Patient-reported quality-of-life outcomes after de-escalated chemoradiation for human papillomavirus-positive oropharyngeal carcinoma: Findings from a phase 2 trial.

BACKGROUND: The current study represents a subset analysis of quality-of-life (QOL) outcomes among patients treated on a phase 2 trial of de-escalated chemoradiation for human papillomavirus (HPV)-associated oropharyngeal cancer. METHODS: Eligibility included newly diagnosed, (American Joint Committee on Cancer, 7th edition) stage III or IV oropharyngeal squamous cell carcinoma, p16 positivity, age ≥ 18 years, and a Zubrod performance status of 0 to 1. Treatment was induction paclitaxel at a dose of 175 mg/m2 and carboplatin at an area under the curve of 6 for 2 cycles followed by response-adapted, dose-reduced radiation of 54 Gy or 60 Gy with weekly concurrent paclitaxel at a dose of 30 mg/m2 . The University of Washington Quality of Life (UW-QOL) and the Functional Assessment of Cancer Therapy-Head and Neck questionnaires were used to assess patient-reported QOL as a secondary endpoint. RESULTS: A total of 45 patients were registered, 40 of whom completed QOL surveys and were evaluable. Nadirs for overall UW-QOL and Functional Assessment of Cancer Therapy-Head and Neck scores were reached at 4 weeks after treatment but returned to baseline at 3 months. Nearly all functional indices returned to baseline levels by 6 to 9 months. The mean overall UW-QOL score was 71.6 at baseline compared with 70.8, 73.0, 83.3, and 81.1, respectively, at 3 months, 6 months, 1 year, and 2 years after therapy. The percentage of patients rating their overall QOL as "very good" or "outstanding" at 6 months, 1 year, and 2 years using the UW-QOL was 50%, 77%, and 84%, respectively. CONCLUSIONS: This de-escalation regimen achieved QOL outcomes that were favorable compared with historical controls. These results serve as powerful evidence that ongoing de-escalation efforts lead to tangible gains in function and QOL. Cancer 2018;124:521-9. © 2017 American Cancer Society.

Identifying predictors for postoperative clinical outcome in lumbar spinal stenosis patients using smart-shoe technology.

BACKGROUND: Approximately 33% of the patients with lumbar spinal stenosis (LSS) who undergo surgery are not satisfied with their postoperative clinical outcomes. Therefore, identifying predictors for postoperative outcome and groups of patients who will benefit from the surgical intervention is of significant clinical benefit. However, many of the studied predictors to date suffer from subjective recall bias, lack fine digital measures, and yield poor correlation to outcomes. METHODS: This study utilized smart-shoes to capture gait parameters extracted preoperatively during a 10 m self-paced walking test, which was hypothesized to provide objective, digital measurements regarding the level of gait impairment caused by LSS symptoms, with the goal of predicting postoperative outcomes in a cohort of LSS patients who received lumbar decompression and/or fusion surgery. The Oswestry Disability Index (ODI) and predominant pain level measured via the Visual Analogue Scale (VAS) were used as the postoperative clinical outcome variables. RESULTS: The gait parameters extracted from the smart-shoes made statistically significant predictions of the postoperative improvement in ODI (RMSE =0.13, r=0.93, and p<3.92×10-7) and predominant pain level (RMSE =0.19, r=0.83, and p<1.28×10-4). Additionally, the gait parameters produced greater prediction accuracy compared to the clinical variables that had been previously investigated. CONCLUSIONS: The reported results herein support the hypothesis that the measurement of gait characteristics by our smart-shoe system can provide accurate predictions of the surgical outcomes, assisting clinicians in identifying which LSS patient population can benefit from the surgical intervention and optimize treatment strategies.

Utilization of a novel digital measurement tool for quantitative assessment of upper extremity motor dexterity: a controlled pilot study.

BACKGROUND: The current methods of assessing motor function rely primarily on the clinician's judgment of the patient's physical examination and the patient's self-administered surveys. Recently, computerized handgrip tools have been designed as an objective method to quantify upper-extremity motor function. This pilot study explores the use of the MediSens handgrip as a potential clinical tool for objectively assessing the motor function of the hand. METHODS: Eleven patients with cervical spondylotic myelopathy (CSM) were followed for three months. Eighteen age-matched healthy participants were followed for two months. The neuromotor function and the patient-perceived motor function of these patients were assessed with the MediSens device and the Oswestry Disability Index respectively. The MediSens device utilized a target tracking test to investigate the neuromotor capacity of the participants. The mean absolute error (MAE) between the target curve and the curve tracing achieved by the participants was used as the assessment metric. The patients' adjusted MediSens MAE scores were then compared to the controls. The CSM patients were further classified as either "functional" or "nonfunctional" in order to validate the system's responsiveness. Finally, the correlation between the MediSens MAE score and the ODI score was investigated. RESULTS: The control participants had lower MediSens MAE scores of 8.09%±1.60%, while the cervical spinal disorder patients had greater MediSens MAE scores of 11.24%±6.29%. Following surgery, the functional CSM patients had an average MediSens MAE score of 7.13%±1.60%, while the nonfunctional CSM patients had an average score of 12.41%±6.32%. The MediSens MAE and the ODI scores showed a statistically significant correlation (r=-0.341, p<1.14×10⁻5). A Bland-Altman plot was then used to validate the agreement between the two scores. Furthermore, the percentage improvement of the the two scores after receiving the surgical intervention showed a significant correlation (r=-0.723, p<0.04). CONCLUSIONS: The MediSens handgrip device is capable of identifying patients with impaired motor function of the hand. The MediSens handgrip scores correlate with the ODI scores and may serve as an objective alternative for assessing motor function of the hand.

