Distinguishing Sepsis from Systemic Inflammation induced by Prolonged PGE1 infusion in Neonates with Duct-dependent Congenital Heart Disease: A Prospective Observational Study

Distinguishing Sepsis from Systemic Inflammation induced by Prolonged PGE1 infusion in Neonates with Duct-dependent Congenital Heart Disease: A Prospective Observational Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Distinguishing Sepsis from Systemic Inflammation induced by Prolonged PGE1 infusion in Neonates with Duct-dependent Congenital Heart Disease: A Prospective Observational Study Sivanesan Sivagnanaganesan, Arun Kumar Baranwal, Ashwini Nair, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8822972/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective : Prolonged PGE1 infusion (PPG E1 I) is common in low- and middle-income countries (LMICs). This prospective observational study was planned to assess role of biomarkers in differentiating sepsis from PGE1-induced systemic inflammation. Study Design: Neonates (gestation age, > 34 weeks) with duct-dependent congenital heart disease having received PGE1 for 5 days were enrolled in Pediatric Cardiac Intensive Care Unit of a tertiary care hospital in an LMIC. Culture-positive sepsis was excluded. Complete blood counts, CRP, procalcitonin and presepsin were obtained at enrolment; every 5 days thereafter; and whenever sepsis was suspected until end point (preoperative death/surgery). Correlations between biomarkers and duration of PGE1 infusion were assessed at non-sepsis observation points (N-SOPs). Comparisons were made between biomarkers at N-SOPs and sepsis observation points (SOPs). ROC analysis was performed to find cut-offs. Results: Eighty-nine observations (N-SOPs, 78; SOPs, 11) were made from 30 patients. At N-SOPs, CRP, presepsisn and platelet count increased linearly with duration of PGE1 (p=0.068, p=0.055 and p<0.0001 respectively). CRP (p<0.001), procalcitonin (p<0.001) and presepsin (p<0.001) were higher, while platelet count was lower (p=0.003) at SOPs compared to at N-SOPs. Cut-offs to detect sepsis were— CRP: 19.6mg/dL (AUC, 0.97; sensitivity, 100%; specificity, 88%), procalcitonin: 0.55ng/mL (0.91; 90%; 82%) and presepsin: 2.23ng/mL (0.98; 100%; 94%). Conclusions: PPG E1 I led to rise in CRP, presepsin and platelet count in dose-dependent manner. CRP >19.6mg/dL seems to detect sepsis with high sensitivity but lower specificity. Presepsin (>2.23ng/mL) seems to outperform CRP, procalcitonin and their combination. Lower platelet count may play a supporting role. Pediatrics Duct-dependent Congenital Heart Disease Prostaglandin E1 Neonatal Sepsis C-Reactive Protein Procalcitonin Presepsin. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 INTRODUCTION Prostaglandin E1 (PGE1) infusion is used to maintain patency of ductus in neonates with duct-dependent congenital heart disease (ddCHD) while waiting for definitive cardiac intervention. In low- and middle- income countries (LMICs), many patients receive it for prolonged periods due to lower birth weight, financial constraints, sepsis and paucity of dedicated pediatric cardiac programs[ 1 , 2 , 3 ]. Prolonged PGE1 infusion (PPG E1 I) induces systemic inflammation, causing fever, apnea, seizures, hypotension, diarrhea, and platelet dysfunction. It also elevates total leukocyte count (TLC) and C-reactive protein (CRP)[ 1 , 4 – 11 ]. Distinguishing sepsis from PGE1-induced systemic inflammation (PISI) is often difficult, and we frequently end up using empirical antimicrobials. This initiates a cycle of delayed surgery, prolonged intensive care unit (ICU) stay, healthcare-associated infections (HAIs), greater resource utilization, physiological and emotional stress— ultimately compromising outcomes[ 8 , 12 ]. High prevalence of community-acquired infections and HAIs in LMICs complicates the situation further. Improving accuracy of tests to distinguish sepsis from PISI is crucial for antimicrobial stewardship, optimal use of limited resources, and better outcomes. There is lack of systematic data on role of inflammatory biomarkers in distinguishing sepsis from PISI[ 12 ]. This prospective observational study seeks to assess utility of TLC, CRP, procalcitonin and presepsin in identifying sepsis among neonates on PP GE1 I. Secondary objectives were to find relationship, if any, between degree of exposure to PGE1 infusion and biomarkers. METHODS Study Setting and Eligibility Criteria Study was conducted in a tertiary care hospital of an LMIC, and was a time-bound dissertation project. All late preterm and term neonates (gestational age > 34 weeks) admitted with CHD between August 2021 and November 2022 (a 16-month period), were screened for eligibility. Inclusion criteria included (a) echocardiographically confirmed ddCHD, (b) initiation of PGE1 infusion within first 28 days of life, and (c) continuous PGE1 infusion for at least 5 days. Neonates were excluded if they had clinically or laboratory-confirmed sepsis at time of PGE1 initiation or enrolment. Patients with heterotaxy syndromes were also excluded for potential association of splenic dysfunction. Laboratory Assessments, Definitions and Data Collection PGE1 was started at 0.01–0.05mcg/kg/min, and titrated down to minimal effective dose. Clinical characteristics were recorded including complete blood counts (CBC), CRP, procalcitonin, presepsin and cardiac anatomy based on echocardiography and computed tomography. Clinical assessment, CBC, CRP, procalcitonin, presepsin and blood cultures were repeated every fifth day after enrolment, until end-point. The end-point was defined by stoppage of PGE1 infusion, surgical procedure or death prior to surgery. Healthcare providers were blinded to biomarker measurements prior to defining sepsis. Also, this work-up was repeated whenever there was clinical suspicion of sepsis. Cultures of urine, endotracheal aspirate, and cerebrospinal fluid (CSF) were obtained if clinically indicated. Laboratory-confirmed sepsis was defined by positive culture from blood and/or other body fluids, and/or radiological evidence of pneumonia. Any growth in blood or CSF was considered positive. However, growth of skin commensals in a single blood culture bottle from asymptomatic neonates was considered contaminant. Cultures of endotracheal aspirate and urine were considered positive with ≥ 10⁵ CFU/mL. Clinically confirmed sepsis was defined if patients had altered secretions, increased requirement of respiratory support, new-onset chest infiltrates, and/or signs of impaired perfusion, and shown improvement in clinical status and biomarkers with antimicrobials. Observation points where patients exhibited clinically or laboratory-confirmed sepsis were designated as sepsis observation points (SOPs), while those without any such evidence were labelled as non-sepsis observation points (N-SOPs). Patients were managed as per unit protocol. CRP and procalcitonin were measured on BN-ProSpec® System (Siemens Healthineers, Germany) and Elecsys® BRAHMS PCT (Roche Diagnostics, USA) respectively. Presepsin was measured from serum stored at 80°C with a commercial solid-phase sandwich ELISA kit (IT4529; G-Biosciences, St. Louis, Missouri, USA), and were read with an ELISA reader (Infinite 200 Pro, Tecan, Switzerland). Levels exceeding 6.0mg/L, 0.5ng/mL and 0.6ng/mL were considered abnormal for CRP, procalcitonin and presepsin respectively[ 13 – 16 ]. Statistical Analysis Statistical analysis was performed using R Statistical Software (v4.3.0; R Core Team, 2023) [ 17 ]. A convenient sample of 30–40 patients was planned. Relationship of duration and cumulative dose of PGE1 infusion with TLC, platelet count, CRP, procalcitonin and presepsin at N-SOPs was assessed with Spearman’s correlation test. Laboratory parameters at SOPs and N-SOPs were compared with Mann–Whitney U test. Cut-offs for CRP, procalcitonin and presepsin to identify sepsis was obtained with receiver operating characteristic (ROC) analysis. Values of biomarkers were missing at several observation points; all of which were at N-SOPs except one. The primary data collector (SS), a postgraduate pediatric trainee rotating through different clinical units during the dissertation period, could not consistently ensure sample collection for biomarker analysis at N-SOPs, i.e., when there was no suspicion and/or confirmation of sepsis. As probability of missing data was related to absence of sepsis, these missing values were classified as 'missing at random' (MAR)[ 18 ]. Primary analysis was conducted with the available values, while sensitivity analysis was performed after having imputed the missing values with multivariate imputation by chained equations (MICE) approach[ 19 ]. A p-value of < 0.05 was considered statistically significant. Ethical Clearance Research methods were in accordance with the Declaration of Helsinki, and with ethical standards of the Institutional Ethics Committee. Protocol was approved vide letter no. INT/IEC/2021/581 − 150 (dated 11.10.2021) and endst. no. 13166/PG-2Trg/2020/15795-96 (dated 14.12.2021) respectively. Written free and informed consent was obtained from legal guardians of all participants prior to enrolment. Identity and patient-specific information were kept confidential. RESULTS Thirty patients (18 males and 12 females) met eligibility criteria ( Figure 1 ). Their demographic, clinical, and laboratory profiles are summarized in Table 1 . Cardiac anatomies included d-transposition of great arteries (n=12), tetralogy of Fallot (n=11), pulmonary atresia (n=4), coarctation of aorta (n=2), and interrupted aortic arch (n=1). Majority were referred late (median age, 3 days; 3 rd quartile, 10.5 days); four patients came during fourth week of age. Only seven patients were referred with PGE1 infusion. Most patients belonged to lower socioeconomic strata— 17 (56%) from lower middle class and 8 (26%) from upper lower class [20]. Median duration of PGE1 infusion and median cumulative dose of PGE1 were 21 days and 570.2 mcg/Kg respectively. Many patients received PGE1 infusion well into fifth week. Twenty-five patients(83%) underwent surgery after median hospitalization of 19 days (IQR; 12, 27) at a median age of 29 days (IQR; 17, 40). PGE1 infusion was tapered and stopped in one patient. One patient died preoperatively, while three refused surgery due to financial constraints. Table 1: Demographic and clinical characteristics of enrolled patients (n=30) Type of Congenital Heart Disease Duct-dependent Pulmonary Circulation (n, %) 16 (53%) Duct-dependent Systemic Circulation (n, %) 3 (10%) Admixture Lesions (n, %) 11 (37%) Characteristics at Hospitalization Birth Weight (gm) 2.8 (2.26, 2.71) Gestational Age at birth (Completed Weeks) 37 (37, 38) Gender (M:F) 18 : 12 Age (days) 3.0 (1.0, 10.5) Total Leucocyte Count (x10 9 /L) 13.07 (9.46, 16.46) Absolute Neutrophil Count (x10 9 /L) 7.06 (4.05, 8.67) Platelet Count (x10 9 /L) 279 (241, 355) Age when PGE1 infusion was initiated (days) 5.00 (3.00, 12.25) Starting dose of PGE1 infusion (ng/kg/min) 20.0 (12.5, 50.0) Characteristics at Enrolment (i.e., the first N-SOP * ) Cumulative dose of PGE1 received by Enrolment (mcg/kg) 205.0 (133.2, 287.1) Total Leucocyte Count (x10 9 /L) 12.56 (9.38, 15.78) Absolute Neutrophil Count (x10 9 /L) 6.31 (4.63, 10.96) Platelet Count (x10 9 /L) 289 (234, 355) Characteristics during Subsequent Hospital Course Duration of PGE1 infusion by the Endpoint (days) 21.0 (11.25, 27.00) Maximum dose of PGE1 infusion at the Endpoint (mcg/kg/min) 0.040 (0.030, 0.058) Cumulative dose of PGE1 received by the Endpoint (mcg/kg) 570.2 (381.6, 1022.4) Number of patients needed Non-Invasive Respiratory Support (n, %) 16 (53%) Number of patients needed Invasive Mechanical Ventilation (n, %) 15 (50%) Duration of Invasive Mechanical Ventilation (n=15) 10 (2.25, 14.75) Number of patients needed Inotropic support (n, %) 7 (23%) Duration of Inotropic support (days) (n=7) 10.0 (5.0, 15.5) Maximum Vasoactive Inotrope Score (n=7) 60.0 (51.5, 81.5) Number of patients needed furosemide (n, %) 8 (26%) Number of patients needed correction for hyponatremia (n, %) 1 (3%) Number of patients needed correction for hypokalemia (n, %) 2 (6%) Number of patients needed correction for