Low Plasma Albumin Levels Should Be Interpreted, but not Ignored

Development of a nutritional screening and assessment indicator system for patients with esophageal cancer in China: Findings from the Delphi method

BACKGROUND

In China, individuals diagnosed with esophageal cancer are confronted with an elevated risk of nutritional inadequacy or malnutrition throughout the course of their disease, a condition that contributes to various adverse clinical outcomes. A vast corpus of data are burgeoning at an unprecedented rate, primarily due to the revolutionary growth of digitalization technologies and artificial intelligence, notably within the domains of health care and medicine. The purpose of this investigation is to initiate the development of a nutritional screening and assessment indicator framework for patients with esophageal cancer within the Chinese context. We seek to furnish an instrumental reference to facilitate preparations for the forthcoming era of advanced, "deep," evidence-based medicine.

METHODS

An integrative methodology was employed to forge the preliminary draft of the nutritional screening and assessment indicator system for preoperative patients with esophageal cancer. This encompassed a rigorous literature survey, in-depth clinical practice investigation, and the facilitation of expert panel discussions. Thereafter, two iterative consultation phases were conducted using the Delphi method in China. The analytic hierarchy process was deployed to ascertain the weighting of each index within the definitive evaluation indicator system.

RESULTS

The effective response rates for the dual rounds of expert consultation were 91.7% and 86.4%, with commensurate authority coefficients of 0.97 and 0.91. The Kendall harmony coefficients were ascertained to be 0.19 and 0.14 (p < 0.01), respectively. The culminating nutritional screening and assessment indicator system for patients with esophageal cancer comprised 5 primary-level indicators and 38 secondary-level indicators.

CONCLUSIONS

The nutritional screening and assessment indicator system contrived for patients with esophageal cancer is underpinned by cogent theoretical principles, leverages an astute research methodology, and manifests dependable outcomes. This system may be appositely utilized as a meaningful reference for the nutritional screening and assessment process in patients afflicted with esophageal cancer.

Estimation of 24-hour urinary creatinine excretion from patient variables: A novel approach to identify patients with low muscle mass and malnutrition and relationship to outcomes

BACKGROUND

Low muscle mass has been correlated with adverse outcomes in patients who are critically ill. Methods to identify low muscularity such as computed tomography scans or bioelectrical impedance analyses are impractical for admission screening. Urinary creatinine excretion (UCE) and creatinine height index (CHI) are associated with muscularity and outcomes but require a 24-h urine collection. The estimation of UCE from patient variables avoids the need for a 24-h urine collection and may be clinically useful.

METHODS

Variables of age, height, weight, sex, plasma creatinine, blood urea nitrogen (BUN), glucose, sodium, potassium, chloride, and carbon dioxide from a deidentified data set of 967 patients who had UCE measured were used to develop models to predict UCE. The model identified with the best predictive ability was validated and then retrospectively applied to a separate sample of 120 veterans who were critically ill to examine if UCE and CHI predicted malnutrition or were associated with outcomes.

RESULTS

A model was identified that included variables of plasma creatinine, BUN, age, and weight and was found to be highly correlated, moderately predictive of UCE, and statistically significant. Patients with model-estimated CHI  ≤  60% had significantly lower body weight, body mass index, plasma creatinine, and sera albumin and prealbumin levels; were 8.0 times more likely to be diagnosed with malnutrition; and were 2.6 times more likely to be readmitted in 6 months.

CONCLUSION

A model that predicts UCE offers a novel method to identify patients with low muscularity and malnutrition on admission without the use of invasive tests.