Nutritional Risk Assessment Scores Effectively Predict Mortality in Critically Ill Patients with Severe COVID-19
Constantin Bodolea, Andrada Nemes, Lucretia Avram, Rares Craciun, Mihaela Coman, Mihaela Ene-Cocis, Cristina Ciobanu, Dana Crisan
Nutrients, doi:10.3390/nu14102105
Background: Malnutrition predicts a worse outcome for critically ill patients. However, quick, easy-to-use nutritional risk assessment tools have not been adequately validated. Aims and Methods: The study aimed to evaluate the role of four biological nutritional risk assessment instruments (the Prognostic Nutritional Index-PNI, the Controlling Nutritional Status Score-CONUT, the Nutrition Risk in Critically Ill-NUTRIC, and the modified NUTRIC-mNUTRIC), along with CT-derived fat tissue and muscle mass measurements in predicting in-hospital mortality in a consecutive series of 90 patients hospitalized in the intensive care unit for COVID-19-associated ARDS. Results: In-hospital mortality was 46.7% (n = 42/90). Non-survivors had a significantly higher nutritional risk, as expressed by all four scores. All scores were independent predictors of mortality on the multivariate regression models. PNI had the best discriminative capabilities for mortality, with an area under the curve (AUC) of 0.77 for a cut-off value of 28.05. All scores had an AUC above 0.72. The volume of fat tissue and muscle mass were not associated with increased mortality risk. Conclusions: PNI, CONUT, NUTRIC, and mNUTRIC are valuable nutritional risk assessment tools that can accurately predict mortality in critically ill patients with COVID-19-associated ARDS.
Conflicts of Interest: The authors declare no conflict of interest.
References
Abate, Chekole, Estifanos, Abate, Kabthymer, Prevalence and Outcomes of Malnutrition among Hospitalized COVID-19 Patients: A Systematic Review and Meta-Analysis, Clin. Nutr. ESPEN,
doi:10.1016/j.clnesp.2021.03.002Bengelloun, Ortega, Ancochea, Sanz-Garcia, Rodríguez-Serrano et al., Usefulness of the CONUT Index upon Hospital Admission as a Potential Prognostic Indicator of COVID-19 Health Outcomes, Chin. Med. J,
doi:10.1097/CM9.0000000000001798Besutti, Pellegrini, Ottone, Cantini, Milic et al., The Impact of Chest CT Body Composition Parameters on Clinical Outcomes in COVID-19 Patients, PLoS ONE,
doi:10.1371/journal.pone.0251768Booth, Reed, Ponzo, Yassaee, Aral et al., Population Risk Factors for Severe Disease and Mortality in COVID-19: A Global Systematic Review and Meta-Analysis, PLoS ONE,
doi:10.1371/journal.pone.0247461Cederholm, Jensen, Correia, Gonzalez, Fukushima et al., GLIM Criteria for the Diagnosis of Malnutrition-A Consensus Report from the Global Clinical Nutrition Community, J. Cachexia Sarcopenia Muscle,
doi:10.1002/jcsm.12383Charlson, Pompei, Ales, Mackenzie, A New Method of Classifying Prognostic in Longitudinal Studies: Development and Validation, J. Chronic Dis,
doi:10.1016/0021-9681(87)90171-8Cheng, Sung, Cheng, Hsu, Guo et al., Prognostic Nutritional Index and the Risk of Mortality in Patients With Acute Heart Failure, J. Am. Heart Assoc,
doi:10.1161/JAHA.116.004876De Ulíbarri, González-Madroño, De Vilar, González, González et al., CONUT: A Tool for Controlling Nutritional Status. First Validation in a Hospital Population, Nutr. Hosp
Heyland, Dhaliwal, Jiang, Day, Identifying Critically Ill Patients Who Benefit the Most from Nutrition Therapy: The Development and Initial Validation of a Novel Risk Assessment Tool, Crit. Care,
doi:10.1186/cc10546Hu, Deng, Wang, Chen, Gu et al., Predictive Value of the Prognostic Nutritional Index for the Severity of Coronavirus Disease, Nutrition,
doi:10.1016/j.nut.2020.111123Hu, Guo, Zhou, Shi, Characteristics of SARS-CoV-2 and COVID-19, Nat. Rev. Microbiol
