The Ventilator-Associated Pneumonia PIRO Score: A Tool for Predicting ICU Mortality and Health-Care Resources Use in Ventilator-Associated Pneumonia

doi:10.1378/chest.08-1106

Chest

Volume 134, Issue 6, December 2008, Pages 1208-1216

https://doi.org/10.1378/chest.08-1106 Get rights and content

Background

No score is available to assess severity and stratify mortality risk in ventilator-associated pneumonia (VAP). Our objective was to develop a severity assessment tool for VAP patients.

Methods

A prospective, observational, cohort study was performed including 441 patients with VAP in three multidisciplinary ICUs. Multivariate logistic regression was performed to identify variables independently associated with ICU mortality. Results were converted into a four-variable score based on the PIRO (predisposition, insult, response, organ dysfunction) concept for ICU mortality risk stratification in VAP patients.

Results

Comorbidities (COPD, immunocompromise, heart failure, cirrhosis, or chronic renal failure); bacteremia; systolic BP < 90 mm Hg; and ARDS. A simple, four-variable VAP PIRO score was obtained at VAP onset. Mortality varied significantly according to VAP PIRO score (p < 0.001). On the basis of observed mortality for each VAP PIRO score, patients were stratified into three levels of risk: (1) mild, 0 to 1 points; (2) high, 2 points; (3) very high, 3 to 4 points. VAP PIRO score was associated with higher risk of death in Cox regression analysis in the high-risk group (hazard ratio, 2.14; 95% confidence interval [CI], 1.19 to 3.86) and the very-high-risk group (hazard ratio, 4.63; 95% confidence interval, 2.68 to 7.99). Moreover, medical resource use after VAP diagnosis was higher in high-risk and very-high-risk levels compared to patients at mild risk, evaluated using length of ICU stay (mean ± SD, 22.0 ± 10.6 d vs 18.7 ± 12.8 d, p < 0.05) and duration of mechanical ventilation (18.3 ± 10.1 d vs 15.1 ± 11.5 d, p < 0.05).

Conclusions

VAP PIRO score is a simple, practical clinical tool for predicting ICU mortality and health-care resources use that is likely to assist clinicians in determining VAP severity.

Section snippets

Materials and Methods

This is a cohort study including all patients with VAP prospectively recorded for surveillance purposes at three different multidisciplinary ICUs. Institutional review board approval was obtained in accordance with local requirements, and informed consent was waived due to the observational nature of the study.

Results

We included 441 patients with VAP diagnosis. Mean APACHE II score was 18.3 ± 6.7, and mean age was 53.8 ± 18.4 years. There were 344 male patients (77.1%). Mean length of stay in the ICU was 30.1 ± 24.9 days, and duration of mechanical ventilation was 27.3 ± 22.3 days ± SD. Distribution of admission diagnosis category included the following: 194 medical (44.0%), 100 surgical (22.7%), and 147 trauma patients (33.3%). Definite etiology was documented in 328 patients (74.4%). There were 58

Discussion

This is the first study designed to stratify severity of VAP episodes, using a score based on the PIRO concept. Our findings suggest that this simple four-variable score (Fig 4) is able to assess severity and to improve prediction of ICU mortality in VAP patients. Additionally, it helps to predict health-care utilization (ICU length of stay and duration of mechanical ventilation) after the VAP episode.

The PIRO concept is a classification scheme for sepsis that includes predisposing conditions,

Acknowledgment

We are indebted to Michael Maudsley for editorial assistance.

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Cited by (100)

