bluuebunny/crossref_metadata_2025_split huggingface dataset:
397. The Three COVID-19 Prognostic Tools (CALL, 4Cs, NLR): Head-to-Head Comparison
Authors: Doumat George , Yazbeck Hady , Itani Hassan , Wazzi-Mkahal Rayyan , Badr Mario , Moussa Elie , Barakat Salim , Osman Wael , Kanafani Zeina 
Published: November 2023

Abstract

Background
Risk stratification of patients is crucial for treating COVID-19 infection, but studies have not compared prognostic tools like neutrophil to lymphocyte ratio (NLR), CALL score, and 4C mortality score. Our aim is to determine the most accurate prognostic score for predicting COVID-19 patient outcomes in a retrospective cohort at a tertiary care center in Lebanon.

Methods
The study included adult patients with COVID-19 infection admitted from 2020 to 2021 at the American University of Beirut Medical Center. We calculated the NLR, CALL, and 4C scores and we recorded various outcomes for each patient. The outcomes of interest were need for mechanical ventilation (MV), intensive care unit (ICU) admission, illness progression, mortality, length of hospital stay, length of ICU stay, and length of MV. We also created a combined score from variables within each score and correlated it with outcome measures.

Results
We enrolled 401 patients with confirmed COVID-19 infection. Patients were predominantly males (67%) with an average age of 67 years. The median length of hospital stay was 15 days and illness progression was recorded in 66% of patients. Around 50% of patients required ICU admission with a median stay of 14 days, and 31% required MV with a median duration of 16 days. The mortality rate was 31%. The CALL score was a significant predictor of need for ICU admission (difference of means [DOM] 0.73), MV (DOM 0.81), disease progression (DOM 0.78), and mortality (DOM 0.90), with p≤0.01 for each outcome). The 4C score was positively associated with disease progression (DOM 1.0) and mortality (DOM 1.9) (p≤0.03 for each outcome), while NLR was only predictive of disease progression (DOM 0.2; p=0.04). The combined score was able to predict mortality (DOM 1.7; p&lt; 0.002) but none of the other outcomes.

Conclusion
The CALL score proves to be the most useful tool in predicting several patient outcomes. However, the 4C score is a better predictor of mortality. There seems to be no added benefit from using a combined score. The evaluation of more advanced scoring systems is highly recommended to optimize risk stratification.

Disclosures
All Authors: No reported disclosures
413. Living with Long COVID: A Study on Persistent Symptoms and Vaccination Outcomes Two Years After COVID-19 Infection
Authors: Kim Yoonjung , Kim Shin-Woo , Bae Sohyun , Chang Hyun-Ha 
Published: November 2023

Abstract

Background
Understanding the characteristics of long COVID and the effect of COVID-19 vaccinations on patients after 24 months of COVID-19 infection is crucial for effective disease management. This study aims to determine the prevalence of long COVID and its impact on lifestyle 24 months after COVID-19 infection.

Methods
This prospective cohort study included 235 adult patients diagnosed with COVID-19 between February 17, 2020, and March 24, 2020. The participants visited the study hospital four times, at 6, 12, 18, and 24 months after acute COVID-19 infection, to assess their symptoms, quality of life, and mental health. The clinical characteristics, self-reported long COVID symptoms, EuroQol 5-dimension 5-level (EQ-5D-5L) index, Korean versions of the Patient Health Questionnaire-9 (PHQ-9), Posttraumatic Stress Disorder Checklist-5 (PCL-5-K), and Generalized Anxiety Disorder-7 (GAD-7) scale scores were investigated 24 months after acute COVID-19 infection. The presence or absence of 24-month long COVID symptoms and COVID-19 vaccination, initial disease severity, and clinical characteristics over time were compared and analyzed.

Results
Out of the 235 patients, 121 (51.5%) completed the study visits, and 75 (62.0%) still experienced long COVID symptoms 24 months after acute infection. Fatigue, amnesia, difficulty concentrating, and insomnia were the most common symptoms (Figure 1). The frequency of neuropsychiatric symptoms did not differ based on vaccination status or the number of doses received. However, patients with long COVID symptoms had significantly higher scores on measures of anxiety and depression than those without long COVID symptoms. The quality of life for the participants improved over time but still affected 32.2% of respondents, particularly in the anxiety/depression dimension.

