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Article ; Online: Using time-course as an essential factor to accurately predict sepsis-associated mortality among patients with suspected sepsis.

Yen, Shih-Chieh / Wu, Chin-Chieh / Tseng, Yi-Ju / Li, Chih-Huang / Chen, Kuan-Fu

2023 , Page(s) 100632

Abstract: Background: Biomarker dynamics in different time-courses might be the primary reason why a static measurement of a single biomarker cannot accurately predict sepsis outcomes. Therefore, we conducted this prospective hospital-based cohort study to ... ...

Abstract	Background: Biomarker dynamics in different time-courses might be the primary reason why a static measurement of a single biomarker cannot accurately predict sepsis outcomes. Therefore, we conducted this prospective hospital-based cohort study to simultaneously evaluate the performance of several conventional and novel biomarkers of sepsis in predicting sepsis-associated mortality on different days of illness among patients with suspected sepsis. Methods: We evaluated the performance of 15 novel biomarkers including angiopoietin-2, pentraxin 3, sTREM-1, ICAM-1, VCAM-1, sCD14 and 163, E-selectin, P-selectin, TNF-alpha, interferon-gamma, CD64, IL-6, 8, and 10, along with few conventional markers for predicting sepsis-associated mortality. Patients were grouped into quartiles according to the number of days since symptom onset. Receiver operating characteristic curve (ROC) analysis was used to evaluate the biomarker performance. Results: From 2014 to 2017, 1,483 patients were enrolled, of which 78% fulfilled the systemic inflammatory response syndrome criteria, 62% fulfilled the sepsis-3 criteria, 32% had septic shock, and 3.3% developed sepsis-associated mortality. IL-6, pentraxin 3, sCD163, and the blood gas profile demonstrated better performance in the early days of illness, both before and after adjusting for potential confounders (adjusted area under ROC curve [AUROC]:0.81-0.88). Notably, the Sequential Organ Failure Assessment (SOFA) score was relatively consistent throughout the course of illness (adjusted AUROC:0.70-0.91). Conclusion: IL-6, pentraxin 3, sCD163, and the blood gas profile showed excellent predictive accuracy in the early days of illness. The SOFA score was consistently predictive of sepsis-associated mortality throughout the course of illness, with an acceptable performance.
Language	English
Publishing date	2023-07-17
Publishing country	United States
Document type	Journal Article
ZDB-ID	2698541-X
ISSN	2320-2890 ; 2320-2890
ISSN (online)	2320-2890
ISSN	2320-2890
DOI	10.1016/j.bj.2023.100632
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Using Machine Learning to Develop and Validate an In-Hospital Mortality Prediction Model for Patients with Suspected Sepsis.

Chao, Hsiao-Yun / Wu, Chin-Chieh / Singh, Avichandra / Shedd, Andrew / Wolfshohl, Jon / Chou, Eric H / Huang, Yhu-Chering / Chen, Kuan-Fu

Biomedicines

2022 Volume 10, Issue 4

Abstract: Background: Early recognition of sepsis and the prediction of mortality in patients with infection are important. This multi-center, ED-based study aimed to develop and validate a 28-day mortality prediction model for patients with infection using ... ...

