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Surveillance of hospital-acquired infections based on electronic hospital registries
Journal of Hospital Infection (2006) 62, 71–79
www.elsevierhealth.com/journals/jhin
Surveillance of hospital-acquired infections based on electronic hospital registries R.A. Leth*, J.K. Møller Department of Clinical Microbiology, Aarhus University Hospital, Skejby, Brendstrupgaardsvej 100, DK-8200 Aarhus N, Denmark Received 29 August 2003; accepted 11 March 2005 Available online 15 August 2005
KEYWORDS Computer-assisted surveillance; Hospitalacquired infections; Infection control
Summary Computer-assisted surveillance of hospital-acquired infections (HAIs) was compared with conventional manual registration (our gold standard i.e. reference method) by chart reviews of nosocomial infections in patients from surgical and medical departments. By combining selected infection parameters from various electronic hospital registries, the computer detected general HAIs with a sensitivity of 94% and a specificity of 47%. However, defining septicaemia, urinary tract infection (UTI), pneumonia and postoperative wound infection (PWI) specifically by sets of simplified criteria (infection parameters), computer-assisted surveillance was able to detect these infections with a sensitivity ranging between 82% (UTI) and 100% (septicaemia), and a specificity ranging between 91% (PWI) and 100% (septicaemia) compared with conventional manual registration. We conclude that computer surveillance based on data collected for other purposes in electronic hospital registries is an effective method for monitoring HAIs. Q 2005 The Hospital Infection Society. Published by Elsevier Ltd. All rights reserved.
Introduction Hospital-acquired infections (HAIs) still remain a major problem, leading to prolonged hospital stay, increased morbidity and mortality for patients, and increased costs for the healthcare system. A Danish prevalence study from 1999 showed that 8–10% of
* Corresponding author. Tel.: C45 89 495 647; fax: C45 89 495 611. E-mail address: [email protected]
patients admitted to medical and surgical departments had an HAI.1 An active surveillance programme with feedback to the clinicians and an infection control team, including one infection control nurse per 250 beds, has been shown to reduce subsequent infection rates by 32%.2,3 Our knowledge of hospital infection rates is generally scarce and the rates are often incomparable. The explanation for this is the use of classical bedside registration of infections, which is time consuming, costly, difficult to standardize and often subjective in the categorization of patients
0195-6701/$ - see front matter Q 2005 The Hospital Infection Society. Published by Elsevier Ltd. All rights reserved. doi:10.1016/j.jhin.2005.04.002
72 as having a particular nosocomial infection. Gastmeier et al.4 and Ehrenkranz et al.5 have both shown that even well-trained registrants come to different results. Definitions of nosocomial infections, e.g. the US Centers for Disease Control and Prevention (CDC) definitions,6 are a mixture of several infection parameters. Combination of selected parameters, e.g. microbiology and antibiotic treatment, has been shown to give a high sensitivity in predicting HAIs.7,8 Some of these data may be electronically registered for other purposes, e.g. data concerning microbiology and biochemistry, which individually or together could indicate the presence of an infection. Several authors have shown that selected electronically registered parameters may be more successful in detecting HAIs than a traditional manual registration.9,10 However, an apparently high sensitivity seems to be at the expense of a low specificity. The aim of this study was to establish and validate computer-assisted surveillance of nosocomial infections in a Danish hospital. To maximize the objectivity of the computer-assisted registration of HAIs, we used data that had been registered for other purposes in various electronic hospital registries. Our computer registration of infections is based on selected laboratory and administrative data, including individual use of antimicrobial agents, in order to increase the specificity of the registration of infections.
Methods Study design This study compared two different methods for surveillance of HAIs: computer-assisted registration based on various electronic hospital registries and conventional manual registration of hospital infections by chart reviews. The computer-assisted infection registration model was established using a random sample of patients previously evaluated in the national prevalence study in 1999 (Part A). The model was evaluated more extensively using patient data collected retrospectively and prospectively from a department of internal medicine and a department of abdominal surgery (Part B). In both studies, the reference (gold standard) was a conventional review of all patient charts in the department during the study period. The classification of patients as having an HAI was made by the authors in Part B of the study, and by a variety of infection control teams in Part A.
R.A. Leth, J.K. Møller
Study wards and patients Part A (prevalence study) was used as the basic reference material for identification of the most important parameters in the computer model, and included all patients (NZ97) from a local department of orthopaedic surgery on the day of the national prevalence study. Part B (incidence study) comprised three different groups of patients. Two of the groups originated from a department of abdominal surgery and comprised 666 patients (763 consecutive admissions). Data from 296 patients (344 admissions) were collected retrospectively (patients discharged from 1 January to 28 February 2001), and data from 370 patients (419 consecutive admissions) were collected prospectively (patients admitted from 22 February to 28 April 2002). The third group consisted of data collected retrospectively from 359 patients from a department of internal medicine (401 consecutive admissions from 1 January to 31 March 2002). During the prospective part of the incidence study, we examined antibiotic treatment using the notes on medication in the patient charts. The three groups of patients were analysed as a combined group because no significant difference was found between the groups in terms of the analysed parameters in the computer model. Admissions lasting less than two days, except for re-admissions, were excluded. Part B thus consisted of 1164 admissions in total. In order to have a more precise evaluation of the computer model for detection of HAIs, a further 35 admissions were excluded because the patients had both a community-acquired infection (CAI) and an HAI. HAIs that occurred after hospital discharge were not registered unless re-admission took place.
Collection of data All Danes have a unique civil registration number (CRN) that is used by all public institutions including the healthcare system. Therefore, in this study, the CRN was the ‘backbone’ of both conventional registration and computer-assisted surveillance of nosocomial infections. Conventional registration was based on a chart review of the clinical records including laboratory requests and culture results for all patients. During the prospective study at a department of abdominal surgery, the chart reviews were supplemented with daily visits to the department to monitor antibiotic treatment of patients and to register new HAIs.
Hospital infection surveillance From the hospital administrative data system, the CRNs and information about time of admission and discharge of the patients in the study were transferred to a HAI registry created in a relational database (Oraclee) at the Department of Clinical Microbiology. These data constitute the basic record in our infection registration model. All data on cultures and other types of microbiological examinations on the study patients were extracted from the local microbiological laboratory information system11 by use of a list of CRNs from the hospital information system. Leukocyte counts and C-reactive protein (CRP) concentrations were obtained from the local biochemistry laboratory information system. Cutoff values of 200 nmol/L for the medical patients and 400 nmol/L for the surgical patients were chosen to optimize the sensitivity of CRP concentration as a predictor of infections based upon analyses including receiver operational curves (data not shown). Data on chest X-ray examinations (date of examination and X-ray diagnosis) were supplied by the Department of Radiology’s information system. Individual data on consumption of antimicrobial agents could only partially be obtained from electronic registries. Therefore, a manual registration by one of the investigators was made daily in the prospective part of the incidence study. In the retrospective studies, the data were extracted from patient charts and the separate notes on medication (nursing Cardex records).
