Roger Pebody / 2025年5月13日 / Medscape 醫學新聞
瑞士研究人員報告稱，人工智慧 (AI) 模型使用常規收集的數據預測了活動性結核病 (TB) 的後續發展。在識別結核病高風險的愛滋病毒陽性患者方面，人工智慧模型的表現優於潛伏性結核病的生物檢測。
除了免疫功能和社會人口統計變數外，人工智慧模型還保留了幾種可指示患者健康和新陳代謝的生物標記。
在瑞士和其他能夠很好地獲得抗反轉錄病毒療法的國家，結核病是愛滋病毒感染者中一種罕見但嚴重的合併感染，通常與愛滋病毒診斷較晚有關。為了防止發展為活動性結核病，已知患有潛伏性結核感染的人可以使用異煙肼和/或利福平 (isoniazid and/or rifampicin) 進行預防性治療。
但檢測潛伏性結核病具有挑戰性，尤其是對愛滋病毒感染者而言。在瑞士先前的分析中，採用丙型干擾素釋放試驗（IGRA，註）和結核菌素皮膚試驗結合的方法，僅識別出30%隨後患有活動性結核病的人。
瑞士蘇黎世大學傳染病和醫院流行病學系主治醫師 Joahnnes Nemeth 醫學博士告訴 Medscape 醫學新聞：「這比拋硬幣還要糟糕」。
問題在於，這些測試依賴免疫反應，而免疫反應可能會受到損害。他解釋道：「你檢測的是愛滋病毒感染過程中出現故障的系統，因此測試結果不佳也就不足為奇了」。
這促使他和他的同事們尋找其他方法來識別有風險的患者。他們利用了瑞士愛滋病世代研究的數據，該研究涵蓋了該國約 70% 接受愛滋病治療的人。
使用機器學習分析了超過 23 年的數據，機器學習是人工智慧的一個子集，它使電腦能夠從數據中學習模式並做出預測，而無需針對每個任務進行明確編程。他們的機器學習模型採用了隨機森林—— 一種結合了多個決策樹的輸出的演算法。
該模型查看了愛滋病毒診斷時收集的數據，以預測至少 6 個月後發展的活動性結核病。該模型不僅考慮研究人員認為是潛在風險因素的變量，還審查了他們擁有足夠數據的所有變量。
「我真正喜歡這種機器學習方法的地方在於，我們將收集到的所有數據都輸入到機器中，然後問它：你能用這些數據做些什麼嗎？」，內梅特說道。「我認為這確實很有回報」。
該模型的第一次迭代 (iteration，數學裡若干次使用一個數學法則所得的量) …包括 48 個變量，敏感度為 70.1%，特異性為 81.0%。簡化的第二個版本保留了 20 個變數——使其計算要求更低——同時提供 57.1% 的靈敏度和 77.8% 的特異性。
鑑於生物測試的敏感性為 30%，特異性為 94%，對於 Nemeth 來說，這「完全出乎意料」。該模型不需要額外的數據收集，也不需要花費 IGRA 的費用。
如預期的那樣，保留的 20 個變數包括免疫學參數、血液學標記和社會人口統計因素，但有些變數更令人驚訝：以及那些與代謝相關的幾個變數（膽固醇、高密度脂蛋白、葡萄糖和肌酸酐）、身體質量指數和平均動脈壓。
研究人員指出，結核病與營養不良有關，並表示其中一些標記可能反映了結核病高風險族群的代謝紊亂和肌肉質量受損。
該模型首先在未經訓練的一部分瑞士人群中進行了驗證，然後在奧地利的一個人群中進行了驗證。儘管兩組之間存在許多相似之處，但該模型最初在奧地利的表現不佳。
研究人員意識到這個問題源自於各國之間不同的移民模式：瑞士的大多數結核病患者來自撒哈拉以南非洲，而奧地利的結核病患者大多來自前蘇聯共和國。只有在修改種族和出生地區變數之後，模型才開始有效發揮作用。
「這是一個警示故事」，內梅特說。「如果你去到一個非常相似的環境，但稍有不同，所有這些方法就會失效。對於機器學習模型，我們必須非常小心，並在依賴它們之前進行嚴格的測試」。
醫學博士 Emily Wong 是阿拉巴馬大學伯明罕分校的副教授，她曾使用人工智慧輔助解釋南非的胸部 X 光檢查，但並未參與這項新研究。
她告訴 Medscape 醫學新聞，瑞士的這項研究「讓人們認識到，透過包含大量臨床變數的大型數據集，你可以辨別出有意義的預測模式，預測某人是否會繼續發展為結核病」。
Nemeth 正在進行一項實施研究，醫生的患者當中從未接受過結核病檢測者將被隨機分配去接受檢測提醒或是基於機器學習模型的風險評分。一個關鍵問題是後者是否足以說服醫生採取進一步行動，例如提供預防性治療。
Wong指出，需要為每位患者權衡預防性治療的潛在益處和風險（包括肝毒性）。但是機器學習模型可以幫助臨床醫生做到這一點。
她說：「未來，基於個人的關鍵人口統計和臨床信息，可能包括他們的胸部 X 光或 IGRA 測試，也可能不包括，我們將擁有一個功能齊全的臨床決策工具，指導醫護人員為他們面前的患者做出結核病預防決策，這絕對是一個值得追求的目標」。
註：新一代的潛伏性感染診斷方法-丙型干擾素血液檢驗 (Interferon-gamma release assay，IGRA)，可避免卡介苗之干擾，然而仍有其局限。 丙型干擾素釋放試驗是一種用來診斷是否感染結核菌的抽血檢查，偵測血液中T細胞對結核菌抗原的免疫反應，適合用於多次接種卡介苗的接觸者或免疫不全的病患。
該研究由瑞士國家科學基金會資助。 Nemeth 宣布將接受 Oxford Immunotec 和 ViiV 的演講酬金。
羅傑‧佩博迪 (Roger Pebody) 是一位駐法國巴黎的自由記者。
引用：AI 模型識別 HIV 感染者活動性結核病的新預測因子 – Medscape – 2025 年 5 月 13 日。
AI Model Identifies Novel Predictors of Active TB in People With HIV
Roger Pebody / May 13, 2025 / Medscape Medical News
An artificial intelligence (AI) model using routinely collected data predicted subsequent development of active tuberculosis (TB), Swiss researchers reported. The AI model outperformed biological tests for latent TB in identifying HIV-positive patients at high risk of developing TB.
