Design and Development of an Antigen Test for SARS-CoV-2 Nucleocapsid Protein to Validate the Viral Quality Assurance Panels.

The continuing mutability of the SARS-CoV-2 virus can result in failures of diagnostic assays. To address this, we describe a generalizable bioinformatics-to-biology pipeline developed for the calibration and quality assurance of inactivated SARS-CoV-2 variant panels provided to Radical Acceleration of Diagnostics programs (RADx)-radical program awardees. A heuristic genetic analysis based on variant-defining mutations demonstrated the lowest genetic variance in the Nucleocapsid protein (Np)-C-terminal domain (CTD) across all SARS-CoV-2 variants. We then employed the Shannon entropy method on (Np) sequences collected from the major variants, verifying the CTD with lower entropy (less prone to mutations) than other Np regions. Polyclonal and monoclonal antibodies were raised against this target CTD antigen and used to develop an Enzyme-linked immunoassay (ELISA) test for SARS-CoV-2. Blinded Viral Quality Assurance (VQA) panels comprised of UV-inactivated SARS-CoV-2 variants (XBB.1.5, BF.7, BA.1, B.1.617.2, and WA1) and distractor respiratory viruses (CoV 229E, CoV OC43, RSV A2, RSV B, IAV H1N1, and IBV) were assembled by the RADx-rad Diagnostics core and tested using the ELISA described here. The assay tested positive for all variants with high sensitivity (limit of detection: 1.72-8.78 ng/mL) and negative for the distractor virus panel. Epitope mapping for the monoclonal antibodies identified a 20 amino acid antigenic peptide on the Np-CTD that an in-silico program also predicted for the highest antigenicity. This work provides a template for a bioinformatics pipeline to select genetic regions with a low propensity for mutation (low Shannon entropy) to develop robust 'pan-variant' antigen-based assays for viruses prone to high mutational rates.

Real-World Use of Control-IQ Technology Is Associated with a Lower Rate of Severe Hypoglycemia and Diabetic Ketoacidosis Than Historical Data: Results of the Control-IQ Observational (CLIO) Prospective Study.

Objective: Severe hypoglycemia (SH) and diabetic ketoacidosis (DKA) remain significant risks with intensive insulin therapy. While these adverse event (AE) rates are generally very low in advanced hybrid closed-loop (AHCL) clinical studies, prospectively collected real-world AE rates are lacking. Research Design and Methods: The Control-IQ Observational (CLIO) study was a single-arm, prospective, longitudinal, postmarket surveillance study of individuals with type 1 diabetes (T1D) age 6 years and older who began the use of t:slim X2 insulin pump with Control-IQ technology in the real-world outpatient setting. AEs were reported monthly over 12 months and were compared to historical data from the T1D Exchange. Patient-reported outcomes were assessed quarterly. All study visits were virtual. Results: Three thousand one hundred fifty-seven participants enrolled from August 2020 through March 2022. Two thousand nine hundred ninety-eight participants completed through 12 months. SH rates were significantly lower than historic rates for children (9.31 vs. 19.31 events/100 patient years, d = 0.29, P < 0.01) and adults (9.77 vs. 29.49 events/100 patient years, d = 0.53, P < 0.01). DKA rates were also significantly lower in both groups. Lower observed rates of AEs occurred independent of baseline hemoglobin A1c or prior insulin delivery method. Time in range 70-180 mg/dL was 70.1% (61.0-78.8) for adults, 61.2% (52.4-70.5) for age 6-13, 60.9% (50.1-71.8) for age 14-17, and 67.3% (57.4-76.9) overall. Reduction in diabetes burden was consistently reported. Conclusions: SH and DKA rates were lower for users of t:slim X2 with Control-IQ technology compared to historical data for both adults and children. Real-world use of this AHCL system proved safe and effective in this virtual study design. The study was registered at clinicaltrials.gov (NCT04503174).

Digital exposure notification tools: A global landscape analysis.

