基於規則、族群與個體分析的醫療相關感染網路監測系統

Abstract

醫療相關感染的發生不僅會增加醫院的花費，也會使病人住院天數增加，以及增加死亡率等，此外，造成醫療相關感染之病原菌的變遷導致治療藥物選擇的困難，以及伴隨而來影響病人的預後及醫療資源的支出，包括多重抗藥性微生物與念珠菌等，故醫療相關感染的預防與控制已經是醫院最重要的問題之一。為此我們開發了基於網路的醫療相關感染監測及管理系統，並在其中使用規則、族群與個體分析等方法，實作了管制圖、群聚分析與資料探勘技術來幫助感染管制師與相關醫護人員執行感染控制業務。此系統建立在台灣某教學醫院，首先，醫療相關感染與包括多重抗藥性微生物的目標菌株會由完善定義的規則選出，選出的標的做進一步的族群與個體分析，或是以結構化的方式呈現給感控護理師。族群層級分析以抗萬古黴素腸球菌 （Vancomycin-Resistant Enterococcus）作為實驗標的，使用管制圖搭配群聚分析以偵測群突發的發生；個體層級分析則是使用資料探勘技術，分辨出念珠菌菌血症與常見細菌菌血症。與標準對照後，得到偵測醫療相關血流感染的敏感性是98.16%，特異性是99.93%，此外，與使用此系統前的收案情況相比，經迴歸分析部門間與時序間的相關性後，可分別得到R平方值為1.00與0.89，收案延遲時間也有顯著縮短（P<.001）。偵測抗萬古黴素腸球菌的群突發的最佳標準為90%信賴區間上限搭配菌株規則和群聚分析，可得到ROC曲線下面積0.93，且在菌株數（P=.001）、個案數（P=.04）以及新個案數（P=.001）的計算條件下，使用群聚分析可使偵測效能顯著提升。在個體分析的實驗中，使用歸納邏輯程式（Inductive logic programming），配合使用者提供的背景知識以及單變數分析算出的背景知識，可得到F1 score 0.437以及正確性0.713，實驗結果也顯示歸納邏輯程式在有配合適當的背景知識的狀況下，可以得到更好的結果 （P=.015）。由此可知，此系統可準確的判別醫療相關感染與多重抗藥性微生物，並且可正確偵測多重抗藥性微生物群突發的發生，以及協助醫師做個體念珠菌感染的決策與判斷。

Healthcare-associated infections (HAIs) are a major patient safety issue, and related pathogen, such as multidrug-resistant organism (MDRO) and Candida species, are causing a global crisis. These adverse events add to the burden of resource use, promote resistance to antibiotics, and contribute to patient deaths and disability. A Web-based HAI surveillance system was developed for automatic integration, analysis, and interpretation of HAIs and related pathogens. Rule-based classification, population-based and patient-based pathogen surveillance were incorporated in the system, and control chart analysis, clustering analysis and data mining algorithm were implemented to facilitate infection control surveillance. Electronic medical records from a 2200-bed teaching hospital in Taiwan were classified according to predefined criteria of HAIs and MDROs in rule-based classification system. The detailed information in each HAI was presented systematically to support infection control personnel decision. Comparing to infection control personnel’s review, this system has sensitivity of 98.16%, specificity of 99.93%, positive predictive value of 95.81% and negative predictive value of 99.97%. The consistency of HAIs’ time trends (R2=0.89) and department distribution (R2=1.00) between in absence and in presence of the system were also proved. The healthcare-associated bloodstream infection detection delay is significantly decreased after using this system (P<.001). Then, the numbers of organisms in each MDRO pattern were presented graphically to describe spatial and time information in population-based pathogen surveillance system. Hierarchical clustering with 7 upper control limits (UCL) was used to detect suspicious outbreaks. The system’s performance was evaluated in three parts: HAIs and MDROs classification, outbreak detection based on vancomycin-resistant enterococcal outbreaks, and infection prediction based on candidiasis. The optimal UCL for MDRO outbreak detection was the upper 90% confidence interval (CI) using germ criterion with clustering (area under ROC curve (AUC) 0.93, 95% CI 0.91 to 0.95). The performance indicators of each UCL were statically significant higher with clustering than those without clustering in germ criterion (P < .001), patient criterion (P = .04), and incident patient criterion (P < .001). Finally, there were 3 data mining algorithms including support vector machine, decision tree and inductive logic programming (ILP) being used for patient-based Candida infection prediction, and a generalized linear model was set as the baseline. In addition, the effect of adding background knowledge into ILP was also evaluated. The optimal Candida infection prediction model was ILP with background knowledge from specialist and computer algorithms, having F1 score of 0.437 and accuracy of 0.713. This research provided a preliminary result of applying data mining algorithms to Candida infection prediction, approving that adopting background knowledge could improve the performance of Candida infection prediction (P=.015). This system automatically identifies HAIs and MDROs, accurately detect suspicious outbreak of MDROs based on the antimicrobial susceptibility of all clinical isolates, and effectively classifies Candida infection.