an overview of predictive scoring systems used in icu · 2020. 10. 2. · expenditure and...

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05 June 2020 No.09

An overview of predictive scoring

systems used in ICU

UV Jaganath

Moderator: S Moodley

School of Clinical Medicine Discipline of Anaesthesiology and Critical Care

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CONTENTS Introduction ......................................................................................................................................... 3

Developing a predictive scoring system ..................................................................................... 4

Choosing a predictive scoring system ........................................................................................ 6

Acute Physiologic and Chronic Health Evaluation (APACHE) .......................................... 7

Sequential (Sepsis-related) Organ Failure Score (SOFA) ................................................. 10

Simplified Acute Physiologic Score (SAPS) ......................................................................... 11

Mortality Predictive Model (MPM) ............................................................................................ 12

Comparison of performance ......................................................................................................... 14

Use in the low to middle-income setting ................................................................................... 15

Other uses of predictive scoring systems ................................................................................ 16

Limitations ......................................................................................................................................... 17

Conclusion and recommendations ............................................................................................. 18

References ......................................................................................................................................... 19

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Introduction

With the rising healthcare costs and shortage of intensive care unit (ICU) beds,

clinicians need to appropriately triage patient admissions into ICU to avoid wasteful

expenditure and unnecessary bed utilisation. While the Critical Care Society of

Southern Africa does recognise the use of predictive scoring systems in ICU to aid in

triage, they have also acknowledged some if its pitfalls.1

Predictive scoring systems are tools that have been developed to describe the severity

of a disease process and subsequently predict outcomes in patients. These tools

typically utilise a combination of patient data, including clinical health information,

physiological and laboratory data to determine a numerical severity of disease score,

which in turn is used to determine outcomes, such as; length of hospital stay and

mortality rates.2-4

Predictive scoring systems can be divided into two broad categories5:

1. Single organ or disease specific scoring systems such as:

• European Systems for Cardiac Operative Risk Evaluation (EuroSCORE)

that is used to predict mortality after cardiac surgery.6

• The Model for End-Stage Liver Disease (MELD) score used in patients

with end-stage liver disease to predict mortality risk.7

• The Glasgow Coma Score (GCS) to assess and predict mortality after

head injury.8

2. Generic scoring systems for the use in all ICU patients, such as:

• Acute Physiologic and Chronic Health Evaluation (APACHE).

• Sequential (Sepsis-related) Organ Failure Score (SOFA).

• Simplified Acute Physiologic Score (SAPS).

• Mortality Predictive Model (MPM).

• Multiple Organ Dysfunction Score (MODS).

The ideal predictive scoring system should use objective and easily measured risk

factors or predictors upon which the outcome prediction is made.

The characteristics of the ideal predictive scoring system includes:5,9

1. Uses easily measurable variables (see below).

2. Has a high level of discrimination.

3. Well calibrated.

4. Validated for use in all patients in the ICU and in different countries or patient

cohorts.

5. Can predict length of hospital stay, quality of life after ICU and/or mortality.

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The idea variables for describing organ dysfunction should be:5,10

1. Simple, cheap and routinely available.

2. Reliable, reproducible and objective to measure.

3. Specific to the acute dysfunction of the organ being evaluated but not chronic

dysfunction.

4. It should not be affected by reversable, transient abnormalities that are

associated with practical or therapeutic interventions.

5. Abnormalities should only be in one direction.

6. Variable should be continuous rather than dichotomous.

It should be noted that the simultaneous use of more than one predictive scoring

system on the same patient should be seen as complementary, as opposed to

competitive or mutually exclusive, as their combined use may possibly offer a more

accurate indication of the true severity of the disease process and hence overall

prognosis.5

Developing a predictive scoring system

To appreciate the basis of predictive scoring systems, it is important to have an

understanding of the steps that are required to develop them. An overview of these

steps are as follows2,4,10,11:

1. Selecting the outcome variable(s)

• Mortality is most commonly used as it is an easy to define endpoint.

Being binary, it easily lends itself to statistical analysis.

• Long term prediction of quality of life after ICU and cost-effective

medical treatment may also be used, however, unlike mortality, they

cannot be represented as a binary endpoint, thus making them more

difficult to measure.

2. Selecting the patient population

• Population bias may be inherent. This is an important factor to

consider when one is developing or choosing to use a given scoring

system. This may be important in the South African setting whereby

our population demographics and disease profiles may not mirror

that of the population sample that the scoring system was originally

developed for and validated against.

