Clinical Report
Factors associated with specific uropathogens in catheter-associated urinary tract infection: Developing a clinical prediction model
Journal of International Medical Research 2014, Vol. 42(6) 1335–1347 ! The Author(s) 2014 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav DOI: 10.1177/0300060514543035 imr.sagepub.com
Won Sup Oh1, Ji-An Hur2, Eu Suk Kim3, Kyung-Hwa Park4, Hee Kyoung Choi5, Chisook Moon6 and Baek-Nam Kim7
Abstract Objectives: To identify characteristics associated with particular groups of uropathogens in catheter-associated urinary tract infection (CA–UTI) and to develop clinical prediction rules for identifying these groups. Methods: Demographic, clinical and microbiological data were analysed from patients with CA– UTI. Infections were categorized into enteric Gram-negative rods, nonfermenters, Gram-positive cocci and fungal. Variables were analysed using univariate and multiple logistic regression analyses, and were used to develop clinical prediction rules. Results: A total of 492 patients were included in the study. Candida species were the most common uropathogens (30.7%), followed by enterococci (17.3%), Escherichia coli (12.0%), Pseudomonas spp. (10.8%), Klebsiella spp. (7.9%) and staphylococci (6.5%). Clinical prediction rules for the bacterial uropathogenic groups showed poor-to-fair discriminatory power, with sensitivities of 90%. However, clinical prediction rules showed good discriminatory power for fungal infections, with a sensitivity of 67.3% and a specificity of 78.1%. Conclusions: Clinical prediction rules developed for identifying specific groups of bacterial uropathogens in patients with CA–UTI had a low sensitivity, whereas those for fungal infections 5
1
Department of Internal Medicine, Kangwon National University School of Medicine, Chuncheon, Republic of Korea 2 Department of Internal Medicine, Yeungnam University College of Medicine, Daegu, Republic of Korea 3 Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Republic of Korea 4 Department of Internal Medicine, Chonnam National University Medical School, Gwangju, Republic of Korea
Department of Internal Medicine, Wonju Christian Hospital, Yonsei University Wonju College of Medicine, Wonju, Republic of Korea 6 Department of Internal Medicine, Inje University BusanPaik Hospital, Busan, Republic of Korea 7 Department of Internal Medicine, Inje University SanggyePaik Hospital, Seoul, Republic of Korea Corresponding author: Dr Baek-Nam Kim, Department of Internal Medicine, Inje University Sanggye-Paik Hospital, Dongil-ro 1342, Nowon-gu, Seoul 139-707, Republic of Korea. Email: [email protected]
Creative Commons CC-BY-NC: This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 3.0 License (http://www.creativecommons.org/licenses/by-nc/3.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as2015 specified on the SAGE and Open Access page Downloaded from imr.sagepub.com by guest on April 13, (http://www.uk.sagepub.com/aboutus/openaccess.htm).
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Journal of International Medical Research 42(6)
showed good discriminatory power. Further studies to develop more refined and sensitive tools for predicting specific bacterial uropathogens in CA–UTI are warranted.
Keywords Catheter-associated urinary tract infection, urinary catheterization, risk factor, aetiology, sensitivity, prediction model Date received: 13 December 2013; accepted: 17 June 2014
Introduction Catheter-associated urinary tract infection (CA–UTI) is the most common nosocomial infection worldwide, comprising 40% of such infections.1,2 The acquisition of CA–UTI is associated with increased morbidity, length of hospital stay and healthcare-associated costs. Therefore, early identification of causative uropathogens and initiation of appropriate antibiotic treatment is of great importance. In contrast to community-acquired, uncomplicated UTI, a wide variety of micro-organisms have been isolated from patients with CA– UTI.3,4 In addition, because uropathogens in CA–UTI tend to show high rates of antibiotic resistance, the selection of appropriate empirical antibiotic treatment may be difficult.5,6 Until microbiological results are available, appropriate selection of antibiotics for CA–UTI depends on known susceptibility patterns of suspected bacterial uropathogens, with third-generation cephalosporins or fluoroquinolones being used for enteric Gram-negative rods, ceftazidime for nonfermenters, ampicillin or vancomycin for enterococci, and methicillin for staphylococci. Both the aetiological diversity and the high rate of resistance in CA– UTI can lead to either the misuse or overuse of antibiotics as empirical treatments. Predicting the presence of specific uropathogens in CA–UTI before microbiological results are available would therefore be useful in selecting appropriate antibiotic treatment strategies.