Conclusion
This study highlights that long COVID symptoms can persist up to 24 months after acute COVID-19 infection, affecting mental health and quality of life. Although COVID-19 vaccination did not seem to impact the frequency of neuropsychiatric symptoms, further research is needed to fully understand the long-term impact of COVID-19 and the efficacy of COVID-19 vaccinations on long COVID symptoms.

Disclosures
All Authors: No reported disclosures
416. Poor clinical outcomes of COVID-19 patients receiving rituximab during the Omicron era
Authors: Lee Chan Mi , Kang Chang Kyung , Jo Hyeon Jae , Choe Pyoeng Gyun , Kim Nam Joong , Park Wan Beom , Oh Myoung-don 
Published: November 2023

Abstract

Background
Rituximab (RTX), a widely used anti-CD20 monoclonal antibody, has been suggested as a risk factor for poor outcomes of coronavirus disease 2019 (COVID-19). However, it remains largely unknown whether COVID-19 outcomes remain worse in patients receiving RTX compared to those not being treated with RTX during the Omicron-dominant period, when patient fatality has significantly decreased compared to the Delta-dominant period. Therefore, we aimed to investigate the outcomes of COVID-19 patients receiving RTX during the Omicron-dominant period.

Methods
Among all laboratory-confirmed COVID-19 patients who were admitted to Seoul National University Hospital from February 2022 to January 2023, those who had received RTX before diagnosis of COVID-19 were included in the RTX group, while those who had the same underlying diseases but had not received RTX were included in the non-RTX group. The COVID-19-related outcomes were compared between the RTX and non-RTX groups. Multivariate logistic regression analyses were used to identify factors associated with severe to critical COVID-19 and COVID-19-related mortality.

Results
The proportion of severe to critical COVID-19 and COVID-19-related mortality were significantly higher in the RTX group than in the non-RTX group (41.9% vs. 28.3%, P = 0.030; 11.8% vs. 2.8%, P = 0.005). RTX therapy (adjusted odds ratio [aOR] 2.21, 95% confidence interval [CI] 1.21–4.04, P = 0.010) and chronic kidney disease (aOR 3.28, 95% CI 1.56–6.91, P = 0.002) were independent risk factors for severe to critical COVID-19, whereas being effectively vaccinated (aOR 0.47, 95% CI 0.25–0.85, P = 0.013) was a protective factor. In addition, only RTX therapy was independently associated with COVID-19-related mortality (aOR 4.03, 95% CI 1.17–13.86, P = 0.027).

Conclusion
RTX therapy was associated with poor clinical outcomes of COVID-19, even in the Omicron-dominant period. This suggests that RTX should be used with caution even during the Omicron-dominant period.

Disclosures
All Authors: No reported disclosures
420. Prevalence of comorbidities and COVID-19 vaccination among COVID-19 deaths
Authors: Lyons Zin , DiBiase Lauren , Sickbert-Bennett Emily , Weber David J 
Published: November 2023

Abstract

Background
It is crucial to note that comorbidities may significantly increase the risk of mortality among COVID-19 patients. That's why it's vital for individuals with multiple comorbidities to make getting fully vaccinated a top priority, in order to safeguard their own health and the health of those around them. This study describes prevalence of comorbidities and COVID-19 vaccination among patients who died of COVID-19.

Methods
This was a retrospective study that assessed 220 inpatients at the UNC Medical Center who died with COVID-19 per discharge diagnosis from 7/11/21-4/3/23. Data included demographics, COVID-19 vaccination status and preexisting comorbidities. Comorbidities were classified based on CDC designated risk factors for severe COVID-19 (CDC, 2023).

Results
Of 220 patients who died, 45% were &gt; 65 years, 60% were male, and 90% of patients had comorbidities ranging from 1 to &gt; 3. The top 6 comorbidities among all genders and ages were hypertension (48%), heart conditions (42%), diabetes (35%), chronic kidney disease (29%), immunocompromised (29%), and obesity (25%), respectively. The proportion vaccinated was 38%; among those vaccinated, doses received were 1 (11%), 2 (40%), 3 (32%), 4 (10%), and 5 (7%). Among those who died and were vaccinated, they were 1.13 times more likely to have comorbidities (97%) compared to those not vaccinated (86%) (Risk ratio (RR) 1.13, 95% CI 1.05-1.22, P=0.004) (Table).
Prevalence of comorbidities and vaccination among COVID-19 deaths

Conclusion
In this cohort study of COVID-19 deaths, the majority of patients dying with COVID-19 were White, non-Hispanic, males &gt; 65 years with comorbidities, most often with multiple comorbidities. Hypertension, heart conditions, diabetes, chronic kidney disease, immunocompromised, and obesity emerged as the most common comorbidities. More than half of the patients had not received COVID-19 vaccination. This highlights the importance of getting fully vaccinated, especially for those with pre-existing conditions.

