Variation in antibiotic treatment for diabetic patients with serious foot infections: A retrospective observational study
© Fincke et al; licensee BioMed Central Ltd. 2010
Received: 30 November 2009
Accepted: 6 July 2010
Published: 6 July 2010
Diabetic foot infections are common, serious, and diverse. There is uncertainty about optimal antibiotic treatment, and probably substantial variation in practice. Our aim was to document whether this is the case: A finding that would raise questions about the comparative cost-effectiveness of different regimens and also open the possibility of examining costs and outcomes to determine which should be preferred.
We used the Veterans Health Administration (VA) Diabetes Epidemiology Cohorts (DEpiC) database to conduct a retrospective observational study of hospitalized patients with diabetic foot infections. DEpiC contains computerized VA and Medicare patient-level data for VA patients with diabetes since 1998, including demographics, ICD-9-CM diagnostic codes, antibiotics prescribed, and VA facility. We identified all patients with ICD-9-CM codes for cellulitis/abscess of the foot and then sub-grouped them according to whether they had cellulitis/abscess plus codes for gangrene, osteomyelitis, skin ulcer, or none of these. For each facility, we determined: 1) The proportion of patients treated with an antibiotic and the initial route of administration; 2) The first antibiotic regimen prescribed for each patient, defined as treatment with the same antibiotic, or combination of antibiotics, for at least 5 continuous days; and 3) The antibacterial spectrum of the first regimen.
We identified 3,792 patients with cellulitis/abscess of the foot either alone (16.4%), or with ulcer (32.6%), osteomyelitis (19.0%) or gangrene (32.0%). Antibiotics were prescribed for 98.9%. At least 5 continuous days of treatment with an unchanged regimen of one or more antibiotics was prescribed for 59.3%. The means and (ranges) across facilities of the three most common regimens were: 16.4%, (22.8%); 15.7%, (36.1%); and 10.8%, (50.5%). The range of variation across facilities proved substantially greater than that across the different categories of foot infection. We found similar variation in the spectrum of the antibiotic regimen.
The large variations in regimen appear to reflect differences in facility practice styles rather than case mix. It is unlikely that all regimens are equally cost-effective. Our methods make possible evaluation of many regimens across many facilities, and can be applied in further studies to determine which antibiotic regimens should be preferred.
Foot infections in diabetic patients are among the most frequent precipitating causes of hospitalization and amputation[1–3]. They may result from many different microbial species, either singly or in combination, and response to treatment may be poor, particularly when vascular disease is also present, as is common in diabetes. The Infectious Diseases Society of America has issued a guideline in an effort to standardize and improve care[4, 5]. Uncertainty remains, however, regarding the optimal antibiotic regimens for the various types of infection that may occur[6–8]. Lack of consensus about optimal treatment means that there is substantial latitude in the choice of antibiotics for diabetic foot infections, with the likely result that there will be substantial variation in practice. The aim of this study was to determine whether this is the case. To the degree that such variation can be documented, it would raise questions about the comparative cost-effectiveness of different regimens and also open the possibility of examining costs and outcomes in order to determine which regimens should be preferred.
The Veterans Health Administration is the largest integrated health care system in the country and includes 147 hospitals. It has an excellent computerized patient record system, which contains patient demographics, diagnoses, diagnostic studies, treatments received and patient outcomes. With this information it is possible to examine the antibiotic regimens of large numbers of diabetic patients with foot infections and the degree to which practice patterns vary across facilities.
In previous work, we reported the construction of a classification system for diabetic foot infections that is expressly designed for use with computerized medical data. We applied it to a database that contains all diabetic patients in the Veterans Health Administration (VA), and found support for our classification in its correlation with patient characteristics, treatments received, and outcomes, including rehospitalization, amputation, transition to long-term care, and death .
In the present study, we have used our classification system to identify diabetic patients with serious active foot infections, as defined by hospitalization with isolated cellulitis or cellulitis accompanied by gangrene, osteomyelitis, or ulcer. We have also developed and applied methods for assessing variation in complex multi-drug antibiotic regimens to examine how the treatment of these patients varies across hospitals in the VA healthcare system. We have found that there is large variation in treatment across facilities and that there is more variation across facilities than across different types of infection. This suggests that practice styles play a larger role in the choice of regimen than does case mix.
