Johannes O Ferstad

All Publications

• Smart Start - Designing Powerful Clinical Trials Using Pilot Study Data. NEJM evidence Ferstad, J. O., Prahalad, P., Maahs, D. M., Zaharieva, D. P., Fox, E., Desai, M., Johari, R., Scheinker, D. 2024; 3 (2): EVIDoa2300164

  Abstract

  Using Pilot Study Data to Design Clinical TrialsDigital health interventions are often studied in a pilot trial before full evaluation in a randomized controlled trial. The authors introduce Smart Start, a framework for using pilot study data to optimize the intervention and design the subsequent randomized controlled trial to maximize the chance of success.

  View details for DOI 10.1056/EVIDoa2300164

  View details for PubMedID 38320487

• The evolving role of data & safety monitoring boards for real-world clinical trials. Journal of clinical and translational science Bunning, B. J., Hedlin, H., Chen, J. H., Ciolino, J. D., Ferstad, J. O., Fox, E., Garcia, A., Go, A., Johari, R., Lee, J., Maahs, D. M., Mahaffey, K. W., Opsahl-Ong, K., Perez, M., Rochford, K., Scheinker, D., Spratt, H., Turakhia, M. P., Desai, M. 2023; 7 (1): e179

  Abstract

  Clinical trials provide the "gold standard" evidence for advancing the practice of medicine, even as they evolve to integrate real-world data sources. Modern clinical trials are increasingly incorporating real-world data sources - data not intended for research and often collected in free-living contexts. We refer to trials that incorporate real-world data sources as real-world trials. Such trials may have the potential to enhance the generalizability of findings, facilitate pragmatic study designs, and evaluate real-world effectiveness. However, key differences in the design, conduct, and implementation of real-world vs traditional trials have ramifications in data management that can threaten their desired rigor.Three examples of real-world trials that leverage different types of data sources - wearables, medical devices, and electronic health records are described. Key insights applicable to all three trials in their relationship to Data and Safety Monitoring Boards (DSMBs) are derived.Insight and recommendations are given on four topic areas: A. Charge of the DSMB; B. Composition of the DSMB; C. Pre-launch Activities; and D. Post-launch Activities. We recommend stronger and additional focus on data integrity.Clinical trials can benefit from incorporating real-world data sources, potentially increasing the generalizability of findings and overall trial scale and efficiency. The data, however, present a level of informatic complexity that relies heavily on a robust data science infrastructure. The nature of monitoring the data and safety must evolve to adapt to new trial scenarios to protect the rigor of clinical trials.

  View details for DOI 10.1017/cts.2023.582

  View details for PubMedID 37745930

  View details for PubMedCentralID PMC10514684

• A quantitative model to ensure capacity sufficient for timely access to care in a remote patient monitoring program. Endocrinology, diabetes & metabolism Chang, A., Gao, M. Z., Ferstad, J. O., Dupenloup, P., Zaharieva, D. P., Maahs, D. M., Prahalad, P., Johari, R., Scheinker, D. 2023: e435

  Abstract

  Algorithm-enabled remote patient monitoring (RPM) programs pose novel operational challenges. For clinics developing and deploying such programs, no standardized model is available to ensure capacity sufficient for timely access to care. We developed a flexible model and interactive dashboard of capacity planning for whole-population RPM-based care for T1D.Data were gathered from a weekly RPM program for 277 paediatric patients with T1D at a paediatric academic medical centre. Through the analysis of 2 years of observational operational data and iterative interviews with the care team, we identified the primary operational, population, and workforce metrics that drive demand for care providers. Based on these metrics, an interactive model was designed to facilitate capacity planning and deployed as a dashboard.The primary population-level drivers of demand are the number of patients in the program, the rate at which patients enrol and graduate from the program, and the average frequency at which patients require a review of their data. The primary modifiable clinic-level drivers of capacity are the number of care providers, the time required to review patient data and contact a patient, and the number of hours each provider allocates to the program each week. At the institution studied, the model identified a variety of practical operational approaches to better match the demand for patient care.We designed a generalizable, systematic model for capacity planning for a paediatric endocrinology clinic providing RPM for T1D. We deployed this model as an interactive dashboard and used it to facilitate expansion of a novel care program (4 T Study) for newly diagnosed patients with T1D. This model may facilitate the systematic design of RPM-based care programs.

