bluuebunny/crossref_metadata_2025_split huggingface dataset:
P-1882. Answering the Call: Volume and Characteristics of External Phone Consultations to Pediatric Infectious Diseases Specialists at a Large Academic Children's Hospital
Authors: Dimo Joana , Ho Erin C , Boguniewicz Juri , Searns Justin B , Messacar Kevin , Cataldi Jessica R 
Published: January 2025

Abstract

Background
Due to the limited supply and geographic distribution of pediatric infectious diseases (Peds ID) specialists, providing unremunerated telephone advice to outside clinicians has become a significant component of day-to-day practice for many academic Peds ID clinicians. This time-consuming service is not classified as direct patient care or adequately captured by traditional billing and productivity metrics. The objective of this study was to quantify and characterize external phone consults to Peds ID specialists at Children’s Hospital Colorado (CHCO), and compare the burden of these phone consults to other pediatric subspecialists.

Methods
External phone consults to pediatric subspecialists at CHCO from April 2019-March 2024 were obtained from the electronic medical record (EMR). Patient and call characteristics for Peds ID were summarized with descriptive statistics.

Results
From 2021-2023, Peds ID received the second most external phone consults (n=6376) of any pediatric subspecialty. From 2019 to 2024, average calls to Peds ID increased 21% from 38 to 46 calls/week. One-quarter of calls were about patients registered in the EMR only for the phone encounter and never for direct patient care. Most calls concerned children &lt; 5 years and 9% were for children 0-7 days old. Eighty-five percent of calls occurred between 8AM-5PM and 23% requested immediate callback. Fridays had the most calls (mean 7.6 calls/day), and Sundays had the least (2.1). Based on dummy time-based RVU codes, the median call time was 11-20 minutes; 36% of calls required at least one follow-up call. Only 11% of calls were expected to lead to direct patient care at CHCO based on clinician recommendation. Peds ID specialists spent over 2000 unbilled hours on external phone consults over the study period and an average of &gt; 10 hours/week in the most recent year of data.

Conclusion
External phone consults to Peds ID at CHCO are increasing and time-consuming, yet do not directly generate revenue and do not reliably lead to direct patient care in our health system. Given limited supply, increased demand, and reimbursement challenges for Peds ID clinicians, different economic models for external phone consults should be explored. Chart review to describe callers, reasons for calls, and patient outcomes may inform these efforts.

Disclosures
Juri Boguniewicz, MD, AstraZeneca: Grant/Research Support
P-1883. Evaluation of IVEnsure Device and Care Platform for Remote Therapeutic Monitoring in OPAT: Interim Analysis of a Pilot Study
Authors: Berenson Mitchell , Dozier Jacob K , Siegrist Emily A , Dvorak Justin D , Meyer Debbie N , Siddique Bushra , Bratzler Dale W , Sassine Joseph 
Published: January 2025

Abstract

Background
Data on medication non-adherence in outpatient parenteral antimicrobial therapy (OPAT) and the implications on clinical outcomes are lacking. Remote therapeutic monitoring (RTM) might offer additional insights into OPAT adherence. We present here a pre-planned interim analysis of a pilot study of IVEnsure, an RTM device, in patients receiving OPAT.

Methods
This prospective study is recruiting adult patients discharged from a tertiary care center on OPAT starting July 2023. After consent, patients are enrolled in an RTM program, including the use of the IVEnsure device, and follow up with a case management team (Figure 1). This FDA-exempt and Medicare-approved healthcare remote monitoring device attaches to the tubing set of any IV medication and transmits infusion-related data in real-time via cellular transmission to generate weekly adherence reports. The study aims to analyze the effect of RTM on hospital readmission outcomes in patients on OPAT, compared to contemporaneous controls in an intention-to-treat analysis.

