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
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