hypocalcemia (n, %) 0 (0%) Abbreviations: PGE1, Prostaglandin E1; N-SOP, non-sepsis observation point. All values are in Median (IQR) unless stated otherwise. * Neonates who hadlaboratory- or clinically-confirmed sepsis at the time of initiation of PGE1 infusion and/or at enrolment were excluded. A total of 89 observations were recorded, with median of three observations per patient. Of these, 11 observation points (12.4%) corresponded to clinically- and/or laboratory- confirmed sepsis episodes (SOPs) from nine patients(30%), while remaining 78 (87.6%) were from periods without any clinical or laboratory evidence of sepsis (N-SOPs). Of 78 N-SOPs, 62 (79%) were from the 21 patients who never developed sepsis, and 16 (21%) were from nine patients who did develop sepsis at some point. Of the 11 SOPs, two had positive blood culture ( Staphylococcus aureus and Staphylococcus epidermidis ), while one each had radiological evidence of pneumonia and CSF findings consistent with meningitis. Remaining seven SOPs were defined by clinical suspicion and subsequent improvement in clinical status, TLC and biomarkers with antimicrobials. Median durations of PGE1 infusion were similar (p=0.82), while median cumulative dose was higher at SOPs than at N-SOPs (p=0.09) ( Table 2 ). Twenty-three percent of biomarker values were missing (61/267 opportunities), predominantly at N-SOPs—CRP: 14/78 (17.9%), procalcitonin: 25/78 (32.1%), and presepsin: 21/78 (26.9%). At SOPs, only one value of procalcitonin was missing ( Table 2 ). Table 2: Clinical and laboratory parameters at Non-Sepsis Observation Points and Sepsis Observation Points. Characteristics Non-Sepsis Observation Points (n=78) Sepsis Observation Points (n=11) p-value Duration of Prostaglandin E1 Infusion (days) 10 (5, 16) 9.5 (5, 18.25) 0.82 Cumulative dose of Prostaglandin E1 (mcg/kg) 324 (208.8, 604.8) 561.6 (381, 961.2) 0.09 Total Leucocyte Count (x10 9 /L) 12.6 (9.30, 15.52) 11.10 (9.24, 13.08) 0.51 Absolute Neutrophil Count (x10 9 /L) 5.57 (4.13, 8.49) 5.46 (4.43, 7.77) 0.98 Platelet Count (x10 9 /L) 370 (270, 480) 173 (69.7, 337) 0.003 C-Reactive Protein (mg/L) Primary Analysis 3.88 (1.75, 7.15); [0.12-73.30] (n=64)* 38.0 (28.65, 78.00); [19.60-203.00] (n=11) <0.001 Sensitivity Analysis # 4.04 (1.80, 7.47); [0.12-73.30] (n=78) # 38.0 (28.65, 78.00); [19.60-203.00] (n=11) <0.001 Procalcitonin (ng/mL) Primary Analysis 0.16 (0.10, 0.45); [0.06-4.24] (n=53)* 3.05 (1.40, 10.98); [0.30-100.00] (n=10)* <0.001 Sensitivity Analysis # 0.16 (0.09, 0.45); [0.06-4.24] (n=78) # 2.5(1.46,9.55); [0.3-100.00] (n=11) # <0.001 Presepsin (ng/mL) Primary Analysis 1.91 (1.72, 2.00); [1.51-2.95] (n=57)* 2.99 (2.61, 3.37); [2.23-3.80] (n=11) <0.001 Sensitivity Analysis # 1.91 (1.70, 2.00); [1.51-2.95] (n=78) # 2.99 (2.61, 3.37); [2.23-3.80] (n=11) <0.001 All values are in Median (Interquartile Range). Additionally, [Min-Max] is given for C-Reactive Protein, Procalcitonin and Presepsin. * Values of C-reactive protein, procalcitonin and presepsin are available only for the mentioned numbers of observation points. # Sensitivity analysiswas done after imputing the missing values of C-Reactive Protein, Procalcitonin and Presepsin with Multivariate Imputation by Chained Equations (MICE) approach. At N-SOPs, CRP increased linearly with cumulative dose and duration of PGE1 infusion. Correlation was statistically significant with the former (p=0.0043) but not with the latter (p=0.068). Nearly 25% of N-SOPs had CRP exceeding 7.15mg/L, with the highest recorded value being 73.3mg/L. Presepsin revealed a positive trend towards duration (p=0.055), but was not affected by cumulative dose (p=0.61) ( Figure 2 ). With sensitivity analysis, strength of positive correlations of CRP and presepsin with duration improved (p=0.013 and p=0.0032 respectively) ( Figure 3 ). In contrast, procalcitonin neither correlated with duration of PGE1 infusion (p=0.25) nor its cumulative dose(p=0.80) irrespective of sensitivity analysis ( Figures 2 and 3) . More than 75% of procalcitonin values were below normal threshold of 0.5ng/mL (3 rd quartile, 0.45ng/mL), while all the presepsin values were markedly above the cut-off of 0.6ng/mL (lowest, 1.51ng/mL) ( Table 2 ). CRP and procalcitonin revealed notable overlap between N-SOPs and SOPs. In contrast, presepsin revealed an excellent separation of correlation lines representing N-SOPs and SOPs; separation is more prominent for duration of PGE1 infusion than its cumulative dose ( Table 2 and Figure 2 ). During sepsis episodes, CRP, procalcitonin and presepsin, all three, got elevated significantly compared to N-SOPs. CRP and procalcitonin got elevated 10-fold (38.0 vs 3.88 mg/L; p<0.001) and 19-fold (3.05 vs 0.16 ng/ml; p<0.001) respectively while presepsin got elevated only 1.5-fold during sepsis episodes (2.99 vs 1.91 ng/ml; p<0.001). Sensitivity analysis yielded similar results ( Table 2 ). CRP above the cut-off of 19.6mg/L was found to identify sepsis episodes (AUC, 0.97), while procalcitonin could identify it above the cut-off of 0.55ng/mL (AUC, 0.91). Combining the two increased the sensitivity (AUC, 0.98). Presepsin, at cut off of 2.23ng/mL, could identify sepsis episodes with better sensitivity and specificity (AUC, 0.98) ( Figure 4, Table 3 ). Sensitivity analysis of ROC neither altered cut-offs for CRP and presepsin, nor their sensitivity and specificity. However, it yielded a much higher cut-off for procalcitonin (1.35ng/mL versus 0.55ng/mL) with lesser sensitivity (82% versus 90%) ( Table 3) . Such a higher cut-off of procalcitonin is likely to miss many neonates with sepsis. A CRP level more than 20mg/L and procalcitonin more than 0.5ng/mL indicated 2.62-fold (95%CI, 1.39-4.94; p=0.003] and 1.7-fold (95%CI, 1.17-2.47; p=0.005) increase in risk of having sepsis respectively, while presepsin more than 2.23ng/mL indicated a 5.92-fold (95%CI, 1.67-20.99; p=0.006) increase in risk. Table 3: Result of Receiver Operating Characteristics analysis for inflammatory biomarkers to distinguish sepsis from PGE1 induced systemic inflammation (Primary and Sensitivity Analyses) Cut-off Value AUC (95% CI) Sensitivity Specificity PPV NPV LR+ LR- C-Reactive Protein Primary Analysis 19.60 mg/L 0.97 (0.92, 1.00) 100% 88% 71% 100% 8.77 0.00 Sensitivity Analysis 19.60 mg/L 0.97 (0.94-0.99) 100% 90% 57.9% 100% 9.75 0.00 Procalcitonin Primary Analysis 0.55 ng/mL 0.91 (0.80, 0.99) 90% 82% 60% 97% 5.26 0.12 Sensitivity Analysis 1.35 ng/mL 0.91 (0.83-0.98) 82% 90% 52.9% 97.2% 7.98 0.20 C-Reactive Protein + Procalcitonin Primary Analysis ---- 0.98 (0.95, 1.00) 100% 88% 71% 100% 17.54 0.00 Sensitivity Analysis ---- 0.98 (0.95-1.00) 100% 89% 55% 100% 8.67 0.00 Presepsin Primary Analysis 2.23ng/mL 0.98 (0.94, 1.00) 100% 94% 83% 100% 17.54 0.00 Sensitivity Analysis 2.23 ng/mL 0.99 (0.97-1.00) 100% 97% 84.6% 100% 39.00 0.00 Abbreviations: AUC, Area Under Curve; 95% CI, 95% Confidence Interval; PPV, Positive Predictive Value; NPV, Negative Predictive Value; LR+, Positive Likelihood Ratio; LR-, Negative Likelihood Ratio. Platelet counts were similar at hospital admission and at enrolment ( Table 1) . However it got elevated significantly at post-enrolment N-SOPs (p=0.001) (Table 4) . Further, platelet counts revealed a linear elevation with increasing duration and cumulative dose at N-SOPs (p<0.0001 and p<0.001 respectively) but not at SOPs (p=0.19 and p=0.71 respectively) ( Figure 5 ). Other important observations include significant fall in platelet counts at SOPs(p=0.003) despite being on PGE1 infusion ( Table 2 ), and an excellent visible separation of values at SOPs from those at N-SOPs ( Figure 5 ). A platelet count less than 200x10 3 /L and less than 100x10 9 /L had 1.68-fold [95%CI, 1.09-2.60; p=0.02] and 2.13-fold [95%CI, 0.9-5.03; p=0.083] increase in relative risk of having sepsis respectively. TLC and ANC were similar at hospital admission, enrolment and post-enrolment N-SOPs ( Tables 1 and 4) . At N-SOPs, TLC and ANC did not reveal any relationship with either duration or cumulative dose ( Figure 5 ). Moreover, lack of their rise during sepsis episodes led to complete overlap in their values at SOPs with those at N-SOPs ( Table 2 , Figure 5 ). Table 4: Comparison of inflammatory biomarkers at ‘Enrolment’ (i.e., at 5 th day of PGE1 infusion, n=30) versus ‘All Non-Sepsis Observation Points’ (i.e., including observations at enrolment; n=78) and ‘Post-Enrolment Non-Sepsis Observation Points’ (i.e., after excluding observations at enrolment; n=48). 1. Comparison of observations at ‘Enrolment’ versus at ‘All Non-Sepsis Observation Points’ Inflammatory Biomarkers Enrolment (n=30) All N-SOPs (n=78) p-value Total Leucocyte Count (x10 9 /L) 12.56 (9.38, 15.78) 12.6 (9.30, 15.52) 0.76 Absolute Neutrophil Count (x10 9 /L) 6.31 (4.63, 10.96) 5.57 (4.13, 8.49) 0.39 Platelet Count (x10 9 /L) 2.89 (2.34, 3.55) 370.0 (270.0, 480.0) 0.01 C-Reactive Protein (mg/L)* 2.5 (1.40, 4.80) (n=27) * 3.88 (1.75, 7.17) (n=64) * 0.13 Procalcitonin (ng/mL)* 0.16 (0.13, 0.76) (n=17) * 0.16 (0.10, 0.45) (n=53) * 0.75 Presepsin (ng/mL)* 1.78 (1.66, 1.92) (n=20) * 1.91 (1.72, 2.0) (n=57) * 0.14 2. Comparison of observations at ‘Enrolment’ versus at ‘Post-Enrolment Non-Sepsis Observation Points’ Inflammatory Biomarkers Enrolment (n=30) Post-Enrolment N-SOPs (n=48) p-value Total Leucocyte Count (x10 9 /L) 12.56 (9.38, 15.78) 12.79 (9.44, 15.54) 0.84 Absolute Neutrophil Count (x10 9 /L) 6.31 (4.63, 10.96) 5.39 (4.12, 8.21) 0.31 Platelet Count (x10 9 /L) 2.89 (2.34, 3.55) 4.17 (3.35, 5.30) 0.001 C-Reactive Protein (mg/L)* 2.5 (1.40, 4.80) (n=27) * 4.3 (2.59, 11.0) (n=37) * 0.05 Procalcitonin (ng/mL)* 0.16 (0.13, 0.76) (n=17) * 0.18 (0.10, 0.45) (n=36) * 0.73 Presepsin (ng/mL)* 1.78 (1.66, 1.92) (n=20) * 1.95 (1.76, 2.08) (n=37) * 0.04 Abbreviation: N-SOPs, Non-Sepsis Observation Points. *Values of C-reactive protein, procalcitonin and presepsin are available for the mentioned numbers of observation points. DISCUSSION Many of adverse effects of PPG E1 I mimic neonatal sepsis, potentially delaying cardiac surgery and prolonging preoperative ICU stay. This study demonstrated behavior of inflammatory biomarkers and their utility in identifying sepsis episodes among neonates on PPG E1 I. CRP showed a dose-dependent linear rise even in the absence of infection, limiting its diagnostic utility to a high cut-off value(~20mg/L). In contrast, procalcitonin levels did not increase with PGE1 infusion and maintained diagnostic accuracy at the conventional threshold of 0.5ng/mL. Presepsin showed a trend toward dose-independent elevation with PPG E1 I, however it performed best in identifying sepsis episodes at cut-off of 2.23ng/mL. Multivariate ROC model did not improve performance. TLC and ANC neither correlated with degree of PGE1 exposure nor distinguished sepsis episodes. A dose-dependent thrombocytosis was observed with PPG E1 I. Sepsis episodes, however, reduced platelet counts significantly; values below 200×10 9 /L indicated a higher risk of sepsis. Relationship of biomarkers with duration of PGE1 infusion and its cumulative dose (a composite of infusion rate and duration) were similar except for presepsin (at N-SOPs and SOPs both) and TLC (at N-SOPs). Prostaglandins play key roles in pro-inflammatory cascade causing a dose-dependent rise in CRP[11,21,22]. The observed linear rise in CRP with duration and cumulative dose of PGE1 aligns with the available experimental and clinical evidence. Maximum CRP recorded at N-SOPs was 73mg/L, though higher levels are reported in literature, e.g., 100mg/L on day 49 of PGE2 infusion at 20ng/kg/min, and 197mg/L on day 11 at 50ng/kg/min [7]. Though CRP rose nearly 10-fold during sepsis episodes, notable overlap with N-SOPs was observed, especially with longer duration and at higher cumulative dose. A stand-alone rise in CRP below 20mg/L does not appear to suggest sepsis. Though pro-inflammatory cytokines stimulate extra-thyroidal tissues to synthesize procalcitonin, the latter does not seem to get affected with PPG E1 I. Despite extensive efforts, we could not find English language literature addressing the effect of PGE1 on procalcitonin. More clinical data are therefore required to support this finding. A cut-off of 0.55ng/mL was optimal in