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Automated surveillance system for hospital-acquired urinary tract infections in Denmark

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Condell, O, Gubbels, S, Nielsen, J, Espenhain, L, Frimodt-Møller, N, Engberg, J, Møller, JK, Ellermann-Eriksen, S, Schønheyder, HC, Voldstedlund, M, Mølbak, K & Kristensen, B 2016, 'Automated surveillance system for hospital-acquired urinary tract infections in Denmark' The Journal of hospital infection, vol. 93, no. 3, pp. 290-6. https://doi.org/10.1016/j.jhin.2016.04.001

APA

CBE

Condell O, Gubbels S, Nielsen J, Espenhain L, Frimodt-Møller N, Engberg J, Møller JK, Ellermann-Eriksen S, Schønheyder HC, Voldstedlund M, Mølbak K, Kristensen B. 2016. Automated surveillance system for hospital-acquired urinary tract infections in Denmark. The Journal of hospital infection. 93(3):290-6. https://doi.org/10.1016/j.jhin.2016.04.001

MLA

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Author

Condell, O ; Gubbels, S ; Nielsen, J ; Espenhain, L ; Frimodt-Møller, N ; Engberg, J ; Møller, J K ; Ellermann-Eriksen, S ; Schønheyder, H C ; Voldstedlund, M ; Mølbak, K ; Kristensen, B. / Automated surveillance system for hospital-acquired urinary tract infections in Denmark. In: The Journal of hospital infection. 2016 ; Vol. 93, No. 3. pp. 290-6.

Bibtex

@article{7b60ea62cafe4d218d1037260c3c498a,
title = "Automated surveillance system for hospital-acquired urinary tract infections in Denmark",
abstract = "BACKGROUND: The Danish Hospital-Acquired Infections Database (HAIBA) is an automated surveillance system using hospital administrative, microbiological, and antibiotic medication data.AIM: To define and evaluate the case definition for hospital-acquired urinary tract infection (HA-UTI) and to describe surveillance data from 2010 to 2014.METHODS: The HA-UTI algorithm defined a laboratory-diagnosed UTI as a urine culture positive for no more than two micro-organisms with at least one at ≥10(4)cfu/mL, and a probable UTI as a negative urine culture and a relevant diagnosis code or antibiotic treatment. UTI was considered hospital-acquired if a urine sample was collected ≥48h after admission and <48h post discharge. Incidence of HA-UTI was calculated per 10,000 risk-days. For validation, prevalence was calculated for each day and compared to point prevalence survey (PPS) data.FINDINGS: HAIBA detected a national incidence rate of 42.2 laboratory-diagnosed HA-UTI per 10,000 risk-days with an increasing trend. Compared to PPS the laboratory-diagnosed HA-UTI algorithm had a sensitivity of 50.0{\%} (26/52) and a specificity of 94.2{\%} (1842/1955). There were several reasons for discrepancies between HAIBA and PPS, including laboratory results being unavailable at the time of the survey, the results considered clinically irrelevant by the surveyor due to an indwelling urinary catheter or lack of clinical signs of infection, and UTIs being considered HA-UTI in PPS even though the first sample was taken within 48h of admission.CONCLUSION: The HAIBA algorithm was found to give valid and valuable information and has, among others, the advantages of covering the whole population and allowing continuous standardized monitoring of HA-UTI.",
keywords = "Journal Article",
author = "O Condell and S Gubbels and J Nielsen and L Espenhain and N Frimodt-M{\o}ller and J Engberg and M{\o}ller, {J K} and S Ellermann-Eriksen and Sch{\o}nheyder, {H C} and M Voldstedlund and K M{\o}lbak and B Kristensen",
note = "Copyright {\circledC} 2016 The Healthcare Infection Society. Published by Elsevier Ltd. All rights reserved.",
year = "2016",
month = "7",
doi = "10.1016/j.jhin.2016.04.001",
language = "English",
volume = "93",
pages = "290--6",
journal = "Journal of Hospital Infection",
issn = "0195-6701",
publisher = "W.B./Saunders Co. Ltd",
number = "3",

}

RIS

TY - JOUR

T1 - Automated surveillance system for hospital-acquired urinary tract infections in Denmark

AU - Condell, O

AU - Gubbels, S

AU - Nielsen, J

AU - Espenhain, L

AU - Frimodt-Møller, N

AU - Engberg, J

AU - Møller, J K

AU - Ellermann-Eriksen, S

AU - Schønheyder, H C

AU - Voldstedlund, M

AU - Mølbak, K

AU - Kristensen, B

N1 - Copyright © 2016 The Healthcare Infection Society. Published by Elsevier Ltd. All rights reserved.