BACKGROUND: As the COVID-19 pandemic continues, digital exposure notification systems are increasingly used to support traditional contact tracing and other preventive strategies. Likewise, a plethora of COVID-19 mobile applications have emerged. Objective: To characterize the global landscape of pandemic related mobile applications, including digital exposure notification and contact tracing tools. DATA SOURCES AND METHODS: The following queries were entered into the Google search engine: "(*country name* COVID app) OR (COVID app *country name*) OR (COVID app *country name*+) OR (*country name*+ COVID app)". The App Store, Google Play, and official government websites were then accessed to collect descriptive data for each application. Descriptive data were qualified and quantified using standard methods. COVID-19 Exposure Notification Systems (ENS) and non-Exposure Notification products were categorized and summarized to provide a global landscape review. RESULTS: Our search resulted in a global count of 224 COVID-19 mobile applications, in 127 countries. Of these 224 apps, 128 supported exposure notification, with 75 employing the Google Apple Exposure Notification (GAEN) application programming interface (API). Of the 75 apps using the GAEN API, 15 apps were developed using Exposure Notification Express, a GAEN turnkey solution. COVID-19 applications that did not include exposure notifications (n = 96) focused on COVID-19 Self-Assessment (35·4%), COVID-19 Statistics and Information (32·3%), and COVID-19 Health Advice (29·2%). CONCLUSIONS: The digital response to COVID-19 generated diverse and novel solutions to support non-pharmacologic public health interventions. More research is needed to evaluate the extent to which these services and strategies were useful in reducing viral transmission.

Exposure notification system activity as a leading indicator for SARS-COV-2 caseload forecasting.

PURPOSE: Digital methods to augment traditional contact tracing approaches were developed and deployed globally during the COVID-19 pandemic. These "Exposure Notification (EN)" systems present new opportunities to support public health interventions. To date, there have been attempts to model the impact of such systems, yet no reports have explored the value of real-time system data for predictive epidemiological modeling. METHODS: We investigated the potential to short-term forecast COVID-19 caseloads using data from California's implementation of the Google Apple Exposure Notification (GAEN) platform, branded as CA Notify. CA Notify is a digital public health intervention leveraging resident's smartphones for anonymous EN. We extended a published statistical model that uses prior case counts to investigate the possibility of predicting short-term future case counts and then added EN activity to test for improved forecast performance. Additional predictive value was assessed by comparing the pandemic forecasting models with and without EN activity to the actual reported caseloads from 1-7 days in the future. RESULTS: Observation of time series presents noticeable evidence for temporal association of system activity and caseloads. Incorporating earlier ENs in our model improved prediction of the caseload counts. Using Bayesian inference, we found nonzero influence of EN terms with probability one. Furthermore, we found a reduction in both the mean absolute percentage error and the mean squared prediction error, the latter of at least 5% and up to 32% when using ENs over the model without. CONCLUSIONS: This preliminary investigation suggests smartphone based ENs can significantly improve the accuracy of short-term forecasting. These predictive models can be readily deployed as local early warning systems to triage resources and interventions.

The physiology of intraoperative error: using electrokardiograms to understand operator performance during robot-assisted surgery simulations.

BACKGROUND: No platform for objective, synchronous and on-line evaluation of both intraoperative error and surgeon physiology yet exists. Electrokardiogram (EKG) metrics have been associated with cognitive and affective features that are known to impact surgical performance but have not yet been analyzed in conjunction with real-time error signals using objective, real-time methods. METHODS: EKGs and operating console point-of-views (POVs) for fifteen general surgery residents and five non-medically trained participants were captured during three simulated robotic-assisted surgery (RAS) procedures. Time and frequency-domain EKG statistics were extracted from recorded EKGs. Intraoperative errors were detected from operating console POV videos. EKG statistics were synchronized with intraoperative error signals. RESULTS: Relative to personalized baselines, IBI, SDNN and RMSSD decreased 0.15% (S.E. 3.603e-04; P = 3.25e-05), 3.08% (S.E. 1.603e-03; P < 2e-16) and 1.19% (S.E. 2.631e-03; P = 5.66e-06), respectively, during error. Relative LF RMS power decreased 1.44% (S.E. 2.337e-03; P = 8.38e-10), and relative HF RMS power increased 5.51% (S.E. 1.945e-03; P < 2e-16). CONCLUSIONS: Use of a novel, on-line biometric and operating room data capture and analysis platform enabled detection of distinct operator physiological changes during intraoperative errors. Monitoring operator EKG metrics during surgery may help improve patient outcomes through real-time assessments of intraoperative surgical proficiency and perceived difficulty as well as inform personalized surgical skills development.

