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Comparison of different methods for nutrition assessment in patients with tumors.

ABSTRACT: Nutrition screening to identify patients at risk of malnutrition is vital for cancer patients because of the high prevalence of malnutrition in this population. The aim of the present study was to compare different methods of nutrition assessment in patients with tumors. From June 2013 to June 2014, we conducted an observational multicenter study to compare the assessment of nutritional status in patients with tumors by anthropometry, biochemical indicators, nutritional risk screening (NRS-2002) and patient-generated subjective global assessment (PG-SGA). Mann-Whitney test and Kruskal-Wallis H non-parametric test were used for intergroup comparisons. Spearmans rank correlation coefficients were calculated to evaluate the association between different methods of nutritional assessment. The κ statistic was used to evaluate the agreement between two assessment methods. A total of 927 oncology inpatients underwent full nutritional assessment and nutrition screening. The PG-SGA tool determined that 13.7% of patients were well-nourished (PG-SGA from 0-1) and the rest (86.3%) were malnourished. Among the malnourished patients, 57.8% were moderately malnourished (PG-SGA from 2-8) and 28.5% were severely malnourished (PG-SGA ≥9). According to NRS-2002, 30.7% of patients were at nutritional risk (NRS-2002 ≥3). There was a significant positive correlation between PG-SGA scores and NRS-2002 scores in both men and women. Compared to albumin, the PG-SGA had a sensitivity of 93.78% and specificity of 21.80%. In comparison, NRS-2002 had a low sensitivity of 43.13% and relatively higher specificity of 82.16%. In conclusion, the relationship between PG-SGA, NRS-2002 and nutritional status is statistically significant. Compared with NRS-2002, PG-SGA is a suitable screening tool for detecting the risk of malnutrition in patients with cancer.

SUBMITTER: Du H

PROVIDER: S-EPMC5494893 | biostudies-other | 2017 Jul

REPOSITORIES: biostudies-other

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Project description:BackgroundSepsis is one of the most life-threatening circumstances for critically ill patients in the United States, while diagnosis of sepsis is challenging as a standardized criteria for sepsis identification is still under development. Disparities in social determinants of sepsis patients can interfere with the risk prediction performances using machine learning.MethodsWe analyzed a cohort of critical care patients from the Medical Information Mart for Intensive Care (MIMIC)-III database. Disparities in social determinants, including race, sex, marital status, insurance types and languages, among patients identified by six available sepsis criteria were revealed by forest plots with 95% confidence intervals. Sepsis patients were then identified by the Sepsis-3 criteria. Sixteen machine learning classifiers were trained to predict in-hospital mortality for sepsis patients on a training set constructed by random selection. The performance was measured by area under the receiver operating characteristic curve (AUC). The performance of the trained model was tested on the entire randomly conducted test set and each sub-population built based on each of the following social determinants: race, sex, marital status, insurance type, and language. The fluctuations in performances were further examined by permutation tests.ResultsWe analyzed a total of 11,791 critical care patients from the MIMIC-III database. Within the population identified by each sepsis identification method, significant differences were observed among sub-populations regarding race, marital status, insurance type, and language. On the 5783 sepsis patients identified by the Sepsis-3 criteria statistically significant performance decreases for mortality prediction were observed when applying the trained machine learning model on Asian and Hispanic patients, as well as the Spanish-speaking patients. With pairwise comparison, we detected performance discrepancies in mortality prediction between Asian and White patients, Asians and patients of other races, as well as English-speaking and Spanish-speaking patients.ConclusionsDisparities in proportions of patients identified by various sepsis criteria were detected among the different social determinant groups. The performances of mortality prediction for sepsis patients can be compromised when applying a universally trained model for each subpopulation. To achieve accurate diagnosis, a versatile diagnostic system for sepsis is needed to overcome the social determinant disparities of patients.

Project description:BACKGROUND:Determining gestational age in resource-poor settings is challenging because of limited availability of ultrasound technology and late first presentation to antenatal clinic. Last menstrual period (LMP), symphysio-pubis fundal height (SFH) and Ballard Score (BS) at delivery are therefore often used. We assessed the accuracy of LMP, SFH, and BS to estimate gestational age at delivery and preterm birth compared to ultrasound (US) using a large dataset derived from a randomized controlled trial in pregnant malaria patients in four African countries. METHODS:Mean and median gestational age for US, LMP, SFH and BS were calculated for the entire study population and stratified by country. Correlation coefficients were calculated using Pearson's rho, and Bland Altman plots were used to calculate mean differences in findings with 95% limit of agreements. Sensitivity, specificity, positive predictive value and negative predictive value were calculated considering US as reference method to identify term and preterm babies. RESULTS:A total of 1630 women with P. falciparum infection and a gestational age?>?24?weeks determined by ultrasound at enrolment were included in the analysis. The mean gestational age at delivery using US was 38.7?weeks (95%CI: 38.6-38.8), by LMP, 38.4?weeks (95%CI: 38.0-38.9), by SFH, 38.3?weeks (95%CI: 38.2-38.5), and by BS 38.0?weeks (95%CI: 37.9-38.1) (p <?0.001). Correlation between US and any of the other three methods was poor to moderate. Sensitivity and specificity to determine prematurity were 0.63 (95%CI 0.50-0.75) and 0.72 (95%CI, 0.66-0.76) for LMP, 0.80 (95%CI 0.74-0.85) and 0.74 (95%CI 0.72-0.76) for SFH and 0.42 (95%CI 0.35-0.49) and 0.77 (95%CI 0.74-0.79) for BS. CONCLUSIONS:In settings with limited access to ultrasound, and in women who had been treated with P. falciparum malaria, SFH may be the most useful antenatal tool to date a pregnancy when women present first in second and third trimester. The Ballard postnatal maturation assessment has a limited role and lacks precision. Improving ultrasound facilities and skills, and early attendance, together with the development of new technologies such as automated image analysis and new postnatal methods to assess gestational age, are essential for the study and management of preterm birth in low-income settings.

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Comparison of different methods for nutrition assessment in patients with tumors.

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