identifying sepsis, and is similar to data obtained from children experiencing systemic inflammation after cardiac surgery[23]. Both CRP and procalcitonin are known to rise during non-infectious inflammation, such as in the post-operative period and following cardiopulmonary bypass (CPB) [23,24]. CRP may peak up to 80mg/L during first two post-operative days losing its ability to reliably distinguish post-operative sepsis[23-25]. Procalcitonin also increases on the first post-operative day and may exceed 3ng/mL, however it outperforms CRP in detecting infections during post-CPB period[23,24]. These are consistent with our findings in patients with PISI. Presepsin, generated by bacterial proteases during sepsis, is considered specific for bacterial infections[27,28]. A meta-analysis of 23 studies involving clinically and culture-confirmed early-onset neonatal sepsis (infected, n=1040; non-infected, n=826) revealed sensitivity and specificity of 93% and 91% respectively[27]. Cut-offs to diagnose neonatal and pediatric sepsis varies from 0.31ng/mL to 0.89ng/mL[28-30]. Presepsin also rises on first post-operative day in pediatric surgical patients, with no difference between those with and without culture-positive infections (0.81 vs 0.72ng/mL, p=0.41)[31]. These findings, along with nearly three-fold elevation in presepsin at N-SOPs, suggest that even presepsin may be a non-specific marker of inflammation[30]. Cut-off observed in our cohort is extraordinarily higher. Notwithstanding, presepsin demonstrated its superior accuracy in distinguishing sepsis compared to CRP or procalcitonin, with separation of values at SOPs from those at N-SOPs. More data are required to support its clinical utility considering its cost akin to procalcitonin. PGE1 downregulates CD11b/CD18 adhesion molecules responsible for neutrophil margination, and thereby increasing circulating neutrophils[9,32]. With PGE1 infusion, TLC is reported to rise by 39% within 24 hours, peaking at 25×10⁹/L before returning to normal by days 2–3 of infusion[9]. We, however, did not observe leucocytosis at N-SOPs. The early leucocytic phase might have missed as patients were enrolled after five days of PGE1 infusion. Absence of leucocytosis during sepsis weakens its diagnostic utility[9]. Though increase in platelet count is described with PGE1 infusion[33,34], the observed linear correlation with duration and cumulative dose has not been reported before. With significant fall during sepsis despite being on PGE1 infusion, platelet counts below 200×10⁹/L may help identify sepsis, whereas counts above 400×10⁹/L seem to rule it out. Infants with ddCHD should ideally undergo early cardiac surgery after stabilization and shortest possible duration of PGE1 infusion. Prompt recognition and aggressive management of sepsis are important considering their multifactorial susceptibility to infections[8,12,35]. Although 40–55% of neonates with PISI may have fever, only a minority have culture-proven sepsis[5,8,11,12]. PISI is also associated with increase in TLC, CRP and presepsin. Empiric antimicrobials may be justified with high degree of clinical suspicion, it is however equally important to beware of clinico-laboratory effects of PPG E1 I closely mimicking neonatal sepsis[12]. In patients with clinical and/or radiological suspicion, presence of more than one of platelet count 20mg/dL, procalcitonin >0.5ng/mL, or presepsin >2.2ng/mL may warrant antimicrobials while awaiting culture results. A scoring system integrating clinical signs with multiple biomarkers may enhance precision of decisions regarding cultures and empirical antimicrobials in this select group of patients[12]. It is likely to reduce pre-operative ICU stay, and healthcare costs. To best of our knowledge, this is the first and largest prospective study evaluating inflammatory biomarkers in neonates on PPG E1 I. However, we were not able to undertake mixed-effects models to analyze longitudinal data with time-dependent variables to account for within-subject correlation due to sparse and unbalanced dataset. Sparsity (e.g., only three observation points per patients, and missing biomarkers at N-SOPs) and unbalanced data (e.g., uneven number of SOPs and N-SOPs, and uneven follow-up duration) reduce stability of model estimation. As reliable convergence and interpretation of mixed-effects models were not feasible, a two-fold pragmatic strategy was adopted to minimize bias and improve interpretability. Firstly, SOPs and N-SOPs were compared with non-parametric methods which are relatively robust for unbalanced dataset. Secondly, missing data were handled transparently— primary analyses were performed without imputation while sensitivity analyses were performed using MICE; the obtained results were consistent. These approaches acknowledge the exploratory nature of findings. Relatively high accuracy observed for all the three biomarkers in distinguishing sepsis from PISI may partly be attributed to methodological factors. Spectrum bias is likely with the sampling limited to every fifth day, or upon clinical suspicion of sepsis. It might have failed to capture early cases of sepsis, thereby enriching the SOP dataset with clinically more severe and advanced episodes. Further, lesser number of SOPs, just a seventh of N-SOPs, is likely to limit intra-group variability. These might have overestimated discriminative performance of the biomarkers. Moreover, influence of center-specific variables may be difficult to exclude. A control group of patients without PGE1 infusion would have strengthened interpretations. Obtaining values of CRP, procalcitonin and presepsin before starting PGE1 infusion, and monitoring TLC and ANC during the first five days of infusion may offer better insight into PISI. Despite limitations, the index study provides clinically relevant information with potential applications in pediatric cardiac programs, especially in LMICs. Confirmation of these findings will require larger multi-centric studies with stratification based on gestational age, capture of complete data, and obtaining pre-infusion and follow-up levels of biomarkers. Future research may investigate molecular mechanisms underlying the observed elevation in presepsin without a corresponding rise in procalcitonin, and explore novel biomarkers like 16s rRNA. CONCLUSIONS PPG E1 I was associated with dose-dependent elevations in CRP, platelet count, and presepsin, while procalcitonin largely remained unaffected. A presepsin >2.2ng/mL, procalcitonin >0.5ng/mL, platelet count 20mg/dL demonstrated high predictive value in identifying sepsis; presepsin has the highest specificity. TLC and ANC failed to distinguish sepsis from PISI. Judicious use of these biomarkers may support antimicrobial stewardship and optimal timing for cardiac surgery. Larger prospective studies are needed to validate these preliminary findings. Declarations Financial support and sponsorship : Procurement of laboratory reagents required for presepsin measurement was supported through an intramural research grant from Postgraduate Institute of Medical Education and Research, Chandigarh-160012, India. Conflicts of interest : There are no conflicts of interest for any of the authors. Praveen K-M is employed by Nference as Director and Head of Clinical Sciences. His contribution in this manuscript was entirely in his independent scholarly capacity and is not related to his employment at Nference. Nference did not provide any resource, data, support, or input of any kind. Views and conclusions expressed herein are solely those of the author and do not represent views, positions, or work of Nference. Declaration of generative AI-assisted technologies in manuscript preparation process : During the preparation of this work the author(s) used ChatGPT in order to improve English language. After using this tool/service, the authors reviewed and edited the content as needed and take full responsibility for the content of the published article. ROLE OF CONTRIBUTORS SS: Data curation; Formal analysis; Investigation; Roles/Writing - original draft; Software. AKB: Conceptualization; Formal analysis; Funding acquisition; Methodology; Project administration; Resources; Supervision; Validation; Visualization; Writing - review & editing. AN: Investigation. RKP: Methodology; Writing - review & editing. AR: Methodology; Writing - review & editing. SSS: Methodology; Writing - review & editing. AA: Methodology; Writing - review & editing. PKM: Data curation; Formal analysis; Software; Writing - review & editing. KK: Formal analysis. References Alhussin W, Verklan MT (2016) Complications of Long-Term Prostaglandin E1 Use in Newborns With Ductal-Dependent Critical Congenital Heart Disease. J Perinat Neonatal Nurs 30:73–79 Baranwal AK, Prinja S, Kaur N (2023) Congenital Heart Disease: Would It Be the Key Driver of Infant Survival During Amrit Kaal (2022–2047)? Indian Pediatr 60:98–102 Baranwal AK, Kaur N, Govardhan SV (2024) Pediatric Cardiac Critical Care: A Vital Link in the Chain-of-Survival of Children with Congenital Heart Disease. Indian Pediatr 61:682–686 Shelton EL, Singh GK, Nichols CG (2018) Novel drug targets for ductus arteriosus manipulation: Looking beyond prostaglandins. Semin Perinatol 42:221–227 Teixeira OH, Carpenter B, MacMurray SB, Vlad P (1984) Long-term prostaglandin E1 therapy in congenital heart defects. J Am Coll Cardiol 3:838–843 Tálosi G, Katona M, Túri S (2007) Side-effects of long-term prostaglandin E(1) treatment in neonates. Pediatr Int 49:335–340 Duggan MI, MacLaren AT, Anand D (2018) Prolonged prostaglandin-E2-associated periosteal reaction and elevated C-reactive protein levels. Cardiol Young 28:482–484 Alghanem F, Rakestraw SL, Schumacher KR, Owens GE (2018) Incidence of Fever and Positive Bacterial Cultures in Neonates Receiving Prostaglandin. Pediatr Cardiol 39:89–97 Arav-Boger R, Baggett HC, Spevak PJ, Willoughby RE (2001) Leukocytosis caused by prostaglandin E1 in neonates. J Pediatr 138:263–265 Saxena A, Sharma M, Kothari SS et al (1998) Prostaglandin E1 in infants with congenital heart disease: Indian experience. Indian Pediatr 35:1063–1069 Cucerea M, Simon M, Moldovan E, Ungureanu M, Marian R, Suciu L (2016) Congenital Heart Disease Requiring Maintenance of Ductus Arteriosus in Critically Ill Newborns Admitted at a Tertiary Neonatal Intensive Care Unit. J Crit Care Med (Targu Mures) 2:185–191 Pollak U, Morag S, Lowenthal A et al (2025) Inter-Specialty Differences in the Management of Febrile Neonates on Prostaglandin E1. J Perinatol. (under review). 