Accuracy of predictions of survival at admission to the intensive care unit
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Prognosis and futility in neurosurgical emergencies: A review
2020, Clinical Neurology and NeurosurgeryCitation Excerpt :Though prognostic information is useful in these situations, it often lacks reliability and tends to be overly pessimistic [3,4]. Furthermore, the validity of prognostic disclosure varies based on provider experience with more experienced providers generally providing more accurate prognostic predictions [5,6]. From this discussion, it is clear that neurosurgeons, housestaff, and physician extenders must have a strong understanding of evidence-based prognostic data and medical futility so that they may correlate a patient’s condition to a prognosis or functional outcome in a non-biased manner.
The ability of intensive care unit physicians to estimate long-term prognosis in survivors of critical illness
2018, Journal of Critical CareCitation Excerpt :However, adding these short stay patients to the study population would not affect the predictive performance as measured by the discrimination for survival (c-index 0.68; 95%CI 0.67–0.70) or for poor outcome including survival and HRQoL (c-index 0.63; 0.62–0.66). Other studies into the prognostic ability of ICU physicians have focused on the different levels of physician experience [6,29]. In these studies, attending intensivists consistently outperformed ICU fellows with regard to the predictive value of their outcome predictions.
Assessing trauma care provider judgement in the prediction of need for life-saving interventions
2015, InjuryCitation Excerpt :How human factors research results can best be made accessible to math-based systems developers remains an open question. The confrontation between the classically expert-judgement-based “art of medicine” point of view and that of “evidence-based medicine” is neither the focus nor the concern of the present study; however, the need to merge the two effectively in computer-based support systems is of concern across many health care disciplines, most particularly in critical care, to allow for better triage and distribution of resources [23-26]. Within this general debate, the clearest role for math-based systems development—computer modelling and instrumentation based on this work—is in improved data-gathering and decision-assist tools [27,28].
Development of a daily mortality probability prediction model from Intensive Care Unit patients using a discrete-time event history analysis
2013, Computer Methods and Programs in BiomedicineCitation Excerpt :The APACHE and MPM scoring systems are global measures of illness severity and outcome prediction; however, the Multiple Organ Dysfunction and Sequential Organ Failure Assessment scores were initially designed to describe organ dysfunction or failure in critically ill patients. As various articles [5,12,22,30,46,49,51] have pointed out, severity scores are imperfect at predicting individual clinical chance of survival. Several reasons may explain this deficit [12,33,46,49].
Preferences for resuscitation and intubation among patients with do-not-resuscitate/do-not-intubate orders
2013, Mayo Clinic Proceedings