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. 2021 Aug 27;128(11):1687–1703. doi: 10.1007/s00702-021-02400-5

COVID-19 outcomes of 10,881 patients: retrospective study of neurological symptoms and associated manifestations (Philippine CORONA Study)

Adrian I Espiritu 1,2,✉,#, Marie Charmaine C Sy 1,#, Veeda Michelle M Anlacan 1, Roland Dominic G Jamora 1; the Philippine CORONA Study Group Investigators
PMCID: PMC8391861  PMID: 34448930

Abstract

Our study aimed to determine the effects of new-onset neurological symptoms (NNS) on clinically relevant outcomes in hospitalized patients with COVID-19 infection. We conducted a nationwide, comparative, retrospective, cohort study among adult, hospitalized COVID-19 patients involving 37 hospital sites from various regions in the Philippines. We included a total of 10,881 patients with confirmed COVID-19 infection (2008 had NNS while 8873 did not have NNS). The adjusted hazard ratios (aHRs) for mortality among the mild and severe cases were significantly higher by 1.660 (95% CI 1.132–2.435) and by 1.352 (95% CI 1.042–1.752), respectively, in the NNS group compared to those in the non-NNS group. The aHRs for respiratory failure in the NNS group were significantly increased by 1.914 (95% CI 1.346–2.722), by 1.614 (95% CI 1.260–2.068), and by 1.234 (95% CI 1.089–1.398) among the mild, severe, and critical cases, respectively. The aHRs for ICU admission in the NNS group were still significantly higher by 1.973 (95% CI 1.457–2.673) and by 1.831 (95% CI 1.506–2.226) among the mild and severe cases, respectively. Patients who had NNS were not significantly associated with a longer duration of ventilator dependence (adjusted odds ratio (aOR) 0.954, 95% CI 0.772–1.179), longer ICU stay (aOR 0.983, 95% CI 0.772–1.252) and longer hospital admission (aOR 1.045, 95% CI 0.947–1.153). The presence of NNS significantly increases the risk of mortality, respiratory failure and ICU admission among COVID-19 patients. Registration and associated protocol publication: ClinicalTrials.gov website (NCT04386083); Espiritu AI, Sy MCC, Anlacan VMM, Jamora RDG. The Philippine COVID-19 Outcomes: a Retrospective study Of Neurological manifestations and Associated symptoms (The Philippine CORONA study): a protocol study. BMJ Open. 2020;10:e040944.

Keywords: COVID-19, New-onset neurological symptoms, Mortality, Respiratory failure, Intensive care unit admission, Cohort study

Introduction

The coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and has affected over 192 million individuals worldwide as of July 23, 2021 (Coronavirus disease (COVID-19) n.d.). During this period in the Philippines, our data breached the 1.5 million mark of confirmed total cases; nearly 54 thousand are active cases and approximately 28 thousand patients died from this infection (Department of Health (Philippines) 2021). Nationwide, about 50% of the intensive care unit (ICU), isolation, and ward beds were occupied and nearly 40% of the total mechanical ventilators were used (Department of Health (Philippines) 2021). In July 2021, only 5.5 million individuals, approximately 5% of the population, were fully vaccinated in the country (Coronavirus (COVID-19) Vaccinations–Statistics and Research–Our World in Data 2021), which is exceedingly distant from the target of about 75–90% vaccinated individuals to achieve herd immunity from this infection (Anderson et al. 2020). With the recent advent of local transmission of the Delta (B.1.617.2) variant in the country which could potentially initiate a surge and overwhelm our healthcare systems, the Department of Health is currently focusing its attention to implement stricter border control policies and to strengthen local COVID-19 responses (Department of Health (Philippines) 2021).

Current reports of neurological symptoms/signs and complications of this infection are limited due to the small number of included patients and relatively short duration of data collection which could hinder more precise estimates and detection of rarer manifestations (Amanat et al. 2021; Benussi et al. 2020; Chachkhiani et al. 2020; Chuang et al. 2021; Collantes et al. 2021; Flores-Silva et al. 2021). Although there were two powered studies that provided a glimpse on the effects of neurological manifestations on mortality (Eskandar et al. 2021; Frontera et al. 2021), there are no comparative cohort studies yet that explored the effects of new-onset neurological symptoms (NNS) on other clinically relevant outcomes such as respiratory failure, duration of ventilator dependence, admission to the intensive care (ICU) unit and length of ICU and hospital stay. Furthermore, these two large studies were conducted in New York City (NYC) where healthcare systems and outcomes may be remarkably different from low–middle income or developing countries like the Philippines.

Therefore, we aimed to determine the clinical/neurological features of hospitalized patients with COVID-19 infection and to investigate the effects of NNS on mortality, respiratory failure, duration of ventilator dependence, ICU admission, length of ICU and hospital stay among these patients.

Methodology

Study design

We performed a nationwide, multicenter, comparative, retrospective, cohort study involving patients with COVID-19 who were admitted to our hospitals/study sites from February 2020 until December 2020. The study’s protocol was registered in ClinicalTrials.gov (NCT04386083) and was previously published (Espiritu et al. 2020).

Setting

The study encompassed a total of 37 major hospitals/study sites from various regions in the Philippines (see the complete list of the sites below).

Patient selection, sampling and cohort description

We included a total enumeration of all patients that fulfilled the inclusion criteria, as follows: adults ≥ 19 years of age; confirmed cases via COVID-19 real-time reverse transcription polymerase chain reaction (RT-PCR) of patients’ nasopharyngeal swab samples which were performed by testing centers accredited by the Department of Health (Philippines); clinical symptoms/signs ascribed to COVID-19 infection; patients with the appropriate disposition by the end of the data collection period (e.g., discharged, transferred to another hospital, or died). Individuals who were transferred to another hospital were excluded to prevent duplication of data.

Adult COVID-19 patients who had new-onset neurological symptom/s (NNS) were grouped under the exposed cohort while those without NNS (non-NNS) were classified under the unexposed cohort.

Information sources, data collection, patient variables, and bias

We collected relevant information from the patient medical charts. Pilot-tested electronic collection forms generated using Epi Info Software (Version 7.2.2.16) were employed. The details of the obtained patient variables were indicated in the published protocol (Espiritu et al. 2020). Recording bias was considered inherent in this retrospective cohort study.

Outcome variables

We obtained the following relevant patient outcomes: mortality; respiratory failure (patients with clinical symptoms/signs of respiratory insufficiency (increased work of breathing/tachypnea [respiratory rate of ≥ 22], a necessity to administer supplemental oxygen, or abnormal blood gases [Partial pressure of oxygen < 60/hypoxemia or partial pressure of carbon dioxide > 45/hypercapnia])); duration of ventilator dependence (DVD) (days from the start of assisted ventilation to cessation); ICU admission (COVID-19 patients admitted to an ICU or ICU-comparable setting; length of ICU stay (LICUS) (days admitted in the ICU); and length of hospital stay (LHS) (days from admission to discharge).

Sample size

The calculation of the sample size, computed at 1342 patients, was specified in the published protocol (Espiritu et al. 2020).

Statistical analysis

We summarized demographic, medical, and neurological characteristics using frequencies (%) for categorical variables and medians (interquartile range, IQR) for continuous variables. We determined distribution differences between two independent samples using Mann–Whitney U and χ2 tests. We employed odds ratios (OR) and hazard ratios (HR) and corresponding 95% confidence intervals (CI) as the outcome measures for our dichotomous and time-to-event outcomes, respectively. The ORs for longer ventilator dependence, longer ICU stay, and longer hospitals stays were computed via multivariate logistic regression. Data on the time to onset of mortality, respiratory failure, ICU admission were used to build Kaplan–Meier curves; the log-rank test was employed to compare the curves. Confounder and effect modifiers were identified by performing stratified analysis. Adjusted HRs and ORs with 95% CI were computed based on prespecified confounders/effect modifiers. Significant factors that affect the outcomes were determined using Cox proportional regression models. The final model was further rectified using likelihood ratio tests and other information criteria (Akaike or Bayesian). Goodness-of-fit was assessed via Hosmer–Lemeshow test and Cox–Snell residuals. A p value < 0.05 (two tailed) was set for all analyses. Statistically significant differences were also detected if the 95% CI did not include number one. We conducted all statistical analyses using Stata®, Version 7.2.2.16 (College Station, TX: StataCorp LP).

