Hepatic improvement within 27 days of avenciguat treatment in Child-Pugh A cirrhosis detected by an oral cholate challenge test.

New methods for measuring hepatic improvement in clinical trials and the clinic are needed. One new method, HepQuant SHUNT, detected dose-dependent improvements in hepatic function and portal physiology in the phase 1b study (NCT03842761) of avenciguat, an activator of soluble guanylyl cyclase that is being developed for the treatment of portal hypertension. Herein, we examined whether HepQuant Duo, an easy-to-administer test version, could similarly detect the effects of avenciguat. Twenty-three patients with Child-Pugh A cirrhosis and liver stiffness >15 kPa received either a placebo (n = 5) or a maximum twice-daily avenciguat dose of 1, 2, or 3 mg (n = 6 per group) for 28 days. The DuO test was performed at baseline and on days 11 and 27 in each subject. The test involved administering 40 mg of d4-cholate orally, measuring d4-cholate concentrations in serum at 20 and 60 minutes, and calculating portal hepatic filtration rate, disease severity index, portal-systemic shunting (SHUNT%), and hepatic reserve (HR%). Avenciguat demonstrated dose-dependent improvement in all test parameters. Changes from baseline in SHUNT% after 27 days' treatment were 0.1 ± 9.0% for placebo, 1.7 ± 5.5% for 1 mg twice-daily, -3.2 ± 2.7% for 2 mg twice-daily, and -6.1 ± 5.0% for 3 mg twice-daily (paired t test for change from baseline p = 0.98, 0.48, 0.04, and 0.03, respectively). The changes detected by HepQuant DuO were similar to those previously observed and reported for HepQuant SHUNT. The results support further study of avenciguat in treating portal hypertension and spotlight the utility of HepQuant DuO in the development of drug therapy for liver disease. HepQuant DuO facilitates the use of function testing to measure hepatic improvement in clinical trials and the clinic.

Smart Diagnostics: Combining Artificial Intelligence and In Vitro Diagnostics.

We are beginning a new era of Smart Diagnostics-integrated biosensors powered by recent innovations in embedded electronics, cloud computing, and artificial intelligence (AI). Universal and AI-based in vitro diagnostics (IVDs) have the potential to exponentially improve healthcare decision making in the coming years. This perspective covers current trends and challenges in translating Smart Diagnostics. We identify essential elements of Smart Diagnostics platforms through the lens of a clinically validated platform for digitizing biology and its ability to learn disease signatures. This platform for biochemical analyses uses a compact instrument to perform multiclass and multiplex measurements using fully integrated microfluidic cartridges compatible with the point of care. Image analysis digitizes biology by transforming fluorescence signals into inputs for learning disease/health signatures. The result is an intuitive Score reported to the patients and/or providers. This AI-linked universal diagnostic system has been validated through a series of large clinical studies and used to identify signatures for early disease detection and disease severity in several applications, including cardiovascular diseases, COVID-19, and oral cancer. The utility of this Smart Diagnostics platform may extend to multiple cell-based oncology tests via cross-reactive biomarkers spanning oral, colorectal, lung, bladder, esophageal, and cervical cancers, and is well-positioned to improve patient care, management, and outcomes through deployment of this resilient and scalable technology. Lastly, we provide a future perspective on the direction and trajectory of Smart Diagnostics and the transformative effects they will have on health care.

Predictive Modeling of Morbidity and Mortality in Patients Hospitalized With COVID-19 and its Clinical Implications: Algorithm Development and Interpretation.