Randomized trial evaluating an mHealth intervention for the early community-based detection and follow-up of cutaneous leishmaniasis in rural Colombia.

BACKGROUND: In Latin America, cutaneous leishmaniasis primarily affects dispersed rural communities, that have limited access to the public health system and medical attention. Mobile health (mHealth) strategies have shown potential to improve clinical management and epidemiological surveillance of neglected tropical diseases, particularly those of the skin. METHODS: The Guaral +ST app for Android was designed to monitor cutaneous leishmaniasis treatment and assess therapeutic response. We carried out a randomized trial in the coastal municipality of Tumaco in southwestern Colombia, with parallel arms comparing a) follow-up aided by the app to b) standard institution-based follow-up. Treatment was prescribed according to national guidelines. Follow-up of therapeutic response was scheduled at the end of treatment and at 7, 13 and 26 weeks after the start of treatment. The primary endpoint was the proportion of participants who were monitored at or around week 26, allowing outcome and effectiveness of treatment to be determined. RESULTS: Follow-up of treatment and outcome assessment was achieved in significantly more patients in the intervention arm than the controls, Of the 75 participants in the two randomized arms, 74 had information on whether or not treatment was followed and outcome determined at or around week 26. Among these, 26/49 (53.1%) were evaluated in the intervention arm, and none (0/25, 0%) in the control arm (difference = 53.1%, 95% confidence interval 39.1-67.0%, p<0.001). Of the 26 participants evaluated at or around week 26 in the intervention arm, 22 (84.6%) had cured. There were no serious adverse events, nor events of severe intensity among patients monitored by CHW using the app. CONCLUSION: This study provides proof of concept for mHealth to monitor treatment of CL in remote and complex settings, deliver improved care and to provide information to the health system on the effectiveness of treatment as it is delivered to affected populations. CLINICAL TRIAL REGISTRATION: ISRCTN54865992.

In Vitro Diagnostic Assay to Detect SARS-CoV-2-Neutralizing Antibody in Patient Sera Using Engineered ACE-2 Mini-Protein.

The recent development and mass administration of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) vaccines allowed for disease control, reducing hospitalizations and mortality. Most of these vaccines target the SARS-CoV-2 Spike (S) protein antigens, culminating with the production of neutralizing antibodies (NAbs) that disrupt the attachment of the virus to ACE2 receptors on the host cells. However, several studies demonstrated that the NAbs typically rise within a few weeks after vaccination but quickly reduce months later. Thus, multiple booster administration is recommended, leading to vaccination hesitancy in many populations. Detecting serum anti-SARS-CoV-2 NAbs can instruct patients and healthcare providers on correct booster strategies. Several in vitro diagnostics kits are available; however, their high cost impairs the mass NAbs diagnostic testing. Recently, we engineered an ACE2 mimetic that interacts with the Receptor Binding Domain (RBD) of the SARS-2 S protein. Here we present the use of this engineered mini-protein (p-deface2 mut) to develop a detection assay to measure NAbs in patient sera using a competitive ELISA assay. Serum samples from twenty-one patients were tested. Nine samples (42.8%) tested positive, and twelve (57.1%) tested negative for neutralizing sera. The data correlated with the result from the standard commercial assay that uses human ACE2 protein. This confirmed that p-deface2 mut could replace human ACE2 in ELISA assays. Using bacterially expressed p-deface2 mut protein is cost-effective and may allow mass SARS-CoV-2 NAbs detection, especially in low-income countries where economical diagnostic testing is crucial. Such information will help providers decide when a booster is required, reducing risks of reinfection and preventing the administration before it is medically necessary.

Biometric recognition of newborns and young children for vaccinations and health care: a non-randomized prospective clinical trial.