• Ideally, the clinician should only use the predictive scoring system for

decision making if the said scoring system was developed for and

validated in a population group similar to that of the patient of interest.

• Should the accuracy of the scoring system be found to be inaccurate

in the population group being examined, it should then be updated or

modified for that population group. An example of such an

undertaking occurs with the APACHE scoring system.

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3. Selecting the variables or risk factors

• Selecting variables to be studied are usually done in one of two ways.

• In the first method, clinicians select variables that are believed to be

related to the chosen outcome (e.g. mortality). Such a technique was

employed to develop the APACHE II and SOFA scoring systems.

• In the second method, statistical methods (e.g. linear discriminant

function analysis) are used to narrow down the initial list of variables

to only those that are individually related to the chosen outcome.

Such a technique was employed to develop the APACHE III, SAPS

and MPM II scoring system.

• It is important that the variables identified are objective and easy to

obtain.

4. Data collection (predictors) and analysis (of the outcome)

• Prospective data collection is preferred as it allows for ongoing

analysis of the data being collected. This helps reduce the risk of bias

or errors which may reduce the accuracy of the predictive scoring

system.

• Another advantage of prospective data collection is that it allows for

evaluation of the timing of the measured variable to the outcome.

While the SAPS and MPM scoring systems use data collected within

1 hour of admission, the APACHE and SOFA scoring systems used

the worst physiological values within 24 hours of admission.

• Missing data from retrospective investigations may become

challenging and result in inappropriate associations of mortality with

a smaller number of organ system derangements, which may be

reduced with prospective data collection methods.

• Once the predictive factors are identified, they are analysed and

presented either as a cumulative score or as a single score which is

calculated from the sum of weights allocated to each individual

predictive factor (as used in APACHE and SAPS).

5. Developing the model

• This is done with the use of various statistical techniques including

multivariant linear regression models (used to assess relationships

between independent variables, i.e. the risk factors and the

dependent outcome i.e. mortality) and logistic transformation (used

when the predicting factors are not linearly related to the outcome).

• Logistic regression methods may be used to help identify additional

predictive factors that may impact the final outcome.

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6. Validation of the predictive scoring system

• This can be done by directly comparing the predictive scoring system

to a “gold standard” or to each other.

• Another technique involves developing the receiver operation

characteristic curve to aid with a more precise cut off point selection.

7. Evaluation of the predictive scoring system’s utility and impact

• Depending on the chosen scoring system, they may be used to triage

patients for ICU and assist in decision making. They may also be

used during clinical trials to prove that the two patient population

groups were similar.

8. Updating the system

• All predictive scoring systems should be updated as its calibration over

time may deteriorate because of case-mix and changes in both disease

profiles and therapeutic interventions. This tends to result in an

overestimation of mortality and subsequently poor discrimination

between outcomes (e.g. survivors vs non-survivors).

• This is important since past studies have shown that in certain

circumstances older scoring systems performed no better, or perhaps

worse than predictions made by clinicians.

Choosing a predictive scoring system

While there are many different predictive scoring systems to choose for, each with

their own advantages and limitations, some of the commonly used models include the

APACHE, SOFA, SAPS and MPM scoring systems. When choosing to use a

predictive model, is it important to select one that was most recently developed for and

validated in the population group being evaluated. Other factors that should be taken

into account include its user-friendliness, accessibility, feasibility, cost implications and

the outcome that is being evaluated (e.g. length of hospital stay vs predicted mortality

rate).2

An example of such limitations is that while the APACHE IV model is able to more

accurately predict mortality over the SAPS model, this benefit is off set by the fact that

it is more challenging and costly to use as it requires more variables and depends on

propriety software. Additional, APACHE IV is better calibrated for predicting length of

hospital stay when used in ICUs within the United States. In contrast, while the SAPS

model is cheaper and easier to use and better suited for use internationally, it is more

prone to case-mix effects and is unable to reliably predict length of stay when

compared to the APACHE IV model.2

A summary of each of these predictive scoring systems will be discussed separately.

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Acute Physiologic and Chronic Health Evaluation (APACHE)

The APACHE scoring system has been found to be efficient in predicting mortality and

estimating length of stay in ICU. There are four versions that is widely used, APACHE

I to IV.

