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Mortality related to hospital-associated infections in a tertiary hospital; repeated cross-sectional studies between 2004-2011

Mortality related to hospital-associated infections in a tertiary hospital; repeated... Background: Hospital-associated infections (HAIs) are reported to increase patient mortality and incur longer hospital stays. Most studies to date have focused on specific groups of hospitalised patients with a rather short follow-up period. In this repeated cross-sectional study, with prospective follow-up of 19,468 hospitalized patients, we aimed to analyze the impact of HAIs on mortality 30 days and 1 year after the prevalence survey date. Methods: The study was conducted at Haukeland University Hospital, Norway, a large combined emergency and referral teaching hospital, from 2004 to 2011 with follow-up until November 2012. Prevalence of all types of HAIs including urinary tract infections (UTI), lower respiratory tract infections (LRTI), surgical site infections (SSI) and blood stream infections (BSI) were recorded four times every year. Information on the date of birth, admission and discharge from the hospital, number of diagnoses (ICD-10 codes) and patient’s mortality was retrieved from the patient administrative data system. The data were analysed by Kaplan-Meier survival analysis and by multiple Cox regression analysis, adjusted for year of registration, time period, sex, type of admission, Charlson comorbidity index, surgical operation, use of urinary tract catheter and time from admission to the prevalence survey date. Results: The overall prevalence of HAIs was 8.5 % (95 % CI: 8.1, 8.9). Patients with HAIs had an adjusted hazard ratio (HR) of 1.5 (95 % CI: 1.3, 1.8,) and 1.4 (95 % CI: 1.2, 1.5) for death within 30-days and 1 year, relative to those without HAIs. Subgroup analyses revealed that patients with BSI, LRTI or more than one simultaneous infection had an increased risk of death. Conclusions: In this long time follow-up study, we found that HAIs have severe consequences for the patients. BSI, LRTI and more than one simultaneous infection were independently and strongly associated with increased mortality 30 days and 1 year after inclusion in the study. Keywords: Hospital associated infections, HAIs, Mortality, Prevalence, Blood stream infection, Lower respiratory tract infection Background affecting hospitalized patients [8]. The risk for HAIs de- In industrialized countries, at any given time, more than pends on patient related factors, various invasive proce- one out of twenty patients has a hospital associated in- dures and treatment provided during hospital stay. fection (HAI) [1–7]. Even if great efforts have been made Medical technology and treatment are becoming more to reduce HAIs during the last decades, such infections complex every year and more patients with severe are still among the most common complications underlying diseases are treated. Consequently, HAIs vary according to the type of clinical department, with the highest infection rate usually found in intensive care * Correspondence: anne.mette.koch@helse-bergen.no Department of Research and Development, Haukeland University Hospital, units (ICU), neonatal and burn units [3, 5, 6, 9, 10]. Jonas Liesv. 65, 5021 Bergen, Norway 2 HAIs affect a large number of patients in terms of Department of Clinical Science, University of Bergen, Jonas Liesv. 87, Bergen, complications, increased mortality and longer hospital Norway Full list of author information is available at the end of the article © 2015 Koch et al. Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Koch et al. Antimicrobial Resistance and Infection Control (2015) 4:57 Page 2 of 8 stay. HAIs may also affect the quality of life like long term disability and psychological trauma and are reported as Recorded patients 2004-2011 n= 26,933 one of the top leading causes of in-hospital deaths world- wide [8, 11]. HAIs also impose significant economic con- sequences on the healthcare system [12–14]. The association between different HAIs and mortal- ity is well established in previously published studies Patients with ≥ 250 [14–19], and such associations are particularly found in days LOS LOS ≥2 days patients with lower respiratory tract (LRTI) [14, 16, 17] n=26 n=6,436 and blood stream infections (BSI) [14, 16, 18, 20]. How- ever, some of the studies are primarily performed in high risk units, with a small number of patients, focusing on one type of HAI, or without taking co-morbidity into Missing information about LOS account. Patients with HAIs n=682 In this study the purpose was to evaluate a possible re- from outside this lationship between various types of HAIs and the risk of hospital or mortality within 30 days and 1 year among 19,468 unknown patients in a combined emergency and referral hospital n=218 in Norway. Methods Setting Total study population The study was conducted at Haukeland University Hos- n=19,468 pital, a hospital trust including a large somatic hospital (1,662 with and 17,806 and psychiatric hospital, a smaller emergency hospital, without HAIs) and a specialized orthopedic hospital. All together the hospital has approximately 1000 somatic beds. It covers LOS = Length of stay about one million inhabitants in Western Norway, and Fig. 1 Flow-chart showing patient inclusion in the study is also an emergency hospital for 300,000 people. It pro- vides all specialties apart from organ transplants, and it includes large intensive care units with approximately 30 Norway, quality assurance projects, surveys and evalua- beds, a neonatal unit with 7 beds, and a national burns tions that are intended to ensure that diagnosis and treat- center with 5 beds. ment actually produce the intended results do not need ethical committee approval and patient consent is not Method required. Hence, the study was only approved by the The study was designed as a repeated cross-sectional hospital’ s privacy ombudsman [Ref: 2013/9818]. study with prospective follow-up of life status. Data collection was performed four times annually from Data collection November 2004 to November 2011, with a one year follow The Department of Infection Control has the overall up for all subjects up to November 2012. All in-patients responsibility for data collection in a local registry on the day of prevalence survey were included in the study established for the mandatory infection surveillance, and a total of 26,933 patients were recorded following 32 which was linked to the patient administrative sys- different surveys. When excluding patients with hospital tem. On the day of prevalence survey, dedicated stay less than 2 days (by definition not at risk for HAI) or nurses or physicians in the somatic wards reviewed longer than 250 days, as well as patients with HAIs trans- all in-patients in an on-line system. The patients’ in- ferred from other hospitals and patients with missing in- fection status was identified, and the inclusion day formation on LOS, we ended up with a patient cohort of was defined as the day of the prevalence survey. 