Abstract	Background: Early recognition of sepsis and the prediction of mortality in patients with infection are important. This multi-center, ED-based study aimed to develop and validate a 28-day mortality prediction model for patients with infection using various machine learning (ML) algorithms. Methods: Patients with acute infection requiring intravenous antibiotic treatment during the first 24 h of admission were prospectively recruited. Patient demographics, comorbidities, clinical signs and symptoms, laboratory test data, selected sepsis-related novel biomarkers, and 28-day mortality were collected and divided into training (70%) and testing (30%) datasets. Logistic regression and seven ML algorithms were used to develop the prediction models. The area under the receiver operating characteristic curve (AUROC) was used to compare different models. Results: A total of 555 patients were recruited with a full panel of biomarker tests. Among them, 18% fulfilled Sepsis-3 criteria, with a 28-day mortality rate of 8%. The wrapper algorithm selected 30 features, including disease severity scores, biochemical parameters, and conventional and few sepsis-related biomarkers. Random forest outperformed other ML models (AUROC: 0.96; 95% confidence interval: 0.93-0.98) and SOFA and early warning scores (AUROC: 0.64-0.84) in the prediction of 28-day mortality in patients with infection. Additionally, random forest remained the best-performing model, with an AUROC of 0.95 (95% CI: 0.91-0.98, Conclusions: Our results demonstrated that ML models provide a more accurate prediction of 28-day mortality with an enhanced ability in dealing with multi-dimensional data than the logistic regression model.
Language	English
Publishing date	2022-03-29
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2720867-9
ISSN	2227-9059
ISSN	2227-9059
DOI	10.3390/biomedicines10040802
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Article: Utilizing Computed Tomography to Analyze the Morphomic Change between Patients with Localized and Metastatic Renal Cell Carcinoma: Body Composition Varies According to Cancer Stage.

Tan, Chin-Chieh / Sheng, Ting-Wen / Chang, Ying-Hsu / Wang, Li-Jen / Chuang, Cheng-Keng / Wu, Chun-Te / Pang, See-Tong / Shao, I-Hung

Journal of clinical medicine

2022 Volume 11, Issue 15

Abstract: Background: This study aimed to elucidate the change of body composition in different clinical stages of renal cell carcinoma (RCC) by analyzing computed tomography (CT) images.: Methods: We enrolled patients diagnosed with RCC in a tertiary medical ... ...

Abstract	Background: This study aimed to elucidate the change of body composition in different clinical stages of renal cell carcinoma (RCC) by analyzing computed tomography (CT) images. Methods: We enrolled patients diagnosed with RCC in a tertiary medical center who did not mention body weight loss or symptoms of cachexia. We grouped patients into those with localized RCC and those with metastatic RCC. Analyses of the volume of skeletal muscles tissue (SMT), subcutaneous adipose tissue (SAT), and visceral adipose tissue (VAT) calculated based on CT images were performed and included subgroup analyses by sex and age. The correlation between tumor size and body composition in localized RCC was also examined. Results: A total of 188 patients were enrolled in this study. There was significantly lower VAT ( Conclusions: In localized RCC, VAT volume was significantly larger in those with large primary tumor size. However, the VAT was significantly lower in those with metastatic status comparing to those with localized disease. The clinical course of cancers closely correlates with body composition.
Language	English
Publishing date	2022-07-30
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2662592-1
ISSN	2077-0383
ISSN	2077-0383
DOI	10.3390/jcm11154444
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Article ; Online: Developing and validating clinical features-based machine learning algorithms to predict influenza infection in influenza-like illness patients

Shang-Kai Hung / Chin-Chieh Wu / Avichandra Singh / Jin-Hua Li / Christian Lee / Eric H. Chou / Andrew Pekosz / Richard Rothman / Kuan-Fu Chen

Biomedical Journal, Vol 46, Iss 5, Pp 100561- (2023)

2023

Abstract: Background: Seasonal influenza poses a significant risk, and patients can benefit from early diagnosis and treatment. However, underdiagnosis and undertreatment remain widespread. We developed and compared clinical feature-based machine learning (ML) ... ...

