Grace Guan

All Publications

• Insights From Refusal Patterns for Deceased Donor Kidney Offers. Transplantation Guan, G., Neelam, S., Studnia, J., Cheng, X. S., Melcher, M. L., Rees, M. A., Roth, A. E., Somaini, P., Ashlagi, I. 2025

  Abstract

  BACKGROUND: The likelihood that a deceased donor kidney will be used evolves during the allocation process. Transplant centers can either decline an organ offer for a single patient or for multiple patients at the same time. We hypothesize that refusals for a single patient indicate issues with individual patients, whereas simultaneous refusals for multiple patients indicate issues with organ quality.METHODS: We investigate offer refusal patterns between January 1, 2022, and December 31, 2023, using Organ Procurement and Transplantation Network data. We aggregate refusals at the same timestamp by a center and define a multiple patient refusal as >1 or >5 patients simultaneously refused. We report the refusal codes associated with single and multiple patient refusals and the nonutilization rate after receiving single and multiple patient refusals by cross-clamp.RESULTS: Patient-related refusal reasons are more commonly single patient refusals, whereas organ-related refusal reasons are more commonly multiple patient refusals. Multiple patient refusals before cross-clamp are associated with nonutilization, but single patient refusals are positively correlated with utilization. The nonutilization rate was 28% for organs without pre-clamp refusals, 35% with a single center sending a multiple patient refusal, but only 12% with a single center sending a single patient refusal.CONCLUSIONS: The risk of nonutilization can be assessed early in the offering process based on the number of single and multiple patient refusals received by a specific time (e.g., cross-clamp). Understanding refusal patterns can guide the development of transparent protocols for accelerated placement.

  View details for DOI 10.1097/TP.0000000000005434

  View details for PubMedID 40407363

• Assessing the Financial Sustainability of a Virtual Clinic Providing Comprehensive Diabetes Care. Journal of diabetes science and technology Dupenloup, P., Guan, G., Aleppo, G., Bergenstal, R. M., Hood, K., Kruger, D., McArthur, T., Olson, B., Oser, S., Oser, T., Weinstock, R. S., Gal, R. L., Kollman, C., Scheinker, D. 2025: 19322968251340664

  Abstract

  The Virtual Diabetes Specialty Clinic (VDiSC) study demonstrated the feasibility of providing comprehensive diabetes care entirely virtually by combining virtual visits with continuous glucose monitoring support and remote patient monitoring (RPM). However, the financial sustainability of this model remains uncertain.We developed a financial model to estimate the variable costs and revenues of virtual diabetes care, using visit data from the 234 VDiSC participants with type 1 or type 2 diabetes. Data included virtual visits with certified diabetes care and education specialists (CDCES), endocrinologists, and behavioral health services (BHS). The model estimated care utilization, variable costs, reimbursement revenue, gross profit, and gross profit margin per member, per month (PMPM) for privately insured, publicly insured, and overall clinic populations (75% privately insured). We performed two-way sensitivity analyses on key parameters.Gross profit and gross profit margin PMPM (95% confidence interval) were estimated at $-4 ($-14.00 to $5.68) and -4% (-3% to -6%) for publicly insured patients; $267.26 ($256.59-$277.93) and 73% (58%-88%) for privately insured patients; and $199.41 ($58.43-$340.39) and 67% (32%-102%) for the overall clinic. Profits were primarily driven by CDCES visits and RPM. Results were sensitive to insurance mix, cost-to-charge ratio, and commercial-to-Medicare price ratio.Virtual diabetes care can be financially viable, although profitability relies on privately insured patients. The analysis excluded fixed costs of clinic infrastructure, and securing reimbursement may be challenging in practice. The financial model is adaptable to various care settings and can serve as a planning tool for virtual diabetes clinics.

