FINALReveiwofLiterature

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Predicting, Preventing, and Managing Pulmonary Complications in the ICU
Patient; A literary review and thoughts for the future
Jacob R. Burd
Rush University Medical Center
August 12th, 2015

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Introduction
The ability to identify hospitalized patients at risk for developing complications and take
appropriate measures to improve outcomes is of great interest in intensive care units (ICUs)
throughout the world, and for a good reason. Fuller and colleagues1 suggest that preventable
complications increase the overall cost of inpatient hospital care by 9 percent. In 2006, the
national estimates of inpatient hospital care were $940 billion in the United States. Factor in the
9 percent of additional costs and one could argue there is a nearly $90 billion problem.
Complications of a pulmonary etiology are among those known to impact mortality and
morbidity the most.
Pulmonary complications are defined broadly as conditions that develop secondary to an
identifiable source, and which adversely affect the respiratory system. This subset of
complications is among the most common that occur within hospitals and are associated with
worse outcomes, particularly in postoperative ICU patients2, 14 in which an overwhelming
majority of these complications occur. Shander et al2 agree that the clinical and economic burden
of postoperative pulmonary complications (PPC’s) is significant. Linde-Zwirble et al3 studied
adult elective surgery cases from 414 U.S. hospitals. Patients that met predefined criteria for
having developed a PPC were selected for further analysis. There was a significant increase in
costs to both the hospitals and patients included in the study due to additional resources used and
increased length of stay (LOS). The total additional cost generalized to the entire U.S. population
was estimated to be over $3.4 billion. The encumbrance of PPC’s like post-operative respiratory
failure, atelectasis, acute bronchospasm, hospital-acquired pneumonia, pneumothorax,
tracheobronchitis, pleural effusions, and non-cardiogenic pulmonary edema has been the origin
of many critical studies4-14 that have focused largely on identifying risk factors and using these to

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develop tools for predicting and preventing their occurrence. Table 1 summarizes the PPC’s that
have been described in several landmark studies.10, 15-17
Table 1. Literature definition of postoperative pulmonary complications10,15-17
Respiratory failure
Postoperative PaO2 <60mmHg on .21 FiO2, P/F ratio <300,
SpO2 <90% requiring O2
therapy
Suspected pulmonary infection
Antibiotic therapy for a respiratory infection, plus at least one of the following;
New or changed sputum
New or changed lung opacities on chest
radiograph
Core body temperature >38.3 °C
Leukocyte count >12,000/mm3
Pleural effusion
Seen as blunted costrophrenic angles on chest radiograph, loss of sharp silhouette of
ipsilateral
diaphragm (patient upright), displacement of adjacent structures,or hazy opacity in one
hemithorax with preserved vascular shadows (patient supine)
Atelectasis
On chest radiograph, visualization of lung opacification with shift of the mediastinum,
hilum,
or hemidiaphragm toward to affected site with compensatory overinflation in the
adjacent
nonatelectatic
lung
Pneumothorax
Air in the pleural space with lack of vascular bed surrounding the visceral pleura
Bronchospasm
Newly detected expiratory wheeze treated with bronchodilators
Aspiration pneumonitis
Respiratory failure following the inhalation of regurgitated gastric contents
_____________________________________________________________________________
_____
PaO2 = partial pressure of oxygen in arterial blood, P/F ratio = ratio of PaO2 to inspired oxygen
fraction,
Sp02 = arterialoxyhemoglobin saturation measured with pulse oximetry

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This review will examine what is currently known about risk factors for developing
pulmonary complications as well as how this knowledge has been applied to the development of
multifactorial scoring systems to predict and prevent their occurrence. Relevant articles
published within the last ten years were acquired from the online-database PubMed with the
exception of several landmark studies that were published before the ten-year search period. An
extensive amount of research exists to support the effect of PPCs on mortality and morbidity as
well as their negative economic impact in healthcare.
The similarities and differences across these studies will be discussed in an effort to
better understand the multifaceted approach needed to reduce pulmonary complications. Notably,
the bulk of research on this topic has focused exclusively on PPC’s. While the vast majority of
pulmonary complications do occur in postoperative patients, pulmonary complications in
intensive care units have the potential to affect any patient regardless of whether there was a
surgical intervention or not.18-21 For this reason, a thorough but more general approach using five
respiratory related components to identify patients with underlying pathophysiology that may
lead to these complications will also be discussed.22
Although progress has been made in identifying patients at risk for postoperative
pulmonary complications, there is a lack of evidence showing that perioperative factors under
our control can actually prevent their occurrence.2, 14 However, there is still value in knowing the
factors that identify a patient as being at risk for pulmonary complications. Risk assessment can
guide clinical decision-making and has been applied through evidence-based scoring systems.
The value of these scoring systems may lie in their ability to identify patients in need of an