Disclosures
David J. Weber, MD, MPH, BD: Advisor/Consultant|Germitic: Advisor/Consultant|GSK: DSMB|PDI: Advisor/Consultant|Pfizer: Advisor/Consultant|Wellair: Advisor/Consultant
423. Development and Validation of a Predictive Model of New-Onset Deep Vein Thrombosis/Pulmonary Embolism Among Hospitalized COVID-19 Patients: A Retrospective Cohort Study
Authors: Meng Qingqing , Rodriguez-Nava Guillermo , Egoryan Goar , Kishmiryan Armen 
Published: November 2023

Abstract

Background
COVID-19 can increase the risk of thromboembolism events, such as deep vein thrombosis (DVT) and pulmonary embolism (PE). This study aimed to develop a risk prediction model for new-onset DVT/PE in hospitalized COVID-19 patients based on baseline characteristics, major complaints, and lab values.

Methods
Retrospective data from 671 hospitalized COVID-19 patients were collected between March and October 2020. After excluding missing values, 241 cases were included in the final analysis (Table 1). Lasso regression was used to select related variables, and a prediction model was built using logistic regression (Figure 1). A nomogram was established based on multivariate analysis, and the model was validated using the bootstrap method.

Results
Among the 241 hospitalized patients with COVID-19 infection, 18 (7.5%) developed new-onset DVT/PE during hospitalization. The final prediction model included the history of diabetes mellitus, chills, sore throat, anorexia, hemoglobin, and D-dimer levels on admission. A predictive nomogram model for new-onset DVT/PE based on these variables was built (Figure 2). Our prediction model showed good predictive performance with an area under the curve (AUC) of 0.89 and a bootstrap-corrected AUC of 0.82 (95% CI: 0.767-0.873) (Figure 3). The calibration plots demonstrated good agreement between the estimated probability and the actual observation (Figure 4A). Decision curve analysis shows the nomogram is clinically useful (Figure 4B).

Conclusion
The developed model for new-onset DVT/PE in hospitalized COVID-19 patients based on baseline characteristics, major complaints, and lab values had good discrimination and calibration. It may help identify high-risk patients who require close monitoring and prophylactic anticoagulation. Future studies are needed to validate the model in different populations and settings.

Disclosures
All Authors: No reported disclosures
424. High Prevalence of Neuropsychiatric Symptoms and Pre-COVID-19 Mental Health Disorders in Patients with Post-Acute COVID
Authors: Mokraoui Nassim , Brenner Sara , Westlake Amanda , Rastegar Vida , Skiest Daniel 
Published: November 2023

Abstract

Background
Individuals with post-acute COVID syndrome (PAC), also known as long-COVID, have new or continuing symptoms lasting more than 1 month from COVID-19 infection. Patients may present with neuropsychiatric (NP) and non-NP symptoms. Whether prior mental health disorders correlate with NP symptoms is unknown. The study aims were to describe the demographics, clinical factors, comorbid conditions, and presenting symptoms of PAC.

Methods
We included patients evaluated at Baystate Health PAC clinic from January 2021-April 2022, with prior COVID-19 infection and new or persistent symptoms of at least 6 weeks. We defined the primary presenting symptoms as NP (cognitive dysfunction (brain fog), anxiety, or depression) or non-NP (dyspnea, fatigue, dizziness, palpitations). The clinician’s diagnostic impression was recorded and characterized as NP or non-NP.
Demographics

Results
We randomly selected 150 of 250 clinic patients for chart review. Most were women, 117 (78%); mean age was 52. Most 106 (71%) had mild acute COVID illness; 36 (24%) were hospitalized. 49% (n=73) had a pre-existing mental health disorder: mood disorder (depression/bipolar) 26 (17%), anxiety 18 (12%), both anxiety and mood disorder 27 (18%). The most common presenting symptoms in order of frequency were fatigue, dyspnea, brain fog, anxiety, depression, and palpitations. 106 (71%) had at least 1 NP presenting symptom or combination of NP symptoms; cognitive dysfunction with anxiety in 23 (15%), and cognitive dysfunction with anxiety and mood disorder in 13 (9%). The clinician's final diagnostic impression was fatigue (47%), cognitive dysfunction (36%), anxiety (29%) depression (33%) and PTSD (3%). A NP diagnosis was made in 81 (54%).
Primary Symptoms
Primary Diagnosis

Conclusion
The majority of PAC clinic patients were female and had mild COVID illness. We noted a high prevalence of pre-existing mental health disorders. More than two-thirds presented with one or more NP symptom. Further studies should explore the association of pre-COVID mental health disorders with PAC.