Results
The interim analysis includes 44 patients (out of 90 planned) and 74 controls. Baseline characteristics are overall similar between both groups (Table 1). Adjusted for patient age at discharge, insurance status, infection class, presence of diabetes, and discharging service, RTM with IVEnsure is associated with a significant decrease in 90-day readmission rate related to the original infection or OPAT (4% vs. 23%, adjusted OR 0.14, 95% CI 0.02-0.54, p=0.01, Tables 2-3), leading to estimated cost savings of $213,825 in the IVEnsure arm, or $4,860 per patient (Table 4). Within the IVEnsure group, 45% of the patients required an intervention by the study team, 75% of which occurred in the first week of OPAT, resulting in improved or sustained adherence in 73% of the patients (Table 5).

Conclusion
RTM with IVEnsure significantly reduced infection-related readmissions and associated costs in patients on OPAT. Study accrual continues to evaluate additional readmission-related outcomes.

Disclosures
Mitchell Berenson, MPH, Community Infusion Solutions: CEO|IVEnsure, Inc.: CEO Jacob K. Dozier, BS, Community Infusion Solutions: COO|IVEnsure, Inc.: COO Debbie N. Meyer, MS, IVEnsure, Inc.: Chief Technology Officer Joseph Sassine, MD, Ansun BioPharma: Grant/Research Support|Cidara Therapeutics: Grant/Research Support|Community Infusion Solutions: Grant/Research Support|F2G: Grant/Research Support|Shionogi: Grant/Research Support
P-1886. Utilizing Artificial Intelligence to Predict Triage in Pediatric Outpatients with Prolonged and Recurrent Fever
Authors: Li Guyu , Huo Nan , Orandi Amir B , Ristagno Elizabeth H 
Published: January 2025

Abstract

Background
Prolonged and recurrent fevers in pediatric outpatients often require specialist consultations, presenting a challenge in accurately triaging to the correct pediatric subspecialty. This study aims to develop an artificial intelligence (AI)-based model to accurately direct pediatric patients to appropriate subspecialties.Table 1.Patient characteristics and the department confirming the final diagnosis
Table 1. Patient characteristics and the department confirming the final diagnosis

Methods
A retrospective study was conducted at the Mayo Clinic, Rochester, from December 1, 2020, to January 1, 2023, involving pediatric patients aged 0 to 18 referred for recurrent fever or fever of unknown origin. Six machine learning models, including gradient boosting, random forest, support vector machines (SVM), k-Nearest Neighbor (KNN), logistic regression (LR) and decision tree, were developed using 7 variables (demographics, chief complaints, referral sources, departments of same-day initial visit, and lab tests results within 30 days before initial visits). Data were extracted from external electronic health records (EHR) using Mayo Data Explorer and Lab Information Management Systems. Precision, Recall, F1 Score, and area under the curve (AUROC) were selected to measure the performance.Table 2.Performance of six machine learning models
Table 2. Performance of six machine learning models

Results
The study evaluated 87 pediatric patients with a median age of 10.2 years (IQR: 4.6-13.8), including 43 (49%) females (Table 1). External provider referrals were noted in 40 (46%) patients. Musculoskeletal pain was the most common chief complaint, identified in 29 (33%) patients, in addition to prolonged or recurrent fever. Infectious disease and rheumatology divisions jointly saw 43 (49%) patients during the initial visit. Sub-optimal performance was observed in six machine learning models (Table 2). The random forest model showed the best results (Accuracy=0.39, Precision=0.34, Recall=0.39, F1 Score=0.35, AUROC=0.60) (Figure).Figure.ROC curves for various models
Figure. ROC curves for various models

Conclusion
This study demonstrates the potential of AI-based models for outpatient triage prediction in pediatric patients with recurrent fever, using EHR data to optimize health resource utilization. This data serves as the groundwork to expand and refine this AI triage tool.

