Introduction
The modern intensive care unit (ICU) is awash in a continuous stream of multivariate data produced from multiple monitors, ventilators, laboratory data and medical staff documentation. The dramatic increase in available information has led to an ICU that is very data-rich. The trauma and critical care communities have turned to these monitors and the data they produce to better understand post-injury physiology and guide resuscitation and treatment. Despite the improvements in, and increasing reliance on monitoring technology, these multivariate data (EKG, arterial blood pressure, ventilator information, and so on) are still recorded intermittently in many ICUs, often as infrequently as every hour, onto a paper chart. Even in ICUs where the paper chart has been replaced by a computerized medical record, these systems are not adequate for the tracking and analysis of complex multivariate relationships. Furthermore, this antiquated, non-relational system of data collection and presentation limits our ability to understand the complex relationship between variables and precludes longitudinal analysis of trends and developing patient pathophysiology. This results in care decisions that are too simplistic in nature. Indeed, most often care orders are written to restrict one variable to a given range (that is, give a fluid bolus for a systolic blood pressure <100) resulting in univariate treatment of complex multivariate physiology. A method to visualize and utilize complex multivariate data is needed, with the ultimate goal of identifying predictive patterns to protocolize and guide medical care. New applications of techniques in bioinformatics and data mining have been developed in the disparate fields of high throughput genomics, physics, and business data management that are aimed at dealing with these increasingly large and complex data sets [
1,
2]. These data-intensive fields apply techniques such as hierarchical clustering, k-means clustering and self-organizing maps to permit pattern recognition in data sets that would otherwise be too complex to visualize. Investigations in genetic research use hierarchical clustering to group gene expression data according to patterns based on deviations from the mean or median. These clusters are then visualized as a heat map and dendrogram to highlight the similarity within clusters. This has led to an improved understanding of complex genomic interactions and the development of new tools for the diagnosis and management of human disease [
3]. We sought to apply these techniques to the complex multivariate physiologic data collected from severely injured patients in a modern ICU.
Here we show that these clustering methodologies from bioinformatics are applicable to continuous rapidly changing multivariate physiologic data in critically injured patients, yielding important insight into patient physiology and outcomes. We define that at any time, the patient state is made up of a complex pattern of variables that together make up the resuscitative and metabolic milieu. We further hypothesize that these patterns are not easily discernable using traditional clinical measures of physiology. We define 10 patient states by applying hierarchical clustering to our multivariate ICU data. These states were then characterized based on clinical parameters and patient outcome. The states identified by clustering were not obvious by traditional physiological measures, yet they proved to have clinical prognostic value: time spent in some patient states was significantly predictive of subsequent mortality, the development of multiple organ failure, and infection. Furthermore, patients transitioned through multiple states during their ICU stay, reflecting changing post injury physiology and the effect of resuscitation and treatment. Together these findings demonstrate the potential of these techniques to integrate complex information and provide new insights in clinical care.
Materials and methods
Data collection
The study was approved by and conducted under supervision of the Committee on Human Research at the University of California San Francisco. Informed consent was obtained from patients or their surrogates per protocol. Physiological data were collected on 17 severely injured poly-trauma patients at one-minute intervals and stored in our Neurotrauma and Critical Care Database using a multimodal bio-informatics system (Aristein Bioinformatics, Palo Alto, CA, USA).
This system integrates continuous data from the bedside patient monitor (heart rate, oxygen saturation and Mean Arterial Pressure (MAP) with ventilator data and tissue oxygen measurements using a date and time stamp. Intermittent laboratory data, medications, and nursing interventions were derived from the computerized nursing documentation system (CareVue, Philips, Amsterdam, The Netherlands) and integrated with continuous data. Data were stored on a dedicated server in the ICU. Clinical blood gas sampling was supplemented for study purposes using a point-of-care analyzer (Opti CCA, Roche, Mannheim, Germany). Plasma lactate levels were measured (Roche Accutrend
® Lactate point of care testing system, Mannheim, Germany). Microdialysis of the deltoid muscle was performed as part of a previously reported study [
4] using the Licox
® Oxygen Catheter (Integra Neurosciences, Plainsboro, NY, USA) to measure the partial pressure of oxygen in the deltoid muscle as continuous surrogate markers for splanchnic perfusion. Catheters and monitoring took place for seven days or until the patient was extubated.