  View details for DOI 10.1177/19322968251340664

  View details for PubMedID 40357670

• Transplant surgeons already account for inaccuracies in the Kidney Donor Profile Index (KDPI) calculation. Clinical transplantation Guan, G., Ashlagi, I., Melcher, M. L. 2024; 38 (5): e15323

  View details for DOI 10.1111/ctr.15323

  View details for PubMedID 38690616

• Resource Utilization and Costs Associated with Approaches to Identify Infants with Early-Onset Sepsis. MDM policy & practice Guan, G., Joshi, N. S., Frymoyer, A., Achepohl, G. D., Dang, R., Taylor, N. K., Salomon, J. A., Goldhaber-Fiebert, J. D., Owens, D. K. 2024; 9 (1): 23814683231226129

  Abstract

  Objective. To compare resource utilization and costs associated with 3 alternative screening approaches to identify early-onset sepsis (EOS) in infants born at ≥35 wk of gestational age, as recommended by the American Academy of Pediatrics (AAP) in 2018. Study Design. Decision tree-based cost analysis of the 3 AAP-recommended approaches: 1) categorical risk assessment (categorization by chorioamnionitis exposure status), 2) neonatal sepsis calculator (a multivariate prediction model based on perinatal risk factors), and 3) enhanced clinical observation (assessment based on serial clinical examinations). We evaluated resource utilization and direct costs (2022 US dollars) to the health system. Results. Categorical risk assessment led to the greatest neonatal intensive care unit usage (210 d per 1,000 live births) and antibiotic exposure (6.8%) compared with the neonatal sepsis calculator (112 d per 1,000 live births and 3.6%) and enhanced clinical observation (99 d per 1,000 live births and 3.1%). While the per-live birth hospital costs of the 3 approaches were similar-categorical risk assessment cost $1,360, the neonatal sepsis calculator cost $1,317, and enhanced clinical observation cost $1,310-the cost of infants receiving intervention under categorical risk assessment was approximately twice that of the other 2 strategies. Results were robust to variations in data parameters. Conclusion. The neonatal sepsis calculator and enhanced clinical observation approaches may be preferred to categorical risk assessment as they reduce the number of infants receiving intervention and thus antibiotic exposure and associated costs. All 3 approaches have similar costs over all live births, and prior literature has indicated similar health outcomes. Inclusion of downstream effects of antibiotic exposure in the neonatal period should be evaluated within a cost-effectiveness analysis.Of the 3 approaches recommended by the American Academy of Pediatrics in 2018 to identify early-onset sepsis in infants born at ≥35 weeks, the categorical risk assessment approach leads to about twice as many infants receiving evaluation to rule out early-onset sepsis compared with the neonatal sepsis calculator and enhanced clinical observation approaches.While the hospital costs of the 3 approaches were similar over the entire population of live births, the neonatal sepsis calculator and enhanced clinical observation approaches reduce antibiotic exposure, neonatal intensive care unit admission, and hospital costs associated with interventions as part of the screening approach compared with the categorical risk assessment approach.

  View details for DOI 10.1177/23814683231226129

  View details for PubMedID 38293656

  View details for PubMedCentralID PMC10826394

• Higher sensitivity monitoring of reactions to COVID-19 vaccination using smartwatches. NPJ digital medicine Guan, G., Mofaz, M., Qian, G., Patalon, T., Shmueli, E., Yamin, D., Brandeau, M. L. 2022; 5 (1): 140

  Abstract

  More than 12 billion COVID-19 vaccination shots have been administered as of August 2022, but information from active surveillance about vaccine safety is limited. Surveillance is generally based on self-reporting, making the monitoring process subjective. We study participants in Israel who received their second or third Pfizer BioNTech COVID-19 vaccination. All participants wore a Garmin Vivosmart 4 smartwatch and completed a daily questionnaire via smartphone. We compare post-vaccination smartwatch heart rate data and a Garmin-computed stress measure based on heart rate variability with data from the patient questionnaires. Using a mixed effects panel regression to remove participant-level fixed and random effects, we identify considerable changes in smartwatch measures in the 72 h post-vaccination even among participants who reported no side effects in the questionnaire. Wearable devices were more sensitive than questionnaires in determining when participants returned to baseline levels. We conclude that wearable devices can detect physiological responses following vaccination that may not be captured by patient self-reporting. More broadly, the ubiquity of smartwatches provides an opportunity to gather improved data on patient health, including active surveillance of vaccine safety.