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Available at https://www.R-project.org/ . [Accessed on Nov 30, 2023) Mack C, Su Z, Westreich D Managing Missing Patient Data in Patient Registries. White Paper, addendum to Registries for Evaluating Patient Outcomes: A User’s Guide, Third Edition. AHRQ Publication No. 17(18)-EHC015-EF. Rockville, MD: Agency for Healthcare Research and Quality; February 2018. Available at https://www.ncbi.nlm.nih.gov/books/NBK493611/ [Accessed on June 14, 2025] an Buuren S, Groothuis-Oudshoorn K, MICE (2011) Multivariate Imputation by Chained Equations in R. J Stat Softw 45:1–67 [Accessed on June 13, 2025]. https://cran.r-project.org/web/packages/mice/index.html Radhakrishnan M, Nagaraja SB (2023) Modified Kuppuswamy socioeconomic scale 2023: Stratification and Updates. Int J Community Med Public Health 10:4415–4418 Hinson RM, Williams JA, Shacter E (1996) Elevated interleukin 6 is induced by prostaglandin E2 in a murine model of inflammation: possible role of cyclooxygenase-2. Proc Natl Acad Sci U S A 93:4885–4890 Inoue H, Takamori M, Shimoyama Y, Ishibashi H, Yamamoto S, Koshihara Y (2002) Regulation by PGE2 of the production of interleukin-6, macrophage colony stimulating factor, and vascular endothelial growth factor in human synovial fibroblasts. Br J Pharmacol 136:287–295 Li X, Wang X, Li S, Yan J, Li D (2017) Diagnostic Value of Procalcitonin on Early Postoperative Infection After Pediatric Cardiac Surgery. Pediatr Crit Care Med 18:420–428 Arkader R, Troster EJ, Abellan DM et al (2004) Procalcitonin and C-reactive protein kinetics in postoperative pediatric cardiac surgical patients. J Cardiothorac Vasc Anesth 18:160–165 Nasser BA, Mesned AR, Tageldein M et al (2017) Can acute-phase response biomarkers differentiate infection from inflammation postpediatric cardiac surgery? Avicenna J Med 7:182–188 Bobillo-Perez S, Girona-Alarcon M, Sole-Ribalta A, et al. Infection… what else? The usefulness of procalcitonin in children after cardiac surgery. PLoS One. 2021; 16:e0254757. Poggi C, Lucenteforte E, Petri D, De Masi S, Dani C (2022) Presepsin for the Diagnosis of Neonatal Early-Onset Sepsis: A Systematic Review and Meta-analysis. JAMA Pediatr 176:750–758 Parri N, Trippella G, Lisi C, De Martino M, Galli L, Chiappini E (2019) Accuracy of presepsin in neonatal sepsis: systematic review and meta-analysis. Expert Rev Anti Infect Ther 17:223–232 van Maldeghem I, Nusman CM, Visser DH (2019) Soluble CD14 subtype (sCD14-ST) as biomarker in neonatal early-onset sepsis and late-onset sepsis: a systematic review and meta-analysis. BMC Immunol 20:17 Yoon SH, Kim EH, Kim HY et al (2019) Presepsin as a diagnostic marker of sepsis in children and adolescents: a systemic review and meta-analysis. BMC Infect Dis 19:760 Puspaningtyas NW, Karyanti MR, Paramita TN et al (2023) Presepsin as a promising biomarker for early detection of post-operative infection in children. Front Pediatr 11:1036993 Ulich TR, Dakay EB, Williams JH, Ni RX (1986) In vivo induction of neutrophilia, lymphopenia, and diminution of neutrophil adhesion by stable analogs of prostaglandins E1, E2, and F2 alpha. Am J Pathol 124:53–58 Locker GJ, Staudinger T, Knapp S et al (1997) Prostaglandin E1 inhibits platelet decrease after massive blood transfusions during major surgery: influence on coagulation cascade? J Trauma 42:525–531 Palma-Barqueros V, Torregrosa JM, Caparrós-Pérez E et al (2020) Developmental Differences in Platelet Inhibition Response to Prostaglandin E1. Neonatology 117:15–23 Das S, Baranwal AK, Rawat A et al (2024) Prevalence of immunological aberrations and 22q11.2 deletion in children with conotruncal anomalies: A cross-sectional study. Ann Pediatr Cardiol 17:339–346 Additional Declarations The authors declare potential competing interests as follows: No, I declare that authors have no interests, affiliations, or associations that might be perceived to influence the results and/or discussion reported in this preprint submission. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8822972","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":587765249,"identity":"7de778e4-739b-43ef-a8a2-3acab948132e","order_by":0,"name":"Sivanesan Sivagnanaganesan","email":"","orcid":"","institution":"Postgraduate Institute of Medical Education and Research, Chandigarh, India","correspondingAuthor":false,"prefix":"","firstName":"Sivanesan","middleName":"","lastName":"Sivagnanaganesan","suffix":""},{"id":587765250,"identity":"4bcc2905-5fb5-420e-b9d2-ae3e919d4138","order_by":1,"name":"Arun Kumar Baranwal","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+ElEQVRIiWNgGAWjYBACAygtw8be/+HAByCLjZ1ILTx8PAcMH84AaWEmVoucRIKxMQ+ISUiLOfvZh58L2+x42CQS0qRtfm2T52NmYPzwMQe3FsuedGPpmW3JPGw8D45J5/bdNmxjZmCWnLkNj8MOpDFI825j5mFjT2yTzu25zQjUwsbMi0/L+WfMv3m31fOwMSSzSVv23LYnrOVGGhvQlsM8bBxpzMYMP24nEtRiOeMZmzXvv+NAv5xhfNjbcDu5jZmxGa9fzPnTmG/znKmWk2/vYTjw489t2/ntzQc/fMSjBRUwtoHJBmLVg8AfUhSPglEwCkbBSAEA7YhJGBW9yQgAAAAASUVORK5CYII=","orcid":"","institution":"Postgraduate Institute of Medical Education and Research, Chandigarh, India","correspondingAuthor":true,"prefix":"","firstName":"Arun","middleName":"Kumar","lastName":"Baranwal","suffix":""},{"id":587765251,"identity":"fb26e07e-6d81-4e6f-8928-30505ae63d15","order_by":2,"name":"Ashwini Nair","email":"","orcid":"","institution":"Postgraduate Institute of Medical Education and Research, Chandigarh, India","correspondingAuthor":false,"prefix":"","firstName":"Ashwini","middleName":"","lastName":"Nair","suffix":""},{"id":587765252,"identity":"b7f25b88-1be9-47b1-8be1-380431058bf8","order_by":3,"name":"Rakesh Kumar Pilania","email":"","orcid":"","institution":"Postgraduate Institute of Medical Education and Research, Chandigarh, India","correspondingAuthor":false,"prefix":"","firstName":"Rakesh","middleName":"Kumar","lastName":"Pilania","suffix":""},{"id":587765253,"identity":"6a6b3909-925c-4d9b-a3da-2c9dbb6bc31e","order_by":4,"name":"Amit Rawat","email":"","orcid":"","institution":"Postgraduate Institute of Medical Education and Research, Chandigarh, India","correspondingAuthor":false,"prefix":"","firstName":"Amit","middleName":"","lastName":"Rawat","suffix":""},{"id":587765254,"identity":"9196251d-093c-4980-ba5b-dae1d6422691","order_by":5,"name":"Shiv Sajan Saini","email":"","orcid":"","institution":"Postgraduate Institute of Medical Education and Research, Chandigarh, India","correspondingAuthor":false,"prefix":"","firstName":"Shiv","middleName":"Sajan","lastName":"Saini","suffix":""},{"id":587765255,"identity":"aae3ae08-d4e0-4846-97dc-85820ba0c689","order_by":6,"name":"Praveen Kumar-M","email":"","orcid":"","institution":"Nference Labs, Bengaluru, India","correspondingAuthor":false,"prefix":"","firstName":"Praveen","middleName":"","lastName":"Kumar-M","suffix":""},{"id":587765256,"identity":"b02cfa78-b785-43ec-9cbe-e0a039730597","order_by":7,"name":"Archana Angrup","email":"","orcid":"","institution":"Postgraduate Institute of Medical Education and Research, Chandigarh, India","correspondingAuthor":false,"prefix":"","firstName":"Archana","middleName":"","lastName":"Angrup","suffix":""}],"badges":[],"createdAt":"2026-02-08 16:22:22","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":true,"conflictsOfInterestStatement":true,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":true},"doi":"10.21203/rs.3.rs-8822972/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8822972/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102310817,"identity":"fdab3d35-9f07-4813-83a2-84af669d821e","added_by":"auto","created_at":"2026-02-10 11:56:24","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":696585,"visible":true,"origin":"","legend":"\u003cp\u003eStudy Flow Diagram\u003c/p\u003e","description":"","filename":"2.Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-8822972/v1/4fd5ec754b675af9b51e8e57.png"},{"id":102311595,"identity":"95e2d865-cbbd-4f42-85a9-62d70afef530","added_by":"auto","created_at":"2026-02-10 11:58:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":4640774,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation of duration of PGE1 infusion and its cumulative dose with C-reactive protein (A \u0026amp; B), procalcitonin (c \u0026amp; D) and presepsin (E \u0026amp; F) at sepsis and non-sepsis observation points (Primary Analysis).\u003c/p\u003e","description":"","filename":"3.Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-8822972/v1/f6480f5eb7f386f4f99864c9.png"},{"id":102310769,"identity":"62cfa7be-d572-4178-b540-bb0ab85378d2","added_by":"auto","created_at":"2026-02-10 11:56:02","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":4955174,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation of duration of PGE1 infusion and its cumulative dose with C-reactive protein (A \u0026amp; B), procalcitonin (C \u0026amp; D) and presepsin (E \u0026amp; F) at sepsis and non-sepsis observation points \u003cstrong\u003e(\u003c/strong\u003eSensitivity Analysis)\u003c/p\u003e","description":"","filename":"4.Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-8822972/v1/1b0dc462038c65741888302d.png"},{"id":102311467,"identity":"37b1127a-dd3c-42ee-a511-6c08a68cf76c","added_by":"auto","created_at":"2026-02-10 11:58:13","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":218867,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver Operator Characteristics Curves for C-reactive protein (CRP), procalcitonin and presepsin in identifying sepsis episodes among neonates on prolonged PGE1 infusion (Primary Analysis; Logistic regression equation for combined CRP and Procalcitonin: Intercept, -6.535; Coefficient for CRP, 0.188; Coefficient for Procalcitonin, 0.496).\u003c/p\u003e","description":"","filename":"5.Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-8822972/v1/e336f46c2d175f314661cf4c.png"},{"id":102310787,"identity":"53302945-5324-4b8d-86db-d0aabbe6e44b","added_by":"auto","created_at":"2026-02-10 11:56:13","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":5818034,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation of duration of PGE1 infusion and its cumulative dose with total leucocyte count (A \u0026amp; B), absolute neutrophil count (C \u0026amp; D) and platelet count (E \u0026amp; F) at sepsis and non-sepsis observation points.\u003c/p\u003e","description":"","filename":"6.Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-8822972/v1/f7f807cb626b77f76f12b018.png"},{"id":102312414,"identity":"e7037f3f-a67a-41a3-a46f-a97bf8179a40","added_by":"auto","created_at":"2026-02-10 12:01:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":16111494,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8822972/v1/bc929f5a-76ed-4c57-92ae-fee4060d5fbd.pdf"}],"financialInterests":"The authors declare potential competing interests as follows: No, I declare that authors have no interests, affiliations, or associations that might be perceived to influence the results and/or discussion reported in this preprint submission.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eDistinguishing Sepsis from Systemic Inflammation induced by Prolonged PGE1 infusion in Neonates with Duct-dependent Congenital Heart Disease: A Prospective Observational Study\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eProstaglandin E1 (PGE1) infusion is used to maintain patency of ductus in neonates with duct-dependent congenital heart disease (ddCHD) while waiting for definitive cardiac intervention. In low- and middle- income countries (LMICs), many patients receive it for prolonged periods due to lower birth weight, financial constraints, sepsis and paucity of dedicated pediatric cardiac programs[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Prolonged PGE1 infusion (PPG\u003csub\u003eE1\u003c/sub\u003eI) induces systemic inflammation, causing fever, apnea, seizures, hypotension, diarrhea, and platelet dysfunction. It also elevates total leukocyte count (TLC) and C-reactive protein (CRP)[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan additionalcitationids=\"CR5 CR6 CR7 CR8 CR9 CR10\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Distinguishing sepsis from PGE1-induced systemic inflammation (PISI) is often difficult, and we frequently end up using empirical antimicrobials. This initiates a cycle of delayed surgery, prolonged intensive care unit (ICU) stay, healthcare-associated infections (HAIs), greater resource utilization, physiological and emotional stress\u0026mdash; ultimately compromising outcomes[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. High prevalence of community-acquired infections and HAIs in LMICs complicates the situation further.\u003c/p\u003e \u003cp\u003eImproving accuracy of tests to distinguish sepsis from PISI is crucial for antimicrobial stewardship, optimal use of limited resources, and better outcomes. There is lack of systematic data on role of inflammatory biomarkers in distinguishing sepsis from PISI[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. This prospective observational study seeks to assess utility of TLC, CRP, procalcitonin and presepsin in identifying sepsis among neonates on PP\u003csub\u003eGE1\u003c/sub\u003eI. Secondary objectives were to find relationship, if any, between degree of exposure to PGE1 infusion and biomarkers.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003e \u003cstrong\u003eStudy Setting and Eligibility Criteria\u003c/strong\u003e \u003cp\u003eStudy was conducted in a tertiary care hospital of an LMIC, and was a time-bound dissertation project. All late preterm and term neonates (gestational age\u0026thinsp;\u0026gt;\u0026thinsp;34 weeks) admitted with CHD between August 2021 and November 2022 (a 16-month period), were screened for eligibility. Inclusion criteria included (a) echocardiographically confirmed ddCHD, (b) initiation of PGE1 infusion within first 28 days of life, and (c) continuous PGE1 infusion for at least 5 days. Neonates were excluded if they had clinically or laboratory-confirmed sepsis at time of PGE1 initiation or enrolment. Patients with heterotaxy syndromes were also excluded for potential association of splenic dysfunction.