19,468 (Fig. 1). When a patient had more than one regis- HAIs were identified according to a simplified ver- tration in the surveillance system during the follow-up sion of the definitions developed and recommended period, only the first admission was included. by the Centres for Disease Control and prevention (CDC), USA [2, 21]. Ethics HAIs were defined as any infection identified at least The data was collected as a part of the hospital’ s infection 48 h after hospital admission without evidence of the in- prevalence survey. According to the Health Research Act, fection being present or incubating at the time of Koch et al. Antimicrobial Resistance and Infection Control (2015) 4:57 Page 3 of 8 admission. All in-patients registered in somatic wards at urinary tract catheter (no, intermittent/permanent). We the hospital at 8 a.m. on the designated day were additionally adjusted for time from hospital admission to included in the survey. study inclusion (i.e., the pre-prevalence period) and All types of HAIs were recorded and analysed, includ- Charlson comorbidity index. Because these two variables ing symptomatic urinary tract infection (UTI), lower re- formed a non-linear relationship with mortality or infec- spiratory tract infection (LRTI), blood stream infection tion status, they were categorized and included as cat- (BSI), and surgical site infection (SSI). HAIs with only a egorical model terms (6 categories each) to achieve few included cases such as skin, soft-tissue infections better adjustment. All covariates were chosen because and gastrointestinal infections were analysed together as they have previously been strongly related with mortality “other infections”. Patients with more than one type of of HAIs. Finally, by visual inspection of the log-log plot infection simultaneously were analysed as a separate of survival, we verified that the proportional-hazards group. The prevalence of surgical site infections was assumption was essentially fulfilled for all variables in analysed including all patients for overall prevalence and the models. All analyses were performed using Stata/IC among operated patients only in the remaining analysis. version 14.0 (StataCorp, Texas, USA) for Windows. All The following variables were recorded for each patient: P values were two sided and values below 0.05 were sex, age, season of admission (spring, summer, autumn considered statistically significant. and winter), elective versus emergency admission, surgi- cal procedure, use of urinary tract catheter (permanent Results and intermittent catheter) and antibiotic therapy. Date Patient characteristics and prevalence of HAIs of admission was automatically collected from the During the study period 19,468 patients were included, patient administrative data system. 1662 patients had HAIs and the remaining 17,806 did Up to seven diagnoses according to ICD-10 (The not have HAIs. The overall prevalence of HAIs was international classification of Diseases, ICD-10) were 8.5 % and the prevalence of the four most frequently re- recorded for each patient at discharge. All diagnoses corded types of infections was for LRTI 2.2 %, UTI were weighted according to Charlson comorbidity index, 2.1 %, BSI 0.5 %, and SSI 1.6 %. Prevalence among oper- a method validated to predict mortality by classifying or ated patients was 4.5 % (Table 1). A general overview of weighting the patient’s comorbid conditions [22, 23]. the analysed variables is shown in Table 2. Fifty-three Information about mortality was recorded from patient percent of the patients were females. The overall preva- administrative data system 30 days and 1 year after lence was higher in males than in females (9.7 % vs. patient’s inclusion in the study (the day of the prevalence 7.5 %) and increased with age. For the oldest patients survey). (>74 years old), we found a prevalence of 11.3 % vs. 2.6 % for the youngest patients (<14 years). A total of Statistical analysis 6925 (35.6 %) patients had undergone surgery and the HAIs were analysed as a binary exposure variable (no prevalence of HAIs among operated patients was 15.0 % HAI, any HAI). We also analysed HAIs by type of infec- compared to 5 % for the non-operated patients. Acute tion (no HAI, UTI, LRTI, BSI, other HAIs, multiple admission patients had a higher prevalence of HAIs than HAIs, SSI), which were mutually exclusive. those with elective admission, 9.6 % and 6.8 %, respect- Descriptive statistics were used to quantify sample ively. Seventeen percent of the patients had urinary tract characteristic whereas the Kaplan Meier survivor func- catheters (13.8 % permanent and 2.9 % intermittent) and tion were used to describe the percentage of survivors 26.2 % of the patients received antibiotics. We found an after 30 days and 1 year after infection status. To test for association between hospital stay before the date of difference in survival functions across HAI categories, prevalence study and the prevalence of HAIs. Charlson we used the log-rank test. We further estimated the as- Table 1 Prevalence of HAIs among 19,468 patients at Haukeland sociations of HAIs with 30 days and 1 year mortality as University hospital, 2004-2011 hazard ratios with 95 % confidence intervals (CIs) using Type of infection n % (95 % CI) Cox regression models. The time from study inclusion (i.e., date of prevalence survey) until death was used as All infections 1662 8.5 % (95 % CI: 8.1, 8.9) the measure of event free time. All patients were moni- Urinary tract 407 2.1 % (95 % CI 1.9, 2.3) tored for up to 30 and 1 year. The hazard ratios were Lower respiratory 428 2.2 % (95 % CI: 2.0, 2.4) estimated by crude models as well as after controlling Blood stream 89 0.5 % (95 CI: 0.4, 0.6) for year of inclusion (continuous), time period (categor- Surgical site 311 1.6 % (95 % CI: 1.4, 1.8) ical calendar quarters), patient’s sex (woman, man), Surgical site 311 4.5 % (95 % CI: 4.2, 4.8) patient’s age (continuous), type of admission to hospital (acute, elective), surgical operation (no, yes), and use of Among 6925 operated patients Koch et al. Antimicrobial Resistance and Infection Control (2015) 4:57 Page 4 of 8 Table 2 Characteristics of 19,468 patients with and without hospital-associated infections (HAIs) treated at Haukeland University Hospital, 2004-2011 HAIs Prevalence of HAIs All patients No Yes Characteristics n (%) n (%) n (%) P value % All 19468 (100) 17806 (100.0) 1662 (100.0) 8.5 Gender <0.001 Women 10140 (52.1) 9378 (52.7) 762 (45.8) 7.5 Men 9328 (47.9) 8428 (47.3) 900 (54.2) 9.7 Age (years) <0.001 0-14 2131 (10.9) 2076 (11.7) 55 (3.3) 2.6 15-34 2447 (12.6) 2331 (13.1) 116 (7.0) 4.7 35-54 3345 (17.2) 3084 (17.3) 261 (15.7) 7.8 55-74 6113 (31.4) 5498 (30.9) 615 (37.0) 10.1 >74 5432 (27.9) 4817 (27.1) 615 (37.0) 11.3 Time period 0.005 Jan-Mar 4924 (25.3) 4512 (25.3) 412 (24.8) 8.4 Apr-Jun 5010 (25.7) 4618 (25.9) 392 (23.6) 7.8 Jul-Sept 4786 (24.6) 4391 (24.7) 395 (23.8) 8.3 Oct-Dec 4748 (24.4) 4285 (24.1) 463 (27.9) 9.8 