Clinical Trial Details
— Status: Recruiting
Administrative data
| NCT number |
NCT04930926 |
| Other study ID # |
2018111033 |
| Secondary ID |
|
| Status |
Recruiting |
| Phase |
|
| First received |
|
| Last updated |
|
| Start date |
November 1, 2019 |
| Est. completion date |
November 30, 2021 |
Study information
| Verified date |
June 2021 |
| Source |
Hospital Galdakao-Usansolo |
| Contact |
PEDRO PABLO ESPAÑA YANDIOLA, PhD |
| Phone |
+34944007002 |
| Email |
PEDROPABLO.ESPANAYANDIOLA[@]OSAKIDETZA.EUS |
| Is FDA regulated |
No |
| Health authority |
|
| Study type |
Observational
|
Clinical Trial Summary
An innovative multicenter project that aims to study the evolution and predictive value of
new leukocyte morphological parameters (CPD) in patients with community-acquired pneumonia.
Our project has 3 objectives: 1.- To demonstrate that the use of some leukocyte morphology
parameters at the time of diagnosis, and their changes in the first 72 hours, can help us to
better identify the severity and prognosis of these patients and to discriminate between
bacterial etiology of viral. 2.- Make a comparison with other more studied inflammation and
cardiovascular biomarkers such as C-reactive protein, pro-calcitonin and pro-adrenomedullin.
3.- Incorporate some of these CPDs parameters to a new prediction rule with greater
sensitivity and specificity than those existing up to now (PSI, CURB-65, SCAP, ATS / IDSA).
Methodology: The study will be carried out in 3 hospitals (Galdakao-Usánsolo, Basurto and San
Pedro de Logroño). Prospective observational study with longitudinal follow-up up to 30 days
after the diagnosis of admitted patients with CAP. Patients will be included consecutively
for 24 months; Sociodemographic variables, duration of symptoms, previous antibiotic therapy,
severity of presentation, etiological diagnosis, treatment administered and evolution during
hospital stay and up to 30 days will be analyzed. As dependent variables of severe CAP we
will use, on the one hand, poor evolution (therapeutic failure, and / or need for admission
to high-monitoring units such as ICU or Intermediate Respiratory Care Unit (ICU) and / or
30-day mortality) and, for another, a microbiological etiological diagnosis. For statistical
processing, univariate and multivariate analyzes and logistic regression models will be used
to create a predictive rule.
Description:
Community-acquired pneumonia (CAP) has a high incidence of 2-8 cases per 1000 inhabitants per
year and a mortality rate of around 5%. Mortality in admitted patients is10-25%, and is much
higher in those requiring admission to intensive care (ICU). Most patients hospitalized for
CAP respond satisfactorily to treatment, but 10-15% experience therapeutic failure and 6% may
develop a rapid and progressive deterioration that can be life-threatening. Mortality from
CAP occurs mainly in patients with therapeutic failure. The prognostic factors of mortality
have been related to the germ-host binomial. Despite the emergence of antibiotic resistance,
there are studies that show that mortality is more associated with patient-dependent factors
than with germ resistance. For the management of CAP, it is essential to improve the
microbiological diagnosis and the assessment of its severity, which will allow the choice of
antimicrobial agents, establish the need for hospital admission, monitoring and care during
admission, the appropriate time for hospital discharge, as well as post-discharge management.
Determining the etiologic agent of CAP remains problematic, due to failure to detect the
microorganism with the usual diagnostic methods. To establish the severity of CAP, severity
scales have been created that allow predicting the patient's evolution. The best known are:
PSI, CURB-65, SCAP score, and ATS / IDSA. The objectives of stratifying patients with CAP are
multiple: defining which patients can be managed out-of-hospital, establishing the patients
who require greater monitoring in intermediate respiratory care units (ICUs) or ICUs,
establishing severity models to select patients for whom perform new diagnostic tests or
therapeutic trials. The prognostic scales measure the physiological effect of the infection
on the host but not the inflammatory response mechanisms against the microorganism.
A better understanding of the early inflammatory response may have clinical significance
determined by greater therapeutic efficacy in patients with more severe CAP. Microbial
invasion of lung tissue causes an inflammatory response aimed at limiting the progression of
the infection and destroying the microorganism. The objective of this response is to
facilitate the arrival of leukocytes and other inflammatory biomarkers to exercise their
defense function. Among the biomarkers we can highlight C-reactive Protein (CRP), studied for
the diagnosis and monitoring of inflammatory processes, and with which a certain relationship
has been established with the severity of CAP. PCT, referred to as a sensitive marker of
severity in bacterial infection and sepsis, and as a guide to adjust antibiotic treatment in
patients with CAP. Pro-adrenomodulin (ProADM) has been associated with a prognostic marker in
patients with sepsis and as a useful marker in the risk stratification of patients with CAP.
