PREDICTORS OF OUTCOMES IN CRITICALLY-ILL PATIENTS

Dr: Hatem O.Qutub MD,FCCP,FCCMAssoc proof Medicine & Critical Care

KFHU / UOD

‘Art’ and ‘science’ of Medicine

“Any physician who continuously provides care to a particular category of patients will be able to initially predict the prognosis with a reasonable degree of accuracy which is the "art" aspect of the clinical practice”

…….H.Q.

Lay out

Morphological analysis [Predictors] Systemic analysis for the predictors Scores and organ failure system Why do we need them ? [ objectives ] Limitation of scoring system

Introduction

GCS TISS ~~ 1974 / Assign points according to the degree of

abnormality in a set of variables known to affect outcome.

Outcome prediction, i.e., the probabilistic estimation of a binary outcome (death or survival, usually at hospital discharge) for a groups of patients

Objectives of predictors {{{ Pre- dict – or}}} Reliable & objective estimation of disease

prognoses Probability of adverse events To compare outcome & survival (hospital

mortality) Risk adjustment [ quality ] Evaluation of care performance [ quality ] Cost-benefit analysis [ budgeting ]!! Clinical decision making!

What determine ICU prognosis

Diseases spectrum Personnel types Methods of monitoring Admission / discharge criteria's Resources utilization Patients allocation

What to Predict ?

Associated illness ( chronic health, co morbidities)

Underlying cause and severity of indication for ICU admission

Physiological derangements{ especially if related to underlying cause}

Response to therapy Complications ( especially if unanticipated)

Creating a Useful Predicting Instrument

Patient selection / populations Outcome selection Variable ( predictor) selection Data collection Relating predictors to outcome Validation Impact evaluation Updates

Evaluating a predictive model

Uniformity of definitions of outcomes Uniformity of definitions of variables Completeness of data, number and

frequency of variables Timeliness and source of data,

development population characteristics Development and testing (validation)

cohorts Calibration and discrimination

The Ideal Scoring System

**The ideal scoring system would have the following characteristics:

On the basis of easily/routinely recordable variables Well calibrated A high level of discrimination Applicable to all patient populations Can be used in different countries The ability to predict functional status or quality of

life after ICU discharge.*No scoring system currently incorporates all these

features.

Invisible

Visible

Severity of illness scoring systems

Severity of illness scoring systems

They are so many [ generation ] Specific or organ failure models Are widely used in ICU practice. Complex systems {basis in mathematics}. Need to appreciate what factors influence

their performance

Utilization of scoring systems

Outcome prediction Clinical research Quality of care analysis Benchmarking in (ICU)

environment

Adult severity-of-illness and organ dysfunction assessment models

APACHE,: Acute Physiology and Chronic Health Evaluation

LODS, :Logistic Organ Dysfunction Score MODS, :Multiple Organ Dysfunction

Syndrome MSOF, :Multiple System Organ Failure MPM, :Mortality Probability Model SAPS, :Simplified Acute Physiology Score SOFA, :Sequential Organ Failure

Assessment Critical Care Clinics - Volume

23, Issue 3 (July 2007

Generations of the ICU severity prognostic models

Fourth generation

Third generation

Second generation

First generation

APACHE IV APACHE III APACHE II APACHE I

SAPS III SAPS II SAPS I

MPM III MPM II MPM I

Critical Care Clinics - Volume 23, Issue 3 (July 2007

Adult severity-of-illness and organ dysfunction assessment models

APACHE~~~ Prediction of: •   ICU and hospital mortality

•    ICU and hospital length of stay•    Duration of mechanical ventilation•    Risk of needing an active treatment during ICU

stay•    Probability of PA Catheter use•    Potential transfer from ICU

Adult severity-of-illness and organ dysfunction assessment models

SAPS : Prediction of hospital mortality MPM :Prediction of hospital mortality SOFA :Assessment of organ dysfunction MODS: Assessment of organ dysfunction LODS :Assessment of organ dysfunction MSOF :Assessment of organ dysfunction

Variables evaluated Age Chronic health conditions Acute Physiological variables HR, SBP, RR, Temp, MAP Urine O P , BUN, Creat , HCT , WBC ABG [ PH , PaO2, PaCO2 / Hco3 ] / A –a gradient / Albumin, bilirubin GCS Glucose / sodium [ RBS / Na / K / CO2 ] MV [ RR]

First generation

APACHE I knaus~ 1981 ~ USA /2 medical center 805

patients Consist : 34 physiological variables &

preadmission health statusMost abnormal variables in 1st 32 hours after

ICU admission Not validated at that time [mortality

approach]

**CCM 9 (8)1981 ~ Knaus et al [ APACHE :a physiological based classification system.

Second generation

APACHE II

SAPS I

MPM I

Third generation

APACHE III

SAPS II

MPM II

Old generation Overall observations1-Models ~ good discrimination , but poor

calibration 2- Underwent customization 3- No consistence improvement in the

performance 4- No reflection to [ current case max &

practice patterns]

