When Children Die Improving Palliative and End-of-Life Care for Children and Their Families (2003) / Chapter Skim
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Appendix B: Prognostication Scores
Appendix B: Prognostication Scores
Pages 449-475
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From page 449... ...
The interest in prognostication tools as potential decision making aids stems from the need to improve prognostic estimates under the conditions of limited clinician experience and limited scientific evidence. Contemporary developments mainly driven by concerns for adult pa~Professor of Pediatrics, George Washington University School of Medicine and Health Sciences; Division Chief, Critical Care Medicine, Children's National Medicine Center; and Executive Director, Center for Hospital-Based Specialties, Children's National Medical Center.
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From page 450... ...
The following discussion considers the preparedness of physicians to undertake complex life and death decisions, the elements of quantitative prognostic tools and scores, the accuracy of clinicians' estimates, and issues in using prognostic scores to guide decisions about individual patients. THE LACK OF PREPAREDNESS Despite the interest in prognostication and its application to end-of-life decisions, educational experiences and resources for health care profession
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From page 451... ...
found that while internists frequently encounter these issues, about 60% find it stressful to assess prognosis explicitly, about 45% wait to discuss prognosis until the patient brings it up, 90% avoid being specific, and almost 60% report inadequate training.9 Similar data are not available for pediatricians, but it is likely that general pediatricians would demonstrate even less familiarity and comfort with end of life prognostication since relatively fewer children die, those that do die are generally cared for by specialists, and childhood death may be difficult for many pediatricians to face. What little information we have about pediatric end-of-life decision making suggests that there is substantial variability in how pediatricians estimate prognosis in similar situations.
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From page 452... ...
Unfortunately, the use of physical disability and/or cognitive impairment as outcomes in pediatrics has been severely hindered by the lack of summary measures describing disability states in a manner that can be used as outcomes in formal prognostication methods. While extensive neuropsychological testing can define functional states, it is time consuming, expensive, and specialized, limiting its use in developing prognostication scores that may require thousands of patients.
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From page 453... ...
Economic outcome indicators have been popular outcomes in the medical literature because they are easily measured and appeal to those concerned about health care costs, especially costs for care that is futile or of very low probable benefit. While this emphasis has immediate appeal, efforts to define futility and to link it to high medical costs or the need for rationing have not produced acceptance or consensus among clinicians and policymakers.~4 For example, one pediatric ICU study using very broad definitions of medical futility found that relatively small amounts of resources were used for "futile" PICU cared Development and Validation of Prognostication Health professionals, administrators, and policy makers interested in the credible and appropriate use of prognostication scores and methods need to understand, in at least a general fashion, several important issues in the development and validation of scores.
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; and multivariate linear or quadratic discriminant function analysis is most often used to predict categorical outcomes such as diagnosis. Care must be taken when developing a score or risk prediction mode!
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Validation in an external sample is the most stringent test and should be performed before any prognostic too! is used to guide decisions about individual patients.
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A mortality prediction mode! developed to guide decisions about individual patients needs to meet the most rigorous standards of thorough investigation, which have been noted.
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A recent systematic review found that CDSS's can enhance clinical performance for such activities as drug dosing and preventive care, but there have not been any positive impacts on clinical outcomes.26 However, the potential of CDSS systems is tremendous, especially if appropriately large data bases were developed and maintained. For example, if huge data bases could be collected and maintained, analyses could be tailored to the individual patient.
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From page 458... ...
A recent analysis compared multiple neonatal severity scores in a low birth weight infant cohort from 1994-1997.39 None of the neonatal severity scores performed well, implying either deficiencies in their development, or advances in neonatology that have made them out of date. Most important, however, the analyses demonstrated that birth weight was still a very powerful outcome predictor if its predictive potential was accounted for with modern statistical techniques.
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From page 459... ...
of physiologic variables, and descriptive and diagnostic data such as CPR status, operative status, and the presence of importance diagnoses such as cancer. PRISM III has been used for 4 national studies and is routinely used in over 50 PICUs nationally and internationally to evaluate quality of care as well as case-mix adjust administrative data.
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From page 460... ...
