IS was supported by NIHR grant RP-DG-1108-10125, and DW was supported in part by the Health Protection Agency during the work described. We declare no other financial relationships with any organisations that might have an interest in the submitted work in the previous three years. IS, DW, TP, DC and ASW have applied for a patent related to the techniques described. TP has been a scientific advisor to Optimer Pharmaceuticals.
Conceived and designed the experiments: DHW ASW. Performed the experiments: DHW ASW IS JF AV DG SO KD MG. Analyzed the data: IS ASW DHW MG. Contributed reagents/materials/analysis tools: JF AV DG SO KD. Wrote the first draft of the manuscript: DHW IS. Contributed to the writing of the manuscript: DHW TP DW IS JF MG DC.
Iryna Schlackow and colleagues investigated whether electronic systems providing early warning of changing severity of infectious conditions can be established using routinely collected laboratory hospital data. They showed that for
Changing clinical impact, as virulent clones replace less virulent ones, is a feature of many pathogenic bacterial species and can be difficult to detect. Consequently, innovative techniques monitoring infection severity are of potential clinical value.
We studied 5,551 toxin-positive and 20,098 persistently toxin-negative patients tested for
Automated electronic systems providing early warning of the changing severity of infectious conditions can be established using routinely collected laboratory hospital data. In the settings studied here these systems have higher performance than those monitoring mortality, at least in
The ability of bacteria to cause infection and disease (that is, their virulence) is in part determined by bacterial genetic makeup. At any time, there is a great deal of genetic diversity within common kinds of bacteria, which naturally generate new variants all the time. Sometimes organisms arise with a genetic makeup that causes more severe infection in humans and even death. For example, in 2005, spread of a particularly virulent strain (called 027/ST1) of the toxin-producing bacterium
In health care settings, general methods of detecting changing virulence to enable the early recognition, control, and optimal management of increasingly severe infections would be highly beneficial. Changing virulence of a bacterial infection is often measured using death rates, but only a small proportion of those with infection die from it, so this may not be the most sensitive method of monitoring. Consequently, in this study the researchers investigated whether the changing virulence of
The researchers examined all
The researchers used statistical models to estimate changes in potential biomarkers (neutrophils, creatinine, and urea) with reference to the
Using these methods, the researchers found that in patients who were positive for
These findings suggest that passively monitoring the severity of infection using routinely measured clinical biomarkers is feasible and can potentially detect important shifts in the virulence of human pathogens, such as
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Bacterial diseases remain one of the most important causes of illness in humans. The genomes, and consequently the clinical behaviours, of many important human pathogens are constantly changing
Indeed, continuing genotypic and phenotypic change is probably the rule, rather than the exception, within bacterial species
Recent investments in information technology systems in the UK National Health Service, and other large health care providers worldwide, have allowed electronic collection of large amounts of patient data. Such collections typically include various laboratory measurements, in addition to hospital admission and diagnosis data. Changes in parameters, such as peripheral blood white cell and neutrophil counts, urea and creatinine concentrations, can be consequences of an innate response to infection present in all humans
Since full blood count and renal function measurements are, at least in high-income countries, measured frequently on diagnosis
We considered all positive
For haematology and biochemical data, we identified the laboratory blood test results closest to the
Prior to 1 October 1999, cell-culture-based toxin testing was performed; subsequently, testing used an A+B toxin enzyme immunoassay (Meridian Biosciences). In response to the recognition of an increase in severity in
We excluded patients admitted to oncological and renal specialities, since their laboratory values are likely to reflect their underlying diagnosis, rather than the impact of
All remaining cases were analysed for changes in mortality and incidence over calendar time. Biomarker analyses were restricted to cases with the relevant blood test results. To decrease influence of outliers, for biomarkers with no prior count restriction, values below the 1st percentile and above the 99th percentile were excluded from the analysis.
We considered patients with only toxin-negative samples as a control group. We excluded patients with any subsequent positive tests to minimise the impact of false negatives. A 28-d deduplication was also made for negative tests, and the same inclusion/exclusion criteria were applied.