Ethical considerations

This study was approved by the individual institutional review and research boards of the hospital sites and the Single Joint Research Ethics Board of the Department of Health of the Philippines (see complete list below).

Results

Inclusion of patients

We identified 10,999 hospitalized patients diagnosed with COVID-19 (i.e., verified via RT-PCR) from the 37 participating study sites. A total of 10,881 were included in the qualitative and quantitative analyses. Two thousand eight patients were identified with the primary exposure (i.e., NNS group) while the remaining 8873 did not have the exposure (i.e., non-NNS group). Missingness of data for relevant outcomes was considered very minimal; thus, we deemed it unnecessary to explore its effects (see Fig. 1).

Fig. 1.

Fig. 1

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Flow of patients in the Philippine CORONA study

Demographic and clinical characteristics of included COVID-19 patients

The median (IQR) age of the full cohort was 52 (36–64) with a female:male ratio was 1:1.13. A higher proportion of patients with NNS was found among those who are elderly (≥ 60 years) compared to non-elderly (19–59 years) (19.8% vs. 17.7%; p = 0.007). No significant difference in the proportion of patients with NNS was found between females and males (18.7% vs. 18.35; p = 0.609). The most typical declared exposure to COVID-19 infection was community/domestic travel (n = 3894, 35.8%). Hypertension (HPN) (n = 3647, 33.5%) and diabetes mellitus (DM) (n = 2191, 20.1%) were the most common comorbidities. Most patients had mild (n = 6690, 61.5%) and severe (n = 2354, 21.6%) COVID-19 infection. A considerable proportion of patients received systemic glucocorticoids (n = 2844, 26.1%), remdesivir (n = 1344, 12.4%), and tocilizumab (n = 1029, 9.4%). Other pertinent clinical features and comparison of characteristics of the NNS and non-NNS groups are displayed in Table 1.

Table 1.

Demographics and clinical features of patients included in the analysis

Features All patients COVID-19 patients with NNS COVID-19 patients without NNS p value*
Sample, n (%) 10,881 (100.0) 2008 (18.4) 8873 (81.5) –
Age in years, median (IQR) 52.0 (36.0–64.0) 54.0 (38.0–65.0) 52.0 (35.0–64.0) < 0.001
Frequencies of age groups, n (%)
 60 years and above 3834 (35.2) 760 (37.8) 3074 (34.6) 0.007
 19–59 years 7047 (64.8) 1248 (62.2) 5799 (65.4)
Sex, n (%)
 Female 5099 (46.9) 951 (47.4) 4148 (46.7) 0.609
 Male 5780 (53.1) 1056 (52.6) 4724 (53.2)
Nationality, n (%)
 Filipino 10,789 (99.2) 1996 (99.4) 8793 (99.1) 0.179
 Others 92 (0.8) 12 (0.6) 80 (0.9)
History of COVID-19 exposure, n (%)
 International travel 323 (3.0) 42 (2.1) 281 (3.2) < 0.001
 Community/domestic travel 3894 (35.8) 871 (43.4) 3023 (34.1)
 Hospital 1362 (12.5) 266 (13.2) 1096 (12.4)
Comorbidities, n (%)
 Hypertension 3647 (33.5) 927 (46.2) 2720 (30.6) < 0.001
 Diabetes mellitus 2191 (20.1) 523 (26.0) 1668 (18.8) < 0.001
 Kidney disease 611 (5.6) 174 (8.7) 437 (4.9) < 0.001
 Bronchial asthma 463 (4.2) 105 (5.2) 358 (4.0) 0.017
 Coronary artery disease 421 (3.9) 100 (5.0) 321 (3.6) 0.004
 Malignancy 244 (2.2) 70 (3.5) 174 (2.0) < 0.001
 Chronic obstructive pulmonary disease 156 (1.4) 24 (1.2) 132 (1.5) 0.32
 Heart failure 127 (1.2) 44 (2.2) 83 (0.9) < 0.001
 Liver disease 60 (0.6) 17 (0.8) 43 (0.5) 0.048
 Human immunodeficiency virus infection 37 (0.3) 9 (0.4) 28 (0.3) 0.357
Smoker, n (%) 1026 (9.4) 278 (13.8) 748 (8.4) < 0.001
Healthcare worker, n (%) 876 (8.0) 235 (11.7) 641 (7.2) < 0.001
Pregnant, n (%) 323 (3.0) 28 (1.4) 295 (3.3) < 0.001
Respiratory and constitutional symptoms, n (%)
 Cough 4411 (40.5) 995 (49.6) 3416 (38.5) < 0.001
 Fever 3927 (36.1) 886 (44.1) 3041 (34.3) < 0.001
 Dyspnea 2703 (24.8) 613 (30.5) 2090 (23.6) < 0.001
 Sore throat 751 (6.9) 211 (10.5) 540 (6.1) < 0.001
 Fatigue 713 (6.6) 220 (11.0) 493 (5.6) < 0.001
 Sputum production 637 (5.8) 194 (9.7) 443 (5.0) < 0.001
 Rhinorrhea 607 (5.6) 200 (10.0) 407 (4.6) < 0.001
 Diarrhea 597 (5.5) 163 (8.1) 434 (4.9) < 0.001
 Arthralgia 175 (1.6) 45 (2.2) 130 (1.5) 0.013
 Hemoptysis 33 (0.3) 6 (0.3) 27 (0.3) 0.968
COVID-19 disease severity, n (%)
 Mild 6690 (61.5) 1114 (55.5) 5576 (62.8) < 0.001
 Severe 2354 (21.6) 413 (20.6) 1941 (21.9)
 Critical 1707 (15.7) 468 (23.3) 1239 (14.0)
Treatment received, n (%)
 Systematic glucocorticoids 2844 (26.1) 743 (37.0) 2101 (23.7) < 0.001
 Remdesivir 1344 (12.4) 325 (16.2) 1019 (11.5) < 0.001
 Tocilizumab 1029 (9.4) 188 (9.4) 841 (9.5) 0.873
 Lopinavir–Ritonavir 579. (5.3) 99 (4.9) 480 (5.4) 0.387
 Hydroxychloroquine 529 (4.9) 116 (5.8) 413 (4.6) 0.035
 Chloroquine 358 (3.3) 68 (3.4) 290 (3.3) 0.789
 Convalescent plasma 263 (2.4) 80 (4.0) 183 (2.1) < 0.001
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NNS new-onset neurological symptom, IQR interquartile range

*Difference between NNS and non-NNS groups

Neurological features of included COVID-19 patients

The most common new-onset neurological symptom was headache (n = 607, 5.58%), anosmia/hyposmia (n = 544, 5.0%), and altered sensorium (n = 479, 4.4%). The proportion of patients who had new-onset neurological disorder/complication was 8.97% (n = 976); most patients had encephalopathy (n = 622, 5.72%), any acute cerebrovascular disease (n = 367, 3.37%), and any seizure event (n = 125, n = 1.16%). Among all the included patients, a number of individuals had a past neurological history of stroke/cerebrovascular diseases (n = 321, 2.95%), dementia (n = 38, 0.35%), and epilepsy (n = 27, 0.25%). Among patients who underwent computed tomography or magnetic resonance imaging (n = 760), most lesions were present in the parietal cortex (n = 113, 14.9%), basal ganglia (n = 112, 14.7%), and frontal cortex (n = 95, 12.5%). Very few patients in our cohort underwent cerebrospinal fluid analysis (n = 38). A more detailed presentation of neurological characteristics is displayed in Tables 2, 3.