The "representativeness heuristic" neither accounts for a suitable number of variables nor incorporates the spectrum of possible clinical outcomes. Physicians' accuracy in estimating mortality risk for patients admitted to ICUs has been variable.55~58 Generally, clinically experienced physicians perform better than less experienced physicians but there are even discrepancies between physicians of equal clinical experience.59 Physician prediction performance may also depend upon the patient's disease or severity of iliness.60 Importantly, there are no data about how well physicians can predict the probability of survival for seriously ill patients at the time that triage decisions must be made, for example, when a patient presents to the emergency department.
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From page 461... ...
The ROC for physicians was 0.85, and for nurses was 0.93, equivalent to the SNAP score of 0.94. Only one study has evaluated the prediction performance of physicians at different experience levels and nurses in a PICU and compared the performance of these health care professionals with the same statistical methods used to evaluate prognostication scores.64 In this study of 642 patients of whom 36 patients died, predictions were made after the first 10 hours and 24 hours of care by bedside nurses, residents, critical care fellows, and critical care attendings.
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, false negatives (incorrectly predicted survivors/total survivors) , positive predictive value (correctly predicted deaths/total predicted deaths)
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False positives = incorrectly predicted deaths/total deaths. False negatives = incorrectly predicted survivors/total survivors.
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UTILITY OF PROGNOSTICATION SCORES FOR INDIVIDUAL PATIENTS Rationale There has been an increased interest in the use of outcome prediction models in providing decision support for individual patients.68~75 In general, these applications have focused on mortality prediction and on resource use for individual patients too healthy to benefit76 or too sick to benefit77~80 from intensive care services. Early identification of patients in the ICU for whom further curative, life-prolonging, or life-sustaining therapies are futile or very unlikely to be beneficial could help with difficult decisions, obviate undue patient suffering, and help to direct scarce resources to more cost effective uses.81 82 Most individuals and societies have cautiously approached the issue of quantitative prognostication for individual patients with such scores as
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Studies of Utility in Individuals Scoring systems may help identify "potentially ineffective care," or isolate patients admitted to the ICU with a negligible chance of survival in whom further care would not be beneficial.8687 The utility of objective prognostication scores will depend in large part on the size of the data base, the number of patients in relevant sub-samples, the confidence level or clinical certainty required by physicians for decision making, and the predicted clinical outcome range given the required certainty level, as well as the intended application. Among pediatric measures, only the PRISM score has received in depth evaluation for use in individuals.
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The statistical concepts of confidence intervals and confidence levels must be thoroughly understood in their relationship to the clinical concepts of survival ranges and clinical certainty if they are to help guide for individuals. In a statistical sense, if a mode!
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In statistical analyses, the trade-off between confidence interval width and confidence level is clear. The analogy to the outcome ranges and clinical certainty, while close, is not always logical.
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For this perspective the authors queried the PRISM data base with the "same" question, but from a health policy perspective: "What is the maximum error rate of a health care policy which limits therapies for patients with PRISM III scores exceeding a very high threshold, and how do these maximums (based on confidence intervals or estimates of survival ranges) change as the confidence levels (as estimates of clinical certainty)
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From page 469... ...
Even in academic neonatal and pediatric ICUs where staff feels the most comfortable with end-of-life decisions and where experience is maximized because of the number of deaths, physicians and nurses make substantial errors in predicting death based on subjective judgment. The false positive rate for predicting death ranges from approximately 25% to 50°/0 in sickest patient groups, clearly a worrisome error rate.
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From page 470... ...
Third, it is not clear that prognostication scores will fit comfortably into physician-patient relationships. The relationship of patient and physician varies greatly depending on the characteristics of individual physicians and individual patients or families.94 Patient values may range from fixed to changeable, from harmonious to conflicting.
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From page 471... ...
In any case, the effective use of prognostication scores will remain limited until huge data bases can be collected that have a sufficient number of patients who can be matched or approximately matched to the individual patient. We will need our largest collective experience to serve the individual in issues of life and death decision making.
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Roberson PK, et al. Relationship of pediatric overall performance category and pediatric cerebral performance category scores at pediatric intensive care unit discharge with outcome measures collected at hospital discharge and 1- and 6-month followup assessments.
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Crit Care Med 1996;24:743-752. 42 Pediatric intensive care unit evaluations.
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Crit Care Med 1989;8:827-833. 60 Perkins HS, Jonsen AR, Epstein WV.
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Parental perspectives on end-of-life care in the pediatric intensive care unit. Crit Care Medicine 2002;30:226-231.
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