The data were extracted from an anonymised linked electronic research database, the Infection in Oxfordshire Research Database (IORD), approved by the Oxford Research Ethics Committee (09/H0606/85) and the National Information Governance Board (5-07(a)/2009).
A STROBE checklist is attached as
Anonymised microbial isolation and biomarker data were extracted from information systems in the University Hospitals Birmingham NHS Foundation Trust (UHB), in order to assess infection severity in the Queen Elizabeth Hospital and Selly Oak Hospital, Birmingham, at the end of this investigation. UHB is a major tertiary referral centre, organisationally distinct from the Oxford centre, being located about 100 km to the north. These hospitals operate a wide range of medical and surgical services, but not obstetric or paediatric services. As in Oxford, toxin-based testing operated as the diagnostic method throughout the period observed. Data were available from 1 January 2000 onwards, and we deduplicated, selected, and excluded biomarker data as for the Oxford dataset; we used the same end date for analysis as for the Oxford cohort analysed.
We used general linear regression models to estimate changes in mean of potential biomarkers of severity over calendar time. Given the known biology of
To estimate how soon any changes in biomarker/mortality trends would have been detected, we used an iterative sequential regression (ISR) technique. Specifically, we reconstructed what would have been observed if the data had been monitored in real-time, and applied gridsearch to sets of samples taken between 1 February 1998 and 1 September 1998, and successively every month through 1 August 2009. On each dataset a one-trend and a succession of two-trend models were fitted, with iterative selection of optimal joinpoints (see
To evaluate the severity monitoring techniques, we performed two simulation studies in which we assumed a more severe strain of
The studies assessed the influence of key parameters (additional virulence of the new strain compared to underlying variability in biomarker measurements, penetrance of the new strain, number of affected patients per year) on the following outcome measures: (a) With what probability is the arrival of the severe strain detected? This key operational question was addressed using the ISR method. Data were simulated on the basis of the assumption that after 1 y of constant virulence, a new, more severe, strain was introduced, and during year 2 it replaced existing strains, fully or partially, at a constant rate. The change was deemed ‘successfully detected’ if the best model suggested that the rates were increasing significantly (
Simulation model parameters were derived from the study data (see
After 28-d deduplication, there were 8,357
Patient Group | Factor | Subcategory | |||||
01/02/1998–2000 | 2001–2003 | 2004–2006 | 2007–01/08/2009 | Total | |||
Total | 1,043 (100%) | 1,638 (100%) | 1,790 (100%) | 1,080 (100%) | 5,551 (100%) | ||
Sex | Male | 440 (42%) | 730 (44%) | 811 (45%) | 463 (42%) | 2,444 (44%) | |
Age at admission | y | 76 (71–86) | 76 (71–86) | 77 (72–86) | 74 (67–85) | 76 (71–86) | |
Previous ORH admission | In the past year | 571 (54%) | 907 (55%) | 1,158 (64%) | 720 (66%) | 3,356 (60%) | |
Admission specialty codes | General medicine | 581 (55%) | 985 (60%) | 1,110 (62%) | 624 (57%) | 3,300 (59%) | |
Other medicine | 226 (21%) | 268 (16%) | 186 (10%) | 90 (8%) | 770 (13%) | ||
General surgery | 65 (6%) | 134 (8%) | 152 (8%) | 127 (11%) | 478 (8%) | ||
Trauma and orthopaedic | 33 (3%) | 63 (3%) | 54 (3%) | 40 (3%) | 190 (3%) | ||
Other specialties | 138 (13%) | 188 (11%) | 288 (16%) | 199 (18%) | 813 (14%) | ||
Missing data | Neutrophil count | 330 (31%) | 578 (35%) | 393 (21%) | 144 (13%) | 1,445 (26%) | |
Urea | 371 (35%) | 621 (37%) | 431 (24%) | 176 (16%) | 1,599 (28%) | ||
Creatinine | 278 (26%) | 531 (32%) | 351 (19%) | 138 (12%) | 1,290 (23%) | ||
Albumin | 460 (44%) | 736 (44%) | 630 (35%) | 313 (28%) | 2,139 (38%) | ||
Total | 3,121 (100%) | 4,330 (100%) | 5,142 (100%) | 7,505 (100%) | 20,098 (100%) | ||
Sex | Male | 1,339 (42%) | 1,933 (44%) | 2,256 (43%) | 3,388 (45%) | 8,916 (44%) | |
Age at admission | y | 68 (57–83) | 69 (58–82) | 69 (58–83) | 70 (60–84) | 69 (58–83) | |
