Generalizable Biomarkers in Critical Care: Toward Precision Medicine

Sweeney, Timothy E. MD, PhD; Khatri, Purvesh PhD

Critical Care Medicine: June 2017 - Volume 45 - Issue 6 - p 934–939

doi: 10.1097/CCM.0000000000002402

http://journals./ccmjournal/Fulltext/2017/06000/Generalizable_Biomarkers_in_Critical_Care___Toward.2.aspx

REPRODUCIBILITY AND GENERALIZABILITY

可重復(fù)性與普適性

The sequencing of the human genome and the subsequent availability of inexpensive, robust methods for “omics” profiling (e.g., genome-wide association studies, gene expression microarrays, and metabolomics) have led to optimism of a new era of biomarkers that would allow for a “precision medicine” approach to critical care. Unfortunately, this promise has yielded few tangible results, as the general biomedical reproducibility crisis (1–3) is particularly troublesome in critical care (4–8) and in omics biomarker studies (9–11). There are two broad problems that lead to seemingly similar studies of biomarkers in critical care producing different results. One problem is traditional nonreproducibility due to false positive biomarker selection or nonrobust statistical models. The other, more importantly, is a lack of generalizability in moving from a narrow study population into broader applications in critical care. We present here a contextual framework for addressing these problems and for assessing new biomarker studies.

可重復(fù)性與普適性：人類基因組測(cè)序和隨后廉價(jià)及自動(dòng)化的“組學(xué)”分析（例如，全基因組關(guān)聯(lián)研究，基因表達(dá)微陣列和代謝組學(xué)）為生物標(biāo)志物迎來(lái)了一個(gè)時(shí)代，使其能以“精準(zhǔn)醫(yī)學(xué)”的方式為重癥監(jiān)護(hù)服務(wù)。不幸的是，這一愿景取得的實(shí)質(zhì)性成果并不多，主要是因?yàn)樯飿?biāo)志物的可重復(fù)性難題在重癥監(jiān)護(hù)和生物標(biāo)志物的組學(xué)研究中尤其顯著。有兩個(gè)廣泛存在的隱患導(dǎo)致相似的重癥醫(yī)學(xué)生物標(biāo)志物研究產(chǎn)生不同的結(jié)果： 其一是傳統(tǒng)的不可重復(fù)性問(wèn)題，主要是由于選擇了假陽(yáng)性的生物標(biāo)志物或非強(qiáng)效的統(tǒng)計(jì)模型。 另一個(gè)，更重要的，相對(duì)狹隘的重癥醫(yī)學(xué)研究向更廣泛的重癥監(jiān)護(hù)轉(zhuǎn)化應(yīng)用時(shí)缺乏普適性。 為此作者在此介紹一個(gè)解決這些問(wèn)題及評(píng)估新的生物標(biāo)志物的背景框架。

SYNDROMIC ILLNESS AND GENERALIZABLE BIOMARKERS

綜合征性疾患與普適化的生物標(biāo)志物

Many critical illnesses are defined syndromically, such as sepsis, acute kidney injury (AKI), acute respiratory distress syndrome (ARDS), and delirium. These syndromes typically have clear, though changing, clinical criteria. Still, a syndrome may arise from multiple causes; as a result, it is unclear whether all cases of the syndrome really represent the same disease. Such uncertainty raises a major problem in the field. For example, if a positive clinical trial for adults with ARDS defined by Berlin criteria has failed to reproduce in an independent population of children with ARDS also defined by Berlin criteria, was the original finding a false positive or do adults and children have a “different” version of ARDS? Our reliance on syndromic definitions and the lack of clear gold-standard diagnostics linked to pathophysiology thus makes it difficult to assess clinical trial results. In theory, if the entire clinical spectrum of a disease has a common molecular pathophysiology, then a molecular biomarker should exist that is generalizable to the disease. Thus, finding a generalizable biomarker can help to define the disease, improving both patient care and clinical trial design, and potentially moving a whole field of study forward.