Novel pneumonia score based on a machine learning model for predicting mortality in pneumonia patients on admission to the intensive care unit
2023, Respiratory Medicine
Scores for predicting the long-term mortality of severe pneumonia are lacking. The purpose of this study is to use machine learning methods to develop new pneumonia scores to predict the 1-year mortality and hospital mortality of pneumonia patients on admission to the intensive care unit (ICU).
The study population was screened from the MIMIC-IV and eICU databases. The main outcomes evaluated were 1-year mortality and hospital mortality in the MIMIC-IV database and hospital mortality in the eICU database. From the full data set, we separated patients diagnosed with community-acquired pneumonia (CAP) and ventilator-associated pneumonia (VAP) for subgroup analysis. We used common shallow machine learning algorithms, including logistic regression, decision tree, random forest, multilayer perceptron and XGBoost.
The full data set of the MIMIC-IV database contained 4697 patients, while that of the eICU database contained 13760 patients. We defined a new pneumonia score, the "Integrated CCI-APS", using a multivariate logistic regression model including six variables: metastatic solid tumor, Charlson Comorbidity Index, readmission, congestive heart failure, age, and Acute Physiology Score III. The area under the curve (AUC) and accuracy of the integrated CCI-APS were assessed in three data sets (full, CAP, and VAP) using both the test set derived from the MIMIC-IV database and the external validation set derived from the eICU database. The AUC value ranges in predicting 1-year and hospital mortality were 0.784–0.797 and 0.691–0.780, respectively, and the corresponding accuracy ranges were 0.723–0.725 and 0.641–0.718, respectively.
The main contribution of this study was a benchmark for using machine learning models to build pneumonia scores. Based on the idea of integrated learning, we propose a new integrated CCI-APS score for severe pneumonia. In the prediction of 1-year mortality and hospital mortality, our new pneumonia score outperformed the existing score.
Screening and early detection of sepsis
2022, The Sepsis Codex
Sepsis is a complex time-dependent clinical syndrome. Therefore, early detection is the cornerstone of proper comprehensive care. The need to consider the possibility that a patient may have sepsis is not a simple process because of the great variability of clinical presentation. We do not have pathognomonic clinical or analytical criteria, so the suspicion will depend on the clinician's training. Screening methods are recommended to improve early detection. In recent years, the use of automated algorithms obtained from the electronic medical record with fixed rules or under artificial intelligence and machine learning techniques can be developed to improve screening and early detection of septic patients in the hospital.
Validation of the Predisposition Infection Response Organ (PIRO) dysfunction score for the prognostic stratification of patients with sepsis in the Emergency Department
2021, Medicina Intensiva
There are many different methods for computing the Predisposition Infection Response Organ (PIRO) dysfunction score. We compared three PIRO methods (PIRO1 (Howell), PIRO2 (Rubulotta) and PIRO3 (Rathour)) for the stratification of mortality and high level of care admission in septic patients arriving at the Emergency Department (ED) of an Italian Hospital.
We prospectively collected clinical data of 470 patients admitted due to infection in the ED to compute PIRO according to three different methods. We tested PIRO variables for the prediction of mortality in the univariate analysis. Calculation and comparison were made of the area under the receiver operating curve (AUC) for the three PIRO methods, SOFA and qSOFA.
Most of the variables included in PIRO were related to mortality in the univariate analysis. Increased PIRO scores were related to higher mortality. In relation to mortality, PIRO 1 performed better than PIRO2 at 30 d ((AUC 0.77 (0.716–0.824) vs. AUC 0.699 (0.64–0.758) (p = 0.03) and similarly at 60 d (AUC 0.767 (0.715–0.819) vs AUC 0.709 (0.656–0.763)(p = 0.55)); PIRO1 performed similarly to PIRO3 (AUC 0.765 (0.71–0.82) at 30 d, AUC 0.754 (0.701–0.806) at 60 d, p = ns). Both PIRO1 and PIRO3 were as good as SOFA referred to mortality (AUC 0.758 (0.699, 0.816) at 30 d vs. AUC 0.738 (0.681, 0.795) at 60 d; p = ns). For high level of care admission, PIRO proved inferior to SOFA.
We support the use of PIRO1, which combines ease of use and the best performance referred to mortality over the short term. PIRO2 proved to be less accurate and more complex to use, suffering from missing microbiological data in the ED setting.
Existen muchos métodos diferentes para calcular la escala PIRO (predisposición, infección respuesta, fallo orgánico). Comparamos 3 métodos (PIRO1 [Howell], PIRO2 [Rubolotta] y PIRO3 [Rathour]) para estratificar la mortalidad y el ingreso con alto nivel de cuidados en pacientes con sepsis atendidos en el servicio de urgencias (SU) de un hospital italiano.
