 |
Drug Target Insights 2022; 16: 12-16 ISSN 1177-3928 | DOI: 10.33393/dti.2022.2422 ORIGINAL RESEARCH ARTICLE |
 |
The association of ESBL Escherichia coli with mortality in patients with Escherichia coli bacteremia at the emergency department
ABSTRACT
Background: Escherichia coli is a common bloodstream infection pathogen in the emergency department (ED). Patients with extended-spectrum beta-lactamase (ESBL) E. coli have a higher risk of morbidity. However, there is still debate surrounding ESBL E. coli-associated mortality in community, intensive care unit, and tertiary care settings. In addition, there have been few studies regarding mortality in ESBL E. coli in ED settings, and results have been contradictory.
Methods: This was a retrospective cohort study conducted at the Department of Emergency Medicine, Faculty of Medicine, Khon Kaen University in Thailand aimed at evaluating the possible association between ESBL E. coli bacteremia and mortality in the ED. The inclusion criteria were age 18 years or over, clinical presentation suspicious of infection, and positive blood culture for E. coli. Predictors for mortality were analyzed by logistic regression analysis.
Results: During the study period, 273 patients presented at the ED with hemoculture positive for E. coli. Of those, 27 (9.89%) died. Five factors remained in the final model, of which plasma glucose levels, serum lactate levels, and ESBL E. coli were significantly associated with 28-day mortality in the ED with adjusted odds ratios of 0.970, 1.258, and 12.885, respectively. Plasma glucose of less than 113 mg/dL yielded a sensitivity of 80.95% and specificity of 64.29%, while serum lactate over 2.4 mmol/L had a sensitivity of 81.48% and specificity of 45.50%.
Conclusion: ESBL E. coli, plasma glucose, and serum lactate levels were associated with 28-day mortality in patients with E. coli bacteremia presenting at the ED.
Keywords: Extended-spectrum beta-lactamase-producing Escherichia coli, Glucose, Lactate
Received: May 11, 2022
Accepted: September 19, 2022
Published online: October 17, 2022
Drug Target Insights - ISSN 1177-3928 - www.aboutscience.eu/dti
© 2022 The Authors. This article is published by AboutScience and licensed under Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0).
Commercial use is not permitted and is subject to Publisher’s permissions. Full information is available at www.aboutscience.eu
Introduction
Bloodstream infection (BSI) with Gram-negative bacteria is common in the emergency department (ED), accounting for 39.4% of ED patients with suspected infection (1). A study from China found that Escherichia coli was the most common Gram-negative BSI in 3,199 patients and accounted for 34.3% of cases (2). One population-based study found the mortality rate of E. coli BSI to be 9.6%. Male patients aged 70 years or older are at higher risk of 30-day mortality with adjusted incidence rate ratios of 1.26 and 10.35 (3). Another study found a mortality rate of 30.6% in patients infected with extended-spectrum beta-lactamase (ESBL) E. coli vs 22.2% in those infected with non-ESBL strains or Klebsiella pneumoniae (4).
The prevalence of drug-resistant Gram-negative bacteria is increasing, particularly in in-hospital, intensive care unit (ICU), and tertiary care settings (5-8). A study from a multispecialty hospital in India found that rates of multidrug-resistant Gram-negative bacteria increased from 26.16% in 2012 to 33.33% in 2014 (6). Additionally, urinary tract infection patients with resistant Enterobacteriaceae have been shown to be 1.447 times more likely to have severe sepsis or septic shock at presentation than those with nonresistant strains (9). Data regarding the association of ESBL E. coli and mortality in community, ICU, and tertiary care settings have been inconclusive. Two studies conducted in community settings, for example, found differences in mortality between patients with ESBL and non-ESBL bacteremia (10,11), whereas a study from a tertiary care setting found comparable rates (9.7% vs 9.2%), as did a study in a teaching hospital in China (12,13). However, another study in a teaching hospital in Japan found higher rate of mortality in patients with ESBL strains (14), as did a study in an ICU (37.5% vs 15.6%; p = 0.04) (15). This study thus aimed to evaluate if ESBL E. coli bacteremia was associated with mortality in an ED setting.
