Computed tomography diagnosed cachexia and sarcopenia in 725 oncology patients: is nutritional screening capturing hidden malnutrition?. Issue 2 (21st December 2017) - Ní Bhuachalla, Éadaoin B.; Daly, Louise E.; Power, Derek G.; Cushen, Samantha J.; MacEneaney, Peter; Ryan, Aoife M.
Abstract: Background: Nutrition screening on admission to hospital is mandated in many countries, but to date, there is no consensus on which tool is optimal in the oncology setting. Wasting conditions such as cancer cachexia (CC) and sarcopenia are common in cancer patients and negatively impact on outcomes; however, they are often masked by excessive adiposity. This study aimed to inform the application of screening in cancer populations by investigating whether commonly used nutritional screening tools are adequately capturing nutritionally vulnerable patients, including those with abnormal body composition phenotypes (CC, sarcopenia, and myosteatosis). Methods: A prospective study of ambulatory oncology outpatients presenting for chemotherapy was performed. A detailed survey incorporating clinical, nutritional, biochemical, and quality of life data was administered. Participants were screened for malnutrition using the Malnutrition Universal Screening Tool (MUST), Malnutrition Screening Tool (MST), and the Nutritional Risk Index (NRI). Computed tomography (CT) assessment of body composition was performed to diagnose CC, sarcopenia, and myosteatosis according to consensus criteria. Results: A total of 725 patients (60% male, median age 64 years) with solid tumours participated (45% metastatic disease). The majority were overweight/obese (57%). However, 67% were losing weight, and CT analysis revealed CC in 42%, sarcopenia in 41%, and myosteatosis in 46%. Among patients with CT‐identified CC, the MUST, MST, and NRI tools categorized 27%, 35%, and 7% of them as 'low nutritional risk', respectively. The percentage of patients with CT‐identified sarcopenia and myosteatosis that were categorised as 'low nutritional risk' by MUST, MST and NRI were 55%, 61%, and 14% and 52%, 50%, and 11%, respectively. Among these tools, the NRI was most sensitive, with scores <97.5 detecting 85.8%, 88.6%, and 92.9% of sarcopenia, myosteatosis, and CC cases, respectively. Using multivariate Cox proportional hazards models, NRI score < 97.5 predicted greater mortality risk (hazard ratio 1.8, confidence interval: 1.2–2.8, P = 0.007). Conclusions: High numbers of nutritionally vulnerable patients, with demonstrated abnormal body composition phenotypes on CT analysis, were misclassified by MUST and MST. Caution should be exercised when categorizing the nutritional risk of oncology patients using these tools. NRI detected the majority of abnormal body composition phenotypes and independently predicted survival. Of the tools examined, the NRI yielded the most valuable information from screening and demonstrated usefulness as an initial nutritional risk grading system in ambulatory oncology patients.