Abstract	Background: Seasonal influenza poses a significant risk, and patients can benefit from early diagnosis and treatment. However, underdiagnosis and undertreatment remain widespread. We developed and compared clinical feature-based machine learning (ML) algorithms that can accurately predict influenza infection in emergency departments (EDs) among patients with influenza-like illness (ILI). Material and methods: We conducted a prospective cohort study in five EDs in the US and Taiwan from 2015 to 2020. Adult patients visiting the EDs with symptoms of ILI were recruited and tested by real-time RT-PCR for influenza. We evaluated seven ML algorithms and compared their results with previously developed clinical prediction models. Results: Out of the 2189 enrolled patients, 1104 tested positive for influenza. The eXtreme Gradient Boosting achieved superior performance with an area under the receiver operating characteristic curve of 0.82 (95% confidence interval [CI] = 0.79–0.85), with a sensitivity of 0.92 (95% CI = 0.88–0.95), specificity of 0.89 (95% CI = 0.86–0.92), and accuracy of 0.72 (95% CI = 0.69–0.76) in the testing set over cut-offs of 0.4, 0.6 and 0.5, respectively. These results were superior to those of previously proposed clinical prediction models. The model interpretation revealed that body temperature, cough, rhinorrhea, and exposure history were positively associated with and the days of illness and influenza vaccine were negatively associated with influenza infection. We also found the week of the influenza season, pulse rate, and oxygen saturation to be associated with influenza infection. Conclusions: The clinical feature-based ML model outperformed conventional models for predicting influenza infection.
Keywords	Influenza infection ; Machine learning ; Influenza-like illness ; Prediction model ; Medicine (General) ; R5-920 ; Biology (General) ; QH301-705.5
Subject code	610
Language	English
Publishing date	2023-10-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
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Article ; Online: Effectiveness of Treatments and Diagnostic Tools and Declining Mortality in Patients With Severe Sepsis: A 12-Year Population-Based Cohort Study.

Chen, Kuan-Fu / Tsai, Meng-Ying / Wu, Chin-Chieh / Han, Shih-Tsung

Journal of intensive care medicine

2019 Volume 35, Issue 12, Page(s) 1418–1425

Abstract: Sepsis is a major cause of morbidity and mortality worldwide. With the advance of medical care, the mortality of sepsis has decreased in the past decades. Many treatments and diagnostic tools still lack supporting evidence. We conducted a retrospective ... ...

Abstract	Sepsis is a major cause of morbidity and mortality worldwide. With the advance of medical care, the mortality of sepsis has decreased in the past decades. Many treatments and diagnostic tools still lack supporting evidence. We conducted a retrospective population-based cohort study with propensity score matched subcohorts based on a prospectively collected national longitudinal health insurance database in Taiwan. Severe sepsis-associated hospital admissions from 2000 to 2011 based on
MeSH term(s)	Cohort Studies ; Hospital Mortality ; Humans ; Retrospective Studies ; Sepsis/mortality ; Sepsis/therapy ; Taiwan/epidemiology
Language	English
Publishing date	2019-01-30
Publishing country	United States
Document type	Journal Article
ZDB-ID	632828-3
ISSN	1525-1489 ; 0885-0666
ISSN (online)	1525-1489
ISSN	0885-0666
DOI	10.1177/0885066619827270
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Article ; Online: Applying symptom dynamics to accurately predict influenza virus infection: An international multicenter influenza-like illness surveillance study.

Li, Jin-Hua / Wu, Chin-Chieh / Tseng, Yi-Ju / Han, Shih-Tsung / Pekosz, Andrew / Rothman, Richard / Chen, Kuan-Fu

Influenza and other respiratory viruses

2022 Volume 17, Issue 1, Page(s) e13081

Abstract: Background: Public health organizations have recommended various definitions of influenza-like illnesses under the assumption that the symptoms do not change during influenza virus infection. To explore the relationship between symptoms and influenza ... ...