Plasma levels of inflammatory mediators appear to correlate with the severity of sepsis or
pneumonia.
The correct diagnosis of infections by the clinic, biochemical and microbiological markers
can be expensive and time consuming. It is important to look for new and low-cost
alternatives to evaluate this condition, with the aim of making an early and timely diagnosis
and evaluation and instituting the best therapeutic strategy and follow-up. Among these new
alternatives, the "Cellular Popular Data" (CPD) stand out, which are morphological parameters
of different types of leukocytes. The CPDs of the XN analyzers (Sysmex Corporation, Kobe,
Japan) report quantitative information on the morphological and functional characteristics of
leukocytes. They are morphological parameters that characterize neutrophils, lymphocytes, and
monocytes and classify them according to their volume, shape, granularity, and their nucleic
acid content. The composition of activated cell membranes is different from that of resting
cells, due to the expression of receptors and signaling molecules on their surface, in
response to activation. This membrane is more sensitive to analyzer reagents, and more
fluorescent dye can penetrate the activated cell, and bind to the cytoplasmic organelles and
nucleic acids. The optical signals are different, which makes it possible to distinguish the
morphological changes produced and that are directly related to the functionality of the
cell. Activated neutrophils and monocytes are characterized by increased "deformability",
mobility, and their ability to adhere, granulate, and release cytokines.
The CPD values reflect the morphological and functional transformation of these activated
cells, offering very valuable information on the state of the cell and the patient at the
time of obtaining the sample. Recent studies have shown that these parameters are valuable
for the detection and control of infections and inflammation. Neutrophil structural
parameters NE-SSC, (NEUT GI granularity index) and NE-SFL (NEUT RI reactivity index) could
predict the appearance of later-stage infection markers, such as the presence of immature
granulocytes, suggesting that they can be used to detect bacterial infections very early. It
has been shown to be useful in acute bacterial infection, particularly in the differentiation
of bacterial infection and early detection of sepsis. The mean volume of the neutrophil and
its variability are more sensitive indicators of bacteremia than the leukocyte count and the
percentage of neutrophils. Neutrophils in sepsis are larger and their volumes more
heterogeneous than in the healthy population; the same happens to monocytes, larger and more
heterogeneous than in localized infections, and in the ROC analysis they had the highest
sensitivity for detection of sepsis. Lymphocyte CPDs show specific changes in viral
infection, providing potential for differential diagnosis between viral and bacterial
infection. Together, the CPDs support the differentiation between viral and bacterial
infections, or between acute or evolving infections, and if there is an inflammatory
condition without infection, with better diagnostic performance, especially in postsurgical
bacterial infection, than the conventional parameters.
The available literature has focused on the potential usefulness of CPD in diagnosis, but we
do not have data on prognostic value or its applicability to pneumonia. The classical
inflammatory biomarkers are expensive and often not accessible in clinical practice, while
the evaluation of new leukocyte markers through hematimetry analysis, cheaper and more
accessible in clinical practice, can help to monitor the inflammatory response and recognize
to patients who may have poor evolution. No study, to date, has related all these parameters
(CPDs) together with the severe evolution of pneumonia or mortality, nor have clear cut-off
points been established for each of them. We propose an observational study, in which these
markers are related to the severity and prognosis of CAP, and a comparison between them, in
addition to incorporating these biomarkers into the prognostic rules currently in use and
seeing how their predictive capacity is modified.
Objectives
1. To evaluate de predictive ability of new biomarkers such as leukocyte morphology
parameters (CPDs) and their short-term change over time, with poor evolution (defined by
therapeutic failure, and/or the need for admission to high-monitoring units such as ICU
or Intermediate Respiratory Care Units and/or mortality in 30 days) in patients admitted
for community-acquired pneumonia.
2. To analyze the relationship between these biomarkers (CPDs) and the etiology of
pneumonia.
3. To compare the predictive capacity at baseline and during evolution of these markers of
leucocyte morphology (CPDs) with other frequently used biomarkers (CRP, PCT and pro-ADM)
in the cohort of patients admitted for community acquired pneumonia (CAP).