Clinical prediction rules are tools designed to assist clinicians in making medical decisions when caring for patients.7,8 They usually consist of several predictors derived from multivariable analyses, such as demographic features, clinical characteristics, laboratory results and radiographic findings. Clinical prediction rules may provide a means of predicting the presence of specific uropathogens in patients with CA– UTI. The present study was performed to identify aetiological micro-organisms in patients with CA–UTI, and to characterize factors associated with the major groups of uropathogens in order to develop clinical prediction rules for identifying specific groups of uropathogens.
Patients and methods Patients This cohort study was conducted at seven university hospitals in the Republic of Korea (Kangwon National University Hospital, Chuncheon; Yeungnam University Medical Centre, Daegu; Seoul National University Bundang Hospital, Seongnam; Chonnam National University Hospital, Gwangju; Wonju Severance Christian Hospital, Wonju; Inje University Busan-Paik Hospital, Busan; Inje University SanggyePaik Hospital, Seoul) between June and August 2012. Urine culture results from the laboratory databases at each hospital were screened to identify patients over 18 years of age who had urine culture performed after 48 h of admission. After a review of their
Downloaded from imr.sagepub.com by guest on April 13, 2015
Oh et al.
1337
medical records, patients who had a urinary catheter either at the time of urine culture or within the preceding 48 h and had CA–UTI (including catheter-associated asymptomatic bacteriuria) meeting the Infectious Diseases Society of America criteria3 were included in the study. Patients with polymicrobial infections were excluded. Only the first CA–UTI episode in each patient during the study period was considered. The study protocol was approved by the institutional review boards of each of the seven hospitals involved; the requirement for informed consent from the study participants was waived by the boards.
Data collection Data were collected using standardized casereport forms as soon as patients were enrolled in the study. Collected data comprised: demographic data; comorbid conditions; presence of urinary tract obstruction or vesico–ureteral reflux; type and duration of urinary catheterization; type and duration of other procedures; use of antibiotics within the preceding 30 days; presence of fever; organisms isolated from urine, blood or other cultures within the preceding 3 months; antibiotic resistance of major uropathogens; immunosuppressive therapy within the preceding 30 days. Demographic data included age, sex, care site (medical ward, surgical ward or intensive care unit), bed-ridden state and duration of stay at the time of urine culture, which was divided into short- ( 14 days) or long-term hospitalization (>14 days). Comorbidity was assessed using the Charlson comorbidity index.9 The type and duration of urinary catheterization was divided into three categories: short-term indwelling catheterization ( 14 days); long-term indwelling catheterization (>14 days); intermittent catheterization.10 Data on other procedures included surgery, tracheostomy (including
endotracheal intubation), gastrostomy (including nasogastric tube insertion), percutaneous nephrostomy, mechanical ventilation, renal replacement therapy, insertion of a biliary drainage tube, double-J stent or central venous catheter and other invasive procedures. In addition, a postvoid residual urine >100 ml, placement of a cystostomy tube within the preceding 48 h and the presence of chronic renal failure or endstage renal disease were recorded. Microbiological data relating to the CA–UTI were collected from all enrolled patients, including the susceptibility of the major uropathogens to antibiotics (thirdgeneration cephalosporins and fluoroquinolones for enteric Gran-negative rods, ceftazidime for nonfermenters, ampicillin and vancomycin for enterococci, and methicillin for staphylococci).