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eLaboratory Assessments, Definitions and Data Collection\u003c/strong\u003e \u003cp\u003ePGE1 was started at 0.01\u0026ndash;0.05mcg/kg/min, and titrated down to minimal effective dose. Clinical characteristics were recorded including complete blood counts (CBC), CRP, procalcitonin, presepsin and cardiac anatomy based on echocardiography and computed tomography. Clinical assessment, CBC, CRP, procalcitonin, presepsin and blood cultures were repeated every fifth day after enrolment, until end-point. The end-point was defined by stoppage of PGE1 infusion, surgical procedure or death prior to surgery. Healthcare providers were blinded to biomarker measurements prior to defining sepsis. Also, this work-up was repeated whenever there was clinical suspicion of sepsis. Cultures of urine, endotracheal aspirate, and cerebrospinal fluid (CSF) were obtained if clinically indicated. Laboratory-confirmed sepsis was defined by positive culture from blood and/or other body fluids, and/or radiological evidence of pneumonia. Any growth in blood or CSF was considered positive. However, growth of skin commensals in a single blood culture bottle from asymptomatic neonates was considered contaminant. Cultures of endotracheal aspirate and urine were considered positive with \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;10⁵ CFU/mL. Clinically confirmed sepsis was defined if patients had altered secretions, increased requirement of respiratory support, new-onset chest infiltrates, and/or signs of impaired perfusion, and shown improvement in clinical status and biomarkers with antimicrobials. Observation points where patients exhibited clinically or laboratory-confirmed sepsis were designated as sepsis observation points (SOPs), while those without any such evidence were labelled as non-sepsis observation points (N-SOPs). Patients were managed as per unit protocol.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eCRP and procalcitonin were measured on BN-ProSpec\u0026reg; System (Siemens Healthineers, Germany) and Elecsys\u0026reg; BRAHMS PCT (Roche Diagnostics, USA) respectively. Presepsin was measured from serum stored at 80\u0026deg;C with a commercial solid-phase sandwich ELISA kit (IT4529; G-Biosciences, St. Louis, Missouri, USA), and were read with an ELISA reader (Infinite 200 Pro, Tecan, Switzerland). Levels exceeding 6.0mg/L, 0.5ng/mL and 0.6ng/mL were considered abnormal for CRP, procalcitonin and presepsin respectively[\u003cspan additionalcitationids=\"CR14 CR15\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eStatistical Analysis\u003c/strong\u003e \u003cp\u003eStatistical analysis was performed using R Statistical Software (v4.3.0; R Core Team, 2023) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. A convenient sample of 30\u0026ndash;40 patients was planned. Relationship of duration and cumulative dose of PGE1 infusion with TLC, platelet count, CRP, procalcitonin and presepsin at N-SOPs was assessed with Spearman\u0026rsquo;s correlation test. Laboratory parameters at SOPs and N-SOPs were compared with Mann\u0026ndash;Whitney U test. Cut-offs for CRP, procalcitonin and presepsin to identify sepsis was obtained with receiver operating characteristic (ROC) analysis. Values of biomarkers were missing at several observation points; all of which were at N-SOPs except one. The primary data collector (SS), a postgraduate pediatric trainee rotating through different clinical units during the dissertation period, could not consistently ensure sample collection for biomarker analysis at N-SOPs, i.e., when there was no suspicion and/or confirmation of sepsis. As probability of missing data was related to absence of sepsis, these missing values were classified as 'missing at random' (MAR)[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Primary analysis was conducted with the available values, while sensitivity analysis was performed after having imputed the missing values with multivariate imputation by chained equations (MICE) approach[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. A p-value of \u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEthical Clearance\u003c/strong\u003e \u003cp\u003e Research methods were in accordance with the Declaration of Helsinki, and with ethical standards of the Institutional Ethics Committee. Protocol was approved vide letter no. INT/IEC/2021/581\u0026thinsp;\u0026minus;\u0026thinsp;150 (dated 11.10.2021) and endst. no. 13166/PG-2Trg/2020/15795-96 (dated 14.12.2021) respectively. Written free and informed consent was obtained from legal guardians of all participants prior to enrolment. Identity and patient-specific information were kept confidential.\u003c/p\u003e \u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eThirty patients (18 males and 12 females) met eligibility criteria (\u003cstrong\u003eFigure 1\u003c/strong\u003e). Their demographic, clinical, and laboratory profiles are summarized in \u003cstrong\u003eTable 1\u003c/strong\u003e. Cardiac anatomies included d-transposition of great arteries (n=12), tetralogy of Fallot (n=11), pulmonary atresia (n=4), coarctation of aorta (n=2), and interrupted aortic arch (n=1). Majority were referred late (median age, 3 days; 3\u003csup\u003erd\u003c/sup\u003e quartile, 10.5 days); four patients came during fourth week of age. Only seven patients were referred with PGE1 infusion. Most patients belonged to lower socioeconomic strata\u0026mdash; 17 (56%) from lower middle class and 8 (26%) from upper lower class [20]. Median duration of PGE1 infusion and median cumulative dose of PGE1 were 21 days and 570.2 mcg/Kg respectively. Many patients received PGE1 infusion well into fifth week. Twenty-five patients(83%) underwent surgery after median hospitalization of 19 days (IQR; 12, 27) at a median age of 29 days (IQR; 17, 40). PGE1 infusion was tapered and stopped in one patient. One patient died preoperatively, while three refused surgery due to financial constraints.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1: Demographic and clinical characteristics of enrolled patients (n=30)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"613\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eType of Congenital Heart Disease\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDuct-dependent Pulmonary Circulation (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e16 (53%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDuct-dependent Systemic Circulation (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3 (10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAdmixture Lesions (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11 (37%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristics at Hospitalization\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBirth Weight (gm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.8 (2.26, 2.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGestational Age at birth (Completed Weeks)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e37 (37, 38)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGender (M:F)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18 : 12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAge (days)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.0 (1.0, 10.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTotal Leucocyte Count (x10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e13.07 (9.46, 16.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAbsolute Neutrophil Count (x10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.06 (4.05, 8.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePlatelet Count (x10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e279 (241, 355)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAge when PGE1 infusion was initiated (days)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.00 (3.00, 12.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStarting dose of PGE1 infusion (ng/kg/min)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e20.0 (12.5, 50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristics at Enrolment (i.e., the first N-SOP\u003csup\u003e*\u003c/sup\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCumulative dose of PGE1 received by Enrolment (mcg/kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e205.0 (133.2, 287.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTotal Leucocyte Count (x10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12.56 (9.38, 15.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAbsolute Neutrophil Count (x10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.31 (4.63, 10.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePlatelet Count (x10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e289 (234, 355)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristics during Subsequent Hospital Course\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDuration of PGE1 infusion by the Endpoint (days)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e21.0 (11.25, 27.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMaximum dose of PGE1 infusion at the Endpoint (mcg/kg/min)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.040 (0.030, 0.058)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCumulative dose of PGE1 received by the Endpoint (mcg/kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e570.2 (381.6, 1022.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNumber of patients needed Non-Invasive Respiratory Support (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e16 (53%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNumber of patients needed Invasive Mechanical Ventilation (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15 (50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDuration of Invasive Mechanical Ventilation (n=15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10 (2.25, 14.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNumber of patients needed Inotropic support (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7 (23%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDuration of Inotropic support (days) (n=7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.0 (5.0, 15.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMaximum Vasoactive Inotrope Score (n=7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e60.0 (51.5, 81.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNumber of patients needed furosemide (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8 (26%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNumber of patients needed correction for hyponatremia (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 (3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNumber of patients needed correction for hypokalemia (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2 (6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNumber of patients needed correction for hypocalcemia (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAbbreviations:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cem\u003ePGE1, Prostaglandin E1; N-SOP, non-sepsis observation point.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAll values are in Median (IQR) unless stated otherwise.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u003csup\u003e*\u003c/sup\u003e\u003c/em\u003e\u003c/strong\u003e\u003cem\u003eNeonates who hadlaboratory- or clinically-confirmed sepsis at the time of initiation of PGE1 infusion and/or at enrolment were excluded.