Risk of in-hospital mortality identified according to the typology of patients with acute heart failure: Classification tree analysis on data from the Acute Heart Failure Database–Main registry

Abstract Background The clinical efficiency of routine oxygen therapy is uncertain in patients with acute heart failure (AHF) who do not have hypoxemia. The aim of this study was to investigate the association between oxygen therapy and clinical outcomes in normoxemic patients hospitalized with AHF using real-world data. Methods Normoxemic patients diagnosed with AHF on ICU admission from the electronic ICU (eICU) Collaborative Research Database were included in the current study, in which the study population was divided into the oxygen therapy group and the ambient-air group. Propensity score matching (PSM) was applied to create a balanced covariate distribution between patients receiving supplemental oxygen and those exposed to ambient air. Linear regression and logistic regression models were performed to assess the associations between oxygen therapy and length of stay (LOS), and all-cause in-hospital as well as ICU mortality rates, respectively. A series of sensitivity and subgroup analyses were conducted to further validate the robustness of our findings. Results A total of 2922 normoxemic patients with AHF were finally included in the analysis. Overall, 42.1% (1230/2922) patients were exposed to oxygen therapy, and 57.9% (1692/2922) patients did not receive oxygen therapy (defined as the ambient-air group). After PSM analysis, 1122 pairs of patients were matched: each patient receiving oxygen therapy was matched with a patient without receiving supplemental oxygen. The multivariable logistic model showed that there was no significant interaction between the ambient air and oxygen group for all-cause in-hospital mortality [odds ratio (OR) 1.30; 95% confidence interval (CI) 0.92–1.82; P = 0.138] or ICU mortality (OR 1.39; 95% CI 0.83–2.32; P = 0.206) in the post-PSM cohorts. In addition, linear regression analysis revealed that oxygen therapy was associated with prolonged ICU LOS (OR 1.11; 95% CI 1.06–1.15; P < 0.001) and hospital LOS (OR 1.06; 95% CI 1.01–1.10; P = 0.009) after PSM. Furthermore, the absence of an effect of supplemental oxygen on mortality was consistent in all subgroups. Conclusion Routine use of supplemental oxygen in AHF patients without hypoxemia was not found to reduce all-cause in-hospital mortality or ICU mortality.

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Determinants of In-Hospital Mortality in Elderly Patients Aged 80 Years or above with Acute Heart Failure: A Retrospective Cohort Study at a Single Rural Hospital

Journal of Clinical Medicine ◽

10.3390/jcm10071468 ◽

2021 ◽

Vol 10 (7) ◽

pp. 1468

Author(s):

Yusuke Watanabe ◽

Kazuko Tajiri ◽

Hiroyuki Nagata ◽

Masayuki Kojima

Keyword(s):

Heart Failure ◽

Regression Analysis ◽

Elderly Patients ◽

Hospital Mortality ◽

Acute Heart Failure ◽

Stepwise Regression ◽

Stepwise Regression Analysis ◽

Antibiotic Administration ◽

Very Elderly ◽

Survivor Group

Heart failure is one of the leading causes of mortality worldwide. Several predictive risk scores and factors associated with in-hospital mortality have been reported for acute heart failure. However, only a few studies have examined the predictors in elderly patients. This study investigated determinants of in-hospital mortality in elderly patients with acute heart failure, aged 80 years or above, by evaluating the serum sodium, blood urea nitrogen, age and serum albumin, systolic blood pressure and natriuretic peptide levels (SOB-ASAP) score. We reviewed the medical records of 106 consecutive patients retrospectively and classified them into the survivor group (n = 83) and the non-survivor group (n = 23) based on the in-hospital mortality. Patient characteristics at admission and during hospitalization were compared between the two groups. Multivariate stepwise regression analysis was used to evaluate the in-hospital mortality. The SOB-ASAP score was significantly better in the survivor group than in the non-survivor group. Multivariate stepwise regression analysis revealed that a poor SOB-ASAP score, oral phosphodiesterase 3 inhibitor use, and requirement of early intravenous antibiotic administration were associated with in-hospital mortality in very elderly patients with acute heart failure. Severe clinical status might predict outcomes in very elderly patients.