Recopilamos datos clínicos prospectivos de 470 pacientes que llegaban con una infección al SU, con el fin de calcular la puntuación PIRO, de acuerdo con 3 métodos diferentes. Evaluamos las variables PIRO para la predicción de la mortalidad en un análisis monovariable. Calculamos y comparamos el área bajo la curva (AUC) característica de operación del receptor (ROC) de los 3 métodos PIRO, SOFA y qSOFA.
La mayoría de las variables incluidas en las puntuaciones PIRO estaban relacionadas con la mortalidad en un análisis de una sola variable. El aumento de la puntuación PIRO se relacionó con una mortalidad más elevada. En cuanto a la mortalidad, PIRO1 presentó un rendimiento mejor que PIRO2 a los 30 días (AUC 0,77 [0,716-0,824] frente a AUC 0,699 [0,64-0,758]; p = 0,03) y similares a los 60 días (AUC 0,767 [0,715-0,819] frente a AUC 0,709 [0,656-0,763]; p = 0,55); PIRO1 presentó un rendimiento similar al de PIRO3 (AUC 0,765 [0,71-0,82] a los 30 días, AUC 0,754 [0,701-0,806] a los 60 días; p = NS). Tanto PIRO1 como PIRO3 presentaron un rendimiento similar al de SOFA para la mortalidad (AUC 0,758 [0,699-0,816) al cabo de 30 días y AUC 0,738 [0,681-0,795] al cabo de 60 días; p = NS). En cuanto al ingreso con alto nivel de cuidados, las puntuaciones PIRO resultaron ser inferiores a SOFA.
Apoyamos el uso de la puntuación PIRO1, que resulta fácil de usar, y presenta el mejor rendimiento en cuanto a la mortalidad a largo plazo. PIRO2 resultó ser menos precisa y más compleja de usar, y se vio afectada por problemas de falta de datos microbiológicos en el entorno del SU.
Comparison of severity score models based on different sepsis definitions to predict in-hospital mortality among sepsis patients in the Intensive Care Unit
2020, Medicina Intensiva
A comparison is made of the accuracy between severity models, based on different sepsis definitions (systemic inflammatory response syndrome (SIRS), predisposition, insult, response, organ dysfunction (PIRO), and sequential organ failure assessment (SOFA) concepts), in predicting outcomes among sepsis patients.
A retrospective study was carried out.
The study was conducted in the Intensive Care Unit (ICU) of a university teaching hospital.
Septic patients admitted to the ICU during 2007–2016.
The primary outcome was in-hospital mortality, with ICU mortality being the secondary outcome.
A total of 2152 septic patient were identified, with ICU and in-hospital mortality rates of 33.3% and 45.9%, respectively. The Moreno PIRO (AUC, 95%CI) (0.835; 0.818–0.852) showed the highest discriminating capacity, followed by SOFA (0.828; 0.811–0.846), qSOFA (0.792; 0.775–0.809), Rubulotta PIRO (0.708; 0.687–0.730), Howell PIRO (0.706; 0.685–0.728) and SIRS (0.578; 0.556–0.600). The AUC of the SOFA score was comparable to that of the Moreno PIRO (p = 0.43), though the AUCs of both of these scores were significantly higher than those of the other scores (p < 0.001 for all other comparisons). However, the SOFA score showed the best discriminating capacity in predicting ICU mortality (0.838; 0.820–0.855), followed by Moreno PIRO (0.804; 0.785–0.823) and qSOFA (0.787; 0.770–0.805). The accuracy of the qSOFA in predicting ICU mortality was comparable to that of the Moreno PIRO score (p = 0.15).
The SOFA score and Moreno PIRO score showed the best accuracy in predicting in-hospital mortality among septic patients admitted to the ICU.
Comparar la precisión entre varios modelos de intensidad, basándose en diferentes definiciones de la sepsis (síndrome de respuesta inflamatoria sistémica [SIRS, por sus siglas en inglés], predisposición, infección, respuesta, disfunción orgánica [PIRO por sus siglas en inglés] y puntuación de la evaluación del fallo orgánico secuencial [SOFA por sus siglas en inglés]) para predecir los desenlaces en los pacientes con sepsis.
Estudio retrospectivo.
El estudio se llevó a cabo en la unidad de cuidados intensivos (UCI) de un hospital universitario.
Enfermos con sepsis ingresados en la UCI durante 2007-2016.
El desenlace principal fue la mortalidad hospitalaria, mientras que la mortalidad en la UCI fue el desenlace secundario.
Se identificó un total de 2.152 pacientes con sepsis, con unas tasas de mortalidad en la UCI e intrahospitalaria del 33,3 y del 45,9%, respectivamente. El modelo Moreno-PIRO (AUC, IC del 95%) (0,835; 0,818-0,852) fue el que presentó una mayor capacidad de discriminación, seguido del modelo SOFA (0,828; 0,811-0,846), modelo qSOFA (0,792; 0,775-0,809), modelo Rubulotta-PIRO (0,708; 0,687-0,730), modelo Howell-PIRO (0,706; 0,685-0,728) y modelo SIRS (0,578; 0,556-0,600). El AUC de la puntuación SOFA fue comparable al de Moreno-PIRO (p = 0,43), si bien el AUC de ambas puntuaciones fue significativamente superior al de otras puntuaciones (p < 0,001 para todas las demás comparaciones). Sin embargo, la puntuación SOFA es la que presenta la mayor capacidad de discriminación para predecir la mortalidad en la UCI (0,838; 0,820-0,855), seguida de Moreno-PIRO (0,804; 0,785-0,823) y qSOFA (0,787; 0,770-0,805). La precisión de la puntuación qSOFA en cuanto a la predicción de la mortalidad en la UCI fue comparable a la de la puntuación Moreno-PIRO (p = 0,15).
