
==== Front
Eur Geriatr Med
Eur Geriatr Med
European Geriatric Medicine
1878-7649
1878-7657
Springer International Publishing Cham

38878221
987
10.1007/s41999-024-00987-y
Research Paper
The Multidimensional Prognostic Index predicts incident delirium among hospitalized older patients with COVID-19: a multicenter prospective European study
http://orcid.org/0000-0001-6214-7276
Morganti Wanda wanda.morganti@galliera.it

1
http://orcid.org/0000-0003-1549-6451
Custodero Carlo 2
http://orcid.org/0000-0002-9328-289X
Veronese Nicola 3
Topinkova Eva 45
Michalkova Helena 45
Polidori M. Cristina 67
Cruz‐Jentoft Alfonso J. 8
von Arnim Christine A. F. 9
Azzini Margherita 10
Gruner Heidi 11
Castagna Alberto 12
Cenderello Giovanni 13
Custureri Romina 1
http://orcid.org/0009-0000-5342-7102
Seminerio Emanuele 1
Zieschang Tania 14
Padovani Alessandro 15
Sanchez‐Garcia Elisabet 8
http://orcid.org/0000-0002-8615-1955
Pilotto Alberto 12
the MPI-COVID-19 Study Group InvestigatorsBarbagallo Mario
Barbagelata Marina
Dini Simone
Diesner Naima Madlen
Fernandes Marilia
Gandolfo Federica
Garaboldi Sara
Musacchio Clarissa
Pilotto Andrea
Pickert Lena
Podestà Silvia
Ruotolo Giovanni
Sciolè Katiuscia
Schlotmann Julia

1 grid.450697.9 0000 0004 1757 8650 Department of Geriatric Care, Neurology and Rehabilitation, Galliera Hospital, Genoa, Italy
2 https://ror.org/027ynra39 grid.7644.1 0000 0001 0120 3326 Department of Interdisciplinary Medicine, “Aldo Moro” University of Bari, Bari, Italy
3 https://ror.org/044k9ta02 grid.10776.37 0000 0004 1762 5517 Department of Internal Medicine and Geriatrics, University of Palermo, Palermo, Italy
4 https://ror.org/024d6js02 grid.4491.8 0000 0004 1937 116X Department of Geriatrics, First Faculty of Medicine, Charles University, Prague, Czech Republic
5 grid.14509.39 0000 0001 2166 4904 Faculty of Health and Social Sciences, University of South Bohemia, Ceske Budejovice, Czech Republic
6 https://ror.org/05mxhda18 grid.411097.a 0000 0000 8852 305X Department II of Internal Medicine and Center for Molecular Medicine Cologne, Faculty of Medicine, Ageing Clinical Research, University Hospital Cologne, Cologne, Germany
7 grid.6190.e 0000 0000 8580 3777 Cologne Excellence Cluster on Cellular Stress Responses in Aging- Associated Diseases (CECAD), University of Cologne, Cologne, Germany
8 https://ror.org/050eq1942 grid.411347.4 0000 0000 9248 5770 Servicio de Geriatría, Hospital Universitario Ramón y Cajal (IRYCIS), Madrid, Spain
9 https://ror.org/021ft0n22 grid.411984.1 0000 0001 0482 5331 Department of Geriatrics, University Medical Center Göttingen, Göttingen, Germany
10 Geriatrics Unit, “Mater Salutis” Hospital, Legnago ULSS 9 Scaligera, Verona, Italy
11 https://ror.org/0353kya20 grid.413362.1 0000 0000 9647 1835 Serviço de Medicina Interna, Hospital Curry Cabral, Centro Hospitalar Universitário Lisboa Central/Universidade Nova de Lisboa, Lisbon, Portugal
12 Geriatrics Unit, “Pugliese Ciaccio” Hospital, Catanzaro, Italy
13 Infectious Disease Unit, Sanremo Hospital, ASL 1 Imperiese, Sanremo, Italy
14 grid.5560.6 0000 0001 1009 3608 University-Clinic for Geriatric Medicine, Klinikum Oldenburg AöR, Oldenburg University, Oldenburg, Germany
15 https://ror.org/02q2d2610 grid.7637.5 0000 0004 1757 1846 Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
15 6 2024
15 6 2024
2024
15 4 961969
18 1 2024
1 5 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by/4.0/ Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Key summary points

Aim

Testing the role of the Multidimensional Prognostic Index (MPI), based on the Comprehensive Geriatric Assessment (CGA), in predicting the risk of incident delirium in hospitalized older patients with COVID-19.

Findings

The MPI showed a good accuracy in predicting incident delirium (AUC = 0.71). Its accuracy is higher than the ones of two validated predictive models (AWOL delirium risk-stratification score’s AUC = 0.63; Martinez Model’s AUC = 0.61; p < 0.0001 for both comparisons).

Message

The MPI is a sensitive tool for risk-stratification of the incident delirium in hospitalized older COVID-19 patients.

Supplementary Information

The online version contains supplementary material available at 10.1007/s41999-024-00987-y.

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.

Supplementary Information

The online version contains supplementary material available at 10.1007/s41999-024-00987-y.

Keywords

Multidimensional Prognostic Index
Delirium prediction
Comprehensive geriatric assessment
COVID-19
Older people
issue-copyright-statement© European Geriatric Medicine Society 2024
==== Body
pmcIntroduction

Delirium, as defined by the DSM-5 [1] criteria, consists of a disturbance in attention and awareness, together with a cognitive change, developed over hours or a few days and representing a severe change from baseline functioning. It is a neuropsychiatric syndrome common among older people and is the most frequent hospital admission complication, [2] with an occurrence ranging from 11 to 42% [3] of patients, especially after surgery [4].

As opposed to prevalent delirium which stands for the insurgence of delirium at admission in the Emergency Department (ED), incident delirium can be applied to patients who were non-delirious at hospital admission and develop delirium during hospitalization or ED stay [5]. Delirium etiology is quite heterogeneous, involving some predisposing factors, such as old age, sensory impairments, presence of severe illnesses and cognitive impairment, and triggering or precipitating factors such as dehydration [6], infection, malnutrition, polypharmacy, environmental changes, and, especially for incident delirium, the occurrence of iatrogenic events [2, 7]. Incident delirium occurring in ED is associated with higher morbidity and mortality risks together with an increase in-hospital stay (21 days Vs. 9 days without delirium), and a greater risk of developing dementia and loss of independence [7].