Table 2.

Past neurological history, and new-onset neurological symptoms and disorders/complications associated with the hospitalized COVID-19 patients included in the analysis (N = 10,881)

Past neurological history Frequency (%) New-onset neurological symptoms Frequency (%) New-onset neurological disorders/complications Frequency (%)
Stroke/cerebrovascular diseases 321 (2.95) Headache 607 (5.58) Any neurological disorder/complication 976 (8.97)
Dementia 3 (0.35) Anosmia/hyposmia 544 (5.00) Encephalopathy 622 (5.72)
Epilepsy 27 (0.25) Altered sensorium 479 (4.40) Any acute cerebrovascular disease 367 (3.37)
Neuropathy 9 (0.08) Ageusia/dysgeusia 338 (3.11)  Acute cerebrovascular infarction 262 (2.41)
Movement disorder 6 (0.06) Myalgia 256 (2.35)  Acute cerebrovascular hemorrhagic stroke 101 (0.93)
Headache syndrome 5 (0.04) Extremity weakness 246 (2.26) Any seizure disorder 125 (1.16)
Central nervous system infection 5 (0.04) Dizziness 159 (1.46)  Acute symptomatic seizure 63 (0.58)
Neuromuscular junction disorder 5 (0.04) Confusion 143 (1.31)  Status epilepticus 19 (0.17)
Central demyelinating syndrome 2 (0.02) Vomiting 126 (1.16)  Epilepsy 17 (0.16)
Myelopathy 2 (0.02) Seizure 96 (0.88) Anoxic brain 51 (0.47)
Peripheral nervoussystem infection 3 (0.03) Dysarthria 83 (0.76) Any movement disorder 3 (0.03)
Myopathy 0 Nausea 82 (0.75) Inflammatory syndromes
Extremity numbness/paresthesia 53 (0.49)  Meningitis 13 (0.12)
Facial weakness 40 (0.37)  Encephalitis 6 (0.06)
Tremor 25 (0.23)  Meningoencephalitis 1 (0.01)
Facial numbness/paresthesia 20 (0.18)  Acute disseminated encephalomyelitis 1 (0.01)
Dysphagia 16 (0.15)  Acute necrotizing hemorrhagic encephalopathy 0
Tongue weakness 8 (0.07)  Cerebellitis 0
Blindness/decreased vision 6 (0.06)  Cerebellitis 0
Ataxia 5 (0.04)  Optic neuritis 1 (0.01)
Meningismus 5 (0.04)  Myelitis 0
Hearing loss/decreased hearing 4 (0.04)  Sensory ganglionitis dorsal radiculitis 0
Dysphonia 4 (0.04)  Anterior horn syndrome polio-like syndrome/ventral radiculitis 2 (0.02)
Neck weakness 3 (0.03)  Peripheral neuritis/GBS-like syndrome 5 (0.04)
Ophthalmoparesis/ophthalmoplegia 2 (0.02)  Peripheral neuritis other than GBS-like syndrome 1 (0.01)
Eye pain 3 (0.03)  Myositis 1 (0.01)
Bradykinesia 3 (0.03) Neuromuscular disorder 3 (0.03)
Dystonia 0
Choreoathethosis 0
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GBS Guillain–Barré syndrome

Table 3.

Summary of findings in COVID-19 patients who underwent computed tomography/magnetic resonance imaging and cerebrospinal fluid (CSF) analysis

Findings Frequency (%)
Radiologic imaging
 Number of patients with any radiologic imaging, n 760
 Number of patients with the affected segment of the central nervous system evidenced by radiologic imaging, n (%)
  Whole brain 25 (3.3)
  Frontal cortex 95 (12.5)
  Temporal cortex 81 (10.6)
  Parietal cortex 113 (14.9)
  Occipital cortex 46 (6.0)
  Subcortical white matter 90 (11.8)
  Basal ganglia 112 (14.7)
  Thalamus 44 (5.8)
  Hypothalamus 3 (0.4)
  Cerebellum 41 (5.4)
  Midbrain 13 (1.7)
  Pons 38 (5.0)
  Medulla 2 (0.3)
  Cervical spinal cord 3 (0.4)
  Thoracic spinal cord 3 (0.4)
  Lumbar spinal cord 3 (0.4)
  Sacral spinal cord 0
CSF analysis
 Number of patients with CSF analysis, n 38
 Median CSF total cell count (IQR), cells/μL 3.0 (2.3–14)
  Number of patients with pleocytosis* in the CSF, n (%) 6 (15.8)
 Median CSF neutrophil count (IQR), cells/μL 1 (0–8)
  Number of patients with neutrophil/s† in the CSF, n (%) 8 (21.0)
 Median CSF lymphocyte count (IQR), cell/μL 13 (0–55)
  Number of patients with lymphocytosis* in the CSF, n (%) 7 (18.4)
 Median CSF protein (IQR), mg/dL 157.5 (37.8–708.0)
  Number of patients with increased protein‡ in the CSF, n (%) 8 (21.0)
 Median CSF glucose (IQR), mg/dL 7.6 (5.2–65.5)
 Median serum glucose (IQR), mg/dL 126.0 (59.9–157.5)
  Number of patients with hypoglycorrhachia§, n (%) 8 (21.0)
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IQR Interquartile range

* > 5 cells/μL of CSF

† ≥ 1 cell/μL of CSF

‡CSF protein concentration ≥ 60 mg/dL

§CSF glucose concentration < 2/3 of serum glucose concentration

Effects of new-onset neurological symptom/s on outcomes of included COVID-19 patients and survival analysis

Table 4 shows the comparison of outcomes between NNS and non-NNS groups among the included COVID-19 patients while Table 5 presents the crude and adjusted hazard ratio for mortality, respiratory failure and ICU admission. Figure 2 shows the Kaplan–Meier cumulative hazard functions for NNS and non-NNS groups in terms of mortality, respiratory failure and ICU admission. For all survival analyses, it was found that the effect of NNS on the outcomes was significantly different depending on COVID-19 severity.

Table 4.

