Original article
Assessing illness severity: Does clinical judgment work?

https://doi.org/10.1016/0021-9681(86)90111-6Get rights and content

Abstract

Accurate classification of clinical severity is important for interpreting casemix in clinical studies and for stratifying patients for clinical trials. To evaluate whether clinical judgment might be an effective method of estimating severity, all 604 patients admitted to the medical service in a one month period were rated at the time of admission by the responsible resident as to how sick they were.

Within the 13 comorbid disease groups, and within the 15 basic categories of reason for admission, the physicians' severity ratings were the most significant predictor of in-hospital mortality. Death rates rose from 0% in those rated as not ill, to 2% in the mildly ill, to 6% in the moderately ill, to 23% in the severely ill, and to 58% in those rated as moribund (p < 0.001). Sickness ratings also predicted time to death: mildly ill patients died after prolonged hospitalizations, while the moribund died shortly after admission.

The patients' age, sex, race, the number of comorbid diseases or problems did not predict mortality. Patients with serious comorbidity (metastases, AIDS, or cirrhosis) had a higher mortality rate than other patients (p < 0.001); however, the severity ratings predicted outcomes within this group (p < 0.001) as well as among those without such serious comorbidity (p < 0.001). Patients who were admitted with acute neurologic (p < 0.05) or acute cardiovascular (p < 0.01) events did have an independently worse prognosis.

In conclusion, physicians' estimates or sickness provided an accurate estimate of illness severity, with mortality rates that essentially tripled from one stratum to the next. Clinical judgment may suffice to classify the clinical severity of patients at the time of enrollment in prospective trials and can provide a useful method of controlling for casemix.

References (35)

  • FE Harrell et al.

    Regression modelling strategies for improved prognostic prediction

    Stat Med

    (1984)
  • Statistical Analysis System Institute

    the FUNCAT Procedure

  • F Harrell

    The LOGIST Procedure

  • Statistical Analysis System Institute

    The GLM Procedure

  • F Harrell

    The PHGLM Procedure

  • LW Eaton et al.

    Regional cardiac dilatation after acute myocardial infarction

    N Engl J Med

    (1979)
  • Cited by (303)

    View all citing articles on Scopus
    1

    Dr Charlson is a Henry J. Kaiser Foundation Faculty Scholar in General Internal Medicine.

    2

    Dr Sax is a Fellow at the National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, Maryland.

    3

    Dr Fields is a Henry J. Kaiser Foundation Fellow in General Internal Medicine.

    View full text