Kosovali, Kucuk, Soyal, Mutlu, Can Prognostic Nutritional Index Predict Mortality in Intensive Care Patients with COVID-19?, Int. J. Clin. Pract,
doi:10.1111/ijcp.14800Kumar, Kumar, Kumar, Pattanayak, Singh et al., Nutric Score as a Predictor of Outcome in COVID-19 ARDS Patients: A Retrospective Observational Study, Indian J. Anaesth,
doi:10.4103/ija.ija_474_21Li, Fang, Li, Pan, Qin et al., CT Image Visual Quantitative Evaluation and Clinical Classification of Coronavirus Disease (COVID-19), Eur. Radiol,
doi:10.1007/s00330-020-06817-6Li, Zhou, Ba, Wang, Song et al., Nutritional Risk and Therapy for Severe and Critical COVID-19 Patients: A Multicenter Retrospective Observational Study, Clin. Nutr
Liberti, Piacentino, Umbrello, Muttini, Comparison between Nutric Score and Modified Nutric Score to Assess ICU Mortality in Critically Ill Patients with COVID-19, Clin. Nutr. ESPEN,
doi:10.1016/j.clnesp.2021.04.026Loi, Wang, Ong, Lee, Nutritional Support of Critically Ill Adults and Children with Acute Respiratory Distress Syndrome: A Clinical Review, Clin. Nutr. ESPEN,
doi:10.1016/j.clnesp.2017.02.005Lu, Ren, Jiang, The Relationship between Prognostic Nutritional Index and All-Cause Mortality in Critically Ill Patients: A Retrospective Study, Int. J. Gen. Med,
doi:10.2147/IJGM.S318896Moctezuma-Velazquez, Miranda-Zazueta, Ortiz-Brizuela, Gonzalez-Lara, Tamez-Torres et al., Low Thoracic Skeletal Muscle Area Is Not Associated with Negative Outcomes in Patients with COVID-19, Am. J. Phys. Med. Rehabil,
doi:10.1097/PHM.0000000000001716Mueller, Compher, Ellen, American Society for Parenteral and Enteral Nutrition (ASPEN) Board of Directors. Clinical Guidelines-Nutrition Screening, Assessment, and Intervention in Adults, J. Parenter. Enter. Nutr,
doi:10.1177/0148607110389335Onodera, Goseki, Kosaki, Prognostic Nutritional Index in Gastrointestinal Surgery of Malnourished Cancer Patients] Japanese, Nihon Geka Gekkai Zasshi
Poros, Becker-Pennrich, Sabel, Stemmler, Wassilowsky et al., Anthropometric Analysis of Body Habitus and Outcomes in Critically Ill COVID-19 Patients, Obes. Med,
doi:10.1016/j.obmed.2021.100358Rahman, Hasan, Agarwala, Martin, Day et al., Identifying Critically-Ill Patients Who Will Benefit Most from Nutritional Therapy: Further Validation of the "Modified NUTRIC" Nutritional Risk Assessment Tool, Clin. Nutr,
doi:10.1016/j.clnu.2015.01.015Schiaffino, Albano, Cozzi, Messina, Arioli et al., CT-Derived Chest Muscle Metrics for Outcome Prediction in Patients with COVID-19, Radiology,
doi:10.1148/radiol.2021204141Shao, Lai, Duan, Ge, Ye, Nutritional Indices at Admission Are Associated with Mortality Rates of Patients in the Intensive Care Unit, Eur. J. Clin. Nutr,
doi:10.1038/s41430-021-00994-3Shimoyama, Umegaki, Kadono, Minami, Presepsin Values and Prognostic Nutritional Index Predict Mortality in Intensive Care Unit Patients with Sepsis: A Pilot Study, BMC Res. Notes,
doi:10.1186/s13104-021-05659-9Shirakabe, Hata, Kobayashi, Okazaki, Matsushita et al., The Prognostic Impact of Malnutrition in Patients with Severely Decompensated Acute Heart Failure, as Assessed Using the Prognostic Nutritional Index (PNI) and Controlling Nutritional Status (CONUT) Score, Heart Vessel,
doi:10.1007/s00380-017-1034-zSinger, Blaser, Berger, Alhazzani, Calder et al., ESPEN Guideline on Clinical Nutrition in the Intensive Care Unit, Clin. Nutr,
doi:10.1016/j.clnu.2018.08.037Song, Ma, Wang, Qin, Xu et al., Nutritional Screening Based on Objective Indices at Admission Predicts In-Hospital Mortality in Patients with COVID-19, Nutr. J,
