bluuebunny/crossref_metadata_2025_split huggingface dataset:
23. Development and Validation of a Risk Score for Post-transplant Lymphoproliferative Disorders among Solid Organ Transplant Recipients
Authors: dos Santos Quenia , Wareham Neval E , Mocroft Amanda , Rasmussen Allan , Gustafsson Finn , Perch Michael , Sørensen Søren Schwartz , Manuel Oriol , Mueller Nicolas , Lundgren Jens , Reekie Joanne 
Published: December 2021

Abstract

Background
Post-transplant lymphoproliferative disease (PTLD) is a well-recognized complication after transplant. This study aimed to develop and independently validate a risk score to predict PTLD among solid organ transplant (SOT) recipients (kidney, liver, lung and heart).

Methods
Poisson regression identified predictors of PTLD with the best fitting model selected for the risk score, where each predictor contributed with a risk coefficient to the risk score, dividing patients in high vs low risk of having a PTLD.

Results
For both cohorts, most of the patients were male, aged more than 16 years old, kidney recipients and with a low-risk pre-transplant Epstein-Barr Virus (EBV) IgG donor/recipient serostatus. The derivation cohort consisted of 2546 SOT transplanted at Rigshospitalet, Copenhagen between 2004-2019; 57 developed PTLD. Predictors of PTLD were high-risk pre-transplant Epstein-Barr Virus (EBV) IgG donor/recipient serostatus, and current plasma EBV DNA positive, abnormal hemoglobin and C-reactive protein levels. A positive EBV DNA was the strongest parameter for the PTLD risk score (figure 1), although the model was able to predict the risk of PTLD cases in both EBV positive and EBV negative individuals. Individuals in the high-risk group had almost 7 times higher incidence of PTLD compared to the low risk group (table 1). In the validation cohort of 1611 SOT recipients between 2008-2018 from University Hospital of Zürich, 24 developed PTLD. A similar seven times higher risk of PTLD was observed in the high-risk group compared to the low risk group (table 1). The discriminatory ability was also similar in derivation (Harrell’s C-statistic of 0.82 95%CI (0.76-0.88) and validation (0.82, 95% CI:0.72-0.92) cohorts.
An explanation about how the risk for PTLD is calculated for the SOT recipients; in this example the risk of developing PTLD is calculated in the next 180 days

Performance of the PTLD score in the derivation and validation cohorts (low-risk group: score&lt;=17 points; high-risk group: score&gt;17 points)

Conclusion
The risk score had a good discriminatory ability in both cohorts and helped to identify patients with higher risk of developing PTLD, so they can be monitored more often. This is the first risk-score developed and externally validated to predict risk of PTLD among SOT recipients.

Disclosures
All Authors: No reported disclosures
26. Risk of Post–COVID-19 Dyspnea and Interstitial Lung Disease (ILD) in a Real-World Cohort of Patients Hospitalized with COVID-19 in the United States
Authors: Zalocusky Kelly , Chawla Devika , Neighbors Margaret , Rizzo Shemra , Tsai Larry 
Published: December 2021

Abstract

Background
While COVID-19 carries substantial morbidity and mortality, the extent of long-term complications remains unclear. Reports suggest that acute lung damage associated with severe COVID-19 can result in chronic respiratory dysfunction. This study: (1) estimated the incidence of dyspnea and ILD after COVID-19 hospitalization, and (2) assessed risk factors for developing dyspnea and ILD in a real-world cohort of patients hospitalized with COVID-19 using US electronic health records (EHR).

Methods
Patients in the Optum de-identified COVID-19 EHR database who were hospitalized for COVID-19 (lab confirmed or diagnosis code) between February 20 and July 2020 and had at least 6 months of follow-up were eligible for analysis. Dyspnea and ILD were identified using diagnosis codes. The effects of baseline characteristics and hospitalization factors on the risk of incident dyspnea or ILD 3 to 6 months’ post discharge were evaluated.