Abstract	Background: Public health organizations have recommended various definitions of influenza-like illnesses under the assumption that the symptoms do not change during influenza virus infection. To explore the relationship between symptoms and influenza over time, we analyzed a dataset from an international multicenter prospective emergency department (ED)-based influenza-like illness cohort study. Methods: We recruited patients in the US and Taiwan between 2015 and 2020 with: (1) flu-like symptoms (fever and cough, headache, or sore throat), (2) absence of any of the respiratory infection symptoms, or (3) positive laboratory test results for influenza from the current ED visit. We evaluated the association between the symptoms and influenza virus infection on different days of illness. The association was evaluated among different subgroups, including different study countries, influenza subtypes, and only patients with influenza. Results: Among the 2471 recruited patients, 45.7% tested positive for influenza virus. Cough was the most predictive symptom throughout the week (odds ratios [OR]: 7.08-11.15). In general, all symptoms were more predictive during the first 2 days (OR: 1.55-10.28). Upper respiratory symptoms, such as sore throat and productive cough, and general symptoms, such as body ache and fatigue, were more predictive in the first half of the week (OR: 1.51-3.25). Lower respiratory symptoms, such as shortness of breath and wheezing, were more predictive in the second half of the week (OR: 1.52-2.52). Similar trends were observed for most symptoms in the different subgroups. Conclusions: The time course is an important factor to be considered when evaluating the symptoms of influenza virus infection.
MeSH term(s)	Humans ; Influenza, Human/diagnosis ; Influenza, Human/epidemiology ; Cough ; Prospective Studies ; Cohort Studies ; Orthomyxoviridae ; Pharyngitis
Language	English
Publishing date	2022-12-08
Publishing country	England
Document type	Multicenter Study ; Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	2274538-5
ISSN	1750-2659 ; 1750-2640
ISSN (online)	1750-2659
ISSN	1750-2640
DOI	10.1111/irv.13081
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Article ; Online: Could we employ the queueing theory to improve efficiency during future mass causality incidents?

Lin, Chih-Chuan / Wu, Chin-Chieh / Chen, Chi-Dan / Chen, Kuan-Fu

Scandinavian journal of trauma, resuscitation and emergency medicine

2019 Volume 27, Issue 1, Page(s) 41

Abstract: Background: Preparation for a disaster or accident-related mass casualty events is often based on experience. The objective measures or tools for evaluating decision-making and effectiveness during such events are underdeveloped. Queueing theory has ... ...

Abstract	Background: Preparation for a disaster or accident-related mass casualty events is often based on experience. The objective measures or tools for evaluating decision-making and effectiveness during such events are underdeveloped. Queueing theory has been suggested to evaluate the effectiveness of mass causality incidents (MCI) plans. Objective: Using different types of real MCI, we aimed to determine if a queueing network model could be used as a tool to assist in preparing plans to address mass causality incidents. Methods: We collected information from two types of mass casualty events: a motor vehicle accident and a dust explosion. Patient characteristics, time intervals of every working station, numbers of physicians and nurses attending, and time required by physicians and nurses during these two MCIs were collected and used for calculation in a queueing network model. Balanced efficiency was determined by calculating the numbers of server, i.e., nurses and physicians, in the two MCIs. Results: Efficient patient flows were found in both MCIs. However, excessive medical manpower supply was revealed when the queueing network model was applied to assess the MCIs. The best fitting result, i.e., the most efficient man power utilization, can be calculated by the queueing network models. Furthermore, balanced efficiency may be a more suitable condition than the highest efficiency man power utilization when faced with MCIs. Conclusion: The queueing network model is a flexible tool that could be used in different types of MCIs to observe the degree of efficiency when handling MCIs.
MeSH term(s)	Adult ; Decision Making ; Disaster Planning/organization & administration ; Female ; Humans ; Male ; Mass Casualty Incidents ; Models, Theoretical ; Physicians/standards ; Young Adult
Language	English
Publishing date	2019-04-11
Publishing country	England
Document type	Journal Article
ZDB-ID	2455990-8
ISSN	1757-7241 ; 1757-7241
ISSN (online)	1757-7241
ISSN	1757-7241
DOI	10.1186/s13049-019-0620-8
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Article ; Online: Diagnostic accuracy of clinical signs and symptoms of COVID-19: A systematic review and meta-analysis to investigate the different estimates in a different stage of the pandemic outbreak.