BACKGROUND: The COVID-19 pandemic began in early 2021 and placed significant strains on health care systems worldwide. There remains a compelling need to analyze factors that are predictive for patients at elevated risk of morbidity and mortality. OBJECTIVE: The goal of this retrospective study of patients who tested positive with COVID-19 and were treated at NYU (New York University) Langone Health was to identify clinical markers predictive of disease severity in order to assist in clinical decision triage and to provide additional biological insights into disease progression. METHODS: The clinical activity of 3740 patients at NYU Langone Hospital was obtained between January and August 2020; patient data were deidentified. Models were trained on clinical data during different parts of their hospital stay to predict three clinical outcomes: deceased, ventilated, or admitted to the intensive care unit (ICU). RESULTS: The XGBoost (eXtreme Gradient Boosting) model that was trained on clinical data from the final 24 hours excelled at predicting mortality (area under the curve [AUC]=0.92; specificity=86%; and sensitivity=85%). Respiration rate was the most important feature, followed by SpO2 (peripheral oxygen saturation) and being aged 75 years and over. Performance of this model to predict the deceased outcome extended 5 days prior, with AUC=0.81, specificity=70%, and sensitivity=75%. When only using clinical data from the first 24 hours, AUCs of 0.79, 0.80, and 0.77 were obtained for deceased, ventilated, or ICU-admitted outcomes, respectively. Although respiration rate and SpO2 levels offered the highest feature importance, other canonical markers, including diabetic history, age, and temperature, offered minimal gain. When lab values were incorporated, prediction of mortality benefited the most from blood urea nitrogen and lactate dehydrogenase (LDH). Features that were predictive of morbidity included LDH, calcium, glucose, and C-reactive protein. CONCLUSIONS: Together, this work summarizes efforts to systematically examine the importance of a wide range of features across different endpoint outcomes and at different hospitalization time points.

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.

Programmable Bio-nanochip Platform: A Point-of-Care Biosensor System with the Capacity To Learn.

The combination of point-of-care (POC) medical microdevices and machine learning has the potential transform the practice of medicine. In this area, scalable lab-on-a-chip (LOC) devices have many advantages over standard laboratory methods, including faster analysis, reduced cost, lower power consumption, and higher levels of integration and automation. Despite significant advances in LOC technologies over the years, several remaining obstacles are preventing clinical implementation and market penetration of these novel medical microdevices. Similarly, while machine learning has seen explosive growth in recent years and promises to shift the practice of medicine toward data-intensive and evidence-based decision making, its uptake has been hindered due to the lack of integration between clinical measurements and disease determinations. In this Account, we describe recent developments in the programmable bio-nanochip (p-BNC) system, a biosensor platform with the capacity for learning. The p-BNC is a "platform to digitize biology" in which small quantities of patient sample generate immunofluorescent signal on agarose bead sensors that is optically extracted and converted to antigen concentrations. The platform comprises disposable microfluidic cartridges, a portable analyzer, automated data analysis software, and intuitive mobile health interfaces. The single-use cartridges are fully integrated, self-contained microfluidic devices containing aqueous buffers conveniently embedded for POC use. A novel fluid delivery method was developed to provide accurate and repeatable flow rates via actuation of the cartridge's blister packs. A portable analyzer instrument was designed to integrate fluid delivery, optical detection, image analysis, and user interface, representing a universal system for acquiring, processing, and managing clinical data while overcoming many of the challenges facing the widespread clinical adoption of LOC technologies. We demonstrate the p-BNC's flexibility through the completion of multiplex assays within the single-use disposable cartridges for three clinical applications: prostate cancer, ovarian cancer, and acute myocardial infarction. Toward the goal of creating "sensors that learn", we have developed and describe here the Cardiac ScoreCard, a clinical decision support system for a spectrum of cardiovascular disease. The Cardiac ScoreCard approach comprises a comprehensive biomarker panel and risk factor information in a predictive model capable of assessing early risk and late-stage disease progression for heart attack and heart failure patients. These marker-driven tests have the potential to radically reduce costs, decrease wait times, and introduce new options for patients needing regular health monitoring. Further, these efforts demonstrate the clinical utility of fusing data from information-rich biomarkers and the Internet of Things (IoT) using predictive analytics to generate single-index assessments for wellness/illness status. By promoting disease prevention and personalized wellness management, tools of this nature have the potential to improve health care exponentially.

Cardiac ScoreCard: A Diagnostic Multivariate Index Assay System for Predicting a Spectrum of Cardiovascular Disease.

