Predicting Delirium Risk Using an Automated Mayo Delirium Prediction Tool

Pagali, Sandeep R.; Miller, Donna; Fischer, Karen M.; Schroeder, Darrell R.; Egger, Norman G.; Manning, Dennis M.; Lapid, Maria I.; Pignolo, Robert J.; Burton, M. Caroline

doi:10.1016/j.mayocp.2020.08.049

Cited by 14 publications

(19 citation statements)

References 20 publications

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“…Hence, delirium prevention, detection, and optimization are critical in hospitalized COVID-19 patients. The COVID-19 pandemic magnified the crucial nature of applying and implementing the validated delirium prediction tools (Pagali, 2021) to prevent and respond to delirium timely. Prediction and early detection of delirium could be linked to effective multicomponent delirium prevention/mitigation activities such as the Hospital Elder Life Program (HELP) and the ABCDEF bundle, both of which are modified to accommodate and address the care needs and challenges of COVID-19 patients.…”

Section: Discussionmentioning

confidence: 99%

Delirium occurrence and association with outcomes in hospitalized COVID-19 patients

Pagali

Lindroth

et al. 2021

Int. Psychogeriatr.

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Delirium is reported to be one of the manifestations of coronavirus infectious disease 2019 (COVID-19) infection. COVID-19 hospitalized patients are at a higher risk of delirium. Pathophysiology behind the association of delirium and COVID-19 is uncertain. We analyzed the association of delirium occurrence with outcomes in hospitalized COVID-19 patients, across all age groups, at Mayo Clinic hospitals. A retrospective study of all hospitalized COVID-19 patients at Mayo Clinic between March 1, 2020 and December 31, 2020 was performed. Occurrence of delirium and outcomes of mortality, length of stay, readmission, and 30-day mortality after hospital discharge were measured. Chi-square test, student t-test, survival analysis, and logistic regression analysis were performed to measure and compare outcomes of delirium group adjusted for age, sex, Charlson comorbidity score, and COVID-19 severity with no-delirium group. A total of 4351 COVID-19 patients were included in the study. Delirium occurrence in the overall study population was noted to be 22.4%. The highest occurrence of delirium was also noted in patients with critical COVID-19 illness severity. A statistically significant OR 4.35 (3.27–5.83) for in-hospital mortality and an OR 4.54 (3.25–6.38) for 30-day mortality after discharge in the delirium group were noted. Increased hospital length of stay, 30-day readmission, and need for skilled nursing facility on discharge were noted in the delirium group. Delirium in hospitalized COVID-19 patients is a marker for increased mortality and morbidity. In this group, outcomes appear to be much worse when patients are older and have a critical severity of COVID-19 illness.

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Section: Discussionmentioning

confidence: 99%

Delirium occurrence and association with outcomes in hospitalized COVID-19 patients

Pagali

Lindroth

et al. 2021

Int. Psychogeriatr.

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“…In the past, more emphasis has been placed on developing predictive models with clinical implications that remain unclear. A number of prediction models have been developed but primarily to predict delirium risk (not POD risk) [ 17 ] or for ICU patients [ 15 , 18 , 20 , 46 ]. A simple translation to surgical patients is problematic, as the needs of surgical patients are not addressed.…”

Section: Discussionmentioning

confidence: 99%

“…Prediction models for ICU delirium include—besides age parameters, which are primarily related to intensive care treatment, such as for coma—use of sedatives and morphine [ 46 ], respiratory failure [ 20 ], vasoactive medication use and requirement of continuous renal replacement therapy and mechanical ventilation [ 15 ]. Some of them were developed through retrospective analysis [ 15 , 18 ].…”

Section: Discussionmentioning

confidence: 99%

“…However, the implementation of these measures requires a considerable allocation of personnel resources and time, and it may thus come as no surprise that these guideline recommendations are poorly implemented [ 14 ]. Furthermore, a number of predictive models have been developed in the past to guide the prediction of POD; however, these also often require extensive assessment, and their clinical implications remain unclear [ 15 , 16 , 17 , 18 , 19 , 20 ]. These examples suggest that a detection tool that would be supportive of use in clinical care needs to be simple to keep the number of personnel and the time costs of the assessment as low as possible.…”