Disclosures
All Authors: No reported disclosures
640. A County-wide Collaboration to Drive Quality Improvement Using Healthcare-associated Infection Surveillance Data from the National Healthcare Safety Network, Centers for Disease Control and Prevention
Authors: Monroe Madeleine , Kang Grace , Torriani Francesca J , Myers Frank , Meyer Karen , de Vera Priscila , Becasen Jarrod , Mabalot Shannon C , Schultz Kerry , binstock Peter , Chang Wun-Ling , Benedek Stacie , Hayes Tamara , jefferson elizabeth A , Warner Shweta , Behm Tamara , Soni Sandeep , Mataya Kyle , Berg Erik A , Menz Aaron , Nickel Alexis , Grace Sadile Mary , Kao Annie S , Johnson Jeffrey , Aiem Heidi , Beatty Mark E , Shah Seema , Chinn Raymond 
Published: January 2025

Abstract

Background
Quality healthcare is dependent on continuous input and feedback. The California Department of Public Health (CDPH) requires that all general acute care hospital (GACH) report healthcare-associated infections (HAI) annually. Comparisons of current facility-specific standardized infection ratios (SIRs) to the National Healthcare Safety Network (NHSN) 2015 SIR baseline provide designations of whether a facility is statistically significantly better, worse, or average are displayed for public reporting purposes. Since then, the annual NHSN and CDPH SIR baselines have dropped below 1 for many HAIs; however, neither NHSN nor CDPH provide facility-specific comparisons to the most recent annual NHSN and CDPH SIRs. Facilities that are performing better or average using the 2015 baseline may be performing average or worse when compared the the most recent baselines (Figure A.).

Methods
The County of San Diego (COSD) HAI program began a collaborative effort with local GACHs in 2020 (Figure B). A work group comprised of Infection Preventionists and Epidemiologists convened to provide input into the format and content. Unique features include: 1) a display of an 18 month select HAI aggregate SIRS for the GACHs; 2) statistical analysis using a NHSN SAS 9.4 Macro to provide facility-specific comparative SIRs to the most recent annual NHSN and CDPH baselines and an 18-month aggregate COSD SIR baselines. Facilities were de-identified; the data were distributed only to Infection Prevention Departments for quality improvement and not used for regulatory purposes,; and met the intent of the NHSN Data Use Agreement.

Results
The first report (8/2023) included comparative SIRs on13 surgeries and central-line associated blood stream and catheter-associated urinary tract infections. The second report (4/2024) adds comparative standardized utilization ratios for central lines and urinary catheters (Figures C, D).

Conclusion
Through this effort, the COSD HAI program has strengthened communication between our local health department and the GACHs and also between GACHs. Future reports will be refined based on the input from the GACHs and could include comparative surveillance data on other HAIs being reported to NHSN and CDPH. Sharing best practices could result in continued decreases in HAIs.

Disclosures
All Authors: No reported disclosures
P-1905. Quality of Life 6 Months After SARS-CoV-2 Infection in Ambulatory and Hospitalized Patients from Lima, Peru: FUNCTION Cohort Study
Authors: Coombes Carolina , Cachay Figueroa Rodrigo Antonio A , Watanabe Takashi , Ballena Isabel , Mejia Fernando , Medina Felix , Gayoso Oscar , Seas Carlos , Otero Larissa , Gotuzzo Eduardo 
Published: January 2025

Abstract

Background
COVID-19 survivors overcome long-lasting symptoms that affect their quality of life (QoL). We assessed the impact on QoL and the factors associated with the persistence and reversibility of poor QoL 6 months after acute COVID 19.