The original APACHE model was developed in 1981 and consisted of two parts. The

former is the Acute Physiological Score (APS) which represents the degree of the

acute illness. A total of 34 physiological variables are measured and allocated a score

between 0 and 4, depending on the degree of severity. The worst value of each

variable measured within the first 32 hours of ICU admission are used. The latter part

of the score, the Chronic Health Evaluation (CHE) that is classified from A to D,

represents the physiological status of the patient before the illness. The classification

of A representing excellent health while a classification of D representing severely

failing health. The initial study demonstrated a direct relationship between the APS

score and the likelihood of mortality. With regards to the CHE, only class D was shown

to be an independent risk factor for mortality.12

Developed in 1985 as a modification of the original model, the APACHE II scoring

system uses a point score based on 12 physiological parameters that is routinely

investigated.13 These physiological measurements are done during the first 24 hours

after admission, in addition to this, the patients age, medical history and surgical

requirements are considered. If a variable has not been measured a point of zero is

allocated. A score between 0 and 71 is then calculated based on these measurements.

Generally, higher scores are more predictable of severe disease and subsequently

higher rates of mortality. From the APACHE II score, the estimated risk of in hospital

mortality is then calculated using a logistic regression equation, utilizing specific beta

co-efficient made for this purpose (Tables 1 and 2).4,14,15

The APACHE III was developed in 1991 using 26 variables. It comprises of two

components, namely the APACHE III score, ranging from 0 – 299, and the APACH III

predictive equation that uses the APACHE III score to predict in hospital mortality

rates.16 With regards to predicting ICU mortality in trauma patients, the APACHE III

has been validated against and found be as accurate as the Trauma Injury Severity

Score (TRISS).17

Developed in 2006, the APACHE IV is more complex and entails the input of 142

variables and 115 various disease groups, however web based calculations can be

done.18 Despite the APACHE IV being the most recent version, the APACHE II score

is still amongst the most commonly model in current clinical use.

When comparing the APACHE II to the APACHE IV model, both demonstrated good

discrimination capabilities, with an area under the receiver operating characteristics

curve of 0.805 and 0.832 respectively. With regards to predicting mortality, the cut-off

points with the best Youden index were 17 and 72 of the APACHE II and APACHE IV

respectively.13

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Table 1. Acute physiologic and chronic health evaluation (APACHE II)4,14

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Table 2. Acute physiologic and chronic health evaluation II-diagnostic category

weight4,14

Advantages: The measurements required to calculate the APACHE II score are

routine parameters that are monitored in the ICU thus no additional investigations are

needed. This predictive scoring system can predict sepsis with a single assessment

at 24 hours. Additionally, it has been validated in several countries and has been

proven to be highly reproducible.4,14,15

Disadvantages: The major setback of the APACHE scoring system is that patients

may have several co-morbid illnesses and therefore selecting only one main

diagnostic category may be challenging. The physiological parameters used in the

calculation of the APACHE score are dynamic and may be easily affected by factors

such as on-going treatment and resuscitation.4,14 Regarding the APACHE IV scoring

system, even though more updated, it is more complex and requires physiological

setting software resulting in added costs.18

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Sequential (Sepsis-related) Organ Failure Score (SOFA)

The SOFA score was developed in 1994 by the European Society of Intensive Care

Medicine, then revised in 1996. It was originally used to understand the natural

progression of individual organ failure and the interaction between failure of other

organs and to describe the sequence of complications (in terms of said organ

dysfunction or failure) in critically ill patients with sepsis.4,19 Intuitively, while any

calculation of morbidity or organ dysfunction should then relate to mortality to some

degree, the SOFA score was originally not intended to predict this outcome. However,

it has since been validated for use in critically ill patients with non-sepsis related organ

dysfunction and as a tool for predicting mortality rate.4,10,19 It measures 6 organ

systems with scores ranging between 0 – 4 for each (Table 5).

Interpretation: Irrespective of the initial SOFA score, an increase in the score within

48 hours of ICU admission is associated with a mortality rate of ≥ 50%, an unchanged

score was associated with a mortality rate of between 27 – 35% (if the initial score was

< 8) and 60% (if the initial score was ≥ 8), whereas a decreasing score is associated

with a mortality rates of < 6% and 27% if the initial score was < 8 or ≥ 8 respectively.20

In the setting of sepsis, a score of ≥ 2 is associated with a predicted mortality rate of ≥

10%, however, in the setting of septic shock with a score of ≥ 2 the predicted mortality

rate is around 40%.2,21 Further information regarding the interpretation of the of SOFA

score may be found in table 5.