Definitions of hospital infections (Table I) Table I shows the conventional definitions based upon slightly modified CDC criteria (gold standard) and the definitions used in the computer model. We did not discriminate between deep and superficial wound infections in either the conventional or the computer-assisted surveillance of HAIs. An infection was considered to be hospital acquired when it occurred during or after hospitalization and was not present or incubating upon hospital admission except for re-admission. For practical purposes, a cut-off of 48 h after admission was chosen.
Surveyors The infection control teams performing the prevalence study in 1999 consisted of an experienced infection control nurse and a clinical microbiologist. The investigators in the present study were an infection control practitioner skilled in microbiological
73 laboratory work and master of public health, and a senior consultant in clinical microbiology.
Statistics Data were analysed using Statistical Package for the Social Sciences, Version 10.1. The ability of the different variables to predict HAIs, individually or in combination, was estimated by calculating the sensitivity, specificity and positive and negative predictive values. A 95% confidence interval (CI) is given for frequencies assuming a binomial distribution.
Results Among the 97 orthopaedic patients (Part A of the study), 10 had a CAI and nine had an HAI according to the reference method. We found that antibiotic treatment alone or in combination with one or more of the other infection parameters (data not shown) were the better methods to detect HAI (eight or nine out of nine). It should be mentioned that only 23 patients (24%) were microbiologically sampled during the week before the day of the prevalence study. However, six of the seven patients with HAI were microbiologically sampled and found to be positive. Within the same week, 34 patients (35%) had had a leukocyte count taken and 40 patients (41%) had a measurement of CRP concentration. In Part B of the study, we found that 153 of 763 admissions to the surgical department had a CAI alone. In 104 admissions, the patients had one or more HAIs alone. In 121 admissions to the medical department, the patients had a CAI alone, and in 48 admissions, the patients had one or more HAIs alone. The association of single infection parameters and the various groups of patients with HAI, CAI or no infection is shown in Table II. All parameters except the leukocyte count and CRP are much less represented among patients with no infection than with HAI or CAI. It is noteworthy that positive microbiology is much more frequent among patients with HAI than CAI, and that abnormal leukocyte counts and increased CRP concentrations seem to be of moderate value in distinguishing CAI, HAI or no infection. The ability of the parameters to predict HAI, individually or in combination, is shown in Figure 1 (data obtained by chart reviews). The sensitivity of the single infection parameter ranged from 61% to 82% and the specificity ranged from 53% to 70%. A high sensitivity and a high negative predictive value were seen for all combinations of the various
74
Table I
Definitions of hospital infections
Conventional definitiona
Computer definitionb
Septicaemia (bacteraemia/fungaemia)
Urinary tract infection (UTI)
Pneumonia
Postoperative wound infection (PWI)
Significant pathogen isolated from blood culture plus one or more of the following criteria: temperature O38 8C or !36 8C, tachycardia (O90/min), tachypnoea (O20/ min), leukocyte count O12 or !4!109/L
1. A urine culture of R105 cfu/mL plus one or more of the following clinical criteria: temperature O38 8C, frequency, urgency dysuria or suprapubic tenderness
Stethoscopic and/or radiological signs of pneumonia plus one or more of the following criteria: temperature O38 8C, pathogen isolated from blood or from specimen from the lower respiratory tract, new onset of purulent sputum, positive reaction with validated diagnostic method
Superficial or deep wound infection occurred within 30 days of surgery. One or more of the following criteria fulfilled: purulent secretion from postoperative wound or drainage/incision, wound spontaneously dehisced or surgical wound revision followed by culture-positive swab sample, pathogen isolated from culture of fluid after wound closed primarily, detection of subfascial abscess by puncture or surgical revision
Positive blood culture with significant pathogen and concomitant antibiotic treatment
Culture of urine of R105 cfu/mL of a dominating pathogen or alternatively R103 cfu/mL of pure culture of pathogen and/or UTI relevant antibiotic treatment
Chest X-ray indicating pneumonia and concomitant antibiotic treatment
Type and time of surgical procedure registered, and one or more of the following criteria: culturepositive swab from wound/ drainage, discharge code for PWI (ICD-10),13 relevant antibiotic treatmentc
2. At least two of the aforementioned clinical criteria: positive dipstick for leukocytes and/or nitrate, physicians’ clinical UTI diagnosis and prescription of antibiotic treatment
R.A. Leth, J.K. Møller
A hospital infection is nosocomial if acquired in the hospital. a The Danish version of the Centers for Disease Control and Prevention definitions12 was used for the chart reviews. b For the computer model, the definitions were simplified to fit the data from the available electronic hospital registries. c Prescribed after surgery and not for other infections, e.g. UTI-specific antibiotics or Helicobacter pylori eradication treatment.
Percentage of admissions with
HAI CAI No infection
Microbiology
Positive microbiology
CRP elevatedb
CRP measured nmol/L
Antibiotic treatmenta
Leukocytes counted
Leukocyte count O12 and !4! 109/L
Number
%
Number
%
Number
%
Number
%
Number
%
Number
%
Number
%
114/152 181/274 103/703
75 66 15
89/114 87/181 10/103
78 48 10
115/152 242/274 49/703
76 88 7
135/152 241/274 474/703
89 88 67
111/135 177/241 162/474
82 73 34
137/152 257/274 506/703
90 94 72
83/137 143/257 129/506
61 56 26
Hospital infection surveillance
Table II The association between single infection parameters and the various groups of patients with hospital-acquired infections (HAI) alone, community-acquired infections (CAI) alone, and no infection
CRP, C-reactive protein. Data obtained by chart review. a Use of antibiotics as prophylactic treatment excluded. b CRP R200 nmol/L for the medical patients and R400 nmol/L for the surgical patients.
Table III Prediction of infections (hospital and community acquired): the computer model vs conventional registration of patients from the Department of Abdominal Surgery Sensitivity No. of cases (%) Septicaemia UTI Pneumonia PWIc
26/26 35/49 29/36 46/49
(100) (71) (81) (94)
a
Specificity b
95% CI
No. of admissions (%)
57–83 64–92 83–99
738/738 710/714 727/727 445/489
(100) (99) (100) (91)
PPV 95% CI 99–100 88–93
No. of cases (%) 26/26 35/39 29/29 46/90
(100) (90) (100) (51)
a
NPV 95% CI 76–97 40–62
No. of admissions (%)b
95% CI
738/738 710/724 727/734 445/448
97–99 98–100 98–100
(100) (98) (99) (99)
UTI, urinary tract infection; PWI, postoperative wound infection; PPV, positive predictive value; NPV, negative predictive value; CI, confidence intervals. a Computer model/conventional registration. More than one case per admission was seen. b Computer model/conventional registration. Number of admissions without any episodes of infection detected. c Only patients having surgery were included (538 of 763 surgical admissions).
