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A Novel COVID-19 Severity Score Is Associated with Survival in Patients Undergoing Percutaneous Dilational Tracheostomy [Meeting Abstract]

Hambrecht, A; Krowsoski, L; DiMaggio, C; Hong, C; Medina, B; McDevitt, J T; McRae, M; Mukherjee, V; Uppal, A; Bukur, M
Introduction: Tracheostomy in COVID-19 patients is a controversial and difficult clinical decision. A recent COVID-19 Severity Score (CSS) was validated to identify high-risk patients requiring hospitalization. We hypothesized that the CSS would be associated with survival in patients considered for tracheostomy.
Method(s): We reviewed 77 mechanically ventilated COVID-19 patients evaluated for percutaneous dilational tracheostomy (PDT) from March-June 2020 at a public tertiary care center. Decision for PDT was based on clinical judgment of the screening surgeons. The CSS was retrospectively calculated using mean biomarker values from admission to time of PDT consult. Primary end point was survival to discharge. The Youden index identified an optimal CSS cut point for survival.
Result(s): Mean CSS for 42 survivors vs 35 nonsurvivors was significantly different (CSS 52 vs 66; p = 0.003). The Youden index returned an optimal CSS of 55 (area under the curve 0.7; 95% CI, 43 to 72). Median CSS was 40 (interquartile range 27 to 49) in the Low CSS (<55 group) and 72 (interquartile range 66 to 93) in the high CSS (>= 55) group (Fig. 1a). Eighty-seven percent of low CSS patients underwent PDT, with 74% survival, and 61% of high CSS patients underwent PDT with only 41% surviving (Fig. 1b). Patients with high CSS had 77% lower odds of survival (odds ratio 0.2; 95% CI, 0.1 to 0.7).
Conclusion(s): Higher CSS was associated with decreased survival to discharge in patients evaluated for PDT, with a score > 55 predictive of mortality. The novel CSS can be a useful adjunct in determining which COVID-19 patients will benefit from tracheostomy. Further prospective validation of this tool is warranted. [Formula presented]
Copyright
EMBASE:2014945417
ISSN: 1879-1190
CID: 5024592

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

Wang, Joshua M; Liu, Wenke; Chen, Xiaoshan; McRae, Michael P; McDevitt, John T; Fenyo, David
BACKGROUND:Retrospective study of COVID-19 positive patients treated at NYU Langone Health (NYULH). OBJECTIVE:Identify clinical markers predictive of disease severity to assist in clinical decision triage and provide additional biological insights into disease progression. METHODS:Clinical activity of 3740 de-identified patients at NYULH between January and August 2020. Models were trained on clinical data during different parts of their hospital stay to predict three clinical outcomes: deceased, ventilated, or admitted to ICU. RESULTS: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). 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.
PMID: 34081611
ISSN: 1438-8871
CID: 4891822

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

Wang, Joshua M; Liu, Wenke; Chen, Xiaoshan; McRae, Michael P; McDevitt, John T; Fenyö, David
Objective/UNASSIGNED:Retrospective study of COVID-19 positive patients treated at NYU Langone Health (NYULH) to identify clinical markers predictive of disease severity to assist in clinical decision triage and provide additional biological insights into disease progression. Materials and Methods/UNASSIGNED:Clinical activity of 3740 de-identified patients at NYULH between January and August 2020. Models were trained on clinical data during different parts of their hospital stay to predict three clinical outcomes: deceased, ventilated, or admitted to ICU. Results/UNASSIGNED: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). Conclusion/UNASSIGNED: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.
PMID: 33300013
ISSN: n/a
CID: 4898872

Nuclear F-actin Cytology in Oral Epithelial Dysplasia and Oral Squamous Cell Carcinoma

