Evidence-Based PrognosticationApril 2010

Christian Sinclair, MD

Contributors

• Michelle Affield, MD– Fellow, Univ of Kansas – Hem/Onc FellowKansas City Hospice &

Palliative Care, Kansas City, MO• Michael Salacz, MD

– Saint Luke’s Hospital, Kansas City, MO– Assistant Professor, University of Missouri, Kansas City, MO– msalacz@saint-lukes.org

• Christian Sinclair, MD– Assoc Fellowship Director & Assoc. Med. Dir., Kansas City Hospice

& Palliative Care, KC, MO– Medical Director, Palliative Care Team, Providence Medical Center,

Kansas City, KS– csinclair@kchospice.org

Prognosis Links

www.pallimed.org

http://www.pallimed.org/2007/05/prognosis-links.html

Overview

1. Define the benefits and limitations of open frequent prognostication

2. Understand theories for accurate formulation of prognostication

3. Apply prognostic information to clinical scenarios

4. Discover tools for more accurate prognostication

Medical Triad

Diagnosis

PrognosisTherapy

What is Prognostication?

• It is not…– Fortune Telling– Playing God– Precognition– Divination

…founded upon a combination of personal experience, statistics, and

validated models

Ethics

Policy Research

Academics

Clinical

Prognosis

Two Parts to Prognostication

• Formulation (Foreseeing)– Anticipated vs. true

• Communication (Foretelling)– Compassionate– To the patient– As much as they want to hear– Many articles about “Breaking bad news”

Theory for Prognostic Model

Clinical Findings

IndividualPrognosis

GeneralPrognosisDiagnosis

PathologicalFindings

PsychosocialFactors

Co-morbiditiesTherapy

Adapted from Vigano 2000

Advantages•Flexible•Incorporates multiple variables•May be aided by models•Immediate access•Ease of communication•? accuracy vs. modeling

Clinician’s Prognosis

Validated Models

Disadvantages•? Accuracy•? Frequency•“Gut feeling”•Open to multiple biases

•Recall bias•Anchoring bias

•Less oversight•Difficulty in communication

Advantages•Greater accuracy•Ability to evaluate efficacy•More objective•Can compare similar cases

Disadvantages•? Accuracy to your case•? Applies to groups not individuals•Models the past•Time lag•Not integrated•Different biases

Error

• Is the error random?

• Does a measurable bias exist?

• In what direction does a bias exist?

• What is the magnitude of the error?– Few studies

• MD’s are frequently and largely inaccurate

• But lack describing a source of the error

Life Expectancy - 1900

• 47.3 years (both sexes, all races)

• Caucasian:

All Male Female

47.6 46.6 48.7

• African-American:

All Male Female

33.0 32.5 33.5

National Center for Health Statistics

NHPCO Guideline Study

Fox 1999

NHPCO Guideline Study

Fox 1999

Narrow Inclusion Criteria, n=19

Broad Inclusion Criteria,n=923

Intermediate Inclusion Criteria, n=300

Survived to Hospital Discharge, n=2607

General Findings

• Repeated estimates may be more accurate

• Possibly more accurate as death is near

• Clinician experience may increase accuracy

• Discipline/specialty may not matter

• Second opinion effect

Prognostic Scales/Tools

• Palliative Prognostic (PaP) Score

• Palliative Performance Scale

• Palliative Prognostic Index

• Terminal Cancer Prognostic Score

• Poor Prognostic Indicator

• Charlson Co-morbidity Index

Palliative Prognostic Score

• Developed in Italy

• Validated in cancer patients– Outpatient and inpatient

• Used for short-term survival

Pirovano 1999

Palliative Prognostic Score

Pirovano 1999, Glare 2004

Palliative Performance Scale

• Quick classification for functional status

• Based off Karnofsky

• Used widely in the Hospice & Palliative Care literature/field

Image from http://www.victoriahospice.org/pdfs/PPSv2.pdf

PPS in Heterogeneous Population

Harold 2005

PPS in Heterogeneous Population

Harold 2005

PPS in Heterogeneous Population

Harold 2005

Cancer = BlackNon-Cancer = Gray

PPS in Prognostication

PPS Mean Median Range

60 64 40 6-348

50 51 27 1-287

40 36 17 1-347

30 18 9 1-295

20 6 2 1-81

10 2 1 1-12

Lau 2006

Palliative Prognostic Index

Morita 2001

Terminal Cancer Prognostic (TCP)

