Surveillance of Infection Severity: A Registry Study of Laboratory Diagnosed
Background:
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.
Methods and Findings:
We studied 5,551 toxin-positive and 20,098 persistently toxin-negative patients tested for Clostridium difficile infection between February 1998 and July 2009 in a group of hospitals based in Oxford, UK, and investigated 28-day mortality and biomarkers of inflammation (blood neutrophil count, urea, and creatinine concentrations) collected at diagnosis using iterative sequential regression (ISR), a novel joinpoint-based regression technique suitable for serial monitoring of continuous or dichotomous outcomes. Among C. difficile toxin-positive patients in the Oxford hospitals, mean neutrophil counts on diagnosis increased from 2003, peaked in 2006–2007, and then declined; 28-day mortality increased from early 2006, peaked in late 2006–2007, and then declined. Molecular typing confirmed these changes were likely due to the ingress of the globally distributed severe C. difficile strain, ST1. We assessed the generalizability of ISR-based severity monitoring in three ways. First, we assessed and found strong (p<0.0001) associations between isolation of the ST1 severe strain and higher neutrophil counts at diagnosis in two unrelated large multi-centre studies, suggesting the technique described might be useful elsewhere. Second, we assessed and found similar trends in a second group of hospitals in Birmingham, UK, from which 5,399 cases were analysed. Third, we used simulation to assess the performance of this surveillance system given the ingress of future severe strains under a variety of assumptions. ISR-based severity monitoring allowed the detection of the severity change years earlier than mortality monitoring.
Conclusions:
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 C. difficile infection. Such systems could have wider applicability for monitoring infections presenting in hospital.
Vyšlo v časopise:
Surveillance of Infection Severity: A Registry Study of Laboratory Diagnosed. PLoS Med 9(7): e32767. doi:10.1371/journal.pmed.1001279
Kategorie:
Research Article
prolekare.web.journal.doi_sk:
https://doi.org/10.1371/journal.pmed.1001279
Souhrn
Background:
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.
Methods and Findings:
We studied 5,551 toxin-positive and 20,098 persistently toxin-negative patients tested for Clostridium difficile infection between February 1998 and July 2009 in a group of hospitals based in Oxford, UK, and investigated 28-day mortality and biomarkers of inflammation (blood neutrophil count, urea, and creatinine concentrations) collected at diagnosis using iterative sequential regression (ISR), a novel joinpoint-based regression technique suitable for serial monitoring of continuous or dichotomous outcomes. Among C. difficile toxin-positive patients in the Oxford hospitals, mean neutrophil counts on diagnosis increased from 2003, peaked in 2006–2007, and then declined; 28-day mortality increased from early 2006, peaked in late 2006–2007, and then declined. Molecular typing confirmed these changes were likely due to the ingress of the globally distributed severe C. difficile strain, ST1. We assessed the generalizability of ISR-based severity monitoring in three ways. First, we assessed and found strong (p<0.0001) associations between isolation of the ST1 severe strain and higher neutrophil counts at diagnosis in two unrelated large multi-centre studies, suggesting the technique described might be useful elsewhere. Second, we assessed and found similar trends in a second group of hospitals in Birmingham, UK, from which 5,399 cases were analysed. Third, we used simulation to assess the performance of this surveillance system given the ingress of future severe strains under a variety of assumptions. ISR-based severity monitoring allowed the detection of the severity change years earlier than mortality monitoring.
Conclusions:
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 C. difficile infection. Such systems could have wider applicability for monitoring infections presenting in hospital.
Zdroje
1. ChambersHF, DeleoFR (2009) Waves of resistance: Staphylococcus aureus in the antibiotic era. Nat Rev Microbiol 7: 629–641.
2. FridkinSK, HagemanJC, MorrisonM, SanzaLT, Como-SabettiK, et al. (2005) Methicillin-resistant Staphylococcus aureus disease in three communities. N Engl J Med 352: 1436–1444.
3. LamagniTL, EfstratiouA, Vuopio-VarkilaJ, JasirA, SchalenC (2005) The epidemiology of severe Streptococcus pyogenes associated disease in Europe. Euro Surveill 10: 179–184.