Previous ORH admission | In the past year | 1,417 (45%) | 2016 (46%) | 2,675 (52%) | 3,934 (52%) | 10,042 (49%) | |
Admission specialty | General medicine | 1,420 (45%) | 2,043 (47%) | 2,529 (49%) | 3,556 (47%) | 9,548 (47%) | |
Other medicine | 664 (21%) | 709 (16%) | 687 (13%) | 797 (10%) | 2,857 (14%) | ||
General surgery | 374 (11%) | 616 (14%) | 757 (14%) | 1,049 (13%) | 2,796 (13%) | ||
Trauma and orthopaedic | 117 (3%) | 163 (3%) | 168 (3%) | 339 (4%) | 787 (3%) | ||
Other specialties | 546 (17%) | 799 (18%) | 1,001 (19%) | 1,764 (23%) | 4,110 (20%) | ||
Missing data | Neutrophil count | 922 (29%) | 1,274 (29%) | 1,024 (19%) | 1,182 (15%) | 4,402 (21%) | |
Urea | 1,095 (35%) | 1,389 (32%) | 1,097 (21%) | 1,462 (19%) | 5,043 (25%) | ||
Creatinine | 779 (24%) | 1,153 (26%) | 872 (16%) | 1,094 (14%) | 3,898 (19%) | ||
Albumin | 1,301 (41%) | 1,743 (40%) | 1,599 (31%) | 2,259 (30%) | 6,902 (34%) |
Changes in incidence over time were similar to national trends
Elevated levels of neutrophils and creatinine have been reported to be associated with CDI severity and mortality
The association between mortality within 7 d (triangles) or 28 d (circles) and three biomarkers measured on admission (peripheral blood neutrophil counts×109/l, left, serum urea concentrations (middle, mmol/l), and serum creatinine concentrations (µmol/l, right) are illustrated among the 5,551 cases from the Oxford centre. For each biomarker, patients were divided into deciles of equal size. Each point represents the observed (and 95% CIs around) mortality for patients. Mortality increases as neutrophil and urea concentrations rise. This is also true for creatinine concentrations over about 100 µmol/l.
Mean quarterly mortality, neutrophil counts (×109/l), and urea concentrations (mmol/l) between 1998 and 2009 in
The introduction of new strains is often characterised by gradual changes over years
The best model for quarterly mortality, neutrophil counts, and urea over the whole study period (solid line) was chosen on the basis of BIC, see
No trend changes in 7-d mortality were identified: rather 7-d mortality was estimated to have increased slightly but non-significantly over the whole study period (
In the best model mean neutrophils started to increase from 2004 (with statistically equivalent models supporting the increase starting between August 2003–February 2005), peaked in 2006 (February 2006–May 2007), and subsequently declined until late 2008 (November 2007–February 2009). Further, some models suggested a weaker earlier rise peaking in 1999 (May 1999–February 2000). Differences between models close (within 3.84 of BIC) to the best model (solid line) were small, with all models identifying a peak in severity in 2006–2007. Similar results were obtained adjusting for age, gender, and previous hospital exposure (unpublished data), suggesting that this biomarker is informative about severity over and above the demographic information. Similar trends were not found in the negative control group (unpublished data), confirming the observed changes are
The same analysis applied to urea found no evidence of any secular trends, with urea decreasing non-significantly (
We used the ISR technique to investigate whether it would have been feasible to use the host response data rather than (or in addition to) post-infection mortality to monitor changes in microbial severity on a regular monthly basis (
Cumulative data on mortality, neutrophils and urea modelled successively to each month, as if such monitoring had been conducted in real-time, see
In contrast, for neutrophil counts we found that an increasing trend from February 1998 changing to a decreasing trend would have been detected in December 2001, with some uncertainty around the exact time of change (April 1999–October 2000), most likely time being January 2000. Interestingly, there was actually an increasing trend in 28-d mortality from February 1998 through January 2000 that did not reach statistical significance (odds ratio [OR] = 1.02 per year,
A new rise in neutrophil levels from February 2003 (January 2003–February 2003) would have been detectable only 3 mo later, in May 2003. Clinically, the ingress of the severe strain into the Oxford hospitals was not detected before national alerts were issued 3 y later and enhanced typing was established in 2006. Neutrophils continued to rise until September 2006 (March 2006–June 2007). The downward change in trend became observable after a year, in September 2007. Intriguingly, the analysis suggests another rise started in late 2008 (