PY - 2016/7

Y1 - 2016/7

N2 - BACKGROUND: The Danish Hospital-Acquired Infections Database (HAIBA) is an automated surveillance system using hospital administrative, microbiological, and antibiotic medication data.AIM: To define and evaluate the case definition for hospital-acquired urinary tract infection (HA-UTI) and to describe surveillance data from 2010 to 2014.METHODS: The HA-UTI algorithm defined a laboratory-diagnosed UTI as a urine culture positive for no more than two micro-organisms with at least one at ≥10(4)cfu/mL, and a probable UTI as a negative urine culture and a relevant diagnosis code or antibiotic treatment. UTI was considered hospital-acquired if a urine sample was collected ≥48h after admission and <48h post discharge. Incidence of HA-UTI was calculated per 10,000 risk-days. For validation, prevalence was calculated for each day and compared to point prevalence survey (PPS) data.FINDINGS: HAIBA detected a national incidence rate of 42.2 laboratory-diagnosed HA-UTI per 10,000 risk-days with an increasing trend. Compared to PPS the laboratory-diagnosed HA-UTI algorithm had a sensitivity of 50.0% (26/52) and a specificity of 94.2% (1842/1955). There were several reasons for discrepancies between HAIBA and PPS, including laboratory results being unavailable at the time of the survey, the results considered clinically irrelevant by the surveyor due to an indwelling urinary catheter or lack of clinical signs of infection, and UTIs being considered HA-UTI in PPS even though the first sample was taken within 48h of admission.CONCLUSION: The HAIBA algorithm was found to give valid and valuable information and has, among others, the advantages of covering the whole population and allowing continuous standardized monitoring of HA-UTI.

AB - BACKGROUND: The Danish Hospital-Acquired Infections Database (HAIBA) is an automated surveillance system using hospital administrative, microbiological, and antibiotic medication data.AIM: To define and evaluate the case definition for hospital-acquired urinary tract infection (HA-UTI) and to describe surveillance data from 2010 to 2014.METHODS: The HA-UTI algorithm defined a laboratory-diagnosed UTI as a urine culture positive for no more than two micro-organisms with at least one at ≥10(4)cfu/mL, and a probable UTI as a negative urine culture and a relevant diagnosis code or antibiotic treatment. UTI was considered hospital-acquired if a urine sample was collected ≥48h after admission and <48h post discharge. Incidence of HA-UTI was calculated per 10,000 risk-days. For validation, prevalence was calculated for each day and compared to point prevalence survey (PPS) data.FINDINGS: HAIBA detected a national incidence rate of 42.2 laboratory-diagnosed HA-UTI per 10,000 risk-days with an increasing trend. Compared to PPS the laboratory-diagnosed HA-UTI algorithm had a sensitivity of 50.0% (26/52) and a specificity of 94.2% (1842/1955). There were several reasons for discrepancies between HAIBA and PPS, including laboratory results being unavailable at the time of the survey, the results considered clinically irrelevant by the surveyor due to an indwelling urinary catheter or lack of clinical signs of infection, and UTIs being considered HA-UTI in PPS even though the first sample was taken within 48h of admission.CONCLUSION: The HAIBA algorithm was found to give valid and valuable information and has, among others, the advantages of covering the whole population and allowing continuous standardized monitoring of HA-UTI.

KW - Journal Article

U2 - 10.1016/j.jhin.2016.04.001

DO - 10.1016/j.jhin.2016.04.001

M3 - Journal article

VL - 93

SP - 290

EP - 296

JO - Journal of Hospital Infection

JF - Journal of Hospital Infection

SN - 0195-6701

IS - 3

ER -

ID: 49907011