However, because delirium could be prevented with tailored interventions [8–11] early identification of patients at risk for delirium seems to be important. In spite of the availability in clinical practice of several tools for the early delirium risk assessment, a systematic method is not universally defined and delirium is often underrated [12]. Furthermore, predictive models are quite heterogeneous, focusing on different risk factors and addressing diverse populations [2].

A recent review identified several delirium prediction models [13] with varying degrees of accuracy (area under the curve-AUC from 0.52 to 0.94). Easy-to-assess but reliable prediction models for the assessment at hospital admission are the AWOL delirium risk-stratification score [14] and the Martinez model [6], both including age and then focusing on cognition, disorientation, and illness severity for the former, and dependence and dementia diagnosis for the latter. Moreover, a systematic review with meta-analysis [15] highlighted a 2.2-fold greater risk of developing delirium in frail individuals, stressing the usefulness of deepening the knowledge about the possible relation between these two conditions and the role of frailty as a predisposing factor for delirium, as it can multidimensionally contribute to susceptibility to negative outcomes [15]. In addition, delirium could be the phenotypic presentation and the neuropsychiatric manifestation of an underlying frailty condition. This has emerged with particular strength during the COVID-19 pandemic, in which delirium often represented an atypical presentation of the disease [16] in frail older adults.

The Multidimensional Prognostic Index (MPI) is a prognostic tool derived from the Comprehensive Geriatric Assessment (CGA) which is able to stratify older adults based on the risk of negative outcomes [17, 18] and may help in daily practice for the clinical decision-making [19]. Recently, the MPI demonstrated to accurately predict pre-operative delirium in older adults undergoing surgery for hip fracture [20]. However, no studies evaluated the potential predictive value of the MPI in general medicine wards to identify the subjects at higher risk of delirium during hospitalization.

Given this background and the inconsistency of the currently available delirium predictive models, along with the importance of reducing assessment time while remaining reliable and precise, we tested the ability of the MPI to predict the risk of incident delirium among older adults hospitalized with COVID-19 disease.

Materials and methods

Study population

This study is a longitudinal observational cohort study that was carried out in compliance with the Declaration of Helsinki and formally authorized by the local ethical committees of each participating institution. Participants were older subjects consecutively admitted to the hospital with a diagnosis of COVID-19 infection, enrolled from April 2020 to August 2021. Patients were hospitalized in general medicine wards (i.e., geriatrics, internal medicine units) from 10 European centers located in Italy (5 centers, 272 participants), Spain (1 center, 46 participants), Czech Republic (1 center, 153 participants), Portugal (1 center, 34 participants), and Germany (2 centers, 43 participants). Inclusion criteria were a) being at least 65 years old, b) consecutively being admitted to the hospital with a COVID-19 diagnosis made through a nasopharyngeal swab, and c) willingness to participate in the study. Exclusion criteria were age under 65 years and being unwilling or unable to provide informed consent.

Informed consent was given by the participants for their clinical records to be used in clinical studies: since the patients could be not able to understand the aims of the study (e.g., for severe hypoxemia), we recorded informed consent until 48 h after the admission. COVID-19 severity was defined as the use of non-invasive ventilation (NIV) or oro-tracheal intubation during the hospitalization. All the patient records were anonymized and de-identified before the analyses.

Exposure: the Multidimensional Prognostic Index (MPI)

The MPI [17] is a widely used and validated CGA-based [19] instrument for the assessment of multidimensional frailty in hospitalized older people, able to predict negative outcomes (e.g., rehospitalization, institutionalization, mortality and falls) [18]. This tool has been already demonstrated to be feasible also in patients with respiratory failure and hospitalized with COVID-19 disease [21, 22]. The MPI explored functional, nutritional, cognitive and social status, levels of mobility, comorbidities and polypharmacy (see Supplementary Materials).

The final score range between 0 = no risk and 1 = higher risk of mortality and can be classified as MPI-1 (low risk of frailty, MPI index under 0.33), MPI-2 (moderate risk of frailty, MPI index between 0.34 and 0.66), or MPI-3 (high risk of frailty, MPI index greater than 0.67). The MPI was administered during the first 24–48 h from the admission by a health-professional.

Delirium assessment: the 4 “A”s test (4AT)

For delirium detection, we used the 4AT, a simple and reliable instrument. The administration takes about 2 min and does not require any training. Furthermore, vision or hearing impairment does not interfere with the examination. It is composed of four items: 1. level of Alertness [23]; 2. a brief cognitive assessment through the Abbreviated Mental Test-4 [24]; 3. Attention evaluation [25]; 4. Acute change or fluctuating mental status occurring within the last 2 weeks and enduring in the last 24 h [26]. Each item was summed to obtain a score from 0 to 12, with 4 as a cut-off for possible delirium [27]. For delirium, the 4AT’s sensitivity is 89.7% and its specificity is 84.1% [27]. In the study, the 4As Test was routinely administered at admission and on discharge and also at any time during hospitalization when delirium is suspected based on clinical observation.

Delirium prediction models

Based on a previous systematic review by Lindroth and colleagues we identified all the potential delirium prediction tools [13]. Given the retrospective nature of the analysis we selected those tools that could be calculated from the available information in our dataset. Thus, as the study’s delirium prediction tools, the AWOL delirium risk-stratification score and the Martinez model (as modified by [28]) were used:The AWOL delirium risk-stratification score is calculated by giving 1 point each to increased nurse-rated illness severity and age over 80 years and 2 points to dementia diagnosis and/or Mini Mental State Examination (MMSE) [29] score < 24, or Abbreviated Mental Test Score (AMTS) [30] score < 9;

The Martinez model predicts delirium based on the presence of three criteria: age over 85 years, loss of independence in at least five ADLs, and cognitive impairment based on MMSE (score < 24) or AMTS (score < 9).

In the present study, we used as cognitive rating an SPMSQ score higher or equal to 8 according to the previously validated comparison with an MMSE score < 24 [31, 32].

