Risk stratification in acute heart failure: Rationale and design of the STRATIFY and DECIDE studies

Sean P. Collins; Christopher J. Lindsell; Cathy A. Jenkins; Frank E. Harrell; Gregory J. Fermann; Karen F. Miller; Sue N. Roll; Matthew I. Sperling; David J. Maron; Allen J. Naftilan; John A. McPherson; Neal Lee Weintraub; Douglas B. Sawyer; Alan B. Storrow

doi:10.1016/j.ahj.2012.07.033

Risk stratification in acute heart failure: Rationale and design of the STRATIFY and DECIDE studies

Sean P. Collins, Christopher J. Lindsell, Cathy A. Jenkins, Frank E. Harrell, Gregory J. Fermann, Karen F. Miller, Sue N. Roll, Matthew I. Sperling, David J. Maron, Allen J. Naftilan, John A. McPherson, Neal Lee Weintraub, Douglas B. Sawyer, Alan B. Storrow

Research output: Contribution to journal › Article › peer-review

29 Scopus citations

Abstract

Background: A critical challenge for physicians facing patients presenting with signs and symptoms of acute heart failure (AHF) is how and where to best manage them. Currently, most patients evaluated for AHF are admitted to the hospital, yet not all warrant inpatient care. Up to 50% of admissions could be potentially avoided and many admitted patients could be discharged after a short period of observation and treatment. Methods for identifying patients that can be sent home early are lacking. Improving the physician's ability to identify and safely manage low-risk patients is essential to avoiding unnecessary use of hospital beds. Methods: Two studies (STRATIFY and DECIDE) have been funded by the National Heart Lung and Blood Institute with the goal of developing prediction rules to facilitate early decision making in AHF. Using prospectively gathered evaluation and treatment data from the acute setting (STRATIFY) and early inpatient stay (DECIDE), rules will be generated to predict risk for death and serious complications. Subsequent studies will be designed to test the external validity, utility, generalizability and cost-effectiveness of these prediction rules in different acute care environments representing racially and socioeconomically diverse patient populations. Results: A major innovation is prediction of 5-day as well as 30-day outcomes, overcoming the limitation that 30-day outcomes are highly dependent on unpredictable, post-visit patient and provider behavior. A novel aspect of the proposed project is the use of a comprehensive cardiology review to correctly assign post-treatment outcomes to the acute presentation. Conclusions: Finally, a rigorous analysis plan has been developed to construct the prediction rules that will maximally extract both the statistical and clinical properties of every data element. Upon completion of this study we will subsequently externally test the prediction rules in a heterogeneous patient cohort.

Original language	English (US)
Pages (from-to)	825-834
Number of pages	10
Journal	American Heart Journal
Volume	164
Issue number	6
DOIs	https://doi.org/10.1016/j.ahj.2012.07.033
State	Published - Dec 2012
Externally published	Yes

ASJC Scopus subject areas

Cardiology and Cardiovascular Medicine

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10.1016/j.ahj.2012.07.033

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Collins, S. P., Lindsell, C. J., Jenkins, C. A., Harrell, F. E., Fermann, G. J., Miller, K. F., Roll, S. N., Sperling, M. I., Maron, D. J., Naftilan, A. J., McPherson, J. A., Weintraub, N. L., Sawyer, D. B., & Storrow, A. B. (2012). Risk stratification in acute heart failure: Rationale and design of the STRATIFY and DECIDE studies. American Heart Journal, 164(6), 825-834. https://doi.org/10.1016/j.ahj.2012.07.033

Collins, SP, Lindsell, CJ, Jenkins, CA, Harrell, FE, Fermann, GJ, Miller, KF, Roll, SN, Sperling, MI, Maron, DJ, Naftilan, AJ, McPherson, JA, Weintraub, NL, Sawyer, DB & Storrow, AB 2012, 'Risk stratification in acute heart failure: Rationale and design of the STRATIFY and DECIDE studies', American Heart Journal, vol. 164, no. 6, pp. 825-834. https://doi.org/10.1016/j.ahj.2012.07.033

@article{0ec4e513760f44559065bde636896ed8,

title = "Risk stratification in acute heart failure: Rationale and design of the STRATIFY and DECIDE studies",

abstract = "Background: A critical challenge for physicians facing patients presenting with signs and symptoms of acute heart failure (AHF) is how and where to best manage them. Currently, most patients evaluated for AHF are admitted to the hospital, yet not all warrant inpatient care. Up to 50% of admissions could be potentially avoided and many admitted patients could be discharged after a short period of observation and treatment. Methods for identifying patients that can be sent home early are lacking. Improving the physician's ability to identify and safely manage low-risk patients is essential to avoiding unnecessary use of hospital beds. Methods: Two studies (STRATIFY and DECIDE) have been funded by the National Heart Lung and Blood Institute with the goal of developing prediction rules to facilitate early decision making in AHF. Using prospectively gathered evaluation and treatment data from the acute setting (STRATIFY) and early inpatient stay (DECIDE), rules will be generated to predict risk for death and serious complications. Subsequent studies will be designed to test the external validity, utility, generalizability and cost-effectiveness of these prediction rules in different acute care environments representing racially and socioeconomically diverse patient populations. Results: A major innovation is prediction of 5-day as well as 30-day outcomes, overcoming the limitation that 30-day outcomes are highly dependent on unpredictable, post-visit patient and provider behavior. A novel aspect of the proposed project is the use of a comprehensive cardiology review to correctly assign post-treatment outcomes to the acute presentation. Conclusions: Finally, a rigorous analysis plan has been developed to construct the prediction rules that will maximally extract both the statistical and clinical properties of every data element. Upon completion of this study we will subsequently externally test the prediction rules in a heterogeneous patient cohort.",

