Optimizable Risk Factors Contributing to Mortality in Patients With Heart Failure Undergoing Noncardiac Surgery | Cardiology | JAMA Surgery | JAMA Network
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Table.  Contribution of Various Demographic and Clinical Factors to the Gap in Postoperative Mortality Risk Between Patients With and Without Heart Failure
Contribution of Various Demographic and Clinical Factors to the Gap in Postoperative Mortality Risk Between Patients With and Without Heart Failure
1.
Lerman  BJ, Popat  RA, Assimes  TL, Heidenreich  PA, Wren  SM.  Association of left ventricular ejection fraction and symptoms with mortality after elective noncardiac surgery among patients with heart failure.   JAMA. 2019;321(6):572-579. doi:10.1001/jama.2019.0156PubMedGoogle ScholarCrossref
2.
Massarweh  NN, Kaji  AH, Itani  KMF.  Practical guide to surgical data sets: Veterans Affairs Surgical Quality Improvement Program (VASQIP).   JAMA Surg. 2018;153(8):768-769. doi:10.1001/jamasurg.2018.0504PubMedGoogle ScholarCrossref
3.
Livingston  EH, Fairlie  RW.  Little effect of insurance status or socioeconomic condition on disparities in minority appendicitis perforation rates.   Arch Surg. 2012;147(1):11-17. doi:10.1001/archsurg.2011.746PubMedGoogle ScholarCrossref
4.
Cleland  JGF, Zhang  J, Pellicori  P,  et al.  Prevalence and outcomes of anemia and hematinic deficiencies in patients with chronic heart failure.   JAMA Cardiol. 2016;1(5):539-547. doi:10.1001/jamacardio.2016.1161PubMedGoogle ScholarCrossref
5.
Ponikowski  P, van Veldhuisen  DJ, Comin-Colet  J,  et al; CONFIRM-HF Investigators.  Beneficial effects of long-term intravenous iron therapy with ferric carboxymaltose in patients with symptomatic heart failure and iron deficiency.   Eur Heart J. 2015;36(11):657-668. doi:10.1093/eurheartj/ehu385PubMedGoogle ScholarCrossref
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    Research Letter
    April 8, 2020

    Optimizable Risk Factors Contributing to Mortality in Patients With Heart Failure Undergoing Noncardiac Surgery

    Author Affiliations
    • 1Department of Health Research and Policy (Division of Epidemiology), Stanford University School of Medicine, Stanford, California
    • 2Department of Surgery, University of California, Los Angeles, Los Angeles
    • 3Palo Alto Veterans Affairs Health Care System, Palo Alto, California
    • 4Department of Surgery, Stanford University School of Medicine, Stanford, California
    JAMA Surg. 2020;155(6):530-531. doi:10.1001/jamasurg.2020.0257

    Heart failure (HF) is a major risk factor for postoperative mortality. A recent study found that veteran patients with preoperative HF had a 90-day postoperative mortality of 5.5% (95% CI, 5.3%-5.7%) compared with patients without HF, who had a mortality of 1.2% (95% CI, 1.1%-1.3%).1 The crude risk of mortality in patients with vs without HF was substantially attenuated by multivariable adjustment, a finding suggesting that a portion of the elevated mortality risk may be attributable to risk factors amenable to preoperative optimization.

    Gap decomposition analysis is a statistical technique that uses repeated regression simulations to estimate the degree to which individual covariates contribute to a difference in outcomes between 2 groups. Gap decomposition analysis was used to determine the relative contributions of various risk factors to the postoperative mortality gap between patients with and without HF and identify risk factors that can be optimized before surgery.

    Methods

    Patients undergoing nonemergency, noncardiac surgery in Veterans Administration hospitals (47 997 with HF and 561 738 without HF) from 2009 through 2016 were identified in its Veterans Affairs Surgical Quality Improvement Program database.1,2 The following covariates were used in this analysis: preoperative hematocrit and creatinine levels (measured as continuous variables), age, body mass index (calculated as weight in kilograms divided by height in meters squared), sex, race/ethnicity, American Society of Anesthesiologists (ASA) score, and alcohol intake, as well as comorbid medical conditions. Gap decomposition was performed for all available covariates for the postoperative mortality gap between patients with vs without HF. A detailed description of gap decomposition analysis, as well as the publicly available code used in this study, has been published by Livingston et al.3

    This study was approved by the Stanford University institutional review board and Department of Veterans Affairs. Because this study involved retrospective use of existing data, a waiver of informed consent was obtained. Data were analyzed from January 2018 to May 2019. A 2-tailed P value less than .05 was considered significant, and SAS version 9.4 (SAS Institute) was used for all analyses.