Comparison of outcomes in COVID-19 patients with NNS vs. without NNS

Outcomes All COVD-19 patients COVID-19 patients with NNS COVID-19 patients without NNS p value*
Mortality and associated causes
 Mortality, n (%) 1702 (15.6) 464 (23.1) 1238 (14.0) < 0.001†
  Acute respiratory distress syndrome 749 (44.0) 216 (10.8) 533 (6.0) < 0.001†
  Septic shock 699 (41.1) 162 (8.1) 537 (6.1) 0.001†
  Multi-organ dysfunction syndrome 150 (8.8) 23 (1.1) 127 (1.4) 0.321
  Acute coronary syndrome 138 (8.1) 33 (1.6) 105 (1.2) 0.096
  Cardiac arrhythmia 106 (6.2) 19 (0.9) 87 (1.0) 0.888
  Brain herniation 66 (3.9) 43 (2.1) 23 (0.3) < 0.001†
  Decompensated heart failure 44 (2.6) 9 (0.4) 35 (0.4) 0.732
Respiratory failure and associated causes
 Respiratory failure, n (%) 1608 (14.8) 514 (25.6) 1094 (12.3) < 0.001†
  Pneumonia 927 (57.6) 324 (16.1) 603 (6.8) < 0.001†
  Acute respiratory distress syndrome 868 (54.0) 218 (10.9) 650 (7.3) < 0.001†
  Shock 147 (9.1) 48 (2.4) 99 (1.1) < 0.001†
  Central neurological cause 89 (5.5) 82 (4.1) 7 (0.1) < 0.001†
  Pulmonary edema 33 (2.0) 16 (0.8) 17 (0.2) < 0.001†
  Pulmonary embolism 21 (1.3) 2 (0.1) 19 (0.2) 0.404
  Peripheral neurological cause 2 (0.1) 0 2 (0.02) 1.000
  Pulmonary hemorrhage 1 (0.1) 1 (0.05) 0 0.185
Duration of ventilator dependence
 Duration of ventilator dependence
  Sample, n 1606 514 1092 0.612
  Median (IQR) 13 (8–20) 13 (8–20) 13 (8–20)
ICU admission and reasons for admission
 ICU admission, n (%) 1740 (16.0) 545 (27.1) 1195 (13.5) < 0.001†
  Acute respiratory distress syndrome 956 (54.9) 236 (11.8) 720 (8.1) < 0.001†
  Shock 180 (10.3) 59 (2.9) 121 (1.4) < 0.001†
  Impaired level of consciousness 110 (6.3) 85 (4.2) 25 (0.3) < 0.001†
  Acute myocardial infarction 80 (4.6) 13 (0.6) 67 (0.8) 0.610
  Acute kidney injury necessitating dialysis 75 (4.3) 27 (1.3) 48 (0.5) < 0.001†
  Treatment-related indication 71 (4.1) 28 (1.4) 43 (0.5) < 0.001†
  Acute stroke 57 (3.3) 51 (2.5) 6 (0.1) < 0.001†
  Cardiac arrhythmia 36 (2.1) 6 (0.3) 30 (0.3) 0.782
  Post-cardiac arrest 23 (1.3) 5 (0.2) 18 (0.2) 0.599
  Cerebral edema 18 (1.0) 15 (0.7) 3 (0.03) < 0.001†
  Venous thromboembolism 13 (0.7) 1 (0.05) 12 (0.1) 0.484
Length ICU stay
 Length of ICU stay
  Sample, n 1737 545 1192 0.887
  Median (IQR) 15 (9.5–21) 15 (10–21) 15 (9–21)
Length of hospital stay
 Length of hospital stay
  Sample, n 10,881 2008 8873 0.002†
  Median (IQR) 13 (10–19) 14 (10–19) 13 (10–19)
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ICU intensive care unit, IQR interquartile range, NNS new-onset neurological symptoms

*Difference between NNS and non-NNS groups

†Statistically significant (i.e., alpha set at 0.5)

Table 5.

Crude and fully adjusted hazard ratio for mortality, ICU admission and respiratory failure comparing patients with NNS vs. patients without NNS

Characteristic HR (95% CI) p value for association†
Mortality
 Crude HR for mortality 1.423 (1.278–1.585) –
 Fully adjusted HR for mortality
  COVID-19 severity*
   Mild 1.660 (1.132–2.434) 0.01‡
   Severe 1.352 (1.042–1.752) 0.023‡
   Critical 1.043 (0.920–1.181) 0.511
 Confounders
  Hypertension 0.863 (0.781–0.954) 0.04‡
  Age 1.010 (1.007–1.013) < 0.001‡
  Sex (male) 0.959 (0.870–1.058) 0.405
Respiratory failure
 Crude HR for respiratory failure 1.857 (1.670–2.065) –
 Fully adjusted HR for respiratory failure
  COVID-19 severity*
   Mild 1.914 (1.346–2.722) < 0.001‡
   Severe 1.614 (1.260–2.068) < 0.001‡
   Critical 1.234 (1.089–1.398) 0.001‡
 Confounders
  Smoker 1.364 (1.889–1.565) < 0.001‡
  Hypertension 1.517 (1.355–1.699) < 0.001‡
  Diabetes mellitus 1.174 (1.055–1.307) 0.003‡
  Age 0.999 (0.996–1.002 0.536
  Sex (male) 0.852 (0.768–0.945) 0.003‡
ICU admission
 Crude HR for ICU admission 1.802 (1.626–1.996) –
 Fully adjusted HR for ICU admission
  COVID-19 severity*
   Mild 1.973 (1.457–2.673) < 0.001‡
   Severe 1.831 (1.506–2.226) < 0.001‡
   Critical 1.122 (0.985–1.279) 0.084
 Confounders
  Smoker 1.443 (1.267–1.643) < 0.001‡
  Hypertension 1.692 (1.516–1.889) < 0.001‡
  Diabetes mellitus 1.268 (1.144–1.404) < 0.001‡
  Age 0.999 (0.996–1.002) 0.630
  Sex (male) 0.884 (0.800–0.977) 0.016‡
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95% CI 95% confidence intervals, NNS new-onset neurological symptoms, HR hazard ratio, ICU Intensive care unit

*Interaction term (likelihood ratio test) p value = 0.03

†Wald’s test

‡Statistically significant (i.e., alpha set at 0.05)

Fig. 2.

Fig. 2

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Cumulative hazard by exposure to new-onset neurological symptoms for A mortality, B intensive care unit admission and C respiratory failure

Mortality

A total of 1702 (15.6%) died in the full cohort; acute respiratory distress syndrome (ARDS) (n = 749, 44.0%) and septic shock (n = 699, 41.1%) were the typical causes of mortality. Among those who died, a significantly higher proportion of patients was found in the NNS compared to those in the non-NNS group (p < 0.001); among those who died due to ARDS (p < 0.001), septic shock (p = 0.001), and brain herniation (p < 0.001), a statistically higher percentage of patients was found in the NNS group. The crude HR for mortality was significantly higher among those in the NNS group by 1.423 (95% CI 1.278–1.585) than those in the non-NNS group (see Fig. 2A). After adjusting for age, sex, and presence of HPN, the risk of mortality differed depending on the disease severity. Among the mild and severe cases, the adjusted HRs remained significantly higher by 1.660 (95% CI 1.132–2.435; p = 0.01) and by 1.352 (95% CI 1.042–1.752; p = 0.023), respectively, in the NNS group compared to those in the non-NNS group. However, among the critical cases, the adjusted HR for mortality became non-significant (HR 1.043, 95% CI 0.920–1.181; p = 0.511).

Respiratory failure

There were 1608 (14.8%) patients who had respiratory failure. Pneumonia (n = 927, 57.6%) and ARDS (n = 868, 54%) were the most common causes of respiratory failure. Among those who had respiratory failure, a higher proportion of patients was found in the NNS compared to the non-NNS group (p < 0.001); among those who had a respiratory failure due to pneumonia (p < 0.001), ARDS (p < 0.001), shock (p < 0.001), central neurological cause (p < 0.001), and pulmonary edema (p < 0.001), a statistically higher percentage of patients was found in the NNS group. The crude HR for respiratory failure was significantly higher among those in the NNS group by 1.857 (95% CI 1.670–2.065) than those in the non-NNS group (see Fig. 2B). After adjusting for age, sex, smoking status, and presence of HPN and DM, the risk of respiratory failure differed depending on the disease severity. Among the mild, severe, and critical cases, the adjusted HRs for respiratory failure were still significantly increased by 1.914 (95% CI 1.346–2.722; p < 0.001), by 1.614 (95% CI 1.260–2.068; p < 0.001) and by 1.234 (95% CI 1.089–1.398; p = 0.001), respectively, in the NNS group than those in the non-NNS group.

Duration of ventilator dependence (DVD)

Among those patients needing mechanical ventilation, the overall median DVD was 13 days (IQR 8–20). There was no significant difference in the median DVD between patients in the NNS and non-NNS groups (p = 0.612). The crude OR for longer DVD (i.e., ≥ 16 days) was insignificant at 0.979 (95% CI 0.793–1.207; p = 0.841). After adjusting for age, sex, and COVID-19 severity, the adjusted OR for longer DVD remained insignificant at 0.954 (95% CI 0.772–1.179; p = 0.663).

