This document discusses the importance and methodology of evidence-based prognostication in medical settings, noting its benefits and limitations. It outlines various prognostic tools and models, emphasizes the necessity for accurate and compassionate communication of prognosis to patients, and reviews specific prognostic factors across different diseases. The conclusion reinforces that effective prognostication is a skill that can be improved with appropriate tools and practices.

1. Evidence-Based Prognostication April 2010 Christian Sinclair, MD

2. 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 [email_address]

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

4. Overview Define the benefits and limitations of open frequent prognostication Understand theories for accurate formulation of prognostication Apply prognostic information to clinical scenarios Discover tools for more accurate prognostication

5. Medical Triad Diagnosis Prognosis Therapy

6. A prognosis is an estimation of possible future outcomes of a treatment or a disease process…

7. What is Prognostication? It is not… Fortune Telling Playing God Precognition Divination

8. … founded upon a combination of personal experience, statistics, and validated models

9. Medical Prognostication Role for prognostication Clinical implications Relationship implications Symbolic implications Contextual issues Administrative issues Cost-benefit analysis Futility/bioethics Christakis, Death Foretold Christakis Death Foretold 1999 Ethics Policy Research Academics Clinical Prognosis

10. 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”

11. Theory for Prognostic Model Clinical Findings Individual Prognosis General Prognosis Diagnosis Pathological Findings Psychosocial Factors Co-morbidities Therapy Adapted from Vigano 2000

12. Print separately with above 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

13. 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

14. 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

15. NHPCO Guideline Study Fox 1999

16. 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

17. Comparison of SUPPORT and MD survival estimates

18. 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

19. Prognostic Scales/Tools Palliative Prognostic (PaP) Score Palliative Performance Scale Palliative Prognostic Index Terminal Cancer Prognostic Score Poor Prognostic Indicator Charlson Co-morbidity Index

20. Palliative Prognostic Score Developed in Italy Validated in cancer patients Outpatient and inpatient Used for short-term survival Pirovano 1999

21. Palliative Prognostic Score Pirovano 1999, Glare 2004

22. Palliative Performance Scale Quick classification for functional status Based off Karnofsky Used widely in the Hospice & Palliative Care literature/field

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

24. PPS in Heterogeneous Population Harold 2005

25. PPS in Heterogeneous Population Harold 2005

26. PPS in Heterogeneous Population Harold 2005 Cancer = Black Non-Cancer = Gray

27. PPS in Prognostication Lau 2006 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

28. Palliative Prognostic Index Morita 2001

29. Terminal Cancer Prognostic (TCP) Yun 2001

30. The Future of Prognostication Seattle Heart Failure Model Adjuvant Online HD Mortality Predictor Perception of prognostication as a skill

31. PubMed MESH Search with Limits: English, Human, Core Clinical Journals (Jan 2008) Therapy Diagnosis Prognosis

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

33. www.adjuvantonline.com

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

37. 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

38. Mortality In Liver Disease Mortality thoroughly studied Organ allocation for liver transplant According to “sickest first” Not location Not waiting times

39. MELD Score M odel for E nd stage L iver D isease 3 factors Bilirubin INR Creatinine 10 { 0.957 Ln(Scr) + 0.378 Ln(Tbil) + 1.12 Ln(INR) + 0.643 } Online calculator (Mayo Clinic)

41. Three Month Mortality in Hospitalized Patients MELD Score </= 9 10-19 20-29 20-39 >/= 40 Death Rate 4% 27% 76% 83% 100% Kamath 2001

42. 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

43. Prognostic Factors in 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

44. Prognosis in Lung Cancer Only 15% of all lung cancer patients are alive 5 years after diagnosis NCCN Guidelines 2006

45. 5-Year Survival Non-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

46. 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

47. 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

48. 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

49. 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%

50. 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

51. Brain Metastases Survival Treatment Survival No primary treatment 1 month Steroids 2-3 months Whole Brian Radiation 3-6 months Surgery/SRS 6-12 months

52. Brain Mets Prognosis Median Survival Group 1 7.1 months Group 2 4.2 months Group 3 2.3 months Gaspar 1997

53. 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)

54. 5-Year Cancer Survival Rates ACS 2007 Guidelines 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

55. 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

56. 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

57. 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

58. 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

59. 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

60. Predicting Outcome From 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

61. 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

62. 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

63. Variables Predicting Poor 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

64. 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

65. Hypoxic-Ischemic Coma Post-Cardiac Arrest 11 studies 1914 patients Determine precision and accuracy of the clinical exam Poor neurological outcome was 77% Booth 2004

66. Hypoxic-Ischemic Coma Post-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

67. Poor Prognostic Factors In 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

68. 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

69. 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

70. 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

71. 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

72. Dementia No statistical correlation: Between guidelines or components and 6 month survival Statistically significant: Greater age Greater functional impairment Anorexia Schonwetter 2003, Mitchell 2004

73. Dementia – MDS-12 ADL > 28 = 1.9 Male =1.9 Cancer = 1.7 O 2 in 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

74. 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%

75. 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

76. 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

77. Mechanical Ventilation 902 ICU Vent patients Young vs. old (70y cut-off) 28d survival rate < 70yo – 75% >70 yo – 50% Ely 2002

78. 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

79. 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

80. 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

81. 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

82. 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

83. 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

84. 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

85. Opioid Use But…. None of them explained more than 10% of the variation Portenoy 2006

86. Recommended Readings Death Foretold: Prophecy and Prognosis in Medical Care by Nicholas A. Christakis The Terminal Phase, Chapter 18. Oxford Textbook of Palliative Medicine , 3 rd ed. 2004 Predicting survival in patients with advanced disease, Chapter 2.4. Oxford Textbook of Palliative Medicine , 3 rd ed. 2004 Storm Watchers by John D. Cox Stone P, Lund S. Predicting prognosis in patietns with advanced cancer . Ann Oncol 2006.

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89. References 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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