75
76
R.A. Leth, J.K. Møller
Figure 1 Prediction of hospital-acquired infections by selected parameters individually or in combination. Deviant leukocyte count was O12 or !4!109/L. Elevated C-reactive protein was O200 nmol/L for medical patients and O400 nmol/L for surgical patients. &, sensitivity; ,, specificity; , positive predictive value; , negative predictive value.
parameters, but the highest degree of sensitivity (94%) was obtained by combining three sets of parameters (antibiotic treatment, microbiology and biochemistry). Tables III and IV show the sensitivity and specificity of the computer model in predicting the four most frequent HAIs: septicaemia, urinary tract infection (UTI), pneumonia and postoperative wound infection (PWI). Septicaemia was predicted with the highest sensitivity (100%) and specificity (100%) by the computer model. There were no statistically significant differences between the results for the medical and the surgical departments examined in the study (Tables III and IV). As shown in Table V, the computer model identified 99 (73%) of the 136 patients with a UTI (HAI or CAI) based upon a positive urine culture and antibiotic treatment. Treatment with a UTI-specific antibiotic (sulphamethizole, mecillinam or trimethoprim) was also used as the sole criterion for a UTI because there is a tradition in Denmark for using these drugs for the treatment of both community- and hospital-acquired UTI. Using both sets of criteria increases the sensitivity to 82%. Based upon a positive chest X-ray and antibiotic treatment, the computer model detected 91 (76%) of the 119 patients with pneumonia (HAI or CAI) assessed by conventional criteria and a manual review of the patient chart. The computer identified 46 of the 49 (94%) PWIs defined as episodes with either a relevant culturepositive sample, antibiotic treatment initiated
after a surgical procedure, or a discharge code indicating a PWI. However, the positive predictive value was only 51% (46/90). The explanation is that for some patients, the computer model could not exclude antibiotic treatment given postoperatively for an HAI other than PWI.
Discussion We have evaluated a surveillance system for HAI based on electronic hospital registries in comparison with conventional manual registration of hospital infections. The study shows that a combination of parameters indicating infection, e.g. antibiotic treatment and positive microbiology, gives a higher sensitivity in detecting HAIs than single parameters, as also shown by others.7,8 We found that 94% of all nosocomial infections using unselected data from surgical and medical patients were detected by our computer surveillance model. A strength of this study was the use of CRNs, which makes the compilation of patient-identifiable data from various electronic registries very efficient and reliable. Furthermore, it strengthens our study that the reference (gold standard) was a conventional registration based on slightly modified CDC definitions for HAIs and a review of the charts of all patients admitted during the study period. Another strength of the study was that data from various medical specialities were employed, because the computer model is intended to be generally applicable.
95–99 91–96 384/384 (100) 320/328 (98) 317/338 (94) 85–97 89–100
95% CI No. of admissions (%)b 95% CI
384/384 (100) 320/324 (99) 317/319 (99) 80–94 64–84
UTI, urinary tract infection; PPV, positive predictive value; NPV, negative predictive value; CI, confidence intervals. a Computer model/conventional registration. More than one case per admission was seen. b Computer model/conventional registration. Number of admissions without any episodes of infection detected.
17/17 (100) 77/83 (93) 62/64 (97) 97–100 98–100
No. of cases (%) 95% CI No. of admissions (%) 95% CI No. of cases (%)
17/17 (100) 77/87 (89) 62/83 (75)
NPV PPV
77
a b
Specificity Sensitivity
a
Septicaemia UTI Pneumonia
Table IV Medicine
Prediction of infections (hospital and community acquired): the computer model vs conventional registration of patients from the Department of Internal
Hospital infection surveillance
A limitation of this study is that the conventional registration (reference) of nosocomial infections, except for Part A of the study, was made by the authors and not by an independent third party. Several authors have described the use of a computerized system for infection control surveillance,9,10,14–18 of which the majority were based on microbiology reports. The objective of these systems has been different, e.g. to monitor PWIs alone. The aim of this model was to let computer registration constitute an overall infection surveillance system, based on a combination of data from electronic hospital registries. This includes surveillance of all patients and the major types of nosocomial infections, not just a selection of cases, and relies on different infection parameters depending on their ability to predict a particular type of infection. The computer model was designed to achieve both a high sensitivity and a high specificity in order to avoid confirmatory methods for separating true- and false-positive infections. Therefore, the computer model embraces simplified sets of selected criteria specific for a particular type of infection. Information about former surgical interventions plus the patient’s date of admission, re-admission or possibly transfer from another department or hospital is required to establish whether the infection is an HAI or a CAI. If some of the proposed criteria are not met, a re-examination or extension of the patient data is needed. This could be done by looking at supplementary data already electronically registered or extracted manually from patient charts. The approach to surveillance of nosocomial infections proposed in the present study is supported indirectly by the results of Bouam et al.,9 who examined two different surveillance techniques compared with reference data consisting of patients with positive bacteriology results alone. Reference data were collected by review of case records by an infection control team and restricted to selected infections. The highest sensitivity (91%) in detecting HAIs was found using an automated surveillance based on results from the microbiology laboratory combined with administrative data, such as the date of admission. In contrast, surveillance with a retrospective verification by ward physicians of positive bacteriology reports sent weekly from the microbiology laboratory detected only 59%. However, as stated by the authors, their model needs more information about the presence of clinical signs or symptoms of infection for patients not examined by microbiological samples to obtain a more adequate registration of all HAIs. Evans et al.10 combined several parameters in a computer surveillance of HAIs and antibiotic use
78
R.A. Leth, J.K. Møller
Table V Parameters used for detecting patients with urinary tract infection (UTI), pneumonia and postoperative wound infection (PWI) UTI (136 cases) Culture positive Culture positive Culture positive Culture positive Culture positive Culture positive UTI-specific antibiotic UTI-specific antibiotic Another antibiotic Another antibiotic Pneumonia (119 cases) Positive chest X-ray Positive chest X-ray Culture-positive sputum Culture-positive sputum Clinical diagnosis Clinical diagnosis PWI (49 cases) Antibiotic treatment Antibiotic treatment Antibiotic treatment Antibiotic treatment Culture positive Culture positive PWI discharge code
CUTI specific antibiotic CUTI specific antibiotic CAnother antibiotic CAnother antibiotic KAntibiotics KAntibiotics KCulture positive KCulture positive KCulture positive KCulture positive
CDischarge KDischarge CDischarge KDischarge CDischarge KDischarge CDischarge KDischarge CDischarge KDischarge
code of code of code of code of code of code of code of code of code of code of
UTI UTI UTI UTI UTI UTI UTI UTI UTI UTI
20 40 12 27 1 17 3 10 4 2
CAntibiotics CAntibiotics CAntibiotics CAntibiotics CAntibiotics CAntibiotics
CDischarge KDischarge CDischarge KDischarge CDischarge KDischarge
code of code of code of code of code of code of
pneumonia pneumonia pneumonia pneumonia pneumonia pneumonia
55 36 1 5 13 9
CCulture positive CCulture positive KCulture positive KCulture positive KAntibiotic treatment KAntibiotic treatment KAntibiotic treatment
CPWI discharge code KPWI discharge code CPWI discharge code KPWI discharge code CPWI discharge code KPWI discharge code KCulture positive
9 15 7 4 2 5 4
CZpresence of characteristic; KZabsence of characteristic.