Methods
We enrolled symptomatic patients who tested positive for SARS-COV-2 with an antigenic or RT-PCR test between August 2021 and October 2022. We classified subjects according to disease severity: B1 (mild: outpatient), B2 (moderate: outpatient with pneumonia), B3 (severe: inpatient with oxygen supply) and B4 (critical: inpatient admitted to intensive care unit). Measurements were taken at baseline (BL), month 1 (M1), 3 (M3), and 6 (M6) after acute infection. We evaluated QoL with the RAND-36 questionnaire which provides a score for the Physical Component Score (PCS) and the Mental Component Score (MCS). The PCS comprises scales for physical functioning, role limitations due to physical health, pain and general health. MCS includes scales of role limitations due to mental health, energy/fatigue, emotional wellbeing, and social functioning. PCS and MCS scores were standardized on a 0-100 scale and poor QoL was considered &lt; 40 in each scale.

Results
We enrolled 147 participants: 46 in B1 (31%), 39 in B2 (27%), 22 in B3 (15%), and 40 in B4 (27%). We lost to follow-up 33 participants (22.5%) from BL to M6. Significant improvements in PCS were observed between BL and M6 within B3 [35.77 (95%CI 29.7-41.8) vs 51.63 (95%CI 47.1-56.2), p=0.0002] and B4 [28.8 (95%CI 24.5-33.2) vs 43.3 (95%CI 39.4-47.2), p&lt; 0.0001]. In the MCS, significant improvement occurred within B3 between BL and M6 [44.0 (95%CI 36.9-51.0) vs 54.4 (95%CI 49.89-58.86) p=0.0229]. Low MCS and PCS at M6 after COVID-19 was associated with both pulmonary and cardiac clinical abnormalities at M1, M3 and M6. Additionally, only low PCS at M6 was associated with diabetes, age older than 60 years and cardiac clinical abnormalities at BL. Patients who reversed their poor QoL at M6 to a normal value were 122 (83%) for MCS and 93 (63.27%) for PCS. Factors associated with increased reversibility in both scales were severity group B3 and male sex.

Conclusion
Our findings, the first cohort study on post COVID-19 from Peru, underscore the need for ongoing physical and mental patient monitoring beyond the acute phase of COVID-19.

Disclosures
All Authors: No reported disclosures
P-1908. The Relationship Between Long COVID and PHQ-4 Measures of Emotional Health
Authors: Kricorian Christopher 
Published: January 2025

Abstract

Background
The COVID-19 pandemic was one of the most devastating public health crises in US history. Millions of Americans experience ongoing symptoms, often termed “Long COVID.” Long COVID (LC) has been defined as symptoms lasting 3 months or longer that were not present prior to the COVID infection. LC’s neurological effects have been associated with psychological issues like depression. Furthermore, given that many individuals with LC report difficulty getting treatment for these symptoms, the emotional effects associated with LC may be profound.

Methods
The US Census Bureau conducts an ongoing study called the Household Pulse Survey, with the purpose of measuring emergent social and economic matters facing U.S. households. Data analyzed was collected January 9 - February 5, 2024. Respondents were asked about their self-reported COVID-19 experiences and their mental health, as measured by the Patient Health Questionnaire-4 (PHQ-4) for depression and anxiety. The survey was conducted in English and Spanish and the survey sample was N=68,544. Data were analyzed using χ2 and between group contrast analysis.

Results
Of those surveyed, N=9,298 or 16.5% reported experiencing LC. When asked about their experiences during the last two weeks, 60.1% reported feeling anxious on several days or more, vs. 40.5% of those who had experienced COVID-19 but had no long term symptoms (termed Short COVID or SC) and 38.7% of those who had not contracted COVID-19 (called No COVID or NC), with LC significantly higher than both other groups (p&lt; .05). Fully 51.1% of LC respondents were unable to stop or control their worrying, vs. 29.1% SC and 31.9% NC= (both comparisons vs. LC, p&lt; .05). Similarly, those with LC were also significantly more likely to report having experienced little pleasure or interest in doing things (47.5% vs. 25.2% SC and 30.4% NC, p&lt; .05) and feeling down, depressed or hopeless in the past two weeks (48.3% vs. 27.4% SC and 30.9% NC, p&lt; .05).