許多重癥疾病被定義為綜合征，如膿毒癥，急性腎損傷（AKI），急性呼吸窘迫綜合征（ARDS）和譫妄。 這些綜合征通常具有明確的臨床標(biāo)準(zhǔn)，盡管其常有變動(dòng)。然而綜合征可能由多種原因引起，因此目前尚不清楚綜合征是否真的代表同一種疾病。 這種不確定性是這一領(lǐng)域的一個(gè)主要問(wèn)題。 例如，根據(jù)柏林標(biāo)準(zhǔn)定義的成年ARDS臨床研究獲得的陽(yáng)性結(jié)論，未能在同樣根據(jù)柏林標(biāo)準(zhǔn)定義的兒童ARDS群體中重現(xiàn)，是成人研究出現(xiàn)了假陽(yáng)性亦或成年人與兒童屬于不同的“ARDS“？ 因缺乏與病理生理學(xué)相關(guān)的明確的金標(biāo)準(zhǔn)，我們依賴癥狀性的定義，而而會(huì)造成難以評(píng)價(jià)臨床試驗(yàn)。 理論上說(shuō)，如果一種疾病的全部臨床譜具有共同的分子病理生理學(xué)機(jī)制，那么應(yīng)該存在一種可廣泛用于該疾病的分子生物標(biāo)志物。 因此，尋找一個(gè)普適的生物標(biāo)志物將有助于確定疾病，改善患者治療和臨床試驗(yàn)設(shè)計(jì)，并可能推動(dòng)整個(gè)領(lǐng)域的研究。

There are other practical reasons to search for biomarkers that are generalizable. First, requiring context-specific biomarkers for every variant on a clinical condition (e.g., a different biomarker for different sources of sepsis, or for each different cause of kidney injury) could end up requiring dozens of tests for each critical syndrome. Tests indicated for such increasingly fragmented populations will fail to overcome barriers to market entry. In addition, those that do make it may have overly specific indications for use, leaving many patients without help. Finally, since off-label uses of tests and therapies are common, if biomarkers fail to deliver similar performance in seemingly similar conditions, patients will be harmed.

尋找可普適的生物標(biāo)志物還有其他較為實(shí)際的 理由。首先，針對(duì)疾患的每種變化對(duì)應(yīng)的專有生物標(biāo)志物（context-specific biomakers，例如針對(duì)膿毒癥不同來(lái)源或腎損傷不同病因而有不同生物標(biāo)志物），能夠終結(jié)每個(gè)重癥綜合征需要一大堆檢驗(yàn)的局面。對(duì)日益分散的人群進(jìn)行多項(xiàng)試驗(yàn)會(huì)因進(jìn)入市場(chǎng)的阻礙而失敗；此外，要這么做的話顯然需要很多特定的適應(yīng)征，這造成很多患者不會(huì)獲檢。最后，現(xiàn)實(shí)中很多檢測(cè)或治療超適應(yīng)征應(yīng)用很常見(jiàn)，如果生物標(biāo)志物在相近的病況下不能有類似的表現(xiàn)，那對(duì)患者是十分不利的。

We thus argue that research should focus first on finding generalizable, disease-defining molecular biomarkers for syndromes in critical illness, or alternatively on showing that such biomarkers do not exist (evidence-of-absence studies). If no generalizable biomarker exists, then more context-specific biomarkers can direct the effort to accurately characterize clinically actionable syndromic subtypes. In other words, we need to clearly define a disease before we begin to divide it into subtypes. Both are necessary components of a precision medicine approach, but due to the high heterogeneity of critical illness, research of both types can be challenging.

因此，我們認(rèn)為對(duì)于重癥領(lǐng)域的綜合征而言，應(yīng)首先重點(diǎn)尋找具有普遍性的，且疾病特異的分子生物標(biāo)志物，或反過(guò)來(lái)證明不存在這種生物標(biāo)志物（無(wú)證據(jù)支持研究）。 若沒(méi)有普適性的生物標(biāo)志物，則應(yīng)尋找更多的專有標(biāo)志物（ context-specific biomarkers）以準(zhǔn)確地確定綜合征的臨床亞型。換句話說(shuō)，在分型之前我們應(yīng)該清晰地確定疾病。兩者都是精準(zhǔn)醫(yī)學(xué)的必要組成部分，但由于重癥疾病的異質(zhì)性高，這兩種研究都是極具挑戰(zhàn)性。