Results
Among eligible patients (n=26,339), 1705 (6.5%) had dyspnea and 220 (0.8%) had ILD 3 to 6 months after discharge. Among patients without prior dyspnea or ILD (n=22,613), 110 (0.5%) had incident ILD (Table 1) and 1036 (4.6%) had incident dyspnea (Table 2) 3 to 6 months after discharge. In multivariate analyses, median (IQR) length of stay (LOS; 5.0 [3.0, 9.0] days in patients who did not develop ILD vs 14.5 [6.0, 26.0] days in patients who developed ILD; RR: 1.12, 95% CI: 1.08, 1.15; P=4.34 x 10-10) and age (RR: 1.02, 95% CI: 1.01, 1.03; P=4.63 x 10-3) were significantly associated with ILD. Median (IQR) LOS (5.0 [3.0, 9.0] days in patients who did not develop dyspnea vs 7 [4.0, 14.0] days in patients who developed dyspnea; RR: 1.04, 95% CI: 1.02, 1.06; P=8.52 x 10-4), number of high-risk comorbidities (RR: 1.18, 95% CI: 1.12, 1.24; P=3.85 x 10-9), and obesity (RR: 1.52, 95% CI: 1.25, 1.86; P=2.59 x 10-4) were significantly associated with dyspnea.
Table 1. Selected Baseline Risk Factors for Incident ILD

Table 2. Selected Baseline Risk Factors for Incident Dyspnea

Conclusion
In a real-world cohort, 4.6% and 0.5% of patients developed dyspnea and ILD, respectively, after COVID-19 hospitalization. Multivariate analyses suggested that LOS, age, obesity, and comorbidity burden may be risk factors for post-COVID-19 respiratory complications. Limitations included sensitivity of diagnosis codes, availability of labs, and care-seeking bias.

Disclosures
Kelly Zalocusky, PhD, F. Hoffmann-La Roche Ltd (Shareholder)Genentech, Inc. (Employee) Devika Chawla, PhD MSPH, F. Hoffmann-La Roche Ltd. (Shareholder)Genentech, Inc. (Employee) Margaret Neighbors, PhD, F. Hoffmann-La Roche Ltd (Shareholder)Genentech, Inc. (Employee) Shemra Rizzo, PhD, F. Hoffmann-La Roche Ltd. (Shareholder)Genentech, Inc. (Employee) Larry Tsai, MD, F. Hoffmann-La Roche Ltd (Shareholder)Genentech, Inc. (Employee)
28. Utilizing the Charleston Comorbidity Index as an Independent Predictor for Outcomes in SARS-Cov-2 Positive Patients
Authors: Guiance Irene Riestra , Char Steven , Gonzaga Ernesto Robalino , Riestra Isabel , Ho Minh Q 
Published: December 2021

Abstract

Background
Since COVID-19 was declared a pandemic, it has seemed that the virus is nondiscriminatory causing 3.73 million deaths worldwide. The Charleston Comorbidity Index (CCI) is a scoring system predicting the one-year mortality for patients with a range of comorbid conditions and is widely used as a predictor of prognosis and survival for a range of pathologies. This study aims to assess if there is an impact of comorbidity burden on COVID-19 patients by utilizing their CCI score.
Charleston Comorbidity Index Score

Scoring system for Charleston Comorbidity Index (CCI). Plus 1 point for every decade age 50 years and over, maximum 4 points. Higher scores indicate a more severe condition and consequently, a worse prognosis.

Methods
Multicenter, retrospective review of patients diagnosed with COVID-19 from January 2020 to September 2020 throughout the HCA Healthcare system. CCI scores for all COVID-19 positive patients were calculated and logistic regression analysis was performed to predict hospitalization and ICU admission by CCI controlling for age, sex and race. A multinomial regression model was also performed to predict discharge status by CCI controlling for age, sex and race. ROC curves to indicate the CCI cut-off point for each outcome (hospitalization, ICU admission and mortality) was performed, and Youden’s Index was used to identify the optimal point.