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intervention as well as give insight to how they may be best managed given individual clinical
circumstances. Treatment of pulmonary complications primarily involves respiratory therapy and
supportive ventilation, with pharmacologic therapy being less effective. Several studies have
experimented with individualized management approaches that will be discussed in greater detail
later on.23-25 This review will also postulate the value of respiratory care protocols in the
identification and management of pulmonary complications in ICU patients. Finally, this review
will draw conclusions from evidence in the literature, suggest how further research in this area
may be most effectively directed, and discuss research the author plans to pursue and how it may
add to the body of knowledge.
Development of a predictive model
Successfully identifying risk factors and developing effective tools to predict PPC’s is largely
dependent on the statistical methods used to develop the models. The commonality among recent
studies seeking to reduce the occurrence of PPC’s is the attempt to identify what predictive
variables, or risk factors, lead to their development. Risk, defined in this case as the potential for
developing a pulmonary complication, is determined by quantifying the probability that an
adverse event will take place. The higher the probability an adverse event may take place, the
greater the risk and likewise, the lower the probability, the less risk. To assess the probability of
a pulmonary complication occurring one must first observe their occurrence in a cohort of
patients with similar characteristics and an equal likelihood of developing a condition. Using
information gathered from these observations, the variables must be carefully assessed to
determine the predictor variables, or covariates, for a given outcome. This is simply done by
selecting the variables that may be correlated with the outcome. With the predictor variables
clearly defined, they should then be measured systematically to establish which have the greatest

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association with the outcome. A common way to do this is with the statistical method of logistic
regression. Through a step-wise algorithm using a multivariable, linear logistics model
equation26 the covariate values are evaluated for their goodness-of-fit to the absence or presence
of a specific event, again, in this case being a pulmonary complication. At the end of an
automatic variable selection process only select covariates remain. These are referred to as the
independent predictors. To reduce the problems associated with this statistical method, most
often occurring with a large number of covariates, the covariates may be tested for significance
with the simple t-test for continuous variables or the chi-squared test for categorical variables
beforehand26. Several other statistical methods such as propensity analysis and Bayesian
approaches are discussed elsewhere in the literature as alternatives to stepwise regression.27, 28
Regardless of the method for the derivation cohort, Pace et al26 suggest there must be
validation and replication of risk factors. The c-statistic is the most commonly used statistic to
reflect discrimination, or how well the model distinguishes between individuals who develop an
outcome from those who do not.26 Internal validation is the next step in developing a predictive
model. Its purpose is to prove the model’s predictive value in the same population in which it
was developed.29 External validation can improve the generalizability of a model. Applying it to
a population different than the one in which it is developed externally validates a model.30
Current Predictive Models
A number of perioperative risk stratification models have been developed in an attempt to
predict the likelihood of a patient developing postoperative respiratory failure (PRF) or
postoperative pulmonary complications (PPCs). In the literature, PRF has been defined as
requiring mechanical ventilation for more than 48 hours after surgery or unplanned intubation

