Many countries throughout the world have experienced an unprecedented health care crisis caused by the new coronavirus-2 (SARS-CoV-2) infection. 1 The coronavirus disease 2019 (COVID- 19) is particularly concerning in older adults, whose underlying multimorbidity and vulnerability increase the risk of adverse outcomes following the infection. A recent report from the Centers for Disease Control and Prevention (CDC) has indicated that geriatric patients account for almost half of hospital admissions and up to 80% of deaths associated with COVID-19 acute respiratory syndrome, the most severe presentation of the disease. 2 The scenario is even more alarming in long-term care facilities, where a combination of the oldest and frailest patients can be found living in close proximity, with a high susceptibility to transmissible diseases. 3 Fever and respiratory symptoms have been described as the most frequent manifestations of COVID-19. 4, 5 However, neurologic symptoms have been commonly found in hospitalized patients. 6 In a case series completed in Wuhan, China, 36% of their patients had neurologic complaints, and 8% had impaired consciousness. 7 Older age is also thought to be a risk factor for atypical manifestations of COVID-19. 8 Recently, a case report from the United Kingdom described delirium, an acute neuropsychiatric syndrome characterized by inattention and fluctuating symptoms, as the sole manifestation of the disease in a frail nonagenarian. 9 Delirium has often been observed in the context of hospitalized patients with infectious diseases and identified as prominent clinical features in acute respiratory syndromes. 10, 11 Their occurrence can be interpreted as the clinical translation of an acute brain failure, most often resulting from multiple precipitating factors (e.g., dehydration, use of psychoactive drugs, infection) and leading to adverse outcomes, such as increased length of hospital stay, functional and cognitive decline, institutionalization, and death. 12, 13 . Given
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that COVID-19 is an acute infectious and inflammatory disease, which in its more severe presentations can lead to respiratory distress syndrome and require mechanical ventilation, it is likely to be associated with delirium. The issue becomes particularly troubling in the context of social distancing and respiratory isolation. Notwithstanding their importance, such measures can become barriers to the prevention and detection of delirium in older adults. 14, 15 Optimum non-pharmacologic management of delirious patients is also hindered by the limitations imposed on the presence of family members and caregivers in the hospital.
A recent investigation demonstrated that 26 out of 40 patients with severe SARS-CoV-2 infection had suggestive clinical features for delirium. 6 Despite these preliminary findings, little is known about the clinical significance of delirium in SARS-CoV-2 infected patients. Previous analyses were limited by their small sample sizes, absence of prognostic estimations 6 , or unclear definitions of delirium. 16 Therefore, our aims were to investigate the occurrence of delirium in a cohort of older adults hospitalized with COVID-19, and explore its association with adverse outcomes in this population.
Our work is part of the ongoing CO-FRAIL study, designed to investigate the association between frailty and adverse outcomes in SARS-CoV-2 infected patients. The CO-FRAIL study is a cohort study recruiting patients at Hospital das Clinicas, a tertiary university hospital affiliated to the University of Sao Paulo Medical School, in Brazil.
Hospital das Clinicas has become a major center for COVID-19 treatment in Sao Paulo, the epicenter of the pandemic in Brazil. In March 2020, the main hospital building was converted to a COVID-19-only facility, dedicating 900 beds to the care of infected patients. Admissions
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to the COVID-19 unit are centrally managed by the Regulatory Central of the State of Sao Paulo, and severely ill patients are preferably referred to the hospital.
We assessed the eligibility of all patients who were consecutively admitted to the hospital between March 30, 2020, and May 18, 2020. We included COVID-19 cases of Our local institutional review board approved the study, and we adhered to the principles expressed in the Declaration of Helsinki. All patient-identifiable information were stored in secure electronic servers, with access restricted to our researchers.
Trained medical investigators completed standardized electronic case report forms using Research Electronic Data Capture (REDCap) resources. 18 They retrospectively collected the study information reviewing electronic medical records, nursing records, consulting notes, laboratory tests, and radiologic exams from the complete hospitalizations.