Results
In the study timeframe, 92,800 patients were diagnosed with COVID-19 and of those, 48,270 were hospitalized. A one-point increase in CCI was associated with higher odds of hospitalization [OR 1.718; 95% CI 1.696-1.74]. The threshold for significance to predict hospitalization was a CCI of 1.5 (AUC 0.804, Youden Index 0.48) with a specificity (73%) and sensitivity (75%). A one-point increase in CCI was associated with 1.444 higher odds of an ICU admission (95% CI 1.134-1.155). A one-point increase in CCI significantly increased the odds of discharge to hospice compared to any discharge other than hospice [OR 1.162; 95% CI 1.142-1.182]). A one-point increase in CCI score was associated with 1.188 higher odds of in-hospital mortality (95% CI, 1.173-1.203) with a CCI threshold of 3.5 having the highest specificity (50.9%) and sensitivity (79.9%) to predict mortality outcome (AUC 0.704, Youden Index 0.31).

Conclusion
In conclusion CCI score is an adequate predictor of hospitalization and in-hospital mortality but less so in predicting ICU admission in COVID-19 positive patients.

Disclosures
All Authors: No reported disclosures
30. SARS-CoV2 Reinfections in a University Teaching Hospital
Authors: Sutton Una , Lynch Judi , Fitzgerald Niamh , Macori Guerrino , Fanning Séamus , Fennell Jérôme 
Published: December 2021

Abstract

Background
The consequences of SARS-CoV2 reinfections for patients, healthcare workers and society are unclear. We reviewed the clinical, laboratory, and epidemiological characteristics of patients re-infected with genetically distinct strains of SARS-CoV2 identified by Whole Virus Genome Sequencing (WvGS).

Methods
Cases were selected based on a positive SARS-CoV-2 Reverse Transcriptase Polymerase Chain Reaction (RT-PCR) test, clinical resolution, a negative interim test and a subsequent positive nasopharyngeal swab. Positive samples were prepared for sequencing by cDNA synthesis, tiled-PCR following the ARTIC protocol and amplicon sequencing using Illumina MiSeq platform. Raw reads were mapped to the reference sequence using bowtie and Samtools was used for variants calling and to generate the consensus sequences. Comparative sequence analysis was conducted by phylogenetic inference maximum likelihood method with RAxML using the multiple sequence aligned by MAFFT. Clades and variants were assigned respectively using Nextstrain and Pangolin COVID-19 lineage assigner (Figure 1). The clinical, radiological and laboratory data were collected from patient medical notes and laboratory information system.

Results
Two cases of SARS-CoV-2 reinfection were detected by RT-PCR (patient 1 and 2). CT values and strain variants are presented in Table 1. The time between detection of the first and second infection was 67 and 270 days respectively. WvGS confirmed that the second episodes were due to a genetically distinct strain of SARS CoV2. These reflected the dominant contemporaneous variants in circulation.
Both patients were immunocompromised from co-morbidities and medications. First and subsequent infections were minimally symptomatic. Both cases were associated with known hospital outbreaks. They passed away within 2 weeks of the second infection of unrelated causes.

Conclusion
Two patients in this study were diagnosed with a SARS-CoV-2 reinfection confirmed by WvGS. A common factor in these cases was immunocompromise. Where a previously infected patient test shows a new positive or an unexpected reduction in CT value is observed, we recommend individual risk assessment to determine the timing of discontinuation of isolation and infection control precautions.

Disclosures
Jérôme Fennell, MB BCh BAO MSc PhD FRCPath FRCPI, Roche Diagnostics (Advisor or Review Panel member)
31. Development a Novel Score (SAD-60) for Predicting Mortality in Hospitalised Patients with COVID-19 Pneumonia: A Multicenter Retrospective Study of 1013 Patients
Authors: Surme Serkan , Karanalbant Hatice Kubra , Tuncer Gulsah , Bayramlar Osman , Copur Betul , Zerdali Esra , Nakir İnci Yilmaz , Yazla Meltem , Buyukyazgan Ahmet , Cinar Ayse Kurt , Kurekci Yesim , Alkan Mustafa , Ozdemir Yusuf Emre , Sengoz Gonul , Pehlivanoglu Filiz 
Published: December 2021

Abstract

Background
We aimed to explore a novel risk score to predict mortality in hospitalised patients with COVID-19 pneumonia. In additoon, we compared the accuracy of the novel risk score with CURB-65, qSOFA and NEWS2 scores.

