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Oncologic Critical Care pp 107–119Cite as

Patient Risk Prediction Model

Severity of Illness Scores

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Abstract

Historically, oncology patients have been refused critical care admission due to pessimistic perceptions of outcomes in the context of terminal illness. This refusal was advocated by some critical care colleges who stated patients with metastatic cancers would be poor candidates for intensive care unit (ICU) admission. More contemporaneously, tremendous advances in antitumor therapies have evolved, and critically unwell cancer patients have demonstrated comparable survival rates to critically unwell non-cancer patients. This has revolutionized medical attitudes, opening the previously closed doors of ICU in facilitating the management of these complex patients.

As with all critically unwell patients, early identification of patients at risk of deleterious outcomes facilitates timely and appropriate therapeutic interventions, directs necessary discussions with multidisciplinary teams and supports frank and open communications with patients and their relatives. In the oncological population, there is a wealth of evidence to support the prognostic value of individual physiological derangements as superior outcome predictors to cancer stage or type. Despite this, general medical attitudes have retained some antiquated nihilism, and in the absence of specialized multidisciplinary teams, risk prediction models specific to oncological patients in ICU would prove to redirect attitudes and better inform of predicted outcomes.

In this chapter, we discuss independent predictors of outcomes in this patient cohort, the challenges of risk prediction in such a vastly heterogeneous population, specific illness severity scoring systems, outcomes following elective surgery, solid organ versus hematological malignancy, therapeutic complications, and long-term sequelae of cancer treatments.

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Authors and Affiliations

  1. Department of Anaesthesia, Critical Care and Pain Medicine, Royal Marsden Hospital, London, UK

    Michelle O’Mahony & Tim Wigmore

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  1. Michelle O’Mahony
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  2. Tim Wigmore
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Correspondence to Michelle O’Mahony .

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Editors and Affiliations

  1. Department of Critical Care and Respiratory Care, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

    Joseph L. Nates

  2. Division of Anesthesiology, Critical Care and Pain Medicine, Department of Critical Care and Respiratory Care, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

    Kristen J. Price

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O’Mahony, M., Wigmore, T. (2020). Patient Risk Prediction Model. In: Nates, J., Price, K. (eds) Oncologic Critical Care. Springer, Cham. https://doi.org/10.1007/978-3-319-74588-6_8

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