Conclusión La puntuación SOFA y la puntuación Moreno-PIRO mostraron la mejor precisión en la predicción Mortalidad intrahospitalaria entre pacientes sépticos ingresados en la UCI.
Biomarker-guided antibiotic stewardship in suspected ventilator-associated pneumonia (VAPrapid2): a randomised controlled trial and process evaluation
2020, The Lancet Respiratory Medicine
Citation Excerpt :
Although extrapulmonary infection was considered unlikely in eligible patients in our study, we cannot be certain that sources of infection outside the lung parenchyma (eg, pleural infection or subphrenic abscess) were absent. We did not collect clinical pulmonary infection scores38 or ventilator-associated pneumonia severity scores,39,40 and we did not ascertain why patients who were taking corticosteroids had been prescribed these. Whether fungi and various bacteria traditionally considered commensals can be considered a cause of ventilator-associated pneumonia when isolated from bronchoalveolar lavage at 104 CFU/mL or more is controversial.5
Ventilator-associated pneumonia is the most common intensive care unit (ICU)-acquired infection, yet accurate diagnosis remains difficult, leading to overuse of antibiotics. Low concentrations of IL-1β and IL-8 in bronchoalveolar lavage fluid have been validated as effective markers for exclusion of ventilator-associated pneumonia. The VAPrapid2 trial aimed to determine whether measurement of bronchoalveolar lavage fluid IL-1β and IL-8 could effectively and safely improve antibiotic stewardship in patients with clinically suspected ventilator-associated pneumonia.
VAPrapid2 was a multicentre, randomised controlled trial in patients admitted to 24 ICUs from 17 National Health Service hospital trusts across England, Scotland, and Northern Ireland. Patients were screened for eligibility and included if they were 18 years or older, intubated and mechanically ventilated for at least 48 h, and had suspected ventilator-associated pneumonia. Patients were randomly assigned (1:1) to biomarker-guided recommendation on antibiotics (intervention group) or routine use of antibiotics (control group) using a web-based randomisation service hosted by Newcastle Clinical Trials Unit. Patients were randomised using randomly permuted blocks of size four and six and stratified by site, with allocation concealment. Clinicians were masked to patient assignment for an initial period until biomarker results were reported. Bronchoalveolar lavage was done in all patients, with concentrations of IL-1β and IL-8 rapidly determined in bronchoalveolar lavage fluid from patients randomised to the biomarker-based antibiotic recommendation group. If concentrations were below a previously validated cutoff, clinicians were advised that ventilator-associated pneumonia was unlikely and to consider discontinuing antibiotics. Patients in the routine use of antibiotics group received antibiotics according to usual practice at sites. Microbiology was done on bronchoalveolar lavage fluid from all patients and ventilator-associated pneumonia was confirmed by at least 10⁴ colony forming units per mL of bronchoalveolar lavage fluid. The primary outcome was the distribution of antibiotic-free days in the 7 days following bronchoalveolar lavage. Data were analysed on an intention-to-treat basis, with an additional per-protocol analysis that excluded patients randomly assigned to the intervention group who defaulted to routine use of antibiotics because of failure to return an adequate biomarker result. An embedded process evaluation assessed factors influencing trial adoption, recruitment, and decision making. This study is registered with ISRCTN, ISRCTN65937227, and ClinicalTrials.gov, NCT01972425.
Between Nov 6, 2013, and Sept 13, 2016, 360 patients were screened for inclusion in the study. 146 patients were ineligible, leaving 214 who were recruited to the study. Four patients were excluded before randomisation, meaning that 210 patients were randomly assigned to biomarker-guided recommendation on antibiotics (n=104) or routine use of antibiotics (n=106). One patient in the biomarker-guided recommendation group was withdrawn by the clinical team before bronchoscopy and so was excluded from the intention-to-treat analysis. We found no significant difference in the primary outcome of the distribution of antibiotic-free days in the 7 days following bronchoalveolar lavage in the intention-to-treat analysis (p=0·58). Bronchoalveolar lavage was associated with a small and transient increase in oxygen requirements. Established prescribing practices, reluctance for bronchoalveolar lavage, and dependence on a chain of trial-related procedures emerged as factors that impaired trial processes.
Antibiotic use remains high in patients with suspected ventilator-associated pneumonia. Antibiotic stewardship was not improved by a rapid, highly sensitive rule-out test. Prescribing culture, rather than poor test performance, might explain this absence of effect.
UK Department of Health and the Wellcome Trust.
Acinetobacter etiology respiratory tract infections associated with mechanical ventilation: What impacts on the prognosis? A retrospective cohort study
2019, Journal of Critical Care

