Shang-Chih Liao | PubMed Health

PubMed2003

Effects of parathyroidectomy on iron homeostasis and erythropoiesis in hemodialysis patients with severe hyperparathyroidism.

AIMS: Secondary hyperparathyroidism (HPT) worsens anemia and may cause hyporesponsiveness to recombinant human erythropoietin therapy (r-HuEPO). To investigate the effect of parathyroidectomy (PTX) on iron homeostasis and erythropoiesis, we conducted a prospective study in chronic hemodialysis patients who underwent PTX. METHODS: Thirty-two patients were enrolled in this study. Based on the increases in hemoglobin level after PTX, patients were divided into responders and nonresponders. Iron homeostasis and erythropoiesis were assessed before and 1 and 3 months after PTX, hemoglobin and parathyroid hormone levels were monitored until 6 months after PTX. RESULTS: In the responders, increased hemoglobin levels were observed in 15 patients at 1 and 3 months after PTX (8.0 +/- 0.8 g/dl vs. 9.2 +/- 1.3 and 10.1 +/- 0.9 g/dl, p < 0.05). The nonresponders had higher pre-PTX hemoglobin levels than the responders (10.3 +/- 1.6 g/dl vs. 8.0 +/- 0.8 g/dl, p < 0.05). There was no further increase in hemoglobin at 6 months compared to 3 months after PTX in both groups. In neither group did PTX affect serum ferritin, transferrin saturation and serum erythropoietin level. Serum soluble transferrin receptor (sTfR) concentration was found to be higher in responders than in nonresponders (3.32 +/- 1.28 mg/l vs. 1.70 +/- 0.31 mg/l, p < 0.05). CONCLUSIONS: We conclude that PTX can improve anemia in hemodialysis patients with severe hyperparathyroidism and greater resistance to r-HuEPO therapy. The reversing of anemia does not involve altering iron mobilization. Pre-PTX hemoglobin and serum sTfR levels can predict the effect of PTX on correcting anemia.

Adult↗

PubMed2002

Appropriate medical data categorization for data mining classification techniques.

Some data mining (DM) methods, or software tools, require normalized data, others rely on categorized data, and some can accommodate multiple data scales. Each DM technique has a specific background theory; therefore, different results are expected when applying multiple methods. The purpose of this study is to find the data format appropriate for each DM classification technique for wider applications, and efficiently to obtain trustworthy results. Considering the nature of medical data, categorical variables are sometimes useful for making decisions and can make it easier to extrapolate knowledge. In this study, three mathematical data categorization methods (Fusinter, minimum description length principle [MDLPC] and Chi-merge) were applied to accommodate five data mining classification techniques (statistics discriminant analysis, supervised classification with Neural Networks, Decision trees, Genetic supervised clustering and Bayesian classification [probability neural networks; PNN]) using a heart disease database with four types of data (continuous data, binary data, nominal data, and ordinal data). Compared with original or normalized data, data categorized by the MDLPC categorization method was found to perform better in most of the DM classification techniques used in this study. Categorical data is good for most DM classification techniques (e.g. classification of disease and non-disease groups) and is relatively easy to use for extracting medical knowledge.

Bayes Theorem↗