The Study Population and Source Data
This study was approved by the Institutional Review Board (IRB) of the Edith Nourse Rogers VA Medical Center in Bedford, Massachusetts. It was conducted in the population of hospitalized veteran diabetic patients receiving care from the Veterans Health Administration (VA) in fiscal year (FY) 2006. We used the national VA Diabetes Epidemiology Cohorts (DEpiC), a linked, computerized research database that serves as a registry of virtually all VA patients with diabetes. Patients are included in DepiC on the basis of having two of the following ICD-9CM codes within a 24-month period: 250.xx, 357.2, 362.0, or 366.41. It contains patient-level data on medical visits, pharmacy and laboratory data, with diagnoses and procedures for VA and non-VA care (from Medicare claims data)[10, 11]. For this study, we used ICD-9CM diagnosis and procedure codes from VA inpatient files along with inpatient antibiotic prescriptions. The records for inpatient antibiotics include generic name of the medication and dose, with separate entries for each date that the antibiotic was administered. For this analysis, we did not include antibiotics prescribed on an outpatient basis or any non-VA hospitalizations.
Identification and classification of diabetic foot infections
We assumed that patients were under treatment for diabetic foot infection while in the hospital if, during that hospitalization, they were assigned an ICD-9-CM code for cellulitis of the foot.
Antibiotic treatment of patients with diabetic foot infections
The proportion of patients treated with an antibiotic and the initial route of administration on the first day that any antibiotic was given.
The first antibiotic regimen prescribed for each patient, defined as treatment with the same antibiotic, or combination of antibiotics, for at least 5 continuous days. This may not be the same as the antibiotics that were given on the first day. We chose this duration of time to allow for consultation about the choice of antibiotics, integration of microbiological information, and observation of clinical response. Also, duration sufficient to observe clinical response is a necessary requirement for future studies to determine which regimens should be preferred.
The antibacterial spectrum of the first antibiotic regimen prescribed for each patient. We used the Medical Letter on Drugs and Therapeutics Handbook of Antimicrobial Therapy to determine whether an antibiotic was a recommended agent for: gram-positive organisms other than S. aureus; methicillin sensitive S. aureus; methicillin resistant S. aureus; Ps. aeruginosa; and B. fragilis. (We considered regimens to target B. fragilis only if they contained either clindamycin or metronidazole.) In addition, we divided the gram-negative spectrum against organisms other than Ps. aeruginosa into broad and narrow. We classified ampicillin, amoxicillin, and second generation cephalosporins as narrow spectrum agents and the remaining agents as broad spectrum.
We used a Bayesian model and an adjusted likelihood method to estimate values of true rates by facilities. This method shrinks the estimate toward the overall mean, considers the strength of the information about a hospital's true rate relative to the variation across hospitals, and allows estimates from hospitals with small sample sizes.
Estimates of the true mean rates incorporate information from a facility's observed rate with information from the distribution of rates of all facilities. If the information from a facility is weak, i.e., the sample size at the facility is small, the estimate of the true rate will be pulled closely toward the overall rate; when the information is strong, the estimate of the true rate will be closer to the facility's observed rate. Although there is statistical error associated with our estimates of true rates, the Bayesian model lets us focus on the parameters of interest rather than focusing on observed rates where random variation is expected.
We characterized the variation in true rates in three ways: range, interquartile range, and range/median. The range is the difference between the lowest and highest values. The interquartile range is the difference between the lowest and highest values in the middle two quartiles of the data. The range/median is similar to the coefficient of variation (standard deviation divided by the mean) in that large values mean that the range of true rates is large relative to the typical value of the true rates. A large value could indicate one or more extreme outliers or true rates with large variation but without extreme outliers.
Demographics of patients with different categories of cellulitis
Age in 2006
Proportion of patients treated with antibiotics and routes of administration
First antibiotic regimen prescribed to patients with foot cellulitis
Of the 3,792 total patients with any cellulitis, 1544 (40.7%) never received a course of antibiotics that met our definition of an antibiotic regimen. This reflects the fact that either a single day's missed dose or 1-4 days of an additional antibiotic would disqualify an antibiotic regimen from consideration.