  View details for DOI 10.1002/edm2.435

  View details for PubMedID 37345227

• Disparities in Hemoglobin A1c Levels in the First Year After Diagnosis Among Youths With Type 1 Diabetes Offered Continuous Glucose Monitoring. JAMA network open Addala, A., Ding, V., Zaharieva, D. P., Bishop, F. K., Adams, A. S., King, A. C., Johari, R., Scheinker, D., Hood, K. K., Desai, M., Maahs, D. M., Prahalad, P. 2023; 6 (4): e238881

  Abstract

  Continuous glucose monitoring (CGM) is associated with improvements in hemoglobin A1c (HbA1c) in youths with type 1 diabetes (T1D); however, youths from minoritized racial and ethnic groups and those with public insurance face greater barriers to CGM access. Early initiation of and access to CGM may reduce disparities in CGM uptake and improve diabetes outcomes.To determine whether HbA1c decreases differed by ethnicity and insurance status among a cohort of youths newly diagnosed with T1D and provided CGM.This cohort study used data from the Teamwork, Targets, Technology, and Tight Control (4T) study, a clinical research program that aims to initiate CGM within 1 month of T1D diagnosis. All youths with new-onset T1D diagnosed between July 25, 2018, and June 15, 2020, at Stanford Children's Hospital, a single-site, freestanding children's hospital in California, were approached to enroll in the Pilot-4T study and were followed for 12 months. Data analysis was performed and completed on June 3, 2022.All eligible participants were offered CGM within 1 month of diabetes diagnosis.To assess HbA1c change over the study period, analyses were stratified by ethnicity (Hispanic vs non-Hispanic) or insurance status (public vs private) to compare the Pilot-4T cohort with a historical cohort of 272 youths diagnosed with T1D between June 1, 2014, and December 28, 2016.The Pilot-4T cohort comprised 135 youths, with a median age of 9.7 years (IQR, 6.8-12.7 years) at diagnosis. There were 71 boys (52.6%) and 64 girls (47.4%). Based on self-report, participants' race was categorized as Asian or Pacific Islander (19 [14.1%]), White (62 [45.9%]), or other race (39 [28.9%]); race was missing or not reported for 15 participants (11.1%). Participants also self-reported their ethnicity as Hispanic (29 [21.5%]) or non-Hispanic (92 [68.1%]). A total of 104 participants (77.0%) had private insurance and 31 (23.0%) had public insurance. Compared with the historical cohort, similar reductions in HbA1c at 6, 9, and 12 months postdiagnosis were observed for Hispanic individuals (estimated difference, -0.26% [95% CI, -1.05% to 0.43%], -0.60% [-1.46% to 0.21%], and -0.15% [-1.48% to 0.80%]) and non-Hispanic individuals (estimated difference, -0.27% [95% CI, -0.62% to 0.10%], -0.50% [-0.81% to -0.11%], and -0.47% [-0.91% to 0.06%]) in the Pilot-4T cohort. Similar reductions in HbA1c at 6, 9, and 12 months postdiagnosis were also observed for publicly insured individuals (estimated difference, -0.52% [95% CI, -1.22% to 0.15%], -0.38% [-1.26% to 0.33%], and -0.57% [-2.08% to 0.74%]) and privately insured individuals (estimated difference, -0.34% [95% CI, -0.67% to 0.03%], -0.57% [-0.85% to -0.26%], and -0.43% [-0.85% to 0.01%]) in the Pilot-4T cohort. Hispanic youths in the Pilot-4T cohort had higher HbA1c at 6, 9, and 12 months postdiagnosis than non-Hispanic youths (estimated difference, 0.28% [95% CI, -0.46% to 0.86%], 0.63% [0.02% to 1.20%], and 1.39% [0.37% to 1.96%]), as did publicly insured youths compared with privately insured youths (estimated difference, 0.39% [95% CI, -0.23% to 0.99%], 0.95% [0.28% to 1.45%], and 1.16% [-0.09% to 2.13%]).The findings of this cohort study suggest that CGM initiation soon after diagnosis is associated with similar improvements in HbA1c for Hispanic and non-Hispanic youths as well as for publicly and privately insured youths. These results further suggest that equitable access to CGM soon after T1D diagnosis may be a first step to improve HbA1c for all youths but is unlikely to eliminate disparities entirely.ClinicalTrials.gov Identifier: NCT04336969.

  View details for DOI 10.1001/jamanetworkopen.2023.8881

  View details for PubMedID 37074715

  View details for PubMedCentralID PMC10116368

• The Association between Patient Characteristics and the Efficacy of Remote Patient Monitoring and Messaging Ferstad, J., Prahalad, P., Maahs, D. M., Fox, E., Johari, R., Scheinker, D. AMER DIABETES ASSOC. 2022

  View details for DOI 10.2337/db22-1009-P

  View details for Web of Science ID 000854899301502

• Adding glycemic and physical activity metrics to a multimodal algorithm-enabled decision-support tool for type 1 diabetes care: Keys to implementation and opportunities. Frontiers in endocrinology Zaharieva, D. P., Senanayake, R., Brown, C., Watkins, B., Loving, G., Prahalad, P., Ferstad, J. O., Guestrin, C., Fox, E. B., Maahs, D. M., Scheinker, D. 2022; 13: 1096325