Whenever necessary, our investigators also performed structured telephone interviews with the participants or their representatives to complement or clarify their information.
Our primary independent variable was the overall occurrence of delirium, which we defined using the Chart-based Delirium Identification Instrument (CHART-DEL). 19, 20 The presence of delirium is characterized by the CHART-DEL instrument based on the documented evidence of acute mental change, combined with at least one of several key
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descriptors during hospital stay (e.g., delirium, confusional state, disorientation, hallucinations, agitation, etc.). Medical records must be reviewed in their entirety to capture the occurrence of delirium, including medical and multidisciplinary notes, consultant notes, and admission and discharge summaries. CHART-DEL is more accurate than other chartbased methods, with an overall sensitivity of 74% and specificity of 83% when performed by trained nurses. 19 In our study, the CHART-DEL assessments were completed by trained physicians with a background in geriatric medicine. Our assessors were also instructed to review medical records in detail for any evidence of preexisting cognitive disorders. All patients were initially evaluated on admission by emergency physicians. Routine assessments in the emergency department included level of consciousness measurements, which we further reviewed for consistency during our study procedures. We also reviewed daily clinical notations to detect changes in level of consciousness from baseline. Delirium was defined as a binary event (yes or no), and recurrent episodes were not pondered in our analyses.
We also collected data on demographics (age, sex, race or ethnicity, marital status, and literacy), clinical history (previous diagnoses and medications, Charlson comorbidity index, physical examination, supplemental oxygen), polypharmacy (five or more medications), premorbid functional status (Katz Activities of Daily Living Scale), and laboratory tests routinely collected on admission (complete blood count, D-dimer, C-reactive protein, urea, creatinine, albumin). We defined the first available results from within the first 48 hours of hospitalization as the admission results of laboratory tests. Combining our detailed chart reviews with telephone interviews, we were able to work with a complete dataset on our variables of interest.
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Our primary outcome was in-hospital death, which we retrieved from electronic medical records. Participants who survived were censored on hospital discharge. Secondary outcomes were length of hospital stay (days), admission to intensive care, and ventilator utilization.
We reported descriptive results for the total sample, comparing the variables of interest according to the overall occurrence of delirium. We used the chi-square test to compare categorical variables, and Student's t-test (normal distribution) or Wilcoxon's ranksum test (non-normal distribution) to compare numerical variables.
We used logistic regression models to explore the association between the overall occurrence of delirium and in-hospital death, admission to intensive care, and ventilator utilization. We selected logistic regressions as our primary multivariable analysis method because we would be unable to determine precise dates of delirium onset using our current design. Even so, we performed a sensitivity analysis to examine the consistency of our primary outcome results using Cox proportional regression models. In this alternate approach, we approximated the onset of delirium using the earliest date on which the key descriptors of delirium were annotated, and defined length of hospital stay as our timedependent variable. The proportionality of hazards was checked using the Schoenfeld residuals test. Finally, we studied the association between delirium and length of hospital stay using negative binomial regression models.
In a secondary analysis, we investigated whether the association between delirium and adverse outcomes was modified according to age subgroups (50-64 years vs. ≥65 years).
We assessed the uniformity of effect estimates across strata using chi-square tests for heterogeneity, and the Mantel-Haenszel method to either calculate pooled adjusted estimates Accepted Article This article is protected by copyright. All rights reserved. when effects were uniform or report stratum-specific estimates in the presence of interactions (P-value < 0.05).
We adjusted all multivariable models for possible confounders, including age, sex, literacy, previous diagnoses, Charlson comorbidity index, polypharmacy, days of symptoms, supplemental oxygen, temperature, mean arterial pressure, lymphocyte count, C-reactive protein, glomerular filtration rate, D-dimer, and albumin. All statistical tests were two-tailed, and an alpha error of up to 5% was accepted to define the statistical significance of any results. Statistical analyses were performed using Stata MP 16.1 (StataCorp, College Station, TX).