    Results

    The 18 variables considered in the gap decomposition analysis explained 66.4% of the observed crude 90-day postoperative mortality difference between patients with and without HF (Table). Three variables contributed the most to the explained mortality variance in patients with HF undergoing surgery: ASA score (20.8%), age (12.5%), and preoperative hematocrit level (16.5%). Approximately one-third of the mortality variance could not be explained by the variables available for this analysis.

    Discussion

    Most of the risk factors examined in this study of the association of HF with postoperative mortality contributed little to explaining variation in mortality in this population. Of the 3 risk factors that had a reasonably large contribution, age and ASA score cannot be optimized before surgery. Preoperative hematocrit level (adjusted odds ratio, 0.90 [95% CI 0.89-0.90] per percentage point)1 contributed to 16.5% of the gap, suggesting that intervention in preoperative anemia might be a target for clinical intervention to improve postoperative mortality in patients with HF.

    Anemia in the HF population is associated with increased all-cause mortality.4 Given their abnormal cardiovascular physiology, patients with HF may be more sensitive to perioperative anemia than those without HF, and an optimal hematocrit level in these patients may need to be higher. If future research can identify this optimal level, then low-risk and low-cost interventions, such as preoperative iron infusions or identification and control of sources of bleeding, have the potential to substantially affect postoperative mortality in patients with HF.5

    One-third of the mortality gap was unexplained in this study. Unmeasured factors contributing to outcomes are a limitation for all observational studies, and gap analysis offers a mechanism to quantify how much of an outcome is unexplained by the available data.

    The code for performing this analysis is publicly available.3 Future observational studies may benefit from performing gap analysis to determined how much any included variable contributes to an outcome and how much of that outcome cannot be explained by the available date.

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    Article Information

    Accepted for Publication: February 2, 2020.

    Corresponding Author: Sherry M. Wren, MD, Palo Alto Veterans Affairs Health Care System G112, 3801 Miranda Ave, Palo Alto, CA 94304 (swren@stanford.edu).

    Published Online: April 8, 2020. doi:10.1001/jamasurg.2020.0257

    Author Contributions: Drs Lerman and Wren had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

    Concept and design: All authors.

    Acquisition, analysis, or interpretation of data: Lerman, Livingston.

    Drafting of the manuscript: Lerman, Livingston.

    Critical revision of the manuscript for important intellectual content: Wren.

    Statistical analysis: Lerman, Livingston.

    Obtained funding: Lerman.

    Administrative, technical, or material support: Wren.

    Supervision: Livingston, Wren.

    Conflict of Interest Disclosures: Dr Lerman reported grants from the National Institutes of Health Center for the Advancement of Clinical and Translational Sciences during the conduct of the study. Dr Livingston reported serving as an associate editor of JAMA during the conduct of the study. No other disclosures were reported.

    Funding/Support: Dr Lerman was supported by a National Institutes of Health Center for Advancing Translational Science Clinical and Translational Science Award (grants TL1TR001084 and UL1TR001085).

    Role of the Funder/Sponsor: The funder had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

    References
    1.
    Lerman  BJ, Popat  RA, Assimes  TL, Heidenreich  PA, Wren  SM.  Association of left ventricular ejection fraction and symptoms with mortality after elective noncardiac surgery among patients with heart failure.   JAMA. 2019;321(6):572-579. doi:10.1001/jama.2019.0156PubMedGoogle ScholarCrossref
    2.
    Massarweh  NN, Kaji  AH, Itani  KMF.  Practical guide to surgical data sets: Veterans Affairs Surgical Quality Improvement Program (VASQIP).   JAMA Surg. 2018;153(8):768-769. doi:10.1001/jamasurg.2018.0504PubMedGoogle ScholarCrossref
    3.
    Livingston  EH, Fairlie  RW.  Little effect of insurance status or socioeconomic condition on disparities in minority appendicitis perforation rates.   Arch Surg. 2012;147(1):11-17. doi:10.1001/archsurg.2011.746PubMedGoogle ScholarCrossref
    4.
    Cleland  JGF, Zhang  J, Pellicori  P,  et al.  Prevalence and outcomes of anemia and hematinic deficiencies in patients with chronic heart failure.   JAMA Cardiol. 2016;1(5):539-547. doi:10.1001/jamacardio.2016.1161PubMedGoogle ScholarCrossref
    5.
    Ponikowski  P, van Veldhuisen  DJ, Comin-Colet  J,  et al; CONFIRM-HF Investigators.  Beneficial effects of long-term intravenous iron therapy with ferric carboxymaltose in patients with symptomatic heart failure and iron deficiency.   Eur Heart J. 2015;36(11):657-668. doi:10.1093/eurheartj/ehu385PubMedGoogle ScholarCrossref
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