HETEROGENEITY IN CRITICAL ILLNESS AND THE CHALLENGE OF CLINICAL TRIALS

重癥疾病的異質(zhì)性與臨床研究的挑戰(zhàn)性

Clinical trials in the critical care setting are among the hardest to carry out, for reasons of practicality, patient protection, and patient heterogeneity; this leads to smaller, mostly homogeneous cohorts that do not represent the broad spectrum of critical illness. First, as described above, similar acute syndromes (such as sepsis, AKI, and ARDS) often have multiple possible definitions and span a range of severities. Second, critically ill patients span the entire range of ages, comorbid conditions, and demographics. Third, the medical, surgical, neurologic, and pediatric pathways of critical illness have widely varying primary problems. Fourth, the practicality of conducting a trial leads to differing sampling times and stages of disease at trial enrollment. Finally, the changing treatment patterns over time (such as the change in early sepsis resuscitation with early goal-directed therapy) can lead to different outcomes for the same intervention. The logistical and budgetary constraints of trying to represent all of these sources of heterogeneity means that most single-cohort studies cannot capture the broad spectrum of critical illness, and thus may have difficulty producing generalizable results.

由于實(shí)際性、保護(hù)患者和異質(zhì)性的原因，重癥監(jiān)護(hù)病房中的臨床試驗(yàn)是最難實(shí)施的；這導(dǎo)致規(guī)模較小，且絕大多數(shù)較為同質(zhì)性的隊(duì)列研究無(wú)法代表危重疾病的廣泛的臨床表現(xiàn)譜。 首先，如上所述，類似的急性綜合征（例如敗血癥，AKI和ARDS）通常具有多種可能的定義且其嚴(yán)重性也不同； 第二，危重病人的年齡范圍，合并疾病和人口特點(diǎn)也完全不同； 第三，內(nèi)科、外科、神經(jīng)學(xué)和兒科中由原發(fā)病導(dǎo)致重癥的發(fā)病途徑明顯不同； 第四，實(shí)際上開(kāi)展臨床試驗(yàn)招募時(shí)也存在采樣時(shí)機(jī)與疾病分期的不同； 最后，不斷變化的治療方式隨著時(shí)間的推移（例如在膿毒癥早期實(shí)施復(fù)蘇的早期目標(biāo)導(dǎo)向治療的變化）可能導(dǎo)致相同干預(yù)措施的不同結(jié)果。 試圖囊括所有異質(zhì)性病源存在著邏輯和預(yù)算的限制，這也意味著大多數(shù)單中心隊(duì)列研究不能代表重癥疾病的廣泛譜系，因此也就難以產(chǎn)生可普適化的結(jié)果。

Still, the bedrock of continued progress toward generalizable biomarkers is continued publication of clinical trials. One way to improve trials is to focus on not just size but also heterogeneity. Single-cohort studies are more likely to yield reproducible results when they are appropriately powered, and are more likely to yield generalizable results when they are designed with broad inclusion criteria that attempt to match the full spectrum of the condition under study. Thus, a biomarker that has been tested in 500 adults with pneumonia and ARDS at admission is more reliable than one that has been tested in only 50 such patients. However, until it is tested in children, or in ARDS arising from other causes, or at other clinical timepoints, its generalizability is unknown. We thus caution against the false security of solely relying on a high sample size in evaluating the robustness of a single study.

誠(chéng)然，通過(guò)臨床試驗(yàn)仍然不斷有普適性的生物標(biāo)志物研究進(jìn)展發(fā)表。 改進(jìn)試驗(yàn)的一個(gè)方法就是不僅要注重研究規(guī)模，而且要注重異質(zhì)性。若效能合適單中心隊(duì)列研究很容易產(chǎn)生具有重復(fù)性的結(jié)果，若寬泛的納入標(biāo)準(zhǔn)是為了納入疾病的全部譜系，則更有可能產(chǎn)生具有普適性的結(jié)果。 也就是說(shuō)，在500例住院成人肺炎和ARDS患者之間測(cè)試的生物標(biāo)志物要比僅在50例此類患者中測(cè)試的生物標(biāo)志物更可靠，然而，只有這些檢驗(yàn)也在兒童中、在ARDS的不同病因中或在不同的臨床時(shí)間點(diǎn)進(jìn)行過(guò)，否則其普適性還是未知的。因此，我們?cè)诖艘嵝阎?jǐn)防虛假的嚴(yán)謹(jǐn)性，即評(píng)價(jià)單中心研究的效能時(shí)僅僅依靠大樣本量。