Admission type <0.001 Acute 12080 (62.3) 10918 (61.6) 1162 (70.2) 9.6 Elective 7304 (37.7) 6810 (38.4) 494 (29.8) 6.8 Surgery <0.001 No 12543 (64.4) 11920 (66.9) 623 (37.5) 5.0 Yes 6925 (35.6) 5886 (33.1) 1039 (62.5) 15.0 Urinary tract catheter <0.001 No 16216 (83.3) 15162 (85.2) 1054 (63.4) 6.5 Yes, permanent 2682 (13.8) 2150 (12.1) 532 (32.0) 19.8 Yes, intermittent 570 (2.9) 494 (2.8) 76 (4.6) 13.3 Use of antibiotics <0.001 No 14372 (73.8) 14241 (80.0) 131 (7.9) 0.9 Yes 5096 (26.2) 3565 (20.0) 1531 (92.1) 30.0 Pre-prevalence period (days) <0.001 2 5182 (26.6) 5112 (28.7) 70 (4.2) 1.4 3-5 4537 (23.3) 4339 (24.4) 198 (11.9) 4.4 6-9 4133 (21.2) 3756 (21.1) 377 (22.7) 9.1 10-15 2295 (11.8) 1938 (10.9) 357 (21.5) 15.6 16-30 2258 (11.6) 1814 (10.2) 444 (26.7) 19.7 >30 1063 (5.5) 847 (4.8) 216 (13.0) 20.3 Charlson comorbidity index <0.001 0 9758 (50.1) 9202 (51.7) 556 (33.5) 5.7 1 3464 (17.8) 3125 (17.6) 339 (20.4) 9.8 2 3234 (16.6) 2827 (15.9) 407 (24.5) 12.6 Koch et al. Antimicrobial Resistance and Infection Control (2015) 4:57 Page 5 of 8 Table 2 Characteristics of 19,468 patients with and without hospital-associated infections (HAIs) treated at Haukeland University Hospital, 2004-2011 (Continued) 3 1118 (5.7) 951 (5.3) 167 (10.0) 14.9 4 420 (2.2) 377 (2.1) 43 (2.6) 10.2 >4 1380 (7.1) 1236 (6.9) 144 (8.7) 10.4 By chi-square test Time from hospital admission to study inclusion Information was missing for 94 patients on Charlson comorbidity index and 84 on admission type comorbidity index up to 3 was associated with a higher 1 year, compared to those without HAIs. BSI, LRTI and prevalence of HAI, whereas patients with a Charlson having more than one HAIs simultaneously were associ- index 4 or higher had a lower prevalence (Table 2). ated with increased mortality, whereas patients with SSI and UTI did not have an increased risk of dying. The Thirty day and 1 year mortality prevalence of HAIs was higher than previously reported Table 3 shows 30 day and 1 year mortality for all pa- from our hospital [7]. The reason for this might be that tients according to patient characteristics. Of all patients patients by definition are not at risk of acquiring infec- 909 (4.7 %) died within 30 days and 3188 (16.4 %) within tion during the first two days in hospital and that all 1 year. We found that mortality was higher among men patients with less than two days length of stay were ex- than women, whereas mortality increased with age for cluded from this study. both men and women. Patients with acute admission to Only a few studies have estimated the global impact of the hospital had higher mortality than patients with HAIs on mortality in hospital, and as in this study, they elective admission. Increased mortality was also related are all reported increased mortality [14, 16, 17, 24]. to a longer pre-prevalence period, with an exception for Comparison of the results between studies remains diffi- patients having a pre-prevalence stay of more than cult since different methods are used in the various 30 days. A high Charlson comorbidity index also gave studies. However, in a study by Kanerva et al., based on increased mortality, and for patients with a Charlson prevalence survey data from more than 7000 patients, index > 4 we found that 17.0 % and 61.4 % died within 28 day mortality rate for patients with HAIs was slightly 30 days and 1 year, respectively. lower than the 30 day mortality found in our study, Among patients with HAIs 10.8 % (95 % CI: 9.3, 12.3) 9.8 % and 10.8 % respectively [17]. died within the first month after they were included in As shown in other studies, we also found that both the study compared to 4.1 % (95 % CI: 3.8, 4.4) in pa- patients with BSI and LRTI had increased risk of dying tients without HAIs. Within 1 year 28.4 % (95 % CI: during the follow-up period [14, 16]. Patients with SSI 26.2, 30.6) with HAIs and 15.3 % (95 % CI: 14.7, 15.8) had no increased mortality risk, the same result has also without HAIs had died. been shown in other studies [16, 17]. We could not con- By Kaplan-Meier survival analyses we found that firm that UTIs led to increased mortality, which con- patients without HAIs had a 1 year survival of 70 %, trasts with the findings from Fabbro-Peray et al. who compared to 85 % in those without HAIs (p < 0.001). reported OR for death after 60 days to be 1.6 (95 % CI: The lowest survival rates were found among patients 1.3-2.1) [16]. with LRTI and BSI. Patients with SSI had the same sur- We identified several patient characteristics which in- vival rates as those without HAIs (Fig. 2). creased the risk of HAIs and death. Male gender, old Following adjustment for confounding factors we found age, use of urinary tract catheter, longer pre-prevalence that patients with HAIs had a significantly increased mor- period, and comorbidity were all factors affecting patient tality risk compared to patients without HAIs. Within outcome. These factors should always be taken into 30 days and 1 year, patients with HAIs had an adjusted account in assessing each patient’s risk of HAIs, and in hazard ratio (HR) of 1.5 (95 % CI: 1.3, 1.8) and 1.4 (95 % targeting infection control and prevention measures in CI: 1.2, 1.5) for death, respectively, relative to those with- care and treatment. out HAIs. The highest mortality risk was observed in To adjust for comorbidity we used the Charlson co- patients with BSI, followed by patients with LRTI. No in- morbidity index [22, 23]. An alternative method for creased risk of death was found in patients with UVI and adjusting risk of death would have been McCabe score, SSI during the follow up periods (Table 4). which assess patients subjectively in three different groups (non-fatal, ultimately fatal and rapidly fatal ill- Discussion ness). According to other studies there is a significant The main findings in this study were that patients with correlation between Charlson index and McCabe class, HAIs had a higher risk of dying within 30 days and although McCabe classifications are assumed to have a Koch et al. Antimicrobial Resistance and Infection Control (2015) 4:57 Page 6 of 8 Table 3 Thirty day and 1 year mortality according to characteristics Table 3 Thirty day and 1 year mortality according to characteristics of 19,468 patients treated at Haukeland University Hospital, of 19,468 patients treated at Haukeland University Hospital, 2004-2011 2004-2011 (Continued) 30-days mortality, 1 year mortality, 2 249 (7.7) 925 (28.6) Characteristics n (%) n (%) 3 134 (12.0) 449 (40.2) All 909 (4.7) 3188 (16.4) 4 49 (11.7) 168 (40.0) Gender >4 234 (17.0) 847 (61.4) Women 413 (4.1) 1449 (14.3) a Time from hospital admission to study inclusion Information was missing for 94 patients on Charlson comorbidity index and Men 496 (5.3) 1739 (18.6) 84 on admission type Age (years) 0-14 7 (0.3) 19 (0.9) better goodness-of-fit for predicting death [16, 17]. McCabe classifications were, however, not part of the 15-34 8 (0.3) 45 (1.8) data set in the prevalence surveys in our hospital, and 35-54 46 (1.4) 259 (7.7) for this reason the