ICU admission

A total of 1740 patients (16.0%) from our full cohort were admitted to the ICU. ARDS (n = 956, 54.9%) and shock (n = 180, 10.3%) were the most common reason for ICU admission. Among those admitted to the ICU, a higher proportion of patients was found in the NNS compared to the non-NNS group (p < 0.001). Similarly, a statistically higher percentage of patients was found in the NNS group among those who were admitted to the ICU due to ARDS (p < 0.001), shock (p < 0.001), impaired level of consciousness (p < 0.001), acute kidney injury necessitating dialysis (p < 0.001), treatment-related indication (p < 0.001), acute stroke (p < 0.001), and cerebral edema (p < 0.001). The crude HR was significantly higher among those in the NNS group by 1.857 (95% CI 1.670–2.065) than those in the non-NNS group (see Fig. 2C). After adjusting for age, sex, smoking status, and presence of HPN and DM, the risk for ICU admission differed depending on disease severity. Among the mild and severe cases, the adjusted HRs for ICU admission were still significantly higher by 1.973 (95% CI 1.457–2.673; p < 0.001) and by 1.831 (95% CI 1.506–2.226; p < 0.001), respectively, in the NNS group compared to those in the non-NNS group.

Length of ICU stay (LICUS)

Among those patients admitted to the ICU, the overall median LICUS was 15 days (IQR 9.5–21). There was no significant difference in median LICUS between patients in the NNS and non-NNS groups (p = 0.887). The crude OR for longer LICUS (i.e., 16 days) was insignificant at 0.969 (95% CI 0.763–1.229; p = 0.793). After adjusting for age, sex, and disease severity, the adjusted OR for longer LICUS remained insignificant at 0.983 (95% CI 0.772–1.252; p = 0.892).

Length of hospital stay (LHS)

Among the full cohort, the overall median LHS was 13 days (10–19). There was a significant difference in the median LHS between patients in the NNS and non-NNS groups (p = 0.002). The crude OR for longer LHS (i.e., ≥ 14 days) was insignificant at 1.037 (95% CI 0.941–1.142; p = 0.465). After adjusting for age, sex, and disease severity, the adjusted OR for longer LHS remained insignificant at 1.045 (95% CI 0.947–1.153; p = 0.378).

Discussion

To the best of our knowledge, we presented the largest Philippine cohort study involving 10,881 hospitalized patients with COVID-19 infection with substantial information on their neurological characteristics. Furthermore, we investigated the effects of the presence of neurological manifestations at the onset on clinically relevant outcomes in these patients.

The incidences of at least one new-onset neurological signs/symptoms in the context of COVID-19 infection were extremely wide-ranging, i.e., ~ 12 to ~ 85%. The wide extent of these percentages was based on retrospective and prospective studies from various countries: Austria (Zifko et al. 2021); China (Mao et al. 2020; Xiong et al. 2020) Egypt (Khedr et al. 2021); Iran (Amanat et al. 2021; Ghaffari et al. 2021); Italy (Benussi et al. 2020; Rifino et al. 2021; Travi et al. 2021); France (Kremer et al. 2020); Germany (Fleischer et al. 2021); Mexico (Flores-Silva et al. 2021); Pakistan (Iltaf et al. 2020); Poland (Wnuk et al. 2021); Portugal (Oliveira et al. 2021); South Korea (Kim et al. 2021); Spain (García-Azorín et al. 2021a, b; García-Moncó et al. 2020; García-Azorín et al. 2021a, b; Romero-Sánchez et al. 2020); Turkey (Karadaş et al. 2020; Yuksel et al. 2021); United States of America (Chachkhiani et al. 2020; Eskandar et al. 2021; Frontera et al. 2021; Liotta et al. 2020); multinational (Chou et al. 2021). Systematic reviews with or without meta-analysis (Cagnazzo et al. 2021; Chua et al. 2020; Collantes et al. 2021; Pinzon et al. 2020; Romoli et al. 2020; Tsai et al. 2020; Vakili et al. 2021) and narrative/scoping reviews (Ahmed et al. 2020; Maury et al. 2021; Roy et al. 2021; Shehata et al. 2021; Wenting et al. 2020; Whittaker et al. 2020; Xu et al. 2021; Yachou et al. 2020; Zubair et al. 2020) were also published. Typically described new-onset neurological manifestations of COVID-19 in the literature included anosmia/ageusia, headache, nausea, myalgia, fatigue, dizziness, acute cerebrovascular events, seizures, and altered mental status/encephalopathy. In our cohort study, we showed that approximately one in twenty COVID-19 patients (i.e., ~ 5%) had a new-onset headache, anosmia/hyposmia, and altered sensorium while one in eleven patients (i.e., ~ 9%) had a new-onset neurological disorder. The most common complications were encephalopathy, stroke and seizure events. It is unknown and difficult to determine if the considerable variations in the percentages of neurological manifestations among the studies are due to differences in the susceptibility among the diverse populations or the neuroinvasive potentials of certain COVID-19 variants. However, we theorize that the varying definitions or ascertainment of neurological manifestations, method of data collection, recording/recall bias, sampling error and other biased played a much larger role in these differences.

Among the full cohort in our study, a substantial number experienced respiratory failure (14.8%), were admitted to the ICU (16.0%) and died (15.6%). Of particular concern was that the new-onset neurological symptoms (NNS) in COVID-19 patients significantly increase the risk of poor outcomes such as mortality, respiratory failure and ICU admission compared to those patients without NNS. On the other hand, the presence of NNS was not significantly associated with a longer length of mechanical ventilation, ICU stay, and hospital admission.

Our data showed that the crude HR of mortality in COVID-19 patients with NNS was increased by 42% over those without NNS. With adjustments for confounders and taking into account the disease severity, mortality risk is significantly higher by 66% among mild cases and by 35% among severe cases. Among critically ill individuals, the presence of NNS may seem to be irrelevant to mortality. We believe that other probable, more fundamental factors may be operating as disease severity progresses such as cardiopulmonary and metabolic complications. Compared to the other matched case–control study with 2324 COVID-19 patients done in NYC, the odds of death was significantly increased by 27% in those with neurological manifestations than those without (Eskandar et al. 2021). A large cohort study also conducted in NYC with 4491 COVID-19 patients showed a significant increase in HR by 38% after adjusting for significant confounders, which was comparable to our own estimates (Frontera et al. 2021). In contrast to these studies, our cohort demonstrated that the proportions of COVID-19 patients with NNS who died due to ARDS, septic shock, multi-organ dysfunction syndrome, and brain herniation were significantly higher compared to those without NNS.

It may be intuitive to suspect that a city with a high population density like NYC would yield worse outcomes particularly in patients with COVID-19; however, other underlying factors may be operative particularly in our country where the healthcare system is at higher risk of being overwhelmed (COVID-19): an ongoing public health crisis in the Philippines 2021). In 2018, there were 23 beds per 10,000 individuals in the National Capital Region while the rest of Luzon, Visayas, and Mindanao had only 8.2, 7.8, and 8.3 beds, respectively (Dayrit et al. 2018). The increase in the role of the private sector in healthcare service delivery induces reliance on out-of-pocket healthcare expenses. In 2018, a total of ~ 54% and ~ 12% is supplied by out-of-pocket payment and voluntary health care payment methods, respectively, to the health expenditure financing scheme (Ignacio et al. 2020; Mapa 2019). The deficient medical infrastructure, insufficient healthcare workforce, and the inadequate government action on contact tracing, mass testing, and vaccine rollout remains to be a major problem that may lead to poor outcomes for our patients infected with COVID-19 (COVID-19): an ongoing public health crisis in the Philippines 2021).

