Prognosticating the outcome of intensive care in older patients—a narrative review

 

by Michael Beil, Rui Moreno, Jakub Fronczek, Yuri Kogan, Rui Paulo Jorge Moreno, Hans Flaatten, Bertrand Guidet, Dylan de Lange, Susannah Leaver, Akiva Nachshon, Peter Vernon van Heerden, Leo Joskowicz, Sigal Sviri, Christian Jung and Wojciech Szczeklik 

 

Annals of Intensive Care volume 14, Article number: 97, Published: 22 June 2024

 

Abstract

Prognosis determines major decisions regarding treatment for critically ill patients. Statistical models have been developed to predict the probability of survival and other outcomes of intensive care. Although they were trained on the characteristics of large patient cohorts, they often do not represent very old patients (age ≥ 80 years) appropriately. Moreover, the heterogeneity within this particular group impairs the utility of statistical predictions for informing decision-making in very old individuals. In addition to these methodological problems, the diversity of cultural attitudes, available resources as well as variations of legal and professional norms limit the generalisability of prediction models, especially in patients with complex multi-morbidity and pre-existing functional impairments. Thus, current approaches to prognosticating outcomes in very old patients are imperfect and can generate substantial uncertainty about optimal trajectories of critical care in the individual. This article presents the state of the art and new approaches to predicting outcomes of intensive care for these patients. Special emphasis has been given to the integration of predictions into the decision-making for individual patients. This requires quantification of prognostic uncertainty and a careful alignment of decisions with the preferences of patients, who might prioritise functional outcomes over survival. Since the performance of outcome predictions for the individual patient may improve over time, time-limited trials in intensive care may be an appropriate way to increase the confidence in decisions about life-sustaining treatment.