The Prognostic Role of Procalcitonin in Critically Ill Patients Admitted in a Medical Stepdown Unit: A Retrospective Cohort Study

Procalcitonin (PCT) is a a marker of bacterial infection. Its prognostic role in the critically-ill patient, however, is still object of debate. Aim of this study was to evaluate the capacity of admission PCT (aPCT) in assessing the prognosis of the critically-ill patient regardless the presence of bacterial infection. A single-cohort, single-center retrospective study was performed evaluating critically-ill patients admitted to a stepdown care unit. Age, sex, Simplified Acute Physiology Score II (SAPS-II), shock, troponin-I, aPCT, serum creatinine, cultures and clinical endpoints (in-hospital mortality or Intensive Care Unit (ICU) transfer) were collected. Time free from adverse event (TF-AE) was defined as the time between hospitalization and occurrence of one of the clinical endpoints, and calculated with Kaplan-Meier curves. We engineered a new predictive model (POCS) adopting aPCT, age and shock.We enrolled 1063 subjects: 450 reached the composite outcome of death or ICU transfer. aPCT was significantly higher in this group, where it predicted TF-AE both in septic and non-septic patients. aPCT and POCS showed a good prognostic performance in the whole sample, both in septic and non-septic patients. aPCT showed a good prognostic accuracy, adding informations on the rapidity of clinical deterioration. POCS model reached a performance similar to SAPS-II.

Inclusion and exclusion criteria. All the consecutive patients admitted in a 36-month period (from 1 st January 2008 to 31 th December 2010) were screened from the EMR. A total of 6562 patients was evaluated.
Currently, there is no univocal definition of critical illness: for this reason, in order to outline the critically ill subjects, we selected all the patients affected by a life-threatening condition with signs of physiological deterioration 11 , defined as the presence of at least one abnormal vital sign and one end-organ dysfunction at the admission.
Exclusion criteria were represented by: (1) age <18 years; (2) absence of critical illness, defined as absence of organ dysfunction; (3) data incompleteness at the moment of the study, particularly (4) absence of serum PCT determination within the first 12 hours of admission.
Patients treated with cardiopulmonary resuscitation before the admission, whose PCT values could be significantly raised, are not admitted to our stepdown unit. PCT values could be significantly reduced after dialysis: all measurements were performed at the admission before any dialytic treatment.
Definitions. We retrospectively collected the following variables: age, sex, presence of shock, admission TnI, aPCT, leucocyte count, aPCT, serum creatinine and biological cultures, if present.
aPCT was defined as the first procalcitonin determination within the first 12 hours from admission to the SDU. Similarly, TnI was defined as the first determination within the first 12 hours from SDU admission. According current literature, TnI was treated as a generic marker of critical illness 8 and tested in all the consecutive patients independently of admission diagnosis. SAPS-II score was calculated for each subject following its original definition 9 with the admission values. Presence of shock was collected as a dichotomous variable: shock was outlined in all the patients admitted with a systolic blood pressure <90 mmHg, mean arterial pressure <70 mmHg and clinical signs of tissue hypoperfusion, defined as cutaneous (hypothermia, mottling), renal (urine output <0.5 mL/kg/hr) or neurological (mental state, obtundation, disorientation and confusion) 16 .
We labelled as "infective" all the patients who had at least one clinical or instrumental criterion of active infection, defined as at least one positive culture (blood, sputum, urine, pleural or peritoneal effusion) and/or a radiogical evidence of infection; a definite diagnosis of infection was put by the attending physician according to current guidelines for each infective condition.
Each enrolled patient underwent to a retrospective chart review (EMR, radiological and biological data) by the group of researchers and unclear cases were retrospectively discussed by the senior experts of the department and assigned to the "infective" or "non infective" group.
Serum We adopted clinical and laboratoristic parameters at the admission in a logistic regression model to engineer a score able to predict in-hospital adverse events. We used the composite endpoint as the main outcome and age, aPCT and presence of shock as main predictors. Variables were treated in binary form with the following cut-offs: www.nature.com/scientificreports www.nature.com/scientificreports/ 65 years for age, 0.5 ng/mL for aPCT, according to the currently suggested cutoffs 7 and presence or absence of shock 16 . We named this composite model "Procalcitonin and Others Clinical Score" (POCS).