Thus, enhanced severity of
We investigated whether changes in severity markers might have been more noticeable in only a subset of the population, such as patients with more severe infection, using non-parametric (quantile) regression for the 75th and 90th quantiles. Both gridsearch and ISR techniques detected the 2000 and the 2006 peaks for neutrophil counts. However, in some models multiple additional joinpoints were suggested (unpublished data), as a consequence of more variability in these quantiles.
One explanation for the observations of increased neutrophil counts in those with CDI is that the strain(s) of
Flowchart (A) illustrates laboratory and typing data available for consecutive faecal samples with positive
To ascertain whether the biomarker-monitoring technique we have described could be generally applicable, we performed three further investigations. Firstly, we determined whether the biomarker∶severe strain association observed in Oxford was present in other settings. Secondly, we performed simulation studies to assess its likely performance in future outbreaks in other settings, relative to the monitoring of mortality. Thirdly, we analysed the trends in severity-associated biomarkers in another hospital using the ISR technique.
We examined unpublished associations between admission biomarker and microbial culture data from two pivotal phase III double-blind randomized non-inferiority licensing trials (studies 003 and 004) of oral fidaxomicin versus vancomycin in the treatment of
Eligibility criteria in the 003/004 studies involved ELISA-based toxin testing, rather than culture. Organisms were cultured and typed from a proportion of the patients included, as discussed
Of the cases who had both culture results and admission blood counts available, there were 600 North American patients, 293 from the US and 307 from Canada. The European group, which was smaller (
To further assess the potential of the severity surveillance techniques described, we simulated two scenarios (see
The study parameters included size of the hospital(s) being monitored (left panel), additional virulence of the new strain relative to biomarker variability (central and right panels); and maximal penetrance of the new strain (all panels, effect represented by lines of different width). The simulated scenarios investigated the probability with which the arrival (top panels) or decline (bottom panels) of the new strain could be detected, see
Based on realistic simulation parameters derived from our observed data (see
As expected, increased virulence and/or penetration of the new strain results in changes being detected more often, regardless of monitoring method. For example, the arrival of a strain with 2-fold increased virulence would have been detected in ORH in 76% as opposed to 30% cases for the baseline case (
Enhanced power could also be achieved using a multi-centre network of hospitals. For example, a 10-fold increase in the hospital-monitored population would have resulted in a 92% detection rate for control of a new organism, in comparison with the 16% detection rate of the baseline case (
The rate of identifying (false-positive) changes in the simulations with no new strain remained under 5% in both simulations.
Finally, we used the ISR technique to analyse trends in neutrophil counts in a second major UK hospital, based in Birmingham. Data were not available prior to 2000 in this centre. Following exclusions identical to those operating in Oxford, there were 5,399 cases available for analysis.
Data are presented over the same time frame as used for the Oxford study (
Left panels contain mean quarterly neutrophil counts, with 95% CIs shown in grey, and black lines representing the loess curves with the span parameter set to 0.5. Right panels show changes in secular trends identified by ISR indicated by ▴ (change from an upward to downward trend), and ▾ (a downward to an upward trend), with+indicating the point in time when this change in secular trend was first detected, and horizontal intervals showing the range of joinpoints within 3.84 of the best model BIC at this time.
