11,430 (4645) 12,210 (5490) 0.036 Neutrophils 16 h (cells/mm3)9713.34 (4140.28) 10,350.64 (5139.92) 0.031 Procalcitonin 24 h (ng/mL) 0.24 (0.24) 0.33 (0.62) <0.001 C-Reactive Protein 24 h (mg/L) 182.65 (104.10) 186.61 (110.81) 0.578 White Blood cells 24 h (cells/mm3)11,960 (5080) 12,860 (5800) 0.003 Neutrophils 24 h (cells/mm3)9877 (4785.94) 11,025 (5498.64) 0.001 Procalcitonin 48 h (ng/mL) 0.28 (0.38) 0.51 (0.96) <0.001 C-Reactive Protein 48 h (mg/L) 173.11 (134.90) 219.90 (133.24) <0.001 White Blood cells 48 h (cells/mm3)11,180 (5265) 11,570 (5622.50) 0.199 Neutrophils 48 h (cells/mm3)8964.78 (4680.39) 9487.68 (4892.40) 0.118 Procalcitonin 72 h (ng/mL) 0.25 (0.25) 0.47 (0.82) <0.001 C-Reactive Protein 72 h (mg/L) 153.80 (140.62) 188.79 (126.82) <0.001 White Blood cells 72 h (cells/mm3)10,270 (5155) 10,230 (5562.50) 0.517 Neutrophils 72 h (cells/mm3)8113.74 (4697.66) 8364.55 (5509.24) 0.311 J. Clin. Med. 2022, 11, x FOR PEER REVIEW 8 of 13 Figure 2. Box plots showing biomarker levels over time based on the presence/absence of renal failure in patients without infection. 3.4. Biomarkers Association with Presence of Renal Failure over Time in Patients with Infection The generalised linear mixed model (GLMM) revealed a significant effect on creatinine clearance of all variables except neutrophils. Under this model, taking time into account and considering each individual as a random factor, we observed that PCT decreases by 0.33 ng/mL for each unit that increases creatinine clearance (Table 4). Table 4. Generalised linear mixed models (GLMM) for evaluating the association between biomarkers and creatinine clearance over time in patients with lung infection. Estimate CI 95% min CI 95% max p-Value Age −0.9334000000 −1.59 −0.28 0.009050 Gender 0.8350000000 −9.82 11.49 0.88058 Time −0.0162300000 −0.05 0.02 0.412307 CRP −0.0378300000 −0.05 −0.03 2.31 × 10−10 PCT −0.3307000000 −0.43 −0.23 1.08 × 10−10 Leukocytes −0.0015920000 0.00 0.00 0.000438 Neutrophils 0.0008293000 0.00 0.00 0.087382 3.5. Biomarker Association with Presence of Renal Failure over Time in Patients without Infection The generalised linear mixed model (GLMM) revealed a significant effect on creatinine clearance of neutrophils alone. Under this model, taking time into account and considering each individual as a random factor, we observed that neutrophils decrease by 0.04 cells/ml for each unit that increases creatinine clearance (Table 5). Figure 2. Cont. J. Clin. Med. 2022,11, 7274 8 of 12 J. Clin. Med. 2022, 11, x FOR PEER REVIEW 8 of 13 Figure 2. Box plots showing biomarker levels over time based on the presence/absence of renal failure in patients without infection. 3.4. Biomarkers Association with Presence of Renal Failure over Time in Patients with Infection The generalised linear mixed model (GLMM) revealed a significant effect on creatinine clearance of all variables except neutrophils. Under this model, taking time into account and considering each individual as a random factor, we observed that PCT decreases by 0.33 ng/mL for each unit that increases creatinine clearance (Table 4). Table 4. Generalised linear mixed models (GLMM) for evaluating the association between biomarkers and creatinine clearance over time in patients with lung infection. Estimate CI 95% min CI 95% max p-Value Age −0.9334000000 −1.59 −0.28 0.009050 Gender 0.8350000000 −9.82 11.49 0.88058 Time −0.0162300000 −0.05 0.02 0.412307 CRP −0.0378300000 −0.05 −0.03 2.31 × 10−10 PCT −0.3307000000 −0.43 −0.23 1.08 × 10−10 Leukocytes −0.0015920000 0.00 0.00 0.000438 Neutrophils 0.0008293000 0.00 0.00 0.087382 3.5. Biomarker Association with Presence of Renal Failure over Time in Patients without Infection The generalised linear mixed model (GLMM) revealed a significant effect on creatinine clearance of neutrophils alone. Under this model, taking time into account and considering each individual as a random factor, we observed that neutrophils decrease by 0.04 cells/ml for each unit that increases creatinine clearance (Table 5). Figure 2. Box plots showing biomarker levels over time based on the presence/absence of renal failure in patients without infection. 