1. Predictors of Outcomes in Critically-ill patients
Dr: Hatem O.Qutub MD,FCCP,FCCM
Assoc proof Medicine & Critical Care
KFHU / UOD

2. ‘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.

4. 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

5. 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

6. Objectives of predictors
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!

7. What determine ICU prognosis
Diseases spectrum
Personnel types
Methods of monitoring
Admission / discharge criteria's
Resources utilization
Patients allocation

8. 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)

9. Creating a Useful Predicting Instrument
Patient selection / populations
Outcome selection
Variable ( predictor) selection
Data collection
Relating predictors to outcome
Validation
Impact evaluation
Updates

10. 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

11. 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.

12. Visible
Invisible 12

13. Severity of illness scoring systems

14. 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

15. Utilization of scoring systems
Outcome prediction
Clinical research
Quality of care analysis
Benchmarking in (ICU) environment

16. 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

17. 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

18. 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

19. 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

20. 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]

22. First generation APACHE I
knaus~ 1981 ~ USA /2 medical center 805 patients
Consist : 34 physiological variables & preadmission health status
Most 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.

23. Second generation
APACHE II
SAPS I
MPM I

24. Third generation
APACHE III
SAPS II
MPM II

25. Old generation
Overall observations 1-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]

26. Fourth generation APACHE IV SAPS III PMP III They excluded readmission
values are normal when not measured / obtained

27. SAPS III Predictive variables
-  Variables included in the fourth-generation prognostic models
MPM0 III
APACHE IV
SAPS III
Predictive variables
Yes
Age No
Length of hospital stay before ICU admission 8 3
ICU admission source
Type of ICU admission 7 6
Chronic comorbidities
Cardiopulmonary resuscitation before ICU admission
Resuscitation status
Surgical status at ICU admission 5
Anatomical site of surgery
116 10
Reasons for ICU admission/Acute diagnosis
Acute infection at ICU admission
Mechanical ventilation
Vasoactive drug therapy before ICU admission 4
Clinical physiologic variables
Laboratory physiologic variables

28. 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

29. 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

30. Biases and Errors in scoring system
Case max
Data collection
Data entry
Flaws in model development
Validation
Pre-ICU location
Acute diagnosis

31. 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

33. Organ Failure Models

34. 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

35. Criteria Organ failure Heart rate ≥ 54/min
Multiple system organ failure
Criteria
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

36. MSOF Common ones [MODS ,SOFA ,LODS] Continuous scales
SOFA & MODS ~ rang 0 to4(severity based)
Subjective evaluation as result of consensus and literature review

37. Variables included in the calculation of the organ failure scores
MODS
LODS
SOFA
Variable
Organ
Yes
PaO2/FIO2
Respiratory MV
Platelets
Hematology
WBC
Bilirubin
Liver
Prothrombin time
Mean arterial pressure
Cardiovascular
Systolic blood pressure
Heart rate
PAR
Dopamine
Dobutamine
Epinephrine
Norepinephrine
Glasgow coma score
CNS
Creatinine
Renal
Blood urea nitrogen
Urine output

38. 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

40. 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)

41. 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 .

42. 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

43. 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

44. THANK YOU
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