  View details for DOI 10.1038/s41746-022-00683-w

  View details for PubMedID 36085312

• Self-Reported and Physiologic Reactions to Third BNT162b2 mRNA COVID-19 (Booster) Vaccine Dose. Emerging infectious diseases Mofaz, M., Yechezkel, M., Guan, G., Brandeau, M. L., Patalon, T., Gazit, S., Yamin, D., Shmueli, E. 2022; 28 (7): 1375-1383

  Abstract

  Despite extensive technological advances in recent years, objective and continuous assessment of physiologic measures after vaccination is rarely performed. We conducted a prospective observational study to evaluate short-term self-reported and physiologic reactions to the booster BNT162b2 mRNA (Pfizer-BioNTech, https://www.pfizer.com) vaccine dose. A total of 1,609 participants were equipped with smartwatches and completed daily questionnaires through a dedicated mobile application. The extent of systemic reactions reported after the booster dose was similar to that of the second dose and considerably greater than that of the first dose. Analyses of objective heart rate and heart rate variability measures recorded by smartwatches further supported this finding. Subjective and objective reactions after the booster dose were more apparent in younger participants and in participants who did not have underlying medical conditions. Our findings further support the safety of the booster dose from subjective and objective perspectives and underscore the need for integrating wearables in clinical trials.

  View details for DOI 10.3201/eid2807.212330

  View details for PubMedID 35654410

• Early detection of COVID-19 outbreaks using human mobility data. PloS one Guan, G., Dery, Y., Yechezkel, M., Ben-Gal, I., Yamin, D., Brandeau, M. L. 2021; 16 (7): e0253865

  Abstract

  Contact mixing plays a key role in the spread of COVID-19. Thus, mobility restrictions of varying degrees up to and including nationwide lockdowns have been implemented in over 200 countries. To appropriately target the timing, location, and severity of measures intended to encourage social distancing at a country level, it is essential to predict when and where outbreaks will occur, and how widespread they will be.We analyze aggregated, anonymized health data and cell phone mobility data from Israel. We develop predictive models for daily new cases and the test positivity rate over the next 7 days for different geographic regions in Israel. We evaluate model goodness of fit using root mean squared error (RMSE). We use these predictions in a five-tier categorization scheme to predict the severity of COVID-19 in each region over the next week. We measure magnitude accuracy (MA), the extent to which the correct severity tier is predicted.Models using mobility data outperformed models that did not use mobility data, reducing RMSE by 17.3% when predicting new cases and by 10.2% when predicting the test positivity rate. The best set of predictors for new cases consisted of 1-day lag of past 7-day average new cases, along with a measure of internal movement within a region. The best set of predictors for the test positivity rate consisted of 3-days lag of past 7-day average test positivity rate, along with the same measure of internal movement. Using these predictors, RMSE was 4.812 cases per 100,000 people when predicting new cases and 0.79% when predicting the test positivity rate. MA in predicting new cases was 0.775, and accuracy of prediction to within one tier was 1.0. MA in predicting the test positivity rate was 0.820, and accuracy to within one tier was 0.998.Using anonymized, macro-level data human mobility data along with health data aids predictions of when and where COVID-19 outbreaks are likely to occur. Our method provides a useful tool for government decision makers, particularly in the post-vaccination era, when focused interventions are needed to contain COVID-19 outbreaks while mitigating the collateral damage from more global restrictions.

  View details for DOI 10.1371/journal.pone.0253865

  View details for PubMedID 34283839