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INTRODUCTION

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Caring for a child with a chronic illness from which he or she is not likely to recover is one of the most difficult challenges we face as healthcare providers. Children with medical complexity (CMC) comprise an increasing proportion of the pediatric population, and although the overall number of pediatric deaths has declined recently in the United States, this population is living longer with numerous and unique healthcare needs. CMC account for a large percentage of technology utilization in pediatrics as well as inpatient hospitalizations. When hospitalized, they face a much higher risk of mortality than non-CMC pediatric patients. Given the increasing disease burden in and the fragility of CMC, there are palliative care issues and needs unique to this population that warrant an in-depth discussion.

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Although a multitude of descriptors and definitions exist for and within this patient population, this chapter will exclusively use the CMC designation. For a discussion of these varying terminologies, please refer to Section 9 of this textbook titled “Children with Medical Complexity.”

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PROGNOSTICATION IN CHILDREN WITH MEDICAL COMPLEXITY

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The term prognosis comes from the Greek meaning “for knowledge” and is used in medicine to communicate expected disease course among members of the healthcare team or to the patient and family, often for decision-making and planning purposes. Prognostication is always a difficult task to perform and difficult to communicate, but it is especially challenging in CMC.

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CHALLENGES

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It is well established that physicians are poor prognosticators and typically overestimate survival. The length of the physician’s relationship with the patient has been suggested to be inversely proportional to the physician’s prognostic accuracy. This is important in CMC as healthcare teams often care for patients from birth through adolescence and even into adulthood and thus may be less accurate in their ability to provide meaningful and precise prognostic information.

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CMC often have numerous problems involving multiple organ systems and sometimes very rare diseases or conditions, leaving providers with little or no precedent to predict disease trajectory. Additionally, every patient has a unique set of social and environmental circumstances that influence how their diseases manifest and how they respond to stressors and treatment over time.

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Existing prognostic models are largely disease specific and based on functional status or laboratory values. Feudtner and colleagues suggest that disease severity, age, previous hospitalizations, and certain chronic conditions can be predictive of mortality 1 year after hospitalization.

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For children diagnosed with a disease that has a typical clinical course (Huntington disease, spinal muscular atrophy type 1), Schwantes and colleagues advise creating a road map with the patient and family that includes the disease course as well as benefits and risks of potential interventions along the way. They use the example of a young boy presenting with asymptomatic Duchenne muscular dystrophy and advise discussing what to expect early in ...

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