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10.1093/clinchem/hvaa200 http://scihub22266oqcxt.onion/10.1093/clinchem/hvaa200 32821907!7499540!32821907     free     free     free Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Clin+Chem 2020 ; 66 (11): 1396-1404 Nephropedia Template TP {{tp|p=32821907|t=2020. Routine Laboratory Blood Tests Predict SARS-CoV-2 Infection Using Machine Learning |pdf=|usr=}}{{32821907}} gab.com Text Routine Laboratory Blood Tests Predict SARS-CoV-2 Infection Using Machine Learning JO: Clin Chem http://www.kidney.de/pget.php?&tw=1&pmid=32821907 #FOAvip BACKGROUND: Accurate diagnostic strategies to identify SARS-CoV-2 positive individuals rapidly for management of patient care and protection of health care personnel are urgently needed. The predominant diagnostic test is viral RNA detection by RT-PCR from nasopharyngeal swabs specimens, however the results are not promptly obtainable in all patient care locations. Routine laboratory testing, in contrast, is readily available with a turn-around time (TAT) usually within 1-2 hours. METHOD: We developed a machine learning model incorporating patient demographic features (age, sex, race) with 27 routine laboratory tests to predict an individual's SARS-CoV-2 infection status. Laboratory testing results obtained within 2 days before the release of SARS-CoV-2 RT-PCR result were used to train a gradient boosting decision tree (GBDT) model from 3,356 SARS-CoV-2 RT-PCR tested patients (1,402 positive and 1,954 negative) evaluated at a metropolitan hospital. RESULTS: The model achieved an area under the receiver operating characteristic curve (AUC) of 0.854 (95% CI: 0.829-0.878). Application of this model to an independent patient dataset from a separate hospital resulted in a comparable AUC (0.838), validating the generalization of its use. Moreover, our model predicted initial SARS-CoV-2 RT-PCR positivity in 66% individuals whose RT-PCR result changed from negative to positive within 2 days. CONCLUSION: This model employing routine laboratory test results offers opportunities for early and rapid identification of high-risk SARS-CoV-2 infected patients before their RT-PCR results are available. It may play an important role in assisting the identification of SARS-CoV-2 infected patients in areas where RT-PCR testing is not accessible due to financial or supply constraints. Twit Text FOAVip Routine Laboratory Blood Tests Predict SARS-CoV-2 Infection Using Machine Learning ????Clin Chem http://www.kidney.de/pget.php?&tw=1&pmid=32821907 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32821907 #FOAvip English Wikipedia <ref>{{Cite pmid|32821907}}</ref> Routine Laboratory Blood Tests Predict SARS-CoV-2 Infection Using Machine Learning #MMPMID32821907 Yang HS; Hou Y; Vasovic LV; Steel PAD; Chadburn A; Racine-Brzostek SE; Velu P; Cushing MM; Loda M; Kaushal R; Zhao Z; Wang F Clin Chem 2020[Nov]; 66 (11): 1396-1404 PMID32821907show ga BACKGROUND: Accurate diagnostic strategies to identify SARS-CoV-2 positive individuals rapidly for management of patient care and protection of health care personnel are urgently needed. The predominant diagnostic test is viral RNA detection by RT-PCR from nasopharyngeal swabs specimens, however the results are not promptly obtainable in all patient care locations. Routine laboratory testing, in contrast, is readily available with a turn-around time (TAT) usually within 1-2 hours. METHOD: We developed a machine learning model incorporating patient demographic features (age, sex, race) with 27 routine laboratory tests to predict an individual's SARS-CoV-2 infection status. Laboratory testing results obtained within 2 days before the release of SARS-CoV-2 RT-PCR result were used to train a gradient boosting decision tree (GBDT) model from 3,356 SARS-CoV-2 RT-PCR tested patients (1,402 positive and 1,954 negative) evaluated at a metropolitan hospital. RESULTS: The model achieved an area under the receiver operating characteristic curve (AUC) of 0.854 (95% CI: 0.829-0.878). Application of this model to an independent patient dataset from a separate hospital resulted in a comparable AUC (0.838), validating the generalization of its use. Moreover, our model predicted initial SARS-CoV-2 RT-PCR positivity in 66% individuals whose RT-PCR result changed from negative to positive within 2 days. CONCLUSION: This model employing routine laboratory test results offers opportunities for early and rapid identification of high-risk SARS-CoV-2 infected patients before their RT-PCR results are available. It may play an important role in assisting the identification of SARS-CoV-2 infected patients in areas where RT-PCR testing is not accessible due to financial or supply constraints. |*Hematologic Tests[MESH] |*Machine Learning[MESH] |Adult[MESH] |Aged[MESH] |COVID-19[MESH] |COVID-19 Testing[MESH] |Clinical Laboratory Techniques[MESH] |Coronavirus Infections/*diagnosis[MESH] |Female[MESH] |Humans[MESH] |Laboratories[MESH] |Male[MESH] |Middle Aged[MESH] |Models, Theoretical[MESH] |Pandemics[MESH] |Pneumonia, Viral/*diagnosis[MESH] |ROC Curve[MESH] |Retrospective Studies[MESH] |Reverse Transcriptase Polymerase Chain Reaction[MESH]Routine Laboratory Blood Tests Predict SARS-CoV-2 Infection Using Mach(epmc).pdf DeepDyve Pubget Overpricing