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eA total of 89 observations were recorded, with median of three observations per patient. Of these, 11 observation points (12.4%) corresponded to clinically- and/or laboratory- confirmed sepsis episodes (SOPs) from nine patients(30%), while remaining 78 (87.6%) were from periods without any clinical or laboratory evidence of sepsis (N-SOPs). Of 78 N-SOPs, 62 (79%) were from the 21 patients who never developed sepsis, and 16 (21%) were from nine patients who did develop sepsis at some point. Of the 11 SOPs, two had positive blood culture (\u003cem\u003eStaphylococcus aureus\u003c/em\u003e and \u003cem\u003eStaphylococcus epidermidis\u003c/em\u003e), while one each had radiological evidence of pneumonia and CSF findings consistent with meningitis. Remaining seven SOPs were defined by clinical suspicion and subsequent improvement in clinical status, TLC and biomarkers with antimicrobials. Median durations of PGE1 infusion were similar (p=0.82), while median cumulative dose was higher at SOPs than at N-SOPs (p=0.09) (\u003cstrong\u003eTable 2\u003c/strong\u003e). Twenty-three percent of biomarker values were missing (61/267 opportunities), predominantly at N-SOPs\u0026mdash;CRP: 14/78 (17.9%), procalcitonin: 25/78 (32.1%), and presepsin: 21/78 (26.9%). At SOPs, only one value of procalcitonin was missing (\u003cstrong\u003eTable 2\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2: Clinical and laboratory parameters at Non-Sepsis Observation Points and Sepsis Observation Points.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"661\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-Sepsis Observation Points (n=78)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSepsis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eObservation Points\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=11)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDuration of \u0026nbsp; \u0026nbsp; Prostaglandin E1 Infusion (days)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10 (5, 16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9.5 (5, 18.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCumulative dose of \u0026nbsp;Prostaglandin E1 (mcg/kg)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e324 (208.8, 604.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e561.6 (381, 961.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal Leucocyte Count (x10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12.6 (9.30, 15.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11.10 (9.24, 13.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAbsolute Neutrophil Count (x10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.57 (4.13, 8.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.46 (4.43, 7.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePlatelet Count (x10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e370 (270, 480)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e173 (69.7, 337)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eC-Reactive Protein (mg/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePrimary Analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.88 (1.75, 7.15);\u003c/p\u003e\n \u003cp\u003e[0.12-73.30]\u003c/p\u003e\n \u003cp\u003e(n=64)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e38.0 (28.65, 78.00);\u003c/p\u003e\n \u003cp\u003e[19.60-203.00]\u003c/p\u003e\n \u003cp\u003e(n=11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSensitivity Analysis\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.04 (1.80, 7.47);\u003c/p\u003e\n \u003cp\u003e[0.12-73.30]\u003c/p\u003e\n \u003cp\u003e(n=78)\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e38.0 (28.65, 78.00);\u003c/p\u003e\n \u003cp\u003e[19.60-203.00]\u003c/p\u003e\n \u003cp\u003e(n=11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eProcalcitonin (ng/mL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePrimary Analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.16 (0.10, 0.45);\u003c/p\u003e\n \u003cp\u003e[0.06-4.24]\u003c/p\u003e\n \u003cp\u003e(n=53)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.05 (1.40, 10.98);\u003c/p\u003e\n \u003cp\u003e[0.30-100.00]\u003c/p\u003e\n \u003cp\u003e(n=10)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSensitivity Analysis\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.16 (0.09, 0.45);\u003c/p\u003e\n \u003cp\u003e[0.06-4.24]\u003c/p\u003e\n \u003cp\u003e(n=78)\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.5(1.46,9.55);\u003c/p\u003e\n \u003cp\u003e[0.3-100.00]\u003c/p\u003e\n \u003cp\u003e(n=11)\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePresepsin (ng/mL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePrimary Analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.91 (1.72, 2.00);\u003c/p\u003e\n \u003cp\u003e[1.51-2.95]\u003c/p\u003e\n \u003cp\u003e(n=57)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.99 (2.61, 3.37);\u003c/p\u003e\n \u003cp\u003e[2.23-3.80]\u003c/p\u003e\n \u003cp\u003e(n=11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSensitivity Analysis\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.91 (1.70, 2.00);\u003c/p\u003e\n \u003cp\u003e[1.51-2.95]\u003c/p\u003e\n \u003cp\u003e(n=78)\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.99 (2.61, 3.37);\u003c/p\u003e\n \u003cp\u003e[2.23-3.80]\u003c/p\u003e\n \u003cp\u003e(n=11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eAll values are in Median (Interquartile Range). Additionally, [Min-Max] is given for C-Reactive Protein, Procalcitonin and Presepsin.\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e*\u003c/em\u003e\u003cem\u003eValues of C-reactive protein, procalcitonin and presepsin are available only for the mentioned numbers of observation points.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003csup\u003e#\u003c/sup\u003e\u003c/em\u003e\u003cem\u003eSensitivity analysiswas done after imputing the missing values of C-Reactive Protein, Procalcitonin and Presepsin with Multivariate Imputation by Chained Equations (MICE) approach.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAt N-SOPs, CRP increased linearly with cumulative dose and duration of PGE1 infusion. Correlation was statistically significant with the former (p=0.0043) but not with the latter (p=0.068). Nearly 25% of N-SOPs had CRP exceeding 7.15mg/L, with the highest recorded value being 73.3mg/L. Presepsin revealed a positive trend towards duration (p=0.055), but was not affected by cumulative dose (p=0.61) (\u003cstrong\u003eFigure 2\u003c/strong\u003e). With sensitivity analysis, strength of positive correlations of CRP and presepsin with duration improved (p=0.013 and p=0.0032 respectively) (\u003cstrong\u003eFigure 3\u003c/strong\u003e). In contrast, procalcitonin neither correlated with duration of PGE1 infusion (p=0.25) nor its cumulative dose(p=0.80) irrespective of sensitivity analysis (\u003cstrong\u003eFigures 2 and 3)\u003c/strong\u003e. More than 75% of procalcitonin values were below normal threshold of 0.5ng/mL (3\u003csup\u003erd\u003c/sup\u003e quartile, 0.45ng/mL), while all the presepsin values were markedly above the cut-off of 0.6ng/mL (lowest, 1.51ng/mL) (\u003cstrong\u003eTable 2\u003c/strong\u003e). CRP and procalcitonin revealed notable overlap between N-SOPs and SOPs. In contrast, presepsin revealed an excellent separation of correlation lines representing N-SOPs and SOPs; separation is more prominent for duration of PGE1 infusion than its cumulative dose (\u003cstrong\u003eTable 2 and Figure 2\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eDuring sepsis episodes, CRP, procalcitonin and presepsin, all three, got elevated significantly compared to N-SOPs. CRP and procalcitonin got elevated 10-fold (38.0 vs 3.88 mg/L; p\u0026lt;0.001) and 19-fold (3.05 vs 0.16 ng/ml; p\u0026lt;0.001) respectively while presepsin got elevated only 1.5-fold during sepsis episodes (2.99 vs 1.91 ng/ml; p\u0026lt;0.001). Sensitivity analysis yielded similar results (\u003cstrong\u003eTable 2\u003c/strong\u003e). CRP above the cut-off of 19.6mg/L was found to identify sepsis episodes (AUC, 0.97), while procalcitonin could identify it above the cut-off of 0.55ng/mL (AUC, 0.91). Combining the two increased the sensitivity (AUC, 0.98). Presepsin, at cut off of 2.23ng/mL, could identify sepsis episodes with better sensitivity and specificity (AUC, 0.98) (\u003cstrong\u003eFigure 4, Table 3\u003c/strong\u003e). Sensitivity analysis of ROC neither altered cut-offs for CRP and presepsin, nor their sensitivity and specificity. However, it yielded a much higher cut-off for procalcitonin (1.35ng/mL versus 0.55ng/mL) with lesser sensitivity (82% versus 90%) (\u003cstrong\u003eTable 3)\u003c/strong\u003e. Such a higher cut-off of procalcitonin is likely to miss many neonates with sepsis. A\u0026nbsp;CRP level more than 20mg/L and procalcitonin more than 0.5ng/mL indicated 2.62-fold (95%CI, 1.39-4.94; p=0.003] and 1.7-fold (95%CI, 1.17-2.47; p=0.005) increase in risk of having sepsis respectively, while presepsin more than 2.23ng/mL indicated a 5.92-fold (95%CI, 1.67-20.99; p=0.006) increase in risk.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3: Result of Receiver Operating Characteristics analysis for inflammatory biomarkers to distinguish sepsis from PGE1 induced systemic inflammation (Primary and Sensitivity Analyses)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"720\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCut-off Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAUC\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSensitivity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpecificity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePPV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNPV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLR+\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLR-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eC-Reactive Protein\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePrimary Analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e19.60 mg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003cp\u003e(0.92, 1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e88%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e71%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSensitivity Analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e19.60 mg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003cp\u003e(0.94-0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e90%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e57.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eProcalcitonin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePrimary Analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.55 ng/mL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003cp\u003e(0.80, 0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e90%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e82%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e60%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e97%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSensitivity Analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.35 ng/mL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003cp\u003e(0.83-0.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e82%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e90%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e52.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e97.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eC-Reactive Protein + Procalcitonin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePrimary Analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e----\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003cp\u003e(0.95, 1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e88%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e71%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e17.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSensitivity Analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e----\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003cp\u003e(0.95-1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e89%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e55%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePresepsin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePrimary Analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.23ng/mL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003cp\u003e(0.94, 1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e94%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e83%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e17.