MULTICOHORT ANALYSIS AND DATA SHARING

多隊(duì)列研究與數(shù)據(jù)分享

An efficient, inexpensive way of tackling the problem of heterogeneity is to combine studies that represent the broad spectrum of disease. At a fixed total sample size, greater reproducibility is gained when the samples are integrated from a greater number of smaller sized studies, rather than vice versa (15). Our group has worked with many collaborators in repeatedly demonstrating that leveraging biological and technical heterogeneity across multiple cohorts can identify generalizable diagnostic and prognostic biomarkers in a diverse set of diseases including organ transplant, cancer, and autoimmune and infectious diseases (16–24). These early successes of multicohort analysis are firmly rooted in the hypothesis that although a broad representation of a disease could make the discovery of a biomarker challenging; such biomarkers are more likely to be reproducible and generalizable when tested in novel circumstances (Fig. 1). On the other hand, making full use of these studies often requires making imperfect comparisons (e.g., integrating datasets that use multiple different definitions for AKI). Although no hard rule can be set, we feel it both reasonable and pragmatic to use data to their fullest extent, even if the statistical methods are simple or some assumptions are slightly violated, as long as such caveats are fully explained and discussed.

解決異質(zhì)性問(wèn)題的一種有效，廉價(jià)的方法是對(duì)納入多種疾病譜系的研究的合并。 在研究規(guī)模固定的情況下，若是由較小規(guī)模的研究合并出較大的樣本量時(shí)，則研究的可重復(fù)性越高，而不是反之亦然。 我們小組與許多合作者的研究多次表明，在包括器官移植，癌癥和自身免疫和傳染病等多種疾病的隊(duì)列研究中，生物學(xué)與技術(shù)的異質(zhì)性有所增強(qiáng)，但仍能發(fā)現(xiàn)具有普適性的診斷與預(yù)后標(biāo)志物。這些早期多隊(duì)列研究分析的成功是源自這樣一個(gè)假設(shè)：雖然疾病的廣泛表現(xiàn)給生物標(biāo)志物的發(fā)現(xiàn)帶來(lái)挑戰(zhàn)，但在新的環(huán)境下這樣的生物標(biāo)志物更可能是可重復(fù)性及普適性（ 圖1 ）。 另一方面，要把這些研究全都用上會(huì)造成不完美的比較（例如，整合使用多種不同定義的數(shù)據(jù)集用于AKI研究）。雖然不能設(shè)定硬性規(guī)則，但我們認(rèn)為最大程度地使用數(shù)據(jù)不僅合理而且是實(shí)用的，盡管統(tǒng)計(jì)學(xué)上較為簡(jiǎn)單或者某些假設(shè)略有偏倚，只要盡可能全面地解釋和討論這些要點(diǎn)即可。

Figure 1. The benefit of incorporating heterogeneity（合并異質(zhì)性的好處）. Biomarkers discovered in a homogeneous cohort are highly likely to work in external cohorts that are similar to the original cohort, but less likely to work in different settings. Biomarkers that are discovered in heterogeneous cohorts are more likely to be generalizable across a broad spectrum of patients.

However, multicohort studies are only possible when data are shared (such as is now required for most genome-wide expression studies). We thus argue for the increased appropriate sharing of molecular data from clinical trials, so that multiple cohorts can be combined in the discovery of new biomarkers (25). In many research areas, data are held privately, preventing such reuse. For instance, we searched the literature for metabolomics, clinical studies in critical care and identified 28 studies (total n = 2,322), out of which only two studies made their raw data publically available (Table 1) (26–53). Public sharing of these data would allow for meta-analysis and data-driven hypotheses generation, avoiding the need for each new cohort to “reinvent the wheel.” It is clear that studies that are performed on single cohorts can be successful at producing robust biomarkers if pitfalls are avoided; but we propose that these investigators make their data available for (and themselves take part in) efforts at later meta-analysis.