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within 30 days of surgery.9 PPC’s have been broadly defined and vary to some extent between
each of these predictive models. To better understand the factors that have been identified as risk
factors for pulmonary complications, several of these predictive models will be assessed.
Although a number of predictive models have been developed and studied, only a few
have been externally validated making them fit for clinical application.17, 31 Of the few validated
models, one is specific to patients undergoing oesophagectomy31 and the other needs
recalibration for application in geographic areas external to where the study was conducted.17
Consequently, the American Society of Anesthesiologist (ASA) physical status classification
system has been used the most clinically to quantify a patient’s perioperative risk of PPCs. This
classification system has received criticism for such use as it lacks objectivity, has low precision
in classes over ASA II, and does not take into consideration the characteristics of surgical
procedures.32
Numerous studies have shown surgical factors such as type of surgery, incision site, and
duration of surgery to be important predictors of PPCs.5, 7-10,34 For example, a prospective cohort
study by Arozullah, et al5 included cases from 44 Veterans Affairs Medical Centers (n = 81, 719)
to develop a risk index Cases from 132 Veterans Affairs Medical Centers (n = 99, 390) were then
used for validation of the risk index. This particular index was developed in an attempt to predict
postoperative respiratory failure (PRF) defined as mechanical ventilation for more than two days
after post-surgical extubation or reintubation after extubation. Participants were selected from the
National Veterans Affairs Surgical Quality Improvement Program (NSQIP). The risk index for
PRF was developed by multivariate logistic regression. A total of 2,746 patients (3.4%)
developed PRF. The most significant predictors of PRF included the type of surgery, albumin
and BUN levels, dependency status, history of COPD, and advanced age. Arozullah et al6 later

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applied the above risk index to predicting postoperative pneumonia (PP). PP rates correlated well
with the risk index scores having a c-statistic of 0.805 and 0.817 for the development and
validation cohorts, respectively. The greatest limitation of these studies is the patient population
selected. The patients from the NSQIP database have greater comorbid illness and for this reason
these findings may not be generalizable to other populations. The participants were also almost
exclusively men and so this risk index is not validated for the female population. The NSQIP
database also lacked pertinent information that could factor into the development of PPC’s such
as prophylactic antibiotic use, accurate COPD classification, and body-mass index.
A very similar study by Gupta et al9 addressed some of the limitations of previous studies
and developed and validated their own risk calculator for predicting PRF. Participants were
selected from the ASA NSQIP data set which, anecdotally, was much improved from when the
previous studied used it. Use of the ASA data set also extended the population of this study
beyond primarily VA hospitals. Data was collected for a development group (n = 211,410) and a
validation group ( n = 257, 385). In the development set 6,531 (3.1%) patients developed PRF
and had a significantly higher 30-day mortality rate. Five predictors of PRF were identified using
multivariate regression analysis. Type of surgery, emergency case, dependent functional status,
preoperative sepsis, and higher ASA class were all identified. A high c-statistic of 0.894 and
0.897 for the development and validation sets indicates good predictive performance. A risk
calculator, as opposed to a point-based scoring system, was developed using the logistic
regression model. The authors anticipate their risk calculator to be used as an aid in surgical
decision-making. The shortcoming of this predictive model is that it is specific to the
development of PRF, defined as requiring mechanical ventilation for more than 48 hours after
surgery or unplanned intubation within 30 days of surgery. This model may miss a number of

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patients who do not meet these criteria but would still meet criteria for being at risk for
developing a PPC. The development of a PPC can lead to PRF but not all patients who develop a
PPC progress to PRF. While this risk calculator may have a positive impact on the mortality and
morbidity of patients at risk for developing PRF, it fails to address the other PPC’s that may also
increase patient length of stay as well as increase patient and hospital costs. A model that is more
specific in terms of predicting less severe but clinically significant PPC’s as well as progression
to PRF may be of greater benefit as a whole.
The ARISCAT study15 is one of the better-known studies in this area of research. The
goal of this study was to develop a scoring system with fewer significant variables to identify
PPC risk in a wider range of clinical settings. Sampling from a large population undergoing a
wide-variety of surgical procedures reduced the sampling-bias identified in previous studies.
Data was prospectively collected from 59 randomly selected hospitals. Inclusion criteria included
the development of respiratory infection, respiratory failure, bronchospasm, atelectasis, pleural
effusion, pneumothorax, or aspiration pneumonitis. Similar to previous studies participants were
divided into developmental and validation subgroups. The regression modeling identified seven
independent predictors including; low preoperative arterial oxygen saturation, preoperative
anemia (hemoglobin <10 mg/dl), acute respiratory infection within one month, age, surgical
duration of 2 or more hours, upper abdominal or intrathoracic surgery, and emergent surgery.
The sample size was relatively small compared to other studies comprising 2,464 patients
undergoing surgical procedures and receiving anesthesia. Similar to what was found in other
studies, 123 (5%) of these patients experienced a PPC. The strength of this study is that this risk
index is more generalizable to other populations based on the sampling technique. Information
on the seven variables used to build the predictive index is also easily obtained in most settings,