Methods
This was a retrospective cohort study conducted at the Department of Emergency Medicine, Faculty of Medicine, Khon Kaen University in Thailand as part of an ED infection project. The inclusion criteria were age 18 years or over, clinical presentation suspicious of infection, and positive blood culture for E. coli. Patients who received prophylactic antibiotics, presented with cardiac arrest or symptoms related to trauma, were referred from other hospitals, or had missing clinical data were excluded. The study period was between 2016 and 2018.
Eligible patients were selected from the hospital database. We reviewed participants’ clinical data at the time of presentation as well as mortality data over the following 28 days. Clinical data included baseline characteristics, laboratory results, and treatment. Baseline characteristics reviewed were age, sex, comorbid diseases, Charlson Comorbidity Index, physical signs, and quick Sepsis Related Organ Failure Assessment (qSOFA) score. Laboratory results included complete blood count, chemistry, arterial blood gas, serum lactate levels, and blood culture results (for ESBL E. coli positivity). The primary outcome was 28-day mortality.
Statistical analyses
Eligible patients were categorized into two groups by mortality. Descriptive statistics were used to calculate differences between the two groups. Predictors for mortality were analyzed using logistic regression analysis. Univariate logistic analysis was used to calculate the unadjusted odds ratio with 95% confidence interval and p value for each factor. Factors with a p value less than 0.05 by univariate logistic regression analysis or those that were clinically significant were subsequently subjected to stepwise, multivariate logistic regression analysis. The final model was tested for goodness of fit using the Hosmer-Lemeshow method. Results were reported as unadjusted/adjusted odds ratios with their 95% confidence intervals. A numerical predictor for mortality as an appropriate diagnostic cutoff point was computed with its sensitivity and specificity. All statistical analyses were performed using STATA version 10.1 (College Station, Texas, USA).
Results
During the study period, 273 patients presented at the ED with hemoculture positive for E. coli. Of those, 27 (9.89%) died. In terms of baseline characteristics and physical signs, there were 12 factors that differed significantly between those who survived and those who died (Tab. I). For example, nonsurvivors had a significantly higher Charlson Comorbidity Index (5 vs 4), respiratory rate (28 vs 24 breaths/min), and qSOFA score (2 vs 1), but oxygen saturation at presentation was lower (96% vs 97%; p 0.040). qSOFA scores were significantly higher in those who died than those who survived (2 vs 1; p < 0.001).
| Factors | Survivors n = 246 | Nonsurvivors n = 27 | p-Value |
|---|---|---|---|
| Age, years | 66 (18-100) | 73 (19-93) | 0.161 |
| Male sex | 125 (50.81) | 10 (37.04) | 0.224 |
| Comorbid diseases | |||
| Liver disease | 50 (20.33) | 10 (37.04) | 0.053 |
| Diabetes | 57 (23.85) | 2 (7.41) | 0.053 |
| CKD (moderate-severe) | 24 (9.76) | 4 (14.81) | 0.499 |
| Solid organ tumor | 74 (30.08) | 15 (55.56) | 0.010 |
| Palliative care | 4 (1.63) | 4 (14.81) | 0.004 |
| Leukemia | 2 (0.81) | 1 (3.70) | 0.269 |
| Lymphoma | 2 (0.81) | 2 (7.41) | 0.050 |
| Hypertension | 90 (36.59) | 7 (25.93) | 0.299 |
| HIV infection | 2 (0.81) | 0 | 0.999 |
| Cholangiocarcinoma | 34 (13.82) | 8 (29.63) | 0.045 |
| Charlson Comorbidity Index | 4 (0-12) | 5 (1-12) | <0.001 |
| Temperature, °C | 38.6 (35.9-41.5) | 38.2 (35.6-41.0) | 0.184 |
| Pulse rate, beats/min | 96 (58-190) | 96 (52-148) | 0.898 |
| Respiratory rate, breaths/min | 24 (18-50) | 28 (18-40) | 0.008 |
| SBP, mm Hg | 126 (64-218) | 112 (80-167) | 0.003 |
| DBP, mm Hg | 70 (33-112) | 67 (37-95) | 0.071 |
| MAP, mm Hg | 91 (48-138) | 80 (56-119) | 0.009 |
| Oxygen saturation, % | 97 (60-100) | 96 (65-100) | 0.040 |
| GCS | 15 (4-15) | 15 (7-15) | <0.001 |
| Sepsis score | |||
| qSOFA | 1 (0-3) | 2 (1-3) | <0.001 |
Data are presented as median (range) or number (percentage).
