Prognostic Tools Compare the Models

Transcription

1 Prognostic Tools Compare the Models 31/07/2008 Dr Ray Viola Mark Corkum 34 Barrie St Kingston, Ontario Canada K7L 3W6 (613) ext

2 Table of Contents Goal... 3 Sample Size... 3 Patients... 4 Setting... 4 Variables... 5 Output... 5 Data Included... 6 Arbitrary Cut-offs... 6 Model Structure Apparent?... 6 Simple Calculation?... 7 Accuracy... 7 Validation... 7 Input Defined?... 8 Patients Defined?... 8 Generalizability... 8 Citations

3 Prognostic Tools Compare the Models The model (10), the Simple & Model for Cancer (1), the Palliative Performance Status () (11), the Palliative Prognostic Score () (21), and the Model Predicting Mortality for Hospitalised Patients with (13) are compared under the following headings: Goal Predict 6 month survival Determine risk of intra-hospital death at the time of hospitalization Determine which variables of functional status predict survival time for terminally ill cancer patients Develop & validate a model that will predict the likelihood of surviving 30 days for terminal cancer patients Develop & validate a model predicting mortality using information available at hospital presentation Sample Size Phase One (derivation) 4301 Phase Two (validation) (retrospective (prospective) 98 Derivation 519 Validation 451 Derivation 2624 Validation

4 Patients Diagnosed with one of COPD, CHF, ESLD, cancer (lung, colon) ARF, MOSF, COMA with expected survival of 50% Cancer patients (non haematological) who died on internal medicine ward of teaching hospital between 1997 & 2000 (retro) and between Dec & Mar (pro). Patient sent from specialised care of primary care physicians and nurses to Home Care Support Team of Madrid s Area 7 with terminal cancer (<6 months survival) Patients admitted to one of 22 Italian cancer centres that were not receiving curative treatment and didn t have myeloma, renal tumours, or haematological neoplasms Community based patients presenting with Framingham heart failure criteria (excluding those developing CHF after admission, transferred from another acute care facility, > 105 years old, non-residents or invalid health cards) Setting 5 US tertiary care adademic cetnres Internal medicine department of teaching hospital Home care support 22 Italian cancer centres Multiple hospitals (rural, urban) in Ontario 4

5 Variables Diagnosis, age, hospital stay, presence of cancer, neurological function, expected survival, various physiological factors ECOG performance status, duration of disease, reason for admission, Hb levels, LDH < 50, HR > 100, RR > 24 Physicians estimate of survival, WBC count, Lymphocyte %, KPS, dyspnea, anorexia Old age, low systolic BP, high RR, high urea nitrogen, hyponatremia, comorbidity Output The output is a graph with the Probability of Survival on the y-axis, and Time (in days) after the report was calculated on the x-axis. The probability for 30, 60, 90, 120, 150, and 180 days is displayed on the graph. Formula outputs the probability with accuracy summarised below Hazard ratios for each parameter not included in a model really 3 Risk Groups: A 30 day survival probability > 70%, B 30-70% & C - < 30% Risk score: Very Low < 60, Low 61-90, Intermediate , High & Very high > 150 5

6 Data Included Readily available data. Many disease specific risk factors were found to have no impact on prognosis due to lack of data Only info readily available in the medical records and clinical parameters deemed relevant were included Socio-demographic, tumour, clinical and functional status variables were included Readily available data including the variables used in the model and a few other treatment related data Candidate variables identified by literature review and by consensus of Canadian expert panel Arbitrary Cut-offs No. The model uses cubic splines No. The model uses cubic splines Yes (e.g. LDH > 378 u/ml, ECOG = 4) Yes (e.g. high lymphocyte % at 8501 cells/mm3) No. This model uses cubic splines. Model Structure Apparent? The mechanism for each variable should be apparent, but the shape and steepness of curve not intuitive The mechanism for each variable should be apparent. The calculation itself is not. Not a model simply a calculation of hazard ratios Yes simply a summation of partial risk scores Point assignment according to various findings 6

7 Simple Calculation? No. Computer input and output Yes. Done either on paper or computer Yes Yes Point scores determined from table Accuracy Area under ROC 0.78 alone, 0.82 when combined with physician estimate Area under ROC of 0.88 in derivation cohort and 0.82 in the validation cohort No ROC provided, but validation studies give corresponding prognoses and confidence intervals No ROC provided; some confidence intervals provided for related data Area under ROC 0.81 at 30-day, 0.78 for 1-year validation arm Validation Phase two is validation; no independent validation Small validation study; no independent validation Several One non-independent validation study Multiple hospitals (rural, urban) in Ontario 7