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making it more clinically applicable. However, the usefulness of this score may be limited, as it
is not validated. The clinical application of this score could be useful in making decisions on
postponing surgery based on risk as well as helping explain preoperative risk to high-risk
patients. It may introduce discrepancy in some cases when deciding whether performing the
surgery or not is more or less of a risk than the calculated possibility of developing a PPC. This
is particularly true for the high-risk patients in whom risk scoring could theoretically be
beneficial. It is unclear whether this risk score would correlate well with the development of
PPC’s. Clinically, its value is limited only to patients who go on to develop the PPC’s defined in
the study.
The previous study was the basis for another study by Canet and colleagues16 using a
large European database of surgical cases referred to as the PERISCOPE (Prospective Evaluation
of a RIsk Score for the postoperative COmPlications in Europe) cohort. Its purpose was for use
in external validation of the ARISCAT score17 but was used also to build a simple risk score for
predicting PRF alone.8 Mazo et al17 studied 5,099 patients of which 725 PPC’s were recorded in
404 (7.9%) of patients. What they found was that the score had good discrimination overall (c-
statistic = 0.80) and also distinguished between three levels of risk: low, intermediate, and high.
In this study a PPC was defined as any one or more of the following; respiratory failure (PaO2
<60mmHg on room air, P/F <300, or Sp02 <90), suspected pulmonary infection (treatment with
antibiotics for a respiratory infection plus at least one of the following; change in sputum, lung
opacities on X-ray, temperature, or leukocyte count >12,000/mm3), pleural effusion, atelectasis,
pneumothorax, bronchospasm, and aspiration pneumonitis. The authors suggest that its ability to
distinguish among levels of risk and the fact that it is externally validated make it a good starting
point for controlled trials and audits of risk-reduction strategies. However, the scale’s calibration

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is unfit for use in certain geographical areas and should be used with caution in predicting risk
for individual patients. This study addressed an important problem of other studies in its
consideration of the level of risk a patient has. It also went to greater lengths to define specific
criteria for having a PPC. The ability to distinguish patients preoperatively based on their level of
risk may be useful in guiding decision making preoperatively as well as postoperatively in an
effort to improve outcomes.
Other studies have sought to develop predictive models of risk to identify patients at
high-risk for developing PPC’s with particularly poor mortality and morbidity rates. It is logical
to focus on identifying these patients and predicting their probability of developing certain PPC’s
that have the worst mortality and morbidity rates associated with them. Kor et al12 studied a
cohort of at-risk surgical patients testing the surgical lung injury prediction (SLIP) model’s
ability to identify patients at risk for developing acute respiratory distress syndrome (ARDS).
The primary outcome, namely, developing ALI or ARDS, was defined by criteria according to
the ARDS/ALI definition that emerged from the1994 American-European consensus conference,
and was endorsed after patient enrollment. The SLIP-2 model, refined from the original SLIP
model, is a point-based predictive tool with several variables each given a point value. The
components include; preoperative sepsis, surgical procedures; high-risk cardiac, vascular, or
thoracic surgery, tachypnea, FiO2>35%, SpO2<95%, admission from source other than home,
and cirrhosis. The study identified 1,562 patients as at-risk, and of these patients, 117 (7.5%)
developed ARDS. The SLIP-2 model is effective in identifying patients at risk for developing
ARDS and discriminates risk as low, moderate, or high. A notable limitation of this study is that
the investigators were not able to consider intraoperative and postoperative risk predictors
despite many of these predictors being associated with postoperative ARDS. Still, the authors