CKD = chronic kidney disease; DBP = diastolic blood pressure; GCS = Glasgow coma scale; MAP = mean arterial pressure; qSOFA = quick Sepsis Related Organ Failure Assessment; SBP = systolic blood pressure.
Data presented as number (percentage) unless indicated otherwise.
With regard to laboratory tests and treatment, seven factors differed significantly between groups (Tab. II). For example, the nonsurvival group had significantly lower levels of serum bicarbonate (17 vs 21 mEq/L) and plasma glucose (94 vs 131 mg/dL), higher serum lactate levels (4.5 vs 2.6 mmol/L), and a greater percentage of patients with ESBL E. coli (14.81% vs 2.03%) than the survival group. In addition, patients in the nonsurvival group underwent significantly more aggressive treatment (such as vasopressor treatment) and had a higher rate of ICU admission. However, duration of hospital stay in the nonsurvival group was shorter (8 vs 11 days; p 0.013).
| Factors | Survivors n = 246 | Nonsurvivors n = 27 | p-Value |
|---|---|---|---|
| Hb, g/dL | 11.0 (4.6-16.0) | 9.6 (4.8-13.8) | 0.002 |
| WBC, ×103/mm3 | 33.8 (13.0-51.7) | 26.6 (14.9-41.9) | 0.010 |
| Platelet, ×106 | 179 (4-584) | 138 (13-451) | 0.098 |
| BUN, mg/dL | 17.9 (3.7-144.8) | 27.3 (6.7-153.2) | 0.009 |
| Creatinine, mg/dL | 1.1 (0.4-10.4) | 1.5 (0.5-11.1) | 0.118 |
| Bicarbonate, mEq/L | 21 (7-30) | 17 (7-27) | <0.001 |
| Total bilirubin, mg/dL | 1.4 (0.2-33.8) | 2.1 (0.3-33.8) | 0.251 |
| Glucose, mg/dL | 131 (53-548) | 94 (35-172) | <0.001 |
| PaO2, mmHg | 76 (23-512) | 89 (33-253) | 0.702 |
| pH | 7.44 (7.16-7.58) | 7.40 (7.11-7.56) | 0.194 |
| Lactate level, mmol/L | 2.6 (0.5-18.3) | 4.5 (1.3-17.9) | 0.003 |
| ESBL E. coli | 5 (2.03) | 4 (14.81) | 0.007 |
| Treatment | |||
| Mechanical ventilator | 21 (8.54) | 5 (18.52) | 0.155 |
| ICU admission | 83 (3.74) | 17 (62.96) | 0.005 |
| Vasopressor* | 62 (25.20) | 21 (77.78) | <0.001 |
| LOS | 11 (2-56) | 8 (1-54) | 0.013 |
Data are presented as median (range) or number (percentage).
BUN = blood urea nitrogen; ESBL E. coli = extended-spectrum beta-lactamase-producing Escherichia coli; Hb = hemoglobin; ICU = intensive care unit; LOS = length of stay; PaO2 = partial pressure of oxygen; pH = power of hydrogen; WBC = white blood cell.
*indicates that the patient received norepinephrine, adrenaline, or dopamine.
Five factors remained in the final model for predicting death (Tab. III). Plasma glucose, serum lactate levels, and ESBL E. coli were significantly associated with mortality, with adjusted odds ratios of 0.970, 1.258, and 12.885, respectively. The final model had a Hosmer-Lemeshow Chi square of 6.73 (p = 0.565). Plasma glucose of 113 mg/dL or lower yielded a sensitivity of 80.95% and specificity of 64.29%, while serum lactate level of over 2.4 mmol/L had a sensitivity of 81.48% and specificity of 45.50%.