Although universal biometrics have been broadly called for, and there are many validated technologies to recognize adults, these technologies have been ineffective in newborns and young children. The present work describes the development and clinical testing of a fingerprint capture system for longitudinal biometric recognition of newborns and young children to support vaccination and clinical follow-up. The reader consists of a high-resolution monochromatic imaging system with an ergonomic industrial design to comfortably support and align infant fingers for imaging without a platen. This imaging approach without a platen, also called free-space imaging, reduces fingerprint distortion and ensures a more consistent finger placement. This system was tested in a newborn ward and immunization clinic at an urban hospital in Baja, California, Mexico, from 2017 to 2019. Nearly five hundred children were enrolled and followed for up to 24 months. With a protocol of imaging all ten fingers, the failure to enroll (FTE) rate was < 1% when acquiring at least two fingers for all ages and < 2% when enrolling at least four fingers. The verification (1:1) true accept rate (TAR) was 77% for newborns enrolled at ≤ 3 days of age and 96% for those enrolled at ≥ 4 days of age, both at a time gap of 15-30 days after enrollment at a false accept rate (FAR) of 0.1%. Using the top-ranked match score, the identification rate (1:many) was 86% for the ≤ 3 days enrollment age and 97% for age ≥ 4 days for a single finger at 15-30 days after enrollment. The enrollment protocol and the frequency of updating will increase for infants compared to adults. However, these data suggest that a high-resolution, free space imaging technique may fill the final gap for universal biometrics across all populations called for by the United Nations Sustainable Development Goal 16.9.

Defining Key Performance Indicators for the California COVID-19 Exposure Notification System (CA Notify).

OBJECTIVES: Toward common methods for system monitoring and evaluation, we proposed a key performance indicator framework and discussed lessons learned while implementing a statewide exposure notification (EN) system in California during the COVID-19 epidemic. MATERIALS AND METHODS: California deployed the Google Apple Exposure Notification framework, branded CA Notify, on December 10, 2020, to supplement traditional COVID-19 contact tracing programs. For system evaluation, we defined 6 key performance indicators: adoption, retention, sharing of unique codes, identification of potential contacts, behavior change, and impact. We aggregated and analyzed data from December 10, 2020, to July 1, 2021, in compliance with the CA Notify privacy policy. RESULTS: We estimated CA Notify adoption at nearly 11 million smartphone activations during the study period. Among 1 654 201 CA Notify users who received a positive test result for SARS-CoV-2, 446 634 (27%) shared their unique code, leading to ENs for other CA Notify users who were in close proximity to the SARS-CoV-2-positive individual. We identified at least 122 970 CA Notify users as contacts through this process. Contact identification occurred a median of 4 days after symptom onset or specimen collection date of the user who received a positive test result for SARS-CoV-2. PRACTICE IMPLICATIONS: Smartphone-based EN systems are promising new tools to supplement traditional contact tracing and public health interventions, particularly when efficient scaling is not feasible for other approaches. Methods to collect and interpret appropriate measures of system performance must be refined while maintaining trust and privacy.

Understanding the needs of people aging with type 1 diabetes: a user-focused research study with prototype diabetes advance directive and setting the stage for future research.

Purpose: People living with Type 1 diabetes (T1D) are living longer than ever and facing the new "luxury" of the challenges of aging. While research is slowly expanding and addressing T1D physiology with regards to aging, there is little research addressing specific challenges and barriers to optimal care by those aging with T1D. To address this gap, this study employed human-centered design research to explore the gaps and barriers to care faced by people aging with T1D. Methods: Researchers employed human-centered design methods of needfinding and user interviews and facilitated participatory workshops. In total, 27 people with T1D (PWT1D), 5 loved ones (partners of PWT1D), and 7 healthcare providers (HCPs) were engaged. Results: Design artifacts were developed, including user personas that help visually articulate the different experiences of PWT1D and their unique needs as they age, as well as a prototype diabetes-specific advance directive that could be further refined to specifically aid those with Type 1 diabetes who are aging and requiring more interactions with the healthcare system. Initial user testing with people with T1D as well as healthcare providers demonstrated the need for such a diabetes advance directive tool or document. Conclusion: This work supports the conclusion that additional focus and scientific enquiry should be given to the needs of people aging with Type 1 diabetes, with a goal of improving the experience of all people with T1D when interacting with their care providers or with the healthcare system as a whole.

Designing a Framework for Remote Cancer Care Through Community Co-design: Participatory Development Study.