The percentage of patients with no first regimen varied across our four categories of cellulitis. Seventy-four percent of patients with Cellulitis-Gangrene received a first regimen, compared to 70% for Cellulitis-Osteomyelitis, 52% for Cellulitis-Ulcer, and 33% for Cellulitis-Only. Since we know that at least 98% of patients in each group were given antibiotics, these declining percentages reveal a corresponding decrease in the proportion of patients who received an unmodified course of treatment for 5 days or more. A total of 199 different antibiotic regimens were used. This large number reflects the many possible combinations of the 52 different antibiotics that were prescribed.
Proportion of patients receiving each of the 17 most common first antibiotic regimens
(n = 2248)
(n = 894)
(n = 503)
(n = 644)
(n = 207)
Observed Proportion (Range*) %
( - )
( - )
( - )
( - )
( - )
( - )
( - )
( - )
( - )
( - )
( - )
( - )
The rows of the table reveal that there are not large differences in the choice of each regimen across the categories of cellulitis. The greatest difference is 11.4% for Piperacillin-tazobactam (high 20.1% for Cellulitis-Gangrene, low 8.7% for Cellulitis-Only). In general, the largest differences in frequency of use across the different categories of cellulitis occur with Cellulitis-Only, in which a substantially smaller proportion of patients received piperacillin-tazobactam and piperacillin-tazobactam plus vancomycin.
The range of use for each regimen across facilities within each category of cellulitis is at least equal to, and usually substantially greater than, the range of observed proportions across the different types of cellulitis (i.e. the range of variation across facilities is generally greater than that across the different categories of cellulitis).
Antibacterial spectrum of first antibiotic regimen prescribed to patients with foot infections
Proportion of patients receiving each of the 10 most common antibacterial spectra
(n = 2248)
(n = 894)
(n = 503)
(n = 644)
(n = 207)
Observed Proportion (Range*) %
G + St(res) G-Ps An
G + St G-Ps An
G + St G-Br An
G + St(res) G-Ps
G + St(res)
G + St G-
G + St
( - )
G + St(res) G-Br An
G + St An
G + St(res) G-Br
There have been numerous studies of variation in antibiotic use in different types of infection [15–22]. The most common underlying premise has been that variations in care will reveal deviations from ideal management, whether judged by medical outcomes, costs, or both. Even when there is not agreement about what constitutes ideal management, there is a presumption that all treatment strategies are unlikely to be equally efficacious, so that the presence of substantial variations reveals the importance of further investigations to determine which approaches are most cost-effective[23, 24].
Evaluation of variation in complex multi-drug regimens presents methodological challenges. Perhaps for this reason, most studies of differences in antibiotic treatment have used strategies that simplify the assessment of variation, such as examining any treatment versus no treatment, deviation in treatment from practice guidelines, or differences in antibiotic regimens when treatment is usually with single drugs, as is the case with infections like otitis media and respiratory tract infections[15, 16]. We have not found any that have evaluated diabetic patients with foot infection, who are often treated with complex regimens that are not well-suited to the foregoing methods of analysis.
We decided to carry out such a study, first, because diabetic foot infections are such an important cause of hospitalization, amputation, and disability and, second, because there is no generally accepted standard antibiotic regimen, so we expected to find large variations in antibiotic management. We used data derived from the computerized medical records of the largest integrated medical care organization in the country, the Veterans Health Administration. This gave us the power to evaluate variation in a large variety of antibiotic regimens, across a large number of hospitals, in a large number of patients. We have focused on patients where antibiotic choices are most important, namely those with serious foot infections as defined by hospital treatment for cellulitis accompanied by gangrene, osteomyelitis, cutaneous ulcer, or occurring in isolation.
The complex antibiotic regimens often used to treat diabetic foot infections present methodological issues that we needed to address before we could begin. First, we had to take into account that the initial antibiotic prescribed might not necessarily represent a final decision about the preferred antibiotic regimen. To address this we examined what we considered to be a stable "course" of antibiotics, given singly or in combination, without change, over a period of at least 5 days. Such a time frame is a requirement for future studies, which will not be able to evaluate the outcomes of different regimens unless they have been given for long enough to assess clinical response. Second, we recognized that variation in the choice of antibiotics would be governed, in part, by the breadth of the anti-bacterial spectrum desired. Hence, we also examined variation in the antibacterial spectrum of treatment well as variation in the antibiotics themselves.
