evidence based prognostication peoria 2010 (1)
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Updated version of Prognostication presentation. Not be used as sole basis for any medical decisions. Please talk with your doctor if you have questions about this information.TRANSCRIPT
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– [email protected]
• 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 protected]
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.
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.
References
MRC CRASH Trial Collaborators. Predicting outcome after traumatic brain injury: practical prognostic models based on large cohort of international patients. BMJ. 2008 February 23; 336(7641): 425–429.
Kinzbrunner BM, Weinreb NJ, Merriman MP. Debility, unspecified: a terminal diagnosis. Am J Hosp Palliat Care. 1996 Nov-Dec;13(6):38-44.
Levy DE, Caronna JJ, Singer BH, Lapinski RH, Frydman H, Plum F. Predicting outcome from hypoxic-ischemic coma. Jama. Mar 8 1985;253(10):1420-1426.
Zandbergen EG, de Haan RJ, Stoutenbeek CP, Koelman JH, Hijdra A. Systematic review of early prediction of poor outcome in anoxic-ischaemic coma. Lancet. Dec 5 1998;352(9143):1808-1812.
Booth CM, Boone RH, Tomlinson G, Detsky AS. Is this patient dead, vegetative, or severely neurologically impaired? Assessing outcome for comatose survivors of cardiac arrest. Jama. Feb 18 2004;291(7):870-879.
References
Zandbergen EG, Hijdra A, Koelman JH, et al. Prediction of poor outcome within the first 3 days of postanoxic coma. Neurology. Jan 10 2006;66(1):62-68.
Holloway RG, Benesch CG, Burgin WS, Zentner JB. Prognosis and decision making in severe stroke. Jama. Aug 10 2005;294(6):725-733.
Schonwetter RS, Han B, Small BJ, Martin B, Tope K, Haley WE. Predictors of six-month survival among patients with dementia: an evaluation of hospice Medicare guidelines. Am J Hosp Palliat Care. Mar-Apr 2003;20(2):105-113.
Mitchell SL, Kiely DK, Hamel MB, Park PS, Morris JN, Fries BE. Estimating prognosis for nursing home residents with advanced dementia. Jama. Jun 9 2004;291(22):2734-2740.
Caraceni A, Nanni O, Maltoni M, et al. Impact of delirium on the short term prognosis of advanced cancer patients. Italian Multicenter Study Group on Palliative Care. Cancer. Sep 1 2000;89(5):1145-1149.
Afessa B, Morales IJ, Scanlon PD, Peters SG. Prognostic factors, clinical course, and hospital outcome of patients with chronic obstructive pulmonary disease admitted to an intensive care unit for acute respiratory failure. Crit Care Med. Jul 2002;30(7):1610-1615.
References
Stupp R et al. Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. N Engl J Med. 2005 Mar 10;352(10):987-96.
Athanassiou H et al. Randomized phase II study of temozolomide and radiotherapy compared with radiotherapy alone in newly diagnosed glioblastoma multiforme.J Clin Oncol. 2005 Apr 1;23(10):2372-7.
Mirimanoff RO et al. Radiotherapy and temozolomide for newly diagnosed glioblastoma: recursive partitioning analysis of the EORTC 26981/22981-NCIC CE3 phase III randomized trial. J Clin Oncol. 2006 Jun 1;24(16):2563-9.
Gaspar L et al. Recursive partitioning analysis (RPA) of prognostic factors in three Radiation Therapy Oncology Group (RTOG) brain metastases trials. Int J Radiat Oncol Biol Phys. 1997 Mar 1;37(4):745-51.
References
Ely EW, Shintani A, Truman B, et al. Delirium as a predictor of mortality in mechanically ventilated patients in the intensive care unit. Jama. Apr 14 2004;291(14):1753-1762.
Beddhu S, Bruns FJ, Saul M, Seddon P, Zeidel ML. A simple comorbidity scale predicts clinical outcomes and costs in dialysis patients. Am J Med. Jun 1 2000;108(8):609-613.
Chan JD, Treece PD, Engelberg RA, et al. Narcotic and benzodiazepine use after withdrawal of life support: association with time to death? Chest. Jul 2004;126(1):286-293.