4. To incorporate these new biomarkers (CPDs) to the clinical prognostic scores such as
PSI, CURB-65, SCAP score and ATS/IDSA score with the aim of complementing them and
better identifying patients at high risk of poor evolution (who require monitoring and
more aggressive therapies) and identifying patients with less severe disease that can be
managed at the outpatient level, creating a new predictive rule with higher sensitivity
and specificity.
Design
- Study design: Multicenter prospective observational study with longitudinal follow-up up
to 25 months (24 months of inclusion and follow-up up to 30 days) of patients who
attended the emergency services of the participating hospitals for community-acquired
pneumonia.
- Scope of study: multicenter study to be carried out in 3 hospitals of the public
network, Galdakao-Usansolo Hospital (Galdakao), Basurto Hospital (Bilbao) and San Pedro
de Logroño Hospital. The first is a general acute teaching hospital that serves a
population of 300,000 inhabitants. The second, Basurto, is a general university acute
hospital that serves a population of 450,000 inhabitants. The third, San Pedro de
Logroño, is a general university acute care hospital with a reference population of
around 320,000 inhabitants.
- Sample size: We estimate, from studies carried out by our groups in previous years, to
include about 1000-1200 useful patients with pneumonia who will require hospital
admission in the 24-month recruitment period. Studies on the development of predictive
models establish that it is necessary to have at least 10 events of the dependent
variable of interest (in our case the dependent variables would be those included in the
poor evolution: therapeutic failure, admission to the ICU or to monitoring units such as
Intermediate Respiratory Care Units and in-hospital mortality/30 days; and on the other
hand the etiological diagnosis) for each independent variable included in the
multivariate logistic regression model. Given that our intention is to initially include
a limited but comprehensive number of variables in the multivariate logistic regression
models (predictably, not less than 2-3 but not more than 5), we estimate that it will be
necessary for 50 to 100 of these to occur. Events of the dependent variable in the
sample from which we will derive the most complex prediction rule to ensure that the
logistic regression model converges properly. Data previously collected tell us that the
number of events of our dependent variable would be a 5-6% mortality at 30 days for
hospitalized patients plus 7% therapeutic failure and 4-5% of patients admitted to
discharge units monitoring, which makes an expected number of events of the primary
variable to be about 100 events in the bypass sample. With the recruitment time shown in
this protocol and with data from 100 useful patients, we believe that it is sufficient
to meet the main objectives set forth and develop and validate the predictive models.
- Sampling: consecutive sampling where all new cases of patients diagnosed with CAP will
be collected consecutively in the 3 participating hospitals during a period of 24
months, who meet the selection criteria and sign the informed consent until the
indicated sample size is achieved.
- Ethical aspects: All participants will sign the informed consent after having discussed
with the investigators the objectives, risks and potential benefits of the study. The
rights of patients will at all times be protected by the Declaration of Helsinki. This
project will have the approval of the Ethics and Clinical Research Committee. The terms
relating to the protection of personal data will be updated in the subject information
sheet (HIP / CI) in relation to Regulation (EU) No. 2016/679 of the European Parliament
and of the Council of April 27, 2016 on the Protection of Data (RGPD) when the data
protection agency incorporates it.
- Study variables:
.Independent variables:
1. Variables related to the patient's condition (socio-demographic, comorbidities,
physical examination, laboratory tests), duration of symptoms at the time of
diagnosis.
2. Variables related to severity at the time of admission. The variables nedeed will
be collected to calculate the risk class established by the Pneumonia Severity
Index (PSI) scale, by the CURB-65 scale (Confusion, Urea nitrogen, Respiratory
rate, Blood pressure, age> 65) by the SCAP scale and by the ATS / IDSA scale
collected during the first 8 hours of diagnosis.
Biomarker analysis (CPDs, PCR, Procalcitonin) will be performed at the time of
diagnosis in all patients and 72 hours after starting treatment. For the pro-ADM
analysis, a plasma extraction will be performed upon admission, 72 hours, which
will be frozen at -70º, for later centralized analysis. The PCR will be measured by
immunoturbidimetry on a Roche Modular platform (CRPLX, reference no. 3002039).
Procalcitonin and Pro-adrenomedullin, by immunolumonometric analysis (Time Resolved
amplified crytate Emission, Brahms Diagnostica, Germany).