As well as immune function and sociodemographic variables, the AI model retained several biomarkers indicative of patients’ well-being and metabolism.
In Switzerland and other countries with good access to antiretroviral therapy, TB is a rare but serious co-infection in people living with HIV, frequently linked with late HIV diagnosis. To prevent progression to active TB disease, people known to have latent TB infection can be offered preventive treatment with isoniazid and/or rifampicin.
But detection of latent TB is challenging, especially in people with HIV. In a previous Swiss analysis, a combined approach using interferon gamma release assays (IGRA) and tuberculin skin tests identified only 30% of people who subsequently developed active TB.
“It was worse than tossing a coin,” Joahnnes Nemeth, MD, an attending physician in the department of infectious diseases and hospital epidemiology at the University of Zürich, Zürich, Switzerland, told Medscape Medical News.
The problem is that the tests rely on immune response, which may be impaired. “You interrogate the very system that is malfunctioning during HIV infection, so it’s not a surprise that the tests perform poorly,” he explained.
This led him and his colleagues to look into alternative ways to identify patients at risk. They leveraged data from the Swiss HIV Cohort Study, which includes around 70% of people receiving HIV care in the country.
Over 23 years’ worth of data were analyzed using machine learning, a subset of AI that enables computers to learn patterns from data and make predictions without being explicitly programmed for each task. Their machine learning model employed a random forest — an algorithm which combines the outputs from multiple decision trees.
The model looked at data collected at HIV diagnosis in order to predict active TB disease that developed at least 6 months later. Rather than only considering variables which the researchers thought were potential risk factors, the model reviewed all the variables for which they had sufficient data.
“What I really liked about this machine learning approach is that we threw all the data we collect into the machine and just asked it: Can you do something with that?” Nemeth said. “I think that really paid off.”
The first iteration of the model included 48 variables and had a sensitivity of 70.1% and a specificity of 81.0%. A streamlined second version retained 20 variables — making it computationally less demanding — while delivering a sensitivity of 57.1% and specificity of 77.8%.
Given that biologic tests had a sensitivity of 30% and specificity of 94%, for Nemeth this “blows everything of the water.” The model doesn’t require additional data collection or have the expense of IGRA.
As might be expected, the 20 retained variables included immunological parameters, hematological markers, and sociodemographic factors, but some were more surprising: along with several variables linked with metabolism (cholesterol, high-density lipoprotein, glucose, and creatinine), body mass index, and mean arterial pressure.
The researchers noted that TB is associated with malnutrition and said that some of these markers may reflect metabolic perturbations and compromised muscle mass in people at risk for TB.
The model was first validated on a portion of the Swiss cohort which it was not trained on, and then on a cohort in Austria. Despite the many parallels between the two cohorts, initially the model performed badly in Austria.
The researchers realized the issue stemmed from different migration patterns between the countries: Most people with TB in Switzerland have moved from sub-Saharan Africa, while in Austria, most come from the former Soviet republics. Only after modifying the ethnicity and region of birth variables did the model begin to work effectively.
“This is a cautionary tale,” said Nemeth. “You go to a very similar setting with a little difference, and all this stops working. With machine learning models, we really have to be careful and test them vigorously before we rely on them.”
Emily Wong, MD, is an associate professor at the University of Alabama at Birmingham who has used AI to aid interpretation of chest radiography in South Africa, but was not involved in the new study.
The Swiss research “opens one’s eyes to the idea that with very large data sets with lots of clinical variables, you can discern meaningful and predictive patterns that predict whether someone will go on to develop TB,” she told Medscape Medical News.
Nemeth is working on an implementation study in which physicians whose patients have never been tested for TB will be randomly allocated to either receive a reminder to test, or a risk score based on the machine learning model. A key question is whether the latter will be enough to convince physicians to take further action, such as offering preventative therapy.
Wong noted that the potential benefits and risks (including liver toxicity) of preventative therapy need to be weighed up for each patient. But a machine learning model could help clinicians to do this.
“The idea that in the future, based on key demographic and clinical information of a person, and maybe including their chest x-ray or IGRA test, or maybe not, we would have a well-functioning clinical decision making tool that would guide a health care worker to make TB prevention decisions for the patient in front of them is definitely a worthy goal,” she said.
The study was funded by the Swiss National Science Foundation. Nemeth declared receiving honoraria for presentations from Oxford Immunotec and ViiV.
Roger Pebody is a freelance journalist based in Paris, France.
Cite this: AI Model Identifies Novel Predictors of Active TB in People With HIV – Medscape – May 13, 2025.