Disclosures
All Authors: No reported disclosures
425. Characterization and predictive risk scoring of long COVID in a South Indian cohort after breakthrough COVID infection; a prospective single centre study
Authors: Moni Merlin , Nair Pranav , Nair Chithira , Kulirankal Kiran , Corley Elizabeth , Edathadathil Fabia , Gutajhr Georg , Sathyapalan Dipu T 
Published: November 2023

Abstract

Background
The majority of existing literature on long COVID has been focused on hospitalised patients. However, in the real-world setting of the post-vaccination era where breakthrough infections are the norm, it becomes a priority to assess the clinical profile of long COVID symptoms among such individuals. Through our study, we aim to describe the incidence, characterise and stratify the risk of developing long COVID breakthrough infections.

Methods
This prospective observational study included adult patients with breakthrough COVID-19 infections diagnosed at a tertiary hospital in India. Post-COVID symptoms at weeks 2, 6 and 12 after testing negative were extracted using a questionnaire after which multi-variate analysis was done.

Results
Out of 414 patients analysed, 164 reported post-COVID symptoms beyond 6 weeks of the infection. Univariate analysis showed that the presence of long COVID was found to be significantly higher among patients above 65 years of age at 31.3% in comparison to the long COVID presence at 29.4% among those below age 65 (p &lt; 0.001). Systemic hypertension was also significantly associated with the presence of long COVID (72.3%), along with bronchial asthma (68.8%) with a p-value of &lt; 0.0001 in both instances. Backwards selection was used leading to a reduced model consisting of age OR 1.053, 95% CI 0.097–1.07), p &lt; 0.001), hypertension (OR 2.59, 95% CI 1.46–4.59, p = 0.001) and bronchial asthma (OR 3.7176, 95% CI 1.24–11.12, p = 0.018) to be significant predictors of long COVID incidence. These were used to develop a propensity score (C statistics – 80.9%).

1A: Long COVID symptomatic profile at weeks 2, 6 and 12 weeks 1B: Organ system wise distribution of long COVID symptoms at 2, 6 and 12 weeks

Conclusion
A significant presence of long COVID at 12 weeks among non-hospitalised breakthrough infections calls for a series of review check-ups for the early detection of long-term complications. The proposed predictive risk scoring based on significant risk factors may assist clinicians in identifying patients at risk of developing long COVID, leading to appropriate, individualised management.
Table 4
Figure 3A & 3B
3A: Logistic regression model for predicting long COVID incidence 3B: Clinical prediction nomogram to depict risk of long COVID incidence
Figure 4A & 4B
4A: Association of propensity risk score for Long COVID incidence with symptom burden 4B: Association of propensity risk score for Long COVID incidence with functional status

Disclosures
All Authors: No reported disclosures
428. Risk Stratification and Clinical Outcomes in a Mixed Vaccination Cohort of COVID-19 Patients Admitted in a Tertiary Hospital Using ISARIC 4C Scores
Authors: Pascual May Anne S , Reyes John Robinson L 
Published: November 2023

Abstract

Background
The risk of clinical deterioration and death with the use of prognostic tools remain necessary for strategic planning among COVID-19 patients. The International Severe Acute Respiratory and Emerging Infections Consortium (ISARIC) developed and validated Coronavirus Clinical Characterization Consortium (4C) scores to predict clinical deterioration and mortality. ISARIC 4C scores uses readily available clinical parameters. With the increasing uptake of COVID-19 vaccine, there is a need to evaluate these tools. The study examined a cohort of fully vaccinated (FV), partially vaccinated (PV) and unvaccinated (UV) COVID-19 patients and determined their clinical outcomes and stratified risks using ISARIC 4C scores.

Methods
We reviewed medical records of confirmed COVID-19 patients admitted to a single tertiary hospital between May 2021 and April 2022. Vaccination status were classified as FV, PV and UV. The primary outcome was in-hospital mortality. Baseline data were collected and predictors recorded upon admission were used to calculate the 4C scores. Multiple logistic regression analysis was used to determine the association of vaccination status and 4C Deterioration and Mortality scores with in-hospital mortality. STATA 14 was used for data analysis and p&lt; 0.05 was considered statistically significant.

