An Automated Algorithm to Preselect Patients to Be Assessed Individually in Point Prevalence Surveys for Hospital-Acquired Infections in Surgery

Streefkerk, Roel H. R. A.; Moorman, Peter W.; Parlevliet, G.A.; Hoeven, Conrad van der; Verbrugh, Henri A.; Vos, Margreet C.; Verkooijen, Roel P.

doi:10.1086/676868

Cited by 10 publications

(18 citation statements)

References 10 publications

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“…[7][8][9][10] Automated monitoring, especially of SSIs, has therefore been attempted at a number of hospitals to address the disadvantages of manual HAI surveillance. [11][12][13][14][15][16][17] However, overestimation of HAI rates, the difficulty in differentiating between infection and colonization, and the failure to record late SSIs due to lack of post-discharge monitoring still have to be resolved. 18,19 The authors of this study have previously reported on an evaluation of an automated monitoring system called HAIR (Hospital-Acquired Infection Registry) based on a combination of laboratory and clinical data available as structured variables in various electronic hospital registries.…”

Section: Introductionmentioning

confidence: 99%

Incidence rates of hospital-acquired urinary tract and bloodstream infections generated by automated compilation of electronically available healthcare data

Redder

Leth

Frimodt‐Møller

2015

Journal of Hospital Infection

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Section: Introductionmentioning

confidence: 99%

Incidence rates of hospital-acquired urinary tract and bloodstream infections generated by automated compilation of electronically available healthcare data

Redder

Leth

Frimodt‐Møller

2015

Journal of Hospital Infection

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“…In total, 55 risk factors were identified across the three types of surgical procedures from literature [15], [18][19][20][21][22][23][24][25][26][27][28]. Of the risk factors identified, ASA class, body mass index (BMI), Preoperative length of stay and diabetes were identified as general risk factors.…”

Section: Resultsmentioning

confidence: 99%

“…The Erasmus MC University Medical Center in Rotterdam, the Netherlands (Erasmus MC), is the largest university medical hospital in the Netherlands with more than 1.300 beds. [21] We used the verified output from the infection control practitioners (ICP) for the SSI output variable.…”

Section: Methodsmentioning

confidence: 99%

“…Weekly (alter two-weekly) prevalence surveys were performed during January 2013 until June 2014 using a semi-automated algorithm [21,22]. In short, an algorithm was used to calculate a nosocomial infection index (NII) which was then verified by infection control practitioners (ICP) in case of a positive outcome to determine whenever HAI was present or not.…”

Section: Methodsmentioning

confidence: 99%

“…Patient related risk factors for SSI, such as obesity, diabetes, surgery duration and the American Society of Anaesthesiologists (ASA) score are risk factors for digestive system, thoracic and orthopaedic surgical procedures [15], [18][19][20][21][22][23][24][25][26][27][28]. Some risk factors are only relevant to certain types of surgical procedures, but the marginal contribution of these surgery specific risk factors to explain the occurrence of SSI has yet to be reported.…”

Section: Introductionmentioning

confidence: 99%

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Risk factor identification and contribution to surgery-specific surgical site infection

Niekerk

Vos

Stein

et al. 2019

Preprint

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Background: To identify common risk factors for the future occurrence of SSI across digestive system surgical procedures, thoracic surgery and orthopaedic procedures and quantify what the marginal contribution of explaining the future occurrence of SSI is of risk factors specific to each group of surgeries. Methods: Retrospective data from serial point-prevalence studies, performed at the Erasmus MC University Medical Center, Rotterdam, The Netherlands were used, together with medical data, from 3.250 surgical procedures, during the period January 2013 to 29 June 2014. Common risk factors across three groups of surgical procedures were identified by scanning literature and univariate analysis. A multivariate forward-step logistic regression model was used to identify the marginal contribution of the risk factor specific to each group of surgical procedure using the increase in the Gini coefficient. Results: For digestive system surgical procedures, antibiotic use, temperature, smoking status, age, CRP, thrombocyte, during of surgery and surgical urgency are risk factors for SSI, where the last four were specific to the digestive system group of surgical procedures and increased the Gini coefficient by 9.5% (0.63 to 0.69). Preoperative length of stay, antibiotic use and Leukocyte are risk factors for orthopaedic surgical procedures. Temperature, age and the use of antibiotics were significant for thoracic surgical procedures. Conclusion: ASA class, body mass index (BMI), Preoperative length of stay, diabetes, antibiotic use, age, Leukocyte, temperature and smoking status as general risk factors common to digestive, orthopaedic and thoracic surgical procedures using existing literature and univariate logistic regression. Risk factors for SSI, specific to a surgical procedure group can increase the ability to explain the future occurrence of SSI by 9.5%. The models developed in this study can aid healthcare workers to improve preoperative counselling and help identify potentially modifiable risk factors.

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Point prevalence of appropriate antimicrobial therapy in a Dutch university hospital

Akhloufi

Streefkerk

Melles∗

et al. 2015

Eur J Clin Microbiol Infect Dis

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Antimicrobial stewardship teams have been shown to increase appropriate empirical antibiotic therapy and reduce medical errors and costs in targeted populations, but the effect in non-targeted populations is still unclear. The aim of this study was to determine the prevalence of inappropriate antibiotic use in a large university hospital and identify areas in which antimicrobial stewardship will be the most effective. In a point prevalence survey we assessed the appropriateness of antibiotic therapy using an electronic surveillance system in combination with a standardized method for duration of therapy, dosage, dosage interval, route of administration, and choice of antibiotic drug. Patients using at least one antibiotic drug were included. Among 996 patients admitted in the surveyed wards, 337 patients (33.8 %) used one or more antibiotic drugs. Two hundred and twenty-one patients (22.2 %) used antibiotic medication therapeutically, with a total of 307 antibiotic prescriptions. Antibiotic therapy was deemed inappropriate in 90 (29.3 %) of these prescribed antibiotics, with an unjustified prescription as the most common reason for an inappropriate prescription. Use of fluoroquinolones and amoxicillin/clavulanic acid and a presumed diagnosis of fever of unknown origin, urinary tract infection, and respiratory tract infection were associated with inappropriate antibiotic therapy. Our study provides insight into the (in)appropriateness of antibiotic prescriptions in a tertiary care center in the Netherlands and identifies areas for improvement. The use of an electronic surveillance system for this point prevalence study is easy and may serve as a baseline measurement for the future effect of antibiotic stewardship.

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An Automated Algorithm to Preselect Patients to Be Assessed Individually in Point Prevalence Surveys for Hospital-Acquired Infections in Surgery

Cited by 10 publications

References 10 publications

Incidence rates of hospital-acquired urinary tract and bloodstream infections generated by automated compilation of electronically available healthcare data

Incidence rates of hospital-acquired urinary tract and bloodstream infections generated by automated compilation of electronically available healthcare data

Risk factor identification and contribution to surgery-specific surgical site infection

Point prevalence of appropriate antimicrobial therapy in a Dutch university hospital

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