Statistical analysis

The descriptive characteristics of the study population were expressed as means and standard deviations for continuous variables and percentages (%) for categorical variables. The Shapiro–Wilk test was used to determine the normality of distributions. For the comparison of continuous variables between subjects with and without incident delirium, independent sample t-tests (or the equivalent nonparametric test) were used. The percentages of the categorical variables were compared using Chi-square tests for the same two subsamples. To test the associations between the diagnosis of incident delirium and MPI (adjusting for age and gender and for age, gender, AWOL delirium risk-stratification score, Martinez model and COVID-19 severity), logistic regression models were used to calculate odds ratios (ORs) and their 95% confidence intervals (CIs).

Finally, the AUC was examined to gage how well the MPI, the AWOL delirium risk-stratification score, and the Martinez model predicted the diagnosis of incident delirium. The AUCs were compared using the test proposed by DeLong et al. [33]. All analyses were conducted using SPSS (Version 26.0) and all two-tailed statistical tests were deemed statistically significant at a p-value of 0.05 or less.

Results

From the initial study population of 502 patients, 206 (41%) were excluded having delirium at admission (4AT score ≥ 4/12). Moreover, three participants had missing data and were also excluded from the analysis. Therefore, the final sample was composed of 293 patients (57% females), of which 33 (11.3%) developed incident delirium during their hospitalization.

Table 1 shows the baseline characteristics of the sample classified according to the development of delirium during the hospitalization. Patients who had delirium were older than the ones who did not develop it (82.7 ± 7.3 vs. 79.2 ± 8.1, p = 0.018). Blood parameters (including pO2) and clinical and immunologic status did not differ between patients with and without delirium. The scores of the delirium predictive models were significantly higher (higher delirium risk) among subjects who developed delirium (AWOL delirium risk-stratification score: 1.394 ± 0.899 vs. 0.850 ± 0.817, p < 0.001; Martinez model: 1.333 ± 0.889 vs. 0.977 ± 0.795; p = 0.002). Moreover, the MPI score in the delirium group was higher than the comparison group without delirium (0.59 ± 0.19 vs. 0.43 ± 0.22; p < 0.001), with significantly poorer scores in the following MPI-domains: IADL, cognitive status, mobility, and number of medications.Table 1 Baseline descriptive characteristics, by incident delirium during the follow-up

Parameter	All sample	
Delirium (n = 33)	No delirium (n = 260)	p-value	
Mean age (mean, SD)	82.7 (7.3)	79.2 (8.1)	0.018*	
Female gender (%)	48.5	58.1	0.294	
MPI domains (means, SDs)				
 ADL score	3.7 (2.0)	4.2 (1.9)	0.185	
 IADL score	3.0 (2.4)	4.8 (2.4)	0.001*	
 SPMSQ score	4.6 (2.5)	3.4 (2.2)	0.006*	
 ESS score	14.4 (4.0)	16.1 (3.2)	0.002*	
 MNA-SF score	8.1 (3.3)	9.2 (3.3)	0.061	
 CIRS-CI score	4.3 (1.8)	3.7 (2.1)	0.101	
 Number of medications	7.4 (3.3)	5.9 (3.1)	0.012*	
 Living alone (%)	9.1	23.9	0.267	
 MPI score	0.59 (0.19)	0.43 (0.22)	 < 0.0001*	
Blood parameters (means, SDs)				
 CRP	16.7 (23.8)	10.8 (16.2)	0.188	
 PO2	57.3 (18.2)	45.3 (25.8)	0.330	
 SatO2	85.3 (15.9)	90.9 (11.5)	0.081	
Clinical and immunologic status (%)				
 Dyspnea	42.4	57.5	0.100	
 Cough	42.4	43.0	0.948	
 Fever	48.5	50.6	0.821	
 Diarrhea	24.2	15.1	0.180	
Clinical scores predicting delirium				
 AWOL delirium risk-stratification score	1.394 (0.899)	0.850 (0.817)	 < 0.0001*	
 Martinez model	1.333 (0.889)	0.977 (0.795)	0.002*	
SD standard deviation; MPI Multidimensional Prognostic Index; ADL activities of daily living; IADL instrumental activities of daily living; SPMSQ short portable mental status questionnaire; ESS exton smith scale; MNA-SF mini-nutritional assessment, short form; CIRS-CI cumulative illness rating scale-comorbidity index; CRP c-reactive protein; PO2 partial pressure of oxygen; SatO2oxygen saturation of arterial blood

*Statistically significant p-value 

Compared to subjects in the low-risk category (MPI-1) at hospital admission, those in the moderate-risk (MPI-2) as well as those in the high-risk MPI category (MPI-3) showed higher risk of developing incident delirium independently of age, gender, AWOL delirium risk-stratification score and Martinez model and COVID-19 severity (OR = 12.72, 95% CI = 2.11–76.86, p = 0.006 for MPI-2 vs. MPI-1; OR = 33.44, 95% CI = 4.55–146.61, p = 0.001 for MPI-3 vs. MPI-1) (Table 2).Table 2 Logistic regression model for the prediction of incident delirium

MPI categories	Model 1	Model 2	
OR	95% CI	p-value	OR	95% CI	p-value	
MPI-1	Reference			Reference			
MPI-2	5.353	1.107–25.886	0.037	12.72	2.11–76.86	0.006	
MPI-3	11.087	2.242–54.825	0.003	33.44	4.55–146.61	0.001	
All data are reported as odds ratios (ORs) with their 95% confidence intervals (CIs). Model 1 was adjusted for age and gender, model 2 was adjusted for age, gender, AWOL delirium risk-stratification score and Martinez model and COVID-19 severity

We calculated the AUC of the MPI and the two previously validated predictive models (the AWOL delirium risk-stratification score and the Martinez model) to test MPI’s accuracy in predicting incident delirium. As shown in Fig. 1 and Table 3, the MPI’s Receiver Operating Characteristic (ROC) Curve Area was 0.71 (p < 0.001), indicating that the MPI can predict nearly 71% of subjects with incident delirium and indicating that MPI was more accurate in predicting incident delirium compared to the AWOL delirium risk-stratification score (AUC = 0.63) and the Martinez model (AUC = 0.61), respectively (p < 0.0001 for both comparisons). An MPI absolute value of 0.33 shows a sensitivity of 95% and a specificity of 25% while a score of 0.66 has a sensitivity of 59% and a specificity of 70%.Fig. 1 Accuracy of Multidimensional Prognostic Index (MPI), AWOL delirium risk-stratification score and Martinez models in predicting incident delirium

Table 3 Area under the curve and confidence intervals of the MPI and of the two other predictive models based on the capacity to predict incident delirium

Parameter	Area	Standard error	p-value	95% Confidence intervals	
Lower	Higher	
MPI	0.707	0.039	 < 0.0001	0.631	0.782	
AWOL delirium risk-stratification score	0.634	0.047	0.006	0.541	0.727	
Martinez model	0.605	0.044	0.031	0.519	0.691	

Discussion

In this study, we found that a CGA-based prognostic instrument, such as the MPI, performed at hospital admission can be an accurate tool for predicting delirium risk during hospitalization in older adults with COVID-19. The MPI identified with good accuracy patients at risk for delirium (AUC = 0.71) and showed greater discriminatory power compared to some currently adopted delirium prediction tools, i.e., the AWOL delirium risk-stratification score and the Martinez model.