Discussion
The prevalence of ESBL E. coli bacteremia at the ED in this study was 3.29%, which is lower than previously reported in community settings (6.7%-9.5%) (10,11,16). In addition to the difference in setting, these results may indicate differing rates among countries, as higher rates have been found in developed countries (South Korea and Spain). A previous report found that frequent visits to the ED increased the risk of ESBL bacteremia by a factor of 9.98, including in those patients who had undergone previous antibiotic treatment. In Thailand, the rate of previous antibiotic use may be lower than in some other countries. Despite the inconsistency in the ESBL E. coli mortality rate in other settings, this study found that patients with ESBL E. coli had a 13 times higher risk of mortality than those with non-ESBL strains. Other factors associated with mortality in patients with E. coli infection may be personal characteristics and inappropriate antibiotic use. A report from Korea found that presenting with septic shock or malignancy increased mortality risk by 26.6 and 11.9 times, respectively, while another study found that mortality rates were comparable in patients with ESBL and non-ESBL E. coli if antibiotics were administered appropriately (p = 0.23) (11,15).
| Factors | Unadjusted odds ratio (95% confidence interval) | Adjusted odds ratio (95% confidence interval) |
|---|---|---|
| Oxygen saturation | 0.933 (0.883, 0.985) | 0.934 (0.860, 1.015) |
| Hemoglobin | 0.732 (0.605, 0.885) | 0.856 (0.662, 1.106) |
| Plasma glucose | 0.971 (0.955, 0.987) | 0.970 (0.954, 0.987) |
| Serum lactate level | 1.185 (1.067, 1.317) | 1.258 (1.090, 1.451) |
| ESBL E. coli* | 8.382 (2.103, 33.407) | 12.885 (1.082, 153.338) |
| Age | 1.008 (0.981, 1.036) | Not retained |
| Sex | 0.569 (0.251, 1.293) | Not retained |
| Liver disease | 2.306 (0.995, 5.344) | Not retained |
| Diabetes | 0.255 (0.058, 1.111) | Not retained |
| Cholangiocarcinoma | 2.625 (1.065, 6.469) | Not retained |
| Solid organ tumor | 2.905 (1.297, 6.508) | Not retained |
| qSOFA | 3.761 (1.974, 7.164) | Not retained |
| DBP | 0.974 (0.947, 1.001) | Not retained |
| GCS | 0.770 (0.630, 0.941) | Not retained |
| Hemoglobin | 0.732 (0.605, 0.885) | Not retained |
| WBC | 1.014 (0.995, 1.032) | Not retained |
| Serum bicarbonate | 0.836 (0.762, 0.918) | Not retained |
Factors in the model included age, sex, liver disease, diabetes, cholangiocarcinoma, solid organ tumor, qSOFA, DBP, GCS, WBC, and serum bicarbonate.
DBP = diastolic blood pressure; ESBL = extended-spectrum beta-lactamase; GCS = Glasgow coma scale; qSOFA = quick Sepsis Related Organ Failure Assessment; WBC = white blood cell.
Hypoglycemia has been shown to be related with higher mortality in sepsis patients and critically ill patients (17-19). Although the causal relationship between hypoglycemia and mortality is not well understood, several mechanisms have been proposed including the inhibition of the physiological responses of hormones such as insulin and epinephrine, increased inflammatory response, and cellular damage from glucose administration (19). Previous studies have also found low plasma glucose to be associated with mortality in patients with sepsis (11,20). This study found that glucose of 113 mg/dL or lower yielded a sensitivity of 80.95% compared to a previous study, in which plasma glucose of 40-69 mg/dL resulted in an adjusted odds ratio of 3.43 (95% confidence interval of 1.51, 7.82) for mortality (19,20). These results may imply that patients with E. coli bacteremia and hypoglycemia may have as high of a risk of mortality as other patients with sepsis. The different plasma glucose cutoff points in the two studies may be due to differences in study population. This study enrolled only patients with E. coli bacteremia at the ED, while the previous study included patients with sepsis, which may have been caused by various pathogens. The plasma glucose cutoff point in this study may be more specific to patients with E. coli bacteremia at the ED.
