European geriatric medicine · 2024
The Multidimensional Prognostic Index predicts incident delirium among hospitalized older patients with COVID-19: a multicenter prospective European study.
СтатьяМногоцентровое исследованиеНазвание и аннотация: Перевод в очереди
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Morganti Wanda, Custodero Carlo, Veronese Nicola, Topinkova Eva, Michalkova Helena, Polidori M Cristina, Cruz-Jentoft Alfonso J, von Arnim Christine A F, Azzini Margherita, Gruner Heidi, Castagna Alberto, Cenderello Giovanni, Custureri Romina, Seminerio Emanuele, Zieschang Tania, Padovani Alessandro, Sanchez-Garcia Elisabet, Pilotto Alberto, MPI-COVID-19 Study Group Investigators
Аннотация
PURPOSE: Incident delirium is a frequent complication among hospitalized older people with COVID-19, associated with increased length of hospital stay, higher morbidity and mortality rates. Although delirium is preventable with early detection, systematic assessment methods and predictive models are not universally defined, thus delirium is often underrated. In this study, we tested the role of the Multidimensional Prognostic Index (MPI), a prognostic tool based on Comprehensive Geriatric Assessment, to predict the risk of incident delirium.
METHODS: Hospitalized older patients (≥ 65 years) with COVID-19 infection were enrolled (n = 502) from ten centers across Europe. At hospital admission, the MPI was administered to all the patients and two already validated delirium prediction models were computed (AWOL delirium risk-stratification score and Martinez model). Delirium occurrence during hospitalization was ascertained using the 4A's Test (4AT). Accuracy of the MPI and the other delirium predictive models was assessed through logistic regression models and the area under the curve (AUC).
RESULTS: We analyzed 293 patients without delirium at hospital admission. Of them 33 (11.3%) developed delirium during hospitalization. Higher MPI score at admission (higher multidimensional frailty) was associated with higher risk of incident delirium also adjusting for the other delirium predictive models and COVID-19 severity (OR = 12.72, 95% CI = 2.11-76.86 for MPI-2 vs MPI-1, and OR = 33.44, 95% CI = 4.55-146.61 for MPI-3 vs MPI-1). The MPI showed good accuracy in predicting incident delirium (AUC = 0.71) also superior to AWOL tool, (AUC = 0.63) and Martinez model (AUC = 0.61) (p < 0.0001 for both comparisons).
CONCLUSIONS: The MPI is a sensitive tool for early identification of older patients with incident delirium.
METHODS: Hospitalized older patients (≥ 65 years) with COVID-19 infection were enrolled (n = 502) from ten centers across Europe. At hospital admission, the MPI was administered to all the patients and two already validated delirium prediction models were computed (AWOL delirium risk-stratification score and Martinez model). Delirium occurrence during hospitalization was ascertained using the 4A's Test (4AT). Accuracy of the MPI and the other delirium predictive models was assessed through logistic regression models and the area under the curve (AUC).
RESULTS: We analyzed 293 patients without delirium at hospital admission. Of them 33 (11.3%) developed delirium during hospitalization. Higher MPI score at admission (higher multidimensional frailty) was associated with higher risk of incident delirium also adjusting for the other delirium predictive models and COVID-19 severity (OR = 12.72, 95% CI = 2.11-76.86 for MPI-2 vs MPI-1, and OR = 33.44, 95% CI = 4.55-146.61 for MPI-3 vs MPI-1). The MPI showed good accuracy in predicting incident delirium (AUC = 0.71) also superior to AWOL tool, (AUC = 0.63) and Martinez model (AUC = 0.61) (p < 0.0001 for both comparisons).
CONCLUSIONS: The MPI is a sensitive tool for early identification of older patients with incident delirium.