Analysis of Risk Factors for Ganciclovir-Induced Thrombocytopenia and Construction of Risk-Prediction Models Using a Decision Tree Analysis

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  • Imai Shungo
    Department of Pharmacy, Hokkaido University Hospital
  • Yamada Takehiro
    Department of Pharmacy, Hokkaido University Hospital
  • Kasashi Kumiko
    Department of Pharmacy, Hokkaido University Hospital
  • Kobayashi Masaki
    Department of Pharmacy, Hokkaido University Hospital
  • Iseki Ken
    Department of Pharmacy, Hokkaido University Hospital Laboratory of Clinical Pharmaceutics and Therapeutics, Faculty of Pharmaceutical Sciences, Hokkaido University

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Other Title
  • ガンシクロビル誘発性血小板減少症の要因分析とDecision tree 解析を用いたリスク推定モデルの構築
  • ガンシクロビル ユウハツセイ ケッショウバン ゲンショウショウ ノ ヨウイン ブンセキ ト Decision tree カイセキ オ モチイタ リスク スイテイ モデル ノ コウチク

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Abstract

<p>Objective: Hematological toxicity, including neutropenia and thrombocytopenia, is a typical side effect of ganciclovir (GCV). We previously developed a risk-prediction model for GCV-induced neutropenia using decision tree (DT) analysis. By employing the DT model, which is a flowchart-like framework, users can predict the combination of factors that may increase neutropenia risk. However, a risk-prediction model for thrombocytopenia has not been established. Here, we aimed to identify the risk factors associated with GCV-induced thrombocytopenia and construct risk-prediction models.</p><p>Method: We retrospectively evaluated the medical records of 386 patients who received GCV between April 2008 and March 2018 at Hokkaido University Hospital. Thrombocytopenia is defined as a decrease in the platelet count (PLT) to ＜50,000 cells/mm3 and to a ＜75% decrease. Risk factors of thrombocytopenia were extracted from the medical records using a multiple logistic regression analysis. Moreover, we employed chi-squared automatic interaction detection (CHAID) and classification and regression tree (CRT) algorithms to develop the DT models. The accuracies of the established models were evaluated to assess their reliability.</p><p>Results: Thrombocytopenia occurred in 47 (12.2%) patients. In the multiple logistic regression analysis, data of patients with white blood cells ＜7,000 cells/mm3,PLT＜101,000 cells/mm3 and total bilirubin ≥ 0.8 mg/dL were extracted. Two risk-prediction models were constructed, and patients were divided into six and seven subgroups. In both algorithms, data on hematopoietic stem cell transplantations, PLT ＜101,000 cells/mm3, serum albumin ＜ 2.8 g/dL, total bilirubin ≥ 0.8 mg/dL, and residence in intensive care unit were extracted. The predictive accuracy of both the CHAID algorithm and the logistic regression models was 87.8% and that of the CRT algorithm was 88.3%, indicating they were reliable.</p><p>Conclusion: We successfully identified the factors associated with GCV-induced thrombocytopenia and constructed useful flowchartlike risk-prediction models.</p>

Journal

  • Iyakuhin Johogaku

    Iyakuhin Johogaku 21 (1), 9-19, 2019-05-31

    Japanese Society of Drug Informatics

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