Delirium is frequently reported as concomitant to SARS-CoV-2 infection, developing roughly in one out of five older subjects hospitalized with COVID-19 [34]. Compared to the pre-pandemic period when delirium prevalence was reported as higher, the estimates across the COVID-19 waves showed a tendency to decrease probably due to the vaccination programs which attenuated the COVID-19 severity [35, 36]. Occurrence of delirium is higher among frail subjects compared to non-frail reaching a prevalence of 37%.

The study was conducted until the third pandemic wave, on a sample of hospitalized older adults with COVID-19 which showed a 41% prevalence of delirium, similar to previously reported estimates, [37] and a slightly lower incidence (about 11%) probably because the delirium was more frequently identified in the emergency department.

Delirium may represent a sentinel event, predisposing to a higher risk of morbidity and mortality [16]. Instruments able to recognize older subjects at risk for delirium are strategic to start early preventive interventions. A recent systematic review summarized evidence on 14 externally validated delirium prediction models [13]. The items more often included in these instruments were cognitive impairment, sensory deficit, advanced age, poor functional status, illness severity, history of alcohol consumption, and presence of infectious disease. Collectively, such tools showed variable and, in most cases, inadequate predictive capabilities [13]. Moreover, very few instruments have been specifically developed for general medicine settings and often showed a high risk of bias and poor generalizability [38, 39]. Some delirium prediction models have also been specifically developed and validated for COVID-19 disease. For example, Castro et al. proposed an electronic health records-based tool built upon a machine-learning approach derived from demographic, clinical, laboratory, and medication information. This model showed an AUC of 0.75 for incident delirium, but the accuracy decreased in older adults (AUC = 0.67) and those with a history of dementia (AUC = 0.58) [40]. Conversely, the MPI, used in our study conducted on an older population, showed better performance in predicting delirium risk (AUC = 0.71), compared to two already validated delirium prediction tools as well (AWOL and the Martinez model).

This might suggest that information routinely collected from a standard CGA could be able to identify older adults hospitalized for COVID-19 who are more prone to develop delirium. Moreover, we found that older in-patients who developed delirium during hospital stay were significantly older, took a higher number of medications, and had lower cognitive performance and functional status compared to those patients who did not have delirium. At admission, subjects who developed delirium showed higher levels of multidimensional impairment assessed by the MPI. Furthermore, higher MPI levels (MPI-2 and MPI-3) may predict a greater risk of developing delirium during the follow-up, compared to the lowest risk MPI category. Previous evidence already highlighted that the MPI can predict pre-operative delirium in older adults with hip fractures [20], emphasizing that a standardized CGA might allow the identification of older subjects at risk for delirium in very heterogeneous settings and independently by age, gender, and setting-specific risk factors. Such a strict association between multidimensional frailty and delirium might be explained also at a biologic level by common pathogenetic mechanisms such as the emerging role of systemic inflammation [41, 42].

Our study demonstrated that a multidimensional assessment using the MPI has greater accuracy in predicting the occurrence of delirium, compared to two other validated prediction models for delirium. Overall, our data corroborate the theory that multidimensional aggregate information, readily available in clinical practice and easy to obtain, could aid physicians in predicting mortality, as previously reported, and the occurrence of delirium as well.

We should acknowledge some limitations of this study. The retrospective nature of the analysis did not allow the collection of potentially relevant information such as delirium motor subtypes (hyperactive, hypoactive, mixed), delirium delay from admission, residual confounders (e.g., other well-known precipitating factors including medications, procedures, and use of devices), and different delirium prediction models. Furthermore, it is well recognized in the scientific literature that clinical judgment might indeed underestimate the adequate identification of delirium [43], thus the use of 4AT assessment prompted by clinical suspicion and not carried out daily could have underestimated the true incidence of delirium in this population. Moreover, follow-up information on delirium occurrence after the index hospitalization was not available. Finally, we did not collect detailed information about the medications used: therefore, we could not consider the role of this specific factor in determining incident delirium.

In conclusion, a CGA-based tool, such as the MPI, when performed at hospital admission, might represent a sensitive instrument predicting delirium in older adults with COVID-19 disease. This tool outperformed prediction models specifically validated for delirium, identifying subjects at risk who may need an individualized approach to prevent delirium occurrence. Future research should test the usefulness of personalized clinical approaches guided by this CGA-based tool in delirium prevention.

Supplementary Information

Below is the link to the electronic supplementary material.Supplementary file1 (DOCX 20 KB)

Acknowledgements

This article is under the auspices of the Special Interest Group in Comprehensive Geriatric Assessment (CGA) of the European Geriatric Medicine Society (EuGMS). MPI-COVID-19 Study Group Investigators: Mario Barbagallo (Department of Internal Medicine and Geriatrics, University of Palermo, Palermo, Italy), Marina Barbagelata (Department of Geriatric Care, Neurology and Rehabilitation, Galliera Hospital, Genoa, Italy), Simone Dini (Department of Geriatric Care, Neurology and Rehabilitation, Galliera Hospital, Genoa, Italy), Naima Madlen Diesner (Division of Geriatrics, University Medical Center Goettingen, Goettingen, Germany), Marilia Fernandes (Serviço de Medicina Interna, Hospital Curry Cabral, Centro Hospitalar Universitário Lisboa Central/Universidade Nova de Lisboa, Lisbon, Portugal), Federica Gandolfo (Department of Geriatric Care, Neurology and Rehabilitation, Galliera Hospital, Genoa, Italy), Sara Garaboldi (Department of Geriatric Care, Neurology and Rehabilitation, Galliera Hospital, Genoa, Italy), Clarissa Musacchio (Department of Geriatric Care, Neurology and Rehabilitation, Galliera Hospital, Genoa, Italy), Andrea Pilotto (Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy), Lena Pickert (Ageing Clinical Research, Department II of Internal Medicine and Center for Molecular Medicine, University of Cologne, Cologne, Germany), Silvia Podestà (Department of Geriatric Care, Neurology and Rehabilitation, Galliera Hospital, Genoa, Italy), Giovanni Ruotolo (Geriatrics Unit, “Pugliese Ciaccio” Hospital, Catanzaro, Italy), Katiuscia Sciolè (Infectious Disease Unit, Sanremo Hospital, ASL 1 Imperiese, Sanremo, Italy), Julia Schlotmann (Klinikum Oldenburg AöR, Oldenburg University, Oldenburg, Germany).