Patients were selected as a sequential convenience sample but all were severely injured patients that required ICU admission and ongoing resuscitation. The patients were followed until discharge or death, and all complications, including infections and organ dysfunction, were documented in the study database. Infectious complications included bacteremia, urinary tract infection, wound infection, fungemia, sepsis, abscess, infected decubitus ulcer, infected hardware, meningitis, and osteomyelitis. The Multiple Organ Failure (MOF) Score was calculated as described by Ciesla et al [
5]. The ordinal MOF score was converted to a binary outcome variable with MOF score ≥4 designated as Multiple Organ Failure. Other outcome variables were mortality and infection.
Hierarchical clustering
A total of 45 variables of physiological, clinical, and treatment data were collected every minute. For the clustering analysis we used only continuous variables for which the data were complete (heart monitor, ventilator, and microdialysis data), resulting in 52,000 points across 14 variables.
The clustering algorithm proceeds in two main steps: pairwise distance calculations and cluster linkage. For distance calculations, we used the standard Euclidean distance between each data point, which is calculated as
with di, jbeing the distance between observations i and j, n being the number of elements per observation, and xk, i/jbeing element k of observation i or j. These distances are calculated for every pair of observations, yielding m*(m-1)/2 distances for m observations.
With a complete enumeration of the pairwise distances between all observations, the linkage algorithm merges the two
closest clusters into one, where a cluster can also be a single data point. For this analysis, we use the complete linkage method, which defines the distance between each cluster as
with C(A, B) the distance from cluster A to cluster B. The maximum function indicates that we take the cluster distance to be the maximal distance between any two points in the cluster.
Univariate Linear Classifier
When using multidimensional analysis techniques, it is important to consider whether simpler univariate techniques could produce similar results. Therefore we attempted to train a univariate linear classifier using linear discriminant analysis (LDA) to classify our binary outcome. LDA produced an a posteriori that each data point falls under our outcome assignments.
In order to provide the LDA algorithm with the best possible chance of providing equivalent or better performance as the multivariate clustering we only use a single set of data rather than splitting our data into distinct training and test sets - a non-standard method that advantages the univariate method over the multivariate. We used all of the data that were input into the clustering algorithm as input into the LDA algorithm.
Between-cluster correlation analysis
We next calculated the Pearson correlation coefficients for each pair of variables within the clusters with the highest and lowest probabilities of death. Significance of correlations was determined using both bootstrapping and label shuffling resampling methods (10,000 iterations of each) to obtain a null distribution for the correlation coefficients. We then compared the corresponding correlation coefficients between the two clusters of interest.
Discussion
We have shown here the utility of hierarchical clustering as an unsupervised non-linear classification schema in the prediction of outcome in severely injured trauma patients. We obtained clusters that were enriched for patients who died, contracted an infection, and suffered multiple organ failure. These clusters were not merely dominated by a few specific patients with a particular outcome. Indeed each of the clusters was made up of multiple patients' data and each patient transitioned through multiple clusters during their ICU stay. Lastly, the prognostic information incorporated in the clustering results was not obtainable by univariate traditional statistical analysis and persists in the face of univariate analyses that could not predict any of these outcomes.
Despite the near continuous monitoring of many physiologic variables and treatment parameters, traditional care in the ICU fails to fully use all these data in an efficient manner. Currently, clinicians base understanding of patient state and appropriate manipulation of that state on intermittent examination of patient variables (vital signs, labs, studies and physical examination). It has been shown, however, that more frequent data collection and analysis better defines patient physiology [
6], and there has been much work in using continuous data, including the alarms built into the standard ICU bedside monitors [
7,
8]. While these monitors are excellent as instant alarms regarding critical parameters, they do nothing to help predict long-term outcomes. Improvements in diagnosis and care have traditionally resulted from both improved clinical acumen and scientific advancement, mostly surrounding scientific examination of a single or small group of adjuncts. Indeed, the critical care literature is full of examinations of monitors, scoring systems, measurements and biomarkers, all of which seek to define and predict the degree of injury, physiological insult and resuscitation [
8,
9]. Despite this proliferation, multivariate understanding of resuscitation state and identification of occult hypoperfusion remain elusive and an open experimental question. Multivariate decision tools using supervised learning algorithms have been implemented to detect hypovolemia [
10] and alarms for critical care patients [
8]. In contrast to our current work, this previous work used relatively few types of data (five and nine, respectively), giving a less complete picture of the patient's physiology. Additionally, multiple logistic regression models have been shown to predict MOF 12 hours post-injury [
11], but these suffer from the inability to discover new physiology or make use of complex multivariate physiological relationships. In ground breaking work in the mid 90s Rixen and collegues utilized K-means clustering to define patient states based on 17 non-continuous variables. Through clustering and comparison to reference states (derived from non-injured controls) this group elegantly proposed that patient state could be defined in multidimentional state space [
12,
13]. This work represented the first attempt at defining patient state as a multivariate entity. Here we extend these analyses using continuous data with no a priori understanding of the relationship between these data and outcome. We then extend these analyses by tracing patient state through the
state space over time.