4. Henriques-NormarkB, BlombergC, DagerhamnJ, BattigP, NormarkS (2008) The rise and fall of bacterial clones: Streptococcus pneumoniae. Nat Rev Microbiol 6: 827–837.
5. OliveiraDC, TomaszA, de LencastreH (2002) Secrets of success of a human pathogen: molecular evolution of pandemic clones of meticillin-resistant Staphylococcus aureus. Lancet Infect Dis 2: 180–189.
6. KumarH, KawaiT, AkiraS (2011) Pathogen recognition by the innate immune system. Int Rev Immunol 30: 16–34.
7. KuehneSA, CartmanST, HeapJT, KellyML, CockayneA, et al. (2010) The role of toxin A and toxin B in Clostridium difficile infection. Nature 467: 711–713.
8. FreemanJ, BauerMP, BainesSD, CorverJ, FawleyWN, et al. (2010) The changing epidemiology of Clostridium difficile infections. Clin Microbiol Rev 23: 529–549.
9. KuijperEJ, CoignardB, TullP (2006) Emergence of Clostridium difficile-associated disease in North America and Europe. Clin Microbiol Infect 12 Suppl 6: 2–18.
10. JacksonRW, JohnsonLJ, ClarkeSR, ArnoldDL (2011) Bacterial pathogen evolution: breaking news. Trends Genet 27: 32–40.
11. CinelI, OpalSM (2009) Molecular biology of inflammation and sepsis: a primer. Crit Care Med 37: 291–304.
12. ChalmersJD, SinganayagamA, AkramAR, MandalP, ShortPM, et al. (2010) Severity assessment tools for predicting mortality in hospitalised patients with community-acquired pneumonia. Systematic review and meta-analysis. Thorax 65: 878–883.
13. BartlettJG, GerdingDN (2008) Clinical recognition and diagnosis of Clostridium difficile infection. Clin Infect Dis 46 Suppl 1: S12–18.
14. WilsonSE, SolomkinJS, LeV, CammarataSK, BrussJB (2003) A severity score for complicated skin and soft tissue infections derived from phase III studies of linezolid. Am J Surg 185: 369–375.
15. KharbandaAB, TaylorGA, FishmanSJ, BachurRG (2005) A clinical decision rule to identify children at low risk for appendicitis. Pediatrics 116: 709–716.
16. KnausWA, WagnerDP, DraperEA, ZimmermanJE, BergnerM, et al. (1991) The APACHE III prognostic system. Risk prediction of hospital mortality for critically ill hospitalized adults. Chest 100: 1619–1636.
17. WyllieDH, BowlerIC, PetoTE (2004) Relation between lymphopenia and bacteraemia in UK adults with medical emergencies. J Clin Pathol 57: 950–955.
18. DH/HPA (2008) Clostridium difficile infection: How to deal with the problem. London: Department of Health.
19. GriffithsD, FawleyW, KachrimanidouM, BowdenR, CrookDW, et al. (2010) Multilocus sequence typing of Clostridium difficile. J Clin Microbiol 48: 770–778.
20. EyreDW, WalkerAS, GriffithsD, WilcoxMH, WyllieDH, et al. (2012) Clostridium difficile mixed infection and reinfection. Journal of clinical microbiology 50: 142–144.
21. WalkerAS, EyreDW, WyllieDH, DingleKE, HardingRM, et al. (2012) Characterisation of Clostridium difficile hospital ward-based transmission using extensive epidemiological data and molecular typing. PLoS Med 9: e1001172 doi:10.1371/journal.pmed.1001172.
22. BoxGEP, CoxDR (1964) An analysis of transformations. J Roy Stat Soc B 26: 211–252.
23. WalkerS, PetoTE, O'ConnorL, CrookDW, WyllieD (2008) Are there better methods of monitoring MRSA control than bacteraemia surveillance? An observational database study. PLoS One 3: e2378 doi:10.1371/journal.pone.0002378.
24. MuggeoV (2003) Estimating regression models with unknown break-points. Stat Med 22: 055–3071.
25. SailhamerEA, CarsonK, ChangY, ZachariasN, SpaniolasK, et al. (2009) Fulminant Clostridium difficile colitis: patterns of care and predictors of mortality. Arch Surg 144: 433–439; discussion 439–440.