Author contributions

Alberto Pilotto and Nicola Veronese conceived and designed the study. Wanda Morganti and Carlo Custodero wrote the original draft. Nicola Veronese performed the statistical analysis and takes responsibility for the accuracy of the data analysis. Data curation was performed by Emanuele Seminerio. Eva Topinkova, Helena Michalkova, Maria Cristina Polidori, Alfonso J. Cruz-Jentoft, Christine A.F. von Arnim, Margherita Azzini, Heidi Gruner, Alberto Castagna, Giovanni Cenderello, Romina Custureri, Tania Zieschang, Alessandro Padovani, Elisabet Sanchez-Garcia contributed to data collection, assisted in data interpretation, and revised the manuscript. Alberto Pilotto critically revised the final manuscript.

Funding

None.

Data availability

The datasets generated and/or analysed during the current study are not publicly available. However, the datasets are available from the corresponding author on reasonable request.

Declarations

Conflict of interest

The Author(s) declared that they were an Editorial Board Member/Editor-in-Chief of European Geriatric Medicine, at the time of submission. The Authors have no relevant financial or non-financial interests to disclose.

Ethical approval

All procedures performed in studies involving human participants were conducted in accordance with the ethical standards of the institutional and national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. The study was approved by the Regional Ethical Committee (CER Liguria, n.131/2020, 20 April 2020), and by the local ethical committees of each participating institution.

Informed consent

The participants provided their written informed consent to participate in this study.

Members of the MPI-COVID-19 Study Group Investigators are listed below in the Acknowledgments.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
==== Refs
References