BACKGROUND: Recent shifts to telemedicine and remote patient monitoring demonstrate the potential for new technology to transform health systems; yet, methods to design for inclusion and resilience are lacking. OBJECTIVE: The aim of this study is to design and implement a participatory framework to produce effective health care solutions through co-design with diverse stakeholders. METHODS: We developed a design framework to cocreate solutions to locally prioritized health and communication problems focused on cancer care. The framework is premised on the framing and discovery of problems through community engagement and lead-user innovation with the hypothesis that diversity and inclusion in the co-design process generate more innovative and resilient solutions. Discovery, design, and development were implemented through structured phases with design studios at various locations in urban and rural Kentucky, including Appalachia, each building from prior work. In the final design studio, working prototypes were developed and tested. Outputs were assessed using the System Usability Scale as well as semistructured user feedback. RESULTS: We co-designed, developed, and tested a mobile app (myPath) and service model for distress surveillance and cancer care coordination following the LAUNCH (Linking and Amplifying User-Centered Networks through Connected Health) framework. The problem of awareness, navigation, and communication through cancer care was selected by the community after framing areas for opportunity based on significant geographic disparities in cancer and health burden resource and broadband access. The codeveloped digital myPath app showed the highest perceived combined usability (mean 81.9, SD 15.2) compared with the current gold standard of distress management for patients with cancer, the paper-based National Comprehensive Cancer Network Distress Thermometer (mean 74.2, SD 15.8). Testing of the System Usability Scale subscales showed that the myPath app had significantly better usability than the paper Distress Thermometer (t63=2.611; P=.01), whereas learnability did not differ between the instruments (t63=-0.311; P=.76). Notable differences by patient and provider scoring and feedback were found. CONCLUSIONS: Participatory problem definition and community-based co-design, design-with methods, may produce more acceptable and effective solutions than traditional design-for approaches.

The neurophysiology of intraoperative error: An EEG study of trainee surgeons during robotic-assisted surgery simulations.

Surgeons operate in mentally and physically demanding workspaces where the impact of error is highly consequential. Accurately characterizing the neurophysiology of surgeons during intraoperative error will help guide more accurate performance assessment and precision training for surgeons and other teleoperators. To better understand the neurophysiology of intraoperative error, we build and deploy a system for intraoperative error detection and electroencephalography (EEG) signal synchronization during robot-assisted surgery (RAS). We then examine the association between EEG data and detected errors. Our results suggest that there are significant EEG changes during intraoperative error that are detectable irrespective of surgical experience level.

A Risk-Based Clinical Decision Support System for Patient-Specific Antimicrobial Therapy (iBiogram): Design and Retrospective Analysis.

BACKGROUND: There is a pressing need for digital tools that can leverage big data to help clinicians select effective antibiotic treatments in the absence of timely susceptibility data. Clinical presentation and local epidemiology can inform therapy selection to balance the risk of antimicrobial resistance and patient risk. However, data and clinical expertise must be appropriately integrated into clinical workflows. OBJECTIVE: The aim of this study is to leverage available data in electronic health records, to develop a data-driven, user-centered, clinical decision support system to navigate patient safety and population health. METHODS: We analyzed 5 years of susceptibility testing (1,078,510 isolates) and patient data (30,761 patients) across a large academic medical center. After curating the data according to the Clinical and Laboratory Standards Institute guidelines, we analyzed and visualized the impact of risk factors on clinical outcomes. On the basis of this data-driven understanding, we developed a probabilistic algorithm that maps these data to individual cases and implemented iBiogram, a prototype digital empiric antimicrobial clinical decision support system, which we evaluated against actual prescribing outcomes. RESULTS: We determined patient-specific factors across syndromes and contexts and identified relevant local patterns of antimicrobial resistance by clinical syndrome. Mortality and length of stay differed significantly depending on these factors and could be used to generate heuristic targets for an acceptable risk of underprescription. Combined with the developed remaining risk algorithm, these factors can be used to inform clinicians' reasoning. A retrospective comparison of the iBiogram-suggested therapies versus the actual prescription by physicians showed similar performance for low-risk diseases such as urinary tract infections, whereas iBiogram recognized risk and recommended more appropriate coverage in high mortality conditions such as sepsis. CONCLUSIONS: The application of such data-driven, patient-centered tools may guide empirical prescription for clinicians to balance morbidity and mortality with antimicrobial stewardship.

Dataset on optimization and development of a point-of-care glucometer-based SARS-CoV-2 detection assay using aptamers.