As previously reported, serum lactate is an indicator for mortality in patients with infection at the ED (21-23). A previous study found that serum lactate greater than 4 mmol/L was associated with higher mortality than at 2 mmol/L (40.7% vs 2.7%) (24). In this study, we found that serum lactate over 2.4 mmol/L yielded sufficient sensitivity to predict fatality in patients with E. coli bacteremia at the ED. Another study found a serum lactate cutoff point of 5.80 mmol/L in patients with necrotizing fasciitis (25). This indicates that E. coli bacteremia may be severe and that the serum lactate cutoff point may vary depending on the causative agents.
Although oxygen saturation and hemoglobin were significantly associated with mortality by univariate logistic regression analysis (Tab. III), they were no longer significant in the final model. These results may indicate that neither factor was a strong predictor compared with the other three. Additionally, there might have been some related confounding factors. Other factors included in the model that had p values of less than 0.05 by univariate analysis were not retained in the final model for the same reasons. Some comorbid diseases, such as diabetes, were found to be significant predictors for mortality in a previous observational study (26). However, comorbid diseases were not significant in this study, as previously mentioned. Additionally, the model used in this study differed from that in the previous study. In this study, we included clinical factors such as ESBL E. coli in the model, while the previous study did not include ESBL E. coli and included treatment-related factors such as peak inspiratory pressure and positive end-expiratory pressure.
There were some limitations to this study. First, the ED at which it was conducted was a single site at a university hospital. Further prospective studies in other settings may be required to confirm the results. In addition, this was an exploratory study without validation. The final predictive model included more factors than event outcomes. There were five factors in the model with only 27 nonsurvivors, resulting in a ratio of more than 1:10. Moreover, the total number of patients in the final model was 130. These limitations could have caused the model to be unbalanced or biased. However, the final model had a high goodness of fit. Another limitation was that some factors were not studied such as previous antibiotic use, previous history of resistant pathogens, or special conditions (27-35). Finally, mortality was defined as 28-day mortality.
ESBL E. coli, plasma glucose, and serum lactate levels were associated with 28-day mortality in patients with E. coli bacteremia presenting at the ED.
Acknowledgments
The authors would like to acknowledge Dr. Dylan Southard for editing the manuscript via the Khon Kaen University Publication Clinic (Thailand).
Disclosures
Conflict of interest: The authors declare that they have no conflicts of interest.
Financial support: This study was funded by a grant from the Khon Kaen University, Faculty of Medicine, Thailand (Grant Number: MN63307).
References
- 1. Chase M, Klasco RS, Joyce NR, Donnino MW, Wolfe RE, Shapiro NI. Predictors of bacteremia in emergency department patients with suspected infection. Am J Emerg Med. 2012;30(9):1691-1697. CrossRef PubMed
- 2. Zhu Q, Zhu M, Li C, et al. Epidemiology and microbiology of Gram-negative bloodstream infections in a tertiary-care hospital in Beijing, China: a 9-year retrospective study. Expert Rev Anti Infect Ther. 2021;19(6):769-776. CrossRef PubMed
- 3. MacKinnon MC, McEwen SA, Pearl DL, et al. Mortality in Escherichia coli bloodstream infections: a multinational population-based cohort study. BMC Infect Dis. 2021;21(1):606. CrossRef PubMed
