Haku Ishida

Utilization of common inflammatory markers in new, symptomatic, primary care outpatients based on their cost-effectiveness

Abstract Few studies have demonstrated the optimal usage of common inflammatory markers, alone or in combination, based on the cost-effectiveness. We analyzed the yield and cost of C-reactive protein (CRP), white blood cell count (WBC), erythrocyte sedimentation rate (ESR), sialic acid, and protein fractionation in 177 new primary care outpatients with inflammation-related symptoms. A useful result (UR) was assigned if tests contributed to a change in physicians diagnosis or decision-making. Costs of testing were calculated based on either single or simultaneous measurement. Five inflammatory markers generated 147 URs in 123 patients. CRP showed the best contribution to generation of UR, followed by sialic acid, protein fractionation, WBC, and ESR. CRP demonstrated poor correlation with WBC (r = 0.458), while sialic acid strongly correlated with total absolute amount of α1 and α2 fractions in protein fractionation (r = 0.855) and moderately with ESR (r = 0.651). The combination of CRP and WBC produced the best cost-effectiveness at a cost of ￥ 1169 (US

Applying a Neural Network to Prostate Cancer Survival Data

9.6 or Euro 9.7)/additional UR against CRP testing alone. Sialic acid, an automated multichannel analyzer-based test, demonstrated the favorable cost-effectiveness over ESR or protein fractionation when combined with CRP (and WBC). Our results indicate that the optimal usage of these inflammatory markers should involve careful cost-effectiveness considerations.

Validating a Markov Model of Treatment for Hepatitis C Virus-related Hepatocellular Carcinoma

Prediction of treatment efficacy for prostate cancer therapies has proven difficult and requires modeling of survival-type data. One reason for the difficulty may be infrequent use of flexible modeling techniques, such as artificial neural networks (ANN). The purpose of this study is to illustrate the use of an ANN to model prostate cancer survival data and compare the ANN to the traditional statistical method, Cox proportional hazards regression. Clinical data and disease follow-up for 983 men were modeled by both an ANN and a Cox model. Repeated sampling of 200 training and testing subsets were supplied to each technique. The concordance index c was calculated for each testing dataset. As further validation, ANN and Cox models were applied to a totally separate dataset. The ANN outperformed the Cox model in internal validation datasets (ANN c = 0.76, Cox c = 0.74) and on the external validation dataset (ANN c = 0.77, Cox c = 0.74). ANNs were more discriminating than Cox models for predicting cancer recurrence. Calibration of the ANN remains a problem. Once solved, it is expected that an ANN will make the most accurate predictions of prostate cancer recurrence and improve treatment decision making.

C-Reactive Protein Kinetics in Newborns: Application of a High-Sensitivity Analytic Method in Its Determination

OBJECTIVE We created and validated a Markov model to simulate the prognosis with treatment for HCV-related hepatocellular carcinoma (HCC) for assessment of cost-effectiveness for alternative treatments of HCC. METHOD Markov state incorporated into the model consisted of the treatment as a surrogate for HCC stage and underlying liver function. Retrospective data of 793 patients from three university hospitals were used to determine Kaplan-Meier survival curves for each treatment and transition probabilities were derived from them. RESULTS There was substantial overlap in the 95% CIs of the Markov model predicted and the Kaplan-Meier survival curves for each therapy. The predicted survival curves were also similar with those from the nationwide survey data supporting the external validity of our model. CONCLUSIONS Our Markov model estimates for prognosis with HCC have both internal and external validity and should be considered applicable for estimating cost-effectiveness related to HCC.