Keywords
Heart Failure
Mortality
Heart failure
Decision making
Computer-Assisted
Cardio-Renal syndrome
How to Cite
Abstract
Background: Acute renal failure (ARF) is a common complication in patients with heart failure (HF) and is associated with increased in-hospital mortality.
Purpose: To determine the factors associated with mortality 14 days after hospital admission in patients with HF admitted for ARF.
Methodology: Analytical cross-sectional study based on 1611 patients with chronic HF admitted for ARF. Biochemical and hemodynamic parameters were analyzed using Mann-Whitney U tests, Odds Ratio, prevalence ratio, Poisson regression, and binary logistics. A decision tree was used using a chi-squared automatic interaction detector (CHAID) to identify predictors of mortality.
Results: The CHAID analysis identified five variables associated with 14-day mortality in patients with HF and ARF: white blood cell count (WBC), respiratory rate, sodium, diastolic blood pressure (DBP), and oxygen saturation (SpO2). The terminal node with the highest association with mortality was comprised of patients with SpO2?93% and WBC>17,000 mm³, presenting an adjusted odds ratio (aOR) of 2.62 (95%CI:2.21-2.76) and an adjusted prevalence ratio (aPR) of 1.91 (95%CI:1.41-1.98). This node showed a specificity of 98% and a negative predictive value of 92%. Patients in this group presented significantly higher values ??of anion gap, BUN, pulse and respiratory rate, and lower values ??of bicarbonate, DBP and SpO2.
Conclusions: In patients with HF hospitalized for ARF, the coexistence of moderate hypoxemia and marked leukocytosis was associated with a higher risk of early mortality. Its identification and monitoring may be key to the implementation of therapeutic strategies.
References
Holgado JL, Lopez C, Fernandez A, Sauri I, Uso R, Trillo JL, et al. Acute kidney injury in heart failure: a population study. ESC Heart Fail. 2020;7(2):415-22. http://dx.doi.org/10.1002/ehf2.12595
Chen JJ, Lee TH, Kuo G, Yen CL, Chen SW, Chu PH, et al. Acute kidney disease after acute decompensated heart failure. Kidney Int Rep. 2022;7(3):526-36. http://dx.doi.org/10.1016/j.ekir.2021.12.033
Laghlam D, Jozwiak M, Nguyen LS. Renin-angiotensin-aldosterone system and immunomodulation: a state-of-the-art review. Cells. 2021;10(7):1767. https://doi.org/10.3390/cells10071767
Lin CY, Wang YH, Chen YM, Hung KY, Chang YC, Fang YT, et al. Dynamic monitoring of kidney injury status over 3 days in the intensive care unit as a sepsis phenotype associated with hospital mortality and hyperinflammation. Biomed J. 2022;45(4):665-74. http://dx.doi.org/10.1016/j.bj.2021.08.006
Hu H, Li L, Zhang Y, Sha T, Huang Q, Guo X, et al. A prediction model for assessing prognosis in critically ill patients with sepsis-associated acute kidney injury. Shock. 2021;56(4):564-72. http://dx.doi.org/10.1097/shk.0000000000001768
Wilson F. Automated, medication-targeted alerts for Acute Kidney Injury - A randomized trial. Dryad; 2023. http://dx.doi.org/10.5061/DRYAD.KH189327P
Wilson FP, Yamamoto Y, Martin M, Coronel-Moreno C, Li F, Cheng C, et al. A randomized clinical trial assessing the effect of automated medication-targeted alerts on acute kidney injury outcomes. Nat Commun. 2023;14(1):2826. https://doi.org/10.1038/s41467-023-38532-3
Hoste EAJ, Bagshaw SM, Bellomo R, Cely CM, Colman R, Cruz DN, et al. Epidemiology of acute kidney injury in critically ill patients: the multinational AKI-EPI study. Intensive Care Med. 2015;41(8):1411-23. https://doi.org/10.1007/s00134-015-3934-7
Benchimol EI, Smeeth L, Guttmann A, Harron K, Moher D, Petersen I, et al. The REporting of studies Conducted using Observational Routinely-collected health Data (RECORD) Statement. PLoS Med. 2015;12(10):e1001885. https://doi.org/10.1371/journal.pmed.1001885
Choi HY, Kim EY, Kim J. Prognostic factors in diabetes: Comparison of Chi-square automatic interaction detector (CHAID) decision tree technology and logistic regression. Medicine. 2022;101(42):e31343. https://doi.org/10.1097/md.0000000000031343
Ye F, Chen ZH, Chen J, Liu F, Zhang Y, Fan QY, et al. Chi-squared automatic interaction detection decision tree analysis of risk factors for infant anemia in Beijing, China. Chin Med J. 2016;129(10):1193-9. https://doi.org/10.4103/0366-6999.181955
IBM. Nodo CHAID [internet]. IBM; 2021. [citado 2025 jun 22]. https://www.ibm.com/docs/es/spss-modeler/saas?topic=nodes-chaid-node
Creative Commons. CC0 1.0 universal Deed [internet]. Creative Commons. [citado 2025 jun 22]. https://creativecommons.org/publicdomain/zero/1.0/
Gao YD, Ding M, Dong X, Zhang JJ, Kursat Azkur A, Azkur D, et al. Risk factors for severe and critically ill COVID?19 patients: a review. Allergy. 2021;76(2):428-55. https://doi.org/10.1111/all.14657
McCollum ED, King C, Ahmed S, Hanif AAM, Roy AD, Islam AA, et al. Defining hypoxaemia from pulse oximeter measurements of oxygen saturation in well children at low altitude in Bangladesh: an observational study. BMJ Open Respir Res. 2021;8(1):e001023. https://doi.org/10.1136/bmjresp-2021-001023
Majumdar SR, Eurich DT, Gamble JM, Senthilselvan A, Marrie TJ. Oxygen saturations less than 92% are associated with major adverse events in outpatients with pneumonia: a population-based cohort study. Clin Infect Dis. 2011;52(3):325-31. https://doi.org/10.1093/cid/ciq076
Smith RJ, Sarma D, Padkins MR, Gajic O, Lawler PR, Van Diepen S, et al. Admission total leukocyte count as a predictor of mortality in cardiac intensive care unit patients. JACC Adv. 2023;3(1):100757. https://doi.org/10.1016/j.jacadv.2023.100757
Kinsey GR, Okusa MD. Role of leukocytes in the pathogenesis of acute kidney injury. Crit Care. 2012;16(2):214. https://doi.org/10.1186/cc11228
Taylor CJ, Ordóñez-Mena JM, Jones NR, Roalfe AK, Lay-Flurrie S, Marshall T, et al. National trends in heart failure mortality in men and women, United Kingdom, 2000-2017. Eur J Heart Fail. 2021;23(1):3-12. https://doi.org/10.1002/ejhf.1996
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.