- 4. Sianipar O, Asmara W, Dwiprahasto I, Mulyono B. Mortality risk of bloodstream infection caused by either Escherichia coli or Klebsiella pneumoniae producing extended-spectrum β-lactamase: a prospective cohort study. BMC Res Notes. 2019;12(1):719. CrossRef PubMed
- 5. Hawkey PM. Multidrug-resistant Gram-negative bacteria: a product of globalization. J Hosp Infect. 2015;89(4):241-247. CrossRef PubMed
- 6. Nagvekar V, Sawant S, Amey S. Prevalence of multidrug-resistant Gram-negative bacteria cases at admission in a multispeciality hospital. J Glob Antimicrob Resist. 2020;22:457-461. CrossRef PubMed
- 7. Morris S, Cerceo E. Trends, epidemiology, and management of multi-drug resistant Gram-negative bacterial infections in the hospitalized setting. Antibiotics (Basel). 2020;9(4):E196. CrossRef PubMed
- 8. Siwakoti S, Subedi A, Sharma A, Baral R, Bhattarai NR, Khanal B. Incidence and outcomes of multidrug-resistant gram-negative bacteria infections in intensive care unit from Nepal – a prospective cohort study. Antimicrob Resist Infect Control. 2018;7(1):114. CrossRef PubMed
- 9. Lee YC, Hsiao CY, Hung MC, et al. Bacteremic urinary tract infection caused by multidrug-resistant Enterobacteriaceae are associated with severe sepsis at admission: implication for empirical therapy. Medicine (Baltimore). 2016;95(20):e3694. CrossRef PubMed
- 10. Kang CI, Song JH, Chung DR, et al; Korean Network for Study of Infectious Diseases (KONSID). Risk factors and treatment outcomes of community-onset bacteraemia caused by extended-spectrum beta-lactamase-producing Escherichia coli. Int J Antimicrob Agents. 2010;36(3):284-287. CrossRef PubMed
- 11. Park SH, Choi SM, Lee DG, et al. Emergence of extended-spectrum β-lactamase-producing Escherichia coli as a cause of community-onset bacteremia in South Korea: risk factors and clinical outcomes. Microb Drug Resist. 2011;17(4):537-544. CrossRef
- 12. Namikawa H, Yamada K, Fujimoto H, et al. Clinical characteristics of bacteremia caused by extended-spectrum beta-lactamase-producing Escherichia coli at a tertiary hospital. Intern Med. 2017;56(14):1807-1815. CrossRef PubMed
- 13. Xiao T, Wu Z, Shi Q, et al. A retrospective analysis of risk factors and outcomes in patients with extended-spectrum beta-lactamase-producing Escherichia coli bloodstream infections. J Glob Antimicrob Resist. 2019;17:147-156. CrossRef PubMed
- 14. Hattori H, Maeda M, Nagatomo Y, et al. Epidemiology and risk factors for mortality in bloodstream infections: a single-center retrospective study in Japan. Am J Infect Control. 2018;46(12):e75-e79. CrossRef PubMed
- 15. Haruki Y, Hagiya H, Haruki M, Sugiyama T. Clinical characteristics and outcome of critically ill patients with bacteremia caused by extended-spectrum β-lactamase-producing and non-producing Escherichia coli. J Infect Chemother. 2018;24(11):944-947. CrossRef PubMed
- 16. Rodríguez-Baño J, Picón E, Gijón P, et al; Spanish Network for Research in Infectious Diseases (REIPI). Community-onset bacteremia due to extended-spectrum beta-lactamase-producing Escherichia coli: risk factors and prognosis. Clin Infect Dis. 2010;50(1):40-48. CrossRef PubMed
- 17. Yao RQ, Ren C, Wu GS, Zhu YB, Xia ZF, Yao YM. Is intensive glucose control bad for critically ill patients? A systematic review and meta-analysis. Int J Biol Sci. 2020;16(9):1658-1675. CrossRef PubMed
- 18. Wang J, Zhu CK, Yu JQ, Tan R, Yang PL. Hypoglycemia and mortality in sepsis patients: a systematic review and meta-analysis. Heart Lung. 2021;50(6):933-940. CrossRef PubMed