Statistical analyses Based on the antibiotic classes for empirical treatment using known susceptibility patterns, patients with CA–UTI were divided into four groups, based on the infection type: enteric Gram-negative rods; nonfermenters; Gram-positive cocci; fungal. For univariate analysis, variables in one group were compared with those in all the other three groups combined. Pearson’s 2-test was used to analyse categorical variables and Student’s t-test for continuous variables. Parameters with a P-value 3. As a surrogate of internal validation while adjusting the model parameters for potential overfitting, 95% confidence intervals of the area under the ROC curve (C-statistic) were calculated by bootstrapping with 1000 replications. A two-tailed P-value 14 days n ¼ 134
2 0 0 1 0 0
(14.3) (0.0) (0.0) (7.1) (0.0) (0.0)
46 (13.4) 29 (8.4) 13 (3.8) 5 (1.5) 6 (1.7) 3 (0.9)
11 10 4 2 1 5
53 (10.8) 14 (2.8) 6 (1.2)
2 (14.3) 0 (0.0) 1 (7.1)
35 (10.2) 11 (3.2) 3 (0.9)
16 (11.9) 3 (2.2) 2 (1.5)
85 (17.3) 32 (6.5) 5 (1.0)
6 (42.9) 1 (7.1) 0 (0.0)
66 (19.2) 30 (8.7) 5 (1.5)
13 (9.7) 1 (0.7) 0 (0.0)
151 (30.7) 8 (1.6)
1 (7.1) 0 (0.0)
89 (25.9) 3 (0.9)
61 (45.5) 5 (3.7)
Total n ¼ 492
Uropathogen Enteric Gram-negative rods, n ¼ 138 Escherichia coli Klebsiella spp. Enterobacter spp. Proteus spp. Citrobacter spp. Othera Nonfermenters, n ¼ 73 Pseudomonas spp. Acinetobacter spp. Otherb Gram-positive cocci, n ¼ 122 Enterococci Staphylococci Streptococci Fungal infections, n ¼ 159 Candida spp. Trichosporon spp.
59 39 17 8 7 8
(12.0) (7.9) (3.5) (1.6) (1.4) (1.6)
(8.2) (7.5) (3.0) (1.5) (0.7) (3.7)
Species, spp. a Morganella spp. (n ¼ 4), Serratia spp.(n ¼ 2), Providencia spp.(n ¼ 1), Raoultella spp.(n ¼ 1). b Alcaligenes spp.(n ¼ 2), Stenotrophomonas spp.(n ¼ 2), Burkholderia spp.(n ¼ 1), Delftia spp.(n ¼ 1). Data presented as n (%) patients.
with CA–UTI, 459 (93.3%) had one or more comorbid conditions, and the median Charlson comorbidity index score was 2 (interquartile range 1–3). Parameters with a P-value 14 days Duration of intensive care unit stay >14 days Duration of indwelling urinary catheterization >14 days Presence of comorbid condition(s) Malignancy Hepatobiliary disease Cardiovascular disease Chronic pulmonary disease Diabetes mellitus Neurological disease Connective tissue disease Charlson comorbidity index 3 Bed-ridden state Immunosuppressive therapy within preceding 30 days Surgery within preceding 30 days Renal replacement therapy within preceding 30 days
Characteristic
42 (30.4) 69 (50.0) 2 (1.4) 42 (30.4)
52 (37.7) 18 (13.0)
31 (22.5)
12 (8.7)
(28.3) (50.2) (2.0) (27.6)
139 247 10 136
177 (36.0) 60 (12.2)
156 (31.7)
34 (6.9)
(23.2) (5.1) (55.1) (4.3)
32 7 76 6
129 (93.5)
459 (93.3)
98 (19.9) 33 (6.7) 251 (51.0) 37 (7.5)
33 (23.9)
134 (27.2)
60 (43.5)
226 (45.9)
22 (15.9)
25 (18.1)
147 (29.9)
92 (18.7)
99 (71.7) 90 (65.2) 51 (37.0)
GNR n ¼ 138
317 (64.4) 280 (56.9) 205 (41.7)
Total n ¼ 492
Downloaded from imr.sagepub.com by guest on April 13, 2015
22 (6.2)
125 (35.3)
125 (35.3) 42 (11.9)
97 (27.4) 178 (50.3) 8 (2.3) 94 (26.6)
66 (18.6) 26 (7.3) 175 (49.4) 31 (8.8)
330 (93.2)
101 (28.5)
70 (19.8)
166 (46.9)
122 (34.5)
218 (61.6) 190 (53.7) 154 (43.5)
Non-GNRa n ¼ 354
GNR versus non-GNR
0.328
0.007
0.676 0.759
0.505 1.000 0.733 0.432
0.260 0.428 0.271 0.127
1.000
0.313
0.369
(17.8) (6.8) (49.3) (9.6)
5 (6.8)
24 (32.9)
27 (37.0) 6 (8.2)
16 (21.9) 37 (50.7) 3 (4.1) 15 (20.5)
13 5 36 7
69 (94.5)
21 (28.8)
18 (24.7)
35 (47.9)
18 (24.7)
Factors associated with specific uropathogens in catheter-associated urinary tract infection: Developing a clinical prediction model
Journal of International Medical Research 2014, Vol. 42(6) 1335–1347 ! The Author(s) 2014 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav DOI: 10.1177/0300060514543035 imr.sagepub.com
Won Sup Oh1, Ji-An Hur2, Eu Suk Kim3, Kyung-Hwa Park4, Hee Kyoung Choi5, Chisook Moon6 and Baek-Nam Kim7