Kollef MH, Ahrens TS, Shannon W. Clinical predictors and outcomes for patients requiring tracheostomy in the intensive care unit. Crit Care Med. Sep 1999;27(9):1714-1720.
Cohen LM, Moss AH, Weisbord SD, Germain MJ. Renal palliative care. J Palliat Med. Aug 2006;9(4):977-992.
Casarett D, Kapo J, Caplan A. Appropriate use of artificial nutrition and hydration--fundamental principles and recommendations. N Engl J Med. Dec 15 2005;353(24):2607-2612.
References
Portenoy RK, Sibirceva U, Smout R, et al. Opioid use and survival at the end of life: a survey of a hospice population. J Pain Symptom Manage. Dec 2006;32(6):532-540.
Kohara H, Ueoka H, Takeyama H, Murakami T, Morita T. Sedation for terminally ill patients with cancer with uncontrollable physical distress. J Palliat Med. Feb 2005;8(1):20-25.
Pirovano M, Maltoni M, Nanni O, et al. A new palliative prognostic score: a first step for the staging of terminally ill cancer patients. Italian Multicenter and Study Group on Palliative Care. J Pain Symptom Manage. Apr 1999;17(4):231-239.
Ely EW, Wheeler AP, Thompson BT, Ancukiewicz M, Steinberg KP, Bernard GR. Recovery rate and prognosis in older persons who develop acute lung injury and the acute respiratory distress syndrome. Ann Intern Med. Jan 1 2002;136(1):25-36.
Glare PA, Eychmueller S, McMahon P. Diagnostic accuracy of the palliative prognostic score in hospitalized patients with advanced cancer. J Clin Oncol. Dec 1 2004;22(23):4823-4828.
References
Virik K, Glare P. Validation of the palliative performance scale for inpatients admitted to a palliative care unit in Sydney, Australia. J Pain Symptom Manage. Jun 2002;23(6):455-457.
Anderson F, Downing GM, Hill J, Casorso L, Lerch N. Palliative performance scale (PPS): a new tool. J Palliat Care. Spring 1996;12(1):5-11.
Morita T, Tsunoda J, Inoue S, Chihara S. Validity of the palliative performance scale from a survival perspective. J Pain Symptom Manage. Jul 1999;18(1):2-3.
Harrold J, Rickerson E, Carroll JT, et al. Is the palliative performance scale a useful predictor of mortality in a heterogeneous hospice population? J Palliat Med. Jun 2005;8(3):503-509.
Lau F, Downing GM, Lesperance M, Shaw J, Kuziemsky C. Use of Palliative Performance Scale in end-of-life prognostication. J Palliat Med. Oct 2006;9(5):1066-1075.
Morita T, Tsunoda J, Inoue S, Chihara S. Improved accuracy of physicians' survival prediction for terminally ill cancer patients using the Palliative Prognostic Index. Palliat Med. Sep 2001;15(5):419-424.
References
Yun YH, Heo DS, Heo BY, Yoo TW, Bae JM, Ahn SH. Development of terminal cancer prognostic score as an index in terminally ill cancer patients. Oncol Rep. Jul-Aug 2001;8(4):795-800.
Lichter I, Hunt E. The last 48 hours of life. J Palliat Care. Winter 1990;6(4):7-15.Nauck F. Symptom control during the last three days of life. European Journal of
Palliative Care. 2001;10:81-84.Conill C. Symptom prevalence in the last week of life. Journal of Pain and
Symptom Management. 1997;21:12-17. Grond S, Zech D, Schug SA, Lynch J, Lehmann KA. Validation of World Health
Organization guidelines for cancer pain relief during the last days and hours of life. J Pain Symptom Manage. Oct 1991;6(7):411-422.
Ellershaw J, Smith C, Overill S, Walker SE, Aldridge J. Care of the dying: setting standards for symptom control in the last 48 hours of life. J Pain Symptom Manage. Jan 2001;21(1):12-17.
Fainsinger R, Miller MJ, Bruera E, Hanson J, Maceachern T. Symptom control during the last week of life on a palliative care unit. J Palliat Care. Spring 1991;7(1):5-11.