不過(guò)，多隊(duì)列研究只有在共享數(shù)據(jù)時(shí)才能進(jìn)行（如大多數(shù)全基因組表達(dá)研究所需要的）。 因此，我們認(rèn)為臨床試驗(yàn)的分子數(shù)據(jù)應(yīng)適當(dāng)共享，從而可以將多個(gè)隊(duì)列合并以發(fā)現(xiàn)新的生物標(biāo)志物。 在許多研究領(lǐng)域，數(shù)據(jù)是私人保有的，無(wú)法如此再次使用。 例如，我們?cè)谖墨I(xiàn)中搜索了重癥監(jiān)護(hù)代謝組學(xué)的臨床研究，并確定了28項(xiàng)研究（合計(jì)n = 2,322），其中只有兩項(xiàng)研究的原始數(shù)據(jù)為公開(kāi)可用（ 表1 ）的。 這些數(shù)據(jù)的公開(kāi)分享將利于薈萃分析和基于數(shù)據(jù)的假設(shè)，避免了所謂“重新發(fā)明輪轂”的新隊(duì)列研究。顯而易見(jiàn)的是，只要能避免缺陷，這些由單一隊(duì)列研究構(gòu)成的薈萃將可以成功地產(chǎn)生強(qiáng)大的生物標(biāo)志物； 我們也建議研究人員們能為之后薈萃分析提供數(shù)據(jù)

。

TABLE 1. Metabolomics Studies in Critical Care

To aid the broader community in this effort, we have made available on our website (http://khatrilab./sepsis) a large number of existing studies of gene expression in sepsis along with source code for analysis (20, 21). This is one resource any researcher can use to further explore their biomarkers in broader clinical context and to test their generalizability in silico prior to embarking on a clinical trial.

為了幫助更廣泛的研究團(tuán)體進(jìn)行這項(xiàng)工作，我們已經(jīng)在自己的網(wǎng)站（ http://khatrilab./sepsis ）上提供了大量已有的膿毒癥基因表達(dá)研究以及分析源代碼。 這是研究人員可以用來(lái)在更廣泛的臨床背景下進(jìn)一步探索其生物標(biāo)志物的一個(gè)資源，并可早與臨床試驗(yàn)之前通過(guò)電腦模擬其普適性。

BIG DATA AND BIOMARKERS

大數(shù)據(jù)與生物標(biāo)志物

One of the biggest benefits of the data-driven omics approach to biomarker discovery is the possibility of discovering novel pathobiology in the heterogeneity of critical illness. Although hypothesis-driven studies of familiar cytokines (e.g., those resulting from activation of the nuclear factor-kB or interferon pathways) may be warranted by preclinical models, many common pathways are activated by multiple stimuli at a cellular level (54) and so are unlikely to be highly specific for a given syndrome. Similarly, clinical scores that use similar data available in an electronic health record (vitals, common laboratories, etc.) are unlikely to be highly specific for multiple conditions. An omics approach, by contrast, can sift through thousands or tens of thousands of candidate biomarkers to find the best fit for a given condition. Unfortunately, the promise of omics is also thus its major pitfall: false positives are likely when there are many more variables than samples in a study. This has contributed to some early failures in the field. It is thus worthwhile to have a general framework with which to approach biomarker development studies.

數(shù)據(jù)驅(qū)動(dòng)的組學(xué)對(duì)發(fā)現(xiàn)生物標(biāo)志物最大的好處之一是在危重疾病的異質(zhì)性中發(fā)現(xiàn)新的病理生物學(xué)機(jī)制。 盡管假說(shuō)驅(qū)動(dòng)的類似細(xì)胞因子的研究（例如激活核因子-kB或干擾素途徑的多種研究）可通過(guò)臨床前模型證明，許多常見(jiàn)的發(fā)病途徑是在細(xì)胞水平上由多重刺激激活，但在指定的綜合征中卻不一定具有高度特異性。 同樣，在電子健康記錄中使用類似數(shù)據(jù)進(jìn)行臨床評(píng)分（生命體征，常用實(shí)驗(yàn)室檢查等），但其對(duì)多種疾病不太可能是高度特異性的。 相比之下，一個(gè)組學(xué)方法可以篩選成千上萬(wàn)個(gè)候選生物標(biāo)志物，從而找出最適合于給定疾病的。 不幸的是，組學(xué)能夠達(dá)成的也正是它的主要缺陷：當(dāng)一項(xiàng)研究中的變量參數(shù)多于樣本量時(shí)，可能會(huì)出現(xiàn)假陽(yáng)性。 這導(dǎo)致了早期該領(lǐng)域的一些失敗。 因此，有必要制定一個(gè)總體框架來(lái)應(yīng)對(duì)生物標(biāo)志物開(kāi)發(fā)研究。

BIOMARKER STUDIES: A CONCEPTUAL FRAMEWORK

生物標(biāo)志物研究：概念架構(gòu)

