Encephalopathy at admission predicts adverse outcomes in patients with SARS‐CoV‐2 infection

Abstract Aims To determine if neurologic symptoms at admission can predict adverse outcomes in patients with severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2). Methods Electronic medical records of 1053 consecutively hospitalized patients with laboratory‐confirmed infection of SARS‐CoV‐2 from one large medical center in the USA were retrospectively analyzed. Univariable and multivariable Cox regression analyses were performed with the calculation of areas under the curve (AUC) and concordance index (C‐index). Patients were stratified into subgroups based on the presence of encephalopathy and its severity using survival statistics. In sensitivity analyses, patients with mild/moderate and severe encephalopathy (defined as coma) were separately considered. Results Of 1053 patients (mean age 52.4 years, 48.0% men [n = 505]), 35.1% (n = 370) had neurologic manifestations at admission, including 10.3% (n = 108) with encephalopathy. Encephalopathy was an independent predictor for death (hazard ratio [HR] 2.617, 95% confidence interval [CI] 1.481–4.625) in multivariable Cox regression. The addition of encephalopathy to multivariable models comprising other predictors for adverse outcomes increased AUCs (mortality: 0.84–0.86, ventilation/ intensive care unit [ICU]: 0.76–0.78) and C‐index (mortality: 0.78 to 0.81, ventilation/ICU: 0.85–0.86). In sensitivity analyses, risk stratification survival curves for mortality and ventilation/ICU based on severe encephalopathy (n = 15) versus mild/moderate encephalopathy (n = 93) versus no encephalopathy (n = 945) at admission were discriminative (p < 0.001). Conclusions Encephalopathy at admission predicts later progression to death in SARS‐CoV‐2 infection, which may have important implications for risk stratification in clinical practice.


| INTRODUC TI ON
The outbreak of coronavirus disease 2019 , caused by novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has been a serious threat to public health. According to the World Health Organization (WHO), there have been 136,996,364 confirmed cases of COVID-19 and 2,951,832 confirmed deaths in 216 countries, areas, and territories until April 14,2021. Approximately 20% of patients develop a severe respiratory illness that ultimately requires mechanical ventilation, with mortality rates exceeding 50% in these severe cases. 1 Due to the high risk of developing critical illness and adverse outcomes and the intensive demands placed on medical resources as the number of severe cases increases, it is critical to identify patients with COVID-19 who may be more susceptible to advanced disease progression at an early stage, preferably at the time of hospital admission.
Recent studies have shown that severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has a myriad of neurological manifestations. [2][3][4][5][6][7][8][9] Several studies specifically described a high prevalence of serious neurologic manifestations in patients with severe COVID-19, which suggests that the nervous system may become more involved as the disease progresses. 5,[9][10][11][12] Despite the continuously increasing reports of the neurological symptoms of SARS-CoV-2, our knowledge about the possible association between early neurologic manifestations and subsequent deterioration leading to intensive care unit (ICU) admission, mechanical ventilation, and death remains unknown. Herein, we will aim to determine associations between encephalopathy and other neurologic manifestations of COVID-19 and adverse outcomes in a large cohort of hospitalized patients. We will also aim to determine the accuracy of early neurologic manifestations in predicting subsequent adverse outcomes in COVID-19 patients and then discuss possible routes of SARS-CoV-2 nervous system involvement based on the current evidence.

| Study design and participants
We conducted a retrospective observational cohort study using data from the hospitals in the University of Pennsylvania A total of 1053 consecutively hospitalized patients with laboratory confirmation of SARS-CoV-2 were included in the analysis ( Figure 1).

| Standard protocol approvals, registrations, and patient consents
The study was approved and the requirement for informed consent was waived by our medical center's Institutional Review Board.

| Data collection
Each patient's electronic medical record was reviewed, with data abstracted from physician and nursing documentation, laboratory results, and radiologic examinations, and recorded in a pre-specified electronic collection form by two medical students in consensus under the supervision of an attending physician. Collected data included patients' demographics (age and sex), comorbidities (hypertension, diabetes, cardiac or cerebrovascular disease, malignancy, and chronic kidney disease), typical COVID-19 symptoms (fever, cough, dyspnea, fatigue, chill, nausea, anorexia, diarrhea, throat pain, chest pain, and abdominal pain), and neurological symptoms. Laboratory results were extracted from medical records on the day of hospital admission (before transferring into ICU). As most patients lack complete laboratory data on the day of admission, only 495 cases were included when analyzing laboratory parameters. Results of all radiologic testing (including brain CT and MRI) were also recorded.

| Neurologic symptoms
We distinguished patient symptoms present at the time of hospital admission from subsequent symptoms that occurred later during hospitalization. We specifically focused on encephalopathy as our primary neurologic symptom of interest, but also included other neurologic symptoms related to the central nervous system (CNS) (dizziness/vertigo, headache, stroke, ataxia, aphasia, and seizure) and peripheral nervous system (PNS) (taste impairment, smell impairment, and vision impairment). 5 Encephalopathy was defined as global disturbance in brain function, which was expressed clinically as either subsyndromal delirium, delirium or coma with possible additional features, such as seizures or extrapyramidal signs. 13 Encephalopathy was categorized as severe if the patient developed coma, and mild/moderate if the patient did not develop coma.