and compared it with the current manually obtained registration by the infection control practitioners. The design of the study was that infectious disease physicians evaluated each method by chart reviews of all cases detected by either of the two methods. Based on the total number of confirmed cases, it was shown that the computer system had a higher sensitivity (90% vs 76%) than the traditional surveillance where infection control practitioners examined all microbiological reports, selectively checked nursing notes and charts, and performed retrospective chart reviews. However, the true sensitivity is not clear because they do not use a proper reference standard, as highlighted by Wenzel and Streed.19 The studies of Bouam et al.9 and Evans et al.,10 together with our own finding that only 75% of the patients with HAIs were microbiologically sampled, demonstrate that microbiology reports can not be used reliably as the sole parameter for detection of all infections. Although supplementary clinical data may be needed to achieve maximum sensitivity and specificity, the inclusion of a few additional infection parameters in the model seems to increase the yield of detection noticeably. In our study, some of the patients with pneumonia did not have a positive
chest X-ray but did have culture-positive sputum and/or a discharge code of pneumonia. By adding these criteria to our computer model, we increased the sensitivity in detecting patients with pneumonia (HAI and CAI) from 76% to 92%. The remaining 8% (nine patients with pneumonia) were treated with antibiotics but could only be detected by a chart review. Similarly, we could increase the sensitivity of UTI (HAI and CAI) detection from 82% to 86% by use of the discharge code for UTI in combination with antibiotic treatment and/or culture-positive urine. This increase in sensitivity by addition of the discharge code is modest and underscores the problem of relying on clinician reporting as the only measure of HAIs. Eighteen patients (13%) were not treated with antibiotics despite a positive urine culture. The reason may be the discharge or death of the patient before report of urine culture, or that patients with a urinary catheter and bacteriuria have their catheters removed rather than antibiotic treatment. There may therefore be a requirement for a computer notification of positive cultures from patients not receiving antibiotic treatment. Three of the patients with a PWI (6%) had only a chart note of purulent secretion from the wound and were consequently not detected. Including
Hospital infection surveillance antibiotic treatment as a criterion for PWI increased the sensitivity of detecting a PWI from 86% to 94%, but also picked up an equal number of patients who did not have a PWI. To exclude those patients without PWI requires electronic or manual follow-up for about 10% of the surgical patients. We are well aware of the limitations of the present computer model and the need for further improvements. Some infections are not included in our model, e.g. gastro-enteritis, catheter-related infection and skin infection; these infections are registered collectively as ‘other HAIs’. In this study, we did not include postdischarge wound infections, but we recognize the need for such inclusion in a future model. A fundamental task henceforward is to find the right balance between a high sensitivity and a minimal number of chart reviews. We think that the more electronic patient data registered, the better identification of HAIs can be expected. The hospitals in Aarhus County are introducing an electronic patient chart system for all patients. With properly structured data, electronic patient charts offer a unique opportunity for electronic surveillance of HAIs. Our model for computer surveillance of HAIs based on data electronically registered for other purposes may, in a healthcare system with an electronic patient chart system, constitute a prototype for an objective registration of all unintended results of hospital treatment. In conclusion, computer surveillance of hospital infections using various sets of data from existing electronic hospital registries may constitute an objective, timesaving and effective alternative to a conventional bedside registration of HAIs. Electronic surveillance based on existing data registries may provide information about groups of patients with a high rate of infection that need further exploration and attention in order to detect risk factors and to establish specific preventive measures. Computer monitoring of infections in clinical departments will also secure a follow-up on the outcome of the interventions directed at HAIs.
Acknowledgements We are grateful to Brian Kristensen and Elisabeth Lund for critical reading. This study was supported in part by the County of Aarhus and a grant from The Danish Medical Research Council (22-01-0411).
References 1. Christensen M, Jepsen OB. Reduced rates of hospitalacquired UTI in medical patients. Prevalence surveys indicate effect of active infection control programmes. J Hosp Infect 2001;47:36—40.