Advantages: Unlike the APACHE and SAPS scores, which are models of

physiological assessment, the SOFA score may be considered to be a multi-organ

scoring system, and as such it can be used to track the response of organ dysfunction

to therapy over time.9 It is also easier and cheaper to use than models such as the

APACHE IV.

Disadvantages: In contrast to the above-mentioned predictive scoring systems, the

SOFA score does not predict individual outcomes, instead it helps identify high risk

patients at risk of sepsis related death as a group.2 It does not take chronic health

status into account.

Table 5. Sequential organ failure assessment score (SOFA)4

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Simplified Acute Physiologic Score (SAPS)

The SAPS II scoring system uses 17 variables i.e. 12 physiological variables which

are measured within the first 24 hours after admission, age of the patient, the type of

admission and three disease-related variables.22 Several of these variables are

assigned a score depending whether they are present or not, whilst the 12

physiological variables are scored according to a range of values. The SAPS II score

may vary between 0 –163 points. The probability of mortality is then calculated using

a logistic regression analysis.4,14

Advantages: The SAPS III which is the latest version has a greater potential for

universal use as it has been designed and validated in ICU settings amongst thirty-

five countries.

Disadvantages: The SAPS II was designed from a database of patients in North

America and Europe. Unfortunately this sample is not representative of the population

and ICUs in other countries where variability in structures and resources are related

to the outcome.23 Unlike APACHE IV, the SAP scoring system cannot be used to

predict length of stay, however it may be used to compare the use of resources

amongst ICUs.24

Table 3. Simplified acute physiology score (SAPS II)4,14

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Mortality Predictive Model (MPM)

The Mortality Probability Model was developed on an international sample of patients

between 1989 – 1990. It assesses patients probability of mortality at hospital

discharge, based on measurements attained within the first hour of ICU admission.25

There are three models. The first version of the model was developed to predict

mortality based on data from admission and after the first 24 hours in the ICU. Later,

models were developed to include data from 48 – 72 hours after ICU admission. This

model uses the patients’ chronic illnesses, acute diagnosis, some physiological

variables and other variables such as mechanical ventilation.4,14

Advantages: The MPM scoring system uses less physiological data in comparison to

other scoring systems thus this scoring system may be preferred in settings where

laboratory resources are constrained. The MPM III has good discrimination and

calibration.25

Disadvantages: The MPM excludes certain patient subsets such as cardiac surgery,

myocardial infarction, and ICU readmissions, which decreases its usefulness to some

ICUs.25

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Table 4. The Mortality Predictive Model (MPM)4,14

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Comparison of performance

Despite having a paucity of high-quality studies comparing the performance of the

various models to each, after evaluating the current evidence, the following

generalisations can be made:

1. When comparing the older models (APACHE II, APACHE III, SAPS II, and

SOFA) to each other, the APACHE II and III predictive scoring system were

shown to be superior in one study whereas the SAPS II was shown to be

superior in another.26 The accuracy of these model in predicting the actual

mortality rate was also limited. As such, it has been suggested that using a

combination of these scoring systems may result in an improvement in the

overall predictive performance.26

2. When comparing the newer models (APACHE IV, SAPS III and MPM III) to the

older models (APACHE II and III, SAPS II, MPM II and SOFA), the newer

models were found to perform better. This was evident by the fact that the older

models had a tendency to over-predict the mortality rate, whereas the newer

models had superior calibration, accuracy and discrimination characteristics.

However, despite these findings, these differences were not found to be

statistically significant. A possible reason for this finding is that the choice to

use one predictive scoring system over another may be due to ease of use and

local preferences.27

3. In general, all of these models have very good discriminatory values with an

area under the receiver operating characteristics curve between 0.80 – 0.90

while simultaneously demonstrating good calibration assessments.9 As such,

no single model has been shown to be significantly superior to another with

regards to predicting mortality.2,27

https://www.sciencedirect.com/topics/medicine-and-dentistry/saps-ii

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Use in the low to middle-income setting

With regards to the South African context, a major cause of concern is that the above-

mentioned predictive scoring systems have been developed and validated

predominantly in high-income countries. As a result, the accuracy of these models

may be reduced given that our population and disease profile and therapeutic

intervention may not mirror that seen in high-income countries. While not specific to