Clinical decision support systems (CDSSs) have the potential to save lives and reduce unnecessary costs through early detection and frequent monitoring of both traditional risk factors and novel biomarkers for cardiovascular disease (CVD). However, the widespread adoption of CDSSs for the identification of heart diseases has been limited, likely due to the poor interpretability of clinically relevant results and the lack of seamless integration between measurements and disease predictions. In this paper we present the Cardiac ScoreCard-a multivariate index assay system with the potential to assist in the diagnosis and prognosis of a spectrum of CVD. The Cardiac ScoreCard system is based on lasso logistic regression techniques which utilize both patient demographics and novel biomarker data for the prediction of heart failure (HF) and cardiac wellness. Lasso logistic regression models were trained on a merged clinical dataset comprising 579 patients with 6 traditional risk factors and 14 biomarker measurements. The prediction performance of the Cardiac ScoreCard was assessed with 5-fold cross-validation and compared with reference methods. The experimental results reveal that the ScoreCard models improved performance in discriminating disease versus non-case (AUC = 0.8403 and 0.9412 for cardiac wellness and HF, respectively), and the models exhibit good calibration. Clinical insights to the prediction of HF and cardiac wellness are provided in the form of logistic regression coefficients which suggest that augmenting the traditional risk factors with a multimarker panel spanning a diverse cardiovascular pathophysiology provides improved performance over reference methods. Additionally, a framework is provided for seamless integration with biomarker measurements from point-of-care medical microdevices, and a lasso-based feature selection process is described for the down-selection of biomarkers in multimarker panels.

Advances in noninvasive measurement of liver function and physiology: The HepQuant DuO test.

Current noninvasive liver tests are surrogates for fibrosis and lack ability to directly measure liver function. HepQuant tests measure liver function and physiology through hepatic uptake of stable cholate isotopes. HepQuant SHUNT (V1.0) involves oral and intravenous dosing and six blood samples over 90 min. We developed simplified test versions: SHUNT V2.0 (oral and intravenous dosing, two blood samples over 60 min) and DuO (oral dosing only, two blood samples over 60 min). The aim of this study was to evaluate equivalency of the simplified tests to the original SHUNT test. Data from three studies comprising 930 SHUNT tests were retrospectively analysed by each method. Equivalence was evaluated in terms of proportion of tests in which the difference between methods was less than any clinically meaningful difference and additionally by two one-sided t-test and bioequivalence methods. DuO and SHUNT V2.0 were equivalent to the original SHUNT test for Disease Severity Index, with >99% and >96% of tests falling within equivalence bounds. DuO and SHUNT V2.0 met equivalency criteria by two one-sided t-tests and bioequivalence. DuO and SHUNT V2.0 are easier to administer, are less invasive than the original SHUNT test and have potential to be more accepted by patients and providers.

Within-individual reproducibility of a dual sample oral cholate challenge test (DuO) and simplified versions of the HepQuant SHUNT test.

Current noninvasive liver tests measure fibrosis, inflammation, or steatosis and do not measure function. The HepQuant platform of noninvasive tests uniquely assesses both liver function and physiology through the hepatic uptake of stable isotopes of cholate. However, the prototypical HepQuant SHUNT test (SHUNT V1.0) is cumbersome to administer, requiring intravenous and oral administration of cholate and six peripheral venous blood samples over 90 min. To alleviate the burden of test administration, we explored whether an oral only (DuO) version, and other simplified versions, of the test could provide reproducible measurements of liver function. DuO requires only oral dosing and two blood samples over 60 min. The simplified SHUNT test versions were SHUNT V1.1 (oral and IV dosing but four blood samples) and SHUNT V2.0 (oral and IV dosing but only two blood samples over 60 min). In this paper, we describe the reproducibility of DuO and the simplified SHUNT tests relative to that of SHUNT V1.0; equivalency is described in a separate paper. Data from two studies comprising 236 SHUNT tests in 94 subjects were analyzed retrospectively by each method. All simplified methods were highly reproducible across test parameters with intraclass correlation coefficients >0.93 for test parameters Disease Severity Index (DSI) and Hepatic Reserve. DuO and SHUNT V2.0 improved reproducibility in measuring portal-systemic shunting (SHUNT%). These simplified tests, particularly DuO and SHUNT V2.0, are easier to administer and less invasive, thus, having the potential to be more widely accepted by care providers administering the test and by patients receiving the test.

Liver function and portal-systemic shunting quantified by the oral cholate challenge test and risk for large oesophageal varices.