79 2. Haley RW, Culver DH, White JW, et al. The efficacy of infection surveillance and control programs in preventing nosocomial infections in US hospitals. Am J Epidemiol 1985; 121:182—205. 3. Haley RW. The scientific basis for using surveillance and risk factor data to reduce nosocomial infection rates. J Hosp Infect 1995;30:S3—S14. 4. Gastmeier P, Kamph G, Hauer T, et al. Experience with two validation methods in a prevalence survey on nosocomial infections. Infect Control Hosp Epidemiol 1998;19:668—673. 5. Ehrenkranz JN, Shultz JM, Richter EI. Recorded criteria as a “gold standard” for sensitivity and specificity estimates for surveillance of nosocomial infection: a novel method to measure job performance. Infect Control Hosp Epidemiol 1995;16:697—702. 6. Garner JS, Jarvis WR, Emori TG, et al. CDC definitions for nosocomial infections. Am J Infect Control 1988;16: 128—140. 7. Gastmeier P, Bra ¨uer H, Hauer T, et al. How many nosocomial infections are missed if identification is restricted to patients with either microbiology reports or antibiotic administration? Infect Control Hosp Epidemiol 1999;20: 124—127. 8. Glenister HM, Taylor LJ, Bartlett CLR, et al. An evaluation of surveillance methods for detecting infections in hospital inpatients. J Hosp Infect 1993;23:229—242. 9. Bouam S, Girou E, Brun-Buisson C, et al. An intranetbased automated system for the surveillance of nosocomial infections: prospective validation compared with physicians’ self-reports. Infect Control Hosp Epidemiol 2003;24:51—55. 10. Evans RS, Larsen RA, Burke JP, et al. Computer surveillance of hospital-acquired infections and antibiotic use. JAMA 1986;256:1007—1011. 11. Møller JK. A microcomputer-assisted bacteriology reporting and information system. Acta Pathol Microbiol Immunol Scand 1984;92:119—126. 12. Statens Serum Institut. Definitions and coding of nosocomial infections. Copenhagen: Statens Serum Institut; 1997. 13. Schloeler G, Mosbech J (Eds). International Statistical Classification of Diseases and Health Related Problems, 10th edn. Copenhagen: The National Health Service and Munksgaard 1993 (in Danish). 14. Sands K, Vineyard G, Livingston J, et al. Efficient identification of postdischarge surgical site infections: use of automated pharmacy dispensing information, administrative data, and medical record information. J Infect Dis 1999;179: 434—441. 15. Evans RS, Burke JP, Classen DC, et al. Computerized identification of patients at high risk for hospital-acquired infection. Am J Infect Control 1992;20:4—10. 16. Jenders RA, Shah A. Challenges in using the Arden Syntax for computer-based nosocomial infection surveillance. J Am Med Inform Assoc 2001;289—293. 17. Hirschhorn LR, Currier JS, Platt R. Electronic surveillance of antibiotic exposure and coded discharge diagnoses as indicators of postoperative infection and other quality assurance measures. Infect Control Hosp Epidemiol 1993; 14:21—28. 18. Friedman C, Sturm LK, Chenoweth C. Electronic chart review as an aid to postdischarge surgical site surveillance: increased case finding. Am J Infect Control 2001;29: 329—332. 19. Wenzel RP, Streed SA. Surveillance and use of computers in hospital infection control. J Hosp Infect 1989;13:217—229.
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Surveillance of hospital-acquired infections based on electronic hospital registries R.A. Leth*, J.K. Møller Department of Clinical Microbiology, Aarhus University Hospital, Skejby, Brendstrupgaardsvej 100, DK-8200 Aarhus N, Denmark Received 29 August 2003; accepted 11 March 2005 Available online 15 August 2005
KEYWORDS Computer-assisted surveillance; Hospitalacquired infections; Infection control
Summary Computer-assisted surveillance of hospital-acquired infections (HAIs) was compared with conventional manual registration (our gold standard i.e. reference method) by chart reviews of nosocomial infections in patients from surgical and medical departments. By combining selected infection parameters from various electronic hospital registries, the computer detected general HAIs with a sensitivity of 94% and a specificity of 47%. However, defining septicaemia, urinary tract infection (UTI), pneumonia and postoperative wound infection (PWI) specifically by sets of simplified criteria (infection parameters), computer-assisted surveillance was able to detect these infections with a sensitivity ranging between 82% (UTI) and 100% (septicaemia), and a specificity ranging between 91% (PWI) and 100% (septicaemia) compared with conventional manual registration. We conclude that computer surveillance based on data collected for other purposes in electronic hospital registries is an effective method for monitoring HAIs. Q 2005 The Hospital Infection Society. Published by Elsevier Ltd. All rights reserved.
Introduction Hospital-acquired infections (HAIs) still remain a major problem, leading to prolonged hospital stay, increased morbidity and mortality for patients, and increased costs for the healthcare system. A Danish prevalence study from 1999 showed that 8–10% of
* Corresponding author. Tel.: C45 89 495 647; fax: C45 89 495 611. E-mail address: [email protected]
patients admitted to medical and surgical departments had an HAI.1 An active surveillance programme with feedback to the clinicians and an infection control team, including one infection control nurse per 250 beds, has been shown to reduce subsequent infection rates by 32%.2,3 Our knowledge of hospital infection rates is generally scarce and the rates are often incomparable. The explanation for this is the use of classical bedside registration of infections, which is time consuming, costly, difficult to standardize and often subjective in the categorization of patients
0195-6701/$ - see front matter Q 2005 The Hospital Infection Society. Published by Elsevier Ltd. All rights reserved. doi:10.1016/j.jhin.2005.04.002
72 as having a particular nosocomial infection. Gastmeier et al.4 and Ehrenkranz et al.5 have both shown that even well-trained registrants come to different results. Definitions of nosocomial infections, e.g. the US Centers for Disease Control and Prevention (CDC) definitions,6 are a mixture of several infection parameters. Combination of selected parameters, e.g. microbiology and antibiotic treatment, has been shown to give a high sensitivity in predicting HAIs.7,8 Some of these data may be electronically registered for other purposes, e.g. data concerning microbiology and biochemistry, which individually or together could indicate the presence of an infection. Several authors have shown that selected electronically registered parameters may be more successful in detecting HAIs than a traditional manual registration.9,10 However, an apparently high sensitivity seems to be at the expense of a low specificity. The aim of this study was to establish and validate computer-assisted surveillance of nosocomial infections in a Danish hospital. To maximize the objectivity of the computer-assisted registration of HAIs, we used data that had been registered for other purposes in various electronic hospital registries. Our computer registration of infections is based on selected laboratory and administrative data, including individual use of antimicrobial agents, in order to increase the specificity of the registration of infections.
Methods Study design This study compared two different methods for surveillance of HAIs: computer-assisted registration based on various electronic hospital registries and conventional manual registration of hospital infections by chart reviews. The computer-assisted infection registration model was established using a random sample of patients previously evaluated in the national prevalence study in 1999 (Part A). The model was evaluated more extensively using patient data collected retrospectively and prospectively from a department of internal medicine and a department of abdominal surgery (Part B). In both studies, the reference (gold standard) was a conventional review of all patient charts in the department during the study period. The classification of patients as having an HAI was made by the authors in Part B of the study, and by a variety of infection control teams in Part A.
R.A. Leth, J.K. Møller
Study wards and patients Part A (prevalence study) was used as the basic reference material for identification of the most important parameters in the computer model, and included all patients (NZ97) from a local department of orthopaedic surgery on the day of the national prevalence study. Part B (incidence study) comprised three different groups of patients. Two of the groups originated from a department of abdominal surgery and comprised 666 patients (763 consecutive admissions). Data from 296 patients (344 admissions) were collected retrospectively (patients discharged from 1 January to 28 February 2001), and data from 370 patients (419 consecutive admissions) were collected prospectively (patients admitted from 22 February to 28 April 2002). The third group consisted of data collected retrospectively from 359 patients from a department of internal medicine (401 consecutive admissions from 1 January to 31 March 2002). During the prospective part of the incidence study, we examined antibiotic treatment using the notes on medication in the patient charts. The three groups of patients were analysed as a combined group because no significant difference was found between the groups in terms of the analysed parameters in the computer model. Admissions lasting less than two days, except for re-admissions, were excluded. Part B thus consisted of 1164 admissions in total. In order to have a more precise evaluation of the computer model for detection of HAIs, a further 35 admissions were excluded because the patients had both a community-acquired infection (CAI) and an HAI. HAIs that occurred after hospital discharge were not registered unless re-admission took place.