Methods
The study was conducted in hospitalised patients with laboratory and radiologically confirmed COVID-19 pneumonia between November 1, 2020 and November 30, 2020. In this retrospective multicenter study. independent predictors were identified using multivariate logistic regression analysis. A receiver operating characteristics (ROC) analysis with area under the curve (AUC) was used to evaluate the performance of the novel score. The optimal cut-off points of the candidate variables were calculated by the Youden’s index of ROC curve. Mortality was defined as all cause in-hospital death.

Results
A total of 1013 patients with COVID-19 were included. The mean age was 60,5 ±14,4 years, and 581 (57,4%) patients were male. In-hospital death was occured in 124 (12,2%) patients. Multivariate analysis revealed that peripheral capillary oxygen saturation (SpO2), albumin, D-dimer, and age were independent predictors for mortality (Table). A novel scoring model was named as SAD-60 (SpO2, Albumin, D-dimer, ≥60 years old). SAD-60 score (0,776) had the highest AUC compared to CURB-65 (0,753), NEWS2 (0,686), and qSOFA (0,628) scores (Figure).

Conclusion
We demonstrated that SAD-60 score had a promising predictive capacity for mortality in hospitalised patients with COVID-19.
Univariate and multivariate analysis of factors predicting mortality

Comparison of CURB-65, qSOFA, NEWS-2 and SAD-60 for predicting pneumonia mortality in hospitalised patients with COVID-19 by ROC analysis

Disclosures
All Authors: No reported disclosures
36. Clinical Features of and Risk Factors for 30-day Readmission after an Initial Hospitalization with COVID-19
Authors: Fukushima Elisa Akagi , Santos Claudia Villatoro , Sharma Mamta , Szpunar Susan M , Saravolatz Louis , Bhargava Ashish 
Published: December 2021

Abstract

Background
Little is known about risk factors for readmission after COVID-19 hospitalizations. Knowledge of these factors may help to identify patients at increased risk and may help to prevent these rehospitalizations.

Methods
This historical cohort study was conducted at a tertiary care academic medical center. We included COVID-19 cases diagnosed by reverse-transcriptase polymerase-chain-reaction (RT-PCR) assay between March 8th and June 14th, 2020. Patients readmitted within 30 days were identified. Using the electronic medical record, we collected data on demographic and clinical information. Data were analyzed using Student’s t-test, the chi-squared test and multivariable logistic regression.

Results
We included 391 patients who survived after the index hospitalization for COVID-19. The readmission rate was 13.3% (52/391). The mean time to readmission was 9.2 ± 7.9 days. The mean age (±SD) was 66.3 ± 18.6 years, 44.2% were male, and 78.8% were black/African-American. The most common presenting complaint was shortness of breath (50%). The most frequent diagnosis during the readmission was infectious process (57.7%). The mortality rate on readmission was 11.5%. Patients with a 30-day readmission were older than those not readmitted, mean age (±SD) 66.3 ± 18.6 vs. 61.0 ± 16.0, respectively (p=0.03). Readmitted patients also had a higher prevalence of heart failure and renal disease as comorbidities. Elevated alanine aminotransferase (AST) and low albumin level were also associated with readmission (Table 1). Intensive care unit (ICU) admission or mechanical ventilation during the index admission did not increase the risk of readmission. From multivariable analysis, independent predictors of 30-day readmission were higher Charlson score (p=0.004), higher creatinine on admission in the index hospitalization (p=0.009), and presence of rhabdomyolysis during the index hospitalization (p=0.039) (Table 2).
Table 1. Univariable Analysis of Predictors for Readmission within 30 days from COVID-19 Infection

Table 2. Multivariable Analysis of Predictors for Readmission within 30 days from COVID-19 Infection

Conclusion
In our cohort, infectious etiologies were common among those readmitted within 30 days of COVID-19. A higher Charlson score, acute renal failure, and rhabdomyolysis during the index admission were independent predictors of a 30-day readmission. Further studies are required to investigate these contributing factors.