8 Input Defined? Not clear Clearly defined Clearly defined Clearly defined Clearly defined Patients Defined? Yes (appendix 1 of paper) Poorly. Generality cannot be assumed Yes (table 1 of paper) Yes Yes Generalizability 9 groups (4 models?) account for 20% of hospital mortality Undetermined Undetermined Undetermined Undetermined 8

9 Citations 1) Bozcuk H, Koyuncu E, Yildiz M et al. A simple and accurate prediction model to estimate the intrahospital mortality risk of hospitalised cancer patients. International Journal of Clinical Practice 2004; 58(11): ) Christakis NA, Lamont EB. Extent and determinants of error in doctors' prognoses in terminally ill patients: prospective cohort study. BMJ 2000; 320: ) Clayton JM, Butow PN, Tattersall MHN. When and How to Initiate Discussion About Prognosis and End-Of-Life Issues with Terminally Ill Patients. Journal of Pain and Symptom Management 2005; 30(2): ) Dilemmas and Opportunities. Inaccurate Predictions of Life Expectancy. Arch Intern Med 1988; 148: ) Esmail N, Walker M. How Good is Canadian Healthcare? 2006 Report; An International Comparison of Healthcare Systems. Fraser Institute Digital Publication, ) Forster LE, Lynn J. Predicting Life Span for Applicants to Inpatient Hospice. Arch Intern Med 1988; 148: ) Hagerty RG, Butow PN, Ellis PM, Dimitry S, Tattersall MHN. Communicating prognosis in cancer care: a systematic review of the literature. Annals of Oncology 2005; 16: ) Harrold J, Rickerson E, Carroll JT et al. Is the palliative performance scale a useful predictor of mortality in a heterogeneous hospice population? Journal of Palliative Medicine 2005; 8(3): ) Head B, Ritchie CS, Smoot TM. Prognostication in hospice care: can the palliative performance scale help? Journal of Palliative Medicine 2005; 8(3): ) Knaus WA, Harrell FE, Jr., Lynn J et al. The prognostic model. Objective estimates of survival for seriously ill hospitalized adults. Study to understand prognoses and preferences for outcomes and risks of treatments. Annals of Internal Medicine 1995; 122(3): ) Lau F, Downing GM, Lesperance M, Shaw J, Kuziemsky C. Use of Palliative Performance Scale in end-of-life prognostication. Journal of Palliative Medicine 2006; 9(5): ) Lamont EB. A Demographic and Prognostic Approach to Defining the End of Life. Journal of Palliative Medicine 2005; 8(1): S12-S21. 9

10 13) Lee DS, Austin PC, Rouleau JL, Liu PP, Naimark D, Tu JV. Predicting mortality among patients hospitalized for heart failure: derivation and validation of a clinical model. JAMA 2003; 290(19): ) Lynn J, Teno JM, Harrell FE. Prognostications of Death; Opportunities and Challenges for Clinicians. West J Med 1995; 163: ) Maltoni M, Nanni O, Pirovanco M et al. Successful Validation of the Palliative Prognostic Score in Terminally Ill Cancer Patients 1999; 17(4): ) Maltoni M, Pirovanco M, Nanni O et al. Prognostic Factors in Terminal Cancer Patients. European Journal of Palliative Care 1994; 1(3): ) Miller RJ. Predicting Survival in the Advanced Cancer Patient. Henry Ford Hospice Medicine Journal 1991; 39(2): ) Morita T, Tsunoda J, Inoue S, Chihara S. Validity of the palliative performance scale from a survival perspective. Journal of Pain & Symptom Management 1999; 18(1): ) Olajide O, Hanson L, Usher BM, Qaqish BF, Schwartz R, Bernard S. Validation of the palliative performance scale in the acute tertiary care hospital setting. Journal of Palliative Medicine 2007; 10(1): ) Parkes CM. Accuracy of Predictions of Survival in Later Stages of Cancer. British Medical Journal 1972; 2: ) Pirovano M, Maltoni M, Nanni O, et al: A New Palliative Prognostic Score: A First Step for the Staging of Terminally Ill Cancer Patients. Journal of Pain and Symptom Management 1999; 17: ) Randolph A, Buchner H, Richardson WS, et al. The Users Guides to the Medical Literature: Chapter 10, Prognosis. 2002; ) Reynolds T. Prognostic Models Abound, but How Useful Are They? Annals of Internal Medicine 2001; 135(6): ) Stambler N, Charatan M, Cedarbaum JM. Prognostic Indicators of Survival in ALS. Neurology 1998; 50: ) Toscani F, Brunelli C, Miccinesi G et al. Predicting survival in terminal cancer patients: clinical observation or quality-of-life evaluation? 2005; 19:

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