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emphasize that the expected use of the prediction model is to assess ARDS even before a surgery
takes place.
Jin et al10 developed a risk index using very similar criteria to that of Mazo et al 17 for
defining PPC’s, but identified somewhat different independent risk factors among the 1,673
Chinese patients included in the study. They identified smoking, respiratory infection in the last
month, preoperative antibiotic use, preoperative oxygen saturation, surgery site, blood loss,
postoperative blood glucose, albumin and ventilation to be independent risk factors. Similar to
other studies the model was validated with a second cohort and performed well with a receiver
operating characteristic curve of 0.90. Due to the geographic and temporal specificity of this
study, further research is needed to confirm it generalizability in other populations. However, the
findings from this study do conclude as others have, that patients at risk for developing PPC’s
should be closely monitored so that intervention may be initiated early to improve outcomes.
One study chose to use post-surgical reintubation as the main outcome and developed and
validated a score predicting reintubation in at-risk patients. Brueckmann et al7 identified risk
factors using multivariable logistic regression analysis. To derive the final model, predictors with
a P value greater than0.05 were excluded, leaving 11 independent risk factors; Age, male sex,
BMI, charlson comorbidity index >3, numerous comorbidities, ASA score >3, type of surgery,
high-risk service, and emergent procedures. Of these, ASA score >3, emergent procedures, high-
risk service, congestive heart failure, and COPD were used in the multivariate model to predict
postoperative reintubation known as SPORC (Score for Prediction of Postoperative Respiratory
Complications). Similar to the study by Kor et al,12 the SPORC score may be useful in
identifying patients at risk of severe PPCs leading to reintubation. This is both the strength and
weakness of the SPORC score. This score is less effective at identifying patients at risk for

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developing mild to moderate PPCs. While they may have a less apparent effect on mortality and
morbidity than severe PPCs, those of a lesser severity can not only still impact costs and hospital
length of stay, they may progress and become severe if not identified and treated early in their
clinical course.
Leo and colleagues13 took a slightly different approach than the majority of studies in
attempting to identify patients at risk for developing PPCs. The researches, like others, sought to
develop a score to identify patients at risk of developing a PPC. However, instead of identifying
preoperative predictor variables with which to develop the model, this score was developed
based on observed postoperative variables. Two of the authors from University of Nice in France
developed the score and their initials (FL, MA) gave rise to the name of the score, the FLAM
score. The key parameters of the FLAM score were chosen by retrospective review of data from
a thoracic surgery database as well as a small pilot study. The seven parameters of the FLAM
score are; dyspnea, chest radiograph, oxygen therapy (3 main parameters), auscultation, cough,
quality and quantity of bronchial secretions (4 minor parameters). The authors defined what they
considered a PPC in their study. They considered 7 PPC’s in their study, which were ARDS,
ALI, pneumonia, atelectasis, pulmonary embolism, pulmonary edema, and bronchospasm. These
PPC’s are very similar to those defined by previously discussed studies. During the postoperative
period a FLAM score was recorded daily on each of the 300 patients included in the studied. 60
(20%) patients developed a PPC. FLAM scores were also measured at 24 and 48 hours post-
surgery to identify any early changes in the FLAM score and to serve as a comparison between
scores of uncomplicated patients and the patients who developed a PPC. On graphical analysis,
higher FLAM scores were seen in all patients who developed any PPC compared to those who
did not at least 24 hours before a clinical diagnosis was made. On further analysis, FLAM scores

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correlated with the incidence and mortality in that they increased progressively with FLAM
scores. The authors then developed 4 separate classes of risk based on ranges of the FLAM score
that PPC’s were most likely to occur. A FLAM score of 9 was able to predict PPC’s with a
sensitivity and specificity of 86% and 95%, respectively. At the time of the diagnosis of a PPC
the FLAM score was reportedly usually 12-21. The results of this study suggest that the FLAM
score has comparable, if not better, predictive value than other risk assessment tools. However, it
has several limitations. At this time, the FLAM score is only applicable to patients undergoing
thoracotomy. Although thoracotomy is known to be associated with high rates of PPCs,4 the need
for a predictive assessment tool extends far beyond this type of procedure. This is evident in the
attempts of other studies to develop predictive models for similar surgical procedures, 3,6,12,33-36
as well as efforts to develop more generalizable models.7-9, 15-17 Another limitation of this study
is the small sample size of patients included in the study. Notably, of the patients included, 216
(72%) were undergoing surgery related to a lung neoplasm. Furthermore, 201 (67%) patients
underwent lobectomy. The very nature of the majority of participants’ preoperative diagnosis
and procedure performed may overestimate the models predictive value in other populations.
However, given the results of this study and the lack of similar studies attempting to reproduce
these results, further research in this area is needed.

