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.
The COVID-19 Severity Score combines multiplex biomarker measurements and risk factors in a statistical learning algorithm to predict mortality.
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.
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.
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.
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.
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.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
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.
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.
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.
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.
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.
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.
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.
A point‐of‐care oral cytology tool has been developed for the noninvasive detection and monitoring of potentially malignant oral lesions. The distribution of cell phenotypes identified by machine learning and a cytology‐on‐a‐chip approach provides useful information as part of the assessment of oral lesions, with improved interpretability, calibration, and generalizability compared with conventional methods.
The lack of standard tools and methodologies and the absence of a streamlined multimarker approval process have hindered the translation rate of new biomarkers into clinical practice for a variety of ...diseases afflicting humankind. Advanced novel technologies with superior analytical performance and reduced reagent costs, like the programmable bio-nano-chip system featured in this article, have potential to change the delivery of healthcare. This universal platform system has the capacity to digitize biology, resulting in a sensor modality with a capacity to learn. With well-planned device design, development, and distribution plans, there is an opportunity to translate benchtop discoveries in the genomics, proteomics, metabolomics, and glycomics fields by transforming the information content of key biomarkers into actionable signatures that can empower physicians and patients for a better management of healthcare. While the process is complicated and will take some time, showcased here are three application areas for this flexible platform that combines biomarker content with minimally invasive or non-invasive sampling, such as brush biopsy for oral cancer risk assessment; serum, plasma, and small volumes of blood for the assessment of cardiac risk and wellness; and oral fluid sampling for drugs of abuse testing at the point of need.
Saliva can be easily obtained in medical and non-medical settings, and contains numerous bio-molecules, including those typically found in serum for disease detection and monitoring. In the past two ...decades, the achievements of high-throughput approaches afforded by biotechnology and nanotechnology allow for disease-specific salivary biomarker discovery and establishment of rapid, multiplex, and miniaturized analytical assays. These developments have dramatically advanced saliva-based diagnostics. In this review, we discuss the current consensus on development of saliva/oral fluid-based diagnostics and provide a summary of recent research advancements of the Texas-Kentucky Saliva Diagnostics Consortium. In the foreseeable future, current research on saliva based diagnostic methods could revolutionize health care.
Abstract Cancer is the 2nd leading cause of death (over 605,000 people) in the US, at an expense of over $200B, with 1 in 3 people projected to have cancer during their lifetime per CDC. Despite the ...significant impact of early detection and screening on prognosis, only some cancers are diagnosed at an early stage. Carcinomas, comprising >80% of cancer incidence, allow ease in cytology sample access chairside, due to the lesions’ epithelial presentation. This presents a unique opportunity for early detection and screening in epithelial cancers. In low-resource healthcare settings, from clinical examination to the long, tedious and expensive diagnostic journey for cancers & pre-cancerous lesions, can lead to missed, delayed or over diagnosis scenarios. This affects treatment initiation and potentially outcome. To facilitate early intervention, there is compelling need to develop accurate and effective minimally invasive screening platforms. Recent advances in the -omics disciplines, microfluidics and AI tools are starting to reveal promising signatures of early disease detection, with potential to drastically improve screening and diagnostic systems. We are developing a novel application of the cytomics-on-chip platform, for enabling chairside, quantitative screening of suspicious epithelial lesions. The biosensor module involves 1. a single use, cytomics platform employing a cartridge with cellular array and high specificity biomarker reagents, that allows single cell molecular imaging to be completed in a portable analyzer. 2. a microfluidics module that allows cytomorphometric measurements to be completed. 3. The results generated are utilized to train machine learning algorithms to detect cyto-signatures and provide an intuitive result that may be utilized by health care practitioners in clinical-decision making. The first cell-based point-of-care oncology tool has recently been validated with high accuracy (99.3%), sensitivity and specificity, in a multi-site prospective clinical study. Here we demonstrate a pilot study towards development of a smart single cell cytomics-on-chip platform for prompt cytomorphometric and biomarker characterization towards diagnosis of urothelial, anal and cervical cancers, and dysplasia lesions, utilizing brush/pap and fresh urine samples. This has potential for continuous quantitative indexing, for disease categorization. As cancers become more pervasive, improved early detection/screening methods that are accurate, cost effective, easy to implement during routine clinical practice, and providing minimal discomfort to the patient, are urgently needed, improving confidence in clinicians’ decisions. Citation Format: Kritika Srinivasan Rajsri, Michael P. McRae, Nicolaos J. Christodoulides, Khaled Algashaamy, Monica T. Garcia-Buitrago, Fei Chen, Fang-Ming Deng, Jennifer S. Smith, John T. McDevitt. Cytomics-on-chip and AI-driven predictive analysis platform for early detection of epithelial cancers abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6087.
Abstract
Background & Objective: Oral squamous cell carcinoma (OSCC) affects over 400,000 individuals globally, every year. When diagnosed early, the 5-year survival rate for OSCC is 64%, but ...two-third of these lesions are diagnosed in later stages, leading to lower survival rates (<40%). Clinical diagnosis of these lesions is complicated by benign and Oral-Potentially-Malignant-Disorders (OPMD) like oral lichenoid conditions (OLC), mimicking OSCC in clinical presentation. Studies in literature demonstrate high rates of incorrect chair-side diagnosis for OSCC and OPMD, making their early and accurate detection challenging. This clinical gap prompts a strong need for clinical chair-side point-of-care (POC) platforms, to aid accurate screening of these lesions and prevent diagnostic delays.
Methods: In this work we present a ‘smart diagnostic approach’ that combines three key capabilities into an integrated sensing modality as follows: i) powerful microfluidic engine that allows for cytology measurements to be completed outside of a sophisticated lab infrastructure, ii) cytomics platform that allows for single cell molecular imaging to be completed in a portable analyzer, iii) AI-linked diagnostic models for early disease detection using cyto-signatures. This platform has been clinically validated across a multi-site prospective clinical study.
Results: Multiple parameters including cellular phenotypes, nuclear parameters, biomarker expressions were indexed. Further, combining these features allowed discrimination and stratification of these lesions with high accuracy (99.3%) and significance. Further examination using logistic regression and receiver operating characteristic curve analyses yielded significant lesion identifiers and AUC values towards positive discrimination of OLC and OSCC (0.824 and 0.95, respectively vs benign lesions), with high sensitivity and specificity.
Conclusion: This rapid (<30 minutes) cytopathology POC solution has the potential to impact OSCC and OPMD screening accurately, characterizing subtle cellular changes to aid long-term monitoring of these lesions. Additionally, this platform has the capability to uncover new parameters that can further aid these assessments and improve confidence in clinicians’ decisions.
Citation Format: Kritika Srinivasan Rajsri, Michael P. McRae, Glennon W. Simmons, Alexander Ross Kerr, Nadarajah Vigneswaran, Spencer W. Redding, Malvin Janal, Stella Kang, Leena Paloma, Nicolaos J. Christodoulides, John T. McDevitt. A smart cytopathology risk-assessment platform for oral potentially malignant disorders and oral squamous cell carcinoma at the point-of-care abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 785.
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