Protein-Energy Malnutrition and Outcomes of Hospitalizations for Heart Failure in the USA

Adejumo, Adeyinka Charles; Adejumo, Kelechi Lauretta; Adegbala, Oluwole; Chinedozi, Ifeanyichukwu; Ndansi, Jordan; Akanbi, Olalekan; Onyeakusi, Nnaemeka; Ogundipe, Olumuyiwa Akinbolaji; Bob‐Manuel, Tamunoinemi; Adeboye, Adedayo

doi:10.1016/j.amjcard.2018.12.014

Cited by 31 publications

(32 citation statements)

References 26 publications

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“…Knowing these medications would have enabled us to reveal medications that might be more beneficial to subgroups of patient with AMI, such as PEM. By using a rigorous propensity‐matching methodology, we ensured optimal matching of comorbidities and eliminated the majority of this difference, as has been done in recent studies . However, residual bias may still exist from unmeasured confounding variables, especially since the NIS does not contain laboratory values and we are unable to calculate and match by other mortality risk scores.…”

Section: Discussionmentioning

confidence: 99%

“…By using a rigorous propensity-matching methodology, we ensured optimal matching of comorbidities and eliminated the majority of this difference, as has been done in recent studies. [48][49][50] However, residual bias may still exist from unmeasured confounding variables, especially since the NIS does not contain laboratory values and we are unable to calculate and match by other mortality risk scores. Fourth, NIS does not allow tracking of patients after discharge, and we could not explore the impact of PEM on readmissions, post-AMI reinfarction, and other posthospitalization events.…”

Section: Limitations and Strengthsmentioning

confidence: 99%

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Inferior Outcomes of Patients With Acute Myocardial Infarction and Comorbid Protein‐Energy Malnutrition

Adejumo

Adegbala

et al. 2019

J Parenter Enteral Nutr

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Background Protein‐energy malnutrition (PEM) diminishes amino acid and energy availability, impairing the body's healing capability after injury, such as in myocardial damage following acute myocardial infarction (AMI). Aims We sought to investigate the influence of PEM on clinical outcomes of AMI. Methods We identified records with a primary discharge diagnosis of AMI from the Nationwide Inpatient Sample (2012–2014), stratified by concomitant PEM. We matched PEM to no‐PEM (1:1) using a greedy algorithm–based propensity methodology and estimated the impact of PEM on health outcomes (SAS 9.4). Results Of the 332,644 hospitalizations for AMI, 11,675 had concomitant PEM accounting for roughly $US 1.5 billion and over 119,792 hospital days. PEM was associated with older age (74.43‐ vs. 66.90‐years; P < 0.0001), female sex (49.19% vs. 38.44%; P < 0.0001), black race (12.78% vs. 10.46%; P < 0.0001), and higher comorbidity burden (Deyo > 3: 32.77% vs. 16.69%; P < 0.0001). After propensity matching, PEM was associated with higher mortality (Adjusted odds ratio [AOR]: 1.59 [1.46–1.73]), cardiogenic shock (AOR: 2.26 [2.08–2.44]), discharge to secondary facilities (AOR: 2.21 [2.10–2.33]), charges ($135,500 [$131,956–139,139] vs. $81,084 [$79,241–82,970]), cardiac artery bypass surgery (AOR:1.81 [1.66–1.97]), intra‐aortic balloon pump placement (AOR: 1.83 [1.65–2.04]) and longer length of stay (10.15‐ vs. 5.52‐days). Conclusions PEM is a predisposing factor for devastating clinical outcomes among AMI hospitalizations. Higher prevention, identification and management of PEM among high‐risk individuals (older age, female sex, and black race) residing in the community are needed.

