Managing COVID-19 With a Clinical Decision Support Tool in a Community Health Network: Algorithm Development and Validation.

BACKGROUND: The coronavirus disease (COVID-19) pandemic has resulted in significant morbidity and mortality; large numbers of patients require intensive care, which is placing strain on health care systems worldwide. There is an urgent need for a COVID-19 disease severity assessment that can assist in patient triage and resource allocation for patients at risk for severe disease. OBJECTIVE: The goal of this study was to develop, validate, and scale a clinical decision support system and mobile app to assist in COVID-19 severity assessment, management, and care. METHODS: Model training data from 701 patients with COVID-19 were collected across practices within the Family Health Centers network at New York University Langone Health. A two-tiered model was developed. Tier 1 uses easily available, nonlaboratory data to help determine whether biomarker-based testing and/or hospitalization is necessary. Tier 2 predicts the probability of mortality using biomarker measurements (C-reactive protein, procalcitonin, D-dimer) and age. Both the Tier 1 and Tier 2 models were validated using two external datasets from hospitals in Wuhan, China, comprising 160 and 375 patients, respectively. RESULTS: All biomarkers were measured at significantly higher levels in patients who died vs those who were not hospitalized or discharged (P<.001). The Tier 1 and Tier 2 internal validations had areas under the curve (AUCs) of 0.79 (95% CI 0.74-0.84) and 0.95 (95% CI 0.92-0.98), respectively. The Tier 1 and Tier 2 external validations had AUCs of 0.79 (95% CI 0.74-0.84) and 0.97 (95% CI 0.95-0.99), respectively. CONCLUSIONS: Our results demonstrate the validity of the clinical decision support system and mobile app, which are now ready to assist health care providers in making evidence-based decisions when managing COVID-19 patient care. The deployment of these new capabilities has potential for immediate impact in community clinics and sites, where application of these tools could lead to improvements in patient outcomes and cost containment.

A low-cost B-mode USB ultrasound probe.

The Universal Serial Bus (USB) is now the ubiquitous interface bus of choice for connecting peripherals to personal computers and laptops. USB 2.0 is a half-duplex bus running at 480 Mb/s and each peripheral can draw as much as 500 mA of current at a nominal 5 V from the USB connector. We have developed a family of USB-based, B-mode probes that connect directly to a personal computer or laptop and that draw as little as 250 mA (1.25 W) when forming ten 5 MHz images/second. The pulser/receiver, high voltage supply, analog-to-digital converter, servo and USB interface are implemented on a small circuit board inside the probe body. After raw data are transferred to the computer, gain compensation, interpolation, filtering and other data processing are performed by the host processor. This gives flexibility to developers and allows enhancements to the system to be incorporated via software updates. In addition, the raw data are available for storage and later postprocessing. There are several advantages to this architectural approach to B-mode imaging, including low cost, portability and optimal signal-to-noise performance. This paper describes the advantages of the architecture of the probe family, discusses the hardware/software division of the required processing steps and presents example images from a 12.5 MHz ophthalmic probe.