McRae, M P; Kerr, A R; Janal, M N; Thornhill, M H; Redding, S W; Vigneswaran, N; Kang, S K; Niederman, R; Christodoulides, N J; Trochesset, D A; Murdoch, C; Dapkins, I; Bouquot, J; Modak, S S; Simmons, G W; McDevitt, J T
Oral cavity cancer has a low 5-y survival rate, but outcomes improve when the disease is detected early. Cytology is a less invasive method to assess oral potentially malignant disorders relative to the gold-standard scalpel biopsy and histopathology. In this report, we aimed to determine the utility of cytological signatures, including nuclear F-actin cell phenotypes, for classifying the entire spectrum of oral epithelial dysplasia and oral squamous cell carcinoma. We enrolled subjects with oral potentially malignant disorders, subjects with previously diagnosed malignant lesions, and healthy volunteers without lesions and obtained brush cytology specimens and matched scalpel biopsies from 486 subjects. Histopathological assessment of the scalpel biopsy specimens classified lesions into 6 categories. Brush cytology specimens were analyzed by machine learning classifiers trained to identify relevant cytological features. Multimodal diagnostic models were developed using cytology results, lesion characteristics, and risk factors. Squamous cells with nuclear F-actin staining were associated with early disease (i.e., lower proportions in benign lesions than in more severe lesions), whereas small round parabasal-like cells and leukocytes were associated with late disease (i.e., higher proportions in severe dysplasia and carcinoma than in less severe lesions). Lesions with the impression of oral lichen planus were unlikely to be either dysplastic or malignant. Cytological features substantially improved upon lesion appearance and risk factors in predicting squamous cell carcinoma. Diagnostic models accurately discriminated early and late disease with AUCs (95% CI) of 0.82 (0.77 to 0.87) and 0.93 (0.88 to 0.97), respectively. The cytological features identified here have the potential to improve screening and surveillance of the entire spectrum of oral potentially malignant disorders in multiple care settings.
PMID: 33179547
ISSN: 1544-0591
CID: 4675972

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

McRae, Michael P; Dapkins, Isaac P; Sharif, Iman; Anderson, Judd; Fenyo, David; Sinokrot, Odai; Kang, Stella K; Christodoulides, Nicolaos J; Vurmaz, Deniz; Simmons, Glennon W; Alcorn, Timothy M; Daoura, Marco J; Gisburne, Stu; Zar, David; McDevitt, John T
BACKGROUND:The COVID-19 pandemic has resulted in significant morbidity and mortality, with large numbers of patients requiring intensive care threatening to overwhelm healthcare systems globally. 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, non-laboratory data to help determine whether biomarker-based testing and/or hospitalization is necessary. Tier 2 predicts probability of mortality using biomarker measurements (CRP, PCT, D-dimer) and age. Both 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 that died vs. those that were not hospitalized or discharged (P < .001). The Tier 1 and Tier 2 internal validation had AUC (95% confidence interval) of 0.79 (0.74-0.84) and 0.95 (0.92-0.98), respectively. The Tier 1 and Tier 2 external validation had AUCs of 0.79 (0.74-0.84) and 0.97 (0.95-0.99), respectively. CONCLUSIONS:Our results demonstrate validity of the clinical decision support system and mobile app, which are now ready to assist healthcare providers in making evidence-based decisions in managing COVID-19 patient care. The deployment of these new capabilities has potential for immediate impact in community clinics, sites whereby application of such tools could lead to improvements in patient outcomes and cost containment. CLINICALTRIAL/UNASSIGNED/:
PMID: 32750010
ISSN: 1438-8871
CID: 4553932

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

McRae, Michael P; Simmons, Glennon W; Christodoulides, Nicolaos J; Lu, Zhibing; Kang, Stella K; Fenyo, David; Alcorn, Timothy; Dapkins, Isaac P; Sharif, Iman; Vurmaz, Deniz; Modak, Sayli S; Srinivasan, Kritika; Warhadpande, Shruti; Shrivastav, Ravi; McDevitt, John T
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.
PMID: 32490853
ISSN: 1473-0189
CID: 4469072

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

McRae, Michael P; Simmons, Glennon W; Christodoulides, Nicolaos J; Lu, Zhibing; Kang, Stella K; Fenyo, David; Alcorn, Timothy; Dapkins, Isaac P; Sharif, Iman; Vurmaz, Deniz; Modak, Sayli S; Srinivasan, Kritika; Warhadpande, Shruti; Shrivastav, Ravi; McDevitt, John T
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.
PMID: 32511607
ISSN: n/a
CID: 4477922

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

McRae, Michael P; Modak, Sayli S; Simmons, Glennon W; Trochesset, Denise A; Kerr, A Ross; Thornhill, Martin H; Redding, Spencer W; Vigneswaran, Nadarajah; Kang, Stella K; Christodoulides, Nicolaos J; Murdoch, Craig; Dietl, Steven J; Markham, Roger; McDevitt, John T
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.
PMID: 32032477
ISSN: 1934-6638
CID: 4300912

