DATABASE Open Access iHMS: a database integrating human histone modification data across developmental stages and tissues
2017

Yanglan Gan
Han Tao
†
Jihong Guan
Shuigeng Zhou
DATABASE Open Access iHMS: a database integrating human histone modification data across developmental stages and tissues

BMC Bioinformatics
18103201710.1186/s12859-017-1461-yHuman histone modificationDevelopmental stagesData integrationDatabase
Background: Differences in chromatin states are critical to the multiplicity of cell states. Recently genome-wide histone modification maps of diverse human developmental stages and tissues have been charted. Description: To facilitate the investigation of epigenetic dynamics and regulatory mechanisms in cellular differentiation processes, we developed iHMS, an integrated human histone modification database that incorporates massive histone modification maps spanning different developmental stages, lineages and tissues (http://www. tongjidmb.com/human/index.html). It also includes genome-wide expression data of different conditions, reference gene annotations, GC content and CpG island information. By providing an intuitive and user-friendly query interface, iHMS enables comprehensive query and comparative analysis based on gene names, genomic region locations, histone modification marks and cell types. Moreover, it offers an efficient browser that allows users to visualize and compare multiple genome-wide histone modification maps and related expression profiles across different developmental stages and tissues. Conclusion: iHMS is of great helpfulness to understand how global histone modification state transitions impact cellular phenotypes across different developmental stages and tissues in the human genome. This extensive catalog of histone modification states thus presents an important resource for epigenetic and developmental studies.

Background

Nearly all cells of an organism share the same genome but exhibit diverse phenotypes and carry out dramatically different functions. In eukaryotic cells the genome is organized into chromatin. Cell-type specific chromatin organization enables differential access and activity of regulatory elements and the manifestation of unique cellular phenotypes [1,2]. Recent genome-wide studies have shown that cooperative chromatin modifications affect the structure of chromatin, shape the macro-environment of DNA, and add an extra layer of information to the genome sequence [3,4]. These chromatin states are distinctive for different developmental stages [5], tissues *Correspondence: firstname.lastname@example.com † Equal contributors 2 Department of Computer Science and Technology, Tongji University, Shanghai, China Full list of author information is available at the end of the article [6], and disease states [7,8], which can play important roles in establishing cell identity during development [9]. Therefore, studying histone modification states of multiple developmental stages and cell types may extend the knowledge of epigenetic dynamics and regulatory mechanisms in cellular differentiation, reprogramming, and disease processes. Large-scale mapping of histone modifications has emerged as a powerful means for characterizing chromatin structures. The technology of chromatin immunoprecipitation followed by sequencing (Chip-Seq) can interrogate chromatin structure across the genome [10], which is increasingly applied for charting genome-wide maps of histone modifications [11,12]. Currently, a large collection of histone modification maps are being generated for diverse developmental stages, lineages and tissues, with the emphasis on mammalian models [5,[13][14][15][16][17]. The expanding body of epigenomic data provides an opportunity to elucidate novel relationships among various histone modifications [18,19], to characterize regulatory elements in the human genome [20], and to understand how global features of histone modifications impact cellular phenotypes across different developmental stages, lineages, and environmental conditions [21,22].

Bearing these promises, a fundamental problem is to integrate histone modification maps of diverse developmental stages and tissues in the public domain. Over the past years, a few epigenomic databases have been developed for the integration of various human epigenomic data from different tissues and experiments [23][24][25]. These widely used databases are designed to catalyze basic biology and disease-oriented research, and mainly provide researchers with a resource for visualizing and downloading whole-genome datasets. However, it is not intuitive and easy to conduct detailed queries and comparisons for specific histone modification states of interested genomic regions. Another human histone modification database HHMD [26] includes only epigenomic data of several cell types, rather than integrate histone modification data from multiple developmental stages. Obviously, there is an urgent need to construct a specialized database that comprehensively provides highresolution genome-wide histone modification data for epigenetic and developmental studies.

Here, we report a database iHMS that integrates human histone modification data covering diverse developmental stages and primary tissues. It also includes genomewide expression data of different conditions and reference genes, GC content and CpG island information. iHMS has an intuitive and user-friendly query interface, which enables both basic and advanced search based on gene names/genomic region locations, histone modification marks and cell types, as three major query options. Moreover, it allows users to visualize and compare multiple genome-wide histone modification maps and related expression profiles at different developmental stages and tissues via a powerful browser. Thus, iHMS can provide a systematic view of the dynamic histone modification landscapes during cellular differentiation and development, which is useful for researchers to compare the variability of histone modification states with underlying gene expression, to identify cell-type-specific histone modification states and their regulatory implications for cellular phenotypes across different developmental stages and tissues.