| Outcomes
For each patient, we recorded the timing of hospital admission, discharge, ICU admission, mechanical ventilation, and death, with clinical outcomes followed up to May 26, 2020. We defined mortality as our primary outcome and mechanical ventilation and ICU admission as a combined secondary outcome, and then calculated time to each event in calendar days.

| Statistical analysis
Means and standard deviations (SD) were used to describe normally distributed baseline data and median and interquartile ranges to describe non-normally distributed baseline data. Categorical variables were expressed as counts and percentages. Kolmogorov-Smirnov test was performed to access the distribution of continuous variables. In this study, all laboratory parameters are subject to non-normal distribution, and the Mann-Whitney U test was used to assess the statistical difference between the encephalopathy group and the non-encephalopathy group in laboratory parameters.

F I G U R E 1 Flow chart of inclusion/ exclusion
Chi-square test was used to compare group differences of categorical variables. Univariable Cox regression analysis based on forward likelihood ratio was performed to evaluate the association of clinical characteristics at admission with later progression to death or mechanical ventilation/ICU admission. Clinical characteristics and laboratory parameters that were significant on univariable analysis were  We then performed receiver operating characteristic (ROC) analysis with calculation of areas under the curve (AUC) to determine the accuracy of our models in predicting progression to death or mechanical ventilation/ICU admission using variables from our final multivariable Cox regression models. Concordance index (C-index) for right-censored data was applied to evaluate the performance of these prediction models. 14 Finally, we stratified patients into subgroups based on the presence or absence of encephalopathy on admission and compared adverse outcome rates using survival statistics. In a sensitivity analysis, we separately considered patients with mild/moderate and severe encephalopathy.
All statistical analyses were performed using R version 4.0.0 software (the R Foundation). The significance threshold was set at a 2sided P value less than 0.05.

| Baseline characteristics and clinical features
We identified a total of 1053 hospitalized patients with confirmed  (Table S1).
Patients with encephalopathy were significantly older and had a higher prevalence of numerous comorbidities compared to patients without encephalopathy, while symptom profiles and laboratory findings also significantly differed between groups (Table 1, Table S2 and   Table S3).
Patients with encephalopathy at admission also had a signifi-  (Table S4). Of note, patients with severe encephalopathy were not considered in our ventilation/ICU analyses due to near-perfect correlation between coma and mechanical ventilation requirements.

| Multivariable Cox regression analysis and sensitivity analysis in encephalopathy
Encephalopathy was an independent predictor for mortality (HR 2.617, 95% CI 1.481-4.625) in our multivariable Cox regression models based on variables significant in univariable analysis (  (Table 2 and Table S5).
The addition of encephalopathy to a multivariable model comprising other risk factors for adverse outcomes increased the Cindex of the model from 0.78 to 0.81 for mortality and from 0.85 to 0.86 for ventilation/ICU (Table 3).
In addition, the AUCs were higher when encephalopathy was added to other factors to predict mortality (0.86 vs. 0.84, p < 0.001, at admission were discriminative (p < 0.001, Figure 3a). Compared to patients without encephalopathy at admission (n = 945), patients with mild/moderate encephalopathy (n = 93) had higher rates of mechanical ventilation/ICU admission (p < 0.001, Figure 3b).

| Radiologic presentations
Among 174 patients diagnosed with encephalopathy during hospitalization, 68 patients had a brain CT, of which 14 had acute abnormalities. These findings included evidence of acute ischemic stroke in four patients and intracranial hemorrhage in six patients, while four patients had other evidence of focal or global ischemia including decreased attenuation and loss of gray-white differentiation ( Figure 4A). Only four patients had a brain MRI performed in addition to their CT, with MRI confirming ischemic stroke in three patients ( Figure 4B and 4C) and revealing an intracranial malignancy in one patient.

| DISCUSS ION
It has been widely reported in the literature that varying forms of encephalopathy are common neurological manifestations of COVID-19, and that patients with severe COVID-19 tend to present with more neurological symptoms. 5,[10][11][12][15][16][17] However, these studies do not provide risk assessments based on these symptoms, which would potentially have significant clinical implications. Our study builds upon this prior work by investigating the potential utility of neurologic symptoms in predicting subsequent progression to adverse outcomes, and by building time-to-event models that assign risk scores to individual patients.
Our results showed that encephalopathy at admission may be an independent risk factor for later progression to death in COVID-19 patients and that it improved the predictive accuracy of our risk factor-based models with its addition. We also found that coma, as a marker of severe encephalopathy, provided further risk stratification and was associated with a significantly higher rate of   (2)

| CON CLUS ION
Encephalopathy at admission predicts later progression to death in COVID-19 patients, which may have important implications for risk stratification in clinical practice.

IRB
Institutional Review Boards of Hospital of University of Pennsylvania was obtained for the study cohort with waiver of consent, with confirmation number (dbhbeegg) and protocol number (843448).

CO N FLI C T O F I NTE R E S T
The authors declare that they have no conflict of interest.

DATA AVA I L A B I L I T Y S TAT E M E N T
Data are not made publicly available due to HIPAA regulations, but anonymized data can be sent to the corresponding author by reasonable requests.