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Article: A feasibility and acceptability study of screening the parents/guardians of pediatric dental patients for the social determinants of health.

Kaur, Raghbir / Lieberman, Martin / Mason, Margaret K / Dapkins, Isaac P / Gallager, Rebecca / Hopkins, Kathleen / Wu, Yinxiang / Troxel, Andrea B / Rashwan, Ayah / Hope, Chelsea / Kane, Daniel J / Northridge, Mary E

Pilot and feasibility studies

2023 Volume 9, Issue 1, Page(s) 36

Abstract: Background: The social determinants of health (SDOH) are the conditions in which people are born, grow, work, live, and age. Lack of SDOH training of dental providers on SDOH may result in suboptimal care provided to pediatric dental patients and their ... ...

Abstract	Background: The social determinants of health (SDOH) are the conditions in which people are born, grow, work, live, and age. Lack of SDOH training of dental providers on SDOH may result in suboptimal care provided to pediatric dental patients and their families. The purpose of this pilot study is to report the feasibility and acceptability of SDOH screening and referral by pediatric dentistry residents and faculty in the dental clinics of Family Health Centers at NYU Langone (FHC), a Federally Qualified Health Center (FQHC) network in Brooklyn, NY, USA. Methods: Guided by the Implementation Outcomes Framework, 15 pediatric dentists and 40 pediatric dental patient-parent/guardian dyads who visited FHC in 2020-2021 for recall or treatment appointments participated in this study. The a priori feasibility and acceptability criteria for these outcomes were that after completing the Parent Adversity Scale (a validated SDOH screening tool), ≥ 80% of the participating parents/guardians would feel comfortable completing SDOH screening and referral at the dental clinic (acceptable), and ≥ 80% of the participating parents/guardians who endorsed SDOH needs would be successfully referred to an assigned counselor at the Family Support Center (feasible). Results: The most prevalent SDOH needs endorsed were worried within the past year that food would run out before had money to buy more (45.0%) and would like classes to learn English, read better, or obtain a high school degree (45.0%). Post-intervention, 83.9% of the participating parents/guardians who expressed an SDOH need were successfully referred to an assigned counselor at the Family Support Center for follow-up, and 95.0% of the participating parents/guardians felt comfortable completing the questionnaire at the dental clinic, surpassing the a priori feasibility and acceptability criteria, respectively. Furthermore, while most (80.0%) of the participating dental providers reported being trained in SDOH, only one-third (33.3%) usually or always assess SDOH for their pediatric dental patients, and most (53.8%) felt minimally comfortable discussing challenges faced by pediatric dental patient families and referring patients to resources in the community. Conclusions: This study provides novel evidence of the feasibility and acceptability of SDOH screening and referral by dentists in the pediatric dental clinics of an FQHC network.
Language	English
Publishing date	2023-03-09
Publishing country	England
Document type	Journal Article
ZDB-ID	2809935-7
ISSN	2055-5784
ISSN	2055-5784
DOI	10.1186/s40814-023-01269-3
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: A Telemedicine Approach to Covid-19 Assessment and Triage.

Reiss, Allison B / De Leon, Joshua / Dapkins, Isaac P / Shahin, George / Peltier, Morgan R / Goldberg, Eric R

Medicina (Kaunas, Lithuania)

2020 Volume 56, Issue 9

Abstract: Covid-19 is a new highly contagious RNA viral disease that has caused a global pandemic. Human-to-human transmission occurs primarily through oral and nasal droplets and possibly through the airborne route. The disease may be asymptomatic or the course ... ...

Abstract	Covid-19 is a new highly contagious RNA viral disease that has caused a global pandemic. Human-to-human transmission occurs primarily through oral and nasal droplets and possibly through the airborne route. The disease may be asymptomatic or the course may be mild with upper respiratory symptoms, moderate with non-life-threatening pneumonia, or severe with pneumonia and acute respiratory distress syndrome. The severe form is associated with significant morbidity and mortality. While patients who are unstable and in acute distress need immediate in-person attention, many patients can be evaluated at home by telemedicine or videoconferencing. The more benign manifestations of Covid-19 may be managed from home to maintain quarantine, thus avoiding spread to other patients and health care workers. This document provides an overview of the clinical presentation of Covid-19, emphasizing telemedicine strategies for assessment and triage of patients. Advantages of the virtual visit during this time of social distancing are highlighted.
MeSH term(s)	Betacoronavirus/isolation & purification ; COVID-19 ; Communicable Disease Control/methods ; Communicable Disease Control/organization & administration ; Coronavirus Infections/diagnosis ; Coronavirus Infections/epidemiology ; Coronavirus Infections/prevention & control ; Coronavirus Infections/therapy ; Disease Transmission, Infectious/prevention & control ; Humans ; Pandemics/prevention & control ; Pneumonia, Viral/diagnosis ; Pneumonia, Viral/epidemiology ; Pneumonia, Viral/etiology ; Pneumonia, Viral/prevention & control ; Pneumonia, Viral/therapy ; SARS-CoV-2 ; Symptom Assessment/methods ; Telemedicine/methods ; Triage/methods ; Triage/organization & administration
Keywords	covid19
Language	English
Publishing date	2020-09-10
Publishing country	Switzerland
Document type	Journal Article ; Review
ZDB-ID	2188113-3
ISSN	1648-9144 ; 1010-660X
ISSN (online)	1648-9144
ISSN	1010-660X
DOI	10.3390/medicina56090461
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Article: A Telemedicine Approach to Covid-19 Assessment and Triage