In our cohort, we also provided initial and relatively precise proof that NNS in COVID-19 infection significantly increases the HR of developing respiratory failure with significantly different magnitudes of effect in terms of disease severity after fully adjusting for factors; 91%, 61% and 23% in mild, severe, and critical cases, respectively. In addition, our data showed that apart from pneumonia, ARDS, shock and pulmonary edema, a significantly higher proportion of COVID-19 patients with NNS had respiratory failure attributed to central neurological causes compared to those in the non-NNS group. These support clinical evidence on the hypothesis that SARS-CoV-2 may infect the medullary cardiorespiratory centers via the peripheral nerve terminals (e.g., baro- and chemoreceptors) in the pulmonary system through synapse-connected routes (Li et al. 2020; Machado-Curbelo 2020). Histopathological studies of several brain specimens demonstrated evidence of viral infiltration or encephalitic characteristics with neuronal cell loss and axonal degeneration affecting brainstem nuclei and tracts without documented infarction (Jaunmuktane et al. 2020; Younger 2021). In a recent comprehensive systematic review of neuropathologic findings in COVID-19 patients, other possible mechanisms that can potentially contribute to damage in cardiorespiratory centers in the brainstem leading to respiratory failure in COVID-19 infection may involve hypoxic/ischemic changes or territorial infarctions, microglial/astrocytic activation, and reactive gliosis (Pajo et al. 2021).

Furthermore, our study was able to shed light on relevant evidence relating NNS and ICU admission in general. In particular, there was a significantly increased risk (i.e., added 80% risk) for ICU admission in COVID-19 patients with NNS compared to those without NNS. Similar to mortality and respiratory failure outcomes, disease severity was found to be a significant effect modifier for ICU admission; 97% and 83% increased risk in mild and severe cases, respectively. These data suggest that NNS may potentially predict increased utilization of ICU/ICU-comparable settings that may lead to elevated healthcare costs among patients with COVID-19 patients. In addition, our data showed that there were significantly higher percentages of patients in the NNS cohort admitted due to ARDS, shock, impaired level of consciousness, acute kidney injury necessitating dialysis, acute stroke, and cerebral edema compared to those in the non-NNS.

Indeed, most previously published reports were focused on the characterization of NNS in COVID-19. Compared to these precursory studies, our study imparted valuable information from a much larger sample size of COVID-19 patients which provides an advantage of limiting sampling error and allows more precise estimates of the associations. To extend the comprehensiveness of our report, the findings in the neuroimaging and CSF were also provided. Although a number of previous studies provided some insights on the association of neurological manifestations to in-hospital mortality, our study provided new information on the relationship of NNS to specific causes of mortality, to the incidence of respiratory failure and its specific causes, to the need for admission to the ICU and the particular reasons for the admission. Furthermore, our study also revealed data on the associations of NNS with a longer duration of ventilator dependence, a longer length of ICU stay, and longer hospital stay. At present, the relationships between NNS and these other outcomes are insufficiently described in the literature. These outcomes can be also considered clinically useful particularly to estimate the magnitude of the risk of having certain poor outcomes when a COVID-19 patient had NNS compared to those without NNS.

Our study has inherent limitations. The data reflected exposures and outcomes of hospitalized COVID-19 patients; thus, information from patients who were not admitted was not captured by our estimates. Moreover, because only admitted patients were involved in this study, mortality and respiratory failure are expected to be overestimated since substantially more severe and critical COVID-19 patients are admitted to the hospital. There may be certain periods during the collection of data when our healthcare system was overwhelmed. Hence, the availability to admit patients to the hospital or to the ICU as well as the capacity to sufficiently document clinical/neurological features and outcomes of patients by our local admitting neurologists/physicians could have affected our estimated exposures/outcomes. In this cohort study, the comparison in the neurological features and outcomes of patients admitted in the urban and rural hospital setting is not feasible because all the included study sites were located in the urban setting. Other study limitations were non-documentation of their psychiatric symptoms and the treatments employed for their neurological symptoms. Recording bias was intrinsic to this study which could have contributed to underreporting of data. Nevertheless, we were able to obtain large amounts of data from the total enumeration of COVID-19 patients from 37 hospital sites located in various regions of our country. We further theorize that prospective collection of data may probably increase the effect estimates of NNS on mortality, respiratory failure, and ICU admission and may facilitate detection of other relevant confounding variables and effect modifiers affecting their relationship.

Our current study encompassed patients admitted from February to December 2020 only. It may be worthwhile to look into the differences in the neurological features of COVID-19 in 2020 compared to 2021 given the emergence of COVID-19 variants and the effects of vaccination. Previous observational studies, including our current study, provided compelling evidence of the involvement of the nervous system particularly the central nervous system (CNS). Beyond the clinical symptomatology, subsequent studies may focus on the viral staging pathology of the brain as well as documenting neuronal-glial changes in patients with COVID-19-infected CNS regions (Riederer and ter Meulen 2020). Long-term sequelae of previously affected brains by the COVID-19 that could potentially promote or precipitate certain neurodegenerative disorders must be evaluated in future clinical and brain coronavirus-related research (Riederer and ter Meulen 2020).