Interestingly, a decline in neutrophil counts on presentation was seen in the Birmingham centre from 2000–2003, as was observed in Oxford, compatible with a period of increased severity of
In summary, similar trends in severity are observed in two UK hospitals, and are detected by the ISR algorithm.
In this study of infection severity surveillance, we used
For evaluation of our passive surveillance technology, we mainly focused on neutrophil counts on diagnosis, because of their frequent measurement prior to therapy, and their known association with mortality in
Visual inspection, gridsearch, and ISR analyses of passively collected laboratory measures of severity (neutrophil counts and, to a lesser extent, creatinine) were all highly suggestive of rising pathogen virulence from 2003–2004 to 2006, followed by virulence decline. The prediction that these changes were due to ingress of the highly virulent ribotype 027/NAP1/ST1 strain is supported by detailed molecular typing data in one centre. Further, such severe strain-associated biomarker associations were also found in two clinical trials performed across multiple hospitals, and are compatible with published literature from diverse sites
The likely utility of the biomarker-monitoring technique described here has been assessed by simulation. Importantly, analysis of both the historical outbreak, and of future simulated outbreaks, shows monitoring of post-infection mortality, as opposed to laboratory measures of severity on diagnosis, affords much less confident detection. This probably reflects the relatively low attributable mortality of
Interestingly, in addition to the known 2006 outbreak, regression models suggest another rise of neutrophil counts peaking around 2000, which was seen in two hospitals. This suggests that waves of virulence may be a recurring feature of
To what extent would one expect this kind of biomarker-based surveillance to be generalizable to other organisms and conditions? It requires that there exists at least one routinely collected biomarker that is associated with disease-related mortality for each target condition. This is likely to be true for many infections; measures of renal function (urea, creatinine), glucose levels, platelet counts, measures of deranged liver function (transaminases, etc.), and coagulation status have all been shown to be prognostic in various infections
With all surveillance systems, the populations monitored need to be carefully defined. Obviously, choosing to run analyses on poorly defined subgroups (or outcomes) may result in ‘effect dilution’ if severity change only occurs in part of the population studied; our simulation studies, presented here, will allow readers to judge the likely impact of such effect dilution in their populations.
Routine monitoring of laboratory measures of severity has limitations. One concerns feasibility. The samples used to predict severity were routinely collected, and came from inpatients; although in many hospitals in high-income countries such samples are taken in the majority of admissions, this may not be the case in less resourced settings.
A less obvious issue is biological, and concerns the constancy of the biomarker∶mortality relationship. Inter-human variation is a contributor to the noise in this relationship
What action should be taken on the basis of ‘signals’ from this kind of surveillance? We suggest, on the basis of the experience with
There are also statistical issues related to the technique used in this paper, which is not necessarily optimal: simultaneous use of multiple measures of severity (such as neutrophil and creatinine counts together), perhaps using Bayesian techniques, may offer increased sensitivity and more rapid detection, without increase in false positive results. Detailed comparison with existing changepoint detection algorithms
Independent of the optimal algorithm, however, we have shown that monitoring severity of infection passively is feasible, can detect important shifts in the phenotype of human pathogens, and offers superior performance relative to mortality monitoring. Since waves of virulence, as shown here with
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We thank all the people of Oxfordshire who contribute to the Infections in Oxfordshire Research Database. Research Database Team: P Bejon, T Berendt, C Bunch, DCW Crook, J Finney, J Gearing (community), H Jones, L O'Connor, TEA Peto (PI), J Robinson (community), B Shine, AS Walker, D Waller, D Wyllie. We thank Sherwood Gorbach for providing 003 and 004 trial data for analysis, and Brian Shine for the provision of laboratory data from Oxford Radcliffe Hospitals.
Bayesian Information Criterion
iterative sequential regression
Oxford Radcliffe Hospitals