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10.2196/22033 http://scihub22266oqcxt.onion/10.2196/22033 32750010!7446714!32750010     free     free     free Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 267.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 267.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 267.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       J+Med+Internet+Res 2020 ; 22 (8): e22033 Nephropedia Template TP {{tp|p=32750010|t=2020. Managing COVID-19 With a Clinical Decision Support Tool in a Community Health Network: Algorithm Development and Validation |pdf=|usr=}}{{32750010}} gab.com Text Managing COVID-19 With a Clinical Decision Support Tool in a Community Health Network: Algorithm Development and Validation JO: J Med Internet Res http://www.kidney.de/pget.php?&tw=1&pmid=32750010 #FOAvip 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. Twit Text FOAVip Managing COVID-19 With a Clinical Decision Support Tool in a Community Health Network: Algorithm Development and Validation ????J Med Internet Res http://www.kidney.de/pget.php?&tw=1&pmid=32750010 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32750010 #FOAvip English Wikipedia <ref>{{Cite pmid|32750010}}</ref> Managing COVID-19 With a Clinical Decision Support Tool in a Community Health Network: Algorithm Development and Validation #MMPMID32750010 McRae MP; Dapkins IP; Sharif I; Anderman J; Fenyo D; Sinokrot O; Kang SK; Christodoulides NJ; Vurmaz D; Simmons GW; Alcorn TM; Daoura MJ; Gisburne S; Zar D; McDevitt JT J Med Internet Res 2020[Aug]; 22 (8): e22033 PMID32750010show ga 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. |Betacoronavirus/*pathogenicity[MESH] |COVID-19[MESH] |Community Networks/*standards[MESH] |Coronavirus Infections/*epidemiology[MESH] |Coronavirus/*pathogenicity[MESH] |Decision Support Systems, Clinical/*standards[MESH] |Female[MESH] |Humans[MESH] |Male[MESH] |Pandemics[MESH] |Pneumonia, Viral/*epidemiology[MESH]Managing COVID-19 With a Clinical Decision Support Tool in a Community(epmc).pdf DeepDyve Pubget Overpricing