BACKGROUND: The quantitative HepQuant SHUNT test of liver function and physiology generates a disease severity index (DSI) that correlates with risk for clinical complications, such as large oesophageal varices (LEVs). A derivative test, HepQuant DuO, generates an equivalent DSI and simplifies testing by requiring only oral administration of the test solution and two blood samples at 20 and 60 min. AIMS: Since the DSIs measured from DuO and SHUNT are equivalent, we compared the diagnostic performance for large oesophageal varices (LEVs) between the DSIs measured from DuO and SHUNT tests. METHODS: This study combined the data from two prospectively conducted US studies: HALT-C and SHUNT-V. A total of 455 subjects underwent both the SHUNT test and esophagogastroduodenoscopy (EGD). RESULTS: DSI scores correlated with the probability of LEVs (p < 0.001) and demonstrated a stepwise increase from healthy lean controls without liver disease to subjects with chronic liver disease and no, small or large varices. Furthermore, a cutoff of DSI ≤ 18.3 from DuO had a sensitivity of 0.98 (missing only one case) and, if applied to the endoscopy (EGD) decision, would have prevented 188 EGDs (41.3%). The AUROC for DSI from DuO did not differ from that of the reference SHUNT test method (0.82 versus 0.81, p = 0.3500). CONCLUSIONS: DSI from HepQuant DuO links liver function and physiology to the risk of LEVs across a wide spectrum of patient characteristics, disease aetiologies and liver disease severity. DuO is minimally invasive, easy to administer, quantitative and may aid the decision to avoid or perform EGD for LEVs.

Hepatic Dysfunction Quantified by HepQuant DuO Outperforms Child-Pugh Classification in Predicting the Pharmacokinetics of Ampreloxetine.

HepQuant tests quantify liver function from clearance of deuterium- and 13C-labeled cholates administered either intravenously and orally (SHUNT) or orally (DuO). Hepatic impairment studies have relied on clinical or laboratory criteria like Child-Pugh classification to categorize the degree of hepatic dysfunction. We compared HepQuant tests with Child-Pugh classification in predicting the pharmacokinetics of ampreloxetine. Twenty-one subjects with hepatic impairment (8 Child-Pugh A, 7 Child-Pugh B, and 6 Child-Pugh C), and 10 age- and sex-matched controls were studied. The pharmacokinetics of ampreloxetine were measured after oral administration of a single dose of 10 mg. Disease severity index (DSI), portal-systemic shunting (SHUNT%), hepatic reserve, and hepatic filtration rates (HFRs) were measured from serum samples obtained after intravenous administration of [24-13C]-cholate and oral administration of [2,2,4,4-2H]cholate. Ampreloxetine plasma exposure (AUC0-inf) was similar to controls in Child-Pugh A, increased 1.7-fold in subjects with Child-Pugh B, and 2.5-fold in subjects with Child-Pugh C and correlated with both Child-Pugh score and HepQuant parameters. The variability observed in ampreloxetine exposure (AUC0-inf) in subjects with moderate (Child-Pugh B) and severe hepatic impairment (Child-Pugh C) was explained by HepQuant parameters. Multivariable regression models demonstrated that DSI, SHUNT%, and Hepatic Reserve from SHUNT and DuO were superior predictors of ampreloxetine exposure (AUC0-inf) compared to Child-Pugh score. HepQuant DSI, SHUNT%, and hepatic reserve were more useful predictors of drug exposure than Child-Pugh class for ampreloxetine and thus may better optimize dose recommendations in patients with liver disease. The simple-to-administer, oral-only DuO version of the HepQuant test could enhance clinical utility.

A cytomics-on-a-chip platform and diagnostic model stratifies risk for oral lichenoid conditions.

OBJECTIVE: A small fraction of oral lichenoid conditions (OLC) have potential for malignant transformation. Distinguishing OLCs from other oral potentially malignant disorders (OPMDs) can help prevent unnecessary concern or testing, but accurate identification by nonexpert clinicians is challenging due to overlapping clinical features. In this study, the authors developed a 'cytomics-on-a-chip' tool and integrated predictive model for aiding the identification of OLCs. STUDY DESIGN: All study subjects underwent both scalpel biopsy for histopathology and brush cytology. A predictive model and OLC Index comprising clinical, demographic, and cytologic features was generated to discriminate between subjects with lichenoid (OLC+) (N = 94) and nonlichenoid (OLC-) (N = 237) histologic features in a population with OPMDs. RESULTS: The OLC Index discriminated OLC+ and OLC- subjects with area under the curve (AUC) of 0.76. Diagnostic accuracy of the OLC Index was not significantly different from expert clinician impressions, with AUC of 0.81 (P = .0704). Percent agreement was comparable across all raters, with 83.4% between expert clinicians and histopathology, 78.3% between OLC Index and expert clinician, and 77.3% between OLC Index and histopathology. CONCLUSIONS: The cytomics-on-a-chip tool and integrated diagnostic model have the potential to facilitate both the triage and diagnosis of patients presenting with OPMDs and OLCs.