The use of unsupervised learning with large multivariate data sets comprised of continuous data represents a rarely used combination of techniques to predict and improve patient outcomes. Nelson
et al. [
14] used self-organizing maps to visualize patterns in microdialysis data from patients with traumatic brain injury, finding that individuals were likely to cluster together, in contrast to our results showing much movement among clusters. The work presented here extends previous observations from our group that employed methods similar to those we report here, except that they used aggregate data from each patient rather than q1 minute data, and our methods provide predictions of outcome in addition to the clinical insights discussed by the authors [
15]. To fully utilize our data, we required a technique to distill all variables into a meaningful single value - in this case, a patient state. This could then, in turn, be defined in terms of clinically relevant patient outcome or physiologic state, as we have done here by associating each cluster with the probability of an outcome. Instead of fixation on one or a few physiologic parameters, transformation of all data into a single reproducible and clinically relevant value allows all available data to be used simultaneously. Furthermore, the complex relationships among multiple variables are preserved and exploited. Our analysis has shown that without inputting any prior knowledge, unsupervised algorithms are able to discern data (unobtainable by
traditional statistics) that are indicative of death, infection, and MOF. With our data obtained every minute, the fact that patients transition through many clusters throughout their observation period attests to rapidly changing complex physiology. We have demonstrated our ability to both define patient state using hierarchical clustering and to track the progress of individual patients through these clusters over time. Indeed, patients tend to move between clusters during their stay, and we would expect most of them to experience under-resuscitation during part of their first 24 hours of care. Future analysis could reveal the potential of assigning transition probabilities between clusters based on physiology, which combined with knowledge of the likelihood of death in each state suggests potential methods of
steering the physiology away from clusters with high mortality towards clusters associated with safety. The ability to do this in real time would greatly improve patient care decisions, leading to potentially enormous gains in outcomes.
We acknowledge that our results are dependent on our choice of similarity measure and clustering method. Our choice of Euclidean distance is natural for the problem at hand, as we were interested in the similarity of all variables to each other, not in how they varied with each other. Though the techniques of traditional linear statistics, correlation and regression analyses, can reveal differences between groups or correlations between pairs of physiological variables, we have shown here that they do not easily define a state made up of many variables with complex interrelationships.
There are several limitations to this preliminary study. First, the analysis here is based on a limited number or patients (17) and data points (52,000). Future studies should incorporate more patients (and more data) representing the primary outcomes. While a potential criticism is that a few clusters were dominated by the few patients with poor outcome, resulting in an overfit model, we stress that the clusters were defined in a way blind to patient outcome yet remained enriched for those outcomes.
Our results, while novel, represent a proof of concept study to show that cluster analysis can reveal complex patterns and predict outcome. Even so, we remain aware that to test the general applicability of these results, future studies will have to use a training data set to produce clusters/states that would then be applied to a test data set from separate patients. While we have tried to address the limitations of our single set data and the existence of serial dependence of data points using bootstrap analysis and by showing that each state was populated by data from many patients, future studies can conclusively address these concerns with separate training and test data sets. It also remains unclear how to select the correct number of clusters. As there is little guidance in the literature and these analyses have never been attempted in this manner we selected 10 clusters as a trade-off between inter- and intra-cluster distance and a usable number of patient states for analysis. Future studies could easily compare the prognostic information obtained from more or fewer clusters thereby discerning the correct number of states for a similar analysis.
Lastly, while our current work is limited to retrospective assignment of data to clusters, future work should include developing a single score that indicates both the patient's current state and their likelihood of dying during their hospital stay.
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
MC collected and processed the data and prepared the manuscript. AG processed the data and prepared the manuscript. GM and DM collected and processed the data and reviewed the manuscript. MMK reviewed the manuscript. AB processed the data and reviewed the manuscript.