There are two excellent guidelines for how to determine the rigor of multivariable prediction models (the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis [TRIPOD] statement [www.tripod-statement.org] [55]) and of diagnostic accuracy studies generally (the Standards for Reporting Diagnostic accuracy studies [STARD] statement [www.stard-statement.org] [56]). In addition to these reporting guidelines, we suggest the following list of questions that we use to help place a study in context:

已有兩個(gè)極佳的指南用于確定多變量預(yù)測(cè)模型的精準(zhǔn)性（即TRIPOD聲明www.tripod-statement.org 和STARD聲明 www.stard-statement.org）。 除了這些指南之外，我們還提出下列問(wèn)題，以幫助您在規(guī)定范疇內(nèi)進(jìn)行研究：

1. What is the context of the reported diagnostic comparison? Are there existing comparators/gold standards for this question, and what is their diagnostic accuracy in practice? Ideally, a study will compare a new biomarker to a gold standard, possibly with a net reclassification, but this is often not possible and only reported in later validations.

所報(bào)告的診斷比較的范疇是什么？ 這個(gè)問(wèn)題是否存在參照物/金標(biāo)準(zhǔn)，在實(shí)踐中他們的診斷準(zhǔn)確度是多少？ 理想情況下，研究中新的生物標(biāo)志物將與金標(biāo)準(zhǔn)進(jìn)行比較，可能需要重新分類，但這通常是不可能的，而且僅在以后的驗(yàn)證中報(bào)告。

2. How “l(fā)ocked down” is the reported biomarker? Are the biomarkers themselves being selected? Is a statistical model being retrained in the new cohort (i.e., if using a regression model, were the coefficients determined prior to testing)? If a cutoff is used, was it determined prospectively, and is it standard?

2.報(bào)告的生物標(biāo)志物如何“恰中要害”？ 生物標(biāo)志物是被挑選的嗎？ 是否在新隊(duì)列中重新驗(yàn)證了統(tǒng)計(jì)模型（即如果使用回歸模型，是否是在測(cè)試之前確定的系數(shù)）？ 如果使用閾值，是否前瞻性地確定過(guò)，是標(biāo)準(zhǔn)的？

3. How generalizable is the validation cohort being studied? Is this merely a random held-out set of the original discovery cohort? Is it from the same center as the discovery cohort? Does it represent a new area of application?

3.研究驗(yàn)證隊(duì)列的普適性如何？ 這只是一個(gè)隨機(jī)推出的原始隊(duì)列嗎？ 是與隊(duì)列相同的中心嗎？ 它是否代表了新的應(yīng)用領(lǐng)域？

4. Is this biomarker useful? If applied clinically, would it change practice?

4.這個(gè)生物標(biāo)志物有用嗎？ 如果臨床應(yīng)用，會(huì)改變臨床實(shí)踐嗎？

5. Is there a link to known biology? In our opinion, this may not be necessary at first, especially if the study is searching in spaces that are not well-studied (outside the “street lamp” of common studies). Procalcitonin, for instance, had not been well-characterized as part of the immune response (and the biology remains somewhat unclear today) at its first testing as a biomarker for bacterial infection .

5.與已知生物學(xué)有聯(lián)系嗎？ 在我們看來(lái)，這可能是沒(méi)有必要的，特別是如果這項(xiàng)研究處于未被很好研究過(guò)的領(lǐng)域（在普通研究的“路燈”之外）。 例如，在降鈣素原第一次被作為細(xì)菌感染的生物標(biāo)志物的檢查中就沒(méi)有被充分定性為免疫應(yīng)答的一部分（并且其生物學(xué)至今仍然有些不清楚）。

6. Can the biomarker be measured in a reasonable amount of time to make it useful in critical care? Although not a reason to dismiss results, many of the diagnostic applications in critical care require a rapid turnaround time. A more complex process, or one that relies on new technologies, may take longer to be clinically translated, and will be harder to replicate in validation studies. For example, neutrophilic CD64 as measured by flow cytometry is highly diagnostic for sepsis but has a turnaround time of several hours .