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This work as supported by Fondo de Investigaciones Sanitarias (CIBERes 06/06/0036) and AGAUR (2005/SGR/920).

The authors have no conflicts of interest to disclose.

: Reproduction of this article is prohibited without written permission from the American College of Chest Physicians (www.chestjournal.org/misc/reprints.shtml).

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Chest

Original ResearchCritical Care MedicineThe Ventilator-Associated Pneumonia PIRO Score: A Tool for Predicting ICU Mortality and Health-Care Resources Use in Ventilator-Associated Pneumonia

Background

Methods

Results

Conclusions

Section snippets

Materials and Methods

Results

Discussion

Acknowledgment

Chest

Chest

Chest

Chest

Global Strategy for the diagnosis, management, and prevention of Chronic Obstructive Pulmonary Disease (GOLD)

Am J Respir Crit Care Med

Clinical classification of pulmonary hypertension

J Am Coll Cardiol

Defining community acquired pneumonia severity on presentation to hospital: an international derivation and validation study

Thorax

C-reactive protein as a marker of ventilator-associated pneumonia

Eur Respir J

CRP correlates with bacterial load and appropriate antibiotic therapy in suspected ventilator-associated pneumonia

Crit Care Med

Procalcitonin kinetics as a prognostic marker of ventilator associated pneumonia

Am J Respir Crit Care Med

2001 SCCM/ESICM/ACCP/ATS/SIS international sepsis definitions conference

Crit Care Med

Sepsis mortality prediction based on predisposition, infection and response

Intensive Care Med

Guidelines for the management of adults with hospital-acquired, ventilator-associated and healthcare-associated Pneumonia

Am J Respir Crit Care Med

Bacteremia in patients with ventilator-associated pneumonia is associated with increased mortality: a study comparing bacteremic vs nonbacteremic ventilator associated pneumonia

Crit Care Med

Management of ventilator-associated pneumonia in a multidisciplinary intensive care unit: does trauma make a difference?

Intensive Care Med

Ventilator-associated pneumonia: a multivariate analysis

JAMA

Implications of COPD in patients admitted to the intensive care unit by community-acquired pneumonia

Eur Respir J

Surviving Sepsis Campaign guidelines for management of severe sepsis and septic shock

Crit Care Med

The American-European Consensus Conference on ARDS: definitions, mechanisms, relevant outcomes, and clinical trial coordination

Am J Respir Crit Care Med

Clinical cure and survival in Gram-positive ventilator-associated pneumonia: retrospective analysis of two double-blind studies comparing linezolid with vancomycin

Intensive Care Med

Therapy for nosocomial pneumonia

Curr Opin Pulm Med

Original Research
Critical Care Medicine
The Ventilator-Associated Pneumonia PIRO Score: A Tool for Predicting ICU Mortality and Health-Care Resources Use in Ventilator-Associated Pneumonia