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Table 2 summarizes categorically the risk factors for PPC’s identified by major studies in
this field.
Table 2. Summary of identified risk factors for development of PPC's (multiple
studies)
Study Preoperative factors Postoperative factors Surgical factors
Agostini et al4
Age>75 years, BMI≥30
kg/m2, ASA ≥3,
smoking history and
COPD
Canet et al8
Low preoperative SpO2
breathing room air,
respiratory symptoms,
heart failure, chronic
liver disease
Open thoracic or abdominal
surgery, duration of surgery,
emergency surgery
Brueckmann et
al7
ASA score ≥3, history of
congestive heart failure,
COPD
Emergency surgery, high-risk
surgical service
Arozullah et al5
Albumin level less than
30g/L, BUN level >30
mg/dL, dependent
functional status,COPD,
and age ≥70
Abdominal aortic aneurysm
repair, thoracic surgery,
neurosurgery,upper abdominal
surgery, peripheral vascular
surgery, neck surgery,
emergency surgery
Ferguson et al34
Underlying lung
function, age, renal,
dysfunction,
performance status,
recent smoking status
Era of operation, surgical
approach
Leo et al13
Dyspnea,chest X-ray,
delivered oxygen,
auscultation,cough,
quality and quantity of
bronchial secretions
Gupta et al9
Dependent functional
status,higherASA class,
preoperative sepsis
Emergency case, brain,
foregut/hepatopancreatobiliary,
and aortic surgeries
Jin et al10
Smoking, respiratory
infection within last
month, antibiotic use
Mechanical ventilation,
albumin, blood glucose
Surgery site and blood loss
Kor et al12
Sepsis, baseline health
status (cirrhosis,
admission from a
location other than
home), FiO2 ≥.35,
tachypnea,SpO2 ≤95
High risk cardiac, vascular, or
thoracic surgery

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A gap exists in the research in postoperative risk factors identified as associated with the
development of PPC’s. Even beyond the studies summarized in Table 2, there have been many
risk scores developed using perioperative patient and surgical characteristics to identify patients
at an increased risk for developing PPC’s. Currently, the only externally validated model, the
ARISCAT score,17 utilizes 4 preoperative and 3 intraoperative factors. The risk assessment tools
that have been developed to date do show promise in being able to stratify preoperative patients
at an increased risk for developing a PPC. However, there is a lack of research that focuses on
the ability to identify those patients as early as possible in the postoperative period. This may be
an important part of reducing the clinical and economic burden of PPC’s as it is rather intuitive
that identifying complications and intervening sooner than later leads to better outcomes.
Preoperative risk stratification may only be one part of what is needed clinically to improve the
outcomes of at-risk patients. For patients identified as high-risk during preoperative assessment,
certain risk reduction strategies may work well towards preventing a negative outcome.
However, the preoperative risk assessment may be less effective in patients who are at low to
moderate risk. Furthermore, the same can be said for at-risk patients who are not identified as
such with preoperative scoring tools. As discussed previously, due to the nature of how
preoperative risk scores are developed, they cannot possibly identify all patients who are at risk
for developing a PPC. The complexity of factors that may predispose any given patient to
developing a PPC or PRF cannot possible be covered in their entirety within one preoperative
risk assessment score.
In addition to preoperative risk stratification, a postoperative tool to identify patients at
risk may be of benefit. This type of score may have several clinically relevant applications. Such
a tool could identify when patients determined preoperatively to be “at-risk” for a PPC begin to