Charlson index, which was already 55-74 309 (5.1) 1172 (19.2) available in the patient administrative system, was >74 539 (9.9) 1693 (31.2) utilised. Time period Our study has some limitations. Many people have Jan-Mar 221 (4.5) 783 (15.9) been involved in data collection, and in spite of written information and validated definitions, different practices Apr-Jun 225 (4.5) 822 (16.4) and assessments may have influenced the results. Fur- Jul-Sept 232 (4.9) 794 (16.6) thermore, we did not investigate if patients without an Oct-Dec 231 (4.9) 789 (16.6) infection on the day of surveillance had a HAI later on Admission type during the hospital stay. This might have resulted in Acute 801 (6.6) 2477 (20.5) misclassification and an underestimation of the impact Elective 108 (1.5) 705 (9.7) of HAIs on mortality. A possible sample bias may also have occurred since Surgery 457 out of 26,833 patents were excluded due to implaus- No 739 (5.9) 2526 (20.1) ible data (Fig. 1). However, since the number of excluded Yes 170 (2.5) 662 (9.6) patients was relatively small, we do not assume that this Urinary tract lead to a systematic bias. catheter We have no information about the length of stay No 526 (3.2) 2288 (14.1) from admission to onset of HAI, and have used the time from admission to prevalence survey (the pre- Yes, permanent 360 (13.4) 818 (30.5) prevalence period) as a surrogate for this. Especially Yes, intermittent 23 (4.0) 82 (14.4) for types of infection with long duration, such as SSI, Use of antibiotics theinfection mayhavestarted severaldaysbeforethe No 512 (3.6) 2035 (14.2) Yes 397 (7.8) 1153 (22.6) Pre-prevalence period (days) 2 100 (1.9) 505 (9.8) 3-5 180 (4.0) 558 (12.3) 6-9 209 (5.1) 750 (18.2) 10-15 163 (7.1) 555 (24.2) 16-30 182 (8.1) 583 (25.8) >30 75 (7.1) 237 (22.3) Charlson comorbidity index 0 93 (1.0) 331 (3.4) 1 150 (4.3) 462 (13.3) Fig. 2 Survival of 19,468 patients with and without hospital-associated infections (HAIs) Koch et al. Antimicrobial Resistance and Infection Control (2015) 4:57 Page 7 of 8 Table 4 Mortality in 19,468 patients with hospital-associated infections at Haukeland University Hospital, 2004-2011 30-days mortality 1-year mortality Infections Mortality Crude hazard ratio Adjusted hazard ratio Mortality Crude hazard ratio Adjusted hazard ratio a b a b Type of infection n n (%) (95 % CI) (95 % CI) n (%) (95 % CI) (95 % CI) All infections 1662 180 (10.8) 2.7 (2.3, 3.2) 1.5 (1.3, 1.8) 472 (28.4) 2.1 (1.9, 2.3) 1.4 (1.2, 1.5) Urinary tract 407 36 (8.9) 2.2 (1.6, 3.1) 0.9 (0.6, 1.3) 100 (24.6) 1.7 (1.4, 2.1) 0.9 (0.7, 1.1) Lower respiratory 428 68 (15.9) 4.1 (3.2, 5.3) 1.9 (1.5, 2.5) 161 (37.6) 3.0 (2.5, 3.5) 1.7 (1.4, 2.0) Blood stream 89 11 (12.4) 3.1 (1.7, 5.7) 2.7 (1.5, 4.9) 36 (40.5) 3.1 (2.3, 4.4) 3.0 (2.1, 4.1) Other 301 36 (12.0) 3.1 (2.2, 4.3) 1.7 (1.2, 2.4) 91 (30.2) 2.2 (1.8, 2.8) 1.5 (1.2, 1.9) >1 infection 126 16 (12.7) 3.3 (2.0, 5.4) 2.6 (1.5, 4.3) 35 (27.8) 2.0 (1.5, 2.8) 1.8 (1.3, 2.6) Surgical site 311 13 (4.2) 2.4 (1.3, 4.2) 1.3 (0.7, 2.3) 49 (15.8) 2.1 (1.6, 2.8) 1.2 (0.9, 1.6) Estimated by Cox regression model Adjusted by year and calendar period of prevalence survey, patient’s sex and age, type of admission, surgery operation, use of urinary tract catheter, time from hospital admission to study inclusion (pre-prevalence period), and Charlson comorbidity index Among 6925 operated patients prevalence survey. Follow-up time will therefore be Author details Department of Research and Development, Haukeland University Hospital, longer than 30 days and 1 year, and possibly different Jonas Liesv. 65, 5021 Bergen, Norway. Department of Clinical Science, for the various types of HAIs. University of Bergen, Jonas Liesv. 87, Bergen, Norway. Norwegian Institute of Even if Charlson index is described as an appropriate Public Health, Postboks 4404Nydalen, 0403 Oslo, Norway. K.G Jebsen Centre for Influenza Vaccine Research, Department of Clinical Science, University of tool to adjust for comorbidity, the use of ICD-codes has Bergen, Jonas Lies v. 87, Bergen, Norway. some limitations. The sensitivity of ICD codes has varied in published studies according to different practice of Received: 11 May 2015 Accepted: 30 November 2015 coding in different hospitals and countries [25, 26]. 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Need for more targeted measures - Only less severe hospital-associated infections Competing interests declined after introduction of an infection control program. J Infect The authors declare that they have no competing interests. Public Health. 2015;8(3):282–90. 8. Burke JP. Infection control - a problem for patient safety. N Engl J Med. Authors’ contributions 2003;348(7):651–6. AMK, RMN, and SH designed the study. RMN and AMK analysed and 9. Klevens RM, Edwards JR, Richards Jr CL, Horan TC, Gaynes RP, Pollock DA, et interpreted the data. AMK drafted the manuscript in discussion with RMN, al. Estimating health care-associated infections and deaths in U.S. hospitals, HME RJC and SH. All authors assisted in manuscript revision and approved 2002. Public Health Rep. 2007;122(2):160–6. the final manuscript for publication. 10. McFee RB. Nosocomial or hospital-acquired infections: an overview. Dis Mon. 2009;55(7):422–38. Acknowledgements 11. Allegranzi B, Bagheri Nejad S, Combescure C, Graafmans W, Attar H, We would like to thank all the nurses and physicians who contributed to the Donaldson L, et al. Burden of endemic health-care-associated infection in prevalence surveys. We also thank infection control nurse Unni Fosse at the developing countries: systematic review and meta-analysis. Lancet. 2011; Centre for infection control, Haukeland University Hospital who administers 377(9761):228–41. the prevalence survey at the hospital and Asgaut Viste for input during the 12. Graves N, Halton K, Lairson D. Economics and preventing hospital-acquired writing process. Also thank to Håkon Ersland who conducted valuable work infection: broadening the perspective. Infect Control Hosp Epidemiol. 2007; in preparing the ICD-10 codes for further analysis. 28(2):178–84. Koch et al. Antimicrobial Resistance and Infection Control (2015) 4:57 Page 8 of 8 13. Stone PW, Larson E, Kawar LN. A systematic audit of economic evidence linking nosocomial infections and infection control interventions: 1990-2000. Am J Infect Control. 2002;30(3):145–52. 14. Vrijens F, Hulstaert F, Devriese S, van de Sande S. Hospital-acquired infections in Belgian acute-care hospitals: an estimation of their global impact on mortality, length of stay and healthcare costs. Epidemiol Infect. 2012;140(1):126–36. 15. Kanerva M, Ollgren J, Virtanen MJ, Lyytikainen O, Prevalence Survey Study G. Estimating the annual burden of health care-associated infections in Finnish adult acute care hospitals. Am J Infect Control. 2009;37(3):227–30. 16. Fabbro-Peray P, Sotto A, Defez C, Cazaban M, Molinari L, Pinede M, et al. Mortality attributable to nosocomial infection: a cohort of patients with and without nosocomial infection in a French university hospital. Infect Control Hosp Epidemiol. 2007;28(3):265–72. 17. Kanerva M, Ollgren J, Virtanen MJ, Lyytikainen O, Prevalence Survey Study G. Risk factors for death in a cohort of patients with and without healthcare- associated infections in Finnish acute care hospitals. J Hosp Infect. 2008; 70(4):353–60. 18. Delgado-Rodriguez M, Gomez-Ortega A, Llorca J, Lecuona M, Dierssen T, Sillero-Arenas M, et al. Nosocomial infection, indices of intrinsic infection risk, and in-hospital mortality in general surgery. J Hosp Infect. 1999;41(3): 203–11. 19. Vincent JL, Rello J, Marshall J, Silva E, Anzueto A, Martin CD, et al. International study of the prevalence and outcomes of infection in intensive care units. JAMA. 2009;302(21):2323–9. 20. Wenzel RP. Health care-associated infections: major issues in the early years of the 21st century. Clin Infect Dis. 2007;45 Suppl 1:S85–8. 21. Garner JS, Jarvis WR, Emori TG, Horan TC, Hughes JM. CDC definitions for nosocomial infections, 1988. Am J Infect Control. 1988;16(3):128–40. 22. Quan H, Sundararajan V, Halfon P, Fong A, Burnand B, Luthi JC, et al. Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data. Med Care. 2005;43(11):1130–9. 23. Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40(5):373–83. 24. Garcia-Martin M, Lardelli-Claret P, Jimenez-Moleon JJ, Bueno-Cavanillas A, Luna-del-Castillo JD, Galvez-Vargas R. Proportion of hospital deaths potentially attributable to nosocomial infection. Infect Control Hosp Epidemiol. 2001;22(11):708–14. 25. Moro ML, Morsillo F. Can hospital discharge diagnoses be used for surveillance of surgical-site infections? J Hosp Infect. 2004;56(3):239–41. 26. Hebden J. Use of ICD-9-CM coding as a case-finding method for sternal wound infections after CABG procedures. Am J Infect Control. 2000; 28(2):202–3. Submit your next manuscript to BioMed Central and we will help you at every step: • We accept pre-submission inquiries � Our selector tool helps you to find the most relevant journal � We provide round the clock customer support � Convenient online submission � Thorough peer review � Inclusion in PubMed and all major indexing services � Maximum visibility for your research Submit your manuscript at www.biomedcentral.com/submit http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Antimicrobial Resistance & Infection Control Springer Journals

Mortality related to hospital-associated infections in a tertiary hospital; repeated cross-sectional studies between 2004-2011

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Springer Journals
Copyright
Copyright © 2015 by Koch et al.
Subject
Biomedicine; Medical Microbiology; Drug Resistance; Infectious Diseases
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2047-2994
DOI
10.1186/s13756-015-0097-9
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26719795
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Abstract

Background: Hospital-associated infections (HAIs) are reported to increase patient mortality and incur longer hospital stays. Most studies to date have focused on specific groups of hospitalised patients with a rather short follow-up period. In this repeated cross-sectional study, with prospective follow-up of 19,468 hospitalized patients, we aimed to analyze the impact of HAIs on mortality 30 days and 1 year after the prevalence survey date. Methods: The study was conducted at Haukeland University Hospital, Norway, a large combined emergency and referral teaching hospital, from 2004 to 2011 with follow-up until November 2012. Prevalence of all types of HAIs including urinary tract infections (UTI), lower respiratory tract infections (LRTI), surgical site infections (SSI) and blood stream infections (BSI) were recorded four times every year. Information on the date of birth, admission and discharge from the hospital, number of diagnoses (ICD-10 codes) and patient’s mortality was retrieved from the patient administrative data system. The data were analysed by Kaplan-Meier survival analysis and by multiple Cox regression analysis, adjusted for year of registration, time period, sex, type of admission, Charlson comorbidity index, surgical operation, use of urinary tract catheter and time from admission to the prevalence survey date. Results: The overall prevalence of HAIs was 8.5 % (95 % CI: 8.1, 8.9). Patients with HAIs had an adjusted hazard ratio (HR) of 1.5 (95 % CI: 1.3, 1.8,) and 1.4 (95 % CI: 1.2, 1.5) for death within 30-days and 1 year, relative to those without HAIs. Subgroup analyses revealed that patients with BSI, LRTI or more than one simultaneous infection had an increased risk of death. Conclusions: In this long time follow-up study, we found that HAIs have severe consequences for the patients. BSI, LRTI and more than one simultaneous infection were independently and strongly associated with increased mortality 30 days and 1 year after inclusion in the study. Keywords: Hospital associated infections, HAIs, Mortality, Prevalence, Blood stream infection, Lower respiratory tract infection Background affecting hospitalized patients [8]. The risk for HAIs de- In industrialized countries, at any given time, more than pends on patient related factors, various invasive proce- one out of twenty patients has a hospital associated in- dures and treatment provided during hospital stay. fection (HAI) [1–7]. Even if great efforts have been made Medical technology and treatment are becoming more to reduce HAIs during the last decades, such infections complex every year and more patients with severe are still among the most common complications underlying diseases are treated. Consequently, HAIs vary according to the type of clinical department, with the highest infection rate usually found in intensive care * Correspondence: anne.mette.koch@helse-bergen.no Department of Research and Development, Haukeland University Hospital, units (ICU), neonatal and burn units [3, 5, 6, 9, 10]. Jonas Liesv. 65, 5021 Bergen, Norway 2 HAIs affect a large number of patients in terms of Department of Clinical Science, University of Bergen, Jonas Liesv. 87, Bergen, complications, increased mortality and longer hospital Norway Full list of author information is available at the end of the article © 2015 Koch et al. Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Koch et al. Antimicrobial Resistance and Infection Control (2015) 4:57 Page 2 of 8 stay. HAIs may also affect the quality of life like long term disability and psychological trauma and are reported as Recorded patients 2004-2011 n= 26,933 one of the top leading causes of in-hospital deaths world- wide [8, 11]. HAIs also impose significant economic con- sequences on the healthcare system [12–14]. The association between different HAIs and mortal- ity is well established in previously published studies Patients with ≥ 250 [14–19], and such associations are particularly found in days LOS LOS ≥2 days patients with lower respiratory tract (LRTI) [14, 16, 17] n=26 n=6,436 and blood stream infections (BSI) [14, 16, 18, 20]. How- ever, some of the studies are primarily performed in high risk units, with a small number of patients, focusing on one type of HAI, or without taking co-morbidity into Missing information about LOS account. Patients with HAIs n=682 In this study the purpose was to evaluate a possible re- from outside this lationship between various types of HAIs and the risk of hospital or mortality within 30 days and 1 year among 19,468 unknown patients in a combined emergency and referral hospital n=218 in Norway. Methods Setting Total study population The study was conducted at Haukeland University Hos- n=19,468 pital, a hospital trust including a large somatic hospital (1,662 with and 17,806 and psychiatric hospital, a smaller emergency hospital, without HAIs) and a specialized orthopedic hospital. All together the hospital has approximately 1000 somatic beds. It covers LOS = Length of stay about one million inhabitants in Western Norway, and Fig. 1 Flow-chart showing patient inclusion in the study is also an emergency hospital for 300,000 people. It pro- vides all specialties apart from organ transplants, and it includes large intensive care units with approximately 30 Norway, quality assurance projects, surveys and evalua- beds, a neonatal unit with 7 beds, and a national burns tions that are intended to ensure that diagnosis and treat- center with 5 beds. ment actually produce the intended results do not need ethical committee approval and patient consent is not Method required. Hence, the study was only approved by the The study was designed as a repeated cross-sectional hospital’ s privacy ombudsman [Ref: 2013/9818]. study with prospective follow-up of life status. Data collection was performed four times annually from Data collection November 2004 to November 2011, with a one year follow The Department of Infection Control has the overall up for all subjects up to November 2012. All in-patients responsibility for data collection in a local registry on the day of prevalence survey were included in the study established for the mandatory infection surveillance, and a total of 26,933 patients were recorded following 32 which was linked to the patient administrative sys- different surveys. When excluding patients with hospital tem. On the day of prevalence survey, dedicated stay less than 2 days (by definition not at risk for HAI) or nurses or physicians in the somatic wards reviewed longer than 250 days, as well as patients with HAIs trans- all in-patients in an on-line system. The patients’ in- ferred from other hospitals and patients with missing in- fection status was identified, and the inclusion day formation on LOS, we ended up with a patient cohort of was defined as the day of the prevalence survey. 19,468 (Fig. 1). When a patient had more than one regis- HAIs were identified according to a simplified ver- tration in the surveillance system during the follow-up sion of the definitions developed and recommended period, only the first admission was included. by the Centres for Disease Control and prevention (CDC), USA [2, 21]. Ethics HAIs were defined as any infection identified at least The data was collected as a part of the hospital’ s infection 48 h after hospital admission without evidence of the in- prevalence survey. According to the Health Research Act, fection being present or incubating at the time of Koch et al. Antimicrobial Resistance and Infection Control (2015) 4:57 Page 3 of 8 admission. All in-patients registered in somatic wards at urinary tract catheter (no, intermittent/permanent). We the hospital at 8 a.m. on the designated day were additionally adjusted for time from hospital admission to included in the survey. study inclusion (i.e., the pre-prevalence period) and All types of HAIs were recorded and analysed, includ- Charlson comorbidity index. Because these two variables ing symptomatic urinary tract infection (UTI), lower re- formed a non-linear relationship with mortality or infec- spiratory tract infection (LRTI), blood stream infection tion status, they were categorized and included as cat- (BSI), and surgical site infection (SSI). HAIs with only a egorical model terms (6 categories each) to achieve few included cases such as skin, soft-tissue infections better adjustment. All covariates were chosen because and gastrointestinal infections were analysed together as they have previously been strongly related with mortality “other infections”. Patients with more than one type of of HAIs. Finally, by visual inspection of the log-log plot infection simultaneously were analysed as a separate of survival, we verified that the proportional-hazards group. The prevalence of surgical site infections was assumption was essentially fulfilled for all variables in analysed including all patients for overall prevalence and the models. All analyses were performed using Stata/IC among operated patients only in the remaining analysis. version 14.0 (StataCorp, Texas, USA) for Windows. All The following variables were recorded for each patient: P values were two sided and values below 0.05 were sex, age, season of admission (spring, summer, autumn considered statistically significant. and winter), elective versus emergency admission, surgi- cal procedure, use of urinary tract catheter (permanent Results and intermittent catheter) and antibiotic therapy. Date Patient characteristics and prevalence of HAIs of admission was automatically collected from the During the study period 19,468 patients were included, patient administrative data system. 1662 patients had HAIs and the remaining 17,806 did Up to seven diagnoses according to ICD-10 (The not have HAIs. The overall prevalence of HAIs was international classification of Diseases, ICD-10) were 8.5 % and the prevalence of the four most frequently re- recorded for each patient at discharge. All diagnoses corded types of infections was for LRTI 2.2 %, UTI were weighted according to Charlson comorbidity index, 2.1 %, BSI 0.5 %, and SSI 1.6 %. Prevalence among oper- a method validated to predict mortality by classifying or ated patients was 4.5 % (Table 1). A general overview of weighting the patient’s comorbid conditions [22, 23]. the analysed variables is shown in Table 2. Fifty-three Information about mortality was recorded from patient percent of the patients were females. The overall preva- administrative data system 30 days and 1 year after lence was higher in males than in females (9.7 % vs. patient’s inclusion in the study (the day of the prevalence 7.5 %) and increased with age. For the oldest patients survey). (>74 years old), we found a prevalence of 11.3 % vs. 2.6 % for the youngest patients (<14 years). A total of Statistical analysis 6925 (35.6 %) patients had undergone surgery and the HAIs were analysed as a binary exposure variable (no prevalence of HAIs among operated patients was 15.0 % HAI, any HAI). We also analysed HAIs by type of infec- compared to 5 % for the non-operated patients. Acute tion (no HAI, UTI, LRTI, BSI, other HAIs, multiple admission patients had a higher prevalence of HAIs than HAIs, SSI), which were mutually exclusive. those with elective admission, 9.6 % and 6.8 %, respect- Descriptive statistics were used to quantify sample ively. Seventeen percent of the patients had urinary tract characteristic whereas the Kaplan Meier survivor func- catheters (13.8 % permanent and 2.9 % intermittent) and tion were used to describe the percentage of survivors 26.2 % of the patients received antibiotics. We found an after 30 days and 1 year after infection status. To test for association between hospital stay before the date of difference in survival functions across HAI categories, prevalence study and the prevalence of HAIs. Charlson we used the log-rank test. We further estimated the as- Table 1 Prevalence of HAIs among 19,468 patients at Haukeland sociations of HAIs with 30 days and 1 year mortality as University hospital, 2004-2011 hazard ratios with 95 % confidence intervals (CIs) using Type of infection n % (95 % CI) Cox regression models. The time from study inclusion (i.e., date of prevalence survey) until death was used as All infections 1662 8.5 % (95 % CI: 8.1, 8.9) the measure of event free time. All patients were moni- Urinary tract 407 2.1 % (95 % CI 1.9, 2.3) tored for up to 30 and 1 year. The hazard ratios were Lower respiratory 428 2.2 % (95 % CI: 2.0, 2.4) estimated by crude models as well as after controlling Blood stream 89 0.5 % (95 CI: 0.4, 0.6) for year of inclusion (continuous), time period (categor- Surgical site 311 1.6 % (95 % CI: 1.4, 1.8) ical calendar quarters), patient’s sex (woman, man), Surgical site 311 4.5 % (95 % CI: 4.2, 4.8) patient’s age (continuous), type of admission to hospital (acute, elective), surgical operation (no, yes), and use of Among 6925 operated patients Koch et al. Antimicrobial Resistance and Infection Control (2015) 4:57 Page 4 of 8 Table 2 Characteristics of 19,468 patients with and without hospital-associated infections (HAIs) treated at Haukeland University Hospital, 2004-2011 HAIs Prevalence of HAIs All patients No Yes Characteristics n (%) n (%) n (%) P value % All 19468 (100) 17806 (100.0) 1662 (100.0) 8.5 Gender <0.001 Women 10140 (52.1) 9378 (52.7) 762 (45.8) 7.5 Men 9328 (47.9) 8428 (47.3) 900 (54.2) 9.7 Age (years) <0.001 0-14 2131 (10.9) 2076 (11.7) 55 (3.3) 2.6 15-34 2447 (12.6) 2331 (13.1) 116 (7.0) 4.7 35-54 3345 (17.2) 3084 (17.3) 261 (15.7) 7.8 55-74 6113 (31.4) 5498 (30.9) 615 (37.0) 10.1 >74 5432 (27.9) 4817 (27.1) 615 (37.0) 11.3 Time period 0.005 Jan-Mar 4924 (25.3) 4512 (25.3) 412 (24.8) 8.4 Apr-Jun 5010 (25.7) 4618 (25.9) 392 (23.6) 7.8 Jul-Sept 4786 (24.6) 4391 (24.7) 395 (23.8) 8.3 Oct-Dec 4748 (24.4) 4285 (24.1) 463 (27.9) 9.8 Admission type <0.001 Acute 12080 (62.3) 10918 (61.6) 1162 (70.2) 9.6 Elective 7304 (37.7) 6810 (38.4) 494 (29.8) 6.8 Surgery <0.001 No 12543 (64.4) 11920 (66.9) 623 (37.5) 5.0 Yes 6925 (35.6) 5886 (33.1) 1039 (62.5) 15.0 Urinary tract catheter <0.001 No 16216 (83.3) 15162 (85.2) 1054 (63.4) 6.5 Yes, permanent 2682 (13.8) 2150 (12.1) 532 (32.0) 19.8 Yes, intermittent 570 (2.9) 494 (2.8) 76 (4.6) 13.3 Use of antibiotics <0.001 No 14372 (73.8) 14241 (80.0) 131 (7.9) 0.9 Yes 5096 (26.2) 3565 (20.0) 1531 (92.1) 30.0 Pre-prevalence period (days) <0.001 2 5182 (26.6) 5112 (28.7) 70 (4.2) 1.4 3-5 4537 (23.3) 4339 (24.4) 198 (11.9) 4.4 6-9 4133 (21.2) 3756 (21.1) 377 (22.7) 9.1 10-15 2295 (11.8) 1938 (10.9) 357 (21.5) 15.6 16-30 2258 (11.6) 1814 (10.2) 444 (26.7) 19.7 >30 1063 (5.5) 847 (4.8) 216 (13.0) 20.3 Charlson comorbidity index <0.001 0 9758 (50.1) 9202 (51.7) 556 (33.5) 5.7 1 3464 (17.8) 3125 (17.6) 339 (20.4) 9.8 2 3234 (16.6) 2827 (15.9) 407 (24.5) 12.6 Koch et al. Antimicrobial Resistance and Infection Control (2015) 4:57 Page 5 of 8 Table 2 Characteristics of 19,468 patients with and without hospital-associated infections (HAIs) treated at Haukeland University Hospital, 2004-2011 (Continued) 3 1118 (5.7) 951 (5.3) 167 (10.0) 14.9 4 420 (2.2) 377 (2.1) 43 (2.6) 10.2 >4 1380 (7.1) 1236 (6.9) 144 (8.7) 10.4 By chi-square test Time from hospital admission to study inclusion Information was missing for 94 patients on Charlson comorbidity index and 84 on admission type comorbidity index up to 3 was associated with a higher 1 year, compared to those without HAIs. BSI, LRTI and prevalence of HAI, whereas patients with a Charlson having more than one HAIs simultaneously were associ- index 4 or higher had a lower prevalence (Table 2). ated with increased mortality, whereas patients with SSI and UTI did not have an increased risk of dying. The Thirty day and 1 year mortality prevalence of HAIs was higher than previously reported Table 3 shows 30 day and 1 year mortality for all pa- from our hospital [7]. The reason for this might be that tients according to patient characteristics. Of all patients patients by definition are not at risk of acquiring infec- 909 (4.7 %) died within 30 days and 3188 (16.4 %) within tion during the first two days in hospital and that all 1 year. We found that mortality was higher among men patients with less than two days length of stay were ex- than women, whereas mortality increased with age for cluded from this study. both men and women. Patients with acute admission to Only a few studies have estimated the global impact of the hospital had higher mortality than patients with HAIs on mortality in hospital, and as in this study, they elective admission. Increased mortality was also related are all reported increased mortality [14, 16, 17, 24]. to a longer pre-prevalence period, with an exception for Comparison of the results between studies remains diffi- patients having a pre-prevalence stay of more than cult since different methods are used in the various 30 days. A high Charlson comorbidity index also gave studies. However, in a study by Kanerva et al., based on increased mortality, and for patients with a Charlson prevalence survey data from more than 7000 patients, index > 4 we found that 17.0 % and 61.4 % died within 28 day mortality rate for patients with HAIs was slightly 30 days and 1 year, respectively. lower than the 30 day mortality found in our study, Among patients with HAIs 10.8 % (95 % CI: 9.3, 12.3) 9.8 % and 10.8 % respectively [17]. died within the first month after they were included in As shown in other studies, we also found that both the study compared to 4.1 % (95 % CI: 3.8, 4.4) in pa- patients with BSI and LRTI had increased risk of dying tients without HAIs. Within 1 year 28.4 % (95 % CI: during the follow-up period [14, 16]. Patients with SSI 26.2, 30.6) with HAIs and 15.3 % (95 % CI: 14.7, 15.8) had no increased mortality risk, the same result has also without HAIs had died. been shown in other studies [16, 17]. We could not con- By Kaplan-Meier survival analyses we found that firm that UTIs led to increased mortality, which con- patients without HAIs had a 1 year survival of 70 %, trasts with the findings from Fabbro-Peray et al. who compared to 85 % in those without HAIs (p < 0.001). reported OR for death after 60 days to be 1.6 (95 % CI: The lowest survival rates were found among patients 1.3-2.1) [16]. with LRTI and BSI. Patients with SSI had the same sur- We identified several patient characteristics which in- vival rates as those without HAIs (Fig. 2). creased the risk of HAIs and death. Male gender, old Following adjustment for confounding factors we found age, use of urinary tract catheter, longer pre-prevalence that patients with HAIs had a significantly increased mor- period, and comorbidity were all factors affecting patient tality risk compared to patients without HAIs. Within outcome. These factors should always be taken into 30 days and 1 year, patients with HAIs had an adjusted account in assessing each patient’s risk of HAIs, and in hazard ratio (HR) of 1.5 (95 % CI: 1.3, 1.8) and 1.4 (95 % targeting infection control and prevention measures in CI: 1.2, 1.5) for death, respectively, relative to those with- care and treatment. out HAIs. The highest mortality risk was observed in To adjust for comorbidity we used the Charlson co- patients with BSI, followed by patients with LRTI. No in- morbidity index [22, 23]. An alternative method for creased risk of death was found in patients with UVI and adjusting risk of death would have been McCabe score, SSI during the follow up periods (Table 4). which assess patients subjectively in three different groups (non-fatal, ultimately fatal and rapidly fatal ill- Discussion ness). According to other studies there is a significant The main findings in this study were that patients with correlation between Charlson index and McCabe class, HAIs had a higher risk of dying within 30 days and although McCabe classifications are assumed to have a Koch et al. Antimicrobial Resistance and Infection Control (2015) 4:57 Page 6 of 8 Table 3 Thirty day and 1 year mortality according to characteristics Table 3 Thirty day and 1 year mortality according to characteristics of 19,468 patients treated at Haukeland University Hospital, of 19,468 patients treated at Haukeland University Hospital, 2004-2011 2004-2011 (Continued) 30-days mortality, 1 year mortality, 2 249 (7.7) 925 (28.6) Characteristics n (%) n (%) 3 134 (12.0) 449 (40.2) All 909 (4.7) 3188 (16.4) 4 49 (11.7) 168 (40.0) Gender >4 234 (17.0) 847 (61.4) Women 413 (4.1) 1449 (14.3) a Time from hospital admission to study inclusion Information was missing for 94 patients on Charlson comorbidity index and Men 496 (5.3) 1739 (18.6) 84 on admission type Age (years) 0-14 7 (0.3) 19 (0.9) better goodness-of-fit for predicting death [16, 17]. McCabe classifications were, however, not part of the 15-34 8 (0.3) 45 (1.8) data set in the prevalence surveys in our hospital, and 35-54 46 (1.4) 259 (7.7) for this reason the Charlson index, which was already 55-74 309 (5.1) 1172 (19.2) available in the patient administrative system, was >74 539 (9.9) 1693 (31.2) utilised. Time period Our study has some limitations. Many people have Jan-Mar 221 (4.5) 783 (15.9) been involved in data collection, and in spite of written information and validated definitions, different practices Apr-Jun 225 (4.5) 822 (16.4) and assessments may have influenced the results. Fur- Jul-Sept 232 (4.9) 794 (16.6) thermore, we did not investigate if patients without an Oct-Dec 231 (4.9) 789 (16.6) infection on the day of surveillance had a HAI later on Admission type during the hospital stay. This might have resulted in Acute 801 (6.6) 2477 (20.5) misclassification and an underestimation of the impact Elective 108 (1.5) 705 (9.7) of HAIs on mortality. A possible sample bias may also have occurred since Surgery 457 out of 26,833 patents were excluded due to implaus- No 739 (5.9) 2526 (20.1) ible data (Fig. 1). However, since the number of excluded Yes 170 (2.5) 662 (9.6) patients was relatively small, we do not assume that this Urinary tract lead to a systematic bias. catheter We have no information about the length of stay No 526 (3.2) 2288 (14.1) from admission to onset of HAI, and have used the time from admission to prevalence survey (the pre- Yes, permanent 360 (13.4) 818 (30.5) prevalence period) as a surrogate for this. Especially Yes, intermittent 23 (4.0) 82 (14.4) for types of infection with long duration, such as SSI, Use of antibiotics theinfection mayhavestarted severaldaysbeforethe No 512 (3.6) 2035 (14.2) Yes 397 (7.8) 1153 (22.6) Pre-prevalence period (days) 2 100 (1.9) 505 (9.8) 3-5 180 (4.0) 558 (12.3) 6-9 209 (5.1) 750 (18.2) 10-15 163 (7.1) 555 (24.2) 16-30 182 (8.1) 583 (25.8) >30 75 (7.1) 237 (22.3) Charlson comorbidity index 0 93 (1.0) 331 (3.4) 1 150 (4.3) 462 (13.3) Fig. 2 Survival of 19,468 patients with and without hospital-associated infections (HAIs) Koch et al. Antimicrobial Resistance and Infection Control (2015) 4:57 Page 7 of 8 Table 4 Mortality in 19,468 patients with hospital-associated infections at Haukeland University Hospital, 2004-2011 30-days mortality 1-year mortality Infections Mortality Crude hazard ratio Adjusted hazard ratio Mortality Crude hazard ratio Adjusted hazard ratio a b a b Type of infection n n (%) (95 % CI) (95 % CI) n (%) (95 % CI) (95 % CI) All infections 1662 180 (10.8) 2.7 (2.3, 3.2) 1.5 (1.3, 1.8) 472 (28.4) 2.1 (1.9, 2.3) 1.4 (1.2, 1.5) Urinary tract 407 36 (8.9) 2.2 (1.6, 3.1) 0.9 (0.6, 1.3) 100 (24.6) 1.7 (1.4, 2.1) 0.9 (0.7, 1.1) Lower respiratory 428 68 (15.9) 4.1 (3.2, 5.3) 1.9 (1.5, 2.5) 161 (37.6) 3.0 (2.5, 3.5) 1.7 (1.4, 2.0) Blood stream 89 11 (12.4) 3.1 (1.7, 5.7) 2.7 (1.5, 4.9) 36 (40.5) 3.1 (2.3, 4.4) 3.0 (2.1, 4.1) Other 301 36 (12.0) 3.1 (2.2, 4.3) 1.7 (1.2, 2.4) 91 (30.2) 2.2 (1.8, 2.8) 1.5 (1.2, 1.9) >1 infection 126 16 (12.7) 3.3 (2.0, 5.4) 2.6 (1.5, 4.3) 35 (27.8) 2.0 (1.5, 2.8) 1.8 (1.3, 2.6) Surgical site 311 13 (4.2) 2.4 (1.3, 4.2) 1.3 (0.7, 2.3) 49 (15.8) 2.1 (1.6, 2.8) 1.2 (0.9, 1.6) Estimated by Cox regression model Adjusted by year and calendar period of prevalence survey, patient’s sex and age, type of admission, surgery operation, use of urinary tract catheter, time from hospital admission to study inclusion (pre-prevalence period), and Charlson comorbidity index Among 6925 operated patients prevalence survey. Follow-up time will therefore be Author details Department of Research and Development, Haukeland University Hospital, longer than 30 days and 1 year, and possibly different Jonas Liesv. 65, 5021 Bergen, Norway. Department of Clinical Science, for the various types of HAIs. University of Bergen, Jonas Liesv. 87, Bergen, Norway. Norwegian Institute of Even if Charlson index is described as an appropriate Public Health, Postboks 4404Nydalen, 0403 Oslo, Norway. K.G Jebsen Centre for Influenza Vaccine Research, Department of Clinical Science, University of tool to adjust for comorbidity, the use of ICD-codes has Bergen, Jonas Lies v. 87, Bergen, Norway. some limitations. The sensitivity of ICD codes has varied in published studies according to different practice of Received: 11 May 2015 Accepted: 30 November 2015 coding in different hospitals and countries [25, 26]. 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Antimicrobial Resistance & Infection ControlSpringer Journals

Published: Dec 29, 2015

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