Publications

Developing a COVID-19 WHO Clinical Progression Scale Inpatient Database from Electronic Health Record Data

Abstract

Objective: There is a need for a systematic method to implement the World Health Organization’s Clinical Progression Scale (WHO-CPS), an ordinal clinical severity score for COVID-19 patients, to electronic health record (EHR) data. We discuss our process of developing guiding principles mapping EHR data to WHO-CPS scores across multiple institutions.
Materials and Methods: Using WHO-CPS as a guideline, we developed the technical blueprint to map EHR data to ordinal clinical severity scores. We applied our approach to data from two medical centers.
Results: Our method was able to classify clinical severity for 100% of patient days for 2,756 patient encounters across two institutions.
Discussion: Implementing new clinical scales can be challenging; strong understanding of health system data architecture was integral to meet the clinical intentions of the WHO-CPS.
Conclusion: We describe a detailed blueprint for how to apply the WHO-CPS scale to patient data from the EHR.

Ramaswamy P, Gong JJ, Saleh SN, McDonald SA, Blumberg S, Medford RJ, Liu X. Developing a COVID-19 WHO Clinical Progression Scale Inpatient Database from Electronic Health Record Data. J Am Med Inform Assoc. 2022. doi:10.1093/jamia/ocac041

Early identification of patients admitted to hospital for Covid-19 at risk of clinical deterioration: Model development and multisite external validation study

Abstract

Objective To create and validate a simple and transferable machine learning model from electronic health record data to accurately predict clinical deterioration in patients with covid-19 across institutions, through use of a novel paradigm for model development and code sharing.
Design Retrospective cohort study.
Setting One US hospital during 2015-21 was used for model training and internal validation. External validation was conducted on patients admitted to hospital with covid-19 at 12 other US medical centers during 2020-21.
Participants 33 119 adults (≥18 years) admitted to hospital with respiratory distress or covid-19.
Main outcome measures An ensemble of linear models was trained on the development cohort to predict a composite outcome of clinical deterioration within the first five days of hospital admission, defined as in-hospital mortality or any of three treatments indicating severe illness: mechanical ventilation, heated high flow nasal cannula, or intravenous vasopressors. The model was based on nine clinical and personal characteristic variables selected from 2686 variables available in the electronic health record. Internal and external validation performance was measured using the area under the receiver operating characteristic curve (AUROC) and the expected calibration error—the difference between predicted risk and actual risk. Potential bed day savings were estimated by calculating how many bed days hospitals could save per patient if low risk patients identified by the model were discharged early.
Results 9291 covid-19 related hospital admissions at 13 medical centers were used for model validation, of which 1510 (16.3%) were related to the primary outcome. When the model was applied to the internal validation cohort, it achieved an AUROC of 0.80 (95% confidence interval 0.77 to 0.84) and an expected calibration error of 0.01 (95% confidence interval 0.00 to 0.02). Performance was consistent when validated in the 12 external medical centers (AUROC range 0.77-0.84), across subgroups of sex, age, race, and ethnicity (AUROC range 0.78-0.84), and across quarters (AUROC range 0.73-0.83). Using the model to triage low risk patients could potentially save up to 7.8 bed days per patient resulting from early discharge.
Conclusion A model to predict clinical deterioration was developed rapidly in response to the covid-19 pandemic at a single hospital, was applied externally without the sharing of data, and performed well across multiple medical centers, patient subgroups, and time periods, showing its potential as a tool for use in optimizing healthcare resources.

Kamran F, Tang S, Otles E, McEvoy D, et al. Early identification of patients admitted to hospital for covid-19 at risk of clinical deterioration: model development and multisite external validation study. BMJ. 2022;376:e068576. doi:10.1136/bmj-2021-068576

Aligning staffing schedules with testing and isolation strategies reduces the risk of COVID-19 outbreaks in carceral and other congregate settings: A simulation study

Summary
Aligning routine testing with work schedules among staff in carceral facilities and other congregate settings can enhance the early detection and isolation of COVID-19 cases, limiting the potential for staff to inadvertently trigger outbreaks in high-risk settings.

Hoover CM, Skaff NK, Blumberg S, Fukunaga R. Aligning staffing schedules with testing and isolation strategies reduces the risk of COVID-19 outbreaks in carceral and other congregate settings: A simulation study. medRxiv. Published online January 1, 2021:2021.10.22.21265396. doi:10.1101/2021.10.22.21265396

Assessing the plausibility of supercritical transmission for an emerging or re-emerging pathogen

Abstract
Rapid assessment of the transmission potential of an emerging or reemerging pathogen is a cornerstone of public health response. A simple approach is shown for using the number of disease introductions and secondary cases to determine whether the upper bound of the reproduction number exceeds the critical value of one.

Blumberg S, Lu P, Lietman TM, Porco TC. Assessing the plausibility of supercritical transmission for an emerging or re-emerging pathogen. medRxiv. Published online January 1, 2021:2020.02.08.20021311. doi:10.1101/2020.02.08.20021311

Mitigating outbreaks in congregate settings by decreasing the size of the susceptible population

Abstract
While many transmission models have been developed for community spread of respiratory pathogens, less attention has been given to modeling the interdependence of disease introduction and spread seen in congregate settings, such as prisons or nursing homes. As demonstrated by the explosive outbreaks of COVID-19 seen in congregate settings, the need for effective outbreak prevention and mitigation strategies for these settings is critical. Here we consider how interventions that decrease the size of the susceptible populations, such as vaccination or depopulation, impact the expected number of infections due to outbreaks. Introduction of disease into the resident population from the community is modeled as a branching process, while spread between residents is modeled via a compartmental model. Control is modeled as a proportional decrease in both the number of susceptible residents and the reproduction number. We find that vaccination or depopulation can have a greater than linear effect on anticipated infections. For example, assuming a reproduction number of 3.0 for density-dependent COVID-19 transmission, we find that reducing the size of the susceptible population by 20% reduced overall disease burden by 47%. We highlight the California state prison system as an example for how these findings provide a quantitative framework for implementing infection control in congregate settings. Additional applications of our modeling framework include optimizing the distribution of residents into independent residential units, and comparison of preemptive versus reactive vaccination strategies.

Hoover CM, Skaff NK, Blumberg S, Fukunaga R. Aligning staffing schedules with testing and isolation strategies reduces the risk of COVID-19 outbreaks in carceral and other congregate settings: A simulation study. medRxiv. Published online January 1, 2021:2021.10.22.21265396. doi:10.1101/2021.10.22.21265396

Modeling transmission of pathogens in healthcare settings

Abstract
Mathematical, statistical, and computational models provide insight into the transmission mechanisms and optimal control of healthcare-associated infections. To contextualize recent findings, we offer a summative review of recent literature focused on modeling transmission of pathogens in healthcare settings.

Stachel A, Keegan LT, Blumberg S. Modeling transmission of pathogens in healthcare settings. Curr Opin Infect Dis. 2021; 34(4): 333-338. doi: 10.1097/QCO.0000000000000742