Construction and content

Database overview

In human, the study of epigenetic mechanisms underlying the regulation of early embryonic development requires access to large amounts of epigenomic data in different developmental stages. iHMS is a web-based integrated platform that enables users to query, compare, analyze and visualize genome-wide histone modification patterns across different human developmental stages and representative tissues. Figure 1 shows the framework of iHMS, which is composed of three layers: data preprocessing, core database and computing unit, and user interface.

In the data preprocessing layer, we reorganize and compress the collected raw data. This process is of great importance to reduce the redundancy among different datasets, to improve query efficiency, and to facilitate data usage in the follow-up analysis. In the second layer, the compressed histone modification maps are imported and stored into a relational database. All histone modification maps are stored in tables to enable efficient management, search and representation. The related expression data and genomic annotations, including reference genes, GC content and CpG island information, are stored in file system for quick access by the JBrowse visualization system [27]. To bridge the core database and the user interface, a computing unit is also developed in the second layer. It processes the dataset according to users' requests from the user interface to guarantee prompt responses. The user interface layer of iHMS provides users with friendly and interactive interfaces for data query, visualization, download and peak detection analysis. With these interfaces, users can easily configure options to access data from the core database. Results of search and analysis will be presented to users and obtained in files. The web-based browser is built for manipulating and displaying these datasets on the whole genome.

System implementation

The integrated system iHMS is built with LampServer (Linux+Apache+MySQL +PHP). LampServer is a fast and open source development environment, allowing users to develop web applications with Apache, PHP and MySQL. Raw datasets of histone modification maps are reorganized and compressed by Perl and Python scripts. The core database of iHMS is implemented with MySQL relational database system (version 5.6.12). To facilitate efficient management and query, all histone modification maps are stored in MySQL tables, whereas the expression data and genomic annotations are stored in files for quick access by JBrowse. The computing unit is implemented by PHP, a server-side scripting language designed for web development. The browser-based interfaces are developed with a collection of web development techniques, including JavaScript, CSS and Ajax. These powerful techniques make data access simple and efficient. To display histone modification profiles of specified genomic regions, we apply Highcharts.js and CanvasJS, which are both effective and open-source painting galleries. Specifically, the Ajax technique enables data to be transferred between Fig. 1 An overview of the iHMS database. iHMS integrates genome-wide histone modification maps of different developmental stages and tissues of human. Related gene expression profiles and genomic annotations are also incorporated. iHMS is composed of three layers: data preprocessing, core database and computing unit, and user interface server and browser asynchronously without interfering with the display of the current web page. Meanwhile, we implement and integrate a peak calling method in the interface, allowing users to identify enrichment sites of different histone modification marks. On the basis of the processed bin-based data, the peak calling procedure first determines a significant enrichement threshold by a percentile rank statistic method and utilizes Monte Carlo simulation method to control false discovery rate [28]. For the visualization of genome-wide histone modification maps, the interactive and user-friendly browser is built on JBrowse, which requires light resource and facilitates fast scrolling and zooming on the whole genome.