Chen, Kuan-Fu / Feng, Tsai-Wei / Wu, Chin-Chieh / Yunusa, Ismaeel / Liu, Su-Hsun / Yeh, Chun-Fu / Han, Shih-Tsung / Mao, Chih-Yang / Harika, Dasari / Rothman, Richard / Pekosz, Andrew

Journal of global health

2023 Volume 13, Page(s) 6026

Abstract: Background: The coronavirus (COVID-19) pandemic caused enormous adverse socioeconomic impacts worldwide. Evidence suggests that the diagnostic accuracy of clinical features of COVID-19 may vary among different populations.: Methods: We conducted a ... ...

Abstract	Background: The coronavirus (COVID-19) pandemic caused enormous adverse socioeconomic impacts worldwide. Evidence suggests that the diagnostic accuracy of clinical features of COVID-19 may vary among different populations. Methods: We conducted a systematic review and meta-analysis of studies from PubMed, Embase, Cochrane Library, Google Scholar, and the WHO Global Health Library for studies evaluating the accuracy of clinical features to predict and prognosticate COVID-19. We used the National Institutes of Health Quality Assessment Tool to evaluate the risk of bias, and the random-effects approach to obtain pooled prevalence, sensitivity, specificity, and likelihood ratios. Results: Among the 189 included studies (53 659 patients), fever, cough, diarrhoea, dyspnoea, and fatigue were the most reported predictors. In the later stage of the pandemic, the sensitivity in predicting COVID-19 of fever and cough decreased, while the sensitivity of other symptoms, including sputum production, sore throat, myalgia, fatigue, dyspnoea, headache, and diarrhoea, increased. A combination of fever, cough, fatigue, hypertension, and diabetes mellitus increases the odds of having a COVID-19 diagnosis in patients with a positive test (positive likelihood ratio (PLR) = 3.06)) and decreases the odds in those with a negative test (negative likelihood ratio (NLR) = 0.59)). A combination of fever, cough, sputum production, myalgia, fatigue, and dyspnea had a PLR = 10.44 and an NLR = 0.16 in predicting severe COVID-19. Further updating the umbrella review (1092 studies, including 3 342 969 patients) revealed the different prevalence of symptoms in different stages of the pandemic. Conclusions: Understanding the possible different distributions of predictors is essential for screening for potential COVID-19 infection and severe outcomes. Understanding that the prevalence of symptoms may change with time is important to developing a prediction model.
MeSH term(s)	United States ; Humans ; COVID-19/diagnosis ; COVID-19/epidemiology ; SARS-CoV-2 ; Myalgia ; Cough ; Pandemics ; COVID-19 Testing ; Dyspnea ; Fatigue
Language	English
Publishing date	2023-07-14
Publishing country	Scotland
Document type	Meta-Analysis ; Systematic Review ; Journal Article
ZDB-ID	2741629-X
ISSN	2047-2986 ; 2047-2986
ISSN (online)	2047-2986
ISSN	2047-2986
DOI	10.7189/jogh.13.06026
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Article: Comparison of the accuracy of neutrophil CD64, procalcitonin, and C-reactive protein for sepsis identification: a systematic review and meta-analysis.

Yeh, Chun-Fu / Wu, Chin-Chieh / Liu, Su-Hsun / Chen, Kuan-Fu

Annals of intensive care

2019 Volume 9, Issue 1, Page(s) 5

Abstract: Background: Neutrophil CD64 is widely described as an accurate biomarker for the diagnosis of infection in patients with septic syndrome. We performed a systematic review and meta-analysis to evaluate the diagnostic accuracy of neutrophil CD64, ... ...

