Literature DB >> 24679020

Nutritional screening; control of clinical undernutrition with analytical parameters.

José Ignacio de Ulíbarri Pérez1, Guillermo Fernández2, Francisco Rodríguez Salvanés3, Ana María Díaz López4.   

Abstract

OBJECTIVE: To update the system for nutritional screening. The high prevalence of nutritional unstability that causes the Clinical Undernutrition (CU), especially within the hospitals and assisted residencies, makes it necessary to use screening tools for the constant control of undernutrition to combat it during its development. CU is not so much due to a nutritional deficiency but to the illness and its treatments. However, the screening systems currently used are aimed at detecting an already established undernutrition rather than at detecting any nutritional risk that may be present. The metabolic changes of the nutritional status that have a trophopathic effect, can be easily and automatically detected in plasma, which allows to make the necessary changes in treatments that might be too aggressive, as well as to apply nutritional support according to each case. The manual screening systems can detect those somatic changes typical of undernutrition only after many days or weeks, which might be too late. Plasma albumin is a very reliable parameter for nutritional control. A lowered amount of it, due to whatever reason, is a clear sign of a possible deficit as well as of a nutritional risk suffered by the cell way before the somatic signs of undernutrition will become apparent. A fast detection of nutritional risk, anticipating undernutrition, offers prognostic abilities, which makes screening tools based on analytic parameters the most useful, ergonomic, reliable and efficient system for nutritional screening and prognosis in the clinical practice.
CONCLUSION: It is necessary to update some concepts, to leave behind old myths and to choose modern screening systems that have proven to be efficient. This is the only way achieving the dream of controlling CU among ill and vulnerable patients. Copyright AULA MEDICA EDICIONES 2014. Published by AULA MEDICA. All rights reserved.

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Year:  2014        PMID: 24679020     DOI: 10.3305/nh.2014.29.4.7275

Source DB:  PubMed          Journal:  Nutr Hosp        ISSN: 0212-1611            Impact factor:   1.057


  24 in total

1.  Preoperative Nutritional Assessment Using the Controlling Nutritional Status Score to Predict Pancreatic Fistula After Pancreaticoduodenectomy.

Authors:  Masashi Utsumi; Hideki Aoki; Seichi Nagahisa; Seitaro Nishimura; Yuta Une; Yuji Kimura; Fumitaka Taniguchi; Takashi Arata; Koh Katsuda; Kohji Tanakaya
Journal:  In Vivo       Date:  2020 Jul-Aug       Impact factor: 2.155

2.  Controlling nutritional status (CONUT) score-based nomogram to predict overall survival of patients with HBV-associated hepatocellular carcinoma after curative hepatectomy.

Authors:  Z-X Lin; D-Y Ruan; C-C Jia; T-T Wang; J-T Cheng; H-Q Huang; X-Y Wu
Journal:  Clin Transl Oncol       Date:  2019-06-14       Impact factor: 3.405

3.  Prognostic Significance of Preoperative Controlling Nutritional Status (CONUT) Score in Patients Undergoing Hepatic Resection for Hepatocellular Carcinoma.

Authors:  Norifumi Harimoto; Tomoharu Yoshizumi; Kazuhito Sakata; Akihisa Nagatsu; Takashi Motomura; Shinji Itoh; Noboru Harada; Toru Ikegami; Hideaki Uchiyama; Yuji Soejima; Yoshihiko Maehara
Journal:  World J Surg       Date:  2017-11       Impact factor: 3.352

4.  Preoperative Controlling Nutritional Status (CONUT) Score for Assessment of Prognosis Following Hepatectomy for Hepatocellular Carcinoma.

Authors:  Kosei Takagi; Takahito Yagi; Yuzo Umeda; Susumu Shinoura; Ryuichi Yoshida; Daisuke Nobuoka; Takashi Kuise; Hiroyuki Araki; Toshiyoshi Fujiwara
Journal:  World J Surg       Date:  2017-09       Impact factor: 3.352

5.  Predicting Overall Survival Using Preoperative Nutritional and Inflammation Status for Colorectal Cancer.

Authors:  Tamuro Hayama; Tsuyoshi Ozawa; Mitsuo Tsukamoto; Yoshihisa Fukushima; Ryu Shimada; Keijiro Nozawa; Keiji Matsuda; Shoichi Fujii; Takeo Fukagawa; Yojiro Hashiguchi
Journal:  In Vivo       Date:  2022 Jan-Feb       Impact factor: 2.155

6.  Preoperative Muscle-Adipose Index: A New Prognostic Factor for Gastric Cancer.

Authors:  Jun Lu; Zhen Xue; Jian-Gao Xie; Bin-Bin Xu; Hai-Bo Yang; Dong Wu; Hua-Long Zheng; Jian-Wei Xie; Jia-Bin Wang; Jian-Xian Lin; Qi-Yue Chen; Ping Li; Chang-Ming Huang; Chao-Hui Zheng
Journal:  Ann Surg Oncol       Date:  2022-03-16       Impact factor: 5.344

7.  Prognostic Utility of Geriatric Nutritional Risk Index After Curative Resection of Colorectal Cancer: A Propensity Score-matched Study.

Authors:  Masahiro Kataoka; Yasumitsu Hirano; Toshimasa Ishii; Shintaro Ishikawa; Atsuko Kataoka; Takatsugu Fujii; Satoshi Shimamura
Journal:  Cancer Diagn Progn       Date:  2021-11-03

8.  The relationship between nutritional status and prognosis in patients with locally advanced and advanced stage lung cancer.

Authors:  Busra Gul; Selma Metintas; Guntulu Ak; Senay Yilmaz; Muzaffer Metintas
Journal:  Support Care Cancer       Date:  2020-10-30       Impact factor: 3.603

9.  Prognostic impact of controlling nutritional status score in resected lung squamous cell carcinoma.

Authors:  Gouji Toyokawa; Yuka Kozuma; Taichi Matsubara; Naoki Haratake; Shinkichi Takamori; Takaki Akamine; Kazuki Takada; Masakazu Katsura; Mototsugu Shimokawa; Fumihiro Shoji; Tatsuro Okamoto; Yoshihiko Maehara
Journal:  J Thorac Dis       Date:  2017-09       Impact factor: 2.895

10.  Impact of the Preoperative Controlling Nutritional Status (CONUT) Score on the Survival after Curative Surgery for Colorectal Cancer.

Authors:  Yasuhito Iseki; Masatsune Shibutani; Kiyoshi Maeda; Hisashi Nagahara; Hiroshi Ohtani; Kenji Sugano; Tetsuro Ikeya; Kazuya Muguruma; Hiroaki Tanaka; Takahiro Toyokawa; Katsunobu Sakurai; Kosei Hirakawa
Journal:  PLoS One       Date:  2015-07-06       Impact factor: 3.240

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