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10.1016/j.jaclp.2020.12.005 http://scihub22266oqcxt.onion/10.1016/j.jaclp.2020.12.005 33688635!7933786!33688635     free     free     free Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 243.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 243.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 243.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       J+Acad+Consult+Liaison+Psychiatry 2021 ; 62 (3): 298-308 Nephropedia Template TP {{tp|p=33688635|t=2021. Development and External Validation of a Delirium Prediction Model for Hospitalized Patients With Coronavirus Disease 2019 |pdf=|usr=}}{{33688635}} gab.com Text Development and External Validation of a Delirium Prediction Model for Hospitalized Patients With Coronavirus Disease 2019 JO: J Acad Consult Liaison Psychiatry http://www.kidney.de/pget.php?&tw=1&pmid=33688635 #FOAvip BACKGROUND: The coronavirus disease 2019 pandemic has placed unprecedented stress on health systems and has been associated with elevated risk for delirium. The convergence of pandemic resource limitation and clinical demand associated with delirium requires careful risk stratification for targeted prevention efforts. OBJECTIVES: To develop an incident delirium predictive model among coronavirus disease 2019 patients. METHODS: We applied supervised machine learning to electronic health record data for inpatients with coronavirus disease 2019 at three hospitals to build an incident delirium diagnosis prediction model. We validated this model in three different hospitals. Both hospital cohorts included academic and community settings. RESULTS: Among 2907 patients across 6 hospitals, 488 (16.8%) developed delirium. Applying the predictive model in the external validation cohort of 755 patients, the c-index was 0.75 (0.71-0.79) and the lift in the top quintile was 2.1. At a sensitivity of 80%, the specificity was 56%, negative predictive value 92%, and positive predictive value 30%. Equivalent model performance was observed in subsamples stratified by age, sex, race, need for critical care and care at community vs. academic hospitals. CONCLUSION: Machine learning applied to electronic health records available at the time of inpatient admission can be used to risk-stratify patients with coronavirus disease 2019 for incident delirium. Delirium is common among patients with coronavirus disease 2019, and resource constraints during a pandemic demand careful attention to the optimal application of predictive models. Twit Text FOAVip Development and External Validation of a Delirium Prediction Model for Hospitalized Patients With Coronavirus Disease 2019 ????J Acad Consult Liaison Psychiatry http://www.kidney.de/pget.php?&tw=1&pmid=33688635 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33688635 #FOAvip English Wikipedia <ref>{{Cite pmid|33688635}}</ref> Development and External Validation of a Delirium Prediction Model for Hospitalized Patients With Coronavirus Disease 2019 #MMPMID33688635 Castro VM; Sacks CA; Perlis RH; McCoy TH J Acad Consult Liaison Psychiatry 2021[May]; 62 (3): 298-308 PMID33688635show ga BACKGROUND: The coronavirus disease 2019 pandemic has placed unprecedented stress on health systems and has been associated with elevated risk for delirium. The convergence of pandemic resource limitation and clinical demand associated with delirium requires careful risk stratification for targeted prevention efforts. OBJECTIVES: To develop an incident delirium predictive model among coronavirus disease 2019 patients. METHODS: We applied supervised machine learning to electronic health record data for inpatients with coronavirus disease 2019 at three hospitals to build an incident delirium diagnosis prediction model. We validated this model in three different hospitals. Both hospital cohorts included academic and community settings. RESULTS: Among 2907 patients across 6 hospitals, 488 (16.8%) developed delirium. Applying the predictive model in the external validation cohort of 755 patients, the c-index was 0.75 (0.71-0.79) and the lift in the top quintile was 2.1. At a sensitivity of 80%, the specificity was 56%, negative predictive value 92%, and positive predictive value 30%. Equivalent model performance was observed in subsamples stratified by age, sex, race, need for critical care and care at community vs. academic hospitals. CONCLUSION: Machine learning applied to electronic health records available at the time of inpatient admission can be used to risk-stratify patients with coronavirus disease 2019 for incident delirium. Delirium is common among patients with coronavirus disease 2019, and resource constraints during a pandemic demand careful attention to the optimal application of predictive models. |Adult[MESH] |Aged[MESH] |Aged, 80 and over[MESH] |Area Under Curve[MESH] |COVID-19/*complications[MESH] |Cohort Studies[MESH] |Delirium/*diagnosis/*etiology/prevention & control[MESH] |Electronic Health Records[MESH] |Female[MESH] |Humans[MESH] |Machine Learning[MESH] |Male[MESH] |Middle Aged[MESH] |Models, Statistical[MESH] |Patient Admission[MESH] |Risk Assessment/methods[MESH] |SARS-CoV-2[MESH]Development and External Validation of a Delirium Prediction Model for(epmc).pdf DeepDyve Pubget Overpricing