We present supplementary data for the published article, "Hitting the diagnostic sweet spot: Point-of-care SARS-CoV-2 salivary antigen testing with an off-the-shelf glucometer" [1]. The assay described is designed to be performed at home or in a clinic without expensive instrumentation or professional training. SARS-CoV-2 is detected by an aptamer-based assay that targets the Nucleocapsid (N) or Spike (S) antigens. Binding of the N or S protein to their respective aptamer results in the competitive release of a complementary antisense-invertase enzyme complex. The released enzyme then catalyzes the conversion of sucrose to glucose that is measured by an off-the-shelf glucometer. The data presented here describe the optimization of the assay parameters and their contribution to developing this aptamer-based assay to detect SARS-CoV-2. The assay performance was checked in a standard buffer, contrived samples, and patient samples validated with well-established scientific methods. The resulting dataset can be used to further develop glucometer-based assays for diagnosing other communicable and non-communicable diseases.

Hitting the diagnostic sweet spot: Point-of-care SARS-CoV-2 salivary antigen testing with an off-the-shelf glucometer.

Significant barriers to the diagnosis of latent and acute SARS-CoV-2 infection continue to hamper population-based screening efforts required to contain the COVID-19 pandemic in the absence of widely available antiviral therapeutics or vaccines. We report an aptamer-based SARS-CoV-2 salivary antigen assay employing only low-cost reagents ($3.20/test) and an off-the-shelf glucometer. The test was engineered around a glucometer as it is quantitative, easy to use, and the most prevalent piece of diagnostic equipment globally, making the test highly scalable with an infrastructure that is already in place. Furthermore, many glucometers connect to smartphones, providing an opportunity to integrate with contact tracing apps, medical providers, and electronic health records. In clinical testing, the developed assay detected SARS-CoV-2 infection in patient saliva across a range of viral loads - as benchmarked by RT-qPCR - within 1 h, with 100% sensitivity (positive percent agreement) and distinguished infected specimens from off-target antigens in uninfected controls with 100% specificity (negative percent agreement). We propose that this approach provides an inexpensive, rapid, and accurate diagnostic for distributed screening of SARS-CoV-2 infection at scale.

Adaptation and performance of a mobile application for early detection of cutaneous leishmaniasis.

BACKGROUND: Detection and management of neglected tropical diseases such as cutaneous leishmaniasis present unmet challenges stemming from their prevalence in remote, rural, resource constrained areas having limited access to health services. These challenges are frequently compounded by armed conflict or illicit extractive industries. The use of mobile health technologies has shown promise in such settings, yet data on outcomes in the field remain scarce. METHODS: We adapted a validated prediction rule for the presumptive diagnosis of CL to create a mobile application for use by community health volunteers. We used human-centered design practices and agile development for app iteration. We tested the application in three rural areas where cutaneous leishmaniasis is endemic and an urban setting where patients seek medical attention in the municipality of Tumaco, Colombia. The application was assessed for usability, sensitivity and inter-rater reliability (kappa) when used by community health volunteers (CHV), health workers and a general practitioner, study physician. RESULTS: The application was readily used and understood. Among 122 screened cases with cutaneous ulcers, sensitivity to detect parasitologically proven CL was >95%. The proportion of participants with parasitologically confirmed CL was high (88%), precluding evaluation of specificity, and driving a high level of crude agreement between the app and parasitological diagnosis. The chance-adjusted agreement (kappa) varied across the components of the risk score. Time to diagnosis was reduced significantly, from 8 to 4 weeks on average when CHV conducted active case detection using the application, compared to passive case detection by health facility-based personnel. CONCLUSIONS: Translating a validated prediction rule to a mHealth technology has shown the potential to improve the capacity of community health workers and healthcare personnel to provide opportune care, and access to health services for underserved populations. These findings support the use of mHealth tools for NTD research and healthcare.

Recognition and management of community-acquired acute kidney injury in low-resource settings in the ISN 0by25 trial: A multi-country feasibility study.