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSensitivity Analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.23 ng/mL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003cp\u003e(0.97-1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e97%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e84.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e39.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAbbreviations:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cem\u003eAUC, Area Under Curve; 95% CI, 95% Confidence Interval; PPV, Positive Predictive Value; NPV, Negative Predictive Value; LR+, Positive Likelihood Ratio; LR-, Negative Likelihood Ratio.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003ePlatelet counts were similar at hospital admission and at enrolment (\u003cstrong\u003eTable 1)\u003c/strong\u003e. However it got elevated significantly at post-enrolment N-SOPs (p=0.001)\u0026nbsp;\u003cstrong\u003e(Table 4)\u003c/strong\u003e. Further, platelet counts revealed a linear elevation with increasing duration and cumulative dose at N-SOPs (p\u0026lt;0.0001 and p\u0026lt;0.001 respectively) but not at SOPs (p=0.19 and p=0.71 respectively) (\u003cstrong\u003eFigure 5\u003c/strong\u003e). Other important observations include significant fall in platelet counts at SOPs(p=0.003) despite being on PGE1 infusion (\u003cstrong\u003eTable 2\u003c/strong\u003e), and an excellent visible separation of values at SOPs from those at N-SOPs (\u003cstrong\u003eFigure 5\u003c/strong\u003e). A platelet count less than 200x10\u003csup\u003e3\u003c/sup\u003e/L and less than 100x10\u003csup\u003e9\u003c/sup\u003e/L had 1.68-fold [95%CI, 1.09-2.60; p=0.02] and 2.13-fold [95%CI, 0.9-5.03; p=0.083] increase in relative risk of having sepsis respectively. TLC and ANC were similar at hospital admission, enrolment and post-enrolment N-SOPs (\u003cstrong\u003eTables 1 and 4)\u003c/strong\u003e. At N-SOPs, TLC and ANC did not reveal any relationship with either duration or cumulative dose (\u003cstrong\u003eFigure 5\u003c/strong\u003e). Moreover, lack of their rise during sepsis episodes led to complete overlap in their values at SOPs with those at N-SOPs (\u003cstrong\u003eTable 2\u003c/strong\u003e, \u003cstrong\u003eFigure 5\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4: Comparison of inflammatory biomarkers at \u0026lsquo;Enrolment\u0026rsquo; (i.e., at 5\u003csup\u003eth\u003c/sup\u003e day of PGE1 infusion, n=30) versus \u0026lsquo;All Non-Sepsis Observation Points\u0026rsquo; (i.e., including observations at enrolment; n=78) and \u0026lsquo;Post-Enrolment Non-Sepsis Observation Points\u0026rsquo; (i.e., after excluding observations at enrolment; n=48).\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"633\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1. Comparison of observations at \u0026lsquo;Enrolment\u0026rsquo; versus at \u0026lsquo;All Non-Sepsis Observation Points\u0026rsquo;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eInflammatory Biomarkers\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEnrolment \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; (n=30)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAll N-SOPs \u0026nbsp; \u0026nbsp; (n=78)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal Leucocyte Count (x10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12.56 (9.38, 15.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12.6 (9.30, 15.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAbsolute Neutrophil Count (x10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.31 (4.63, 10.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.57 (4.13, 8.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePlatelet Count (x10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.89 (2.34, 3.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e370.0 (270.0, 480.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eC-Reactive Protein (mg/L)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.5 (1.40, 4.80) (n=27)\u003cstrong\u003e\u003csup\u003e*\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.88 (1.75, 7.17)\u003c/p\u003e\n \u003cp\u003e(n=64)\u003cstrong\u003e\u003csup\u003e*\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eProcalcitonin (ng/mL)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.16 (0.13, 0.76)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(n=17)\u003cstrong\u003e\u003csup\u003e*\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.16 (0.10, 0.45)\u003c/p\u003e\n \u003cp\u003e(n=53)\u003cstrong\u003e\u003csup\u003e*\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePresepsin (ng/mL)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.78 (1.66, 1.92)\u003c/p\u003e\n \u003cp\u003e(n=20)\u003cstrong\u003e\u003csup\u003e*\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.91 (1.72, 2.0)\u003c/p\u003e\n \u003cp\u003e(n=57)\u003cstrong\u003e\u003csup\u003e*\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e2. Comparison of observations at \u0026lsquo;Enrolment\u0026rsquo; versus at \u0026lsquo;Post-Enrolment Non-Sepsis Observation Points\u0026rsquo;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eInflammatory Biomarkers\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEnrolment \u0026nbsp; \u0026nbsp; \u0026nbsp; (n=30)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePost-Enrolment\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eN-SOPs (n=48)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal Leucocyte Count (x10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12.56 (9.38, 15.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12.79 (9.44, 15.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAbsolute Neutrophil Count (x10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.31 (4.63, 10.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.39 (4.12, 8.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePlatelet Count (x10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.89 (2.34, 3.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.17 (3.35, 5.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eC-Reactive Protein (mg/L)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.5 (1.40, 4.80) (n=27)\u003cstrong\u003e\u003csup\u003e*\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.3 (2.59, 11.0)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(n=37)\u003cstrong\u003e\u003csup\u003e*\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eProcalcitonin (ng/mL)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.16 (0.13, 0.76) (n=17)\u003cstrong\u003e\u003csup\u003e*\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.18 (0.10, 0.45)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(n=36)\u003cstrong\u003e\u003csup\u003e*\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePresepsin (ng/mL)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.78 (1.66, 1.92) (n=20)\u003cstrong\u003e\u003csup\u003e*\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.95 (1.76, 2.08)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(n=37)\u003cstrong\u003e\u003csup\u003e*\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAbbreviation:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cem\u003eN-SOPs, Non-Sepsis Observation Points.\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e*Values of C-reactive protein, procalcitonin and presepsin are available for the mentioned numbers of observation points.\u003c/em\u003e\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eMany of adverse effects of PPG\u003csub\u003eE1\u003c/sub\u003eI mimic neonatal sepsis, potentially delaying cardiac surgery and prolonging preoperative ICU stay. This study demonstrated behavior of inflammatory biomarkers and their utility in identifying sepsis episodes among neonates on PPG\u003csub\u003eE1\u003c/sub\u003eI. CRP showed a dose-dependent linear rise even in the absence of infection, limiting its diagnostic utility to a high cut-off value(~20mg/L). In contrast, procalcitonin levels did not increase with PGE1 infusion and maintained diagnostic accuracy at the conventional threshold of 0.5ng/mL. Presepsin showed a trend toward dose-independent elevation with PPG\u003csub\u003eE1\u003c/sub\u003eI, however it performed best in identifying sepsis episodes at cut-off of 2.23ng/mL. Multivariate ROC model did not improve performance. TLC and ANC neither correlated with degree of PGE1 exposure nor distinguished sepsis episodes. A dose-dependent thrombocytosis was observed with PPG\u003csub\u003eE1\u003c/sub\u003eI. Sepsis episodes, however, reduced platelet counts significantly; values below 200×10\u003csup\u003e9\u003c/sup\u003e/L indicated a higher risk of sepsis.\u0026nbsp;Relationship of biomarkers with duration of PGE1 infusion and its cumulative dose (a composite of infusion rate and duration) were similar except for presepsin (at N-SOPs and SOPs both) and TLC (at N-SOPs).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eProstaglandins play key roles in pro-inflammatory cascade causing a dose-dependent rise in CRP[11,21,22]. The observed linear rise in CRP with duration and cumulative dose of PGE1 aligns with the available experimental and clinical evidence. Maximum CRP recorded at N-SOPs was 73mg/L, though higher levels are reported in literature, e.g., 100mg/L on day 49 of PGE2 infusion at 20ng/kg/min, and 197mg/L on day 11 at 50ng/kg/min [7]. Though CRP rose nearly 10-fold during sepsis episodes, notable overlap with N-SOPs was observed, especially with longer duration and at higher cumulative dose. A stand-alone rise in CRP below 20mg/L does not appear to suggest sepsis. Though pro-inflammatory cytokines stimulate extra-thyroidal tissues to synthesize procalcitonin, the latter does not seem to get affected with PPG\u003csub\u003eE1\u003c/sub\u003eI. Despite extensive efforts, we could not find English language literature addressing the effect of PGE1 on procalcitonin. More clinical data are therefore required to support this finding. A cut-off of 0.55ng/mL was optimal in identifying sepsis, and is similar to data obtained from children experiencing systemic inflammation after cardiac surgery[23]. Both CRP and procalcitonin are known to rise during non-infectious inflammation, such as in the post-operative period and following cardiopulmonary bypass (CPB) [23,24]. CRP may peak up to 80mg/L during first two post-operative days losing its ability to reliably distinguish post-operative sepsis[23-25]. Procalcitonin also increases on the first post-operative day and may exceed 3ng/mL, however it outperforms CRP in detecting infections during post-CPB period[23,24]. These are consistent with our findings in patients with PISI.\u003c/p\u003e\n\u003cp\u003ePresepsin, generated by bacterial proteases during sepsis, is considered specific for bacterial infections[27,28]. A meta-analysis of 23 studies involving clinically and culture-confirmed early-onset neonatal sepsis (infected, n=1040; non-infected, n=826) revealed sensitivity and specificity of 93% and 91% respectively[27]. Cut-offs to diagnose neonatal and pediatric sepsis varies from 0.31ng/mL to 0.89ng/mL[28-30]. Presepsin also rises on first post-operative day in pediatric surgical patients, with no difference between those with and without culture-positive infections (0.81 vs 0.72ng/mL, p=0.41)[31]. These findings, along with nearly three-fold elevation in presepsin at N-SOPs, suggest that even presepsin may be a non-specific marker of inflammation[30]. Cut-off observed in our cohort is extraordinarily higher. Notwithstanding, presepsin demonstrated its superior accuracy in distinguishing sepsis compared to CRP or procalcitonin, with separation of values at SOPs from those at N-SOPs. More data are required to support its clinical utility considering its cost akin to procalcitonin. