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Management of the Critically Ill Geriatric Patient

  • Paul E. Marik

Abstract

People who are older than 65 years of age are the fastest growing segment of the US population.1 By 2030 the population older than 65 years will double to approximately 70 million, and the fastest growing segment of the population, those older than 84 years, will triple.1–3 Age is associated with an increasing prevalence of multiple diseases and disabilities. Age is also associated with a decline of the functional reserve of multiple organ systems and a progressive restriction in personal and social resources. It is, therefore, not surprising that elderly patients will utilize a disproportionate share of health care resources. It is estimated that by the year 2030, 55% of all health care expenses in the USA will be spent on caring for the elderly.4 By virtue of having lived longer, increasing numbers of elderly patients (age >65 years) are being admitted to intensive care units (ICUs), with diagnoses ranging from exacerbations of chronic illnesses and new onset of catastrophic health problems to trauma caused by home-related incidents and injury-resultant accidents that have occurred outside of the home. Elderly patients currently account for 42–52% of ICU admissions and for almost 60% of all ICU days.3,5–7 A disproportionate number of these ICU days is spent by elderly patients before their death. Forty percent of Medicare dependants are admitted to an ICU during their terminal illness, accounting for a quarter of all Medicare expenditure.8,9 Clearly ICU utilization by the elderly will increase exponentially over the next three decades. With the current unitization patterns, a severe shortage of intensivists and ICU beds has therefore been predicted.3 The reality of our aging society dictates that we must focus on how to best care for the elderly who develop critical illness.

Keywords

Elderly Patient Glomerular Filtration Rate Diastolic Dysfunction Comprehensive Geriatric Assessment Postoperative Delirium 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Paul E. Marik
    • 1
  1. 1.Eastern Virginia Medial SchoolNorfolkUSA

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