Yun 2001

The Future of Prognostication

• Seattle Heart Failure Model

• Adjuvant Online

• HD Mortality Predictor

• Perception of prognostication as a skill

PubMed MESH Search with Limits: English, Human, Core Clinical Journals (Jan 2008)

Therapy

Diagnosis

Prognosis

http://depts.washington.edu/shfm/index.php

www.adjuvantonline.com

REFERENCE:Cohen et al. Predicting Six-Month Mortality for Patients who are on Maintenance HemodialysisClin J Am Soc Nephrol. 2009 Dec 3

Min: Min. Hrs Dys Wks Mos.

Avg: Min. Hrs Dys Wks Mos.

Max: Min. Hrs Dys Wks Mos.

Prognosis:

Disc. w /

Pt Fam

Conclusions

• Physicians have a duty to prognosticate– Accurately, openly, dynamically

• Prognostication can be scientifically based

• Tools exist to aid clinical prognostication

• Prognostication is a skill that can be honed

Mortality In Liver Disease

• Mortality thoroughly studied

• Organ allocation for liver transplant

• According to “sickest first”

• Not location

• Not waiting times

MELD Score

• Model for End stage Liver Disease

• 3 factors– Bilirubin– INR– Creatinine

• 10 {0.957 Ln(Scr) + 0.378 Ln(Tbil) + 1.12 Ln(INR) + 0.643}

• Online calculator (Mayo Clinic)

Three Month Mortalityin Hospitalized Patients

• MELD Score

</= 9

10-19

20-29

20-39

>/= 40

• Death Rate

4%

27%

76%

83%

100%

Kamath 2001

Additional Prognostic Factors

• Low serum sodium (MELD-Na) ability to predict 3 & 6 month mortality

• Na <126– independent predictor of wait-list mortality

Biggins 2005, Ruf 2005

Prognostic Factorsin Lung Cancer

• Staging• Performance status• Weight loss• Gender• Tumor histology

– small cell associated with severe disease and debility

• Suppressor oncogene mutations – p53• Oncogene overexpression – c-myc, K-ras, erb-B2

NCCN Guidelines 2006

Prognosis in Lung Cancer

• Only 15% of all lung cancer patients are alive 5 years after diagnosis

NCCN Guidelines 2006

5-Year SurvivalNon-Small Cell Lung Cancer

• Stage IA 67%

• Stage IB 57%

• Stage IIA 55%

• Stage IIB 38-39%

• Stage IIIA 23-25%

• Stage IIIB 3-7%

• Stage IV 1%

NCCN Guidelines 2006

Survival In Small Cell Lung Cancer

• Limited Stage– Median Survival 15-18 months– 2-Year Survival 30-40%– 5-Year Survival 10-15%

• Extensive Stage– Median Survival 9-10 months– 2-Year Survival < 10%– 5-Year Survival rarely reported

Jahan 2002

Malignant Pleural Effusion

• Indicative of poor prognosis– Especially poor if secondary to:

• GI, lung, or ovarian

• Survival– Average Range 3-6 months– Median 4 months– 65% mortality in 3 months– 80% mortality in 6 months

Sahn 2001

Glioma (Astrocytoma) Survival

• Glioblastoma = 50% of all gliomas

Tumor Type 5-Yr (%) 10-Yr (%) Median (y)