Data collection and preprocessing

iHMS integrates a collection of over 200 histone modification maps for phenotypically diverse human developmental stages and tissues, produced by the NIH Roadmap Epigenomics Mapping Consortium [5,16]. These epigenetic maps depict the dynamic landscapes of important histone modifications. In recent studies, to investigate early human developmental decisions, H1 human embryonic stem cells (hESCs) were differentiated into a variety of precursor cell types [5,14], including trophoblast-like cells (TBL) [29], mesendoderm (ME) [30], neural progenitor cells (NPCs) [31], and mesenchymal stem cells (MSCs) [32]. These lineages represent extra-embryonic and embryonic lineages at early stages of development. Specifically, the first three states represent developmental events that mirror critical developmental decisions in the embryo. MSCs are fibroblastoid cells that are capable of expansion and multi-lineage differentiation to bone, cartilage, adipose, muscle, and connective tissues [28]. Also, H9 human embryonic stem cells were differentiated into neurons and neural progenitors [33]. Accordingly, the subsequent primary tissues, representatives of all three germlayers were also investigated, including adipose, adrenal gland, adult liver, aorta, esophagus, gastric, left ventricle, lung, ovary, pancreas, psoas muscle, right ventricle, right atrium, sigmoid colon, spleen, thymus, small intestine, breast, brain and bladder. In these tissues, genomewide maps of major chromatin marks were generated using ChIP-seq [5]. In detail, the chromatin marks including H3K4me1/2/3, H3K36me3, H3K9me3, H3K27me3, H3K79me1, H2AK5ac, H2bK120ac, H2BK5ac, H3K18ac, H3K23ac, H3K27ac, H3K4ac, H3K9ac and H4K8ac were profiled. As gene expression correlates closely with histone modification status, expression profiles of the investigated cell types were also incorporated in iHMS. RNA expression profiles of these cell types were generated by RNA-seq technology. The summary of these datasets is shown in Fig. 2. On the webpage, we can view more detailed information by clicking each solid node. To efficiently integrate and access these datasets, we preprocessed the raw data in the following steps. The whole-genome data were divided into non-overlapping 200 bp bins. Then, we summed neighboring reads and assigned an integer for each bin. We obtained a total 288 genome-wide data sets, including 254 histone modification data sets, 34 RNA-seq data sets, covering 8 early developmental cell lines and 26 primary tissues, representatives of all three germlayers. These integrated datasets enable in-depth investigation of histone modification data, and facilitate users to explore the dynamic histone modifications and transcriptional changes that drive developmental fate decisions. These histone modification datasets were originally classified by cell type and histone modification mark. In this way, the genomic locations were repeatedly recorded, leading to severe data redundancy. Thus, to reduce the redundancy and save the storage cost, we further reorganized and compressed these processed data sets, as shown in Fig. 3. The histone modification data of different tissues were reorganized into matrices. In the matrix, the rows represent different genomic locations, the columns indicate different attributes. Here, each attribute is a combination of the tissue name and histone modification mark (eg H1_h3k4me3). The processed datasets became simpler and more compact for the following indexing and analysis. The scripts of these preprocessing steps and the processed data were deposited into the repository Figshare, which can be downloaded from the following link (https://figshare. com/s/f52b6032884637984711).

Utility and discussion

Data query

The iHMS database can be accessed through web interface to search for specific histone modification states of any interested regions across different developmental stages and tissues (Fig. 4a). The single query page allows searching interested region by genomic location or gene ID. For users' convenience, the matched genes will be recommended to select when users input part of the geneId, and the gene locations are auto-completed in the location input fields. In support of comparative and differential analysis, iHMS offers users with different options: (i) query by developmental stages/tissues, and (ii) query by histone modification marks, as shown in Fig. 4b. By selecting a specific development stage or tissue and multiple histone modification marks, users can compare the histone modification patterns and identify combinatorial modification patterns among theses marks. In the second way, users are able to search for a particular histone modification mark in multiple developmental stages or tissues, which facilitates the differential analysis. Furthermore, users are allowed to submit more comprehensive queries by combining multiple histone modification marks and cell types. Once the search is finished, all the matched results are displayed at the bottom of the webpage (Fig. 4c). By clicking the label on the left panel, the profile of each histone modification could be viewed or hided. To check the details of the matched results, users can zoom in and move to any interested local region in the figure. All results can be saved in csv format for downstream analysis.  We also designed a batch query option for users, which allows searching for many interested genomic locations at the same time. To conduct batch query, the developmental stages/tissues and the histone modification marks are set as in the single query page. The locations of multiple regions or the interested gene list can be uploaded from files or submitted in the input box (Fig. 4d). After clicking the search and export button, users can retrieve the matched results in a txt file [34].

Visualization and comparison

To visualize histone modification maps on a genomewide scale, iHMS deploys a user-friendly and interactive browser which is built on JBrowse. It provides an integrated visualization tool for viewing different histone modification marks, gene expression, reference gene annotations, GC content and CpG island information (Fig. 5). For all available developmental stages or tissues, users could easily specify interested tracks to display by clicking the icons on the left panel. Also, users are able to browse, zoom and scroll any region along the genome. By clicking a gene or region on a specific track, the corresponding details will be displayed. For example, on clicking a gene on the RefSeq gene profile, the related annotations will pop up.