Abstract Objectives: To identify characteristics associated with particular groups of uropathogens in catheter-associated urinary tract infection (CA–UTI) and to develop clinical prediction rules for identifying these groups. Methods: Demographic, clinical and microbiological data were analysed from patients with CA– UTI. Infections were categorized into enteric Gram-negative rods, nonfermenters, Gram-positive cocci and fungal. Variables were analysed using univariate and multiple logistic regression analyses, and were used to develop clinical prediction rules. Results: A total of 492 patients were included in the study. Candida species were the most common uropathogens (30.7%), followed by enterococci (17.3%), Escherichia coli (12.0%), Pseudomonas spp. (10.8%), Klebsiella spp. (7.9%) and staphylococci (6.5%). Clinical prediction rules for the bacterial uropathogenic groups showed poor-to-fair discriminatory power, with sensitivities of 90%. However, clinical prediction rules showed good discriminatory power for fungal infections, with a sensitivity of 67.3% and a specificity of 78.1%. Conclusions: Clinical prediction rules developed for identifying specific groups of bacterial uropathogens in patients with CA–UTI had a low sensitivity, whereas those for fungal infections 5
1
Department of Internal Medicine, Kangwon National University School of Medicine, Chuncheon, Republic of Korea 2 Department of Internal Medicine, Yeungnam University College of Medicine, Daegu, Republic of Korea 3 Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Republic of Korea 4 Department of Internal Medicine, Chonnam National University Medical School, Gwangju, Republic of Korea
Department of Internal Medicine, Wonju Christian Hospital, Yonsei University Wonju College of Medicine, Wonju, Republic of Korea 6 Department of Internal Medicine, Inje University BusanPaik Hospital, Busan, Republic of Korea 7 Department of Internal Medicine, Inje University SanggyePaik Hospital, Seoul, Republic of Korea Corresponding author: Dr Baek-Nam Kim, Department of Internal Medicine, Inje University Sanggye-Paik Hospital, Dongil-ro 1342, Nowon-gu, Seoul 139-707, Republic of Korea. Email: [email protected]
Creative Commons CC-BY-NC: This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 3.0 License (http://www.creativecommons.org/licenses/by-nc/3.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as2015 specified on the SAGE and Open Access page Downloaded from imr.sagepub.com by guest on April 13, (http://www.uk.sagepub.com/aboutus/openaccess.htm).
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Journal of International Medical Research 42(6)
showed good discriminatory power. Further studies to develop more refined and sensitive tools for predicting specific bacterial uropathogens in CA–UTI are warranted.
Keywords Catheter-associated urinary tract infection, urinary catheterization, risk factor, aetiology, sensitivity, prediction model Date received: 13 December 2013; accepted: 17 June 2014
Introduction Catheter-associated urinary tract infection (CA–UTI) is the most common nosocomial infection worldwide, comprising 40% of such infections.1,2 The acquisition of CA–UTI is associated with increased morbidity, length of hospital stay and healthcare-associated costs. Therefore, early identification of causative uropathogens and initiation of appropriate antibiotic treatment is of great importance. In contrast to community-acquired, uncomplicated UTI, a wide variety of micro-organisms have been isolated from patients with CA– UTI.3,4 In addition, because uropathogens in CA–UTI tend to show high rates of antibiotic resistance, the selection of appropriate empirical antibiotic treatment may be difficult.5,6 Until microbiological results are available, appropriate selection of antibiotics for CA–UTI depends on known susceptibility patterns of suspected bacterial uropathogens, with third-generation cephalosporins or fluoroquinolones being used for enteric Gram-negative rods, ceftazidime for nonfermenters, ampicillin or vancomycin for enterococci, and methicillin for staphylococci. Both the aetiological diversity and the high rate of resistance in CA– UTI can lead to either the misuse or overuse of antibiotics as empirical treatments. Predicting the presence of specific uropathogens in CA–UTI before microbiological results are available would therefore be useful in selecting appropriate antibiotic treatment strategies.