We have found very large differences in antibiotic use across VA facilities. It is evident in the antibiotics that make up the initial regimen, as well as in the antibacterial spectra of the chosen regimens. The variation may be partly driven by differences in case mix, but this is unlikely, because we did not find substantial differences in case mix across facilities as judged by the comparative prevalence of our different categories of cellulitis. It is probably truly related to foot infections, because the percentage of patients treated with any antibiotic is close to 100%, the great majority of treatments are parenteral, and the spectrum of antibacterial activity is broad, all of which are to be expected in the treatment of these infections[5, 25–28]. In addition, there are expected patterns of treatment related to our different categories of cellulitis. In particular, patients with the least complicated infection (Cellulitis-Only) receive more oral treatment and less often receive 5 or more continuous days of the same medication.
There is evidence in this study that antibiotic choice is driven by the existence of differing practice styles across VA facilities. The variation in antibacterial spectrum across facilities within each category of infection almost certainly reflects different views about how broadly to treat. (The difference between the three most commonly used regimens, for example, is whether they cover methicillin resistant S. aureus (MRSA), Ps. aeruginosa, or neither. Each of these organisms is recognized as a cause of serious foot infections, but they are not so common as to mandate routine coverage.) There is also evidence for practice variation in the agents chosen to achieve each spectrum, in that the number of spectra is substantially smaller than the number of regimens. Finally, we saw in some facilities a particular preference for one or another of a pair of antibiotics that are very similar in antibacterial spectrum as well as side effects. Facilities almost always used one or the other of these pairs, rarely both. Examples are imipenem versus ertapenem and ticarcillin/clavulanate versus piperacillin/tazobactam.
Even though there is no general agreement about antibiotic choice in the infections we have studied, our findings raise concern about the cost-effectiveness of the care that is being given. It is unlikely that all regimens are equally advantageous. For example, there is some evidence that ampicillin-sulbactam may be more cost-effective than imipenem[30, 31]. There is also a controlled trial that has shown ertapenem to be as effective in the treatment of diabetic foot infections as piperacillin/tazobactam (one of the most commonly used agents in our study), and a comparative study of costs estimated ertapenem to be less expensive, primarily because it had to be administered less frequently [32, 33]. We note, however, that such studies may not be determinative, because other considerations may be more important. It may be advisable, for example, to hold a broad-spectrum antibiotic in reserve if frequent use is known to generate resistant organisms.
Clearly, further studies are needed to identify which antibiotic regimens are most cost-effective. This study has established a number of methodological approaches that can facilitate the conduct of such investigations using computerized medical data. These include, 1) a classification system for diabetic foot infections that can be applied to computerized medical data; 2) an operational definition of the initial antibiotic regimen, including the requirement to exclude drugs unlikely to be used for foot infection; 3) a taxonomy for classifying the spectra of antibacterial agents that has proven to be useful in evaluating variation in complex antibiotic regimens, and 4) the benefit of using of shrunken means to make possible the analysis of antibiotic variation across large numbers of facilities, each of which may see only a few patients treated with a given antibiotic regimen. These methods set stage for observational studies of the relationship between different antibiotic regimens, treatment outcomes, and costs. They are also applicable to other conditions, such as intra-abdominal infections, that require empiric treatment with broad-spectrum regimens that may be achieved using many different antibiotic combinations.
There are a number of limitations to this study. First, 41% of the patients never met our definition of a stable antibiotic regimen. This occurred more commonly among patients with milder infections, which indicates that our findings regarding the chosen antibiotic regimens are biased toward patients with greater severity. This might result in the use of broader spectrum regimens and therefore reduce observed differences in treatment across our categories of infection. Second, the antibiotic regimens that we studied may have been customized in response to microbiologic data. To explain our results, however, would require very large differences across facilities in the prevalence of organisms such as MRSA and Ps. aeruginosa. In a recent study, each of these was isolated from less than 5% of cultures, which makes this possibility very unlikely. Third, ICD-9CM coding for foot infections is known to contain inaccuracies. Despite this, we have found in prior work that that our classification of foot infections permits meaningful distinctions to be made . Fourth, we do not have information about comorbidities that might influence prescribing, such as allergies or renal disease. While this probably accounts for some variation, it is unlikely that differences across facilities are great enough to explain the large variation in antibiotic use that we observed across hospitals. For example, the most commonly used regimen that would be affected by penicillin allergy (piperacillin-tazobactam) has a range of use across hospitals of 23%, while that of the most common regimen that would be affected by chronic renal disease (piperacillin-tazobactam plus vancomycin) is 36%. Fifth, the great majority of patients in our study were male, but it is unlikely that gender would have a large effect on antibiotic choice. Sixth, there is no gold standard for assigning antibacterial spectra to antibiotic regimens and differences of opinion are likely. Changing the assignment of some of the regimens, however, would not alter our finding that there is substantial variation in antibiotic treatment, it would simply move the source of some of the variation from one spectrum to another. Last, despite the relatively large patient population studied, there were small numbers in many of the facilities and antibiotic groups studied. While we applied Bayesian shrinkage of the means to address this problem, the range of true variation may still have been overestimated.