Section: Introductionmentioning

confidence: 99%

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Forecasting Postoperative Delirium in Older Adult Patients with Fast-and-Frugal Decision Trees

Heinrich

Woike

Spies

et al. 2022

JCM

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Postoperative delirium (POD) is associated with increased complication and mortality rates, particularly among older adult patients. However, guideline recommendations for POD detection and management are poorly implemented. Fast-and-frugal trees (FFTrees), which are simple prediction algorithms, may be useful in this context. We compared the capacity of simple FFTrees with two more complex models—namely, unconstrained classification trees (UDTs) and logistic regression (LogReg)—for the prediction of POD among older surgical patients in the perioperative setting. Models were trained and tested on the European BioCog project clinical dataset. Based on the entire dataset, two different FFTrees were developed for the pre-operative and postoperative settings. Within the pre-operative setting, FFTrees outperformed the more complex UDT algorithm with respect to predictive balanced accuracy, nearing the prediction level of the logistic regression. Within the postoperative setting, FFTrees outperformed both complex models. Applying the best-performing algorithms to the full datasets, we proposed an FFTree using four cues (Charlson Comorbidity Index (CCI), site of surgery, physical status and frailty status) for the pre-operative setting and an FFTree containing only three cues (duration of anesthesia, age and CCI) for the postoperative setting. Given that both FFTrees contained considerably fewer criteria, which can be easily memorized and applied by health professionals in daily routine, FFTrees could help identify patients requiring intensified POD screening.

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“…Comorbidities (prior history) were measured with previously validated lists of International Classification of Disease codes (Appendix ) 23,24 . The inclusion of fall risk assessment and their components as potential candidate predictors was based on a recent study for prediction of inpatient delirium 25 . The Memorial ED Fall Risk Assessment Tool 22 was routinely applied by nurses early in the course of patients' ED stay, transforming its results to information that could be used as potential predictors of delirium in the ED.…”

Section: Methodsmentioning

confidence: 99%

REcognizing DElirium in geriatric Emergency Medicine: The REDEEM risk stratification score

Silva

Stanich

Jeffery

et al. 2021

Academic Emergency Medicine

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Objective:The objective was to derive a risk score that uses variables available early during the emergency department (ED) encounter to identify high-risk geriatric patients who may benefit from delirium screening. Methods:This was an observational study of older adults age ≥ 75 years who presented to an academic ED and who were screened for delirium during their ED visit.Variable selection from candidate predictors was performed through a LASSOpenalized logistic regression. A risk score was derived from the final prediction model, and predictive accuracy characteristics were calculated with 95% confidence intervals (CIs).Results: From the 967 eligible ED visits, delirium was detected in 107 (11.1%). The area under the curve for the REcognizing DElirium in Emergency Medicine (REDEEM) score was 0.901 (95% CI = 0.864-0.938). The REEDEM risk score included 10 different variables (seven based on triage information and three obtained during early history taking) with a score ranging from -3 to 66. Using an optimal cutoff of ≥11, we found a sensitivity of 84.1% (90 of 107 ED delirium patients, 95% CI = 75.5%-90.2%) and a specificity of 86.6% (745 of 860 non-ED delirium patients, 95% CI = 84.1%-88.8%). A lower cutoff of ≥5 was found to minimize false negatives with an improved sensitivity at 91.6% (98 of 107 ED delirium patients, 95% CI = 84.2%-95.8%). Conclusion:A risk stratification score was derived with the potential to augment delirium recognition in geriatric ED patients. This has the potential to assist on deliriumtargeted screening of high-risk patients in the ED. Validation of REDEEM, however, is needed prior to implementation.

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Predicting Delirium Risk Using an Automated Mayo Delirium Prediction Tool

Cited by 14 publications

References 20 publications

Delirium occurrence and association with outcomes in hospitalized COVID-19 patients

Delirium occurrence and association with outcomes in hospitalized COVID-19 patients

Forecasting Postoperative Delirium in Older Adult Patients with Fast-and-Frugal Decision Trees

REcognizing DElirium in geriatric Emergency Medicine: The REDEEM risk stratification score

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