背景：縱向式醫療保健資料可以與自發性通報資料互補，提高藥物安全訊號偵測能力，特別是當與樹式掃描統計（TreeScan）和處方序列對稱分析（PSSA）等數據探勘技術相結合時。研究指出這些探勘工具具有良好表現力且應用性佳，可用於醫療保健資料庫中進行安全訊號偵測並產生假說。然而，目前還沒有準則針對縱向式資料庫中進行訊號偵測後的分類和排序提出明確建議。因此，建立和評估一個適合自身國家需求的藥物安全訊號偵測及分類系統至關重要。
目標：本研究將使用台灣縱向式醫療保健資料為來源，建構並評估一個新的藥物安全訊號分類系統的適用性。該系統將整合TreeScan與PSSA作為數據探勘工具進行安全訊號偵測。由新的分類系統偵測到的藥物安全訊號將進一步進行分析與評估。
方法：本研究將召集來自台灣藥害救濟基金會的藥物安全專家組成團隊，共同構建新的藥物安全訊號分類系統。主要將參考台灣現行的藥物安全監視體系及國際間藥物安全監視指引建議的分類系統架構，並整合TreeScan與PSSA作為藥物訊號偵測的數據探勘工具。本研究將利用台灣全民健保資料庫進行探索性分析，並選擇代表不同情境的兩個藥物類別作為例子，以測試所提出的分類系統之可行性和適應性。第一類藥物為葡萄糖-鈉共轉運輸體2抑制劑（SGLT2i），代表一種用於治療慢性疾病的新型藥物類別；另一類藥物是非氟奎諾酮類（NFQs），代表一種用於治療感染的舊藥物類別，這兩類藥物將分別與活性對照組雙基胜肽酶抑制劑（DPP4i）及氟奎諾酮類（FQs）進行比較。由這兩類藥物識別出的所有安全訊號中，本研究將選擇一個訊號作為示例，進一步依據目標試驗模擬架構（target trial emulation framework）進行假說驗證型的世代研究來評估訊號。
結果：新的藥物安全訊號分類系統包含訊號偵測及分類兩部分，首先依研究藥物是否有適合對照品，選擇以世代研究或自我對照的研究設計執行TreeScan與PSSA，經過兩種資料探勘工具所產出的統計警訊將進一步進入分類為已知藥物不良反應、適應症相關警訊、已知藥物不良反應相關事件、使用族群特性相關警訊、反向因果關係導致的protopathic bias及未知訊號，未知訊號將進一步進入相關驗證、排序及評估流程。藥物案例分析結果發現：SGLT2i警訊以使用族群特性相關警訊、已知藥物不良反應以相關事件為主，其中PSSA發現4項未知訊號須進一步探討；相較於SGLT2i，NFQs所產出的訊號較多，可能與其臨床適應症及使用族群較廣泛相關，警訊類型以使用族群特性相關警訊及適應症相關警訊為大宗，但無發現未知訊號。不同情境之藥物例子，由TreeScan及PSSA產出的警訊可互相對應之比例不一，表示兩種方法能彼此相互補充；產出訊號中與使用族群特性相關的警訊數量不少，顯示雖有應用活性對照組，仍存在一定程度、因疾病嚴重度或其他因素在組別之間分佈不同所產生之偏誤。針對SGLT2i所產出的未知訊號，本研究選擇巴金森氏症作為進一步評估的訊號，研究結果顯示，在適當控制干擾因素後，SGLT2i相比DPP4i降低了20%新發生巴金森氏症之風險，這一發現強調了初始訊號易受干擾影響、需進一步評估之重要性。
結論：本研究所建構之藥物安全訊號分類系統能實際運用到不同類型之藥物進行系統性安全訊號掃描，並成功針對偵測到的警訊進行分類、協助後續訊號之排序及評估。研究成果展示了此系統於台灣縱向式醫療保健資料進行藥物安全訊號管理的可行性與潛在應用性，未來發展可探討如何將該系統與現行藥物安全監視流程進行整合，以期能提升整體藥物安全監視之效率與價值。

Background: Longitudinal healthcare data can complement spontaneous reporting data and improve signal detection, particularly when integrating with data-mining techniques such as tree-based scan statistics (TreeScan) and sequence symmetry analysis (SSA). These tools have shown good performance and flexibility, and can be used for hypothesis-free detection in healthcare databases. However, there is currently no standardized criteria for signal triage and prioritization following quantitative signal detection in longitudinal databases. Therefore, it is crucial to establish and evaluate a signal detection and triage system that is tailored to a country's needs.
Objectives: To construct and assess the feasibility of a new proposed triage system for drug safety signal detection and classification that integrate TreeScan and SSA as data-mining tools using longitudinal healthcare data in Taiwan. Further, to evaluate the signals found from the previous triage system.
Methods: We assembled a team of drug safety experts from the Taiwan Drug Relief Foundation to develop this new system. Based on relevant guidelines and structural frameworks in Taiwan's pharmacovigilance system, we proposed a triage system integrating TreeScan and SSA as data-mining tools for detecting safety signals. Using Taiwan’s National Health Insurance Database for exploratory analysis, we selected two classes of drugs representing different scenarios to test the feasibility and adaptability of the proposed triage system. The first medication is sodium-glucose co-transporter-2 inhibitors (SGLT2i), which represent a newer class of drug used in chronic disease, and the other medication is non-fluorinated quinolones (NFQs), which represent an older class of drug that used as episodic treatment for infection. These medications were compared to existing active comparators, dipeptidyl peptidase-4 inhibitors (DPP4i) and fluoroquinolones (FQs), respectively. From all signals identified from these two examples, we selected one signal as example and conduct a hypothesis-testing cohort study under target trial emulation framework to further evaluate the signal.
Results: The new triage system comprises two parts: signal detection and classification. Depending on whether a suitable control group exists for the study drug, we executed TreeScan and PSSA using either a cohort-based or a self-controlled study design. The statistical alerts generated by these two data mining tools are further classified into known adverse drug reactions or related events, indications-related alerts, user characteristics-related alerts, protopathic bias caused by reverse causation, and unknown signals. Unknown signals would undergo further validation, prioritization, and evaluation processes. Analysis of drug examples revealed that SGLT2i alerts were mainly related to population characteristics and known adverse drug reactions, with PSSA identifying 4 unknown signals for further investigation. Compared to SGLT2i, NFQs generated more signals, possibly due to their broader clinical indications and user population. The majority of alerts for NFQs were related to population characteristics and indications, but no unknown signals were found. The proportion of overlapping alerts between TreeScan and PSSA varied for different drug examples, indicating complementary capabilities between the two methods. The output signals comprised a significant proportion of alerts related to patients’ characteristics. This indicates that, although an active control group is applied, there still exists a degree of bias, potentially due to differences in disease severity or other factors between groups. For the unknown signals produced by SGLT2i, we selected Parkinson's disease as the signal for further evaluation. The results showed that, after appropriately controlling for confounding, SGLT2i reduced the risk of new-onset Parkinson's disease by 20% compared to DPP4 inhibitors, highlighting the importance of further evaluating initial signals that are susceptible to bias.
Conclusion: The new drug safety triage system constructed in this study can be practically applied across various drug types for systematic safety signal scanning and successfully assists in classifying and evaluating detected alerts. The results demonstrate the system's feasibility and potential applicability using Taiwan's longitudinal healthcare data, suggesting future development could explore integrating this system with existing pharmacovigilance framework to enhance overall surveillance efficiency and value.

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