Probability and the patient state space

Gabriele Sganga, Ivo Giovannini, Andrea De Gaetano, Wp Coleman, Jh Siegel, S Goodarzi, Rm Tacchino

Risultato della ricerca: Contributo in rivistaArticolo in rivista

4 Citazioni (Scopus)

Abstract

This paper describes work to develop a model-based system to support clinical decision-making. In previous articles, we have developed (from 695 measurement sets obtained from 148 patients) a physiologic state classification based on a set of 11 cardiovascular and metabolic measurements. There is an R or reference state, for stable ICU patients. Patients under (operative, traumatic, or compensated septic) stress, or with (septic or hepatic) metabolic, respiratory, or cardiac insufficiency are in the A, B, C, or D states, respectively. We wished to make the state easier to measure and eventually available continuously, automatically, and noninvasively, as well as reflecting a wider group of bodily systems. The 5 centers define a 4 dimensional affine subspace, designated the cardiovascular state space. Using eigenvector analysis, we have found four new derived physiologic variables CV1, CV2, CV3, and CV4 that span the state space. We have fit sets of linear regression equations that allow the patient's position in the state space, and therefore his state, to be determined from more easily obtainable sets of measurements. Further, we selected 1966 measurement sets from 512 patients at two hospitals. We used the data from 250 of these patients to define 13 prototypical types, namely survivors and deaths from various combinations of sepsis, cardiogenic decompensation, cirrhosis, and pneumonitis, following trauma or general surgery. For any future patient, the statistical theory of Bayesian inference allows one to infer back from the measurements observed to the probability of his being of any of these types and of surviving or dying. We used this method to predict the outcome of the other 262 patients, prospectively. Statistically, the predictions of survival or death were not significantly different from the actual. For individual patients, the method predicts a clinical course that closely follows the actual episodes in their history. These results confirm and explain the validity of the concept of the patient state and make the state easier to compute. The patient state and the probability plot together help to stage, select, and evaluate therapy. They do not replace the clinician's judgement, but rather are tools that help the clinician to exercise judgement.
Lingua originaleEnglish
pagine (da-a)201-215
Numero di pagine15
RivistaInternational Journal of Clinical Monitoring and Computing
Volume7
Stato di pubblicazionePubblicato - 1990

Keywords

  • Adult
  • Artificial Intelligence
  • Bayes Theorem
  • Humans
  • Male
  • Models, Biological
  • Monitoring, Physiologic
  • Prospective Studies
  • Survival Analysis
  • Wounds, Nonpenetrating

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