26. ReadeMC, YendeS, AngusDC (2011) Revisiting Mars and Venus: understanding gender differences in critical illness. Crit Care 15: 180.
27. LyrasD, O'ConnorJR, HowarthPM, SambolSP, CarterGP, et al. (2009) Toxin B is essential for virulence of Clostridium difficile. Nature 458: 1176–1179.
28. DingleKE, GriffithsD, DidelotX, EvansJ, VaughanA, et al. (2011) Clinical Clostridium difficile: clonality and pathogenicity locus diversity. PloS One 6: e19993 doi:10.1371/journal.pone.0019993.
29. CarterGP, DouceGR, GovindR, HowarthPM, MackinKE, et al. (2011) The anti-sigma factor TcdC modulates hypervirulence in an epidemic BI/NAP1/027 clinical isolate of Clostridium difficile. PLoS Pathog 7: e1002317 doi:10.1371/journal.ppat.1002317.
30. PallenMJ, LomanNJ, PennCW (2010) High-throughput sequencing and clinical microbiology: progress, opportunities and challenges. Curr Opin Microbiol 13: 625–631.
31. GravelD, MillerM, SimorA, TaylorG, GardamM, et al. (2009) Health care-associated Clostridium difficile infection in adults admitted to acute care hospitals in Canada: a Canadian Nosocomial Infection Surveillance Program Study. Clin Infect Dis 48: 568–576.
32. BelmaresJ, JohnsonS, ParadaJP, OlsonMM, ClabotsCR, et al. (2009) Molecular epidemiology of Clostridium difficile over the course of 10 years in a tertiary care hospital. Clin Infect Dis 49: 1141–1147.
33. SchlackowI, StoesserN, WalkerAS, CrookDW, PetoTE, et al. (2012) Increasing incidence of E. coli bacteraemia is driven by an increased in antibiotic resistant isolates: electronic database study in Oxfordshire 1999–2011. J Antimicrob Chemother 67: 1514–1524.
34. WongW-K, MooreA, CooperG, WagnerM (2005) What's Strange About Recent Events (WSARE): an algorithm for the early detection of disease outbreaks. J Mach Learn Res 6: 1961–1998.
35. Lawson A, Kleinman K (2005) Spatial and syndromic surveillance for public health. Hoboken (New Jersey): Wiley.
36. Hohle M, Mazick A (2010) Aberration detection in R illustrated by Danish mortality monitorin. Biosurveillance: a health protection priority. Kass-Hout T, Zhang X, editors. Boca Raton (Florida): CRC Press.
37. Killick R, Fearnhead P, Eckley IA (2011) Optimal detection of changepoints with a linear computational cost. Arxiv e-prints (arXiv:11011438).
38. Rigaill G (2010) Pruned dynamic programming for optimal multiple change-point detection. Arxiv e-prints (arXiv:10040887).
39. KulldorffM, HuangL, KontyK (2009) A scan statistic for continuous data based on the normal probability model. Int J Health Geogr 8: 58.
40. HuangSS, YokoeDS, StellingJ, PlaczekH, KulldorffM, et al. (2010) Automated detection of infectious disease outbreaks in hospitals: a retrospective cohort study. PLoS Med 7: e1000238 doi:10.1371/journal.pmed.1000238.
Štítky
Interné lekárstvoČlánok vyšiel v časopise
PLOS Medicine
2012 Číslo 7
- Statinová intolerance
- Očkování proti virové hemoragické horečce Ebola experimentální vakcínou rVSVDG-ZEBOV-GP
- Parazitičtí červi v terapii Crohnovy choroby a dalších zánětlivých autoimunitních onemocnění
- Metamizol v liečbe pooperačnej bolesti u detí do 6 rokov veku
- Co dělat při intoleranci statinů?
Najčítanejšie v tomto čísle
- HIV Treatment as Prevention: Issues in Economic Evaluation
- HIV Treatment as Prevention: The Utility and Limitations of Ecological Observation
- Consequences of Gestational Diabetes in an Urban Hospital in Viet Nam: A Prospective Cohort Study
- HIV Treatment as Prevention: Optimising the Impact of Expanded HIV Treatment Programmes