Acknowledgements

The authors would like to thank the Philippine Neurological Association and the Expanded Hospital Research Office of the University of the Philippines- Philippine General Hospital for funding this research. The Philippine CORONA Study Group Investigators: Asian Hospital and Medical Center, Muntinlupa City (Corina Maria Socorro A. Macalintal, MD; Joanne B. Robles, MD), Baguio General Hospital and Medical Center, Baguio City (Paulo L. Cataniag, MD; Manolo Kristoffer C. Flores, MD, MBA), Cagayan Valley Medical Center, Tuguegarao City (Noreen Jhoanna C. Tangcuangco-Trinidad, MD), Capitol Medical Center, Quezon City (Dan Neftalie A. Juangco, MD; Giuliani Renz G. Paas, MD), Cardinal Santos Medical Center, San Juan City (Audrey Marie U. Chua, MD, Valmarie S. Estrada, MD, Philip Rico P. Mejia, MD, Therese Franz B. Reyes, MD), Chong Hua Hospital, Cebu City (Maria Teresa A. Cañete, MD; Ferdinand Renfred A. Zapata, MD), De La Salle University Medical and Health Sciences Institute, Dasmariñas City, (Franko Eugenio B. Castillo, MD; Romulo U. Esagunde, MD; Jean B. Gantioque, MD), Dr. Jose N. Rodriguez Memorial and Sanitarium Hospital, Caloocan City (Maritoni C. Abbariao, MD; Geramie M. Acebuque, MD), Dr. Pablo O. Torre Memorial Hospital, Bacolod City (Evram V. Corral, MD), East Avenue Medical Center, Quezon City (Marian Irene C. Escasura, MD; Marissa T. Ong, MD), Institute of Dementia Care Asia, Quezon City (Krizelle Cleo Fowler), Jose B. Lingad Memorial Regional Hospital, City of San Fernando (Arnold Angelo M. Pineda, MD; Khasmeen D. Aradani, MD), Jose R. Reyes Memorial Medical Center, Manila (Joseree-Ann S. Catindig, MD; Mark Timothy T. Cinco, MD; Mark Erving H. Ramos, MD), Lung Center of the Philippines, Quezon City (Romulus Emmanuel H. Cruz, MD; Marita B. Dantes, MD; Norberto A. Francisco, MD; Rosalia A. Teleg, MD), Makati Medical Center, Makati City (Krisverlyn B. Bellosillo, MD; Jean Paolo M. Delfino, MD; Cid Czarina E. Diesta, MD; Rosalina B. Espiritu-Picar, MD; Julie Anne V. Gamboa, MD; Cara Camille M. Matute, MD; Franzelle P. Padilla, MD; John Joshua Q. Punsalan, MD), Manila Doctors Hospital, Manila (Ma. Epifania V. Collantes, MD; Charmaine B. Que, MD; Hanifa A. Sampao, MD; Maxine Camela S. Sta. Maria, MD), Medical Center Manila, Manila (Marita M. Fuentes, MD; Jennifer Justice F. Manzano, MD; Rizza J. Umali, MD), New Era General Hospital, Quezon City (Marc Conrad C. Molina, MD), Northern Mindanao Medical Center, Cagayan de Oro City (Hazel Claire Minerva-Ang, MD; Arturo F. Surdilla, MD; Loreto P. Talabucon Jr., MD; Natasha F. Wabe, MD), Quirino Memorial Medical Center, Quezon City (Maria Victoria G. Manuel, MD; Al Inde John A. Pajantoy, MD; Josephine Cecilia V. Roque, MD; Paul Emmanuel L. Yambao, MD), Ospital ng Makati, Makati City (Christian Paul B. Banday, MD; Chritopher C. Cipriano, MD; Nehar A. Pangandaman, MD; Avery Gail C. Wasil, MD), Perpetual Succour Hospital, Cebu City (Elrey P. Inocian, MD; Jarungchai Anton S. Vatanagul, MD), Philippine General Hospital, Manila (Almira Doreen Abigail O. Apor, MD; Carissa Paz C. Dioquino, MD), Philippine Heart Center, Quezon City (Prinz Andrew M. Dela Cruz, MD; Maricar P. Yumul, MD) Research Institute for Tropical Medicine, Muntinlupa City (Ma. Alma E. Carandang-Concepcion, MD), San Juan De Dios Educational Foundation Inc.– Hospital, Pasay City (Ma. Caridad V. Desquitado, MD; Carl Kevin L. Julao, MD), San Lazaro Hospital, Manila (Dante P. Bornales, MD), Southern Isabela Medical Center, Santiago City (Generaldo D. Maylem, MD; Mark Joseph F. Cuntapay, MD), Southern Philippines Medical Center, Davao City (Annabelle Y. Lao-Reyes, MD; Aileen Mae B. Lee, MD; Nadia O. Manlegro, MD; Dave Mar L. Pelere, MD) St. Luke’s Medical Center—Global City, Taguig City (Lina C. Laxamana, MD; Diana-Lynn S. Que, MD; Jeryl Ritzi T. Yu, MD), St. Luke’s Medical Center, Quezon City (Ma. Socorro C. Martinez, MD; Alexandria E. Matic, MD; John Angelo Luigi S. Perez, MD), The Medical City, Pasig City (Glenn Anthony A. Constantino, MD; Aldanica R. Olano, MD; Liz Edenberg P. Quiles, MD; Artemio A. Roxas, Jr., MD; Jo Ann R. Soliven, MD; Michael Dorothy Frances Montojo-Tamayo, MD), University of Santo Tomas Hospital, Manila (Ma. Lourdes C. Joson, MD; Jojo R. Evangelista, MD), University of the East Ramon Magsaysay Memorial Medical Center Inc., Quezon City (Ma. Clarissa B. Nuñez, MD; Marietta C. Olaivar, MD; Dominique Q. Perez, MD), Veterans Memorial Medical Center, Quezon City (Mark Deneb O. Armeña, MD; Robert A. Barja, MD), Vicente Sotto Memorial Medical Center, Cebu City (Joshua Emmanuel E. Abejero, MD; Maritzie R. Eribal, MD), Western Visayas Medical Center, Iloilo City (Ryndell G. Alava, MD), Zamboanga City Medical Center, Zamboanga City (Muktader A. Kalbi, MD; Nasheera W. Radja, MD; Mohammad Elshad S. Sali, MD).

Abbreviations

95% CI

95% confidence intervals

ARDS

Acute respiratory distress syndrome

COVID-19

Coronavirus disease 2019

DM

Diabetes mellitus

DVD

Duration of ventilator dependence

HPN

Hypertension

HR

Hazard ratio

ICU

Intensive care unit

IQR

Interquartile range

LHS

Length of hospital stay

LICUS

Length of ICU stay

NNS

New-onset neurological symptoms

OR

Odds ratio

RT-PCR

Reverse transcription polymerase chain reaction

SARS-CoV-2

Severe acute respiratory syndrome coronavirus 2

Author contributions

AIE: conceptualization (lead); data curation (lead); formal analysis (lead); investigation (lead); methodology (lead); project administration (lead); software (lead); supervision (lead); validation (lead); visualization (lead); writing-original draft (lead); writing-review and editing (lead). MCCS: conceptualization (lead); data curation (lead); formal analysis (equal); investigation (equal); methodology (equal); project administration (lead); supervision (lead); validation (equal); writing-original draft (lead); writing-review and editing (lead). VMMA: conceptualization (lead); data curation (equal); formal analysis (equal); funding acquisition (lead); investigation (equal); methodology (equal); project administration (equal); resources (lead); supervision (lead); validation (equal); writing-original draft (equal); writing-review and editing (equal). RDGJ: conceptualization (lead); data curation (equal); formal analysis (equal); funding acquisition (lead); investigation (equal); methodology (equal); project administration (equal); resources (lead); supervision (lead); validation (equal); writing-original draft (equal); writing-review and editing (equal). Philippine CORONA Study Group Investigators: conceptualization, data acquisition, data curation, and writing—review and editing.

Funding

(1) Philippine Neurological Association, Room 1006, Rear Tower Cathedral Height Building Complex, St. Luke’s Medical Center, E. Rodriguez Avenue, Manila, Philippines (Grant number: not applicable); and (2) Expanded Hospital Research Office, University of the Philippines- Philippine General Hospital, Taft Avenue, Manila, Philippines (Grant number: not applicable). Our funding sources were not involved in the design of the protocol, study execution, analysis and interpretation of data, writing and editing of the contents of the manuscript, and in the publication of this article.

Availability of data and material

Anonymized data not published within this article will be made available by request from any qualified investigator.

Code availability

Not applicable.

Declarations

Conflict of interest

All authors have stated explicitly that there are no conflicts of interest in connection with this article.

Ethical approval

Our protocol was approved and endorsed by the local institutional review boards (code): Asian Hospital and Medical Center, Muntinlupa City (2020- 010-A); Baguio General Hospital and Medical Center (BGHMC), Baguio City (BGHMC-ERC-2020–13); Cagayan Valley Medical Center (CVMC), Tuguegarao City; Capitol Medical Center, Quezon City; Cardinal Santos Medical Center (CSMC), San Juan City (CSMC REC 2020–020); Chong Hua Hospital, Cebu City (IRB 2420–04); De La Salle Medical and Health Sciences Institute (DLSMHSI), Cavite (2020–23-02-A); East Avenue Medical Center (EAMC), Quezon City (EAMC IERB 2020- 38); Jose R. Reyes Memorial Medical Center, Manila; Jose B. Lingad Memorial Regional Hospital, City of San Fernando, Pampanga; Dr. Jose N. Rodriguez Memorial and Sanitarium Hospital, Caloocan City; Lung Center of the Philippines (LCP), Quezon City (LCP-CT-010–2020); Manila Doctors Hospital, Manila (MDH IRB 2020–006); Makati Medical Center, Makati City (MMC IRB 2020–054); Medical Center Manila, Manila (MMERC 2020–09); Northern Mindanao Medical Center, Cagayan de Oro City (025–2020); Quirino Memorial Medical Center (QMMC), Quezon City (QMMC REB GCS 2020–28); Ospital ng Makati, Makati City; University of the Philippines – Philippine General Hospital (UP-PGH), Manila (2020–314-01 SJREB); Philippine Heart Center, Quezon City; Research Institute for Tropical Medicine, Muntinlupa City (RITM IRB 2020–16); San Lazaro Hospital, Manila; San Juan De Dios Educational Foundation Inc – Hospital, Pasay City (SJRIB 2020–0006); Single Joint Research Ethics Board of the DOH, Philippines (SJREB-2020–24); Southern Isabela Medical Center, Santiago City (2020–03); Southern Philippines Medical Center (SPMC), Davao City (P20062001); St. Luke’s Medical Center, Quezon City (SL-20116); St. Luke’s Medical Center, Bonifacio Global City, Taguig City (SL-20116); Southern Philippines Medical Center, Davao City; The Medical City, Pasig City; University of Santo Tomas Hospital, Manila (UST-REC-2020–04-071-MD); University of the East Ramon Magsaysay Memorial Medical Center, Inc, Quezon City (0835/E/2020/063); Veterans Memorial Medical Center (VMMC), Quezon City (VMMC- 2020–025) and Vicente Sotto Memorial Medical Center, Cebu City (VSMMC-REC-O-2020–048).