We included 707 patients in our final analysis, with a mean (± standard deviation) age of 66 (±11) years and a predominance of the male sex (57%; N=402). We identified delirium in 234 participants (33%), of which 86 (12%) already had descriptors of delirium on admission. Only 30 (4%) participants had a baseline diagnosis of dementia, of whom 22 (73%) experienced delirium during hospital stay. Patients with delirium also had a higher prevalence of other comorbidities, such as cerebrovascular disease, heart failure, and cancer (Table 1) . Abnormal laboratory findings were more prominent in delirious patients, including higher C-reactive protein and d-dimer levels, more cases of lymphocytopenia, and lower albumin levels. Overall, in-hospital mortality was 39% (N=273), reaching 55% (N=129) in patients who experienced delirium and 30% (N=144) in those who did not (p<0.001). Length of hospital stay, number of days in intensive care, and ventilator utilization were different across groups as well, with unfavorable estimates in the delirium group. Detailed descriptive findings are reported in Table 1 and supplementary materials (Supplementary Table S1 ).
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We verified that the overall occurrence of delirium was independently associated with in-hospital death (adjusted odds ratio [aOR]=1.75; 95%CI=1.15-2.66) ( Table 2) . The association was confirmed in the Cox proportional hazards regression (Figure 1) In our interaction analyses (Table 3) , we verified that the association between delirium and in-hospital death held both for participants aged 65 years or more (aOR=2.33; 95%CI=1.29-4.21), and for younger patients (aOR=1.93; 95%CI=1.01-3.68). The association between delirium and secondary outcomes was consistent across age subgroups as well, except for ventilator utilization, which was associated with delirium only in middle-aged patients ( Table 3 ).
We found that one in three patients with COVID-19 experienced delirium in the hospital. We observed high mortality rates in our cohort and verified that delirium was an independent predictor of in-hospital death. Delirium was also independently associated with increased length of hospital stay, admission to intensive care, and ventilator utilization. In addition, we found that while delirium was particularly relevant in patients aged 65 years and over, its prognostic significance held for middle-aged adults as well.
Previous studies have recognized the association between neurological manifestations of COVID-19 and illness severity. 6, 7, 16, 21 Most of these studies have either focused on impaired consciousness 7,21 , or did not provide a clear construct of delirium as their main exposure 16 . In a cohort study of 214 adult patients, Mao et al. (2020) reported that CNS
This article is protected by copyright. All rights reserved. symptoms were present in 25% of the cases, the most common being dizziness (17%), and headache (13%). Impaired consciousness was detected in 8% in their sample but was six times more common in patients with severe forms of the disease (15% vs. 2%). Despite these preliminary reports, their results were limited by smaller sample sizes and the absence of additional prognostic analyses. A recent prediction model for critical outcomes in patients with COVID-19 included unconsciousness as one of the key risk factors to be evaluated on hospital admission, along with the presence of hemoptysis, cancer history, and X-ray abnormalities. 21 Although these findings stress the relevance of impaired consciousness as a predictor of adverse outcomes in hospitalized patients with COVID-19, the clinical meaning of delirium as a full syndrome was not addressed in the study.
In our study, we found consistent evidence that delirium is associated with adverse outcomes in hospitalized patients with COVID-19. Delirium has been associated with postdischarge functional and cognitive decline, but its long-term implications in COVID-19, which frequently leads to severe cardiovascular and lung damage, are still unknown.
However, we hope that our findings will stimulate health providers and researchers to work together and creatively overcome the present challenges to patient care to implement new and effective strategies to manage delirium and its complications.