Statistical analysis.
All the variables were collected in an electronic database. We synthesized each variable with mean, standard deviation and 95% confidence interval if normally distributed, or by median, interquantile range (IQR) or percentile confidence interval in case of non-normal data distribution; continuous variables were compared, in normally distributed variables, with t-test or, in non-normally distributed variables, with Mann-Whitney U test; comparison among more than two populations of continuous data have been performed with analysis of variance; binary and categorical data were compared with the chi-squared test.
Temporal events were described with Kaplan-Meier curves, and the comparison between curves was performed with the log-rank test. Sensitivity and specificity were calculated with the standard method, as their confidence intervals. ROC curve analyses were performed according to the standard procedure, and the comparison between curves has been done with the Z-score test.
Multivariate analysis was performed with a linear logistic regression for continuous and discrete data. The model has been engineered with the assumption of logit(y) = xB (where B was taken from logistic analysis coefficients) and event probability has been calculated with the assumption of prob(y) = 1/(1 + exp-xB).
A difference was deemed as statistically significant if p resulted <0.05 in a 2-tailed test. The statistical analysis was performed with the NCSS 2009 package for Windows systems.

Results
From an initial screened sample of 6562 patients admitted to the medical SDU of the University-Hospital "Azienda Ospedaliero-Universitaria Ospedali Riuniti" of Ancona (Italy), we excluded 5499 subjects for absence of inclusion criteria or presence of at least one exclusion criteria. The study flow is described in Fig. 1.
We obtained a final sample 1063 critically-ill patients. Baseline characteristics of the sample are summarised in Table 1. At the end of enrolment, we observed that 67.55% of the sample had a definite diagnosis of infection according to validated diagnostic criteria, while the remaining subjects had a "non-infective" critical illness. Diagnoses at the discharge are synthesized in Table 2.  www.nature.com/scientificreports www.nature.com/scientificreports/ clinical end-points. The all-cause in-hospital mortality was 16.8% (n:179); this observation is in concordance with the mean SAPS-II value in the observed population (33.1 ± 12.1), which corresponds to a predicted mortality of 18.1%. The proportion of patients transferred to ICU for clinical deterioration was 25.5% (n:271). We considered the occurrence of ICU transfer as a therapeutic failure and aggregated this event to in-hospital death.
The group of patients who underwent to the composite clinical endpoint (death or ICU transfer) was defined as a "worse prognosis" (WP) group (n:450,42.3%). The remaining subjects, discharged or transferred to non-intensive care departments, were defined as a "good prognosis" (GP) group (n:613,57.7%).
In the overall sample, the prognostic performance of aPCT was similar to TnI (AUC:0.657; 95%CI:0.609-0.699; p < 0.05) but inferior to SAPS-II (AUC:0.760; 95%CI:0.717-0.798; p < 0.05), as shown in Fig. 4   the admission to engineer a model able to predict in-hospital adverse events. We named this composite model "Procalcitonin and Others Clinical Score" (POCS). In POCS we considered the following parameters: (1) aPCT, taking a cut-off > 0.5 ng/mL as suggested by literature 7 ; (2) age, taking a cutoff of 65 years, as derived from literature data 17 ; (3) shock criteria, as previously defined by literature 16 . The POCS equation is synthesized in Table 3. Hosmer and Lemeshow test showed no evidence of poor fit of the model (p = 0.241).
A definite diagnosis of bacterial infection was present in 61% of the subgroup with aPCT <2.00 ng/mL (n: 455); the number of patients with diagnosis of bacterial infection increased to 82% in the group of subjects with aPCT ≥2.00 ng/mL (n: 263).