- 19. Park S, Kim DG, Suh GY, et al. Mild hypoglycemia is independently associated with increased risk of mortality in patients with sepsis: a 3-year retrospective observational study. Crit Care. 2012;16(5):R189. CrossRef PubMed
- 20. Van den Berghe G, Wilmer A, Hermans G, et al. Intensive insulin therapy in the medical ICU. N Engl J Med. 2006;354(5):449-461. CrossRef PubMed
- 21. Shapiro NI, Howell MD, Talmor D, et al. Serum lactate as a predictor of mortality in emergency department patients with infection. Ann Emerg Med. 2005;45(5):524-528. CrossRef PubMed
- 22. Vorwerk C, Loryman B, Coats TJ, et al. Prediction of mortality in adult emergency department patients with sepsis. Emerg Med J. 2009;26(4):254-258. CrossRef PubMed
- 23. Puskarich MA, Illich BM, Jones AE. Prognosis of emergency department patients with suspected infection and intermediate lactate levels: a systematic review. J Crit Care. 2014;29(3):334-339. CrossRef PubMed
- 24. Bou Chebl R, El Khuri C, Shami A, et al. Serum lactate is an independent predictor of hospital mortality in critically ill patients in the emergency department: a retrospective study. Scand J Trauma Resusc Emerg Med. 2017;25(1):69. CrossRef PubMed
- 25. Chang CP, Fann WC, Wu SR, Lin CN, Hsiao CT. Lactate on emergency department arrival as a predictor of in-hospital mortality in necrotizing fasciitis: a retrospective study. J Orthop Surg Res. 2019;14(1):73. CrossRef PubMed
- 26. Rezoagli E, McNicholas BA, Madotto F, Pham T, Bellani G, Laffey JG; LUNG SAFE Investigators, the ESICM Trials Group. Presence of comorbidities alters management and worsens outcome of patients with acute respiratory distress syndrome: insights from the LUNG SAFE study. Ann Intensive Care. 2022;12(1):42. CrossRef PubMed
- 27. Charoentanyarak S, Sawunyavisuth B, Deepai S, Sawanyawisuth K. A point-of-care serum lactate level and mortality in adult sepsis patients: a community hospital setting. J Prim Care Community Health. 2021;12:21501327211000233. CrossRef PubMed
- 28. Jeerasuwannakul B, Sawunyavisuth B, Khamsai S, Sawanyawisuth K. Prevalence and risk factors of proteinuria in patients with type 2 diabetes mellitus. Asia-Pac J Sci Technol. 2021 Jul 1;26(04):APST-26-04-02. Online (Accessed May 2022).
- 29. Soontornrungsun B, Khamsai S, Sawunyavisuth B, et al. Obstructive sleep apnea in patients with diabetes less than 40 years of age. Diabetes Metab Syndr. 2020;14(6):1859-1863. CrossRef PubMed
- 30. Tongdee S, Sawunyavisuth B, Sukeepaisarnjaroen W, Boonsawat W, Khamsai S, Sawanyawisuth K. Clinical factors predictive of appropriate treatment in COPD: a community hospital setting. Drug Target Insights. 2021;15:21-25. CrossRef PubMed
- 31. Namwaing P, Chaisuksant S, Sawadpanich R, et al. Oxygen saturation is associated with recurrent primary spontaneous pneumothorax treated by intercostal chest drainage. Asian J Surg. 2022;45(1):431-434. CrossRef PubMed
- 32. Namwaing P, Chaisuksant S, Sawadpanich R, et al. Factors associated with duration of intercostal chest drainage in patients with primary spontaneous pneumothorax and the role of pulmonary rehabilitation. Open Access Emerg Med. 2021;13:569-573. CrossRef PubMed
- 33. Boonwang T, Namwaing P, Srisaphonphusitti L, et al. Esports may improve cognitive skills in soccer players: a systematic review. Asia-Pac J Sci Technol. 2022;27(3). Online (Accessed May 2022).
- 34. Ruangritkul P, Tiamkao S, Chainirun N, et al. The efficacy and safety profile of generic intravenous levetiracetam in a real-world setting. Curr Ther Res Clin Exp. 2021;95:100648. CrossRef PubMed
- 35. Sanlung T, Sawanyawisuth K, Silaruks S, et al. Clinical characteristics and complications of obstructive sleep apnea in Srinagarind Hospital. J Med Assoc Thai. 2020;103(1):36-39. Online (Accessed May 2022).