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show clinical signs of deteriorating sooner, suggesting need for an intervention earlier in the
clinical course. It may also prove valuable in identifying those patients who are at-risk but would
otherwise gone unidentified as such using only preoperative risk evaluation. Finally, more
efficient and effective allocation of respiratory care may be an additional foreseeable benefit of
such a scoring tool. To our knowledge, only one study13 has developed a score for predicting
PPC’s using postoperatively assessed parameters to identify patients at risk for developing a
PPC.
A pilot study was done by Vines et al22 to assess the value of a scoring tool similar to that
developed by Leo et al.13 The Respiratory Assessment and Allocation of Therapy (RAAT) score
was developed at Rush University Medical Center using 5 easily assessed respiratory related
components; respiratory distress, chest radiograph, oxygen therapy, clearance of secretions, and
spontaneous vital capacity. Each of these components is scored separately during patient
assessment. A value of 0, 5, or 10 is assigned to each of the 5 components based on what is
observed by a respiratory care practitioner during an evaluation. The researchers determined that
a RAAT score of 10 in any of the components met indications for respiratory therapy based on
clinical practice guidelines. In this study, to determine if RAAT scores of 10 or higher were
associated with pulmonary complications, 154 patients in medical and surgical ICUs at an
academic medical center were scored with the tool. Subsequent to obtaining a RAAT score for
each patient, each medical chart was reviewed to determine if a pulmonary complication
developed. In this study the PPCs considered were; tracheobronchitis, ARDS, hospital-associated
pneumonia (HAP), need for positive pressure ventilation, and atelectasis. Information was also
collected on the diagnosis, physiologic variables, chest radiographs, and any respiratory care
interventions. Of the 50 patients with a RAAT score of 10 or higher, 39 (78%) received some

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form of respiratory therapy or therapy was stopped due to more severe complications occurring
(pulmonary edema, large effusion, pneumothorax). A chi-square test was used to compare the
outcomes of patients with a score of 0 or 5 to those with a score of 10 or higher or stopped due to
more severe complications. The test indicated a significant relationship between a score of 10 or
higher, or stopped with the development of atelectasis compared to a score of 0 or 5. Similarly,
using a Fisher-exact test, a significant association was indicated between a score of 10 or higher,
or stopped with the development of HAP, tracheobronchitis (p = .015, phi = .213), or need for
positive pressure ventilation (p = .001, phi = .292) in comparison to those with a score of 0 or 5.
This study suggests that patients with a higher RAAT score may be at-risk for developing a
pulmonary complication. A recently published abstract37 described a preliminary investigation of
the predictive value of the RAAT scoring tool, which involved analysis of the correlation
between a preoperative risk assessment tool and the development of PPC’s. RAAT scores were
prospectively collected on 98 ICU patients. The patients were then retrospectively evaluated to
determine their respiratory failure risk index (RFRI),5 and postoperative pneumonia risk index
(PPRI)6, both previously described preoperative risk assessment tools. The RAAT tool and the
PPRI had a weak correlation (rs = 0.254, p = 0.015) and the RAAT and RFRI scores had no
correlation (rs = 0.150, p = 0.141). Amid these findings, RAAT scores were significantly higher
in patients who developed PRF (10 versus 5, p <0.0001) or postoperative pneumonia (10 versus
5, p = 0.003).37 Further research is needed to confirm these findings as well as compare the
RAAT score to other preoperative risk assessment tools such as the ARISCAT.15, 17 These
findings suggest the RAAT scoring tool may have value in identifying patients in the ICU who
are at risk of develop pulmonary complications due to underlying pathophysiology. The second
component of the RAAT scoring tool is the respiratory care protocols that were developed

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concomitantly to guide evidence-based respiratory therapy. The RAAT score is used to identify
patients who meet specific criteria for evaluation of need for respiratory care services.
Algorithms were developed to quickly and accurately guide respiratory therapy based on clinical
practice guidelines. The algorithms cover respiratory therapy services regularly delivered to
patients in the ICU setting and include; refractory hypoxemia, lung expansion, and bronchial
hygiene therapy’s.
The use of respiratory care protocols is well supported in the literature. The first mention
of respiratory care protocols was in 1992 in the AARCTimes.38 Nearly 25 years later, respiratory
care protocols are known to be the most appropriate way to safely and effectively deliver
respiratory therapy.39 Tietsort explains that protocols, by definition, are meant to improve upon
the efficiency, quality, and appropriateness of care delivered.39 Metcalf et al40 discuss the need
for formal and efficient care delivery systems that can enhance care, lower costs, and maintain a
balance between supply and demand. The demand for respiratory therapists is expected to grow
nearly 20% by the year 2022. With the increased need for and costs associated with providing
respiratory care services, better allocation of therapy while maintaining the quality of care
delivered is needed. Metcalf et al40 studied factors that have an impact on respiratory protocol
use. They found that physician support and the availability of high quality information systems
seem to be necessary conditions for their successful implementation. Evidence exists to support
that increased empowerment of first-line employees can enhance organizational performance.40
Moreover; the use of protocols gives therapists more autonomy. Increasing therapist
empowerment increases support from the respiratory therapist and will influence the frequency
of their use. According to Modrykamien et al41 the scientific basis for the use of respiratory care
protocols lies in meeting two criteria. The therapy guided by protocols must benefit the patient's