We have found large variations in the antibiotic regimens used to treat patients with diabetic foot infections. They appear to reflect facility differences in practice styles rather than case mix. It is unlikely that these regimens are equally cost-effective. The methods we have developed can be applied in further studies to examine both costs and outcomes in order to determine which regimens should be preferred. They also can be extended to the evaluation of any condition, infective or not, that is often treated with a multidrug regimen.
Appendix - ICD-9CM codes
040.0 Gas Gangrene
440.24 Atherosclerosis of the extremities with gangrene
785.4 Gangrene but only if any one of the following is also present:
250.7 Diabetes with peripheral circulatory disorders
440.2 Atherosclerosis of native arteries of the extremities
Any condition classifiable to 440.21, 440.22, and 440.23
730.07 Acute osteomyelitis of ankle and foot
730.17 Chronic osteomyelitis of ankle and foot
730.27 Unspecified osteomyelitis of ankle and foot
730.97 Unspecified infection of bone of ankle and foot
440.23 Atherosclerosis of the extremities with ulceration
707.14 Ulcer of heel and mid foot
707.15 Ulcer of other part of foot
707.1 Ulcer of lower limbs
Cellulitis or abscess of foot
680.7 Carbuncle and furuncle of foot, heel, toe
682.7 Cellulitis and abscess of foot, except toes
Cellulitis or abscess of toe
681.1 Cellulitis and abscess of toe
681.10 Cellulitis, toe nos
681.11 Onychia and paronychia of toe
Presented at the American Society of Health-System Pharmacists Summer Meeting in Seattle, WA in June 2008.
Supported by a research grant from Merck & Co., Inc.
Robin S. Turpin was affiliated with Merck & Co., Inc. during the study period.
- Pinzur MS, Slovenkai MP, Trepman E, Shields NN: Guidelines for diabetic foot care: recommendations endorsed by the Diabetes Committee of the American Orthopaedic Foot and Ankle Society. Foot Ankle Int. 2005, 26 (1): 113-119.PubMedGoogle Scholar
- Lipsky BA: A report from the international consensus on diagnosing and treating the infected diabetic foot. Diabetes Metab Res Rev. 2004, 20 (Suppl 1): S68-77. 10.1002/dmrr.453.View ArticlePubMedGoogle Scholar
- Lavery LA, Armstrong DG, Wunderlich RP, Mohler MJ, Wendel CS, Lipsky BA: Risk factors for foot infections in individuals with diabetes. Diabetes Care. 2006, 29 (6): 1288-1293. 10.2337/dc05-2425.View ArticlePubMedGoogle Scholar
- Joseph WS: Classification of diabetic foot infections. Cutis. 2004, 73 (5 Suppl): 20-22.PubMedGoogle Scholar
- Lipsky BA, Berendt AR, Deery HG, Embil JM, Joseph WS, Karchmer AW, LeFrock JL, Lew DP, Mader JT, Norden C, et al: Diagnosis and treatment of diabetic foot infections. Plast Reconstr Surg. 2006, 117 (7 Suppl): 212S-238S. 10.1097/01.prs.0000222737.09322.77.View ArticlePubMedGoogle Scholar
- Matthews PC, Berendt AR, Lipsky BA: Clinical management of diabetic foot infection: diagnostics, therapeutics and the future. Expert Rev Anti Infect Ther. 2007, 5 (1): 117-127. 10.1586/1478722.214.171.124.View ArticlePubMedGoogle Scholar
- Nelson EA, O'Meara S, Craig D, Iglesias C, Golder S, Dalton J, Claxton K, Bell-Syer SE, Jude E, Dowson C, et al: A series of systematic reviews to inform a decision analysis for sampling and treating infected diabetic foot ulcers. Health Technol Assess. 2006, 10 (12): iii-iv-ix-x. 1-221View ArticleGoogle Scholar