With the browser, users are able to conduct further analysis. On one hand, the browser is of usefulness to compare the histone modification states of specific genes or regions at different developmental stages by taking account of multiple relevant data tracks. It facilitates users to identify tissue-restricted histone modification patterns, which is important in maintaining the identity of cell/tissue type. On the other hand, as iHMS enable users to visualize high-resolution gene expression data as well as histone modification profiles in an interactive manner, it can also help identify the correlation between histone modification pattern and gene expression level, and develop new hypotheses regarding the regulatory functions of these chromatin features in the cell differentiation process.

Download

Considering that researchers may need these datasets for downstream analysis, iHMS provides an effective interface for data download. The whole-genome histone modification maps and gene expression data can be downloaded in the txt format. iHMS allows users to download data by chromosome. Selecting one chromosome, users can download all available histone modification maps and gene expression data at different developmental stages and tissues. The downloaded data is processed and reorganized as matrix. Rows represents the genomic locations, while columns are the histone modification marks of different cell types. Also, genome-wide expression data across all developmental stages and tissues can be downloaded.

Conclusions

The expanding body of chromatin data in public domain has fostered many computational efforts that aim to integrate different data types. Different from previous databases, iHMS focuses on (1) integrating the whole genome histone modification maps covering a wide spectrum of developmental states, including embryonic stem cells, early embryonic lineages and somatic primary tissue types; (2) relating histone modification maps with other related omics data, including gene expression data and sequence-based genome annotations, which allows the investigation between histone modification and gene expression; (3) enabling detailed query and comparison of histone modification states of different developmental stages and tissues for specific genomic regions or genes. The query result can be retrieved as matrix, which is convenient for further comparison and analysis; and (4) building an efficient browser for visualization of all types of data in a genome-wide manner. In general, through integration of histone modification maps with expression profiles and sequence-based genome annotations, iHMS enables the discovery of cell-type specific functional histone modification states, and the gaining of insights into the epigenetic basis of cellular phenotypes across different developmental stages and tissues. This extensive catalog of histone modification states thus provides wealth information of chromatin structure and function, which may help researchers understand the epigenetic mechanisms of the differentiation and development processes.

In the foreseeable future, more histone modification data will become available with the rapid advancement of high-throughput sequencing technologies. Further development for iHMS will integrate whole-genome histone modification data from more developmental stages and tissues of human. Meanwhile, iHMS will continually incorporate the gene expression data and interconnect them with the histone modification data. As the amount of histone modification data increases, it is also important to develop efficient web tools to support quick incorporation and analysis of these newly produced data.

Fig. 2
2The summary of iHMS data content. More detailed information can be viewed on the web page by clicking each solid node

Fig. 3
3The illustration of data redundancy reduction. Histone modification datasets are reorganized and compressed before being imported to the core database

Fig. 4
4Screenshots of data query in iHMS. This figure describes the search for multiple histone modification marks of a specific cell type. a In the Entry Screen, we choose Single Query. b A search for all profiled histone modification marks of H1 cell line. c The matched results are displayed on the bottom of the query page. Each line in the graph represents the histone modification states along the genomic region. By clicking the line on the left panel, we can view or hide the corresponding histone modification mark. d In the Batch Query page, multiple genomic locations can be searched at the same time

Fig. 5
5Snapshots of visualization results in iHMS. a Gene information, GC content and CpG island. b Histone modification profiles of interested developmental stages and tissues. c Gene expression profiles
Availability and requirementsThe iHMS is freely available online at http://www. tongjidmb.com/human/index.html.AbbreviationsChip-Seq: chromatin immunoprecipitation followed by sequencing; hESCs: H1 human embryonic stem cells; ME: mesendoderm; MSCs: mesenchymal stem cells; NPCs: neural progenitor cells; TBL: trophoblast-like cellsAcknowledgmentsAuthors are grateful to NIH Roadmap Epigenome Project for providing the genome-wide data to carry out this work. We thank anonymous reviewers for their useful comments on the manuscript.FundingThis work was supported by the National Natural Science Foundation of China (61272380, 61300100, 61303096), Shanghai Natural Science Foundation (13ZR1451000, 13ZR1454600, 15ZR1400900), Chen Guang project supported by Shanghai Municipal Education Commission and Shanghai Education Development Foundation, and the Fundamental Research Funds for the Central Universities (16D111208).Availability of data and materialsDatasets contained in iHMS are available on the site to download as excel spreadsheets or txt format. The preprocessed histone modification and expression data can be accessed at the following URL http://www.tongjidmb. com/human/download.php. The scripts of the preprocessing steps can be downloaded at the following URL https://figshare.com/s/ f52b6032884637984711.Authors' contributionsYLG, HT, JHG and SGZ have designed the database system and the web interfaces. YLG and HT have implemented and tested the system. JHG and SGZ have coordinated data preprocessing and supervised the effort. All authors have read and approved the final manuscript.Competing interestsThe authors declare that they have no competing interests.Consent for publicationNot applicable.Ethics approval and consent to participateNot applicable.• We accept pre-submission inquiries • Our selector tool helps you to find the most relevant journal Submit your next manuscript to BioMed Central and we will help you at every step:
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Is critical care ready for an economic surrogate endpoint?

