Kinect-based objective assessment of the acute levodopa challenge test in parkinsonism: a feasibility study.

INTRODUCTION: The acute levodopa challenge test (ALCT) is an important and valuable examination but there are still some shortcomings with it. We aimed to objectively assess ALCT based on a depth camera and filter out the best indicators. METHODS: Fifty-nine individuals with parkinsonism completed ALCT and the improvement rate (IR, which indicates the change in value before and after levodopa administration) of the Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale part III (MDS-UPDRS III) was calculated. The kinematic features of the patients' movements in both the OFF and ON states were collected with an Azure Kinect depth camera. RESULTS: The IR of MDS-UPDRS III was significantly correlated with the IRs of many kinematic features for arising from a chair, pronation-supination movements of the hand, finger tapping, toe tapping, leg agility, and gait (rs = - 0.277 ~ - 0.672, P < 0.05). Moderate to high discriminative values were found in the selected features in identifying a clinically significant response to levodopa with sensitivity, specificity, and area under the curve (AUC) in the range of 50-100%, 47.22%-97.22%, and 0.673-0.915, respectively. The resulting classifier combining kinematic features of toe tapping showed an excellent performance with an AUC of 0.966 (95% CI = 0.922-1.000, P < 0.001). The optimal cut-off value was 21.24% with sensitivity and specificity of 94.44% and 87.18%, respectively. CONCLUSION: This study demonstrated the feasibility of measuring the effect of levodopa and objectively assessing ALCT based on kinematic data derived from an Azure Kinect-based system.

Nutritional assessment and prognosis of oral cancer patients: a large-scale prospective study.

BACKGROUND: To evaluate and compare the prognostic performance of four nutritional indicators body mass index (BMI), serum albumin (ALB), prognostic nutritional index (PNI) and nutritional risk index (NRI) in oral cancer patients, and to predict the response to chemotherapy in patients with different nutritional status. METHODS: This prospective study which involved 1395 oral cancer patients was conducted in Fujian, China from September 2007 to November 2018. The BMI, PNI and NRI were calculated according to the following formulas: BMI = weight / height2 (kg/m2), PNI = albumin (g/l) + 0.005 × lymphocyte (count/µl) and NRI = (1.519 × albumin, g/l) + (41.7× present/ideal body weight), respectively. The univariate and multivariate Cox proportional hazards models were used to compare the prognostic value of BMI, ALB, PNI and NRI in overall survival (OS) in oral cancer. RESULTS: Patients with BMI < 18.5 kg/m2 (VS 18.5 kg/m2 ≤ BMI < 24 kg/m2) had a poor survival outcome (HR = 1.585; 95% CI: 1.207-2.082 ). ALB, PNI, NRI were inversely correlated with OS of oral cancer (HR = 0.716; 95% CI: 0.575-0.891; HR = 0.793; 95% CI: 0.633-0.992; HR = 0.588; 95% CI: 0.469-0.738, respectively). In addition, the prognostic predictive performance of NRI was superior to BMI or ALB or PNI. Interestingly, compared with patients with better nutritional status, chemotherapy was significantly associated with poorer OS in malnourished oral cancer patients. CONCLUSIONS: BMI, ALB, PNI and NRI are of prognostic value in patients with oral cancer and the prognostic performance of NRI was superior to BMI or ALB or PNI. Malnutrition (BMI < 18.5 kg/m2 or ALB< 40 g/l or PNI < 49.3 or NRI < 97.5) could predict an unfavorable response to chemotherapy in oral cancer patients.