Consent to participant

Acquiring informed consent was not needed for this study since the design was a retrospective cohort study employing medical chart review and the data obtained for this study were completely anonymized.

Consent for publication

We, the authors, consent to publish our manuscript entitled “COVID-19 Outcomes of 10,881 patients: Retrospective study of Neurological symptoms and Associated manifestations (Philippine CORONA Study)” in the Journal of Neural Transmission. This manuscript has never been published in part nor concurrently submitted elsewhere. The authors have seen and approved the manuscript, and significantly contributed to the work.

Technical review

Our protocol underwent technical review from the following institutions: Department of Neurosciences, Philippine General Hospital and College of Medicine, University of the Philippines Manila, from the Cardinal Santos Medical Center, San Juan City, and from the Research Center for Clinical Epidemiology and Biostatistics, De La Salle Medical and Health Sciences Institute, Dasmariñas City, Cavite.

Protocol registration and publication

Our protocol was registered at the ClinicalTrials.gov website (NCT04386083). The full protocol was published by the British Medical Journal (BMJ) Open (see the following article: Espiritu AI, Sy MCC, Anlacan VMM, Jamora RDG. The Philippine COVID-19 Outcomes: a Retrospective study Of Neurological manifestations and Associated symptoms (The Philippine CORONA study): a protocol study. BMJ Open 2020; 10:e040944. Available at: https://bmjopen.bmj.com/lookup/doi/10.1136/bmjopen-2020-040944.

List of study sites

Asian Hospital and Medical Center, Muntinlupa City; Baguio General Hospital and Medical Center, Baguio City; Cagayan Valley Medical Center, Tuguegarao City; Capitol Medical Center, Quezon City; Cardinal Santos Medical Center, San Juan City; Chong Hua Hospital, Cebu City; De La Salle University Medical and Health Sciences Institute, Dasmariñas City; East Avenue Medical Center, Quezon City; Jose B. Lingad Memorial Regional Hospital, City of San Fernando, Pampanga; Dr. Jose N. Rodriguez Memorial and Sanitarium Hospital, Caloocan City; Jose R. Reyes Memorial Medical Center, Manila; Lung Center of the Philippines, Quezon City; Manila Doctors Hospital, Manila; Medical Center Manila, Manila; Makati Medical Center, Makati City; New Era General Hospital, Quezon City; Northern Mindanao Medical Center, Cagayan de Oro City; Quirino Memorial Medical Center, Quezon City; Ospital ng Makati, Makati City; Perpetual Succour Hospital, Cebu City; Dr. Pablo O. Torre Memorial Hospital (Riverside Medical Center, Inc.), Bacolod City; University of the Philippines—Philippine General Hospital, Manila; Philippine Heart Center, Quezon City; Research Institute for Tropical Medicine, Muntinlupa City; San Lazaro Hospital, Manila; San Juan De Dios Educational Foundation Inc.—Hospital, Pasay City; Southern Isabela Medical Center, Santiago City; St. Luke’s Medical Center, Quezon City; St. Luke’s Medical Center, Bonifacio Global City, Taguig City; Southern Philippines Medical Center, Davao City; The Medical City, Pasig City; University of the East Ramon Magsaysay Memorial Medical Center, Inc., Quezon City; University of Santo Tomas Hospital, Manila; Veterans Memorial Medical Center, Quezon City; Vicente Sotto Memorial Medical Center, Cebu City; Western Visayas Medical Center, Iloilo City; Zamboanga City Medical Center, Zamboanga City.

Footnotes

The members of the Philippine CORONA Study Group Investigators are mentioned in Acknowledgements section.

Publisher's Note

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Adrian I. Espiritu, MD, and Marie Charmaine C. Sy, MD, MBA, contributed equally to this manuscript.

Contributor Information

Adrian I. Espiritu, Email: aiespiritu@up.edu.ph, Email: espirituadrian@gmail.com

the Philippine CORONA Study Group Investigators:

Corina Maria Socorro A. Macalintal, Joanne B. Robles, Paulo L. Cataniag, Manolo Kristoffer C. Flores, Noreen Jhoanna C. Tangcuangco-Trinidad, Dan Neftalie A. Juangco, Giuliani Renz G. Paas, Audrey Marie U. Chua, Valmarie S. Estrada, Philip Rico P. Mejia, Therese Franz B. Reyes, Maria Teresa A. Cañete, Ferdinand Renfred A. Zapata, Franko Eugenio B. Castillo, Romulo U. Esagunde, Jean B. Gantioque, Maritoni C. Abbariao, Geramie M. Acebuque, Evram V. Corral, Marian Irene C. Escasura, Marissa T. Ong, Krizelle Cleo Fowler, Arnold Angelo M. Pineda, Khasmeen D. Aradani, Joseree-Ann S. Catindig, Mark Timothy T. Cinco, Mark Erving H. Ramos, Romulus Emmanuel H. Cruz, Marita B. Dantes, Norberto A. Francisco, Rosalia A. Teleg, Krisverlyn B. Bellosillo, Jean Paolo M. Delfino, Cid Czarina E. Diesta, Rosalina B. Espiritu-Picar, Julie Anne V. Gamboa, Cara Camille M. Matute, Franzelle P. Padilla, John Joshua Q. Punsalan, Ma. Epifania V. Collantes, Charmaine B. Que, Hanifa A. Sampao, Maxine Camela S. Sta. Maria, Marita M. Fuentes, Jennifer Justice F. Manzano, Rizza J. Umali, Marc Conrad C. Molina, Hazel Claire Minerva-Ang, Arturo F. Surdilla, Loreto P. Talabucon, Natasha F. Wabe, Maria Victoria G. Manuel, Al Inde John A. Pajantoy, Josephine Cecilia V. Roque, Paul Emmanuel L. Yambao, Christian Paul B. Banday, Chritopher C. Cipriano, Nehar A. Pangandaman, Avery Gail C. Wasil, Elrey P. Inocian, Jarungchai Anton S. Vatanagul, Almira Doreen Abigail O. Apor, Carissa Paz C. Dioquino, Prinz Andrew M. Dela Cruz, Maricar P. Yumul, Ma. Alma E. Carandang-Concepcion, Ma. Caridad V. Desquitado, Carl Kevin L. Julao, Dante P. Bornales, Generaldo D. Maylem, Mark Joseph F. Cuntapay, Annabelle Y. Lao-Reyes, Aileen Mae B. Lee, Nadia O. Manlegro, Dave Mar L. Pelere, Lina C. Laxamana, Diana-Lynn S. Que, Jeryl Ritzi T. Yu, Ma. Socorro C. Martinez, Alexandria E. Matic, John Angelo Luigi S. Perez, Glenn Anthony A. Constantino, Aldanica R. Olano, Liz Edenberg P. Quiles, Artemio A. Roxas, Jr., Jo Ann R. Soliven, Michael Dorothy Frances Montojo-Tamayo, Ma. Lourdes C. Joson, Jojo R. Evangelista, Ma. Clarissa B. Nuñez, Marietta C. Olaivar, Dominique Q. Perez, Mark Deneb O. Armeña, Robert A. Barja, Joshua Emmanuel E. Abejero, Maritzie R. Eribal, Ryndell G. Alava, Muktader A. Kalbi, Nasheera W. Radja, and Mohammad Elshad S. Sali

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