1. American Psychiatric Association (2013) Diagnostic and Statistical Manual of Mental Disorders [Internet]. Fifth Edition. American Psychiatric Association; [citato 13 dicembre 2023]. Disponibile su: https://psychiatryonline.org/doi/book/10.1176/appi.books.9780890425596
2. Inouye SK Predisposing and precipitating factors for delirium in hospitalized older patients Dement Geriatr Cogn Disord 1999 10 5 393 400 10.1159/000017177 10473946
Inouye SK (1999) Predisposing and precipitating factors for delirium in hospitalized older patients. Dement Geriatr Cogn Disord 10(5):393–40010473946 10.1159/000017177
3. Siddiqi N House AO Holmes JD Occurrence and outcome of delirium in medical in-patients: a systematic literature review Age Ageing 2006 35 4 350 364 10.1093/ageing/afl005 16648149
Siddiqi N, House AO, Holmes JD (2006) Occurrence and outcome of delirium in medical in-patients: a systematic literature review. Age Ageing 35(4):350–36416648149 10.1093/ageing/afl005
4. McCusker J Cole M Abrahamowicz M Han L Podoba JE Ramman-Haddad L Environmental risk factors for delirium in hospitalized older people J Am Geriatr Soc 2001 49 10 1327 1334 10.1046/j.1532-5415.2001.49260.x 11890491
McCusker J, Cole M, Abrahamowicz M, Han L, Podoba JE, Ramman-Haddad L (2001) Environmental risk factors for delirium in hospitalized older people. J Am Geriatr Soc 49(10):1327–133411890491 10.1046/j.1532-5415.2001.49260.x
5. Silva e LOJ Berning MJ Stanich JA Gerberi DJ Han J Bellolio F Risk factors for delirium among older adults in the emergency department a systematic review protocol BMJ Open 2020 10 7 e039175 10.1136/bmjopen-2020-039175
Silva e LOJ, Berning MJ, Stanich JA, Gerberi DJ, Han J, Bellolio F (2020) Risk factors for delirium among older adults in the emergency department a systematic review protocol. BMJ Open 10(7):e03917510.1136/bmjopen-2020-039175
6. Martinez JA Belastegui A Basabe I Goicoechea X Aguirre C Lizeaga N Derivation and validation of a clinical prediction rule for delirium in patients admitted to a medical ward: an observational study BMJ Open 2012 2 5 e001599 10.1136/bmjopen-2012-001599 22983876
Martinez JA, Belastegui A, Basabe I, Goicoechea X, Aguirre C, Lizeaga N et al (2012) Derivation and validation of a clinical prediction rule for delirium in patients admitted to a medical ward: an observational study. BMJ Open 2(5):e00159922983876 10.1136/bmjopen-2012-001599
7. Pérez-Ros P Martínez-Arnau FM Delirium assessment in older people in emergency departments a literature review Diseases 2019 7 1 14 10.3390/diseases7010014 30704024
Pérez-Ros P, Martínez-Arnau FM (2019) Delirium assessment in older people in emergency departments a literature review. Diseases 7(1):1430704024 10.3390/diseases7010014
8. Han JH Eden S Shintani A Morandi A Schnelle J Dittus RS Delirium in older emergency department patients is an independent predictor of hospital length of stay Acad Emerg Med 2011 18 5 451 457 10.1111/j.1553-2712.2011.01065.x 21521405
Han JH, Eden S, Shintani A, Morandi A, Schnelle J, Dittus RS et al (2011) Delirium in older emergency department patients is an independent predictor of hospital length of stay. Acad Emerg Med 18(5):451–45721521405 10.1111/j.1553-2712.2011.01065.x
9. Han JH Shintani A Eden S Morandi A Solberg LM Schnelle J Delirium in the emergency department: an independent predictor of death within 6 months Ann Emerg Med 2010 56 3 244 252.e1 10.1016/j.annemergmed.2010.03.003 20363527
Han JH, Shintani A, Eden S, Morandi A, Solberg LM, Schnelle J et al (2010) Delirium in the emergency department: an independent predictor of death within 6 months. Ann Emerg Med 56(3):244–252.e120363527 10.1016/j.annemergmed.2010.03.003
10. Han JH Wilson A Ely EW Delirium in the older emergency department patient: a quiet epidemic Emerg Med Clin North Am 2010 28 3 611 631 10.1016/j.emc.2010.03.005 20709246
Han JH, Wilson A, Ely EW (2010) Delirium in the older emergency department patient: a quiet epidemic. Emerg Med Clin North Am 28(3):611–63120709246 10.1016/j.emc.2010.03.005
11. Leslie DL Inouye SK The importance of delirium economic and societal costs J Am Geriatr Soc 2011 59 s2 S241 243 10.1111/j.1532-5415.2011.03671.x 22091567
Leslie DL, Inouye SK (2011) The importance of delirium economic and societal costs. J Am Geriatr Soc 59(s2):S241–24322091567 10.1111/j.1532-5415.2011.03671.x
12. Bellelli G Nobili A Annoni G Morandi A Djade CD Meagher DJ Under-detection of delirium and impact of neurocognitive deficits on in-hospital mortality among acute geriatric and medical wards Eur J Internal Med 2015 26 9 696 704 10.1016/j.ejim.2015.08.006 26333532
Bellelli G, Nobili A, Annoni G, Morandi A, Djade CD, Meagher DJ et al (2015) Under-detection of delirium and impact of neurocognitive deficits on in-hospital mortality among acute geriatric and medical wards. Eur J Internal Med 26(9):696–70426333532 10.1016/j.ejim.2015.08.006
13. Lindroth H Bratzke L Purvis S Brown R Coburn M Mrkobrada M Systematic review of prediction models for delirium in the older adult inpatient BMJ Open 2018 8 4 e019223 10.1136/bmjopen-2017-019223 29705752
Lindroth H, Bratzke L, Purvis S, Brown R, Coburn M, Mrkobrada M et al (2018) Systematic review of prediction models for delirium in the older adult inpatient. BMJ Open 8(4):e01922329705752 10.1136/bmjopen-2017-019223
14. Douglas VC Hessler CS Dhaliwal G Betjemann JP Fukuda KA Alameddine LR The AWOL tool: derivation and validation of a delirium prediction rule J Hosp Med 2013 8 9 493 499 10.1002/jhm.2062 23922253
Douglas VC, Hessler CS, Dhaliwal G, Betjemann JP, Fukuda KA, Alameddine LR et al (2013) The AWOL tool: derivation and validation of a delirium prediction rule. J Hosp Med 8(9):493–49923922253 10.1002/jhm.2062
15. Persico I Cesari M Morandi A Haas J Mazzola P Zambon A Frailty and delirium in older adults: a systematic review and meta-analysis of the literature J Am Geriatr Soc 2018 66 10 2022 2030 10.1111/jgs.15503 30238970
Persico I, Cesari M, Morandi A, Haas J, Mazzola P, Zambon A et al (2018) Frailty and delirium in older adults: a systematic review and meta-analysis of the literature. J Am Geriatr Soc 66(10):2022–203030238970 10.1111/jgs.15503
16. White L Jackson T Delirium and COVID-19: a narrative review of emerging evidence Anaesthesia 2022 77 S1 49 58 10.1111/anae.15627 35001383
White L, Jackson T (2022) Delirium and COVID-19: a narrative review of emerging evidence. Anaesthesia 77(S1):49–5835001383 10.1111/anae.15627
17. Pilotto A Ferrucci L Franceschi M D’Ambrosio LP Scarcelli C Cascavilla L Development and validation of a multidimensional prognostic index for one-year mortality from comprehensive geriatric assessment in hospitalized older patients Rejuvenation Res 2008 11 1 151 161 10.1089/rej.2007.0569 18173367