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Classifying and prioritizing the success and failure factors of technology commercialization of public R&D in South Korea: using classification tree analysis

The Journal of Technology Transfer ◽

10.1007/s10961-014-9376-5 ◽

2014 ◽

Vol 40 (5) ◽

pp. 877-898 ◽

Cited By ~ 4

Author(s):

Mijung Jung ◽

Yi-beck Lee ◽

Heesang Lee

Keyword(s):

South Korea ◽

Classification Tree ◽

Technology Commercialization ◽

Classification Tree Analysis ◽

Tree Analysis ◽

Failure Factors

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Early prediction of poor outcome in extremely low birth weight infants by classification tree analysis

The Journal of Pediatrics ◽

10.1016/j.jpeds.2005.11.042 ◽

2006 ◽

Vol 148 (4) ◽

pp. 438-444.e1 ◽

Cited By ~ 29

Author(s):

N. Ambalavanan ◽

A. Baibergenova ◽

W.A. Carlo ◽

S. Saigal ◽

B. Schmidt ◽

...

Keyword(s):

Birth Weight ◽

Low Birth Weight ◽

Classification Tree ◽

Extremely Low Birth Weight ◽

Early Prediction ◽

Classification Tree Analysis ◽

Low Birth Weight Infants ◽

Tree Analysis ◽

Poor Outcome

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Classification tree analysis to examine influences on colorectal cancer screening

Cancer Causes & Control ◽

10.1007/s10552-015-0523-6 ◽

2015 ◽

Vol 26 (3) ◽

pp. 443-454 ◽

Cited By ~ 7

Author(s):

Gregory M. Dominick ◽

Mia A. Papas ◽

Michelle L. Rogers ◽

William Rakowski

Keyword(s):

Colorectal Cancer ◽

Cancer Screening ◽

Colorectal Cancer Screening ◽

Classification Tree ◽

Classification Tree Analysis ◽

Tree Analysis

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Substance Use Relapse Among Veterans at Termination of Treatment for Substance Use Disorders

Military Medicine ◽

10.1093/milmed/usab280 ◽

2021 ◽

Author(s):

Christian A Betancourt ◽

Panagiota Kitsantas ◽

Deborah G Goldberg ◽

Beth A Hawks

Keyword(s):

Risk Factors ◽

Substance Use ◽

Logistic Regression ◽

Substance Use Disorders ◽

Classification Tree ◽

Treatment Completion ◽

Classification Tree Analysis ◽

Data Set ◽

Tree Analysis ◽

Substance Addiction

ABSTRACT Introduction Military veterans continue to struggle with addiction even after receiving treatment for substance use disorders (SUDs). Identifying factors that may influence SUD relapse upon receiving treatment in veteran populations is crucial for intervention and prevention efforts. The purpose of this study was to examine risk factors that contribute to SUD relapse upon treatment completion in a sample of U.S. veterans using logistic regression and classification tree analysis. Materials and Methods Data from the 2017 Treatment Episode Data Set—Discharge (TEDS-D) included 40,909 veteran episode observations. Descriptive statistics and multivariable logistic regression analysis were conducted to determine factors associated with SUD relapse after treatment discharge. Classification trees were constructed to identify high-risk subgroups for substance use after discharge from treatment for SUDs. Results Approximately 94% of the veterans relapsed upon discharge from outpatient or residential SUD treatment. Veterans aged 18-34 years old were significantly less likely to relapse than the 35-64 age group (odds ratio [OR] 0.73, 95% confidence interval [CI]: 0.66, 0.82), while males were more likely than females to relapse (OR 1.55, 95% CI: 1.34, 1.79). Unemployed veterans (OR 1.92, 95% CI: 1.67, 2.22) or veterans not in the labor force (OR 1.29, 95% CI: 1.13, 1.47) were more likely to relapse than employed veterans. Homeless vs. independently housed veterans had 3.26 (95% CI: 2.55, 4.17) higher odds of relapse after treatment. Veterans with one arrest vs. none were more likely to relapse (OR 1.52, 95% CI: 1.19, 1.95). Treatment completion was critical to maintain sobriety, as every other type of discharge led to more than double the odds of relapse. Veterans who received care at 24-hour detox facilities were 1.49 (95% CI: 1.23, 1.80) times more likely to relapse than those at rehabilitative/residential treatment facilities. Classification tree analysis indicated that homelessness upon discharge was the most important predictor in SUD relapse among veterans. Conclusion Aside from numerous challenges that veterans face after leaving military service, SUD relapse is intensified by risk factors such as homelessness, unemployment, and insufficient SUD treatment. As treatment and preventive care for SUD relapse is an active field of study, further research on SUD relapse among homeless veterans is necessary to better understand the epidemiology of substance addiction among this vulnerable population. The findings of this study can inform healthcare policy and practices targeting veteran-tailored treatment programs to improve SUD treatment completion and lower substance use after treatment.

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