Results
A total of 301 confirmed COVID-19 patients were included and 46.5% were fully vaccinated. FV patients had significantly lower CRP (p&lt; 0.0001), had higher mean oxygen saturation (p&lt; 0.0001), had lower mean mortality score (p=0.004), had lower mean % risk for deterioration (p&lt; 0.0001) and had significantly lower 30-day mortality (&lt; 0.0001) compared to those UV and PV. Patient who died had significantly higher mean age (p=0.001), lower mean lymphocyte ratio (p=0.0004), had significantly higher mean deterioration and mortality score (p&lt; 0.0001). Being fully vaccinated was associated with lower risk for mortality (OR=0.4, 95%CI=0.2 to 0.7).

Conclusion
Fully vaccinated patients were at lower risk for deterioration and mortality. High ISARIC 4C scores were also significantly associated with higher risk for mortality. Risk stratification tools should be utilized in COVID-19 management as they provide a helpful means to identify patients at risk for worse outcomes.

Disclosures
All Authors: No reported disclosures
446. Post-Acute Sequelae of COVID-19 in a Rural Wisconsin Community Prospective Cohort
Authors: Sundaram Maria , Alonge Oluwakemi , Petrie Joshua , Feldstein Leora R , Rolfes Melissa A , Kawaoka Yoshihiro , Neumann Gabriele , Belongia Edward , McLean Huong 
Published: November 2023

Abstract

Background
Some individuals experience post-acute sequelae of SARS-CoV-2 (PASC), a variety of persistent symptoms related to different organ systems after a SARS-CoV-2 infection. There are limited data on the prevalence of PASC after SARS-CoV-2 Omicron variant infection. We assessed the relative risk of persistent symptoms consistent with PASC, during a period of SARS-CoV-2 Omicron circulation.

Methods
A longitudinal cohort in rural Wisconsin was followed from November 2020 – July 2022 to assess incidence of SARS-CoV-2 infection. In March – April 2023, participants completed a survey about ongoing symptoms lasting ≥ 4 consecutive weeks and beginning on or after January 2022 (Omicron period). Poisson regression models were used to assess the relative risk of persistent symptoms overall and by symptom category among those with RT-PCR-confirmed or self-reported SARS-CoV-2 infection since January 1, 2022 (Omicron infection) vs. no history of SARS-CoV-2 infection (by self-report or RT-PCR detection), controlling for participant age group (17-49 vs. ≥50 years), having ≥2 COVID-19 vaccine doses before 2022 (yes/no) and chronic health conditions (yes/no).

Results
Of 573 respondents aged ≥17 years (66% response rate), 63% were female, 60% were ≥50 years old, and 51% had a chronic health condition. The presence of ≥2 persistent symptoms was 27% among Omicron-infected individuals and 24% among never-infected individuals (28% for those with SARS-CoV-2 infection prior to 2022). The risk of having ≥2 persistent symptoms was similar for Omicron-infected vs. never-infected individuals (adjusted relative risk [aRR]: 1.1; 95% CI: 0.9, 1.4). In models investigating symptom categories, aRRs for cardiac, pulmonary, neurologic, and menstrual symptoms were elevated (but not statistically significant) for Omicron-infected vs. never-infected individuals (Figure 1).Figure 1.Relative risk of PASC-related symptoms by category among individuals with Omicron infection, compared to individuals with no history of SARS-CoV-2 infection.

Conclusion
Preliminary data from this cohort show presence of persistent symptoms with and without a history of SARS-CoV-2 infection. We observed elevated though non-significant aRRs for cardiac, pulmonary, and neurologic symptoms for Omicron-infected individuals, similar to other findings in the literature. Although data are sparse, elevated aRRs for auditory and menstrual outcomes may warrant further analysis.

Disclosures
Maria Sundaram, PhD, MSPH, GlaxoSmithKline: Grant/Research Support Joshua Petrie, PhD, CSL Seqirus: Grant/Research Support Gabriele Neumann, Ph.D., FluGen: US-20110150925-A1, US-20100021499-A1, US 8,163,523 B2|FluGen: Ownership Interest|FluGen: Stocks/Bonds Edward Belongia, MD, Seqirus: Grant/Research Support Huong McLean, PhD, MPH, Seqirus: Grant/Research Support