Disclosures
All Authors: No reported disclosures
39. Anti-Spike Monoclonal Antibody Therapy for Kidney Transplant Recipients with COVID-19
Authors: Hardesty Anna , Klein Elizabeth , Vieira Kendra , Farmakiotis Dimitrios 
Published: December 2021

Abstract

Background
Organ transplant recipients may not mount an adequate immune response to COVID-19 infection, and therefore may benefit greatly from passive immunization with anti-spike monoclonal antibodies (mAb), which have been shown to decrease hospitalization rates in the general outpatient population. We evaluated the efficacy of mAb therapy in decreasing hospitalizations or emergency room (ER) visits among kidney transplant recipients (KTR) with COVID-19.

Methods
We identified KTR with COVID-19 between 3/1/2020 and 4/30/2021. Patients were excluded if they had multiorgan transplant or hospital-acquired COVID-19. Data were analyzed by Cox regression with mAb administration as time-dependent variable, and the day of symptom onset as baseline.

Results
We studied 95 KTR; 20 received mAb. Comorbidities and immunosuppression were balanced between the two groups. mAb administration was associated with a significant decrease in hospitalizations or ER visits (15 vs. 76%, P&lt; 0.001). This association remained significant after adjustment for confounders and by analyzing mAb administration as a time-dependent variable (Table: adj. HR 0.2, P=0.04). No KTR who received mAb died or required mechanical ventilation. Black or Hispanic KTR were less likely to receive mAb and more likely to be admitted to the hospital or visit the ER (Table).
Table

Factors significantly associated with hospitalization or ER visit.

Conclusion
In our KTR population, mAb therapy for COVID-19 may have helped decrease hospitalizations and ER visits. Healthcare inequities, including access to investigational treatments, were exacerbated by the COVID-19 pandemic. Acknowledging the nonconcurrent control group as a limitation, we found a strong signal for benefit from mAb treatment. Antiviral mAb are a promising therapeutic modality for immunosuppressed patients.

Disclosures
Dimitrios Farmakiotis, M.D., Astellas (Grant/Research Support)Merck (Grant/Research Support)Viracor (Grant/Research Support)
43. SARS-CoV-2 Cycle threshold (Ct) values predict future COVID-19 cases
Authors: Phillips Matthew , Quintero David , Butler-Wu Susan 
Published: December 2021

Abstract

Background
The threat of surging COVID-19 cases prompted many hospitals in the United States to preemptively suspend elective procedures throughout the pandemic. Utilizing samples from a large hospital in Los Angeles, we sought to determine if temporal trends in SARS-CoV-2 Cycle threshold (Ct) values (proxy for viral RNA loads) were predictive for the number of future COVID-19 cases.

Methods
Nasopharyngeal specimens on symptomatic patients and asymptomatic admissions were tested using the Xpert Xpress SARS-CoV-2 and SARS-CoV-2/Flu/RSV assays (Cepheid). Ct values for all SARS-CoV-2 detections between October 2020 to March 2021 were compiled for analysis.