South Africa, the performance these models to predict mortality in critically ill patients

in low or middle-income countries was demonstrated to be moderate at best.19,28

One reason for this relates to the lack of calibration of these models to the population

cohort. Ideally, if a predictive scoring system is to be used, it should exhibit good

calibration for that population cohort. However, it has been suggested that should a

model with a poor calibration demonstrate a good discriminatory ability, it may still be

of benefit if it is used it to identify high-risk patients for diagnostic and/or therapeutic

interventions.19,29

Another given reason is due to and the possibility of missing predictor variables due

to resource constraints. This problem may be overcome by developing scoring

systems with fewer and more commonly available variables (e.g. the Rwanda MPM

and TropICS models) or by allowing for the substation of certain inaccessible variables

with its more commonly available counterparts (e.g. substituting PaO2 for oxygen

saturation).19,28

A major advantage of developing setting-specific models is that they address the

above two problems. One such example is the Rwanda MPM (R-MPM), which was

developed using five easily attainable variables, namely: age, heart rate and GCS at

the time of ICU admission, hypotension or shock as a reason for ICU admission and

suspected or confirmed infection within 24 hours of ICU admission. Despite having

fewer variables, the R-MPM demonstrated better calibration, discrimination and

prediction compared to the MPM when used in the same population of Rwandan ICU

patients.28

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Other uses of predictive scoring systems

Apart from the clinical uses describe able, predictive scoring systems do play a role in

other aspects of medicine. Two key areas include its role during research and as a

quality care bench mark tool.

1. Research

• Scoring systems may be used in clinical trials to compare the baseline

risks between comparative groups to ensure that they are similar. This

is commonly used during clinical trials in patients with acute respiratory

distress syndrome or sepsis whereby possible therapeutic interventions

are being evaluated.2,11

2. Quality care bench mark

• Predictive scoring systems help evaluate the quality of care by

confirming that patients with the same or similar baseline mortality risks

are being compared. An example of such practices are studies that

compare ICU outcomes with other ICUs within the same hospital or in

other hospitals. The implications of such findings are that policies and

practices from ICUs with favourable mortality rates may then be adopted

and incorporated by other units to help improve their quality or care.30,31

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Limitations

The ICU is the perfect environment for using predictive scoring systems since both the

population group and patient care tends to be is well defined and the most significant

predictor of mortality is the severity of the illness. However, there are some limitations

with regards to their use as follows:32,33

1. The scoring system may not be validated in the population group that it is being

used to evaluate. Similarly, they may not be able to accurately predict outcomes

for specific patient population groups (e.g. the use of APACHE II was found to

be unreliable to predict outcomes in pregnant women receiving critical care) or

disease processes.34 The predictiveness of the system may be reduced due to

the unusual nature of a given population for which it was not designed or

validated in.33

2. The predictiveness of the scoring system deteriorates over time and as such,

failure to periodically update the system results in a gradual loss of

discrimination and/or calibration. The net effect it that an overestimation of the

predicted mortality rate may be seen.35

3. A phenomenon known as lead-time bias may occur. This was seen when

patient who were transferred in from other ICUs or hospitals had a higher

mortality rate than that predicted by the APACHE II scoring system. This

variable, the location of treatment, was subsequently added to the APACHE

scoring system but not the others mentioned above.36

4. The quality of care is better or worse than expected resulting in a lower or higher

patient mortality rate. In this scenario, the model will lose its predictive accuracy

as they will be a higher or lower actual survival rate due to the better or worse

quality of care respectively.33

5. Unlike SAPS, MPM and SOFA, models like the APACHE require proprietary

software and more data points to use, resulting in it being more burdensome,

however, the integration of electronic record keeping into health systems may

alleviate some of these challenges.2

6. When predicting mortality within 24 hours of admission into the ICU, the current

evidence suggests that scoring systems are not yet superior to clinical

judgment.37

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Conclusion and recommendations

Due to the limited availability of ICU resources in South Africa it is important that we

utilise multiple tools to aid in its rational use. After reviewing the literature, predictive

scoring systems do have a role to play in this. The accuracy of predictive scoring

systems will continue to improve with time. This is possible due to and improvements

in computing power and the integration of “big data” into our heath care systems.

When deciding to use predictive scoring systems, it is important for clinicians to be

aware that the predicted mortality rate relates to patients within a similar cohort and

not to an individual. As a result, while we may be able to predict the mortality of a

group of patients with a similar score, we are unable to predict which individual patient

may survive and who may die. Consequently, caution should be used when predicting

mortality for individual cases.

Conversely, as scoring systems allow for an objective assessment of the clinical status

of the patient, they may be used to assist the clinical decision-making since they mirror

the probability of mortality in a similar cohort of patients. Ultimately, predictive scoring

systems should be considered as a tool to assist, rather than replace the clinician.

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