Pilotto A, Ferrucci L, Franceschi M, D’Ambrosio LP, Scarcelli C, Cascavilla L et al (2008) Development and validation of a multidimensional prognostic index for one-year mortality from comprehensive geriatric assessment in hospitalized older patients. Rejuvenation Res 11(1):151–16118173367 10.1089/rej.2007.0569
18. Pilotto A Veronese N Daragjati J Cruz-Jentoft AJ Polidori MC Mattace-Raso F Using the multidimensional prognostic index to predict clinical outcomes of hospitalized older persons: a prospective, multicenter, international study J Gerontol A Biol Sci Med Sci 2019 74 10 1643 1649 10.1093/gerona/gly239 30329033
Pilotto A, Veronese N, Daragjati J, Cruz-Jentoft AJ, Polidori MC, Mattace-Raso F et al (2019) Using the multidimensional prognostic index to predict clinical outcomes of hospitalized older persons: a prospective, multicenter, international study. J Gerontol A Biol Sci Med Sci 74(10):1643–164930329033 10.1093/gerona/gly239
19. Cruz-Jentoft AJ Daragjati J Fratiglioni L Maggi S Mangoni AA Mattace-Raso F Using the Multidimensional Prognostic Index (MPI) to improve cost-effectiveness of interventions in multimorbid frail older persons: results and final recommendations from the MPI_AGE European Project Aging Clin Exp Res 2020 32 5 861 868 10.1007/s40520-020-01516-0 32180170
Cruz-Jentoft AJ, Daragjati J, Fratiglioni L, Maggi S, Mangoni AA, Mattace-Raso F et al (2020) Using the Multidimensional Prognostic Index (MPI) to improve cost-effectiveness of interventions in multimorbid frail older persons: results and final recommendations from the MPI_AGE European Project. Aging Clin Exp Res 32(5):861–86832180170 10.1007/s40520-020-01516-0
20. Musacchio C Custodero C Razzano M Raiteri R Delrio A Torriglia D Association between multidimensional prognostic index (MPI) and pre-operative delirium in older patients with hip fracture Sci Rep 2022 12 1 16920 10.1038/s41598-022-20734-2 36209284
Musacchio C, Custodero C, Razzano M, Raiteri R, Delrio A, Torriglia D et al (2022) Association between multidimensional prognostic index (MPI) and pre-operative delirium in older patients with hip fracture. Sci Rep 12(1):1692036209284 10.1038/s41598-022-20734-2
21. Pilotto A Topinkova E Michalkova H Polidori MC Cella A Cruz-Jentoft A Can the multidimensional prognostic index improve the identification of older hospitalized patients with COVID-19 likely to benefit from mechanical ventilation? an observational, prospective, multicenter study J Am Med Dir 2022 23 9 1608.e1 1608.e8 10.1016/j.jamda.2022.06.023
Pilotto A, Topinkova E, Michalkova H, Polidori MC, Cella A, Cruz-Jentoft A et al (2022) Can the multidimensional prognostic index improve the identification of older hospitalized patients with COVID-19 likely to benefit from mechanical ventilation? an observational, prospective, multicenter study. J Am Med Dir 23(9):1608.e1–1608.e810.1016/j.jamda.2022.06.023
22. Custodero C Gandolfo F Cella A Cammalleri LA Custureri R Dini S Multidimensional prognostic index (MPI) predicts non-invasive ventilation failure in older adults with acute respiratory failure Arch Gerontol Geriatr 2021 94 104327 10.1016/j.archger.2020.104327 33485005
Custodero C, Gandolfo F, Cella A, Cammalleri LA, Custureri R, Dini S et al (2021) Multidimensional prognostic index (MPI) predicts non-invasive ventilation failure in older adults with acute respiratory failure. Arch Gerontol Geriatr 94:10432733485005 10.1016/j.archger.2020.104327
23. Quispel-Aggenbach DWP Holtman GA Zwartjes HAHT Zuidema SU Luijendijk HJ Attention arousal and other rapid bedside screening instruments for delirium in older patients a systematic review of test accuracy studies Age Ageing 2018 47 5 644 653 10.1093/ageing/afy058 29697753
Quispel-Aggenbach DWP, Holtman GA, Zwartjes HAHT, Zuidema SU, Luijendijk HJ (2018) Attention arousal and other rapid bedside screening instruments for delirium in older patients a systematic review of test accuracy studies. Age Ageing 47(5):644–65329697753 10.1093/ageing/afy058
24. Schofield I Stott DJ Tolson D McFadyen A Monaghan J Nelson D Screening for cognitive impairment in older people attending accident and emergency using the 4-item abbreviated mental test Eur J Emerg Med 2010 17 6 340 342 10.1097/MEJ.0b013e32833777ab 20164778
Schofield I, Stott DJ, Tolson D, McFadyen A, Monaghan J, Nelson D (2010) Screening for cognitive impairment in older people attending accident and emergency using the 4-item abbreviated mental test. Eur J Emerg Med 17(6):340–34220164778 10.1097/MEJ.0b013e32833777ab
25. Van de Meeberg EK Festen S Kwant M Georg RR Izaks GJ Ter Maaten JC Improved detection of delirium, implementation and validation of the CAM-ICU in elderly emergency department patients Eur J Emerg Med 2017 24 6 411 416 10.1097/MEJ.0000000000000380 26894309
Van de Meeberg EK, Festen S, Kwant M, Georg RR, Izaks GJ, Ter Maaten JC (2017) Improved detection of delirium, implementation and validation of the CAM-ICU in elderly emergency department patients. Eur J Emerg Med 24(6):411–41626894309 10.1097/MEJ.0000000000000380
26. Mitchell G (2023) Delirium: prevention, diagnosis and management in hospital and long-term care. NICE Clinical Guidelines, No. 103. National Institute for Health and Care Excellence (NICE), London. https://www.ncbi.nlm.nih.gov/books/NBK553009/
27. Bellelli G Morandi A Davis DHJ Mazzola P Turco R Gentile S Validation of the 4AT a new instrument for rapid delirium screening a study in 234 hospitalised older people Age Ageing 2014 43 4 496 502 10.1093/ageing/afu021 24590568
Bellelli G, Morandi A, Davis DHJ, Mazzola P, Turco R, Gentile S et al (2014) Validation of the 4AT a new instrument for rapid delirium screening a study in 234 hospitalised older people. Age Ageing 43(4):496–50224590568 10.1093/ageing/afu021
28. Pendlebury ST Lovett N Smith SC Cornish E Mehta Z Rothwell PM Delirium risk stratification in consecutive unselected admissions to acute medicine: validation of externally derived risk scores Age Ageing 2016 45 1 60 65 10.1093/ageing/afv177 26764396
Pendlebury ST, Lovett N, Smith SC, Cornish E, Mehta Z, Rothwell PM (2016) Delirium risk stratification in consecutive unselected admissions to acute medicine: validation of externally derived risk scores. Age Ageing 45(1):60–6526764396 10.1093/ageing/afv177
29. Folstein MF Folstein SE McHugh PR Mini-mental state examination Arch Gen Psychiatry 1983 40 7 812 10.1001/archpsyc.1983.01790060110016 6860082
Folstein MF, Folstein SE, McHugh PR (1983) Mini-mental state examination. Arch Gen Psychiatry 40(7):8126860082 10.1001/archpsyc.1983.01790060110016
30. Hodkinson HM Evaluation of a mental test score for assessment of mental impairment in the elderly Age Ageing 1972 1 4 233 238 10.1093/ageing/1.4.233 4669880
