Introduction
We previously defined cellulitis as a clinical syndrome consisting of erythema,
oedema and warmth, which may be accompanied by acute pain or
tenderness; and may be associated with overt or latent bacterial or fungal
infection and constitutional disturbance.1 The epidemiology of cellulitis
worldwide is unclear but is generally accepted as being between 1 to 4 per
1,000,000; to 2 per 1000 per year depending on the inclusion criteria 2, 3. In our
previous paper, we described the rationale, design and characteristics of the
North of England Cellulitis Treatment Assessment (NECTA).1 In brief, we
measured the standard of in-patient care due to two treatment protocols for
cellulitis in two hospitals in the North East of England and determined which
one was associated with more robust outcomes.4 We studied 1337 admission
episodes involving 568 patients between January 2001 and December 2003
as shown in Fig 1.
Aim
We aimed to determine the antibiotic treatment associated with the best
patient outcomes 360 days from a hospital admission for cellulitis, by
performing a head-to-head comparison of two commonly used antibiotic
regimens. The first antibiotic regimen was empirical treatment with intra
venous (IV) Flucloxacillin with further alteration to treatment as guided by
microbiology sensitivities, whilst the second was IV Benzylpenicillin plus IV
Flucloxacillin for 5 to 10 days.
1
Methods
We sought and obtained the permission of the audit committees in the two
hospitals. The audit proforma we used was based on the method previously
described by Dong et al (2001) and identified 17 variables from the protocols
being assessed.5 We related the variables to survival at different time points
(Fig 2). Some of the 17 variables had well known associations with soft tissue
infections. 2, 6, 7 Others were not clearly defined. They included lymphoedema,
thrombophlebitis, deep vein thrombosis (DVT), injuries (Suppl Fig 1), recent
surgery, (defined as any surgical procedure in the calendar month preceding
admission); malignancy, ulceration, lower limb oedema, long-term use of
drugs that caused salt and water retention, liver disease, cardiovascular
disease, door-to-needle time for IV antibiotics (Fig 3), re-admission within 90
days of previous cellulitis admission, recent foreign travel, (defined as any trip
outside the UK & Ireland in the calendar month preceding admission); being
bed bound or immobile during the hospital admission being assessed,
diabetes and obesity, (defined in as a body mass index [BMI] > 30).
Statistics
An independent statistician using SPSS 12.0.1 and R 1.9 software carried out
all statistical analyses. 8 Results were expressed as mean (SD) or as
percentages. Multivariate analysis was by stepwise logistic regression to test
the independent role of each variable.9 Goodness-of-fit of each model was
measured by the pseudo R2 measure, which is an indication of the proportion
2
of variance of the predicted outcome measure. Odds ratios were computed
from regression parameters adjusted for all significant effects. 10
Results
We found that 112 patients (19.7%) had died within 360 days (95% CI: 0.16 0.23). The mean time to death from first presentation for hospital admission
was 100.89 days. The characteristics of those that had died within 360 days
of presentation are presented in Table 1.
Mortality
Characteristics like animal bites or insect bites were not associated with any
mortality in this study. We found the following to be statistically significant for
mortality at the specified test statistic (z): Age (z=5.46, p<0.001) was a
significant predictor of mortality at all time points except day 7. Re-admission
within 90 days for cellulitis (z=3.96, p<0.001), previous MI (z=3.73, p<0.001)
and penetrating injuries (z=3.48, p<0.001) were powerful independent
predictors of mortality. Other factors that predicted mortality were long-term
use of drugs that caused salt and water retention like phenelzine and steroids
(z=3.38, p<0.001), being bed bound or immobile (z=3.42, p=0.001), liver
disease or cirrhosis (z=2.96, p=0.003) and chronic lower limb oedema or
chronic lymphoedema (z=2.49, p=0.01). IV Flucloxacillin was a significant
negative predictor of mortality. (z=-3.79, p<0.001). The pseudo R2 for this
model was 0.39.