Pliocytic (1) 91 89

Diffuse (2) 47 39 5

Anaplastic (3) 29 22 2-3

Glioblastoma (4) 3 2 1

Results

EORTC Greek

RT RT+TMZ RT RT+TMZ

Median Survival

12.1m 14.6m 7.7m 13.4m

% 12m Survival

50% 61% 16% 56%

% 18m Survival

21% 39% 5% 25%

Median Survival

• Class III– 17 months– 32% at 2 yr

• Class IV– 15 months – 19% at 2 yr

• Class V– 10 months– 11% at 2 yr

Mirimanoff 2006

Brain Metastases Survival

Treatment SurvivalNo primary treatment

1 month

Steroids 2-3 months

Whole Brian Radiation

3-6 months

Surgery/SRS 6-12 months

Brain Mets Prognosis

• Median Survival– Group 1

• 7.1 months

– Group 2• 4.2 months

– Group 3• 2.3 months

Gaspar 1997

Prostate Cancer

• Gleason Score

• PSA Level

American Cancer Society, www.cancerresearch.uk

Stage Description 5-Yr Survival

1 Small local 98%

2 Large local 65%

3 Outside prostate 60%

4 Bladder, bone or LN 30% (mean 2y)

5-Year Cancer Survival Rates

 All

Stages Local Reg Distant

  % % % %

Breast 89 98 83 26

Colon 64 90 68 10

Esophagus 16 34 17 3

Kidney 66 90 62 10

Larynx 64 84 50 14

Liver 11 22 7 3

Lung 15 49 16 2

Melanoma 92 99 65 15

Oropharynx 59 81 52 26

 All

Stages Local Reg Distant

  % % % %

Ovary 45 93 69 30

Pancreas 5 20 8 2

Prostate 99.9 100 -- 33

Stomach 24 62 22 3

Testis 96 99.5 96 70

Thyroid 97 99.7 97 56

Bladder 81 94 46 6

Cervix 72 92 56 15

Uterine 83 96 67 23

ACS 2007 Guidelines

Amyotrophic Lateral Sclerosis

• NHPCO guidelines available (not validated)

• Event based decline model– Loss of Ambulation– Lower vital capacity = vent support

• Older age = higher mortality

• Bulbar signs = higher mortality– Median time (Dx->death) = 20 months

Zoccolella, S et al. 2008

Amyotrophic Lateral Sclerosis

• Median survival from first symptoms– 28 months

• Median survival from ALS diagnosis– 16 months

• 4-year survival rate 30%

• No validated prognostic tools

Zoccolella, S et al. 2008

Trauma

• Multiple prognostic tools– Traumatic Brain Injury – Online

• http://www.crash2.lshtm.ac.uk/Risk%20calculator/index.html• Risk of 14 d mortality and unfavorable 6 month outcome• Based on:

– Country– Age– GCS– Pupils– Extracranial injury– CT Scan findings

MRC CRASH Trial Collaborators, 2008

Predicting Death From Debility

• No easy method• International Classification of Functioning,

Disability and Health– Body Functions & Structures– Activities and Participation– Environmental Factors– Personal Factors

• Palliative Performance Scale

Kinzbrunner 1996

Congestive Heart Failure

• 2 Prognostic Tools available– EFFECT– Seattle Heart Failure Model

• NHPCO Criteria Available

• Event based prediction models

• Sudden death/arrythmias confound most predictions

Predicting OutcomeFrom Hypoxic-Ischemic Coma

• First comprehensive multivariate approach

• Newly constructed, empirically derived guidelines

• First few days after a cardiac arrest or similar global hypoxic-ischemic insult

• Good vs. poor outcome

Levy 1985

Signs Related to ± Recovery

• 0/52 patients initially lacking pupillary reflex ever became independent

• At 3 days– absent or posturing motor responses were

incompatible with future independence

• At initial exam– most favorable sign - incomprehensible speech

Levy 1985

Hypoxic-Ischemic Coma

• At day 1:– Confused or inappropriate speech– Orienting spontaneous eye movements– Normal OC or OV responses– Obedience to commands

• Each of the above associated with at least 50% chance of gaining independence

Levy 1985

Variables PredictingPoor Outcome

• 100% specific in all studies (no good outcome if factors were present)– Absence of pupillary light reflex on the day 3

– Absence of motor response to pain on the day 3

– Bilateral absence of cortical response to median nerve SSEP (somatosensory evoked potential) < 1 week

• One variable was 100% specific in 5/6 studies– Burst-suppression or iso-electric pattern on EEG

within the first week

Zandbergen 1998

Cardiac Arrest As Cause of Coma

• Survival for pre-hospital cardiac arrest– 2 to 33%

• Survival for inpatient cardiac arrest– 0 to 29%

• Meaningful neurological recovery– 10-30%

Booth 2004

Hypoxic-Ischemic ComaPost-Cardiac Arrest

• 11 studies

• 1914 patients

• Determine precision and accuracy of the clinical exam

• Poor neurological outcome was 77%

Booth 2004

Hypoxic-Ischemic ComaPost-Cardiac Arrest

• No clinical findings– Strongly predicted good neurological outcome

• No pupillary or corneal reflex at 24 hours and no motor response at 72 hours– extremely small chance of neurologic recovery

• No clinical signs immediately after cardiac arrest accurately predicted outcome