Point-of-care characterization and risk-based management of oral lesions in primary dental clinics: A simulation model

Kang, Stella K; Mali, Rahul D; Braithwaite, R Scott; Kerr, Alexander R; McDevitt, John
OBJECTIVES/OBJECTIVE:Oral potentially malignant disorders (OPMDs) encompass histologically benign, dysplastic, and cancerous lesions that are often indistinguishable by appearance and inconsistently managed. We assessed the potential impact of test-and-treat pathways enabled by a point-of-care test for OPMD characterization. MATERIALS AND METHODS/METHODS:We constructed a decision-analytic model to compare life expectancy of test-treat strategies for 60-year-old patients with OPMDs in the primary dental setting, based on a trial for a point-of-care cytopathology tool (POCOCT). Eight strategies of OPMD detection and evaluation were compared, involving deferred evaluation (no further characterization), prompt OPMD characterization using POCOCT measurements, or the commonly recommended usual care strategy of routine referral for scalpel biopsy. POCOCT pathways differed in threshold for additional intervention, including surgery for any dysplasia or malignancy, or for only moderate or severe dysplasia or cancer. Strategies with initial referral for biopsy also reflected varied treatment thresholds in current practice between surgery and surveillance of mild dysplasia. Sensitivity analysis was performed to assess the impact of variation in parameter values on model results. RESULTS:Requisite referral for scalpel biopsy offered the highest life expectancy of 20.92 life-years compared with deferred evaluation (+0.30 life-years), though this outcome was driven by baseline assumptions of limited patient adherence to surveillance using POCOCT. POCOCT characterization and surveillance offered only 0.02 life-years less than the most biopsy-intensive strategy, while resulting in 27% fewer biopsies. When the probability of adherence to surveillance and confirmatory biopsy was ≥ 0.88, or when metastasis rates were lower than reported, POCOCT characterization extended life-years (+0.04 life-years) than prompt specialist referral. CONCLUSION/CONCLUSIONS:Risk-based OPMD management through point-of-care cytology may offer a reasonable alternative to routine referral for specialist evaluation and scalpel biopsy, with far fewer biopsies. In patients who adhere to surveillance protocols, POCOCT surveillance may extend life expectancy beyond biopsy and follow up visual-tactile inspection.
PMCID:7774939
PMID: 33382762
ISSN: 1932-6203
CID: 4747502

Development of a cytology-based multivariate analytical risk index for oral cancer

Abram, Timothy J; Floriano, Pierre N; James, Robert; Kerr, A Ross; Thornhill, Martin H; Redding, Spencer W; Vigneswaran, Nadarajah; Raja, Rameez; McRae, Michael P; McDevitt, John T
OBJECTIVES/OBJECTIVE:The diagnosis and management of oral cavity cancers are often complicated by the uncertainty of which patients will undergo malignant transformation, obligating close surveillance over time. However, serial biopsies are undesirable, highly invasive, and subject to inherent issues with poor inter-pathologist agreement and unpredictability as a surrogate for malignant transformation and clinical outcomes. The goal of this study was to develop and evaluate a Multivariate Analytical Risk Index for Oral Cancer (MARIO) with potential to provide non-invasive, sensitive, and quantitative risk assessments for monitoring lesion progression. MATERIALS AND METHODS/METHODS:A series of predictive models were developed and validated using previously recorded single-cell data from oral cytology samples resulting in a "continuous risk score". Model development consisted of: (1) training base classification models for each diagnostic class pair, (2) pairwise coupling to obtain diagnostic class probabilities, and (3) a weighted aggregation resulting in a continuous MARIO. RESULTS AND CONCLUSIONS/CONCLUSIONS:Diagnostic accuracy based on optimized cut-points for the test dataset ranged from 76.0% for Benign, to 82.4% for Dysplastic, 89.6% for Malignant, and 97.6% for Normal controls for an overall MARIO accuracy of 72.8%. Furthermore, a strong positive relationship with diagnostic severity was demonstrated (Pearson's coefficient = 0.805 for test dataset) as well as the ability of the MARIO to respond to subtle changes in cell composition. The development of a continuous MARIO for PMOL is presented, resulting in a sensitive, accurate, and non-invasive method with potential for enabling monitoring disease progression, recurrence, and the need for therapeutic intervention of these lesions.
PMID: 31010626
ISSN: 1879-0593
CID: 3819192