Conclusion
Many Americans surveyed report experiencing LC and this appears to be associated with significant negative mental health effects, as measured by the PHQ-4. Further research should examine the relationship between LC and mental health, as well as mechanisms of how these factors relate to one another.

Disclosures
All Authors: No reported disclosures
P-1910. Long COVID Resolution Status and Health Impacts
Authors: Kricorian Christopher 
Published: January 2025

Abstract

Background
Many Americans continue to experience Long COVID (LC), which can affect a broad range of physical and mental functions. Understanding symptom prevalence is important to developing public health response.

Methods
An online survey was conducted by the US Census Bureau (N=68,544). Self-reported health metrics (mobility, seeing, hearing and concentration) were compared between those reporting unresolved LC (LC-U), resolved LC (LC-R), previous short COVID (SC), and those not contracting COVID (NC). Data were analyzed using χ2 and between group contrast analysis.

Results
Among those surveyed, 42.2% experienced SC, 41.3% did not experience COVID-19 (NC), 9.7% experienced LC-R, and 6.8% had LC-U. At least some mobility impairment was reported by 28.7% of NC respondents and 27.6% of LC-R (ns). LC-U reported significantly greater mobility impairment (38.1%, p&lt; .05) and SC reported less (15.8%, p&lt; .05). LC-U were most likely to report difficulty seeing (46.8%), followed by LC-R (37.2%), NC (30.5%), and SC (23.3%) (all comparisons p&lt; .05). NC (19.5%) and LC-R (19.6%) were about equally likely to report any hearing difficulty (ns). LC-U reported significantly greater difficulty hearing (27.0%, p&lt; .05) and SC reported significantly less (15.0%, p&lt; .05). LC-U were most likely to report any difficulty concentrating or remembering (55.1%), followed by LC-R (40.4%), NC (28.9%) and SC (22.2%, all comparisons p&lt; .05).

Conclusion
LC-U respondents reported the greatest difficulty across all four variables while SCreported the least. NC and LC-R reported similar hearing and mobility challenges, but LC-R performed worse than NC on seeing and concentration. It seems paradoxical that SC respondents reported fewer challenges than NC, but perhaps those who experienced SC were healthier to begin with and thus less vigilant about avoiding COVID. Despite claiming resolved LC, LC-R still reported worse seeing and concentration than both SC and NC (albeit better than LC-U). Additional research is needed to examine both the factors associated with contracting LC and the long-term effects that may remain even after people believe they have recovered from LC.

Disclosures
All Authors: No reported disclosures
P-1955. Trends in Skilled Nursing Facility Outbreak Characteristics by Prevalent SARS-CoV-2 Variant - Los Angeles County, July 2020 - January 2024
Authors: O’Shea Brendan , Neikirk Amanda , Khanqadri Israa , Babaie Camellia , Rubin Zachary A , Gounder Prabhu 
Published: January 2025

Abstract

Background
Los Angeles County (LAC) skilled nursing facilities (SNFs) experienced recurrent COVID-19 surges coinciding with the emergence of new SARS-CoV-2 variants. The impact of new SARS-CoV-2 variants on SNF outbreaks relative to other measures, such as the use of COVID-19 vaccines and policies to control COVID-19, in SNFs is unknown. We described trends in LAC SNF COVID-19 outbreak size and duration by SARS-CoV-2 variants detected.

Methods
All SNF COVID-19 outbreaks, defined as &gt; 1 case in a resident, were reportable to LAC Department of Public Health (DPH); SNFs were required to follow DPH recommendations for control including testing all staff and residents weekly and offering COVID-19 vaccines according to national guidelines. An outbreak ended when no cases were detected for &gt; 14 days. We obtained SARS-CoV-2 isolates from respiratory specimen for whole genome sequencing when possible and attributed outbreaks to the predominant SARS-CoV-2 variant detected among sequenced cases. Kruskal-Wallis test was conducted to assess for differences in outbreak characteristics by variant.