3.4. Biomarkers Association with Presence of Renal Failure over Time in Patients with Infection The generalised linear mixed model (GLMM) revealed a significant effect on creatinine clearance of all variables except neutrophils. Under this model, taking time into account and considering each individual as a random factor, we observed that PCT decreases by 0.33 ng/mL for each unit that increases creatinine clearance (Table 4). Table 4. Generalised linear mixed models (GLMM) for evaluating the association between biomarkers and creatinine clearance over time in patients with lung infection. Estimate CI 95% min CI 95% max p-Value Age −0.9334000000 −1.59 −0.28 0.009050 Gender 0.8350000000 −9.82 11.49 0.88058 Time −0.0162300000 −0.05 0.02 0.412307 CRP −0.0378300000 −0.05 −0.03 2.31 ×10−10 PCT −0.3307000000 −0.43 −0.23 1.08 ×10−10 Leukocytes −0.0015920000 0.00 0.00 0.000438 Neutrophils 0.0008293000 0.00 0.00 0.087382 3.5. Biomarker Association with Presence of Renal Failure over Time in Patients without Infection The generalised linear mixed model (GLMM) revealed a significant effect on creatinine clearance of neutrophils alone. Under this model, taking time into account and considering each individual as a random factor, we observed that neutrophils decrease by 0.04 cells/mL for each unit that increases creatinine clearance (Table 5). Table 5. Generalised linear mixed models (GLMM) for evaluating the association between biomarkers and creatinine clearance over time in patients without infection. Estimate CI 95% min CI 95% max p-Value Age 125.5000000000 78.30 172.98 7.81 ×10−6 Gender −0.9157000000 −1.57 −0.27 0.01010 Time 0.9311000000 −9.58 11.43 0.865820 CRP −0.0082450000 −0.10 0.09 0.863720 PCT 0.0005547000 0.00 0.00 0.611500 Leukocytes −0.0011590000 0.00 0.00 0.249550 Neutrophils −0.0419700000 −0.07 −0.02 0.00299 J. Clin. Med. 2022,11, 7274 9 of 12 The area under the ROC curve of PCT in patients without kidney failure was 0.70 (95% CI 0.52–0.88), while it was 0.74 (95% CI 0.63–0.85) for patients with kidney failure (Figure 3). The optimal threshold of the PCT value for the diagnosis of infection in patients without renal failure was 0.78 ng/mL (sensitivity of 0.6 and specificity of 0.86), while it was 1.00 ng/mL (sensitivity of 0.7 and specificity of 0.79) in patients with renal failure. When comparing the ROC curves in the two subgroups according to renal function, the diagnostic accuracy of PCT was not significantly different between these two subgroups (Figure 3), but the optimal threshold differed significantly (Table 6). In contrast, the diagnostic accuracy of PCT increased significantly when the threshold was adapted to renal function (Table 7). J. Clin. Med. 2022, 11, x FOR PEER REVIEW 9 of 13 Table 5. Generalised linear mixed models (GLMM) for evaluating the association between biomarkers and creatinine clearance over time in patients without infection. Estimate CI 95% min CI 95% max p-Value Age 125.5000000000 78.30 172.98 7.81 × 10-6 Gender −0.9157000000 −1.57 −0.27 0.01010 Time 0.9311000000 −9.58 11.43 0.865820 CRP −0.0082450000 −0.10 0.09 0.863720 PCT 0.0005547000 0.00 0.00 0.611500 Leukocytes −0.0011590000 0.00 0.00 0.249550 Neutrophils −0.0419700000 −0.07 −0.02 0.00299 The area under the ROC curve of PCT in patients without kidney failure was 0.70 (95% CI 0.52–0.88), while it was 0.74 (95% CI 0.63–0.85) for patients with kidney failure (Figure 3). The optimal threshold of the PCT value for the diagnosis of infection in patients without renal failure was 0.78ng/mL (sensitivity of 0.6 and specificity of 0.86), while it was 1.00 ng/mL (sensitivity of 0.7 and specificity of 0.79) in patients with renal failure. When comparing the ROC curves in the two subgroups according to renal function, the diagnostic accuracy of PCT was not significantly different between these two subgroups (Figure 3), but the optimal threshold differed significantly (Table 6). In contrast, the diagnostic accuracy of PCT increased significantly when the threshold was adapted to renal function (Table 7). Figure 3. Comparison of the receiver-operating characteristic (ROC) curves in determining the predictive value of procalcitonin for the diagnosis of postoperative infection in patients without or with postoperative renal dysfunction. Figure 3. Comparison of the receiver-operating characteristic (ROC) curves in determining the predictive value of procalcitonin for the diagnosis of postoperative infection in patients without or with postoperative renal dysfunction. Table 6. Comparison of the AUROC of procalcitonin for the diagnosis of postoperative infection according to postoperative renal function. Renal Function Impairment (n= 228) No Renal Function Impairment (n= 574) Threshold of procalcitonin (ng/mL) 1.00 0.78 AUROC 0.74 (0.63–0.85) 0.70 (0.51–0.88) p-value vs. no discrimination curve <0.001 0.01 Table 7. Comparison of the efficiency of procalcitonin either with a fixed threshold or a threshold adapted to postoperative renal dysfunction. Variable Procalcitonin 0.78 ng/mL Adapted Procalcitonin 1.00 ng/mL Sensitivity 0.65 (0.47–0.80) 0.62 (0.44–0.77) Specificity 0.67 (0.60–0.73) 0.74 (0.67–0.79) Positive predictive value 0.23 (0.15–0.33) 0.26 (0.17–0.37) Negative predictive value 0.93 (0.88–0.96) 0.92 (0.88–0.96) Accuracy 0.67 0.72