BACKGROUND: Acute kidney injury (AKI) is increasingly encountered in community settings and contributes to morbidity, mortality, and increased resource utilization worldwide. In low-resource settings, lack of awareness of and limited access to diagnostic and therapeutic interventions likely influence patient management. We evaluated the feasibility of the use of point-of-care (POC) serum creatinine and urine dipstick testing with an education and training program to optimize the identification and management of AKI in the community in 3 low-resource countries. METHODS AND FINDINGS: Patients presenting to healthcare centers (HCCs) from 1 October 2016 to 29 September 2017 in the cities Cochabamba, Bolivia; Dharan, Nepal; and Blantyre, Malawi, were assessed utilizing a symptom-based risk score to identify patients at moderate to high AKI risk. POC testing for serum creatinine and urine dipstick at enrollment were utilized to classify these patients as having chronic kidney disease (CKD), acute kidney disease (AKD), or no kidney disease (NKD). Patients were followed for a maximum of 6 months with repeat POC testing. AKI development was assessed at 7 days, kidney recovery at 1 month, and progression to CKD and mortality at 3 and 6 months. Following an observation phase to establish baseline data, care providers and physicians in the HCCs were trained with a standardized protocol utilizing POC tests to evaluate and manage patients, guided by physicians in referral hospitals connected via mobile digital technology. We evaluated 3,577 patients, and 2,101 were enrolled: 978 in the observation phase and 1,123 in the intervention phase. Due to the high number of patients attending the centers daily, it was not feasible to screen all patients to assess the actual incidence of AKI. Of enrolled patients, 1,825/2,101 (87%) were adults, 1,117/2,101 (53%) were females, 399/2,101 (19%) were from Bolivia, 813/2,101 (39%) were from Malawi, and 889/2,101 (42%) were from Nepal. The age of enrolled patients ranged from 1 month to 96 years, with a mean of 43 years (SD 21) and a median of 43 years (IQR 27-62). Hypertension was the most common comorbidity (418/2,101; 20%). At enrollment, 197/2,101 (9.4%) had CKD, and 1,199/2,101 (57%) had AKD. AKI developed in 30% within 7 days. By 1 month, 268/978 (27%) patients in the observation phase and 203/1,123 (18%) in the intervention phase were lost to follow-up. In the intervention phase, more patients received fluids (observation 714/978 [73%] versus intervention 874/1,123 [78%]; 95% CI 0.63, 0.94; p = 0.012), hospitalization was reduced (observation 578/978 [59%] versus intervention 548/1,123 [49%]; 95% CI 0.55, 0.79; p < 0.001), and admitted patients with severe AKI did not show a significantly lower mortality during follow-up (observation 27/135 [20%] versus intervention 21/178 [11.8%]; 95% CI 0.98, 3.52; p = 0.057). Of 504 patients with kidney function assessed during the 6-month follow-up, de novo CKD arose in 79/484 (16.3%), with no difference between the observation and intervention phase (95% CI 0.91, 2.47; p = 0.101). Overall mortality was 273/2,101 (13%) and was highest in those who had CKD (24/106; 23%), followed by those with AKD (128/760; 17%), AKI (85/628; 14%), and NKD (36/607; 6%). The main limitation of our study was the inability to determine the actual incidence of kidney dysfunction in the health centers as it was not feasible to screen all the patients due to the high numbers seen daily. CONCLUSIONS: This multicenter, non-randomized feasibility study in low-resource settings demonstrates that it is feasible to implement a comprehensive program utilizing POC testing and protocol-based management to improve the recognition and management of AKI and AKD in high-risk patients in primary care.

Computing SARS-CoV-2 Infection Risk From Symptoms, Imaging, and Test Data: Diagnostic Model Development.

BACKGROUND: Assigning meaningful probabilities of SARS-CoV-2 infection risk presents a diagnostic challenge across the continuum of care. OBJECTIVE: The aim of this study was to develop and clinically validate an adaptable, personalized diagnostic model to assist clinicians in ruling in and ruling out COVID-19 in potential patients. We compared the diagnostic performance of probabilistic, graphical, and machine learning models against a previously published benchmark model. METHODS: We integrated patient symptoms and test data using machine learning and Bayesian inference to quantify individual patient risk of SARS-CoV-2 infection. We trained models with 100,000 simulated patient profiles based on 13 symptoms and estimated local prevalence, imaging, and molecular diagnostic performance from published reports. We tested these models with consecutive patients who presented with a COVID-19-compatible illness at the University of California San Diego Medical Center over the course of 14 days starting in March 2020. RESULTS: We included 55 consecutive patients with fever (n=43, 78%) or cough (n=42, 77%) presenting for ambulatory (n=11, 20%) or hospital care (n=44, 80%). In total, 51% (n=28) were female and 49% (n=27) were aged <60 years. Common comorbidities included diabetes (n=12, 22%), hypertension (n=15, 27%), cancer (n=9, 16%), and cardiovascular disease (n=7, 13%). Of these, 69% (n=38) were confirmed via reverse transcription-polymerase chain reaction (RT-PCR) to be positive for SARS-CoV-2 infection, and 20% (n=11) had repeated negative nucleic acid testing and an alternate diagnosis. Bayesian inference network, distance metric learning, and ensemble models discriminated between patients with SARS-CoV-2 infection and alternate diagnoses with sensitivities of 81.6%-84.2%, specificities of 58.8%-70.6%, and accuracies of 61.4%-71.8%. After integrating imaging and laboratory test statistics with the predictions of the Bayesian inference network, changes in diagnostic uncertainty at each step in the simulated clinical evaluation process were highly sensitive to location, symptom, and diagnostic test choices. CONCLUSIONS: Decision support models that incorporate symptoms and available test results can help providers diagnose SARS-CoV-2 infection in real-world settings.