Results
We analyzed characteristics of 418 LAC SNF outbreaks with &gt; 1 sequenced case during 7/4/2020–1/5/2024 (Table 1). Median number of cases/100 licensed beds ranged from 10 (1st quartile [Q1]–3rd quartile [Q3]: 5–21) for Delta- to 62 (Q1–Q3: 47–78) for Omicron (BA.1)-associated outbreaks. Median outbreak duration ranged from 29 days (Q1–Q3: 22–37) for Omicron (BA.2/XBB)- to 58 days (Q1–Q3: 46–70) for Omicron (BA.1)-associated outbreaks. The median licensed bed size of outbreak-associated SNFs was similar across variant groups.

Conclusion
Our results indicated that variations in SNF outbreak size and duration by SARS-CoV-2 lineage did not ecologically correlate with changes in vaccine policy (Table 2 and Figure 1) or with DPH recommendations for controlling COVID-19 outbreaks in SNFs (which remained consistent during the evaluation period). Our results provide additional evidence that certain SARS-CoV-2 variants are more transmissible (Table 3); conducting genomic surveillance can guide public health policymakers and SNF providers on implementing enhanced measures to mitigate COVID-19 outbreaks.

Disclosures
All Authors: No reported disclosures
P-1959. B.1.617.2 Outbreak Mitigation on an International Steel Cargo Ship Utilizing a Multidisciplinary Approach
Authors: Mann Yasmeen , Numi Mariia , Edwards Ryan A , Lai Joyce , Joshi Seema , Patel Pragnesh , Blankenship Heather M , Soehnlen Marty K , Zervos Marcus , Mossing Michael , Rehman Najibah K 
Published: January 2025

Abstract

Background
The SARS-CoV-2 B.1.617.2 (Delta) variant emerged in India and became widespread in the United States in 2021. Evidence suggested it was more transmissible than other variants.Figure 1:SARS-CoV-2 Cases on the Steel Cargo Ship Timeline

Methods
From 01 May to 22 May 2021, 14 cases of COVID-19 were linked to an international steel cargo ship docked on the Detroit River, after 5 new members joined the crew from India. Cases were reported to the Detroit Health Department (DHD) on 08 May 2021. A strict weekly testing schedule was maintained by DHD. Control measures included a multidisciplinary partnership between DHD, local universities and hospitals. The multidisciplinary team (MDT) considered environmental health, infection control and behavioral health interventions. Measures for control included testing, and quarantine, vaccine, antibody therapy, and access to hospital care.

Results
14 out of 21 total crew members were confirmed positive (attack rate 67%). Once no new cases were found, a 14-day working quarantine was applied per the Centers for Disease Control (CDC) guidance. On 13 May 2021, the DHD vaccine outreach team administered Janssen COVID-19 vaccine, without incident, to all medically eligible crew (n = 19). Janssen was selected considering the ship’s upcoming departure from Detroit and the limitation for follow-up doses.

Conclusion
Response measures taken for this ship involved quarantine, isolation, disinfection and ventilation protocols, installation of filter units, behavioral health recommendations for anxiety-reducing strategies, antibody therapy, hospital care, vaccine administration and education. A MDT approach and multicomponent control strategy was critical to mitigate further spread. DHD’s communicable diseases investigations, vaccination, and testing capabilities were essential, including continued guidance from the Michigan Department of Health and Human Services (MDHHS) and CDC. Full transparency from the ship crew prevented potential spread of the variant in Detroit.
On 02 June 2021, the ship was cleared by DHD, MDHHS, and CDC to depart. This incident led to the approval and vaccination of 3 subsequent ships whose crews also originated from the Indian subcontinent. This study provides a model for investigation and control of infection related to international ships.

Disclosures
Seema Joshi, MD, AbbVie: Grant/Research Support Marcus Zervos, MD, Johnson and johnson: Grant/Research Support|Moderna: Grant/Research Support