Collection of data All Danes have a unique civil registration number (CRN) that is used by all public institutions including the healthcare system. Therefore, in this study, the CRN was the ‘backbone’ of both conventional registration and computer-assisted surveillance of nosocomial infections. Conventional registration was based on a chart review of the clinical records including laboratory requests and culture results for all patients. During the prospective study at a department of abdominal surgery, the chart reviews were supplemented with daily visits to the department to monitor antibiotic treatment of patients and to register new HAIs.
Hospital infection surveillance From the hospital administrative data system, the CRNs and information about time of admission and discharge of the patients in the study were transferred to a HAI registry created in a relational database (Oraclee) at the Department of Clinical Microbiology. These data constitute the basic record in our infection registration model. All data on cultures and other types of microbiological examinations on the study patients were extracted from the local microbiological laboratory information system11 by use of a list of CRNs from the hospital information system. Leukocyte counts and C-reactive protein (CRP) concentrations were obtained from the local biochemistry laboratory information system. Cutoff values of 200 nmol/L for the medical patients and 400 nmol/L for the surgical patients were chosen to optimize the sensitivity of CRP concentration as a predictor of infections based upon analyses including receiver operational curves (data not shown). Data on chest X-ray examinations (date of examination and X-ray diagnosis) were supplied by the Department of Radiology’s information system. Individual data on consumption of antimicrobial agents could only partially be obtained from electronic registries. Therefore, a manual registration by one of the investigators was made daily in the prospective part of the incidence study. In the retrospective studies, the data were extracted from patient charts and the separate notes on medication (nursing Cardex records).
Definitions of hospital infections (Table I) Table I shows the conventional definitions based upon slightly modified CDC criteria (gold standard) and the definitions used in the computer model. We did not discriminate between deep and superficial wound infections in either the conventional or the computer-assisted surveillance of HAIs. An infection was considered to be hospital acquired when it occurred during or after hospitalization and was not present or incubating upon hospital admission except for re-admission. For practical purposes, a cut-off of 48 h after admission was chosen.
Surveyors The infection control teams performing the prevalence study in 1999 consisted of an experienced infection control nurse and a clinical microbiologist. The investigators in the present study were an infection control practitioner skilled in microbiological
73 laboratory work and master of public health, and a senior consultant in clinical microbiology.
Statistics Data were analysed using Statistical Package for the Social Sciences, Version 10.1. The ability of the different variables to predict HAIs, individually or in combination, was estimated by calculating the sensitivity, specificity and positive and negative predictive values. A 95% confidence interval (CI) is given for frequencies assuming a binomial distribution.
Results Among the 97 orthopaedic patients (Part A of the study), 10 had a CAI and nine had an HAI according to the reference method. We found that antibiotic treatment alone or in combination with one or more of the other infection parameters (data not shown) were the better methods to detect HAI (eight or nine out of nine). It should be mentioned that only 23 patients (24%) were microbiologically sampled during the week before the day of the prevalence study. However, six of the seven patients with HAI were microbiologically sampled and found to be positive. Within the same week, 34 patients (35%) had had a leukocyte count taken and 40 patients (41%) had a measurement of CRP concentration. In Part B of the study, we found that 153 of 763 admissions to the surgical department had a CAI alone. In 104 admissions, the patients had one or more HAIs alone. In 121 admissions to the medical department, the patients had a CAI alone, and in 48 admissions, the patients had one or more HAIs alone. The association of single infection parameters and the various groups of patients with HAI, CAI or no infection is shown in Table II. All parameters except the leukocyte count and CRP are much less represented among patients with no infection than with HAI or CAI. It is noteworthy that positive microbiology is much more frequent among patients with HAI than CAI, and that abnormal leukocyte counts and increased CRP concentrations seem to be of moderate value in distinguishing CAI, HAI or no infection. The ability of the parameters to predict HAI, individually or in combination, is shown in Figure 1 (data obtained by chart reviews). The sensitivity of the single infection parameter ranged from 61% to 82% and the specificity ranged from 53% to 70%. A high sensitivity and a high negative predictive value were seen for all combinations of the various
74
Table I
Definitions of hospital infections
Conventional definitiona
Computer definitionb
Septicaemia (bacteraemia/fungaemia)
Urinary tract infection (UTI)
Pneumonia
Postoperative wound infection (PWI)
Significant pathogen isolated from blood culture plus one or more of the following criteria: temperature O38 8C or !36 8C, tachycardia (O90/min), tachypnoea (O20/ min), leukocyte count O12 or !4!109/L
1. A urine culture of R105 cfu/mL plus one or more of the following clinical criteria: temperature O38 8C, frequency, urgency dysuria or suprapubic tenderness
Stethoscopic and/or radiological signs of pneumonia plus one or more of the following criteria: temperature O38 8C, pathogen isolated from blood or from specimen from the lower respiratory tract, new onset of purulent sputum, positive reaction with validated diagnostic method
Superficial or deep wound infection occurred within 30 days of surgery. One or more of the following criteria fulfilled: purulent secretion from postoperative wound or drainage/incision, wound spontaneously dehisced or surgical wound revision followed by culture-positive swab sample, pathogen isolated from culture of fluid after wound closed primarily, detection of subfascial abscess by puncture or surgical revision
Positive blood culture with significant pathogen and concomitant antibiotic treatment
Culture of urine of R105 cfu/mL of a dominating pathogen or alternatively R103 cfu/mL of pure culture of pathogen and/or UTI relevant antibiotic treatment
Chest X-ray indicating pneumonia and concomitant antibiotic treatment
Type and time of surgical procedure registered, and one or more of the following criteria: culturepositive swab from wound/ drainage, discharge code for PWI (ICD-10),13 relevant antibiotic treatmentc
2. At least two of the aforementioned clinical criteria: positive dipstick for leukocytes and/or nitrate, physicians’ clinical UTI diagnosis and prescription of antibiotic treatment
R.A. Leth, J.K. Møller
A hospital infection is nosocomial if acquired in the hospital. a The Danish version of the Centers for Disease Control and Prevention definitions12 was used for the chart reviews. b For the computer model, the definitions were simplified to fit the data from the available electronic hospital registries. c Prescribed after surgery and not for other infections, e.g. UTI-specific antibiotics or Helicobacter pylori eradication treatment.
