Predictors of Outcomes in Critically-ill patients Dr: Hatem O.Qutub MD,FCCP,FCCM Assoc 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 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.

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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 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.

Second generation APACHE II SAPS I MPM I

Third generation APACHE III SAPS II MPM II

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]

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

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

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

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

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 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

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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