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Can intensive care unit delirium be prevented and reduced? Lessons learned and future directions.

ABSTRACT: Delirium is a form of acute brain injury that occurs in up to 80% of critically ill patients. It is a source of enormous societal and financial burdens due to increased mortality, prolonged intensive care unit (ICU) and hospital stays, and long-term neuropsychological and functional deficits in ICU survivors. These poor outcomes are not only independently associated with the development of delirium but are also associated with increasing delirium duration. Therefore, interventions should strive both to prevent the occurrence of ICU delirium and to limit its persistence. Both patient-centered and ICU-acquired risk factors need to be addressed early in the ICU course to maximize the efficacy of prevention strategies and to improve long-term outcomes of ICU patients. In this article, we review strategies for early detection of patients who are delirious and who are at high risk for developing delirium, and we present a clinically useful ICU delirium prevention and reduction strategy for clinicians to incorporate into their daily practice.

SUBMITTER: Hsieh SJ

PROVIDER: S-EPMC3960966 | biostudies-literature | 2013 Dec

REPOSITORIES: biostudies-literature

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Can intensive care unit delirium be prevented and reduced? Lessons learned and future directions.

Hsieh S Jean SJ Ely E Wesley EW Gong Michelle N MN

Annals of the American Thoracic Society 20131201 6

Delirium is a form of acute brain injury that occurs in up to 80% of critically ill patients. It is a source of enormous societal and financial burdens due to increased mortality, prolonged intensive care unit (ICU) and hospital stays, and long-term neuropsychological and functional deficits in ICU survivors. These poor outcomes are not only independently associated with the development of delirium but are also associated with increasing delirium duration. Therefore, interventions should strive ...[more]

PMID: 24364769

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Project description:Due to the rapid increase in the availability of patient data, there is significant interest in precision medicine that could facilitate the development of a personalized treatment plan for each patient on an individual basis. Radiation oncology is particularly suited for predictive machine learning (ML) models due to the enormous amount of diagnostic data used as input and therapeutic data generated as output. An emerging field in precision radiation oncology that can take advantage of ML approaches is radiogenomics, which is the study of the impact of genomic variations on the sensitivity of normal and tumor tissue to radiation. Currently, patients undergoing radiotherapy are treated using uniform dose constraints specific to the tumor and surrounding normal tissues. This is suboptimal in many ways. First, the dose that can be delivered to the target volume may be insufficient for control but is constrained by the surrounding normal tissue, as dose escalation can lead to significant morbidity and rare. Second, two patients with nearly identical dose distributions can have substantially different acute and late toxicities, resulting in lengthy treatment breaks and suboptimal control, or chronic morbidities leading to poor quality of life. Despite significant advances in radiogenomics, the magnitude of the genetic contribution to radiation response far exceeds our current understanding of individual risk variants. In the field of genomics, ML methods are being used to extract harder-to-detect knowledge, but these methods have yet to fully penetrate radiogenomics. Hence, the goal of this publication is to provide an overview of ML as it applies to radiogenomics. We begin with a brief history of radiogenomics and its relationship to precision medicine. We then introduce ML and compare it to statistical hypothesis testing to reflect on shared lessons and to avoid common pitfalls. Current ML approaches to genome-wide association studies are examined. The application of ML specifically to radiogenomics is next presented. We end with important lessons for the proper integration of ML into radiogenomics.

Project description:BACKGROUND:Accurate prediction of delirium in the intensive care unit (ICU) may facilitate efficient use of early preventive strategies and stratification of ICU patients by delirium risk in clinical research, but the optimal delirium prediction model to use is unclear. We compared the predictive performance and user convenience of the prediction model for delirium (PRE-DELIRIC) and early prediction model for delirium (E-PRE-DELIRIC) in ICU patients and determined the value of a two-stage calculation. METHODS:This 7-country, 11-hospital, prospective cohort study evaluated consecutive adults admitted to the ICU who could be reliably assessed for delirium using the Confusion Assessment Method-ICU or the Intensive Care Delirium Screening Checklist. The predictive performance of the models was measured using the area under the receiver operating characteristic curve. Calibration was assessed graphically. A physician questionnaire evaluated user convenience. For the two-stage calculation we used E-PRE-DELIRIC immediately after ICU admission and updated the prediction using PRE-DELIRIC after 24 h. RESULTS:In total 2178 patients were included. The area under the receiver operating characteristic curve was significantly greater for PRE-DELIRIC (0.74 (95% confidence interval 0.71-0.76)) compared to E-PRE-DELIRIC (0.68 (95% confidence interval 0.66-0.71)) (z score of -?2.73 (p?<?0.01)). Both models were well-calibrated. The sensitivity improved when using the two-stage calculation in low-risk patients. Compared to PRE-DELIRIC, ICU physicians (n?=?68) rated the E-PRE-DELIRIC model more feasible. CONCLUSIONS:While both ICU delirium prediction models have moderate-to-good performance, the PRE-DELIRIC model predicts delirium better. However, ICU physicians rated the user convenience of E-PRE-DELIRIC superior to PRE-DELIRIC. In low-risk patients the delirium prediction further improves after an update with the PRE-DELIRIC model after 24 h. TRIAL REGISTRATION:ClinicalTrials.gov, NCT02518646 . Registered on 21 July 2015.

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Can intensive care unit delirium be prevented and reduced? Lessons learned and future directions.

Publications

Can intensive care unit delirium be prevented and reduced? Lessons learned and future directions.

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