caa a22 4500 465813615 CHVBK 20180323112121.0 cr unu---uuuuu 170327e19901201xx s 000 0 eng 10.1007/BF02919382 doi (NATIONALLICENCE)springer-10.1007/BF02919382 Probability and the patient state space [Elektronische Daten] [William Coleman, John Siegel, Ivo Giovannini, Andrea De Gaetano, Shirin Goodarzi, Roberto Tacchino, Gabriele Sganga] 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 thecardiovascular 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. Kluwer Academic Publishers, 1990 patient state nationallicence Bayesian inference nationallicence ICU nationallicence critical care severity index nationallicence probability nationallicence decision-making nationallicence artificial intelligence nationallicence Coleman William Maryland Institute for Emergency Medical Services Systems, UMAB, 21201, Baltimore, MD, USA aut Siegel John Maryland Institute for Emergency Medical Services Systems, UMAB, 21201, Baltimore, MD, USA aut Giovannini Ivo C.N.R. Centro di Studio per lo Fisiopathologia dello Shock, Istituto di Clinica Chirurgica, Università Cattolica del Sacro Cuore, Via Pineta Sacchetti 644, I-00168, Roma, Italia aut De Gaetano Andrea C.N.R. Centro di Studio per lo Fisiopathologia dello Shock, Istituto di Clinica Chirurgica, Università Cattolica del Sacro Cuore, Via Pineta Sacchetti 644, I-00168, Roma, Italia aut Goodarzi Shirin Maryland Institute for Emergency Medical Services Systems, UMAB, 21201, Baltimore, MD, USA aut Tacchino Roberto C.N.R. Centro di Studio per lo Fisiopathologia dello Shock, Istituto di Clinica Chirurgica, Università Cattolica del Sacro Cuore, Via Pineta Sacchetti 644, I-00168, Roma, Italia aut Sganga Gabriele C.N.R. Centro di Studio per lo Fisiopathologia dello Shock, Istituto di Clinica Chirurgica, Università Cattolica del Sacro Cuore, Via Pineta Sacchetti 644, I-00168, Roma, Italia aut International journal of clinical monitoring and computing Springer Netherlands 7/4(1990-12-01), 201-215 0167-9945 7:4<201 1990 7 12521 https://doi.org/10.1007/BF02919382 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/BF02919382 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Coleman William Maryland Institute for Emergency Medical Services Systems, UMAB, 21201, Baltimore, MD, USA aut NATIONALLICENCE 700 1- Siegel John Maryland Institute for Emergency Medical Services Systems, UMAB, 21201, Baltimore, MD, USA aut NATIONALLICENCE 700 1- Giovannini Ivo C.N.R. Centro di Studio per lo Fisiopathologia dello Shock, Istituto di Clinica Chirurgica, Università Cattolica del Sacro Cuore, Via Pineta Sacchetti 644, I-00168, Roma, Italia aut NATIONALLICENCE 700 1- De Gaetano Andrea C.N.R. Centro di Studio per lo Fisiopathologia dello Shock, Istituto di Clinica Chirurgica, Università Cattolica del Sacro Cuore, Via Pineta Sacchetti 644, I-00168, Roma, Italia aut NATIONALLICENCE 700 1- Goodarzi Shirin Maryland Institute for Emergency Medical Services Systems, UMAB, 21201, Baltimore, MD, USA aut NATIONALLICENCE 700 1- Tacchino Roberto C.N.R. Centro di Studio per lo Fisiopathologia dello Shock, Istituto di Clinica Chirurgica, Università Cattolica del Sacro Cuore, Via Pineta Sacchetti 644, I-00168, Roma, Italia aut NATIONALLICENCE 700 1- Sganga Gabriele C.N.R. Centro di Studio per lo Fisiopathologia dello Shock, Istituto di Clinica Chirurgica, Università Cattolica del Sacro Cuore, Via Pineta Sacchetti 644, I-00168, Roma, Italia aut NATIONALLICENCE 773 0- International journal of clinical monitoring and computing Springer Netherlands 7/4(1990-12-01), 201-215 0167-9945 7:4<201 1990 7 12521 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer