Literature DB >> 35346073

Peripherally inserted central catheters have a protective role and the effect of fluctuation curve feature in the risk of bloodstream infection compared with central venous catheters: a propensity-adjusted analysis.

Yu Lv1, Xiaobo Huang2, Yunping Lan2, Qi Xia3, Fuli Chen4, Jiayu Wu5, Wei Li5, Hongrong Cao5, Caixia Xie6, Luting Li7, Hukui Han8, Hui Wang9, Qian Xiang10.   

Abstract

BACKGROUND: The prevention of peripherally inserted central catheters (PICC)-associated BSI and central venous catheters (CVC)-associated BSI have been a topic of national importance in China. Therefore, we aimed to explore the epidemiological characteristics of central line-associated bloodstream infection (CLABSI), and to evaluate whether PICCs were associated with a protective effect for CLABSI.
METHODS: A retrospective cohort study was conducted in teaching hospital in Western China. All adult patients received a CVC or PICC during their hospital stay were included from January 2017 to December 2020. Primary endpoint was CLABSI up to 30 days after CVC or PICC placement. Propensity scores with a 2:1 match was used to account for potential confounders, and restricted cubic spline was used to visualize the risk of CLABSI at different time points during the catheterization.
RESULTS: A total of 224687 devices (180522 PICCs and 45965 CVCs) in 24879 patients were included. The overall incidence was 1.8 CLABSIs per 1000 catheter-days. The odds ratio (OR) value increased day by day after PICC insertion, reached a relatively high point on the 4th day, and decreased from days 5 through 8. From the 9th day of intubation the OR value began to gradually increase day by day again. After covariate adjustment using propensity scores, CVCs were associated with higher risk of CLABSI (adjHR = 3.27, 95% CI 2.38-4.49) compared with PICCs.
CONCLUSIONS: PICCs have a protective role and the effect of fluctuation curve feature in CLABSI when compared to CVCs, and the first 8 calendar days after CVC insertion are the acute stage of CVC-associated BSI.
© 2022. The Author(s).

Entities:  

Keywords:  Bloodstream infection; Central venous catheter; Peripherally inserted central catheter; Propensity score matching; Restricted cubic spline regression

Mesh:

Year:  2022        PMID: 35346073      PMCID: PMC8961920          DOI: 10.1186/s12879-022-07265-x

Source DB:  PubMed          Journal:  BMC Infect Dis        ISSN: 1471-2334            Impact factor:   3.090


Introduction

Central line-associated bloodstream infections (CLABSIs), one of the common healthcare-associated infections (HAIs), are associated with significant morbidity, mortality, prolonged hospital length of stay (LOS) and excess health care costs [1-3]. Although it is now recognized that CLABSIs are preventable when the evidence-based guidelines are followed [4-6], the surveillance data of the CHN National Clinical Improvement System (NCIS) in recent years show that the incidence of CLABSIs has not decreased significantly [7]. Then in the "national medical quality and safety improvement goals in 2021” issued by the National Health Commission of the People`s Republic of China (PRC) on February 9, 2021 [8], the ninth of the top ten improvement goals is to reduce the incidence of intravascular catheter-related bloodstream infection, and requires that the focus of improvement should be on CLABSI due to central venous catheters (CVCs) and peripherally inserted central catheters (PICCs). Since CLABSI prevention has been a topic of national importance, it is necessary to make in-depth analysis on the characteristics of CVC-associated BSI and PICC-associated BSI. However, there may be different incidence rate, risk factors or other characteristics between the two most commonly used catheters in CLABSI. Due to the influence of some selective bias or mixed bias, conflicting conclusions commonly found in previous studies about whether there is a difference between CVC-associated BSI and PICC-associated BSI are not conducive to guiding clinical practice [9-11]. Although the risk of CLABSI in different catheters may vary [11, 12], it is recognized that prolonged catheterization can significantly increase the risk of CLABSI no matter what kind of central line is used [13]. At present, linear or curvilinear associations of CLABSI risk over time have not been confirmed, and previous studies have been limited to univariate analysis [13]. Therefore, we conducted this study to assess the association of CLABSI risk over time by restricted cubic spline (RCS), and to evaluate whether PICCs were associated with a protective effect for CLABSI when compared to CVCs after the bias was eliminated by propensity score matching (PSM).

Material and methods

Setting

This study was conducted at Sichuan Academy of Medical Sciences, Sichuan People's Hospital, School of Medicine, University of Electronic Science and Technology of China, a 4400-bed tertiary care teaching hospital in Chengdu in the region of Sichuan (Western China). The hospital has an average of 130,000 admissions per year, and comprises 9 (total 167 beds) intensive care units (ICUs), 25 surgical units, 37 medicine wards, 2 transplant units, 1 dialysis centers, 2 traditional Chinese medicine wards and numerous rehabilitation units.

Study design and patients

We performed a retrospective cohort study from January 2017 to December 2020, to compare the different effects of CLABSI risk between CVCs and PICCs, and to assess the dynamic changes of CLABSI risk during intubation. All adult patients received a CVC or PICC during their hospital stay were included. Patients under the age of 18 years at the time of catheter insertion or those whose catheters were removed within 2 calendar days after placement were excluded. This study was approved by the Ethics Committee of Sichuan Academy of Medical Sciences, Sichuan People's Hospital, School of Medicine, University of Electronic Science and Technology of China.

Definitions and data collection

Primary endpoint was CLABSI up to 30 days after CVC or PICC placement. CLABSI was defined according with Centers for Disease Control and Prevention (CDC)/National Healthcare Safety Network (NHSN) surveillance definitions and criteria [14]. To accomplish the study objectives, the independent CLABSI monitoring information system, which was developed by the healthcare-associated infections quality control center in Sichuan province, was used to continuously monitor every patient with CVC or PICC. For each patient with CVC and PICC, suspicious CLABSI cases were automatically screened out by the intelligent identification program of the CLABSI monitoring information system if any of the following conditions were met: ① Fever (> 38 °C), ② Hypotension (systolic blood pressure < 90 mmHg and/or diastolic blood pressure < 60 mmHg), ③ Oliguria(< 400 ml/day), ④ Positive blood specimen, ⑤ CLABSI cases that have been prospectively entered into the HAI electronic system by clinicians before retrospective analysis. For this study, four infectious disease specialists revised the electronic medical records of all screened CLABSI cases to check if all NHSN criteria were fulfilled. The baseline characteristics of patients were retrieved from electronic medical records, including age, gender, principal diagnosis [International Classification of Diseases (ICD)-10 coded] [15], diabetes mellitus, hypertension (systolic blood pressure ≥ 140 mmHg and/or diastolic blood pressure ≥ 90 mmHg) [16], chronic obstructive pulmonary disease (COPD), hemodialysis, mechanical ventilation, urinary catheterization, tracheotomy, surgery, malignancy and community infections. Additional file 1: Table S1 in the Supplement presents the additional data information for our study.

Prevention of central line-associated bloodstream infections

On the basis of referring to several guidelines in China and the United States [6, 17], we have formulated a bundled strategy for central line-associated bloodstream infections prevention and control (IPC). Additional file 1: Table S2 in the Supplement presents the IPC measures for CLABSI. In order to ensure the implementation of IPC measures, we have established a multi-disciplinary teamwork (MDT) including medical department, nursing department and HAI management department. The division of responsibilities in MDT was as follows: the medical department was responsible for the authorization management of catheterization operators, the nursing department was responsible for the preparation of catheterization related materials, central-line maintenance and quality verification, and the HAI management department was responsible for the coordination, quality control, monitoring, analysis and summary in the whole process.

Propensity score matching

To minimize the impact of potential bias, a propensity score matching (PSM) was performed in the whole study cohorts using R Package Matching version 4.9–2 (CRAN.R-project.org/pack-age = Matching). The first step, standardized mean differences (SMD) were determined to compare baseline characteristics of patients. And the SMD of less than 0.1 was considered as an indicator of good balance between CVC and PICC group [18]. The second step, the propensity scores were calculated using the logistic regression model that accounted for the baseline characteristics with statistical significance. The third step, using the caliper of width equal to 0.2 of the standard deviation of the legit of the propensity score a k-nearest neighbor algorithm was used to make a 2:1 match without replacement between CVC and PICC group [19]. The fourth step, SMD was used again to test the balance between groups after PSM.

Statistical analysis

Statistical analysis of the data was performed using STATA version 20.0 (StataCorp. College Station, Texas, USA) and R software (v3.6.1) under RStudio (v1.2.5001). Categorical variables are shown as percentages. The normally distributed variables were presented as mean ± standard deviation and the non-normally distributed variables were expressed as median and interquartile range (IQR). Restricted cubic spline was used to visualize the risk of CLABSI at different time points during the catheterization. Time-to-event analyses were performed using the Kaplan–Meier survival functions to estimate the cumulative CLABSI hazard and the likelihood ratio test was used to compare hazard fraction in different groups. All tests were 2-sided with an α level of 0.05.

Results

During the study period, a total of 30,148 patients were catheterized for 256,879 days. We excluded 774 patients younger than 18 years and 4495 patients who did not meet CLABSI diagnostic criteria, resulting in 4775 patients with CVC and 20,104 patients with PICC were included in the analysis (Fig. 1). In the whole cohort, 54.3% participants were male, and the mean (SD) ages were 60.69 (17.30) and 56.84 (14.78) years old for those with CVC and PICC, respectively (Table 1). We observed an overall incidence of 1.80 CLABSIs per 1000 catheter-days (407 CLABSIs in 226,487 catheter-days). Compared with patients with PICC, those with CVC had a significantly higher crude CLABSI rate (5.55 events per 1000 catheter-days vs 0.84 events per 1000 catheter-days, P < 0.001). The standardized mean differences (SMD) between the groups were over 10% for most investigated baseline characteristics except for year and hemodialysis (Table 1).
Fig. 1

Cases selection algorithm for our study population. We excluded 774 patients younger than 18 years and 4495 patients who did not meet CLABSI diagnostic criteria, resulting in 4775 patients with CVC and 20,104 patients with PICC were included in the analysis

Table 1

Baseline characteristics

VariablePre-PSM, No. (%)Post-PSM, No. (%)
PICC (n = 20,104)CVC (n = 4775)SMDPICC (n = 5390)CVC (n = 2695)SMD
Age, mean (SD), y56.84 (14.78)60.69 (17.30)0.23957.89 (16.27)58.57 (17.20)0.041
Male10,466 (52.1)3038 (63.6)0.2363050 (56.6)1581 (58.7)0.042
Year
 20173976 (19.8)1018 (21.3)0.0381243 (23.1)623 (23.1)0.001
 20184788 (23.8)1176 (24.6)0.0191462 (27.1)707 (26.2)0.020
 20195373 (26.7)1218 (25.5)0.0281345 (25.0)665 (24.7)0.006
 20205967 (29.7)1363 (28.5)0.0251340 (24.9)700 (26.0)0.026
Community Infections902 (4.5)1123 (23.5)0.570757 (14.0)414 (15.4)0.037
Blood Transfusion6632 (33.0)3064 (64.2)0.6573020 (56.0)1548 (57.4)0.028
Urinary Catheterization8842 (44.0)3472 (72.7)0.6093393 (62.9)1806 (67.0)0.085
Hemodialysis174 (0.9)65 (1.4)0.04756 (1.0)27 (1.0)0.004
Mechanical Ventilation3625 (18.0)3222 (67.5)1.1542453 (45.5)1325 (49.2)0.073
Tracheotomy191 (1.0)570 (11.9)0.459163 (3.0)115 (4.3)0.066
Hypertension5005 (24.9)1629 (34.1)0.2031599 (29.7)844 (31.3)0.036
Diabetes mellitus2418 (12.0)958 (20.1)0.220868 (16.1)478 (17.7)0.044
COPD379 (1.9)505 (10.6)0.366211 (3.9)129 (4.8)0.043
Malignancy9434 (46.9)471 (9.9)0.902572 (10.6)343 (12.7)0.066
Liver Failure152 (0.8)274 (5.7)0.284130 (2.4)81 (3.0)0.037
Renal Failure609 (3.0)692 (14.5)0.414355 (6.6)250 (9.3)0.095
Heart Failure477 (2.4)418 (8.8)0.281259 (4.8)169 (6.3)0.064
Respiratory Failure392 (1.9)1573 (32.9)0.895362 (6.7)265 (9.8)0.113
Principal diagnosis
 Certain infectious diseases and parasites601 (3.0)258 (5.4)0.121291 (5.4)147 (5.5)0.002
 Tumor9595 (47.7)313 (6.6)1.045485 (9.0)289 (10.7)0.058
 Blood and hematopoietic diseases and certain diseases involving immune mechanisms197 (1.0)15 (0.3)0.08337 (0.7)15 (0.6)0.017
 Endocrine, nutritional, and metabolic diseases136 (0.7)86 (1.8)0.102123 (2.3)55 (2.0)0.017
 Mental and behavioral disorders9 (0.0)16 (0.3)0.0679 (0.2)8 (0.3)0.027
 Nervous system diseases83 (0.4)113 (2.4)0.16774 (1.4)44 (1.6)0.021
 Eye and appendage diseases2 (0.0)0 (0.0)0.0140 (0.0)0 (0.0)/
 Ear and mastoid diseases2 (0.0)0 (0.0)0.0140 (0.0)0 (0.0)/
 Circulatory diseases1892 (9.4)1098 (23.0)0.3751627 (30.2)738 (27.4)0.062
 Respiratory diseases730 (3.6)1054 (22.1)0.573484 (9.0)251 (9.3)0.012
 Digestive diseases1464 (7.3)755 (15.8)0.2691079 (20.0)526 (19.5)0.013
 Skin and subcutaneous tissue diseases11 (0.1)10 (0.2)0.04310 (0.2)7 (0.3)0.016
 Musculoskeletal system and connective tissue diseases288 (1.4)42 (0.9)0.05268 (1.3)33 (1.2)0.003
 Genitourinary diseases1524 (7.6)142 (3.0)0.207279 (5.2)128 (4.7)0.020
 Pregnancy, childbirth, and puerperium99 (0.5)20 (0.4)0.01143 (0.8)16 (0.6)0.025
 Congenital malformations, deformation, and chromosomal abnormalities407 (2.0)11 (0.2)0.17118 (0.3)11 (0.4)0.012
 Abnormal symptoms, signs, clinical and laboratory results, and cannot be classified in other categories41 (0.2)28 (0.6)0.06140 (0.7)18 (0.7)0.009
 Injury, poisoning and other external pathogenic factors723 (3.6)810 (17.0)0.451717 (13.3)405 (15.0)0.049
 External causes of illness and death2300 (11.4)4 (0.1)0.5036 (0.1)4 (0.1)0.010
Cases selection algorithm for our study population. We excluded 774 patients younger than 18 years and 4495 patients who did not meet CLABSI diagnostic criteria, resulting in 4775 patients with CVC and 20,104 patients with PICC were included in the analysis Baseline characteristics A higher incidence of CLABSIs was observed in catheters of femoral (FEM) site as compared to others before (χ2 = 386.381, P < 0.001) and after PSM (χ2 = 39.603, P < 0.001). More specifically, the incidence of CLABSIs was as follows: FEM: 6.2% before PSM and 3.8% after PSM, Subclavian (SC): 5.2% before PSM and 3.5% after PSM, Internal Jugular (IJ): 4.5% before PSM and 3.3% after PSM, PICC: 0.8% before PSM and 1.3% after PSM (Table 2).
Table 2

CLABSI among the 4 sites of catheterization

Catheter locationPre-PSM, No. (%)P-valuePost-PSM, No. (%)P-value
CLABSI (n = 407)Non-CLABSI (n = 24,472)CLABSI (n = 164)Non-CLABSI (n = 7921)
Catheter insertion sites < 0.001 < 0.001
 PICC152 (0.8)19,952 (99.2)71 (1.3)5319 (98.7)
 Femoral74 (6.2)1128 (93.8)28 (3.8)707 (96.2)
 Subclavian71 (5.2)1284 (94.8)25 (3.5)697 (96.5)
 Internal Jugular53 (4.5)1115 (95.5)22 (3.3)640 (96.7)
 Unclear7 (2.7)255 (97.3)5 (3.1)156 (96.9)
 Femoral + Subclavian46 (6.4)670 (93.6)12 (3.2)366 (96.8)
 Femoral + Internal Jugular3 (6.0)47 (94.0)1 (3.8)25 (96.2)
 Subclavian + Internal Jugular1 (4.8)20 (95.2)0 (0.0)10 (100.0)
 Femoral + Subclavian + Internal Jugular0 (0.0)1 (100.0)0 (0.0)1 (100.0)
CLABSI among the 4 sites of catheterization In the first 8 calendar days after CVC insertion, the risk of CLABSI increased rapidly, and the OR value increased day by day. However, after the 8th calendar day the OR value became stable (Fig. 2A). Compared with the CLABSI risk of CVC, the change of CLABSI risk of PICC showed more complex curve characteristics. In the first 4 calendar days after PICC insertion, the OR value increased day by day, reached a relatively high point on the 4th calendar day, and decreased from days 5 through 8. However, after the 8th calendar day the OR value began to increase day by day again (Fig. 2B).
Fig. 2

Restricted cubic spline of CLABSI risk. A OR value increased rapidly in the first 8 calendar days after CVC insertion. However, after the 8th calendar day the OR value became stable. B In the first 4 calendar days after PICC insertion, the OR value increased day by day, reached a relatively high point on the 4th calendar day, and decreased from days 5 through 8. However, after the 8th calendar day the OR value began to increase day by day again

Restricted cubic spline of CLABSI risk. A OR value increased rapidly in the first 8 calendar days after CVC insertion. However, after the 8th calendar day the OR value became stable. B In the first 4 calendar days after PICC insertion, the OR value increased day by day, reached a relatively high point on the 4th calendar day, and decreased from days 5 through 8. However, after the 8th calendar day the OR value began to increase day by day again Propensity score matching (PSM) improved the balance on the investigated baseline characteristics with all SMD between groups decreasing to 0.1 or less except for the respiratory failure, which was 0.113 (Table 1 and Fig. 3). After PSM, the final analyzed cohort consisted of 2695 patients with CVC and 5390 patients with PICC (Table 1 and Fig. 1). In the whole cohort, patients with CVC had a significantly higher 30-day CLABSI hazard compared with patients with PICC (HR = 6.76, 95% CI 5.53–8.27, Fig. 4A). In the PSM cohort, patients with CVC also had a significantly higher 30-day CLABSI hazard compared with patients with PICC (adjHR = 3.27, 95% CI 2.38–4.49, Fig. 4B).
Fig. 3

Standardized mean differences before and after matching. PSM improved the balance on the investigated baseline characteristics with all SMD between groups decreasing to 0.1 or less except for the respiratory failure

Fig. 4

30-day CLABSI hazard before and after propensity score matching. A In the whole cohort, patients with CVC had a significantly higher 30-day CLABSI hazard compared with patients with PICC (HR = 6.76, 95% CI 5.53–8.27). B In the PSM cohort, patients with CVC also had a significantly higher 30-day CLABSI hazard compared with patients with PICC (adjHR = 3.27, 95% CI 2.38–4.49)

Standardized mean differences before and after matching. PSM improved the balance on the investigated baseline characteristics with all SMD between groups decreasing to 0.1 or less except for the respiratory failure 30-day CLABSI hazard before and after propensity score matching. A In the whole cohort, patients with CVC had a significantly higher 30-day CLABSI hazard compared with patients with PICC (HR = 6.76, 95% CI 5.53–8.27). B In the PSM cohort, patients with CVC also had a significantly higher 30-day CLABSI hazard compared with patients with PICC (adjHR = 3.27, 95% CI 2.38–4.49)

Discussion

This is the first propensity-adjusted study to compare the incidence of CLABSI in PICCs and CVCs in Southwest China. We observed an overall incidence of 1.80 CLABSIs per 1000 catheter-days, higher than the average incidence of 1.00 CLABSIs per 1000 catheter-days of the three-level medical institutions reported in the National Report on the Services, Quality and Safety in Medical Care System [7]. More efforts and actions are needed to further reduce the incidence rate of CLABSI. The prevention of CLABSI, which was clearly defined as one of the ten medical quality and safety improvement goals in 2021 by the CHN National Health Commission, is expected to get more resources and support [20]. In our opinion, this kind of practice that the government only sets goals but lacks unified steps or plans would have a certain effect in the short term, but this effect might be limited and unsustainable, because in the absence of supporting strategies (such as medical security, pay-for-performance appraisal, etc.), medical institutions were more willing to practice in a way suitable for the existing management mode, and evidence based guidelines would not be mandatory to be followed completely [21]. Taking the Centers for Medicare & Medicaid Services (CMS) in the United States as a guide [22], if the incidence of CLABSI could be used as an indicator to examine the reimbursement ratio of medical insurance or the pay-for-performance appraisal of hospitals, this goal might be easier to achieve in China. We observed that PICCs were associated with a protective effect for CLABSI when compared to CVCs, regardless of adjusting for potential confounding factors. Yamaguchi et al. found similar results in a large sample of critically ill children [23]. Multiple reasons, including longer of length, lower of density in extremities bacteria, ease of insertion, and fewer number of lumen, accounted for the protective role of PICCs in CLABSI prevention [11, 23, 24]. Our study observed that PICCs have been used for a large proportion (around 80%). With the increasing use of PICC, it was easier to achieve the goal of reducing the incidence of CLABSI. Of note, in the first 4 calendar days after PICC insertion, there was a transient risk of PICC-associated BSI after puncture. As the trend reversed from days 5 through 8 after PICC insertion, the risk of PICC-associated BSI increased day by day from the 9th calendar day. Because of the trend reversal, the risk of PICC-associated BSI presents the characteristics of fluctuation curve. Sengupta et al. also found that the reversal trend occurred in neonates from days 19 through 35 after PICC insertion [25]. Although the specific reason for this curve feature was not clear, its existence might be one of the reasons why PICCs were associated with lower risk of CLABSI when compared to CVCs. We observed a higher risk of CLABSI with CVCs than with PICCs after adjusting for several potential confounding factors. It was reasonable to focus more on CVCs in CLABSI prevention. The present study has shown that CLABSIs rates are differing among the three commonly used CVC insertion sites. Consistent with many previous studies [26, 27], CVC placement in the FEM site was associated with a substantially higher risk of CLABSI compared to the SC insertion and the IJ insertion. Notably, although avoidance of the FEM insertion was recognized to be very effective in reducing CLABSI rates and recommended by many guidelines, the FEM route was often chosen due to the ease and perceived lower insertion risk of this site [28]. Furthermore, before and after the 8th day of CVC intubation, the trend of CLABSI risk was different. Given the rapid increase in CLABSI risk during the first 8 calendar days after CVC insertion, we believe that this period should be considered an important period for infection control, and that substantial benefits could be obtained if the guidelines were well followed during this period. In contrast, the relative stable risk of CLABSI after the 8th day of CVC insertion meant that the benefits of CLABSI prevention might be lower than expected. Some clinical implications for this result could be highlighted. First, the first 8 calendar days after CVC insertion, the acute stage of CLABSI, were an important period of infection control, and it was worth doing our best to comply with standardized practices for the management of central lines. Most notably, compared with the study from Cobb and coworkers 30 years ago [29], the incidence rate of CVC-associated CLABSI was decreasing, but the acute phase might be prolonged. Second, routine replacement of CVCs with frequency of once every 8 days or longer interval did not prevent infection. In previous two trials, a strategy of changing the catheter every 7 days was used to assess the effect of scheduled catheter replacement, and there was no significant difference in their results [30, 31]. We suggested that this interval should be increased to 8 days in future trials. Although evidence-based IPC guidelines have provided exact solutions to reduce the risk of CLABSI, how to make these methods operate effectively and maximize their effects should be highlighted. During our study period, the MDT team handled the problems that might hinder the implementation of IPC from the perspective of macro management, such as resource allocation, authorization management, verification, quality control and information monitoring. Consistent with the study from Jingjing Han, many systemic obstacles in China were considered to hold back evidence-based guidelines implementation exist, and the MDT CLABSI infection control program was considered to be effective in reducing hospital-wide CLABSI [32]. We acknowledge several limitations in our study. Most importantly, the CLABSI monitoring information system was the main way of data collection in this study. Although it helped the staff to realize the daily prospective monitoring of CLABSI, due to the initial design defects, some risk factors of CLABSI that had been confirmed in previous studies had not been collected (such as ICU admission now or in the past). Furthermore, our study was retrospective and only involved the analysis of a hospital. Larger multi-center randomized studies should be conducted to validate our findings.

Conclusion

Our study found that PICCs have a protective role and the effect of fluctuation curve feature in CLABSI when compared to CVCs, and the first 8 calendar days after CVC insertion was the acute stage of CVC-associated CLABSI. CLABSI prevention was clearly defined as one of the ten medical quality and safety improvement goals in 2021 by the CHN National Health Commission. The popularization of PICC and intensive infection control in the acute stage of CVC-associated CLABSI would help to achieve this goal. Additional file 1: Table S1. Variable assignment table of baseline characteristics of our study population. Table S2. Bundled strategy for central line-associated bloodstream infections prevention and control (IPC).
  24 in total

1.  An intervention to decrease catheter-related bloodstream infections in the ICU.

Authors:  Peter Pronovost; Dale Needham; Sean Berenholtz; David Sinopoli; Haitao Chu; Sara Cosgrove; Bryan Sexton; Robert Hyzy; Robert Welsh; Gary Roth; Joseph Bander; John Kepros; Christine Goeschel
Journal:  N Engl J Med       Date:  2006-12-28       Impact factor: 91.245

Review 2.  The Burden of Healthcare-Associated Infections in Southeast Asia: A Systematic Literature Review and Meta-analysis.

Authors:  Moi Lin Ling; Anucha Apisarnthanarak; Gilbert Madriaga
Journal:  Clin Infect Dis       Date:  2015-02-12       Impact factor: 9.079

3.  Impact of self-reported guideline compliance: Bloodstream infection prevention in a national collaborative.

Authors:  Yea-Jen Hsu; Kristina Weeks; Ting Yang; Melinda D Sawyer; Jill A Marsteller
Journal:  Am J Infect Control       Date:  2014-10       Impact factor: 2.918

4.  Differences in Central Line-Associated Bloodstream Infection Rates Based on the Criteria Used to Count Central Line Days.

Authors:  David Scheinker; Andrew Ward; Andrew Y Shin; Grace M Lee; Roshni Mathew; Lane F Donnelly
Journal:  JAMA       Date:  2020-01-14       Impact factor: 56.272

5.  Catheter duration and risk of CLA-BSI in neonates with PICCs.

Authors:  Arnab Sengupta; Christoph Lehmann; Marie Diener-West; Trish M Perl; Aaron M Milstone
Journal:  Pediatrics       Date:  2010-03-15       Impact factor: 7.124

6.  Catheter-related sepsis: prospective, randomized study of three methods of long-term catheter maintenance.

Authors:  S Eyer; C Brummitt; K Crossley; R Siegel; F Cerra
Journal:  Crit Care Med       Date:  1990-10       Impact factor: 7.598

7.  [2010 Chinese guidelines for the management of hypertension].

Authors:  Li-Sheng Liu
Journal:  Zhonghua Xin Xue Guan Bing Za Zhi       Date:  2011-07

8.  Hospitalization costs due to healthcare-associated infections: An analysis of propensity score matching.

Authors:  Yu Lv; Lan Chen; Wu Yu J; Qian Xiang; Qiong S Tang; Feng D Wang; Hong M Cai; Kun Zou; Qiong D Wei; Hua Z Zhou; Hui Wang; Chen Wang; Jing Chen; Ting L Li; Ying Fang; Huan Wang
Journal:  J Infect Public Health       Date:  2019-02-27       Impact factor: 3.718

9.  Central Line Associated Blood Stream Infections in Pediatric Hematology/Oncology Patients With Different Types of Central Lines.

Authors:  Jeffrey D Hord; John Lawlor; Eric Werner; Amy L Billett; David G Bundy; Cindi Winkle; Aditya H Gaur
Journal:  Pediatr Blood Cancer       Date:  2016-05-16       Impact factor: 3.167

10.  Optimal caliper widths for propensity-score matching when estimating differences in means and differences in proportions in observational studies.

Authors:  Peter C Austin
Journal:  Pharm Stat       Date:  2011 Mar-Apr       Impact factor: 1.894

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.