Compartmental model describing the physiological basis for the HepQuant SHUNT test.

The HepQuant SHUNT test quantifies hepatic functional impairment from the simultaneous clearance of cholate from the systemic and portal circulations for the purpose of monitoring treatment effects or for predicting risk for clinical outcome. Compartmental models are defined by distribution volumes and transfer rates between volumes to estimate parameters not defined by noncompartmental analyses. Previously, a noncompartmental analysis method, called the minimal model (MM), demonstrated reproducible and reliable measures of liver function (Translational Research 2021). The aim of this study was to compare the reproducibility and reliability of a new physiologically based compartmental model (CM) vs the MM. Data were analyzed from 16 control, 16 nonalcoholic steatohepatitis (NASH), and 16 hepatitis C virus (HCV) subjects, each with 3 replicate tests conducted on 3 separate days. The CM describes transfer of cholates between systemic, portal, and liver compartments with assumptions from measured or literature-derived values and unknown parameters estimated by nonlinear least-squares regression. The CM was compared to the MM for 6 key indices of hepatic disease in terms of intraclass correlation coefficient (ICC) with a lower acceptable limit of 0.7. The CM correlated well with the MM for disease severity index (DSI) with R2 (95% confidence interval) of 0.96 (0.94-0.98, P < 0.001). Acceptable reproducibility (ICC > 0.7) was observed for 6/6 and 5/6 hepatic disease indices for CM and MM, respectively. SHUNT, a measure of the absolute bioavailability, had ICC of 0.73 (0.60-0.83, P = 0.3095) for MM and 0.84 (0.76-0.90, P = 0.0012) for CM. The CM, but not the MM, allowed determination of anatomic shunt and hepatic extraction and improved the within individual reproducibility.

Simultaneous Quantitative SARS-CoV-2 Antigen and Host Antibody Detection and Pre-Screening Strategy at the Point of Care.

As COVID-19 pandemic public health measures are easing globally, the emergence of new SARS-CoV-2 strains continue to present high risk for vulnerable populations. The antibody-mediated protection acquired from vaccination and/or infection is seen to wane over time and the immunocompromised populations can no longer expect benefit from monoclonal antibody prophylaxis. Hence, there is a need to monitor new variants and its effect on vaccine performance. In this context, surveillance of new SARS-CoV-2 infections and serology testing are gaining consensus for use as screening methods, especially for at-risk groups. Here, we described an improved COVID-19 screening strategy, comprising predictive algorithms and concurrent, rapid, accurate, and quantitative SARS-CoV-2 antigen and host antibody testing strategy, at point of care (POC). We conducted a retrospective analysis of 2553 pre- and asymptomatic patients who were tested for SARS-CoV-2 by RT-PCR. The pre-screening model had an AUC (CI) of 0.76 (0.73-0.78). Despite being the default method for screening, body temperature had lower AUC (0.52 [0.49-0.55]) compared to case incidence rate (0.65 [0.62-0.68]). POC assays for SARS-CoV-2 nucleocapsid protein (NP) and spike (S) receptor binding domain (RBD) IgG antibody showed promising preliminary results, demonstrating a convenient, rapid (<20 min), quantitative, and sensitive (ng/mL) antigen/antibody assay. This integrated pre-screening model and simultaneous antigen/antibody approach may significantly improve accuracy of COVID-19 infection and host immunity screening, helping address unmet needs for monitoring vaccine effectiveness and severe disease surveillance.

A Rapid and Sensitive Microfluidics-Based Tool for Seroprevalence Immunity Assessment of COVID-19 and Vaccination-Induced Humoral Antibody Response at the Point of Care.