Results
A total of 2,114 SARS-CoV-2-positive samples were included. The number of tests performed per week increased dramatically in December peaking the first week of January before returning to pre-surge numbers by Mid-February. Ct values fell during this same period with values in December and January (25.6±7.8 and 27±7.9, respectively) significantly lower than those of the other months (30±9.3 to 37.7±6.3). Average weekly Ct values for all patients were significantly, negatively correlated with the number of tests run the following week (R= -0.71, P&lt; 0.001) and two weeks later (R= -0.75, P&lt; 0.0001). Ct values for patients who were asymptomatic at the time of testing most strongly correlated with total number of tests performed one month later (R= -0.86, P&lt; 0.0001).
Average weekly Ct values and number of test run

As cases (light grey) increased during December and January, there was a significant decrease in Ct values (dark grey) during that same time period.
Average Ct values are a leading indicator of cases

Average weekly Ct values for all patients (light grey) were significantly, negatively correlated with the number of tests run the following week (R= -0.71, P&lt;0.001) and two weeks later (R= -0.75, P&lt;0.0001). Ct values for patients who were asymptomatic at the time of testing (dark grey) most strongly correlated with total number of tests performed one month later (R= -0.86, P&lt;0.0001).

Conclusion
Lower Ct values, representing higher levels of viral RNA, have been associated with risk of intubation and infectivity. During the winter surge, we observed significantly lower Ct values suggesting that the increased transmission and morbidity of COVID-19 was temporarily associated with higher viral loads. Interestingly, Ct values for asymptomatic patients were most strongly associated with number of cases observed 1 months in the future, suggesting that asymptomatic viral load may be a leading indicator for forthcoming outbreaks. Given this association, Ct values may be a useful tool for predicting regional outbreaks of COVID-19 and more judicious cessation of elective procedures.

Disclosures
All Authors: No reported disclosures
48. Time Between Viral Loads for Suppressed and Non-Suppressed People with HIV During the COVID-19 Pandemic Compared to Pre-Pandemic
Authors: El-Nahal Walid , Shen Nicola , Lesko Catherine , Fojo Anthony , Lau Bryan , Keruly Jeanne , Manabe Yukari C , Jones Joyce , Moore Richard , Gebo Kelly , Chander Geetanjali 
Published: December 2021

Abstract

Background
During the COVID-19 pandemic, patients at the John G. Bartlett Specialty practice experienced disruptions in viral load (VL) monitoring due to 1) conversion to telemedicine visits and 2) closure of the onsite lab from March 16-July 13, 2021. We described the impact of the pandemic on VL monitoring.

Methods
We measured time from all index VLs collected during 3 periods: January 1, 2019 to March 15, 2020 (pre-pandemic); March 16 to July 12, 2020 (pandemic, closed onsite lab); and July 13 to December 31, 2020 (pandemic, open onsite lab) until a subsequent VL, 1 year after the index VL, or administrative censoring on December 31, 2020, whichever came first. We classified follow-up time according to these periods (treating period as a time-varying variable). We report hazard ratios (HRs) and 95% Confidence Intervals (CI) from a Cox proportional hazards model comparing the hazard of a VL during the pandemic periods to the pre-pandemic period, stratified by whether the index VL was suppressed (≤200 copies/mL). We tested for interactions between patient characteristics (age, sex at birth, race, ethnicity, and recent substance use) and period, to investigate differential effects of the pandemic on delayed VL.

Results
After 7,760 suppressed VL measurements, median times to subsequent VL during the pre-pandemic, pandemic (closed lab) and pandemic (open lab) periods, were 4.6 (HR=1.0), 8.9 (HR=0.34, CI:0.30, 0.37), and 5.8 (HR=0.73, CI:0.68,0.78) months respectively. After 1,025 non-suppressed VL measurements, median times to subsequent VL were 2.0 (HR=1.0), 3.9 (HR=0.57, CI:0.42,0.79), and 2.1 (HR=0.92, CI:0.76,1.10) months respectively. Time to subsequent VL after an index suppressed VL was less affected by the pandemic for patients who are white; had private insurance; or had no recent cocaine or heroin use. The effect of the pandemic on time to subsequent VL after a non-suppressed index VL did not significantly differ across patient characteristics.

Conclusion
Onsite lab closure disrupted VL collection for all groups. Once the onsite lab opened, the pandemic period was still associated with a delay among suppressed patients, but not non-suppressed patients. Further studies are needed to investigate if these delays are associated with lapses in viral suppression.

Disclosures
All Authors: No reported disclosures