6.生物標(biāo)志物能否在合理的時(shí)間量中檢測(cè)，以使其在重癥醫(yī)學(xué)可用？ 盡管沒(méi)有理由拋棄研究結(jié)果，但重癥監(jiān)護(hù)中的許多診斷應(yīng)用需要快速的周轉(zhuǎn)時(shí)間。若監(jiān)測(cè)過(guò)程更復(fù)雜，或需依賴新技術(shù)則其臨床轉(zhuǎn)化可能需要更長(zhǎng)的時(shí)間，并且驗(yàn)證研究中更加難以重現(xiàn)。 例如，通過(guò)流式細(xì)胞術(shù)測(cè)量的嗜中性白細(xì)胞CD64對(duì)于敗血癥是具有高度診斷性的，但需要數(shù)小時(shí)的試驗(yàn)周轉(zhuǎn)時(shí)間。

In addition, it is helpful to put a study into context in terms of biomarker development (Fig. 2). Early validation may be simply the generation of receiver-operating characteristic curves in similar cohorts to the initial discovery cohort. As evidence accumulates, however, such studies should 1) investigate the application of the biomarker in a broader variety of cohorts that represent the full spectrum of disease and 2) compare the test to known standards for easy comparison. For instance, the later-stage validation of a biomarker for the prognosis of sepsis that is not compared to either lactate or clinical severity scores (e.g., Sequential Organ Failure Assessment) is unhelpful. Similarly, a study examining the diagnostic power for a locked, commercially available biomarker is important, but not as helpful as one examining outcome after intervention.

此外，將研究至于生物標(biāo)志物的研發(fā)會(huì)比較好（ 圖2 ）。 早期驗(yàn)證可能只是通過(guò)與初始研究隊(duì)列相似的研究中生成操作者特征曲線。 然而，隨著證據(jù)的積累，這樣的研究應(yīng)該：1）調(diào)查生物標(biāo)志物在更廣泛的各種群體中的應(yīng)用，代表疾病的全部范圍; 2）將檢測(cè)與已知標(biāo)準(zhǔn)進(jìn)行比較。 例如，在膿毒癥預(yù)后生物標(biāo)志物的后期驗(yàn)證中，不與乳酸或臨床嚴(yán)重性評(píng)分（例如，序貫器官功能衰竭評(píng)估）進(jìn)行比較將是無(wú)益的。 類似地，考核一個(gè)給定的商業(yè)生物標(biāo)志物的診斷效能是重要的，但不會(huì)比考察干預(yù)治療后結(jié)更有價(jià)值。

Figure 2. Maturity of biomarkers: a conceptual framework. ROC = receiver-operating characteristic.

MOVING FORWARD IN THE BIG DATA ERA

走向大數(shù)據(jù)時(shí)代！

The promise of precision medicine is to have the right treatment for the right patient at the right time. In critical care, our immediate need is to get the basics right. For instance, we should first try to answer urgent clinical questions (such as which patients need antibiotics), and then pose new ones that may not have been previously answerable (such as whether there are molecular subtypes of sepsis). As omics and big data technologies proliferate, so too will studies utilizing them as biomarkers in critical illness (studying the genome, epigenome, transcriptome, proteome, metabolome, lipidome, microbiome, and quantified self, to name a few). In all cases, we must remember the extreme heterogeneity of critical illness, and strive for generalizable disease-defining diagnostics and robust biomarkers that can help the entire spectrum of critical care research and delivery.

精準(zhǔn)醫(yī)學(xué)承諾的是在正確的時(shí)間對(duì)正確的患者進(jìn)行正確的治療。 在重癥醫(yī)學(xué)中，我們迫切需要的是打好基礎(chǔ)。例如，我們應(yīng)首先解決急迫的臨床問(wèn)題（例如哪些患者需要抗生素），然后對(duì)以前無(wú)法解決的問(wèn)題提出新的方案（如是否存在膿毒癥的分子亞型）。 隨著組學(xué)和大數(shù)據(jù)技術(shù)的興起，許多研究也會(huì)利用其開(kāi)展重癥疾病的生物標(biāo)志物的考查（基因組，表觀基因組，轉(zhuǎn)錄組，蛋白質(zhì)組學(xué)，代謝組學(xué)，脂質(zhì)體，微生物學(xué)和自身定量等）。 無(wú)論怎樣，我們必須記住危重疾病的極端異質(zhì)性，并致力于探尋具有普適性的判定疾病的診斷方法和強(qiáng)力的生物標(biāo)志物，以幫助整個(gè)重癥醫(yī)學(xué)研究和推廣。