doi:10.1186/s12937-021-00702-8Sugita, Miyazaki, Shimada, Shimizu, Kunimoto et al., Correlation of Nutritional Indices on Admission to the Coronary Intensive Care Unit with the Development of Delirium, Nutrients,
doi:10.3390/nu10111712Sun, Chen, Xu, Li, He, The Prognostic Significance of the Prognostic Nutritional Index in Cancer: A Systematic Review and Meta-Analysis, J. Cancer Res. Clin. Oncol,
doi:10.1007/s00432-014-1714-3Taylor, Marson, Elhadi, Macleod, Yu et al., Factors Associated with Mortality in Patients with COVID-19 Admitted to Intensive Care: A Systematic Review and Meta-Analysis, Anaesthesia,
doi:10.1111/anae.15532Thibault, Seguin, Tamion, Pichard, Singer, Nutrition of the COVID-19 Patient in the Intensive Care Unit (ICU): A Practical Guidance, Crit. Care,
doi:10.1186/s13054-020-03159-zTseng, Tu, Chen, Wang, Chen et al., Significance of the Modified Nutric Score for Predicting Clinical Outcomes in Patients with Severe Community-Acquired Pneumonia, Nutrients,
doi:10.3390/nu14010198Van Heusden, Swartz, Chargi, De Jong, Van Baal et al., Feasibility of Assessment of Skeletal Muscle Mass on a Single Cross-Sectional Image at the Level of the Fourth Thoracic Vertebra, Eur. J. Radiol,
doi:10.1016/j.ejrad.2021.109879Vincent, Moreno, Takala, Willatts, De Mendonça et al., The SOFA (Sepsis-Related Organ Failure Assessment) Score to Describe Organ Dysfunction/Failure, Intensive Care Med
Wang, He, Kang, Jianguo, Controlling Nutritional Status (CONUT) Score Is a Predictive Marker for Patients with Traumatic Brain Injury, Clin. Neurol. Neurosurg,
doi:10.1016/j.clineuro.2020.105909Wang, Lin, Wei, Li, Liao et al., Predictive Value of Prognostic Nutritional Index on COVID-19 Severity, Front. Nutr,
doi:10.3389/fnut.2020.582736Wei, Wu, Jin, Mu, Gu et al., Predictive Significance of the Prognostic Nutritional Index (PNI) in Patients with Severe COVID-19, J. Immunol. Res,
doi:10.1155/2021/9917302Zhang, He, Yu, Peng, Feng et al., The Modified NUTRIC Score Can Be Used for Nutritional Risk Assessment as Well as Prognosis Prediction in Critically Ill COVID-19 Patients, Clin. Nutr
Zhou, Ma, Xiang, Tao, Yu et al., A Correlation Analysis Between the Nutritional Status and Prognosis of COVID-19 Patients, J. Nutr. Health Aging,
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'abstract': '<jats:p>Background: Malnutrition predicts a worse outcome for critically ill patients. '
'However, quick, easy-to-use nutritional risk assessment tools have not been adequately '
'validated. Aims and Methods: The study aimed to evaluate the role of four biological '
'nutritional risk assessment instruments (the Prognostic Nutritional Index—PNI, the '
'Controlling Nutritional Status Score—CONUT, the Nutrition Risk in Critically Ill—NUTRIC, and '
'the modified NUTRIC—mNUTRIC), along with CT-derived fat tissue and muscle mass measurements '
'in predicting in-hospital mortality in a consecutive series of 90 patients hospitalized in '
'the intensive care unit for COVID-19-associated ARDS. Results: In-hospital mortality was '
'46.7% (n = 42/90). Non-survivors had a significantly higher nutritional risk, as expressed by '
'all four scores. All scores were independent predictors of mortality on the multivariate '
'regression models. PNI had the best discriminative capabilities for mortality, with an area '
'under the curve (AUC) of 0.77 for a cut-off value of 28.05. All scores had an AUC above 0.72. '
'The volume of fat tissue and muscle mass were not associated with increased mortality risk. '
'Conclusions: PNI, CONUT, NUTRIC, and mNUTRIC are valuable nutritional risk assessment tools '
'that can accurately predict mortality in critically ill patients with COVID-19-associated '
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