As of 8 August 2022, SARS-CoV-2, the causative agent of COVID-19, has infected over 585 million people and resulted in more than 6.42 million deaths worldwide. While approved SARS-CoV-2 spike (S) protein-based vaccines induce robust seroconversion in most individuals, dramatically reducing disease severity and the risk of hospitalization, poorer responses are observed in aged, immunocompromised individuals and patients with certain pre-existing health conditions. Further, it is difficult to predict the protection conferred through vaccination or previous infection against new viral variants of concern (VoC) as they emerge. In this context, a rapid quantitative point-of-care (POC) serological assay able to quantify circulating anti-SARS-CoV-2 antibodies would allow clinicians to make informed decisions on the timing of booster shots, permit researchers to measure the level of cross-reactive antibody against new VoC in a previously immunized and/or infected individual, and help assess appropriate convalescent plasma donors, among other applications. Utilizing a lab-on-a-chip ecosystem, we present proof of concept, optimization, and validation of a POC strategy to quantitate COVID-19 humoral protection. This platform covers the entire diagnostic timeline of the disease, seroconversion, and vaccination response spanning multiple doses of immunization in a single POC test. Our results demonstrate that this platform is rapid (~15 min) and quantitative for SARS-CoV-2-specific IgG detection.

Predictive modeling of morbidity and mortality in COVID-19 hospitalized patients and its clinical implications.

Clinical activity of 3740 de-identified COVID-19 positive patients treated at NYU Langone Health (NYULH) were collected between January and August 2020. XGBoost model trained on clinical data from the final 24 hours excelled at predicting mortality (AUC=0.92, specificity=86% and sensitivity=85%). Respiration rate was the most important feature, followed by SpO2 and age 75+. Performance of this model to predict the deceased outcome extended 5 days prior with AUC=0.81, specificity=70%, sensitivity=75%. When only using clinical data from the first 24 hours, AUCs of 0.79, 0.80, and 0.77 were obtained for deceased, ventilated, or ICU admitted, respectively. Although respiration rate and SpO2 levels offered the highest feature importance, other canonical markers including diabetic history, age and temperature offered minimal gain. When lab values were incorporated, prediction of mortality benefited the most from blood urea nitrogen (BUN) and lactate dehydrogenase (LDH). Features predictive of morbidity included LDH, calcium, glucose, and C-reactive protein (CRP). Together this work summarizes efforts to systematically examine the importance of a wide range of features across different endpoint outcomes and at different hospitalization time points.

Clinical decision support tool and rapid point-of-care platform for determining disease severity in patients with COVID-19.

SARS-CoV-2 is the virus that causes coronavirus disease (COVID-19) which has reached pandemic levels resulting in significant morbidity and mortality affecting every inhabited continent. The large number of patients requiring intensive care threatens to overwhelm healthcare systems globally. Likewise, there is a compelling need for a COVID-19 disease severity test to prioritize care and resources for patients at elevated risk of mortality. Here, an integrated point-of-care COVID-19 Severity Score and clinical decision support system is presented using biomarker measurements of C-reactive protein (CRP), N-terminus pro B type natriuretic peptide (NT-proBNP), myoglobin (MYO), D-dimer, procalcitonin (PCT), creatine kinase-myocardial band (CK-MB), and cardiac troponin I (cTnI). The COVID-19 Severity Score combines multiplex biomarker measurements and risk factors in a statistical learning algorithm to predict mortality. The COVID-19 Severity Score was trained and evaluated using data from 160 hospitalized COVID-19 patients from Wuhan, China. Our analysis finds that COVID-19 Severity Scores were significantly higher for the group that died versus the group that was discharged with median (interquartile range) scores of 59 (40-83) and 9 (6-17), respectively, and area under the curve of 0.94 (95% CI 0.89-0.99). Although this analysis represents patients with cardiac comorbidities (hypertension), the inclusion of biomarkers from other pathophysiologies implicated in COVID-19 (e.g., D-dimer for thrombotic events, CRP for infection or inflammation, and PCT for bacterial co-infection and sepsis) may improve future predictions for a more general population. These promising initial models pave the way for a point-of-care COVID-19 Severity Score system to impact patient care after further validation with externally collected clinical data. Clinical decision support tools for COVID-19 have strong potential to empower healthcare providers to save lives by prioritizing critical care in patients at high risk for adverse outcomes.

Clinical Decision Support Tool and Rapid Point-of-Care Platform for Determining Disease Severity in Patients with COVID-19.