Booth 2004

Poor Prognostic FactorsIn Severe Stroke

• Most powerful predictors of death and poor outcome– Persistent coma– Absent pupillary or corneal reflexes at day 2 or 3

• Further variables associated with poor outcome– Co-morbidities– Midline shift– Fever

• Poor outcome specifically in hemorrhagic stroke– Volume of blood and intraventricular hemorrhage– Hydrocephalus– Hypertension

Holloway 2005

Favorable Prognostic Factors In Severe Stroke

• More favorable outcome (both types)– Intubation for seizure or pulmonary reason– Younger age– Minimal co-morbidities– Spouse at home– Early neurological recovery– Lower body temp

Holloway 2005

PEG Tube

• In patients with stroke who required PEG tube– 6 month mortality is nearly 50%– Mortality increases to 80% by 3 years– 78% who survived to 6 months had severe

disability

Holloway 2005

Tracheostomy

• Of patients who required tracheostomy and survived 1 year:– 18% had minimal or no disability– 26% had moderate disability– 56% had severe disability

Holloway 2005

Stroke Syndromes Associated With Poor Outcome

• Higher mortality– Pontine hemorrhage with hyperthermia– Basilar artery occlusion with coma and apnea

• Severe disability– Large MCA infarcts– Pontine strokes resulting in locked-in syndrome

Holloway 2005

Dementia

• No statistical correlation:• Between guidelines or components and 6 month

survival

• Statistically significant:– Greater age– Greater functional impairment– Anorexia

Schonwetter 2003, Mitchell 2004

Dementia – MDS-12

• ADL > 28 = 1.9• Male =1.9• Cancer = 1.7

• O2in last 14d = 1.6

• CHF =1.6• SOA = 1.5

Total score = 0-19

• <25% meals = 1.5• Unstable med cond = 1.5• Bowel incont = 1.5• Bedfast = 1.5• 83yo+ = 1.4• Asleep >50% = 1.4

Mitchell 2004

Dementia - MDS-12

AUROC for >6 (0.64) was better than FAST 7c (0.51)Mitchell 2004

Total Risk

Score

Mortality Estimate @ 6m

0 9%

1-2 10%

3-5 23%

6-8 40%

9-11 57%

>12 70%

Delirium

• 109/393 (28%) palliative care patients

• Confusion Assessment Method (CAM)

• Median survival (95%CI)– Delirium – 21d (16-27)– No Delirium – 39d (33-49)

• 70% accuracy for 30d survival– Delirium + PaP

Caraceni 2000

ICU Admission With COPD

• COPD– 61% required invasive mechanical ventilation– Expected hospital mortality – 30%– Actual hospital mortality – 15%– APACHE-II and # of organ failures

• Independent predictors of hospital outcome

Afessa 2002

Mechanical Ventilation

• 902 ICU Vent patients

• Young vs. old (70y cut-off)– 28d survival rate– < 70yo – 75%– >70 yo – 50%

Ely 2002

Delirium & Ventilation

• Mechanical ventilation– Higher 6-month mortality rates

• 34% vs. 15%, P =.03

• Spent 10 days longer in the hospital (P<.001)

Ely 2004

Ventilator Withdrawal

• 75 heterogeneous ventilated patients• Median survival (range)

– 35 min (1-890min)

• Average meds– 16 mg/h opioid– 7.5 mg/h benzodiazepine

• Every 1mg increase in benzo…– 13 min longer survival (p=0.015)

Chan 2004

Tracheostomy

• 521 patients in ICU requiring mech vent

• 51 (10%) received trach

• Mortality less with trach– 14% vs. 27% (p=0.48)

• Longer vent and hospitalization

• 44 survivors of hospital– 86% alive 30d post hospital

Kollef 1999

Chronic Kidney Disease

• Stage 5 (<15mL/min)– 1 year survival – 80% (>65y = 65%)– 2 year survival – 65%– 5 year survival – 38%

• Multiple independent predictors– Albumin– Functional status

Beddhu 2000

Acute Renal Failure/HD Withdrawal

• In the ICU– Mortality 50-65%

• Septic– Mortality – 75%

• Bone Marrow Transplant– Mortality – 85%

• Range 1-20d– Mean 8d

Cohen 2006

Artificial Nutrition & Hydration

• Improved survival– PVS– Extreme short-bowel syndrome– Bulbar amyotrophic lateral sclerosis.– Acute phase of a stroke or head injury – Patients receiving short-term critical care