The PDCs markers will be measured using the Sysmex XN analyzer that reports as
research parameters those related to leukocyte morphology (CPD), 6 numerical values
for each subpopulation, which describe each cell type according to size (volume),
complexity (cytoplasmic granules) and activation (nucleic acid content) as
described below:
A.For Neutrophils:
- NE-SSC, mean value for cytoplasmic granularity; NE-WX dispersion of NE-SSC
values
- NE-SFL, mean value RNA / DNA content; NE-WY dispersion of NE-SFL values
- NE-FSC, mean cell volume; NE-WZ dispersion of NE-FSC values
B. For Lymphocytes:
- LY-X, mean value for cytoplasmic granularity; LY-WX dispersion of LY X values
- LY-Y mean value RNA / DNA content; LY-WY dispersion of LY-Y values
- LY-Z, mean cell volume; LY-WZ dispersion of LY-Z values
C.For Monocytes
- MO-X, mean value for cytoplasmic granularity; MO-WX dispersion of MO-X values
- MO-Y, mean value of RNA / DNA content; MO-WY dispersion of MO-Y values
- MO-Z, mean cell volume; MO-WZ dispersion of MO-Z values
3. Variables related to evolution (variables that can be analyzed as independent in
some cases and as dependent in others).
.Early therapeutic failure (first 72 hours of treatment): when the clinical
situation deteriorates and is accompanied by hemodynamic instability, the
appearance or worsening of respiratory failure, the need for mechanical
ventilation, radiological progression or the appearance of a new infectious focus.
.Late therapeutic failure (after the first 72 hours of treatment): Admission to the
Intensive Care Unit and / or admission to the Intermediate Respiratory Care Unit
(ICU).
.Complications established during its evolution: shock, respiratory failure (Po2 /
Fio2 <250), renal failure (plasma creatinine> 2 mg), pleural effusion.
4. Variables related to the treatment administered.
.Antibiotic administration prior to diagnosis and days of treatment .Class of
antibiotic used at the time of diagnosis. .Adherence of antibiotic treatment to
SEPAR regulations (categorical variable). .Time to go from intravenous to oral
medication. .Use of invasive mechanical ventilation and time with this treatment.
.Use of non-invasive mechanical ventilation and time with this treatment.
5. Variables related to bacteriological diagnosis: At the time of diagnosis, all
patients will undergo a nasopharyngeal smear to perform an RT-PCR. In addition, the
bacteriological diagnosis will include 2 blood cultures, the determination of
urinary antigens of pneumococcus and legionella in the acute phase (BinaxNOW) and
the Serological tests for atypical bacteria and viruses both during the acute phase
and in remission or convalescence.
- Main dependent variables:
1. Main dependent variable: 30-day mortality, presence of therapeutic failure and need
for ICU and / or ICU.
2. Microbiological diagnosis (bacterial / atypical / viral ..)
3. Mortality will be initially determined by means of a consultation established in
that period of time and in the absence of a telephone interview 30 days after
diagnosis. Deaths and their corresponding dates will be confirmed through hospital
computer support and by public records of death certificates.
- Statistical analysis:
The data processing procedure of this project will be established by following the following
steps:
1. A descriptive analysis of the recruited sample will be carried out.
2. To create predictive models: a. The collected sample will be divided into two
subsamples: Derivation Group 1: The total sample will be divided into 60% for the
derivation of the predictive models for each of the results studied; Group 2 of
validation of the predictive rules: the models will be validated in this sample (40% of
the sample). b. In Group 1, you will identify the risk factors of risk factors and
create predictive models. The unit of study will be the patient (each patient can be
included only once). A bivariate analysis will be carried out to study which variables,
of the possible predictors, are related to each outcome parameter. Those variables with
a p-value <0.20 and using variable selection techniques (LARS, LASSO, shrinkage), the
potential predictors to be introduced in a multivariate logistic regression model will
be identified. Those variables that are statistically significant will be chosen for the
final scale. C. In the same way, statistical techniques of machine learning will be
applied (random forest, neural networks, machine support vectors, nearest neighbor
classification methods). d. The outcome variable will also be taken as temporary
dependent variables (time until the event) and Cox regression models, competitive risk
models and joint models will be used following the same process as that described above.
3. Goodness of fit and comparison of the developed predictive models. On the one hand, in
the case of dichotomous dependent variables, the area under the ROC curve (AUC,
discriminative capacity) will be calculated, considering a value> 0.80 as a robust
predictive model. In addition to the AUC, the calibration of the model will be estimated
through the Hosmer-Lemeshow test (good calibration for a p-value ≥0.05). Finally, the
different proposed models will be contrasted by constructing ROC curves and comparing
the respective ROC curves.
4. Internal validation of predictive models:
The validation of the predictive model will be carried out in the validation group (Group 2).
The predictive model and the scale will be validated in the second subsample, making use of
the predicted values obtained in the derivation sample.