3
Survival
Fig 4 is an odds ratio of the factors that affected survival at 360 days. We
reproduced this from our previous publication to put the predictors of mortality
in the context overall survival.1 Previous myocardial infarction (MI) was a
significant negative predictor of survival at 360 days (OR = 0.30, z = -3.38,
p<0.001), but not for survival at other time points. In comparison, coronary
artery bypass graft (CABG), was a significant negative predictor of survival at
90 days (z=-2.31, p=0.02), but not at 360 days. Patient’s age was a significant
negative predictor of survival at 90 days (z=-5.28, p<0.001), 30 days (z=-4.09,
p<0.001) and 10 days (z=-2.59, p=0.01). Whilst penetrating injury (z=-4.06,
p<0.001) and the presence of underlying malignancy (z= -2.39, p=0.02) were
significant negative predictors at 90 days. Long-term use of drugs that caused
salt and water retention like steroids or phenelzine negatively predicted
survival at 30 days (z=-1.94, p=0.05) and 90 days (z=-4.68, p<0.001). Being
bed bound or immobile was a significant negative predictor at 30 days (z=3.53, p<0.001), 10 days (z=-3.12, p=0.002) and 7 days (z=-3.19, p=0.001). IV
Flucloxacillin was a significant predictor of survival at 30 days (z=2.37,
p=0.02); 90 days and 360 days (z=4.92, p<0.001). Suppl Table 1 gives the
Odds ratio (OR) and the test-statistic (z) for the statistically significant factors
that affected survival at all time points studied.
Discussion
Mortality is a good measure of in-hospital care when looking at a disease
process associated with hospital admission. 11, 12 In Table 1 we identified a
4
number of factors that seemed to be related to mortality in cellulitis
admissions. The clinical features we report are similar to those reported by
Dupuy et al (1999). 13 However, Dupuy et al focused mainly on presenting
features like injuries, lymphoedema, venous insufficiency, weight and leg
oedema. They did not investigate nor report the association of cellulitis with
mortality, MI or liver disease. Similarly, Cox et al (1998) audited the
management of cellulitis in two UK hospitals but did not comment on mortality
or age. 14 In our study, we sought to relate cellulitis to mortality. We found age
to be a profound predictor of mortality in cellulitis over time. Its predictive
power at days 7 and 10 was not as strong as at days 30, 90 and 360.
Conversely, the odds ratio of being bed bound increased over time, implying
that being bed bound became less of a risk as time progressed. This may be
because the patient would have in that time frame been appropriately treated
for cellulitis, become more mobile and discharged from hospital. Table 1 also
shows other factors that we associated with mortality but did not contribute to
the predictive power of our model. Gender, for example, was not statistically
significant though more females died. This finding agrees with the observation
of McNamara et al (2007) who reported that the difference between the sexes
was not statistically significant in a population-based study of lower limb
cellulitis.2 Hypertension was more common in the group of patients that died
in our study but was not a significant predictor. Obesity was more prevalent in
the group that died but again, the difference was not statistically significant.
The door-to-needle time for IV antibiotics was longer in the group of patients
that died than for those that survived but was not a significant predictor of
mortality (Fig 3). The majority of those that received IV antibiotics more that
5
50 hours from presentation were those who were admitted into hospital for
other conditions mainly orthopaedic surgery and developed cellulitis as a
complication of treatment.
In our study, survival was worse for elderly patients with cardiovascular
disease and penetrating injury, who were bed bound and did not receive IV
flucloxacillin (Suppl Fig 3). The addition of IV benzylpenicillin to IV
flucloxacillin was not associated with a survival advantage (Suppl Fig 2) when
compared to IV flucloxacillin alone (Suppl Fig 3). This finding is similar to that
of Leman et al (2005) who reported that the addition of benzylpenicillin to
flucloxacillin did not significantly alter patient outcomes nor patients’
perception of treatment in cellulitis.15 One possible reason for this finding
could be that the most common organism isolated from the microbiology
samples of the NECTA patients was Staphylococcus aureus. This finding
however contradicts the Spanish multi-centre mortality study in cellulitis
(STIMG) in which amoxicillin with clavulanate was the most commonly
prescribed antibiotic and in which the investigators reported a mortality of
10.4%.16 The STIMG investigators limited their observations to 30 days but in
our study, most of the deaths occurred later than this (Fig 2). Early mortality
in NECTA using the same timeframe as STIMG was 9.5%. Fig 5 show the
Kaplan-Meier curves of the other antibiotic treatments identified by our study
as compared to the two main antibiotic treatments assessed. Fig 5 seems to
suggest that Cephalosporin worsened survival. This contradicts a similar
study of the Emergency Departments of five Canadian hospitals by Dong et al
(2001) in which better patient outcomes were reported with cephalosporins. 5
6
Survival for the Cephalosporin group in our study was probably worse
because they did not receive Flucloxacillin in addition to Cephalosporin.