Abstract	Background: Neutrophil CD64 is widely described as an accurate biomarker for the diagnosis of infection in patients with septic syndrome. We performed a systematic review and meta-analysis to evaluate the diagnostic accuracy of neutrophil CD64, comparing it with C-reactive protein (CRP) and procalcitonin (PCT) for the diagnosis of infection in adult patients with septic syndrome, based on sepsis-2 criteria. We searched the PubMed and Embase databases and Google Scholar. Original studies reporting the performance of neutrophil CD64 for sepsis diagnosis in adult patients were retained. The pooled sensitivity, specificity, diagnostic odds ratio (DOR), and hierarchical summary receiver operating characteristic (SROC) curve were calculated. Results: We included 14 studies (2471 patients) from 2006 to 2017 in the meta-analysis. The pooled sensitivity and specificity of neutrophil CD64 for diagnosing infection in adult patients with septic syndrome were 0.87 (95% CI 0.80-0.92) and 0.89 (95% CI 0.82-0.93), respectively. The area under the SROC curve and the DOR were 0.94 (95% CI 0.92-0.96) and 53 (95% CI 22-128), respectively. There was significant heterogeneity between the studies included. Subgroup analyses showed that this heterogeneity was due to differences in sample size and the proportions of patients with sepsis included in the studies. Six studies (927 patients) compared neutrophil CD64 and CRP determinations, and six studies (744 patients) compared neutrophil CD64 and PCT determinations. The area under the SROC curve was larger for neutrophil CD64 than for CRP (0.89 [95% CI 0.87-0.92] vs. 0.84 [95% CI 0.80-0.88], P < 0.05) or PCT (0.89 [95% CI 0.84-0.95] vs. 0.84 [95% CI 0.79-0.89], P < 0.05). Conclusions: In adult patients with septic syndrome, neutrophil CD64 levels are an excellent biomarker with moderate accuracy outperforming both CRP and PCT determinations.
Language	English
Publishing date	2019-01-08
Publishing country	Germany
Document type	Journal Article
ZDB-ID	2617094-2
ISSN	2110-5820
ISSN	2110-5820
DOI	10.1186/s13613-018-0479-2
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Article: Current Evidence and Limitation of Biomarkers for Detecting Sepsis and Systemic Infection.

Hung, Shang-Kai / Lan, Hao-Min / Han, Shih-Tsung / Wu, Chin-Chieh / Chen, Kuan-Fu

Biomedicines

2020 Volume 8, Issue 11

Abstract: Sepsis was recently redefined as a life-threatening disease involving organ dysfunction caused by a dysregulated host response to infection. Biomarkers play an important role in early detection, diagnosis, and prognostication. We reviewed six promising ... ...

Abstract	Sepsis was recently redefined as a life-threatening disease involving organ dysfunction caused by a dysregulated host response to infection. Biomarkers play an important role in early detection, diagnosis, and prognostication. We reviewed six promising biomarkers for detecting sepsis and systemic infection, including C-reactive protein (CRP), procalcitonin (PCT), interleukin-6 (IL-6), CD64, presepsin, and sTREM-1. Among the recent studies, we found the following risks of bias: only a few studies adopted the random or consecutive sampling strategy; extensive case-control analysis, which worsened the over-estimated performance; most of the studies used post hoc cutoff values; and heterogeneity with respect to the inclusion criteria, small sample sizes, and different quantitative synthesis methods applied in meta-analyses. We recommend that CD64 and presepsin should be considered as the most promising biomarkers for diagnosing sepsis. Future studies should enroll a larger sample size with a cohort rather than a case-control study design. A random or consecutive study design with a pre-specified laboratory threshold, consistent sampling timing, and an updated definition of sepsis will also increase the reliability of the studies. Further investigations of appropriate specimens, testing assays, and cutoff levels for specific biomarkers are also warranted.
Language	English
Publishing date	2020-11-12
Publishing country	Switzerland
Document type	Journal Article ; Review
ZDB-ID	2720867-9
ISSN	2227-9059
ISSN	2227-9059
DOI	10.3390/biomedicines8110494
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