Clinical prediction rules are tools designed to assist clinicians in making medical decisions when caring for patients.7,8 They usually consist of several predictors derived from multivariable analyses, such as demographic features, clinical characteristics, laboratory results and radiographic findings. Clinical prediction rules may provide a means of predicting the presence of specific uropathogens in patients with CA– UTI. The present study was performed to identify aetiological micro-organisms in patients with CA–UTI, and to characterize factors associated with the major groups of uropathogens in order to develop clinical prediction rules for identifying specific groups of uropathogens.
Patients and methods Patients This cohort study was conducted at seven university hospitals in the Republic of Korea (Kangwon National University Hospital, Chuncheon; Yeungnam University Medical Centre, Daegu; Seoul National University Bundang Hospital, Seongnam; Chonnam National University Hospital, Gwangju; Wonju Severance Christian Hospital, Wonju; Inje University Busan-Paik Hospital, Busan; Inje University SanggyePaik Hospital, Seoul) between June and August 2012. Urine culture results from the laboratory databases at each hospital were screened to identify patients over 18 years of age who had urine culture performed after 48 h of admission. After a review of their
Downloaded from imr.sagepub.com by guest on April 13, 2015
Oh et al.
1337
medical records, patients who had a urinary catheter either at the time of urine culture or within the preceding 48 h and had CA–UTI (including catheter-associated asymptomatic bacteriuria) meeting the Infectious Diseases Society of America criteria3 were included in the study. Patients with polymicrobial infections were excluded. Only the first CA–UTI episode in each patient during the study period was considered. The study protocol was approved by the institutional review boards of each of the seven hospitals involved; the requirement for informed consent from the study participants was waived by the boards.
Data collection Data were collected using standardized casereport forms as soon as patients were enrolled in the study. Collected data comprised: demographic data; comorbid conditions; presence of urinary tract obstruction or vesico–ureteral reflux; type and duration of urinary catheterization; type and duration of other procedures; use of antibiotics within the preceding 30 days; presence of fever; organisms isolated from urine, blood or other cultures within the preceding 3 months; antibiotic resistance of major uropathogens; immunosuppressive therapy within the preceding 30 days. Demographic data included age, sex, care site (medical ward, surgical ward or intensive care unit), bed-ridden state and duration of stay at the time of urine culture, which was divided into short- ( 14 days) or long-term hospitalization (>14 days). Comorbidity was assessed using the Charlson comorbidity index.9 The type and duration of urinary catheterization was divided into three categories: short-term indwelling catheterization ( 14 days); long-term indwelling catheterization (>14 days); intermittent catheterization.10 Data on other procedures included surgery, tracheostomy (including
endotracheal intubation), gastrostomy (including nasogastric tube insertion), percutaneous nephrostomy, mechanical ventilation, renal replacement therapy, insertion of a biliary drainage tube, double-J stent or central venous catheter and other invasive procedures. In addition, a postvoid residual urine >100 ml, placement of a cystostomy tube within the preceding 48 h and the presence of chronic renal failure or endstage renal disease were recorded. Microbiological data relating to the CA–UTI were collected from all enrolled patients, including the susceptibility of the major uropathogens to antibiotics (thirdgeneration cephalosporins and fluoroquinolones for enteric Gran-negative rods, ceftazidime for nonfermenters, ampicillin and vancomycin for enterococci, and methicillin for staphylococci).