- Lipsky BA: Evidence-based antibiotic therapy of diabetic foot infections. FEMS Immunol Med Microbiol. 1999, 26 (3-4): 267-276. 10.1111/j.1574-695X.1999.tb01398.x.View ArticlePubMedGoogle Scholar
- Fincke BG, Miller DR, Turpin R: A classification of diabetic foot infections using ICD-9 Codes: application to a large computerized medical database. BMC Health Services Research. 2010, 10: 192.View ArticlePubMedPubMed CentralGoogle Scholar
- Miller DR, Safford MM, Pogach LM: Who has diabetes? Best estimates of diabetes prevalence in the Department of Veterans Affairs based on computerized patient data. Diabetes Care. 2004, 27 (Suppl 2): B10-21. 10.2337/diacare.27.suppl_2.B10.View ArticlePubMedGoogle Scholar
- Kupersmith J, Francis J, Kerr E, Krein S, Pogach L, Kolodner RM, Perlin JB: Advancing evidence-based care for diabetes: lessons from the Veterans Health Administration. Health Aff (Millwood). 2007, 26 (2): W156-168. 10.1377/hlthaff.26.2.w156.View ArticleGoogle Scholar
- Abramowicz M: Handbook of antimicrobial therapy. 2005, The Medical Letter, Inc, 17Google Scholar
- Christiansen CL, Morris CN: Improving the statistical approach to health care provider profiling. Ann Intern Med. 1997, 127 (8 Pt 2): 764-768.View ArticlePubMedGoogle Scholar
- Christiansen CL, Morris CN: Hierarchical Poisson Regression Modeling. J Am Stat Assoc. 1997, 92: 618-632. 10.2307/2965709.View ArticleGoogle Scholar
- Berman S, Byrns PJ, Bondy J, Smith PJ, Lezotte D: Otitis media-related antibiotic prescribing patterns, outcomes, and expenditures in a pediatric medicaid population. Pediatrics. 1997, 100 (4): 585-592. 10.1542/peds.100.4.585.View ArticlePubMedGoogle Scholar
- Steinman MA, Landefeld CS, Gonzales R: Predictors of broad-spectrum antibiotic prescribing for acute respiratory tract infections in adult primary care. Jama. 2003, 289 (6): 719-725. 10.1001/jama.289.6.719.View ArticlePubMedGoogle Scholar
- Mettler J, Simcock M, Sendi P, Widmer AF, Bingisser R, Battegay M, Fluckiger U, Bassetti S: Empirical use of antibiotics and adjustment of empirical antibiotic therapies in a university hospital: a prospective observational study. BMC Infect Dis. 2007, 7: 21-10.1186/1471-2334-7-21.View ArticlePubMedPubMed CentralGoogle Scholar
- Linder JA, Bates DW, Lee GM, Finkelstein JA: Antibiotic treatment of children with sore throat. Jama. 2005, 294 (18): 2315-2322. 10.1001/jama.294.18.2315.View ArticlePubMedGoogle Scholar
- van Duijn HJ, Kuyvenhoven MM, Schellevis FG, Verheij TJ: Determinants of prescribing of second-choice antibiotics for upper and lower respiratory tract episodes in Dutch general practice. J Antimicrob Chemother. 2005, 56 (2): 420-422. 10.1093/jac/dki214.View ArticlePubMedGoogle Scholar
- Mortensen EM, Restrepo MI, Anzueto A, Pugh JA: Antibiotic therapy and 48-hour mortality for patients with pneumonia. Am J Med. 2006, 119 (10): 859-864. 10.1016/j.amjmed.2006.04.017.View ArticlePubMedGoogle Scholar
- Houck PM, MacLehose RF, Niederman MS, Lowery JK: Empiric antibiotic therapy and mortality among medicare pneumonia inpatients in 10 western states: 1993-1995, and 1997. Chest. 2001, 119 (5): 1420-1426. 10.1378/chest.119.5.1420.View ArticlePubMedGoogle Scholar