Discussion
In this single-cohort, retrospective study we observed that a single determination of aPCT was associated to in-hospital prognosis in a population of critically-ill patients admitted to an Internal Medicine SDU from the ED.
The magnitude of this association is similar to other biomarkers, as admission TnI, but lower than SAPS-II. The elaboration of a composite clinical and laboratoristic model, based on a reducted set of variables, allowed us to improve the prognostic performance of aPCT. Age and shock are recognised prognostic factors for sepsis 18 that could improve the prediction of infective patients at higher risk of in-hospital adverse events. With this model, aPCT performance was superior to absolute aPCT and admission TnI and similar to SAPS-II.
Our score, however, needs less items than SAPS-II and, if validated in larger cohorts, could be easier to use, especially in patients in critical conditions. aPCT levels were also able to predict the time free from adverse events (death or ICU transfer) in the group of subjects with a worse prognosis.
Several studies have already underlined the ability of serum PCT, both as an absolute value and in terms of non-clearance, to septic patients prognosis 6 . However, PCT levels are increased not only during bacterial infections 1 , but also during other conditions, commonly present in critically-ill patients, such as burns, trauma, necrosis, organ failure and surgery 2,3 .
Thus, we can hypothesise that PCT could have a role in the prognostic evaluation of the patients affected by sepsis or septic shock but also, generally, in all the critically-ill patients, independently of the presence of infection. This has already been postulated and confirmed in other studies: a PCT increase in 24 hours in the critically-ill patient has been associated with an increase of in-hospital mortality at 90 days 7 .

Figure 5.
Kaplan-Meier survival analysis within WP group, according to aPCT values and adopting 0.5 ng/mL as cutoff. This curves confirmed that TF-AE can be significantly modified by aPCT levels at a cutoff of 0.50 ng/mL (p < 0.0001).
With the present study, we underline the importance of a single PCT determination at the admission in a medical SDU in the prediction of in-hospital prognosis of the critically-ill patient: aPCT levels were significantly higher in patients with worse prognosis, represented by in-hospital death or ICU transfer, than the ones observed in patients with a more favourable outcome. ROC curves shown a good accuracy in the defining the in-hospital prognosis in the whole population and independently of the presence of an infection. Thus, we can postulate that the prognostic ability of aPCT could represent the increase of the systemic inflammation during a critical illness that could finally evolve into a multiorgan failure.
Actually, several composite clinical and instrumental prognostic indices have been validated for the critically-ill patient, as SAPS-II 9 , APACHE II 19 and MEWS 20 . Some biomarkers, as TnI, have also been associated to a worse prognosis in the critically-ill patient 8 .
Our data underline that an early aPCT determination could represent, alone or in association with clinical features, an easy, economic and fast approach to predict prognosis in this setting, allowing an earlier risk stratification and suggesting a more aggressive diagnostic and therapeutic strategy in the patients at risk.
However, according to our data, an extensive screening of critically-ill subjects with aPCT should be considered only for an accurate risk stratification. aPCT can also be useful to improve diagnosis in several medical conditions, but its results must be carefully interpreted in the setting of patient's history, physical examination, radiologic and microbiologic tests in order to reduce unnecessary treatment 21 .
The strengths of this work are represented by the large number of subjects and the well-defined population under analysis: to date, this is the largest study evaluating the role of PCT in critically-ill patients. However, this study has several limitations: the retrospective nature of the analysis limits the generalizability of the results. Moreover, due to its single-center design, its results should be validated in prospective, multicenter studies. conclusions aPCT could represent a potential tool to stratify the risk of adverse events in the critically-ill patient admitted in medical SDU. Our clinical-laboratoristic model, if validated in larger samples, could be easy and useful to stratify earlier patients' risk of adverse events. These data, in a critical, time-dependent medicine, represent a further implementation of previous studies that correlated the mortality of the patient to a serial evaluation of PCT 7 .