  Abstract

  Algorithm-enabled patient prioritization and remote patient monitoring (RPM) have been used to improve clinical workflows at Stanford and have been associated with improved glucose time-in-range in newly diagnosed youth with type 1 diabetes (T1D). This novel algorithm-enabled care model currently integrates continuous glucose monitoring (CGM) data to prioritize patients for weekly reviews by the clinical diabetes team. The use of additional data may help clinical teams make more informed decisions around T1D management. Regular exercise and physical activity are essential to increasing cardiovascular fitness, increasing insulin sensitivity, and improving overall well-being of youth and adults with T1D. However, exercise can lead to fluctuations in glycemia during and after the activity. Future iterations of the care model will integrate physical activity metrics (e.g., heart rate and step count) and physical activity flags to help identify patients whose needs are not fully captured by CGM data. Our aim is to help healthcare professionals improve patient care with a better integration of CGM and physical activity data. We hypothesize that incorporating exercise data into the current CGM-based care model will produce specific, clinically relevant information such as identifying whether patients are meeting exercise guidelines. This work provides an overview of the essential steps of integrating exercise data into an RPM program and the most promising opportunities for the use of these data.

  View details for DOI 10.3389/fendo.2022.1096325

  View details for PubMedID 36714600

• A Platform for the Personalized Management of Diabetes and Cardiovascular Disease at Population Scale With Data From Multiple Sensors AHA Senanayake, R., Ferstad, J. O., Thapa, I., Giammarino, F., Vasu, M., Zaharieva, D., Prahalad, P., Maahs, D. M., Rosenthal, D. N., Rodriguez, F., Bambos, N., Miller, D., Shin, A., Roth, S. J., Guestrin, C., Fox, E. B., Scheinker, D. 2022

  View details for DOI 10.1161/circ.146.suppl_1.13358

• Population-level management of Type 1 diabetes via continuous glucose monitoring and algorithm-enabled patient prioritization: Precision health meets population health. Pediatric diabetes Ferstad, J. O., Vallon, J. J., Jun, D., Gu, A., Vitko, A., Morales, D. P., Leverenz, J., Lee, M. Y., Leverenz, B., Vasilakis, C., Osmanlliu, E., Prahalad, P., Maahs, D. M., Johari, R., Scheinker, D. 2021

  Abstract

  OBJECTIVE: To develop and scale algorithm-enabled patient prioritization to improve population-level management of type 1 diabetes (T1D) in a pediatric clinic with fixed resources, using telemedicine and remote monitoring of patients via continuous glucose monitor (CGM) data review.RESEARCH DESIGN AND METHODS: We adapted consensus glucose targets for T1D patients using CGM to identify interpretable clinical criteria to prioritize patients for weekly provider review. The criteria were constructed to manage the number of patients reviewed weekly and identify patients who most needed provider contact. We developed an interactive dashboard to display CGM data relevant for the patients prioritized for review.RESULTS: The introduction of the new criteria and interactive dashboard was associated with a 60% reduction in the mean time spent by diabetes team members who remotely and asynchronously reviewed patient data and contacted patients, from 3.2±0.20 to 1.3±0.24minutes per patient per week. Given fixed resources for review, this corresponded to an estimated 147% increase in weekly clinic capacity. Patients who qualified for and received remote review (n=58) have associated 8.8 percentage points (pp) (95% CI=0.6-16.9pp) greater time-in-range (70-180mg/dL) glucoses compared to 25 control patients who did not qualify at twelve months after T1D onset.CONCLUSIONS: An algorithm-enabled prioritization of T1D patients with CGM for asynchronous remote review reduced provider time spent per patient and was associated with improved time-in-range. This article is protected by copyright. All rights reserved.

  View details for DOI 10.1111/pedi.13256

  View details for PubMedID 34374183

• Stanford Medicine-Engineering Partnership Launches an Interactive Model to Facilitate COVID-19 Response Planning for Hospital and Regional Leaders (4/1) Health Management, Policy and Innovation (HMPI.org) Ferstad, J. O., Gu, A., Lee, R. Y., Thapa, I., Schulman, K., Scheinker, D., Shin, A. Y. 2020; 5 (1)
• A model to forecast regional demand for COVID-19 related hospital beds Ferstad, J. O., Gu, A. J., Lee, R. Y., Thapa, I., Shin, A. Y., Salomon, J. A., Glynn, P., Shah, N. H., Milstein, A., Schulman, K., Scheinker, D. medRxiv. medRxiv. 2020

  Abstract

  COVID-19 threatens to overwhelm hospital facilities throughout the United States. We created an interactive, quantitative model that forecasts demand for COVID-19 related hospitalization based on county-level population characteristics, data from the literature on COVID-19, and data from online repositories. Using this information as well as user inputs, the model estimates a time series of demand for intensive care beds and acute care beds as well as the availability of those beds. The online model is designed to be intuitive and interactive so that local leaders with limited technical or epidemiological expertise may make decisions based on a variety of scenarios. This complements high-level models designed for public consumption and technically sophisticated models designed for use by epidemiologists. The model is actively being used by several academic medical centers and policy makers, and we believe that broader access will continue to aid community and hospital leaders in their response to COVID-19. Link to online model: https://surf.stanford.edu/covid-19-tools/covid-19/