Statistical analyses Based on the antibiotic classes for empirical treatment using known susceptibility patterns, patients with CA–UTI were divided into four groups, based on the infection type: enteric Gram-negative rods; nonfermenters; Gram-positive cocci; fungal. For univariate analysis, variables in one group were compared with those in all the other three groups combined. Pearson’s 2-test was used to analyse categorical variables and Student’s t-test for continuous variables. Parameters with a P-value 3. As a surrogate of internal validation while adjusting the model parameters for potential overfitting, 95% confidence intervals of the area under the ROC curve (C-statistic) were calculated by bootstrapping with 1000 replications. A two-tailed P-value 14 days n ¼ 134
2 0 0 1 0 0
(14.3) (0.0) (0.0) (7.1) (0.0) (0.0)
46 (13.4) 29 (8.4) 13 (3.8) 5 (1.5) 6 (1.7) 3 (0.9)
11 10 4 2 1 5
53 (10.8) 14 (2.8) 6 (1.2)
2 (14.3) 0 (0.0) 1 (7.1)
35 (10.2) 11 (3.2) 3 (0.9)
16 (11.9) 3 (2.2) 2 (1.5)
85 (17.3) 32 (6.5) 5 (1.0)
6 (42.9) 1 (7.1) 0 (0.0)
66 (19.2) 30 (8.7) 5 (1.5)
13 (9.7) 1 (0.7) 0 (0.0)
151 (30.7) 8 (1.6)
1 (7.1) 0 (0.0)
89 (25.9) 3 (0.9)
61 (45.5) 5 (3.7)
Total n ¼ 492
Uropathogen Enteric Gram-negative rods, n ¼ 138 Escherichia coli Klebsiella spp. Enterobacter spp. Proteus spp. Citrobacter spp. Othera Nonfermenters, n ¼ 73 Pseudomonas spp. Acinetobacter spp. Otherb Gram-positive cocci, n ¼ 122 Enterococci Staphylococci Streptococci Fungal infections, n ¼ 159 Candida spp. Trichosporon spp.
59 39 17 8 7 8
(12.0) (7.9) (3.5) (1.6) (1.4) (1.6)
(8.2) (7.5) (3.0) (1.5) (0.7) (3.7)
Species, spp. a Morganella spp. (n ¼ 4), Serratia spp.(n ¼ 2), Providencia spp.(n ¼ 1), Raoultella spp.(n ¼ 1). b Alcaligenes spp.(n ¼ 2), Stenotrophomonas spp.(n ¼ 2), Burkholderia spp.(n ¼ 1), Delftia spp.(n ¼ 1). Data presented as n (%) patients.
with CA–UTI, 459 (93.3%) had one or more comorbid conditions, and the median Charlson comorbidity index score was 2 (interquartile range 1–3). Parameters with a P-value 14 days Duration of intensive care unit stay >14 days Duration of indwelling urinary catheterization >14 days Presence of comorbid condition(s) Malignancy Hepatobiliary disease Cardiovascular disease Chronic pulmonary disease Diabetes mellitus Neurological disease Connective tissue disease Charlson comorbidity index 3 Bed-ridden state Immunosuppressive therapy within preceding 30 days Surgery within preceding 30 days Renal replacement therapy within preceding 30 days
Characteristic
42 (30.4) 69 (50.0) 2 (1.4) 42 (30.4)
52 (37.7) 18 (13.0)
31 (22.5)
12 (8.7)
(28.3) (50.2) (2.0) (27.6)
139 247 10 136
177 (36.0) 60 (12.2)
156 (31.7)
34 (6.9)
(23.2) (5.1) (55.1) (4.3)
32 7 76 6
129 (93.5)
459 (93.3)
98 (19.9) 33 (6.7) 251 (51.0) 37 (7.5)
33 (23.9)
134 (27.2)
60 (43.5)
226 (45.9)
22 (15.9)
25 (18.1)
147 (29.9)
92 (18.7)
99 (71.7) 90 (65.2) 51 (37.0)
GNR n ¼ 138
317 (64.4) 280 (56.9) 205 (41.7)
Total n ¼ 492
Downloaded from imr.sagepub.com by guest on April 13, 2015
22 (6.2)
125 (35.3)
125 (35.3) 42 (11.9)
97 (27.4) 178 (50.3) 8 (2.3) 94 (26.6)
66 (18.6) 26 (7.3) 175 (49.4) 31 (8.8)
330 (93.2)
101 (28.5)
70 (19.8)
166 (46.9)
122 (34.5)
218 (61.6) 190 (53.7) 154 (43.5)
Non-GNRa n ¼ 354
GNR versus non-GNR
0.328
0.007
0.676 0.759
0.505 1.000 0.733 0.432
0.260 0.428 0.271 0.127
1.000
0.313
0.369
(17.8) (6.8) (49.3) (9.6)
5 (6.8)
24 (32.9)
27 (37.0) 6 (8.2)
16 (21.9) 37 (50.7) 3 (4.1) 15 (20.5)
13 5 36 7
69 (94.5)
21 (28.8)
18 (24.7)
35 (47.9)
18 (24.7)