Fourth generation

APACHE IV

SAPS III

PMP III

They excluded readmission values are normal when not measured /

obtained

 -Variables included in the fourth-generation prognostic modelsMPM0 III APACHE IV SAPS III

Predictive variables

Yes Yes Yes Age

No Yes Yes Length of hospital stay before ICU admission

No 8 3 ICU admission source

Yes Yes Yes Type of ICU admission

3 7 6 Chronic comorbidities

Yes No No Cardiopulmonary resuscitation before ICU admission

Yes No No Resuscitation status

No Yes Yes Surgical status at ICU admission

No No 5 Anatomical site of surgery

5 116 10 Reasons for ICU admission/Acute diagnosis

No No Yes Acute infection at ICU admission

Yes Yes Yes Mechanical ventilation

No No Yes Vasoactive drug therapy before ICU admission

3 6 4 Clinical physiologic variables

0 10 6 Laboratory physiologic variables

Fourth generation observations

Performances are good MPM0 III & SAPS III ~ with 1 hr can assess

severity of illness before ICU interventions Missing data do vary in their effects APACHE IV more complex / bought software No standardized lab testing for individual

unit Computers / manually data entry

Fourth generation observations

APACHE & MPM ~ USA SAPS ~ Europe MPM 0 III least complex SAPS III more for customized ~ good

international benchmark Overall are good research tools SAPS III & MPM 0 III potential for supporting

ICU admission triage

Biases and Errors in scoring system

Case max Data collection Data entry Flaws in model development Validation Pre-ICU location Acute diagnosis

Biases and Errors in scoring system

Physiological reserve Patients’ preference for the life-support No long-term survival nor quality of life

issues Not for pediatric Not for specific condition Cost-mortality not been addressed

Organ Failure Models

Failing organs

Organ failure are process not an event Major causes of morbidity & mortality Need initial & sequential assessment Reflect patient outcome & the

effectiveness of the treatment Organs studies [ respiratory , hepatic,

renal cardiovascular, hematology and CNS]

GI T & Endocrine ~ not included

Multiple system organ failureCriteria

Organ failure

Heart rate ≥ 54/min Cardiovascular

Mean arterial pressure ≤ 49 mm Hg or systolic blood pressure < 60 mm Hg  

Ventricular tachycardia or fibrillation  

PH ≤ 7.24 with PaCO2 ≤ 49 mm Hg  

Respiratory rate ≤ 5/min or ≥ 49/min Respiratory

PaCO2 ≥ 50 mm Hg  

Alveolar to arterial oxygen tension gradient ≥ 350 mm Hg  

Dependent on ventilator or CPAP on second day of OSF  

Urine output ≤ 479/mL/24 hours or ≤ 159 mL/8 hours Renal

Blood urea nitrogen ≥ 100 mg/dL  

Creatinine ≥ 3.5 mg/dL  

White blood cell count ≤ 1000/mm3 Hematologic

Platelets ≤ 20,000/mm3  

Hematocrit ≤ 20%  

Glasgow coma score ≤ 6 (in the absence of sedation) Neurologic

MSOF

Common ones [MODS ,SOFA ,LODS] Continuous scales SOFA & MODS ~ rang 0 to4(severity based) Subjective evaluation as result of

consensus and literature review

Variables included in the calculation of the organ failure scoresMODS LODS SOFA Variable

Organ

Yes Yes Yes PaO2/FIO2 Respiratory

  Yes Yes MV  

Yes Yes Yes Platelets Hematology

  Yes   WBC  

Yes Yes Yes Bilirubin Liver

  Yes   Prothrombin time  

    Yes Mean arterial pressure Cardiovascular

  Yes   Systolic blood pressure  

  Yes   Heart rate  

Yes     PAR  

    Yes Dopamine  

    Yes Dobutamine  

    Yes Epinephrine  

    Yes Norepinephrine  

Yes Yes Yes Glasgow coma score CNS

Yes Yes Yes Creatinine Renal

  Yes   Blood urea nitrogen  

  Yes Yes Urine output  

MOF benefits / false

Describe sequence of complication Do not predict mortality Discriminate between survival & non-

survival Paucity of data comparing performance . Used as trend not individual reading Trends response ↔ therapeutic intervention Resources utilization Not been used in large samples

In Reality

The four major intensive care unit (ICU) predictive scoring systems are :

Acute Physiologic and Chronic Health Evaluation (APACHE) scoring system

Simplified Acute Physiologic Score (SAPS)

Mortality Prediction Model (MPM) Sequential Organ Failure Assessment

(SOFA)

Summary

Since each ICU serves a different patient population, each score system must be calibrated in the individual hospital to ensure that the model is applicable.

Outcome of ICU therapy should incorporate not only survival but should also take into account quality of life, morbidity and disability.

Severity scores have no role in clinical decision making for an individual patient .

Summary

Proper implementation of scoring system will help in resources a location and paged utilization

Illness severity scores will never be indicative of absolute irreversibility of disease or impossibility of survival

Summary

The ICU predictive scoring systems require periodic updating, may be inaccurate in patients with certain disease (eg, liver failure, obstetrical diseases, AIDS), and may be limited by lead time bias

THANK YOU

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