M Elizabeth Wilcox
Interdepartmental Division of Critical Care
University of Toronto
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Gordon D Rubenfeld firstname.lastname@example.com
Interdepartmental Division of Critical Care
University of Toronto
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Department of Critical Care Medicine and Sunnybrook Research Institute
Sunnybrook Health Sciences Centre
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Is critical care ready for an economic surrogate endpoint?
10.1186/s13054-015-0947-0C O M M E N T A R Y Open Access See related research by Turunen et al., * Correspondence: Full list of author information is available at the end of the article Domain Dedication waiver (http:// creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Wilcox and Rubenfeld Critical Care ( 2 0 1 5 ) 1 9 : 2 4 8
Intensive care is expensive, and thus a body of research has focused on strategies to reduce its costs. However, efforts to reduce the total cost of intensive care have met with limited success, partly because of the challenges of calculating how much a day in the ICU actually costs. We discuss these challenges and introduce the concept of total cost savings as an outcome of critical care trials, assuming statistically negative effects on mortality and quality of life.Healthcare is expensive, and intensive care is very expensive. ICUs are estimated to consume up to one-third of hospital costs and approximately 0.5 % of the US gross domestic product [1]. It is therefore not surprising that a lot of research has focused on strategies to reduce the cost of intensive care. Many studies have calculated the ICU cost by multiplying days in the ICU by cost per day, but what is truly surprising is the complexity of calculating the cost of a day in the ICU.There are two common accounting formulas for healthcare costing, conveniently termed top down and bottom up. The top-down method takes the overall cost of care for an ICU divided by the number of patient-days of care delivered to calculate the average cost per day. Similarly, the bill that a hospital sends to a payer for an ICU day can be adjusted by some estimate of hospital overheads to obtain an idea of the average cost for that day. This method provides convenient numbers that are used frequently in the literature. For example, the cost-effectiveness study of antimicrobial-coated catheters used this approach to estimate that an ICU day cost US$1,152 in 1999, or roughly three times the cost of a hospital day[2]. In contrast, the bottom-up approach requires very granular cost data. Every bandage, catheter, lab test, and nursing and therapist hour must be accounted for. Even with this level of detail, assigning a dollar figure to these costs is challenging. A recent Time magazine article infuriated readers by reporting that hospitals charged US$18 for a glucose test strip that could be purchased at Amazon for US$0.55[3]. Unlike the Amazon shopper, the hospital has to recoup the costs to order, store, track and, of course, bill for glucose strips. A tiny part of the hospital's bill to dispose of hazardous wastes must also be built into the cost of glucose test strips. Of course, the hospital can decide to charge US$0.55 for glucose test strips, as long as it makes up these costs on aspirin or gauze. The rules for bottom-up accounting are quite complicated, and it is not clear how comparable these costs are between hospitals that might use different methods. Even when bottom-up data are available, the relative costs of an ICU day or a hospital day are determined by the room and board billing rate of the hospital[4]. Importantly, bottom-up and top-down approaches give different cost estimates[5].Clinicians, researchers and policy-makers have found top-down numbers useful despite their limitations. For example, if catheter-related bloodstream infections extend the ICU length of stay by 4 days, then eliminating each infection should save the healthcare system US$4,608 (4 × US$1,152) in ICU costs. If a new sedative drug reduces the ICU length of stay by 0.5 days and the ICU treats 300 patients, the ICU should see an additional US$172,800 (0.5 × 300 × US$1,152) in the budget, minus the cost of the drug. However, while these average ICU costs and cost savings are easy to calculate and impressively large, they are deeply misleading. In this article we discuss some of the reasons why and explore the methods and rationale for studying total cost savings as a surrogate endpoint in critical care.