20
clinical condition they were ordered for and they must maintain or improve the allocation of
appropriate respiratory therapy. The use of respiratory care protocols in ICUs has focused
largely on their use in arterial blood gas sampling, ventilator management, ventilator weaning,
and discontinuation of mechanical ventilation.41 In non-ICU settings protocols for oxygen and
bronchodilator therapy, bronchial hygiene, and step-down assessment have been studied.41 To
our knowledge, the use of respiratory care protocol guided therapy in an effort to decrease
pulmonary complications and improve outcomes in ICU patients has not been studied. While the
use of respiratory care protocols has been increasingly adopted over time, there have been few
hospitals to fully develop and implement this kind of respiratory care delivery system. Several
randomized trials have shown the benefits of respiratory care protocol use in regards to cost-
savings and better allocation of respiratory therapy.42, 43 However, further studies are needed in
this area to determine the impact of implementing this kind of protocol-based care systems on
patient outcomes, specifically in the ICU patient.
In conclusion, PPC’s remain a major issue in hospitals in the United States and around
the world. They are associated with higher cost of care to hospitals and patients, increased length
of stay, and an increase in morbidity and mortality. A great deal of research has been done in an
effort to reduce the negative impact of PPC’s including the development of numerous
perioperative risk assessment tools. The ARISCAT score17 and the Ferguson pulmonary risk
score31 have been externally validated and have potential to be used clinically as preoperative
risk stratification tools. The ARISCAT needs further research on its value in other geographical
areas, and the Ferguson pulmonary risk score is specific to patients undergoing oesophagectomy.
The RAAT score is a newly developed tool using 5 assessable respiratory related components to
identify the ICU patient at risk for developing pulmonary complications. Preliminary studies

21
have shown its potential for being more useful than, or an adjunct to existing preoperative risk
assessment tools to identify patients at risk for developing postoperative pulmonary
complications. Further studies are needed to evaluate its clinical application and impact on
patient outcomes. Additional research on the RAAT score should aim to confirm previous study
results as well as address new questions about its use. Research questions might include: Is there
any correlation between the ARISCAT score & RAAT score and PPCs? Does use of the RAAT
score and its associated respiratory care protocols allocate appropriate respiratory care in non-
intubated ICU patients? Can respiratory therapist use the RAAT score to allocate appropriate
respiratory care? Does use of the RAAT score to identify patients at risk for developing
postoperative pulmonary complications improve patient outcomes? The author plans to conduct
research to determine if upward trending of RAAT scores in non-intubated ICU patients has any
association with pulmonary complications. Evidence in the literature suggests that preoperative
risk assessment scores can identify patients at an increased risk for developing postoperative
pulmonary complications. Despite the plethora of research aimed at developing scoring and
classification systems for pre-operative risk assessment to potentially improve surgical
outcomes, the evidence regarding the predictive value of these tools is inconclusive. There is an
absence of knowledge about the postoperative management of these patients and its ability to
improve outcomes. There is a gap in existing research regarding whether postoperative
assessment of ICU patients may be more useful, or an adjunct to existing risk assessment tools in
identifying patients at risk for developing pulmonary complications. The use of respiratory care
protocols for certain procedures in ICU and non-ICU settings have been shown to improve
quality of care delivered and improve the allocation of respiratory care services. Further research

22
is needed to determine if they have equal value in improving outcomes of ICU patients at risk for
developing pulmonary complications.

23
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