A comprehensive assessment for community-based, person-centered care for older adults.

BACKGROUND: Many health and social needs can be assessed and met in community settings, where lower-cost, person-centered, preventative and proactive services predominate. This study reports on the development and implementation of a person-centered care model integrating dental, social, and health services for low-income older adults at a community dental clinic co-located within a senior wellness center. METHODS: A digital comprehensive geriatric assessment (CGA) and referral system linking medical, dental, and psychosocial needs by real-time CGA-derived metrics for 996 older adults (age ≥ 60) was implemented in 2016-2018 as part of a continuous quality improvement project. This study aims to describe: 1) the development and content of a new CGA; 2) CGA implementation, workflows, triage, referrals; 3) correlations between CGA domains, and adjusted regression models, assessing associations with self-reported recent hospitalizations, emergency department (ED) visits, and clinically-assessed dental urgency. RESULTS: The multidisciplinary team from the senior wellness and dental centers planned and implemented a CGA that included standard medical history along with validated instruments for functional status, mental health and social determinants, and added oral health. Care navigators employed the CGA with 996 older adults, and made 1139 referrals (dental = 797, care coordination = 163, social work = 90, mental health = 32). CGA dimensions correlated between oral health, medical status, depressive symptoms, isolation, and reduced quality of life (QoL). Pain, medical symptoms, isolation and depressive symptoms were associated with poorer self-reported health, while general health was most strongly correlated with lower depressive symptoms, and higher functional status and QoL. Isolation was the strongest correlate of lower QoL. Adjusted odds ratios identified social and medical factors associated with recent hospitalization and ED visits. General and oral health were associated with dental urgency. Dental urgency was most strongly associated with general health (AOR = 1.78,95%CI [1.31, 2.43]), dental symptoms (AOR = 2.39,95%CI [1.78, 3.20]), dental pain (AOR = 2.06,95%CI [1.55-2.74]), and difficulty chewing (AOR = 2.80, 95%CI [2.09-3.76]). Dental symptoms were associated with recent ED visits (AOR = 1.61, 95%CI [1.12-2.30]) or hospitalizations (AOR = 1.47, 95%CI [1.04-2.10]). CONCLUSION: Community-based inter-professional care is feasible with CGAs that include medical, dental, and social factors. A person-centered care model requires coordination supported by new workflows. Real-time metrics-based triage process provided efficient means for client review and a robust process to surface needs in complex cases.

Mending Disconnects in Cancer Care: Setting an Agenda for Research, Practice, and Policy.

Cancer in the United States accounts for $600 billion in health care costs, lost work time and productivity, reduced quality of life, and premature mortality. The future of oncology delivery must mend disconnects to equitably improve patient outcomes while constraining costs and burden on patients, caregivers, and care teams. Embedding learning health systems into oncology can connect care, engaging patients and providers in fully interoperable data systems that remotely monitor patients; generate predictive and prescriptive analytics to facilitate appropriate, timely referrals; and extend the reach of clinicians beyond clinic walls. Incorporating functional learning systems into the future of oncology and follow-up care requires coordinated national attention to 4 synergistic strategies: (1) galvanize and shape public discourse to develop and adopt these systems, (2) demonstrate their value, (3) test and evaluate their use, and (4) reform policy to incentivize and regulate their use.