Intensive care is expensive, and thus a body of research has focused on strategies to reduce its costs. However, efforts to reduce the total cost of intensive care have met with limited success, partly because of the challenges of calculating how much a day in the ICU actually costs. We discuss these challenges and introduce the concept of total cost savings as an outcome of critical care trials, assuming statistically negative effects on mortality and quality of life.

Healthcare is expensive, and intensive care is very expensive. ICUs are estimated to consume up to one-third of hospital costs and approximately 0.5 % of the US gross domestic product [1]. It is therefore not surprising that a lot of research has focused on strategies to reduce the cost of intensive care. Many studies have calculated the ICU cost by multiplying days in the ICU by cost per day, but what is truly surprising is the complexity of calculating the cost of a day in the ICU.

There are two common accounting formulas for healthcare costing, conveniently termed top down and bottom up. The top-down method takes the overall cost of care for an ICU divided by the number of patient-days of care delivered to calculate the average cost per day. Similarly, the bill that a hospital sends to a payer for an ICU day can be adjusted by some estimate of hospital overheads to obtain an idea of the average cost for that day. This method provides convenient numbers that are used frequently in the literature. For example, the cost-effectiveness study of antimicrobial-coated catheters used this approach to estimate that an ICU day cost US$1,152 in 1999, or roughly three times the cost of a hospital day [2]. In contrast, the bottom-up approach requires very granular cost data. Every bandage, catheter, lab test, and nursing and therapist hour must be accounted for. Even with this level of detail, assigning a dollar figure to these costs is challenging. A recent Time magazine article infuriated readers by reporting that hospitals charged US$18 for a glucose test strip that could be purchased at Amazon for US$0.55 [3]. Unlike the Amazon shopper, the hospital has to recoup the costs to order, store, track and, of course, bill for glucose strips. A tiny part of the hospital's bill to dispose of hazardous wastes must also be built into the cost of glucose test strips. Of course, the hospital can decide to charge US$0.55 for glucose test strips, as long as it makes up these costs on aspirin or gauze. The rules for bottom-up accounting are quite complicated, and it is not clear how comparable these costs are between hospitals that might use different methods. Even when bottom-up data are available, the relative costs of an ICU day or a hospital day are determined by the room and board billing rate of the hospital [4]. Importantly, bottom-up and top-down approaches give different cost estimates [5].

Clinicians, researchers and policy-makers have found top-down numbers useful despite their limitations. For example, if catheter-related bloodstream infections extend the ICU length of stay by 4 days, then eliminating each infection should save the healthcare system US$4,608 (4 × US$1,152) in ICU costs. If a new sedative drug reduces the ICU length of stay by 0.5 days and the ICU treats 300 patients, the ICU should see an additional US$172,800 (0.5 × 300 × US$1,152) in the budget, minus the cost of the drug. However, while these average ICU costs and cost savings are easy to calculate and impressively large, they are deeply misleading. In this article we discuss some of the reasons why and explore the methods and rationale for studying total cost savings as a surrogate endpoint in critical care.

Reducing the total cost of hospital care is hard because most hospital costs are fixed, rather than variable. A detailed study of hospital costs at Cook County Hospital in the United States found that the majority of the costs were fixed and related to buildings, equipment, salaried labor and overheads [6]. In addition, variable costs tend to decrease as the length of stay increases; attempts to reduce costs by reducing variable costs such as length of stay or laboratory tests or medications have thus met with limited success. For example, significant reductions in hospital length of stay in the United States led to eventual increases in healthcare costs because patient care was simply shifted from hospitals to nursing homes and other post-acute care services [7]. A study evaluating reductions in length of stay in surgical patients similarly found small financial opportunities by eliminating the low-cost last days in the hospital [8]. The authors concluded that a focus on utilization earlier in the course of admission would yield greater cost savings.