Percentage of admissions with
HAI CAI No infection
Microbiology
Positive microbiology
CRP elevatedb
CRP measured nmol/L
Antibiotic treatmenta
Leukocytes counted
Leukocyte count O12 and !4! 109/L
Number
%
Number
%
Number
%
Number
%
Number
%
Number
%
Number
%
114/152 181/274 103/703
75 66 15
89/114 87/181 10/103
78 48 10
115/152 242/274 49/703
76 88 7
135/152 241/274 474/703
89 88 67
111/135 177/241 162/474
82 73 34
137/152 257/274 506/703
90 94 72
83/137 143/257 129/506
61 56 26
Hospital infection surveillance
Table II The association between single infection parameters and the various groups of patients with hospital-acquired infections (HAI) alone, community-acquired infections (CAI) alone, and no infection
CRP, C-reactive protein. Data obtained by chart review. a Use of antibiotics as prophylactic treatment excluded. b CRP R200 nmol/L for the medical patients and R400 nmol/L for the surgical patients.
Table III Prediction of infections (hospital and community acquired): the computer model vs conventional registration of patients from the Department of Abdominal Surgery Sensitivity No. of cases (%) Septicaemia UTI Pneumonia PWIc
26/26 35/49 29/36 46/49
(100) (71) (81) (94)
a
Specificity b
95% CI
No. of admissions (%)
57–83 64–92 83–99
738/738 710/714 727/727 445/489
(100) (99) (100) (91)
PPV 95% CI 99–100 88–93
No. of cases (%) 26/26 35/39 29/29 46/90
(100) (90) (100) (51)
a
NPV 95% CI 76–97 40–62
No. of admissions (%)b
95% CI
738/738 710/724 727/734 445/448
97–99 98–100 98–100
(100) (98) (99) (99)
UTI, urinary tract infection; PWI, postoperative wound infection; PPV, positive predictive value; NPV, negative predictive value; CI, confidence intervals. a Computer model/conventional registration. More than one case per admission was seen. b Computer model/conventional registration. Number of admissions without any episodes of infection detected. c Only patients having surgery were included (538 of 763 surgical admissions).
75
76
R.A. Leth, J.K. Møller
Figure 1 Prediction of hospital-acquired infections by selected parameters individually or in combination. Deviant leukocyte count was O12 or !4!109/L. Elevated C-reactive protein was O200 nmol/L for medical patients and O400 nmol/L for surgical patients. &, sensitivity; ,, specificity; , positive predictive value; , negative predictive value.
parameters, but the highest degree of sensitivity (94%) was obtained by combining three sets of parameters (antibiotic treatment, microbiology and biochemistry). Tables III and IV show the sensitivity and specificity of the computer model in predicting the four most frequent HAIs: septicaemia, urinary tract infection (UTI), pneumonia and postoperative wound infection (PWI). Septicaemia was predicted with the highest sensitivity (100%) and specificity (100%) by the computer model. There were no statistically significant differences between the results for the medical and the surgical departments examined in the study (Tables III and IV). As shown in Table V, the computer model identified 99 (73%) of the 136 patients with a UTI (HAI or CAI) based upon a positive urine culture and antibiotic treatment. Treatment with a UTI-specific antibiotic (sulphamethizole, mecillinam or trimethoprim) was also used as the sole criterion for a UTI because there is a tradition in Denmark for using these drugs for the treatment of both community- and hospital-acquired UTI. Using both sets of criteria increases the sensitivity to 82%. Based upon a positive chest X-ray and antibiotic treatment, the computer model detected 91 (76%) of the 119 patients with pneumonia (HAI or CAI) assessed by conventional criteria and a manual review of the patient chart. The computer identified 46 of the 49 (94%) PWIs defined as episodes with either a relevant culturepositive sample, antibiotic treatment initiated
after a surgical procedure, or a discharge code indicating a PWI. However, the positive predictive value was only 51% (46/90). The explanation is that for some patients, the computer model could not exclude antibiotic treatment given postoperatively for an HAI other than PWI.
Discussion We have evaluated a surveillance system for HAI based on electronic hospital registries in comparison with conventional manual registration of hospital infections. The study shows that a combination of parameters indicating infection, e.g. antibiotic treatment and positive microbiology, gives a higher sensitivity in detecting HAIs than single parameters, as also shown by others.7,8 We found that 94% of all nosocomial infections using unselected data from surgical and medical patients were detected by our computer surveillance model. A strength of this study was the use of CRNs, which makes the compilation of patient-identifiable data from various electronic registries very efficient and reliable. Furthermore, it strengthens our study that the reference (gold standard) was a conventional registration based on slightly modified CDC definitions for HAIs and a review of the charts of all patients admitted during the study period. Another strength of the study was that data from various medical specialities were employed, because the computer model is intended to be generally applicable.
95–99 91–96 384/384 (100) 320/328 (98) 317/338 (94) 85–97 89–100
95% CI No. of admissions (%)b 95% CI
384/384 (100) 320/324 (99) 317/319 (99) 80–94 64–84
UTI, urinary tract infection; PPV, positive predictive value; NPV, negative predictive value; CI, confidence intervals. a Computer model/conventional registration. More than one case per admission was seen. b Computer model/conventional registration. Number of admissions without any episodes of infection detected.
