Oncologic Risk Stratification Following Cytoreductive Surgery and Hyperthermic Intraperitoneal Chemotherapy for Appendiceal Carcinomatosis.

INTRODUCTION: Patients with peritoneal carcinomatosis (PC) of appendiceal origin demonstrate variable oncologic outcomes, despite aggressive cytoreductive surgery and hyperthermic intraperitoneal chemotherapy (CRS-HIPEC). We sought to devise a prognostic risk stratification system for oncologic outcomes following CRS-HIPEC. METHODS: A total of 197 patients undergoing CRS-HIPEC for the treatment of appendiceal PC were reviewed from a prospective database. Kaplan-Meier survival curves and multivariate Cox regression models were used to identify prognostic factors affecting oncologic outcomes. Clinicopathologic variables affecting overall survival (OS) were utilized to develop a prognostic staging system and nomograms. RESULTS: Univariate and multivariate Cox regression analysis indicated that high-grade tumor histology, lymph node metastasis, and incomplete cytoreduction were high-risk features, adversely affecting OS. Patients were stratified on the presence of high-risk features as follows: low-risk patients had no risk factors (n = 102); intermediate-risk patients had one risk factor (n = 49); and high-risk patients had more than one risk factor (n = 46). Median OS for low-risk patients was not reached, and was 43 and 22 months for intermediate-risk and high-risk patients, respectively. Five-year OS was 72, 43, and 13 % for low-, intermediate- and high-risk patients, respectively (p < 0.0003 for low vs. intermediate risk, and p = 0.06 for intermediate vs. high risk). CONCLUSIONS: We propose a three-tier staging system for appendiceal PC following CRS-HIPEC, based on histologic grade, lymph node involvement, and completeness of cytoreduction. The presence of any one or more of these high-risk features significantly decreased survival in our single-institution database and provided the basis for a prognostic staging system and corresponding nomograms.

Classification of cardiosynchronous waveforms by projection to a Legendre Polynomial sub-space.

UNLABELLED: The use of Radio Frequency Impedance Interrogation (RFII) is being investigated for use as a noninvasive hemodynamic monitoring system and in the capacity of a biometric identifier. Biometric identification of subjects by cardiosynchronous waveform generated through RFII technology could allow the identification of subjects in operational and potentially hostile environments. Here, the filtering methods for extracting a unique biometric signature from the RFII signal are examined, including the use of Cepstral analysis for dynamically estimating the filter parameters. METHODS: The projection of that signature to a Legendre Polynomial sub-space is proposed for increased class separability in a low dimensional space. Support Vector Machine (SVM) and k-Nearest Neighbor (k=3) classification are performed in the Legendre Polynomial sub-space on a small dataset. RESULTS: Both the k-Nearest Neighbor and linear SVM methods demonstrated highly successful classification accuracy, with 93-100% accuracy demonstrated by various classification methods. CONCLUSIONS: The results are highly encouraging despite the small sample size. Further analysis with a larger dataset will help to refine this process for the eventual application of RFII as a robust biometric identifier.

Preliminary feasibility analysis of remote subject identification during hemodynamic monitoring by Radio Frequency Impedance.

Non-contact, non-invasive monitoring of hemodynamic parameters would be ideal for medical monitoring in a variety of environments. Radio Frequency Impedance Interrogation (RFII) measures hemodynamic function via resonance frequency coupling to a hydrophilic protein molecule. While the application of this technology to hemodynamic monitoring has demonstrated initial success, this preliminary study examined the use of RFII for subject identification by waveform signal analysis, which would allow confirmation of the identity of a subject in an operational setting prior to rescue efforts. Preliminary results demonstrate an excellent recognition rate using the RFII signature and pattern classification. Each individual has a consistent pattern during the initial waveform identification period that is visually distinct from the other individuals in the data set. These results suggest that RFII may be of great utility in the pre-hospital triage setting for patient monitoring and for the rapid identification of subjects in the operational setting.