SARS-CoV-2 is the virus that causes coronavirus disease (COVID-19) which has reached pandemic levels resulting in significant morbidity and mortality affecting every inhabited continent. The large number of patients requiring intensive care threatens to overwhelm healthcare systems globally. Likewise, there is a compelling need for a COVID-19 disease severity test to prioritize care and resources for patients at elevated risk of mortality. Here, an integrated point-of-care COVID-19 Severity Score and clinical decision support system is presented using biomarker measurements of C-reactive protein (CRP), N-terminus pro B type natriuretic peptide (NT-proBNP), myoglobin (MYO), D-dimer, procalcitonin (PCT), creatine kinase-myocardial band (CK-MB), and cardiac troponin I (cTnI). The COVID-19 Severity Score combines multiplex biomarker measurements and risk factors in a statistical learning algorithm to predict mortality. The COVID-19 Severity Score was trained and evaluated using data from 160 hospitalized COVID-19 patients from Wuhan, China. Our analysis finds that COVID-19 Severity Scores were significantly higher for the group that died versus the group that was discharged with median (interquartile range) scores of 59 (40-83) and 9 (6-17), respectively, and area under the curve of 0.94 (95% CI 0.89-0.99). These promising initial models pave the way for a point-of-care COVID-19 Severity Score system to impact patient care after further validation with externally collected clinical data. Clinical decision support tools for COVID-19 have strong potential to empower healthcare providers to save lives by prioritizing critical care in patients at high risk for adverse outcomes.

Point-of-care oral cytology tool for the screening and assessment of potentially malignant oral lesions.

BACKGROUND: The effective detection and monitoring of potentially malignant oral lesions (PMOL) are critical to identifying early-stage cancer and improving outcomes. In the current study, the authors described cytopathology tools, including machine learning algorithms, clinical algorithms, and test reports developed to assist pathologists and clinicians with PMOL evaluation. METHODS: Data were acquired from a multisite clinical validation study of 999 subjects with PMOLs and oral squamous cell carcinoma (OSCC) using a cytology-on-a-chip approach. A machine learning model was trained to recognize and quantify the distributions of 4 cell phenotypes. A least absolute shrinkage and selection operator (lasso) logistic regression model was trained to distinguish PMOLs and cancer across a spectrum of histopathologic diagnoses ranging from benign, to increasing grades of oral epithelial dysplasia (OED), to OSCC using demographics, lesion characteristics, and cell phenotypes. Cytopathology software was developed to assist pathologists in reviewing brush cytology test results, including high-content cell analyses, data visualization tools, and results reporting. RESULTS: Cell phenotypes were determined accurately through an automated cytological assay and machine learning approach (99.3% accuracy). Significant differences in cell phenotype distributions across diagnostic categories were found in 3 phenotypes (type 1 ["mature squamous"], type 2 ["small round"], and type 3 ["leukocytes"]). The clinical algorithms resulted in acceptable performance characteristics (area under the curve of 0.81 for benign vs mild dysplasia and 0.95 for benign vs malignancy). CONCLUSIONS: These new cytopathology tools represent a practical solution for rapid PMOL assessment, with the potential to facilitate screening and longitudinal monitoring in primary, secondary, and tertiary clinical care settings.

Sensors that Learn: The Evolution from Taste Fingerprints to Patterns of Early Disease Detection.

The McDevitt group has sustained efforts to develop a programmable sensing platform that offers advanced, multiplexed/multiclass chem-/bio-detection capabilities. This scalable chip-based platform has been optimized to service real-world biological specimens and validated for analytical performance. Fashioned as a sensor that learns, the platform can host new content for the application at hand. Identification of biomarker-based fingerprints from complex mixtures has a direct linkage to e-nose and e-tongue research. Recently, we have moved to the point of big data acquisition alongside the linkage to machine learning and artificial intelligence. Here, exciting opportunities are afforded by multiparameter sensing that mimics the sense of taste, overcoming the limitations of salty, sweet, sour, bitter, and glutamate sensing and moving into fingerprints of health and wellness. This article summarizes developments related to the electronic taste chip system evolving into a platform that digitizes biology and affords clinical decision support tools. A dynamic body of literature and key review articles that have contributed to the shaping of these activities are also highlighted. This fully integrated sensor promises more rapid transition of biomarker panels into wide-spread clinical practice yielding valuable new insights into health diagnostics, benefiting early disease detection.