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

O Condell, S Gubbels, J Nielsen, L Espenhain, N Frimodt-Møller, J Engberg, J K Møller, S Ellermann-Eriksen, H C Schønheyder, M Voldstedlund, K Mølbak, B Kristensen

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

Resumé

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.

OriginalsprogEngelsk
TidsskriftJournal of Hospital Infection
Vol/bind93
Udgave nummer3
Sider (fra-til)290-296
ISSN0195-6701
DOI
StatusUdgivet - 2016

Fingeraftryk

Denmark
Databases
Hospital Laboratories
Urine
Indwelling Catheters
Incidence
Surveys and Questionnaires
Population

Emneord

  • Surveillance
  • Urinary Tract Infections
  • Hospital

Citer dette

Condell, O., Gubbels, S., Nielsen, J., Espenhain, L., Frimodt-Møller, N., Engberg, J., ... Kristensen, B. (2016). Automated surveillance system for hospital-acquired urinary tract infections in Denmark. Journal of Hospital Infection, 93(3), 290-296. https://doi.org/10.1016/j.jhin.2016.04.001
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. I: Journal of Hospital Infection. 2016 ; Bind 93, Nr. 3. s. 290-296.
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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.",
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year = "2016",
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language = "English",
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pages = "290--296",
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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', Journal of Hospital Infection, bind 93, nr. 3, s. 290-296. https://doi.org/10.1016/j.jhin.2016.04.001

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

I: Journal of Hospital Infection, Bind 93, Nr. 3, 2016, s. 290-296.

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

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

Y1 - 2016

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

KW - Surveillance

KW - Urinary Tract Infections

KW - Hospital

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 -

Condell O, Gubbels S, Nielsen J, Espenhain L, Frimodt-Møller N, Engberg J et al. Automated surveillance system for hospital-acquired urinary tract infections in Denmark. Journal of Hospital Infection. 2016;93(3):290-296. https://doi.org/10.1016/j.jhin.2016.04.001