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Section: Discussionmentioning

confidence: 99%

Section: Limitations and Strengthsmentioning

confidence: 99%

Inferior Outcomes of Patients With Acute Myocardial Infarction and Comorbid Protein‐Energy Malnutrition

Adejumo

Adegbala

et al. 2019

J Parenter Enteral Nutr

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“…Many common features of HF, which we will explore further, place patients at an increased risk for developing malnutrition. Malnutrition is known to affect the course of many chronic conditions and has been shown to predict adverse outcomes for those diagnosed with HF . Although the exact number of patients with HF and malnutrition is unknown, several studies have evaluated national databases to seek these answers.…”

Section: Heart Failure Patient Populationmentioning

confidence: 99%

“…Although the exact number of patients with HF and malnutrition is unknown, several studies have evaluated national databases to seek these answers. An evaluation of the National Inpatient Sample (NIS) from 2012 to 2014 revealed that among patients with a primary hospitalization for HF, 6% also carried a diagnosis of malnutrition . A 2010 study of the national database showed that 16.2% of hospitalized patients diagnosed with malnutrition carried HF as a comorbidity .…”

Section: Heart Failure Patient Populationmentioning

confidence: 99%

“…A 2010 study of the national database showed that 16.2% of hospitalized patients diagnosed with malnutrition carried HF as a comorbidity . Because of the presence of edema and fluid‐related weight fluctuations, malnutrition is thought to be underdiagnosed in this population . In a recent study of the NIS database from January 2012 to December 2014, HF patients identified with protein‐energy malnutrition were shown to have over twice the rate of mortality (8.34% vs 3.55%) and higher incidence of shock (cardiogenic and noncardiogenic), cardiac arrest, cerebrovascular accident, acute kidney failure, hemodialysis for acute kidney failure, need for parenteral nutrition, blood product transfusion, acute respiratory failure, and mechanical ventilation .…”

Section: Heart Failure Patient Populationmentioning

confidence: 99%

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Using Global Criteria to Detect Malnutrition: Application in Disease States

2019

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The accurate diagnosis and subsequent treatment of malnutrition is an essential component of nutrition care because of the known negative impacts of malnutrition on patient outcomes. The detection of malnutrition is influenced by disease-specific characteristics, practice settings, and the availability of resources. This article explores how malnutrition diagnosis through the application of the Global Leadership Initiative on Malnutrition global consensus on malnutrition diagnostic criteria is impacted by the unique characteristics of the heart failure, oncology, and trauma populations. (Nutr Clin Pract. 2020;35:85-97)

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Development and validation of a multifactorial nomogram to predict 48 month mortality in decompensated heart failure

Zhuang,

Chen,

Weng

et al. 2024

ESC Heart Failure

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Background and aimsAs the incidence of heart failure (HF) increases, the need for practical tools to evaluate the long‐term prognosis in these patients remains critical. Our study aimed to develop a 48 month prediction model for all‐cause mortality in decompensated HF patients using available clinical indicators.MethodsHF patients (n = 503), 60 years or older, were divided into a training cohort (n = 402) and a validation cohort (n = 101). Data on demographics, comorbidities, laboratory results and medications were gathered. Prediction models were developed using the Prognostic Nutritional Index (PNI), cholinesterase (ChE) and a multifactorial nomogram incorporating clinical variables. These models were constructed using the least absolute shrinkage and selection operator algorithm and multivariate logistic regression analysis. The performance of the model was assessed in terms of calibration, discrimination and clinical utility.ResultsThe mean age was 77.11 ± 8.85 years, and 216 (42.9%) were female. The multifactorial nomogram included variables of ChE, lymphocyte count, albumin, serum creatinine and N‐terminal pro‐brain natriuretic peptide (all P < 0.05). In the training cohort, the nomogram's C‐index was 0.926 [95% confidence interval (CI) 0.896–0.950], outperforming the PNI indices at 0.883 and ChE at 0.804 (Z‐tests, P < 0.05). The C‐index in the validation cohort was 0.913 (Z‐tests, P < 0.05). Calibration and decision curve analysis confirmed model reliability, indicating a more significant net benefit than PNI and ChE alone.ConclusionsBoth the ChE‐ and PNI‐based prediction models effectively predict the long‐term prognosis in patients over 60 years of age with decompensated HF. The multifactorial nomogram model shows superior performance, improving clinical decision‐making and patient outcomes.

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Protein-Energy Malnutrition and Outcomes of Hospitalizations for Heart Failure in the USA

Cited by 31 publications

References 26 publications

Inferior Outcomes of Patients With Acute Myocardial Infarction and Comorbid Protein‐Energy Malnutrition

Inferior Outcomes of Patients With Acute Myocardial Infarction and Comorbid Protein‐Energy Malnutrition

Using Global Criteria to Detect Malnutrition: Application in Disease States

Development and validation of a multifactorial nomogram to predict 48 month mortality in decompensated heart failure

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