There are three important reasons why reducing the length of stay in the ICU may not necessarily lead to overall cost savings. The first reason is that the variable costs of the days or hours that are saved at the end of the ICU stay are small compared with the first days [9]. In fact, the last days in the ICU have similar variable costs to the first days on the ward [10]. An analysis that assigns average costs to these inexpensive days therefore grossly overestimates the potential savings for reductions in length of stay. Second, depending on the reimbursement strategy and perspective of the cost analysis, reducing the ICU length of stay can paradoxically increase total ICU costs, because the new patient replacing the one leaving the ICU will require more intense and expensive care. Increasing access to ICU beds for patients who require them, and decreasing wait times in the emergency department, ward or post-anesthetic care unit is clearly a positive outcome, but it may not be a cost-saving one from the ICU perspective. Finally, if an intervention reduces the ICU length of stay by tacking on an equal or greater number of hospital days, the cost reduction will be significantly less than that calculated by a focus on ICU average costs.

In a recent issue of Critical Care, Turunen and colleagues explore the economic benefits of dexmedetomidine compared with standard sedatives in reducing days of mechanical ventilation [11]. The pharmaceutical company that markets the drug performed this economic analysis, using data from the Dexmedetomidine Versus Midazolam for Continuous Sedation in the Intensive Care Unit (MIDEX) and Dexmedetomidine Versus Propofol for Continuous Sedation in the Intensive Care Unit (PRODEX) trials, to measure cost savings attributable to dexmedetomidine. Readers who were skeptical of the two trials published in Journal of the American Medical Association because they thought that midazolam was an inappropriate comparator will be equally skeptical of this analysis. Careful readers will note that this study finds a statistically significant reduction in ICU length of stay whereas the parent trials did not, because it used different assumptions in counting the length of stay for patients who died [12].

However, there are some strengths to Turunen and colleagues' study that can serve as a guide for future investigations. First, the authors account for the fact that different days in the ICU cost different amounts. They used a hybrid of activity-based bottom-up cost accounting to measure nursing activity using the Therapeutic Intervention Scoring System to estimate, for example, that a day on invasive mechanical ventilation cost 33 % more than a day not ventilated. Second, several sensitivity analyses were performed to see whether costing assumptions changed the results. Dexmedetomidine saved money in all of the scenarios; but in some, particularly in comparison with propofol, the savings are quite small and must be considered against the increased rates of bradycardia and hypotension.

The real question for intensivists to consider is posed by the article's claim that if dexmedetomidine reduces the ICU length of stay by 30 h, then the same ICU staff could care for 19 extra patients for every 100 patients treated with the drug. If true, this important effect might let you treat an additional 80 patients per year in a typical 10-bed ICU. The problem with this conclusion is that you could do it with the same staff. In most ICUs, the last 30 h of an 8-day stay is provided by fewer nurses, but in this example 80 of those last days will be traded for new high-acuity first days of care. Unless the ICU uses a fixed staffing ratio for patients regardless of acuity, it is unlikely that the same staff could deliver this additional care, because it is different care.

Critical care has struggled with meaningful surrogate endpoints for trials [13]. Cost-minimization, the analysis presented here, assumes that the considered treatments have equivalent effects on patient-centered outcomes such as mortality and quality of life. This strategy overcomes a problem inherent in all nonmortality outcomes in critical care, namely informative censoring from mortality [14]. For example, both early death and successful weaning can reduce the duration of mechanical ventilation in a weaning trial, but we routinely ignore this and focus on duration of mechanical ventilation. Cost-minimization analysis addresses the problem of differences in outcomes by assuming they do not exist. This assumption has resulted in cost-minimization analysis being largely replaced by cost-effectiveness models that capture the uncertainty in outcomes from statistically negative clinical trials rather than ignoring them [15,16].

Regardless of the technique, we believe that the research question posed by this study is fundamental to critical care. In the absence of compelling effects on mortality or long-term quality of life, the only valuable surrogate outcome in critical care might be safe and significant reductions in total cost. This outcome will capture much of what we already look for in our surrogate outcomes of duration of mechanical ventilation, organ failure, rescue therapies and length of stay, and will present them in a persuasive form to society and patients. Critical care is a long way from adopting this outcome. We need to develop understandable methods to express the uncertainty in safety and outcomes, reliable techniques to address informative censoring by death, and accurate accounting that includes post-ICU costs, international variations in labor and resource costs, and adequately addresses the issue of declining variable costs over time. Finally, and, most importantly, we need to see whether these modeled cost savings result in efficiencies that benefit our patients.
 Competing interestsThe authors declare that they have no competing interests.
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