Reiss, Allison B / De Leon, Joshua / Dapkins, Isaac P / Shahin, George / Peltier, Morgan R / Goldberg, Eric R

Medicina (Kaunas)

Abstract	Covid-19 is a new highly contagious RNA viral disease that has caused a global pandemic. Human-to-human transmission occurs primarily through oral and nasal droplets and possibly through the airborne route. The disease may be asymptomatic or the course may be mild with upper respiratory symptoms, moderate with non-life-threatening pneumonia, or severe with pneumonia and acute respiratory distress syndrome. The severe form is associated with significant morbidity and mortality. While patients who are unstable and in acute distress need immediate in-person attention, many patients can be evaluated at home by telemedicine or videoconferencing. The more benign manifestations of Covid-19 may be managed from home to maintain quarantine, thus avoiding spread to other patients and health care workers. This document provides an overview of the clinical presentation of Covid-19, emphasizing telemedicine strategies for assessment and triage of patients. Advantages of the virtual visit during this time of social distancing are highlighted.
Keywords	covid19
Publisher	WHO
Document type	Article
Note	WHO #Covidence: #769368
Database	COVID19

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Article ; Online: 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

Lab on a chip

2020 Volume 20, Issue 12, Page(s) 2075–2085

Abstract: 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 ... ...

Abstract	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.
MeSH term(s)	Algorithms ; Biomarkers ; COVID-19 ; Comorbidity ; Coronavirus Infections/diagnosis ; Coronavirus Infections/physiopathology ; Critical Care ; Decision Support Systems, Clinical/organization & administration ; Humans ; Image Processing, Computer-Assisted ; Immunoassay/methods ; Machine Learning ; Pandemics ; Pneumonia, Viral/diagnosis ; Pneumonia, Viral/physiopathology ; Point-of-Care Systems ; Predictive Value of Tests ; Risk Factors ; Severity of Illness Index ; Software ; Treatment Outcome
Chemical Substances	Biomarkers
Keywords	covid19
Language	English
Publishing date	2020-06-03
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	2056646-3
ISSN	1473-0189 ; 1473-0197
ISSN (online)	1473-0189
ISSN	1473-0197
DOI	10.1039/d0lc00373e
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: 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 / Anderman, 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

Journal of medical Internet research

2020 Volume 22, Issue 8, Page(s) e22033

Abstract: 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- ... ...

Abstract	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.
MeSH term(s)	Betacoronavirus/pathogenicity ; COVID-19 ; Community Networks/standards ; Coronavirus/pathogenicity ; Coronavirus Infections/epidemiology ; Decision Support Systems, Clinical/standards ; Female ; Humans ; Male ; Pandemics ; Pneumonia, Viral/epidemiology ; SARS-CoV-2
Keywords	covid19
Language	English
Publishing date	2020-08-24
Publishing country	Canada
Document type	Journal Article
ZDB-ID	2028830-X
ISSN	1438-8871 ; 1438-8871
ISSN (online)	1438-8871
ISSN	1438-8871
DOI	10.2196/22033
Database	MEDical Literature Analysis and Retrieval System OnLINE

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

medRxiv : the preprint server for health sciences

2020

Abstract	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.
Keywords	covid19
Language	English
Publishing date	2020-04-22
Publishing country	United States
Document type	Preprint
DOI	10.1101/2020.04.16.20068411
Database	MEDical Literature Analysis and Retrieval System OnLINE

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

Lab Chip

Abstract	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.
Keywords	covid19
Publisher	WHO
Document type	Article
Note	WHO #Covidence: #506003
Database	COVID19

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

J Med Internet Res

Abstract: 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- ... ...

Abstract	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.
Keywords	covid19
Publisher	WHO
Document type	Article
Note	WHO #Covidence: #694384
Database	COVID19

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Article ; Online: Managing COVID-19 With a Clinical Decision Support Tool in a Community Health Network

Journal of Medical Internet Research, Vol 22, Iss 8, p e

Algorithm Development and Validation

2020 Volume 22033

Abstract: BackgroundThe 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 ... ...

Abstract	BackgroundThe 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. ObjectiveThe 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. MethodsModel 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. ResultsAll 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. ConclusionsOur 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.
Keywords	Computer applications to medicine. Medical informatics ; R858-859.7 ; Public aspects of medicine ; RA1-1270
Subject code	610
Language	English
Publishing date	2020-08-01T00:00:00Z
Publisher	JMIR Publications
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: 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.

Lab on a Chip

2020 Volume 20, Issue 12, Page(s) 2075–2085

Abstract: The COVID-19 Severity Score combines multiplex biomarker measurements and risk factors in a statistical learning algorithm to predict mortality. ...

Abstract	The COVID-19 Severity Score combines multiplex biomarker measurements and risk factors in a statistical learning algorithm to predict mortality.
Keywords	Biochemistry ; Bioengineering ; General Chemistry ; Biomedical Engineering ; covid19
Language	English
Publisher	Royal Society of Chemistry (RSC)
Publishing country	uk
Document type	Article ; Online
ZDB-ID	2056646-3
ISSN	1473-0189 ; 1473-0197
ISSN (online)	1473-0189
ISSN	1473-0197
DOI	10.1039/d0lc00373e
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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