Usefulness of the CONUT index upon hospital admission as a potential prognostic indicator of COVID-19 health outcomes

Background: In-hospital mortality in patients with coronavirus disease 2019 (COVID-19) is high. Simple prognostic indices are needed to identify patients at high-risk of COVID-19 health outcomes. We aimed to determine the usefulness of the CONtrolling NUTritional status (CONUT) index as a potential prognostic indicator of mortality in COVID-19 patients upon hospital admission. Methods: Our study design is of a retrospective observational study in a large cohort of COVID-19 patients. In addition to descriptive statistics, a Kaplan-Meier mortality analysis and a Cox regression were performed, as well as receiver operating curve (ROC). Results: From February 5, 2020 to January 21, 2021, there was a total of 2969 admissions for COVID-19 at our hospital, corresponding to 2844 patients. Overall, baseline (within 4 days of admission) CONUT index could be scored for 1627 (57.2%) patients. Patients' age was 67.3 ± 16.5 years and 44.9% were women. The CONUT severity distribution was: 194 (11.9%) normal (0-1); 769 (47.2%) light (2-4); 585 (35.9%) moderate (5-8); and 79 (4.9%) severe (9-12). Mortality of 30 days after admission was 3.1% in patients with normal risk CONUT, 9.0% light, 22.7% moderate, and 40.5% in those with severe CONUT (P < 0.05). An increased risk of death associated with a greater baseline CONUT stage was sustained in a multivariable Cox regression model (P < 0.05). An increasing baseline CONUT stage was associated with a longer duration of admission, a greater requirement for the use of non-invasive and invasive mechanical ventilation, and other clinical outcomes (all P < 0.05). The ROC of CONUT for mortality had an area under the curve (AUC) and 95% confidence interval of 0.711 (0.676-0746). Conclusion: The CONUT index upon admission is potentially a reliable and independent prognostic indicator of mortality and length of hospitalization in COVID-19 patientsThe work is supported by a grant from the European Union’s Horizon 2020 Research and Innovation Programme under Grant Agreement (No 101016216

Prognostic value of simple frailty and malnutrition screening tools in patients with acute heart failure due to left ventricular systolic dysfunction

Background: Frailty and malnutrition are common in patients with heart failure (HF), and are associated with adverse outcomes. We studied the prognostic value of three malnutrition and three frailty indices in patients admitted acutely to hospital with HF. Methods: 265 consecutive patients [62% males, median age 80 (interquartile range (IQR): 72–86) years, median NTproBNP 3633 (IQR: 2025–6407) ng/l] admitted with HF between 2013 and 2014 were enrolled. Patients were screened for frailty using the Derby frailty index (DFI), acute frailty network (AFN) frailty criteria, and clinical frailty scale (CFS) and for malnutrition using the geriatric nutritional risk index (GNRI), controlling nutritional status (CONUT) score and prognostic nutritional index (PNI). Results: According to the CFS (> 4), DFI, and AFN, 53, 50, and 53% were frail, respectively. According to the GNRI (≤ 98), CONUT score (> 4), and PNI (≤ 38), 46, 46, and 42% patients were malnourished, respectively. During a median follow-up of 598 days (IQR 319–807 days), 113 patients died. One year mortality was 1% for those who were neither frail nor malnourished; 15% for those who were either malnourished or frail; and 65% for those who were both malnourished and frail. Amongst the malnutrition scores, PNI, and amongst the frailty scores, CFS increased model performance most compared with base model. A final model, including CFS and PNI, increased c-statistic for mortality prediction from 0.68 to 0.84. Conclusion: Worsening frailty and malnutrition indices are strongly related to worse outcome in patients hospitalised with HF

Clinical undernutrition in 2014: pathogenesis, early diagnosis and consequences; undernutrition and trophopathy

The last ten years have allowed me to mature some concepts and criteria in relation to malnutrition in the clinical practice. A lot of us have devoted all our efforts in an attempt to take it under control. The results, however, have shown to be insufficient in the clinical practice, because Hospital Undernutrition still persists in our hospitals and in fact, its prevalence is growing due to an ageing population. I think it is necessary to insist in renaming it as Clinical Undernutrition because it not only appears in hospital settings but it is present before and persists even after hospitalization; the latter reinforces the condition by forcing a change in the habits of the patient and the consequences of the treatments. I would also like to sustain that the risk is not caused by the undernutrition in itself but rather in the disruption of the nutritional balance which is a consequence of the aforementioned conditions and which is defined in a term: Trophopathy; that is, a disruption in the trophism or in the normal functioning of the nutritional status. This disruption constitutes the core risk that is associated with clinical undernutrition and the physical consequences of it. The disruption occurs internally and it will play havoc on cellular nutrition, tissues and further. It appears simultaneously in the blood, so it should be searched and detected there as it is the closest possible place to its origin. The new therapeutic procedures make it possible to cure some cases that in the past were impossible to treat. However, this also means increased risks and so requires a strict control to achieve the best results. Both illness and its treatment put homeostasis at risk and they will definitely impact the nutritional balance, being the latter the key objective in order to achieve or restore the healing process and health. Apart from the benefit obtained with the treatment, it is necessary to apply an appropriate nutritional support that will guarantee the least amount of risks which could derive from an imbalanced nutritional status. The use of automated systems to predict and control the risk factors during the clinical phase makes it possible to have a more thorough control of the illness from its origins, allowing an early diagnosis and treatment of it