• Observational data lacking on survival after W/D of artificial nutrition and/or hydration

Casarett 2005

Opioid Use

• National Hospice Outcomes Project– PoPCRN coordinated study

• 13 hospices, 1300+ patients

• Significant association with shorter survival:• Higher opioid dose

• Cancer diagnosis

• Unresponsiveness

• Pain of <5 on a 0-10 scale

Portenoy 2006

Opioid Use

• But….– None of them explained more than 10% of the

variation

Portenoy 2006

Recommended Readings

• Death Foretold: Prophecy and Prognosis in Medical Care by Nicholas A. Christakis

• The Terminal Phase, Chapter 18. Oxford Textbook of Palliative Medicine, 3rd ed. 2004

• Predicting survival in patients with advanced disease, Chapter 2.4. Oxford Textbook of Palliative Medicine, 3rd ed. 2004

• Storm Watchers by John D. Cox• Stone P, Lund S. Predicting prognosis in patietns

with advanced cancer. Ann Oncol 2006.

References

Vigano A, Dorgan M, Buckingham J, Bruera E, Suarez-Almazor ME. Survival prediction in terminal cancer patients: a systematic review of the medical literature. Palliat Med. Sep 2000;14(5):363-374.

Christakis N. Death Foretold: Prophecy and Prognosis in Medical Care. Chicago: University of Chicago Press; 1999.

Parkes CM. Accuracy of predictions of survival in later stages of cancer. Br Med J. Apr 1 1972;2(5804):29-31.

Christakis NA, Lamont EB. Extent and determinants of error in doctors' prognoses in terminally ill patients: prospective cohort study. Bmj. Feb 19 2000;320(7233):469-472.

Detsky AS, Stricker SC, Mulley AG, Thibault GE. Prognosis, survival, and the expenditure of hospital resources for patients in an intensive-care unit. N Engl J Med. Sep 17 1981;305(12):667-672.

Glare P, Virik K, Jones M, et al. A systematic review of physicians' survival predictions in terminally ill cancer patients. Bmj. Jul 26 2003;327(7408):195.

References

Fox E, Landrum-McNiff K, Zhong Z, Dawson NV, Wu AW, Lynn J. Evaluation of prognostic criteria for determining hospice eligibility in patients with advanced lung, heart, or liver disease. SUPPORT Investigators. Study to Understand Prognoses and Preferences for Outcomes and Risks of Treatments. Jama. Nov 3 1999;282(17):1638-1645.

Kamath PS, Wiesner RH, Malinchoc M, et al. A model to predict survival in patients with end-stage liver disease. Hepatology. Feb 2001;33(2):464-470.

Heuman DM, Abou-Assi SG, Habib A, et al. Persistent ascites and low serum sodium identify patients with cirrhosis and low MELD scores who are at high risk for early death. Hepatology. Oct 2004;40(4):802-810.

Biggins SW, Rodriguez HJ, Bacchetti P, Bass NM, Roberts JP, Terrault NA. Serum sodium predicts mortality in patients listed for liver transplantation. Hepatology. Jan 2005;41(1):32-39.

Ruf AE, Kremers WK, Chavez LL, Descalzi VI, Podesta LG, Villamil FG. Addition of serum sodium into the MELD score predicts waiting list mortality better than MELD alone. Liver Transpl. Mar 2005;11(3):336-343.

Cardenas A. Hepatorenal syndrome: a dreaded complication of end-stage liver disease. Am J Gastroenterol. Feb 2005;100(2):460-467.

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Medical Guidelines for Determining Prognosis in Selected Non Cancer Diseases: National Hospice and Palliative Care Organization; 1996.

National Comprehensive Cancer Network. NCCN Guidelines; 2006.Jahan T. Small Cell Lung Cancer.

http://www.cancersupportivecare.com/smallcell.html. Accessed February 01, 2007, 2007.

Sahn SA. Malignant pleural effusions. Semin Respir Crit Care Med. Dec 2001;22(6):607-616.

Zoccolella, S et al. for the SLAP Registry. Analysis of survival and prognostic factors in amyotrophic lateral sclerosis: a population based study. J Neurol Neurosurg Psychiatry. Volume 79(1), January 2008, pp 33-7.

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References

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Holloway RG, Benesch CG, Burgin WS, Zentner JB. Prognosis and decision making in severe stroke. Jama. Aug 10 2005;294(6):725-733.

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