Hodkinson HM (1972) Evaluation of a mental test score for assessment of mental impairment in the elderly. Age Ageing 1(4):233–2384669880 10.1093/ageing/1.4.233
31. Hooijer C Dinkgreve M Jonker C Lindeboom J Kay DWK Short screening tests for dementia in the elderly population I A comparison between AMTS MMSE MSQ and SPMSQ Int J Geriat Psychiatry 1992 7 8 559 571 10.1002/gps.930070805
Hooijer C, Dinkgreve M, Jonker C, Lindeboom J, Kay DWK (1992) Short screening tests for dementia in the elderly population I A comparison between AMTS MMSE MSQ and SPMSQ. Int J Geriat Psychiatry 7(8):559–57110.1002/gps.930070805
32. Angleman SB Santoni G Pilotto A Fratiglioni L Welmer AK Multidimensional prognostic index in association with future mortality and number of hospital days in a population-based sample of older adults: results of the EU funded MPIAGE project PLoS ONE 2015 10 7 e0133789 10.1371/journal.pone.0133789 26222546
Angleman SB, Santoni G, Pilotto A, Fratiglioni L, Welmer AK (2015) Multidimensional prognostic index in association with future mortality and number of hospital days in a population-based sample of older adults: results of the EU funded MPIAGE project. PLoS ONE 10(7):e013378926222546 10.1371/journal.pone.0133789
33. DeLong ER DeLong DM Clarke-Pearson DL Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach Biometrics 1988 44 3 837 845 10.2307/2531595 3203132
DeLong ER, DeLong DM, Clarke-Pearson DL (1988) Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44(3):837–8453203132 10.2307/2531595
34. Zazzara MB Ornago AM Cocchi C Serafini E Bellelli G Onder G A pandemic of delirium: an updated systematic review and meta-analysis of occurrence of delirium in older adults with COVID-19 Eur Geriatr Med 2024 15 2 397 406 10.1007/s41999-023-00906-7 38498073
Zazzara MB, Ornago AM, Cocchi C, Serafini E, Bellelli G, Onder G (2024) A pandemic of delirium: an updated systematic review and meta-analysis of occurrence of delirium in older adults with COVID-19. Eur Geriatr Med 15(2):397–40638498073 10.1007/s41999-023-00906-7
35. Minnema J Tap L Van Der Bol JM Van Deudekom FJA Faes MC Jansen SWM Delirium in older patients with COVID-19: prevalence, risk factors and clinical outcomes across the first three waves of the pandemic Int J Geriatr Psychiatry 2023 38 11 e6024 10.1002/gps.6024 37909117
Minnema J, Tap L, Van Der Bol JM, Van Deudekom FJA, Faes MC, Jansen SWM et al (2023) Delirium in older patients with COVID-19: prevalence, risk factors and clinical outcomes across the first three waves of the pandemic. Int J Geriatr Psychiatry 38(11):e602437909117 10.1002/gps.6024
36. Reppas-Rindlisbacher C Boblitz A Fowler RA Lapointe-Shaw L Sheehan KA Stukel TA Trends in delirium and new antipsychotic and benzodiazepine use among hospitalized older adults before and after the onset of the COVID-19 pandemic JAMA Netw Open 2023 6 8 e2327750 10.1001/jamanetworkopen.2023.27750 37548976
Reppas-Rindlisbacher C, Boblitz A, Fowler RA, Lapointe-Shaw L, Sheehan KA, Stukel TA et al (2023) Trends in delirium and new antipsychotic and benzodiazepine use among hospitalized older adults before and after the onset of the COVID-19 pandemic. JAMA Netw Open 6(8):e232775037548976 10.1001/jamanetworkopen.2023.27750
37. Schulthess-Lisibach AE Gallucci G Benelli V Kälin R Schulthess S Cattaneo M Predicting delirium in older non-intensive care unit inpatients: development and validation of the DELIrium risK Tool (DELIKT) Int J Clin Pharm 2023 45 5 1118 1127 10.1007/s11096-023-01566-0 37061661
Schulthess-Lisibach AE, Gallucci G, Benelli V, Kälin R, Schulthess S, Cattaneo M et al (2023) Predicting delirium in older non-intensive care unit inpatients: development and validation of the DELIrium risK Tool (DELIKT). Int J Clin Pharm 45(5):1118–112737061661 10.1007/s11096-023-01566-0
38. Kobayashi D Takahashi O Arioka H Koga S Fukui T A prediction rule for the development of delirium among patients in medical wards: chi-square automatic interaction detector (chaid) decision tree analysis model Am J Geriatr Psychiatry 2013 21 10 957 962 10.1016/j.jagp.2012.08.009 23567433
Kobayashi D, Takahashi O, Arioka H, Koga S, Fukui T (2013) A prediction rule for the development of delirium among patients in medical wards: chi-square automatic interaction detector (chaid) decision tree analysis model. Am J Geriatr Psychiatry 21(10):957–96223567433 10.1016/j.jagp.2012.08.009
39. Snigurska UA Liu Y Ser SE Macieira TGR Ansell M Lindberg D Risk of bias in prognostic models of hospital-induced delirium for medical-surgical units: a systematic review PLoS ONE 2023 18 8 e0285527 10.1371/journal.pone.0285527 37590196
Snigurska UA, Liu Y, Ser SE, Macieira TGR, Ansell M, Lindberg D et al (2023) Risk of bias in prognostic models of hospital-induced delirium for medical-surgical units: a systematic review. PLoS ONE 18(8):e028552737590196 10.1371/journal.pone.0285527
40. Castro VM Hart KL Sacks CA Murphy SN Perlis RH McCoy TH Longitudinal validation of an electronic health record delirium prediction model applied at admission in COVID-19 patients Gen Hosp Psychiatry 2022 74 9 17 10.1016/j.genhosppsych.2021.10.005 34798580
Castro VM, Hart KL, Sacks CA, Murphy SN, Perlis RH, McCoy TH (2022) Longitudinal validation of an electronic health record delirium prediction model applied at admission in COVID-19 patients. Gen Hosp Psychiatry 74:9–1734798580 10.1016/j.genhosppsych.2021.10.005
41. Forget MF Del Degan S Leblanc J Tannous R Desjardins M Durand M Delirium and inflammation in older adults hospitalized for COVID-19 a cohort study CIA 2021 10.2147/CIA.S315405
Forget MF, Del Degan S, Leblanc J, Tannous R, Desjardins M, Durand M et al (2021) Delirium and inflammation in older adults hospitalized for COVID-19 a cohort study. CIA. 10.2147/CIA.S31540510.2147/CIA.S315405
42. Pilotto A Custodero C Maggi S Polidori MC Veronese N Ferrucci L A multidimensional approach to frailty in older people Ageing Res Rev 2020 60 101047 10.1016/j.arr.2020.101047 32171786
Pilotto A, Custodero C, Maggi S, Polidori MC, Veronese N, Ferrucci L (2020) A multidimensional approach to frailty in older people. Ageing Res Rev 60:10104732171786 10.1016/j.arr.2020.101047
43. Mossello E Tesi F Di Santo SG Mazzone A Torrini M Cherubini A Recognition of delirium features in clinical practice: data from the “delirium day 2015” national survey J Am Geriatr Soc 2018 66 2 302 308 10.1111/jgs.15211 29206286
Mossello E, Tesi F, Di Santo SG, Mazzone A, Torrini M, Cherubini A et al (2018) Recognition of delirium features in clinical practice: data from the “delirium day 2015” national survey. J Am Geriatr Soc 66(2):302–30829206286 10.1111/jgs.15211