author = "Collins, {Sean P.} and Lindsell, {Christopher J.} and Jenkins, {Cathy A.} and Harrell, {Frank E.} and Fermann, {Gregory J.} and Miller, {Karen F.} and Roll, {Sue N.} and Sperling, {Matthew I.} and Maron, {David J.} and Naftilan, {Allen J.} and McPherson, {John A.} and Weintraub, {Neal Lee} and Sawyer, {Douglas B.} and Storrow, {Alan B.}",

note = "Funding Information: This work was supported by National Heart, Lung and Blood Institute grants K23HL085387 and R01HL088459, and by the National Center for Advancing Translational Sciences of the National Institute of Health under Award Number UL1 TR000445. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The authors are solely responsible for the design and conduct of this study, all study analyses and drafting and editing of the paper. ",

year = "2012",

month = dec,

doi = "10.1016/j.ahj.2012.07.033",

language = "English (US)",

volume = "164",

pages = "825--834",

journal = "American Heart Journal",

issn = "0002-8703",

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number = "6",

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TY - JOUR

T1 - Risk stratification in acute heart failure

T2 - Rationale and design of the STRATIFY and DECIDE studies

AU - Collins, Sean P.

AU - Lindsell, Christopher J.

AU - Jenkins, Cathy A.

AU - Harrell, Frank E.

AU - Fermann, Gregory J.

AU - Miller, Karen F.

AU - Roll, Sue N.

AU - Sperling, Matthew I.

AU - Maron, David J.

AU - Naftilan, Allen J.

AU - McPherson, John A.

AU - Weintraub, Neal Lee

AU - Sawyer, Douglas B.

AU - Storrow, Alan B.

N1 - Funding Information: This work was supported by National Heart, Lung and Blood Institute grants K23HL085387 and R01HL088459, and by the National Center for Advancing Translational Sciences of the National Institute of Health under Award Number UL1 TR000445. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The authors are solely responsible for the design and conduct of this study, all study analyses and drafting and editing of the paper.

PY - 2012/12

Y1 - 2012/12

N2 - Background: A critical challenge for physicians facing patients presenting with signs and symptoms of acute heart failure (AHF) is how and where to best manage them. Currently, most patients evaluated for AHF are admitted to the hospital, yet not all warrant inpatient care. Up to 50% of admissions could be potentially avoided and many admitted patients could be discharged after a short period of observation and treatment. Methods for identifying patients that can be sent home early are lacking. Improving the physician's ability to identify and safely manage low-risk patients is essential to avoiding unnecessary use of hospital beds. Methods: Two studies (STRATIFY and DECIDE) have been funded by the National Heart Lung and Blood Institute with the goal of developing prediction rules to facilitate early decision making in AHF. Using prospectively gathered evaluation and treatment data from the acute setting (STRATIFY) and early inpatient stay (DECIDE), rules will be generated to predict risk for death and serious complications. Subsequent studies will be designed to test the external validity, utility, generalizability and cost-effectiveness of these prediction rules in different acute care environments representing racially and socioeconomically diverse patient populations. Results: A major innovation is prediction of 5-day as well as 30-day outcomes, overcoming the limitation that 30-day outcomes are highly dependent on unpredictable, post-visit patient and provider behavior. A novel aspect of the proposed project is the use of a comprehensive cardiology review to correctly assign post-treatment outcomes to the acute presentation. Conclusions: Finally, a rigorous analysis plan has been developed to construct the prediction rules that will maximally extract both the statistical and clinical properties of every data element. Upon completion of this study we will subsequently externally test the prediction rules in a heterogeneous patient cohort.

AB - Background: A critical challenge for physicians facing patients presenting with signs and symptoms of acute heart failure (AHF) is how and where to best manage them. Currently, most patients evaluated for AHF are admitted to the hospital, yet not all warrant inpatient care. Up to 50% of admissions could be potentially avoided and many admitted patients could be discharged after a short period of observation and treatment. Methods for identifying patients that can be sent home early are lacking. Improving the physician's ability to identify and safely manage low-risk patients is essential to avoiding unnecessary use of hospital beds. Methods: Two studies (STRATIFY and DECIDE) have been funded by the National Heart Lung and Blood Institute with the goal of developing prediction rules to facilitate early decision making in AHF. Using prospectively gathered evaluation and treatment data from the acute setting (STRATIFY) and early inpatient stay (DECIDE), rules will be generated to predict risk for death and serious complications. Subsequent studies will be designed to test the external validity, utility, generalizability and cost-effectiveness of these prediction rules in different acute care environments representing racially and socioeconomically diverse patient populations. Results: A major innovation is prediction of 5-day as well as 30-day outcomes, overcoming the limitation that 30-day outcomes are highly dependent on unpredictable, post-visit patient and provider behavior. A novel aspect of the proposed project is the use of a comprehensive cardiology review to correctly assign post-treatment outcomes to the acute presentation. Conclusions: Finally, a rigorous analysis plan has been developed to construct the prediction rules that will maximally extract both the statistical and clinical properties of every data element. Upon completion of this study we will subsequently externally test the prediction rules in a heterogeneous patient cohort.

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Risk stratification in acute heart failure: Rationale and design of the STRATIFY and DECIDE studies

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