Unobtrusive and Continuous Monitoring of Alcohol-impaired Gait Using Smart Shoes.

BACKGROUND: Alcohol ingestion influences sensory-motor function and the overall well-being of individuals. Detecting alcohol-induced impairments in gait in daily life necessitates a continuous and unobtrusive gait monitoring system. OBJECTIVES: This paper introduces the development and use of a non-intrusive monitoring system to detect changes in gait induced by alcohol intoxication. METHODS: The proposed system employed a pair of sensorized smart shoes that are equipped with pressure sensors on the insole. Gait features were extracted and adjusted based on individual's gait profile. The adjusted gait features were used to train a machine learning classifier to discriminate alcohol-impaired gait from normal walking. In experiment of pilot study, twenty participants completed walking trials on a 12 meter walkway to measure their sober walking and alcohol-impaired walking using smart shoes. RESULTS: The proposed system can detect alcohol-impaired gait with an accuracy of 86.2 % when pressure value analysis and person-dependent model for the classifier are applied, while statistical analysis revealed that no single feature was discriminative for the detection of gait impairment. CONCLUSIONS: Alcohol-induced gait disturbances can be detected with smart shoe technology for an automated monitoring in ubiquitous environment. We demonstrated that personal monitoring and machine learning-based prediction could be customized to detect individual variation rather than applying uniform boundary parameters of gait.

A Prediction Model for Functional Outcomes in Spinal Cord Disorder Patients Using Gaussian Process Regression.

Predicting the functional outcomes of spinal cord disorder patients after medical treatments, such as a surgical operation, has always been of great interest. Accurate posttreatment prediction is especially beneficial for clinicians, patients, care givers, and therapists. This paper introduces a prediction method for postoperative functional outcomes by a novel use of Gaussian process regression. The proposed method specifically considers the restricted value range of the target variables by modeling the Gaussian process based on a truncated Normal distribution, which significantly improves the prediction results. The prediction has been made in assistance with target tracking examinations using a highly portable and inexpensive handgrip device, which greatly contributes to the prediction performance. The proposed method has been validated through a dataset collected from a clinical cohort pilot involving 15 patients with cervical spinal cord disorder. The results show that the proposed method can accurately predict postoperative functional outcomes, Oswestry disability index and target tracking scores, based on the patient's preoperative information with a mean absolute error of 0.079 and 0.014 (out of 1.0), respectively.

Non-invasive urine testing of EGFR activating mutation and T790M resistance mutation in non-small cell lung cancer.

BACKGROUND: The increasing understanding of non-small cell lung cancer (NSCLC) biology over the last two decades has led to the identification of multiple molecular targets. This led to the development of multiple targeted therapies in the primary and secondary resistance setting and the epidermal growth factor receptor (EGFR) gene remains the most frequently observed molecular target in NSCLC. Tissue biopsies remain the standard for the identification of such EGFR mutations. Obtaining serial tissue biopsies, especially in the secondary resistance setting is associated with multiple medical and logistical challenges. Utilizing circulating tumor DNA (ctDNA) fragments for molecular analysis can overcome these challenges and aid in therapeutic decision-making. CASE PRESENTATION: Here we present a present a 72-year-old Korean woman with metastatic, EGFR L858R mutated bronchogenic adenocarcinoma. She developed skeletal progression on treatment with first and second generation tyrosine kinase inhibitors (TKIs). Repeated biopsies failed to provide informative molecular test results. A novel urine ctDNA assay was utilized and confirmed T790M positive status. The patient was started on a third generation TKI, which led to a measurable clinical response. CONCLUSIONS: Utilization of urine liquid biopsies for EGFR diagnostics are feasible and provided critical clinical information in this patient's case. Urine liquid biopsy represents a viable alternative to tissue biopsy, particularly in the secondary resistance setting, when tissue is not available for molecular testing.

Use of multivariate linear regression and support vector regression to predict functional outcome after surgery for cervical spondylotic myelopathy.

This study introduces the use of multivariate linear regression (MLR) and support vector regression (SVR) models to predict postoperative outcomes in a cohort of patients who underwent surgery for cervical spondylotic myelopathy (CSM). Currently, predicting outcomes after surgery for CSM remains a challenge. We recruited patients who had a diagnosis of CSM and required decompressive surgery with or without fusion. Fine motor function was tested preoperatively and postoperatively with a handgrip-based tracking device that has been previously validated, yielding mean absolute accuracy (MAA) results for two tracking tasks (sinusoidal and step). All patients completed Oswestry disability index (ODI) and modified Japanese Orthopaedic Association questionnaires preoperatively and postoperatively. Preoperative data was utilized in MLR and SVR models to predict postoperative ODI. Predictions were compared to the actual ODI scores with the coefficient of determination (R(2)) and mean absolute difference (MAD). From this, 20 patients met the inclusion criteria and completed follow-up at least 3 months after surgery. With the MLR model, a combination of the preoperative ODI score, preoperative MAA (step function), and symptom duration yielded the best prediction of postoperative ODI (R(2)=0.452; MAD=0.0887; p=1.17 × 10(-3)). With the SVR model, a combination of preoperative ODI score, preoperative MAA (sinusoidal function), and symptom duration yielded the best prediction of postoperative ODI (R(2)=0.932; MAD=0.0283; p=5.73 × 10(-12)). The SVR model was more accurate than the MLR model. The SVR can be used preoperatively in risk/benefit analysis and the decision to operate.