There were a few confounders in our model that could weaken the validity of
the predictions. First, to avoid multi-co-linearity, we omitted readmission from
the odds ratio for survival. We thus prevented the inter-dependence of the
other characteristics and readmission causing the occurrence of a type 2
error. Second, patients that were bed bound or immobile had an average
door-to-needle time of 69 hours but this was 16 hours for those that were not
bed bound. Thus patients that were bed bound in NECTA may have had a
worse outcome because they did not receive IV antibiotics in time. Third,
mortality in our study measured only mortality in acute hospital admissions
and not mortality due to the prevalence of cellulitis in the North East of
England. During the study period, about the same number of patients were
seen in the Accident & Emergency department and treated as day cases with
follow-up oral antibiotics. Further, an unknown number with mild-to-moderate
disease were being treated within the community by general practitioners as
is the practise in many parts of the world.17, 18 Thus, those that were admitted
into the NECTA hospitals may have been selectively biased towards
community and outpatient treatment failures or severe disease. For this
reason, our findings may only apply to cellulitis that result in an acute hospital
admission. We agree with Stevens et al (2005) who argue that irrespective of
the risk profile of the patient that cellulitis should not be allowed to progress
towards systemic infection and sepsis. 19 We report a 19.7% chance of death
within 360 days of an acute admission for cellulitis whether or not it is
7
complicated by systemic infection and sepsis. Our figures suggest that
stratifying a patient’s risk of death with a composite of the predictors of
mortality followed by the early administration of IV antibiotics would improve
360-day survival. Finally, in interpreting these figures, we did not actively
seek to confirm that the high number of patients lost to follow up (Fig 1) or
those discharged into the community had not died since the admission we
assessed. We may thus have under-estimated the final number of deaths due
to cellulitis admissions at 360 days.
Limitations
This was a retrospective study. The endpoints were not pre-specified and a
dedicated research team did not administer the treatments assessed. The
population studied was not homogeneous and that might have influenced the
reported outcomes. Our findings may at best be regarded as hypothesis
forming and should provide the background for a randomised clinical trial.
Conclusion
The risk of death or survival in patients admitted with cellulitis can be
recognised from the clinical features during in-hospital treatment. They
include: age, re-admission for cellulitis, presence of penetrating injury, chronic
lower limb oedema, liver disease, history of previous MI, history of long term
use of steroids continuing into the acute admission for cellulitis and being bed
bound or immobile during that hospital admission. We report a death rate of
8
19.7% at 360 days from the initial presentation. We also report that empirical
IV Flucloxacillin alone was superior to other antibiotics or combination of
antibiotics in NECTA.
Competing Interests:
None declared
Acknowledgements:
None.
9
Table 1. Characteristics Of Those That Died
“Risk” factor
Age
Mean/Percentage
of those alive after
360 days
55.3
Mean/Percentage
of those dead after
360 days
76.9
Males
52.8
40.0
Previous MI
Hyperlipidaemia
CABG
Hypertension
Peripheral vascular disease
Any CVD factor
13.6
14.8
0.8
30.6
4.5
38.6
48.8
20.0
2.5
60.0
17.5
76.3
Toeweb Maceration
Liver disease
Bed bound/immobility
Re-admission within 90 days
Ulceration
Lower limb oedema
Recent foreign travel
Long term use of drugs
Other risk factors
12.1
0.8
7.6
15.0
12.5
36.3
8.1
9.8
24.6
26.9
4.2
6.3
10.6
25.0
56.3
2.5
24.4
10.0
Blunt injury
Penetrating injury
Minor injury
Recent surgery
Animal bite
Insect bite
Other injury
Any of the above injuries
21.5
5.3
17.5
8.0
7.6
6.4
16.3
43.2
17.5
07.5
13.8
8.8
0.0
0.0
12.5
30.0
Diabetes
Obese
Lymphoedema
DVT
Thrombophlebitis
Malignancy
Other
11.3
54.4
15.1
1.5
12.8
4.9
11.4
25.0
56.0
25.0
6.3
23.8
21.3
32.5
Antibiotic
Benzylpenicillin
Flucloxacillin
Cephalosporin
Clindamycin
Other
68.3
81.5
6.0
3.8
42.6
35.0
46.3
20.0
2.5
55.0
Other
Treatments
Limb Elevation
Compression for leg ulceration,
venous insufficiency or oedema
Terbinafine cream for tinea pedis
24.3
12.5
21.8
20.5
15.3
12.8
Time to antibiotics
23.2 hours
56.9 hours
Patient
characteristics
Cardiovascular
disease
Risk factors
Injury
Co-morbidities
10
Fig 1: Flow Chart of NECTA
Admissions Assessed
(n=1,337)
Ineligible
Re-admissions (n=592)
Case notes not located (n=75)
Audit Population (n=670)
Lost to follow-up (n=102)
Deaths within 360 days
(n=112)
Survivors at 360 days
(n=456)
Factors Affecting Death
Factors Affecting Survival
-Chronic lower limb oedema
-Age < 40
-IV Flucloxacillin
-Previous MI
-Penetrating injury
-Liver disease
-Long term steroids
-Re-admission within 90 days
-Being bed bound or immobile
11
Fig 2: Survival
12
Fig 3: Door-To-Needle Time For IV Antibiotics
13
Fig 4: Odds Ratio of Factors Influencing Survival At 360 Days
14
Fig 5: Kaplan-Meier Curves Of Antibiotic Treatments
15
References
1
Tan RD, Newberry D, Arts G, Onwuamaegbu ME. The design, characteristics and predictors of
mortality in the North of England Cellulitis Treatment Assessment (NECTA). Int J Clin Pract 2007; 61:
1889 - 1893
2
McNamara DR, Tleyjeh IM, Berbari EF, Lahr BD, Martinez JW, Mirzoyev SA, Baddour LM.