- Frei CR, Restrepo MI, Mortensen EM, Burgess DS: Impact of guideline-concordant empiric antibiotic therapy in community-acquired pneumonia. Am J Med. 2006, 119 (10): 865-871. 10.1016/j.amjmed.2006.02.014.View ArticlePubMedGoogle Scholar
- Kern WV, Rose AD, Hay B, Muche R, Frank U: Antimicrobial expenditures and usage at four university hospitals. Baden-Wurttemberg Interuniversity Study Group. Infection. 2001, 29 (3): 127-137. 10.1007/s15010-001-1075-0.View ArticlePubMedGoogle Scholar
- Roumie CL, Halasa NB, Edwards KM, Zhu Y, Dittus RS, Griffin MR: Differences in antibiotic prescribing among physicians, residents, and nonphysician clinicians. Am J Med. 2005, 118 (6): 641-648. 10.1016/j.amjmed.2005.02.013.View ArticlePubMedGoogle Scholar
- Cunha BA: Antibiotic selection for diabetic foot infections: a review. J Foot Ankle Surg. 2000, 39 (4): 253-257. 10.1016/S1067-2516(00)80009-5.View ArticlePubMedGoogle Scholar
- Grayson ML: Diabetic foot infections. Antimicrobial therapy. Infect Dis Clin North Am. 1995, 9 (1): 143-161.PubMedGoogle Scholar
- Lipsky BA, Berendt AR: Principles and practice of antibiotic therapy of diabetic foot infections. Diabetes Metab Res Rev. 2000, 16 (Suppl 1): S42-46. 10.1002/1520-7560(200009/10)16:1+<::AID-DMRR109>3.0.CO;2-B.View ArticlePubMedGoogle Scholar
- Lipsky BA: Empirical therapy for diabetic foot infections: are there clinical clues to guide antibiotic selection?. Clin Microbiol Infect. 2007, 13 (4): 351-353. 10.1111/j.1469-0691.2007.01697.x.View ArticlePubMedGoogle Scholar
- Citron DM, Goldstein EJ, Merriam CV, Lipsky BA, Abramson MA: Bacteriology of moderate-to-severe diabetic foot infections and in vitro activity of antimicrobial agents. J Clin Microbiol. 2007, 45 (9): 2819-2828. 10.1128/JCM.00551-07.View ArticlePubMedPubMed CentralGoogle Scholar
- Grayson ML, Gibbons GW, Habershaw GM, Freeman DV, Pomposelli FB, Rosenblum BI, Levin E, Karchmer AW: Use of ampicillin/sulbactam versus imipenem/cilastatin in the treatment of limb-threatening foot infections in diabetic patients. Clin Infect Dis. 1994, 18 (5): 683-693.View ArticlePubMedGoogle Scholar
- Akalin HE: The role of beta-lactam/beta-lactamase inhibitors in the management of mixed infections. Int J Antimicrob Agents. 1999, 12 (Suppl 1): S15-20. 10.1016/S0924-8579(99)00087-4. discussion S26-17View ArticlePubMedGoogle Scholar
- Lipsky BA, Armstrong DG, Citron DM, Tice AD, Morgenstern DE, Abramson MA: Ertapenem versus piperacillin/tazobactam for diabetic foot infections (SIDESTEP): prospective, randomised, controlled, double-blinded, multicentre trial. Lancet. 2005, 366 (9498): 1695-1703. 10.1016/S0140-6736(05)67694-5.View ArticlePubMedGoogle Scholar
- Tice AD, Turpin RS, Hoey CT, Lipsky BA, Wu J, Abramson MA: Comparative costs of ertapenem and piperacillin-tazobactam in the treatment of diabetic foot infections. Am J Health Syst Pharm. 2007, 64 (10): 1080-1086.PubMedGoogle Scholar
- De Coster C, Li B, Quan H: Comparison and validity of procedures coded With ICD-9-CM and ICD-10-CA/CCI. Med Care. 2008, 46 (6): 627-634. 10.1097/MLR.0b013e3181649439.View ArticlePubMedGoogle Scholar
- The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1472-6963/10/193/prepub
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.