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePGE1 downregulates CD11b/CD18 adhesion molecules responsible for neutrophil margination, and thereby increasing circulating neutrophils[9,32]. With PGE1 infusion, TLC is reported to rise by 39% within 24 hours, peaking at 25×10⁹/L before returning to normal by days 2–3 of infusion[9]. We, however, did not observe leucocytosis at N-SOPs. The early leucocytic phase might have missed as patients were enrolled after five days of PGE1 infusion. Absence of leucocytosis during sepsis weakens its diagnostic utility[9]. Though increase in platelet count is described with PGE1 infusion[33,34], the observed linear correlation with duration and cumulative dose has not been reported before. With significant fall during sepsis despite being on PGE1 infusion, platelet counts below 200×10⁹/L may help identify sepsis, whereas counts above 400×10⁹/L seem to rule it out. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eInfants with ddCHD should ideally undergo early cardiac surgery after stabilization and shortest possible duration of PGE1 infusion. Prompt recognition and aggressive management of sepsis are important considering their multifactorial susceptibility to infections[8,12,35]. Although 40–55% of neonates with PISI may have fever, only a minority have culture-proven sepsis[5,8,11,12]. PISI is also associated with increase in TLC, CRP and presepsin. Empiric antimicrobials may be justified with high degree of clinical suspicion, it is however equally important to beware of clinico-laboratory effects of PPG\u003csub\u003eE1\u003c/sub\u003eI closely mimicking neonatal sepsis[12]. In patients with clinical and/or radiological suspicion, presence of more than one of platelet count \u0026lt;200×10³/L, CRP \u0026gt;20mg/dL, procalcitonin \u0026gt;0.5ng/mL, or presepsin \u0026gt;2.2ng/mL may warrant antimicrobials while awaiting culture results. A scoring system integrating clinical signs with multiple biomarkers may enhance precision of decisions regarding cultures and empirical antimicrobials in this select group of patients[12]. It is likely to reduce pre-operative ICU stay, and healthcare costs.\u003c/p\u003e\n\u003cp\u003eTo best of our knowledge, this is the first and largest prospective study evaluating inflammatory biomarkers in neonates on PPG\u003csub\u003eE1\u003c/sub\u003eI. However, we were not able to undertake mixed-effects models to analyze longitudinal data with time-dependent variables to account for within-subject correlation due to sparse and unbalanced dataset.\u0026nbsp;Sparsity (e.g., only three observation points per patients, and missing biomarkers at N-SOPs) and unbalanced data (e.g., uneven number of SOPs and N-SOPs, and uneven follow-up duration) reduce stability of model estimation. As reliable convergence and interpretation of mixed-effects models were not feasible, a two-fold pragmatic strategy was adopted to minimize bias and improve interpretability. Firstly, SOPs and N-SOPs were compared with non-parametric methods which are relatively robust for unbalanced dataset. Secondly, missing data were handled transparently— primary analyses were performed without imputation while sensitivity analyses were performed using MICE; the obtained results were consistent. These approaches acknowledge the exploratory nature of findings.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRelatively high accuracy observed for all the three biomarkers in distinguishing sepsis from PISI may partly be attributed to methodological factors. Spectrum bias is likely with the sampling limited to every fifth day, or upon clinical suspicion of sepsis. It might have failed to capture early cases of sepsis, thereby enriching the SOP dataset with clinically more severe and advanced episodes. Further, lesser number of SOPs, just a seventh of N-SOPs, is likely to limit intra-group variability. These might have overestimated discriminative performance of the biomarkers. Moreover, influence of center-specific variables may be difficult to exclude. A control group of patients without PGE1 infusion would have strengthened interpretations. Obtaining values of CRP, procalcitonin and presepsin before starting PGE1 infusion, and monitoring TLC and ANC during the first five days of infusion may offer better insight into PISI. Despite limitations, the index study provides clinically relevant information with potential applications in pediatric cardiac programs, especially in LMICs. Confirmation of these findings will require larger multi-centric studies with stratification based on gestational age, capture of complete data, and obtaining pre-infusion and follow-up levels of biomarkers. Future research may investigate molecular mechanisms underlying the observed elevation in presepsin without a corresponding rise in procalcitonin, and explore novel biomarkers like 16s rRNA.\u003c/p\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003ePPG\u003csub\u003eE1\u003c/sub\u003eI was associated with dose-dependent elevations in CRP, platelet count, and presepsin, while procalcitonin largely remained unaffected. A presepsin \u0026gt;2.2ng/mL, procalcitonin \u0026gt;0.5ng/mL, platelet count \u0026lt;200\u0026times;10⁹/L and CRP \u0026gt;20mg/dL demonstrated high predictive value in identifying sepsis; presepsin has the highest specificity. TLC and ANC failed to distinguish sepsis from PISI. Judicious use of these biomarkers may support antimicrobial stewardship and optimal timing for cardiac surgery. Larger prospective studies are needed to validate these preliminary findings.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cu\u003eFinancial support and sponsorship\u003c/u\u003e\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003eProcurement of laboratory reagents required for presepsin measurement was supported through an intramural research grant\u0026nbsp;from\u0026nbsp;Postgraduate Institute of Medical Education and Research, Chandigarh-160012, India.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cu\u003eConflicts of interest\u003c/u\u003e\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003eThere are no conflicts of interest for any of the authors. Praveen K-M is employed by Nference as Director and Head of Clinical Sciences. His contribution in this manuscript was entirely in his independent scholarly capacity and is not related to his employment at Nference. Nference did not provide any resource, data, support, or input of any kind. Views and conclusions expressed herein are solely those of the author and do not represent views, positions, or work of Nference.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of generative AI-assisted technologies in manuscript preparation process\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e During the preparation of this work the author(s) used ChatGPT in order to improve English language. After using this tool/service, the authors reviewed and edited the content as needed and take full responsibility for the content of the published article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eROLE OF CONTRIBUTORS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSS:\u003c/strong\u003e Data curation; Formal analysis; Investigation; Roles/Writing - original draft; Software. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAKB:\u003c/strong\u003e Conceptualization; Formal analysis; Funding acquisition; Methodology; Project administration; Resources; Supervision; Validation; Visualization; Writing - review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAN:\u003c/strong\u003e Investigation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRKP:\u003c/strong\u003e Methodology; Writing - review \u0026amp; editing. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAR:\u003c/strong\u003e Methodology; Writing - review \u0026amp; editing. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSSS:\u003c/strong\u003e Methodology; Writing - review \u0026amp; editing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAA:\u003c/strong\u003e Methodology; Writing - review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePKM:\u003c/strong\u003e Data curation; Formal analysis; Software; Writing - review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eKK:\u003c/strong\u003e Formal analysis.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAlhussin W, Verklan MT (2016) Complications of Long-Term Prostaglandin E1 Use in Newborns With Ductal-Dependent Critical Congenital Heart Disease. 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J Trauma 42:525\u0026ndash;531\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePalma-Barqueros V, Torregrosa JM, Caparr\u0026oacute;s-P\u0026eacute;rez E et al (2020) Developmental Differences in Platelet Inhibition Response to Prostaglandin E1. Neonatology 117:15\u0026ndash;23\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDas S, Baranwal AK, Rawat A et al (2024) Prevalence of immunological aberrations and 22q11.2 deletion in children with conotruncal anomalies: A cross-sectional study. Ann Pediatr Cardiol 17:339\u0026ndash;346\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Postgraduate Institute of Medical Education and Research, Chandigarh, India.","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Duct-dependent Congenital Heart Disease, Prostaglandin E1, Neonatal Sepsis, C-Reactive Protein, Procalcitonin, Presepsin.","lastPublishedDoi":"10.21203/rs.3.rs-8822972/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8822972/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective\u003c/strong\u003e: Prolonged\u0026nbsp; PGE1 infusion (PPG\u003csub\u003eE1\u003c/sub\u003eI) is common in low- and middle-income countries (LMICs). This prospective observational study was planned to assess role of biomarkers in differentiating sepsis from PGE1-induced systemic inflammation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy Design: \u003c/strong\u003eNeonates (gestation age, \u003cu\u003e\u0026gt;\u003c/u\u003e34 weeks) with duct-dependent congenital heart disease having received PGE1 for 5 days were enrolled in Pediatric Cardiac Intensive Care Unit of a tertiary care hospital in an LMIC. Culture-positive sepsis was excluded. Complete blood counts, CRP, procalcitonin and presepsin were obtained at enrolment; every 5 days thereafter; and whenever sepsis was suspected until end point (preoperative death/surgery). Correlations between biomarkers and duration of PGE1 infusion were assessed at non-sepsis observation points (N-SOPs). Comparisons were made between biomarkers at N-SOPs and sepsis observation points (SOPs). ROC analysis was performed to find cut-offs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eEighty-nine observations (N-SOPs, 78; SOPs, 11) were made from 30 patients. At N-SOPs, CRP, presepsisn and platelet count increased linearly with duration of PGE1 (p=0.068, p=0.055 and p\u0026lt;0.0001 respectively). CRP (p\u0026lt;0.001), procalcitonin (p\u0026lt;0.001) and presepsin (p\u0026lt;0.001) were higher, while platelet count was lower (p=0.003) at SOPs compared to at N-SOPs. Cut-offs to detect sepsis were— CRP: 19.6mg/dL (AUC, 0.97; sensitivity, 100%; specificity, 88%), procalcitonin: 0.55ng/mL (0.91; 90%; 82%) and presepsin: 2.23ng/mL (0.98; 100%; 94%).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003ePPG\u003csub\u003eE1\u003c/sub\u003eI led to rise in CRP, presepsin and platelet count in dose-dependent manner. CRP \u0026gt;19.6mg/dL seems to detect sepsis with high sensitivity but lower specificity. Presepsin (\u0026gt;2.23ng/mL) seems to outperform CRP, procalcitonin and their combination. Lower platelet count may play a supporting role. \u0026nbsp;\u0026nbsp;\u003c/p\u003e","manuscriptTitle":"Distinguishing Sepsis from Systemic Inflammation induced by Prolonged PGE1 infusion in Neonates with Duct-dependent Congenital Heart Disease: A Prospective Observational Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-10 11:46:26","doi":"10.21203/rs.3.rs-8822972/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e295ee7a-801c-4631-8d0a-79a7a6ccf16d","owner":[],"postedDate":"February 10th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":62533432,"name":"Pediatrics"}],"tags":[],"updatedAt":"2026-02-10T11:46:27+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-10 11:46:26","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8822972","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8822972","identity":"rs-8822972","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}