The possible mechanisms behind the association between COVID-19 and delirium are also intriguing. Although neurologic manifestations can happen in a broad spectrum of acute and infectious diseases, 22 previous evidence suggests that the coronavirus family is specially neurotropic. 23, 24 Coronaviruses have been shown to have neuroinvasive capacity and breach the central nervous system (CNS) either through the olfactory nerve or through the blood circulation and neuronal pathways. After invading the CNS, coronaviruses can cause direct brain damage by increasing demyelination, interleukins release, and the permeability of the blood-brain barrier. 23 These neuroinflammatory pathways are known to participate in the Accepted Article This article is protected by copyright. All rights reserved. We must also recognize that COVID-19 is an acute respiratory disease and that its overall systemic effects could be sufficient to justify both the occurrence of delirium and adverse in-hospital outcomes. In our study, we attempted to capture the systemic component of the disease by adjusting our analysis for important markers of acute illness, including Creactive protein, albumin, glomerular filtration rate, and vital signs. We found that even after these adjustments, delirium remained an independent predictor of adverse outcomes in the hospital, underlining its role as a prognostic marker in COVID-19 patients.
Our study had limitations. We identified delirium using a chart-based method, which could raise concerns regarding measurement bias. Misclassifications by the CHART-DEL instrument mostly occur in three scenarios: (1) populations with a high risk of delirium (defined by the presence of at least three of the following factors: cognitive impairment, severe illness, visual impairment, and high serum urea nitrogen:creatinine ratio); (2) populations with a high prevalence of dementia; and (3) poor documentation of delirium signs and symptoms. 19 While the first two contribute to the overestimation of delirium occurrence, the third leads to its underestimation. Most of our patients had a low-to-moderate baseline risk of delirium (96%), and the prevalence of preexisting cognitive disorders was low (4%). Therefore, we were at a higher risk of underestimating delirium, which would be more likely to drive our results towards the null, suggesting that the strengths of the Accepted Article This article is protected by copyright. All rights reserved.
associations we verified could be even higher than what we estimated. Moreover, all CHART-DEL assessments were completed by trained medical investigators with a background in geriatric medicine, and the CHART-DEL instrument has been demonstrated to have good accuracy for delirium detection, even when performed by non-medical professionals. 19 Other limitations included the retrospective nature of our analyses, which prevented assessments regarding delirium duration, severity, and its temporal association with intensive care and other therapeutic measures. Finally, our study was performed in a single center dedicated to high-complexity medical care and our results should be read with parsimony before being generalized to different populations.
The study also has notable strengths. We were able to collect detailed clinical data from a large sample of patients and provide new evidence indicating the prognostic relevance of delirium and impaired consciousness in COVID-19. Unlike previous studies, we demonstrated the association between delirium not only with in-hospital death but also with length of stay, intensive care admission and ventilator utilization. We were powered to perform multivariable analyses and confirm our results in regression models adjusted for several possible confounders and in different subgroup analyses. middle-aged and older adults with COVID-19. The prompt recognition of delirium is critical to ensure appropriate clinical care and prevent adverse outcomes in this population.
We thank members of the CO-FRAIL Study Group for their efforts in collecting data for our work. Sponsor's Role: The study did not receive any external funding. 
This article is protected by copyright. All rights reserved. Occurrence results are presented as N (%) for categorical outcomes and median (IQR) for continuous outcomes. CI: confidence interval. a Estimates are presented as odds ratios for dichotomous outcomes, and incidence rate ratios for length of stay. All multivariable analyses were adjusted for age, sex, literacy, previous diagnoses, Charlson comorbidity index, polypharmacy, days of symptoms, oxygen support, temperature, mean arterial pressure, lymphocyte count, Creactive protein, glomerular filtration rate, D-dimer, and albumin.
This article is protected by copyright. All rights reserved. Occurrence results are presented as N (%) for categorical outcomes and median (IQR) for continuous outcomes. CI: confidence interval. a Estimates are presented as odds ratios for dichotomous outcomes, and incidence rate ratios for length of stay. All multivariable analyses were adjusted for age, sex, literacy, previous diagnoses, Charlson comorbidity index, polypharmacy, days of symptoms, oxygen support, temperature, mean arterial pressure, lymphocyte count, Creactive protein, glomerular filtration rate, D-dimer, and albumin.
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