Incidence of lower-extremity cellulitis: a population-based study in Olmsted county, Minnesota. Mayo
Clin Proc 2007; 82: 817-821
3
Goettsch, W.G., J.N. Bouwes Bavinck, and R.M. Herings, Burden of illness of bacterial cellulitis and
erysipelas of the leg in the Netherlands. J Eur Acad Dermatol Venereol 2006; 20: 834-839.
4
Berwick D. Measuring NHS Productivity. How much health for the pound, and not how many
events for the pound. BMJ 2005; 330: 975-976
5
Dong SL, Kelly KD, Oland RC, Holroyd BR, Rowe BH. ED management of cellulitis: A review of
five urban centers. American J Emerg Med 2001; 19: 535-540
6
Bisno AL, Stevens DL. Streptococcal infections of skin and soft tissues. N Eng J Med 1996; 334:
240 –245
7
Low DE, McGeer A. Skin and soft tissue infection: necrotizing fasciitis. Curr Opin Infect Dis 1998;
11: 119 – 123
8
Field A. Discovering Statistics using SPSS for Windows. Sage, London 2000
9
Cramer D. Advanced quantitative data analysis. Open University Press, Maidenhead. 2003
10
Wetherill GB. Intermediate statistical methods. Chapman & Hall, London. 1981
Mason A, Goldacre M, Daly E. Using case fatality rates as a health outcome indicator – a literature
review. Report to the Department of Health. Oxford: National centre for Health Outcomes
Development, 2000
11
12
Lakhani A, Coles J, Eayres Daniel, Spence C, Rachet B. Creative use of existing clinical and health
outcomes data to assess NHS performance in England: part 1 – performance indicators closely linked
to clinical care. BMJ 2005; 330: 1426 – 1431
13
Dupuy A, Benchikhi H, Roujeau J, Bernard P, Vaillant L, Chosidow O, Sassolas B, Guillaume J,
Grob J, Bastuji-Garin S. Risk factors for erysipelas of the leg (cellulitis): case-control study. BMJ
1999; 318: 1591 - 1594
14
Cox NH, Colver GB, Paterson WD. Management and morbidity of cellulitis of the leg. J R Med
1998; 91: 634-637
15
Leman P, Murkherjee D. Flucloxacillin alone or combined with benzylpenicillin to treat lower limb
cellulitis: A randomised controlled trial. Emerg Med J 2005; 22: 342- 346
16
Salgado OF, Villar JJ, Hidalgo CA, Villalobos SA, de la Torre LJ, Aquilar GJ, da Rocha CI, Garcia
OMA, Nuno AE, Ramos CC. Martin PM. Epidemiological characteristics and mortality risk factors in
patients admitted in hospitals with soft tissue infections. A multicentric STIMG (Soft Tissue Infections
Malacitan Group) study results. An Med Interna 2006; 23: 310-316
17
Vinh, D.C., Embil, J.M. Rapidly progressive soft tissue infections. Lancet Infect Dis 2005; 5: 501513
16
18
Hepburn, M.J., Dooley, D.P., Skidmore, P.J., Ellis, M.W., Starnes, W.F., Hasewinkle, W.C.
Comparison of short-course (5 days) and standard (10 days) treatment for uncomplicated cellulitis.
Arch Int Med 2004; 164: 1669-1674
19
Stevens DL, Bisno AL, Chambers HF, Everett HF, Dellinger P, Goldstein EJC, Gorbach SL,
Hirschmann JV, Kaplan EL, Montoya JG, Wade JC. Practice guidelines for the diagnosis and
management of skin and soft-tissue infections. Clin Infect Dis 2005; 41: 1373 –1406
17