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10.1016/j.resuscitation.2016.02.005 http://scihub22266oqcxt.onion/10.1016/j.resuscitation.2016.02.005 C4834231!4834231!26898412     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Resuscitation 2016 ; 102 (ä): 1-5 Nephropedia Template TP {{tp|p=26898412|t=2016. The value of vital sign trends for detecting clinical deterioration on the wards |pdf=|usr=}}{{26898412}} gab.com Text The value of vital sign trends for detecting clinical deterioration on the wards JO: Resuscitation http://www.kidney.de/pget.php?&tw=1&pmid=26898412 #FOAvip Aim: Early detection of clinical deterioration on the wards may improve outcomes, and most early warning scores only utilize a patient?s current vital signs. The added value of vital sign trends over time is poorly characterized. We investigated whether adding trends improves accuracy and which methods are optimal for modelling trends. Methods: Patients admitted to five hospitals over a five-year period were included in this observational cohort study, with 60% of the data used for model derivation and 40% for validation. Vital signs were utilized to predict the combined outcome of cardiac arrest, intensive care unit transfer, and death. The accuracy of models utilizing both the current value and different trend methods were compared using the area under the receiver operating characteristic curve (AUC). Results: A total of 269,999 patient admissions were included, which resulted in 16,452 outcomes. Overall, trends increased accuracy compared to a model containing only current vital signs (AUC 0.78 vs. 0.74; p<0.001). The methods that resulted in the greatest average increase in accuracy were the vital sign slope (AUC improvement 0.013) and minimum value (AUC improvement 0.012), while the change from the previous value resulted in an average worsening of the AUC (change in AUC ?0.002). The AUC increased most for systolic blood pressure when trends were added (AUC improvement 0.05). Conclusion: Vital sign trends increased the accuracy of models designed to detect critical illness on the wards. Our findings have important implications for clinicians at the bedside and for the development of early warning scores. Twit Text FOAVip The value of vital sign trends for detecting clinical deterioration on the wards ????Resuscitation http://www.kidney.de/pget.php?&tw=1&pmid=26898412 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26898412 #FOAvip English Wikipedia <ref>{{Cite pmid|26898412}}</ref> The value of vital sign trends for detecting clinical deterioration on the wards #MMPMID26898412 Churpek MM; Adhikari R; Edelson DP Resuscitation 2016[May]; 102 (ä): 1-5 PMID26898412show ga Aim: Early detection of clinical deterioration on the wards may improve outcomes, and most early warning scores only utilize a patient?s current vital signs. The added value of vital sign trends over time is poorly characterized. We investigated whether adding trends improves accuracy and which methods are optimal for modelling trends. Methods: Patients admitted to five hospitals over a five-year period were included in this observational cohort study, with 60% of the data used for model derivation and 40% for validation. Vital signs were utilized to predict the combined outcome of cardiac arrest, intensive care unit transfer, and death. The accuracy of models utilizing both the current value and different trend methods were compared using the area under the receiver operating characteristic curve (AUC). Results: A total of 269,999 patient admissions were included, which resulted in 16,452 outcomes. Overall, trends increased accuracy compared to a model containing only current vital signs (AUC 0.78 vs. 0.74; p<0.001). The methods that resulted in the greatest average increase in accuracy were the vital sign slope (AUC improvement 0.013) and minimum value (AUC improvement 0.012), while the change from the previous value resulted in an average worsening of the AUC (change in AUC ?0.002). The AUC increased most for systolic blood pressure when trends were added (AUC improvement 0.05). Conclusion: Vital sign trends increased the accuracy of models designed to detect critical illness on the wards. Our findings have important implications for clinicians at the bedside and for the development of early warning scores. äThe value of vital sign trends for detecting clinical deterioration on(epmc).pdf DeepDyve Pubget Overpricing