17/17 (100) 77/83 (93) 62/64 (97) 97–100 98–100
No. of cases (%) 95% CI No. of admissions (%) 95% CI No. of cases (%)
17/17 (100) 77/87 (89) 62/83 (75)
NPV PPV
77
a b
Specificity Sensitivity
a
Septicaemia UTI Pneumonia
Table IV Medicine
Prediction of infections (hospital and community acquired): the computer model vs conventional registration of patients from the Department of Internal
Hospital infection surveillance
A limitation of this study is that the conventional registration (reference) of nosocomial infections, except for Part A of the study, was made by the authors and not by an independent third party. Several authors have described the use of a computerized system for infection control surveillance,9,10,14–18 of which the majority were based on microbiology reports. The objective of these systems has been different, e.g. to monitor PWIs alone. The aim of this model was to let computer registration constitute an overall infection surveillance system, based on a combination of data from electronic hospital registries. This includes surveillance of all patients and the major types of nosocomial infections, not just a selection of cases, and relies on different infection parameters depending on their ability to predict a particular type of infection. The computer model was designed to achieve both a high sensitivity and a high specificity in order to avoid confirmatory methods for separating true- and false-positive infections. Therefore, the computer model embraces simplified sets of selected criteria specific for a particular type of infection. Information about former surgical interventions plus the patient’s date of admission, re-admission or possibly transfer from another department or hospital is required to establish whether the infection is an HAI or a CAI. If some of the proposed criteria are not met, a re-examination or extension of the patient data is needed. This could be done by looking at supplementary data already electronically registered or extracted manually from patient charts. The approach to surveillance of nosocomial infections proposed in the present study is supported indirectly by the results of Bouam et al.,9 who examined two different surveillance techniques compared with reference data consisting of patients with positive bacteriology results alone. Reference data were collected by review of case records by an infection control team and restricted to selected infections. The highest sensitivity (91%) in detecting HAIs was found using an automated surveillance based on results from the microbiology laboratory combined with administrative data, such as the date of admission. In contrast, surveillance with a retrospective verification by ward physicians of positive bacteriology reports sent weekly from the microbiology laboratory detected only 59%. However, as stated by the authors, their model needs more information about the presence of clinical signs or symptoms of infection for patients not examined by microbiological samples to obtain a more adequate registration of all HAIs. Evans et al.10 combined several parameters in a computer surveillance of HAIs and antibiotic use
78
R.A. Leth, J.K. Møller
Table V Parameters used for detecting patients with urinary tract infection (UTI), pneumonia and postoperative wound infection (PWI) UTI (136 cases) Culture positive Culture positive Culture positive Culture positive Culture positive Culture positive UTI-specific antibiotic UTI-specific antibiotic Another antibiotic Another antibiotic Pneumonia (119 cases) Positive chest X-ray Positive chest X-ray Culture-positive sputum Culture-positive sputum Clinical diagnosis Clinical diagnosis PWI (49 cases) Antibiotic treatment Antibiotic treatment Antibiotic treatment Antibiotic treatment Culture positive Culture positive PWI discharge code
CUTI specific antibiotic CUTI specific antibiotic CAnother antibiotic CAnother antibiotic KAntibiotics KAntibiotics KCulture positive KCulture positive KCulture positive KCulture positive
CDischarge KDischarge CDischarge KDischarge CDischarge KDischarge CDischarge KDischarge CDischarge KDischarge
code of code of code of code of code of code of code of code of code of code of
UTI UTI UTI UTI UTI UTI UTI UTI UTI UTI
20 40 12 27 1 17 3 10 4 2
CAntibiotics CAntibiotics CAntibiotics CAntibiotics CAntibiotics CAntibiotics
CDischarge KDischarge CDischarge KDischarge CDischarge KDischarge
code of code of code of code of code of code of
pneumonia pneumonia pneumonia pneumonia pneumonia pneumonia
55 36 1 5 13 9
CCulture positive CCulture positive KCulture positive KCulture positive KAntibiotic treatment KAntibiotic treatment KAntibiotic treatment
CPWI discharge code KPWI discharge code CPWI discharge code KPWI discharge code CPWI discharge code KPWI discharge code KCulture positive
9 15 7 4 2 5 4
CZpresence of characteristic; KZabsence of characteristic.
and compared it with the current manually obtained registration by the infection control practitioners. The design of the study was that infectious disease physicians evaluated each method by chart reviews of all cases detected by either of the two methods. Based on the total number of confirmed cases, it was shown that the computer system had a higher sensitivity (90% vs 76%) than the traditional surveillance where infection control practitioners examined all microbiological reports, selectively checked nursing notes and charts, and performed retrospective chart reviews. However, the true sensitivity is not clear because they do not use a proper reference standard, as highlighted by Wenzel and Streed.19 The studies of Bouam et al.9 and Evans et al.,10 together with our own finding that only 75% of the patients with HAIs were microbiologically sampled, demonstrate that microbiology reports can not be used reliably as the sole parameter for detection of all infections. Although supplementary clinical data may be needed to achieve maximum sensitivity and specificity, the inclusion of a few additional infection parameters in the model seems to increase the yield of detection noticeably. In our study, some of the patients with pneumonia did not have a positive
chest X-ray but did have culture-positive sputum and/or a discharge code of pneumonia. By adding these criteria to our computer model, we increased the sensitivity in detecting patients with pneumonia (HAI and CAI) from 76% to 92%. The remaining 8% (nine patients with pneumonia) were treated with antibiotics but could only be detected by a chart review. Similarly, we could increase the sensitivity of UTI (HAI and CAI) detection from 82% to 86% by use of the discharge code for UTI in combination with antibiotic treatment and/or culture-positive urine. This increase in sensitivity by addition of the discharge code is modest and underscores the problem of relying on clinician reporting as the only measure of HAIs. Eighteen patients (13%) were not treated with antibiotics despite a positive urine culture. The reason may be the discharge or death of the patient before report of urine culture, or that patients with a urinary catheter and bacteriuria have their catheters removed rather than antibiotic treatment. There may therefore be a requirement for a computer notification of positive cultures from patients not receiving antibiotic treatment. Three of the patients with a PWI (6%) had only a chart note of purulent secretion from the wound and were consequently not detected. Including
Hospital infection surveillance antibiotic treatment as a criterion for PWI increased the sensitivity of detecting a PWI from 86% to 94%, but also picked up an equal number of patients who did not have a PWI. To exclude those patients without PWI requires electronic or manual follow-up for about 10% of the surgical patients. We are well aware of the limitations of the present computer model and the need for further improvements. Some infections are not included in our model, e.g. gastro-enteritis, catheter-related infection and skin infection; these infections are registered collectively as ‘other HAIs’. In this study, we did not include postdischarge wound infections, but we recognize the need for such inclusion in a future model. A fundamental task henceforward is to find the right balance between a high sensitivity and a minimal number of chart reviews. We think that the more electronic patient data registered, the better identification of HAIs can be expected. The hospitals in Aarhus County are introducing an electronic patient chart system for all patients. With properly structured data, electronic patient charts offer a unique opportunity for electronic surveillance of HAIs. Our model for computer surveillance of HAIs based on data electronically registered for other purposes may, in a healthcare system with an electronic patient chart system, constitute a prototype for an objective registration of all unintended results of hospital treatment. In conclusion, computer surveillance of hospital infections using various sets of data from existing electronic hospital registries may constitute an objective, timesaving and effective alternative to a conventional bedside registration of HAIs. Electronic surveillance based on existing data registries may provide information about groups of patients with a high rate of infection that need further exploration and attention in order to detect risk factors and to establish specific preventive measures. Computer monitoring of infections in clinical departments will also secure a follow-up on the outcome of the interventions directed at HAIs.
Acknowledgements We are grateful to Brian Kristensen and Elisabeth Lund for critical reading. This study was supported in part by the County of Aarhus and a grant from The Danish Medical Research Council (22-01-0411).
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