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Takada T, Yoshida K, Hamaguchi S, Fukuhara S. Role of Inflammatory Markers in the Assessment of Meningitis in Adult Patients with Fever and Headache. J Infect Chemother 2024:S1341-321X(24)00125-9. [PMID: 38679384 DOI: 10.1016/j.jiac.2024.04.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Revised: 04/20/2024] [Accepted: 04/25/2024] [Indexed: 05/01/2024]
Abstract
BACKGROUND Meningitis, especially of bacterial origin, is a medical emergency that must be diagnosed promptly. However, due to the associated risks of complications of lumbar puncture, it is crucial to identify individuals who truly need it. The aim of this study was to assess the diagnostic role of inflammatory markers in distinguishing among patients without meningitis, those with aseptic meningitis, and those with bacterial meningitis. METHODS This was a retrospective, diagnostic study at an acute care hospital, involving adult patients who presented to either ambulatory care or the emergency department with fever and headache, but without altered mental status or neurological deficits. Inflammatory markers (C-reactive protein [CRP], mean platelet volume, neutrophil-lymphocyte ratio, and red cell distribution width) were assessed as index tests. An expert panel classified patients into three groups: no meningitis, aseptic meningitis, and bacterial meningitis using predefined criteria. RESULTS Of the 80 patients, 52 had no meningitis, 27 had aseptic meningitis, and 1 had bacterial meningitis. Of the inflammatory markers investigated, only CRP showed potential usefulness in differentiating these three diagnostic groups, with median values of 5.6 (interquartile range [IQR] 2.1, 11.3) mg/dL in those without meningitis, 0.2 (IQR 0.1, 1.2) mg/dL in those with aseptic meningitis, and notably elevated at 21.7 mg/dL in the patient with bacterial meningitis. CONCLUSION In adult patients presenting with fever and headache in an emergency setting, CRP was the only marker that demonstrated potential diagnostic utility in distinguishing among those with no meningitis, aseptic meningitis, and bacterial meningitis.
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Affiliation(s)
- Toshihiko Takada
- Department of General Medicine, Shirakawa Satellite for Teaching And Research (STAR), Fukushima Medical University, 2-1 Toyochi Kamiyajiro, Shirakawa, Fukushima 961-0005, Japan.
| | - Kenji Yoshida
- Department of General Medicine, Shirakawa Satellite for Teaching And Research (STAR), Fukushima Medical University, 2-1 Toyochi Kamiyajiro, Shirakawa, Fukushima 961-0005, Japan
| | - Sugihiro Hamaguchi
- Department of General Internal Medicine, Fukushima Medical University, 1 Hikarigaoka, Fukushima 960-1295, Japan
| | - Shunichi Fukuhara
- Department of General Medicine, Shirakawa Satellite for Teaching And Research (STAR), Fukushima Medical University, 2-1 Toyochi Kamiyajiro, Shirakawa, Fukushima 961-0005, Japan; Section of Clinical Epidemiology, Department of Community Medicine, Graduate School of Medicine, Kyoto University, 54 Shogo-in Kawaramachi, Sakyo-ku, Kyoto 606-8507, Japan
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2
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Nan N, Tian L. A new accuracy metric under three classes when subclasses are involved and its confidence interval estimation. Stat Med 2023; 42:5207-5228. [PMID: 37779490 DOI: 10.1002/sim.9908] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 07/26/2023] [Accepted: 09/04/2023] [Indexed: 10/03/2023]
Abstract
"Compound multi-class classification" refers to the setting where three or more main classes are involved and at least one of the main classes have multiple subclasses. A common practice in evaluating biomarker performance under "compound multi-class classification" is "subclasses pooling." In this article, we first explore the downsides of accuracy metrics based on pooled data. Then we propose a new accuracy measure proper for "compound multi-class classification" with three ordinal main classes, namely "volume under compoundR O C $$ ROC $$ surface (V U S C $$ VU{S}_C $$ )." The proposedV U S C $$ VU{S}_C $$ evaluates the accuracy of a biomarker appropriately by identifying main classes without requiring specification of an ordering for marker values of subclasses within each main class. For confidence interval estimation ofV U S C $$ VU{S}_C $$ , both parametric and nonparametric methods are studied, and simulation studies are carried out to assess coverage probabilities. A subset of Alzheimer's Disease Neuroimaging Initiative study dataset is analyzed.
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Affiliation(s)
- Nan Nan
- Department of Biostatistics, University at Buffalo, Buffalo, New York, USA
| | - Lili Tian
- Department of Biostatistics, University at Buffalo, Buffalo, New York, USA
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3
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Liang JX, Ampuero J, Niu H, Imajo K, Noureddin M, Behari J, Lee DH, Ehman RL, Rorsman F, Vessby J, Lacalle JR, Mózes FE, Pavlides M, Anstee QM, Harrison SA, Castell J, Loomba R, Romero-Gómez M. An individual patient data meta-analysis to determine cut-offs for and confounders of NAFLD-fibrosis staging with magnetic resonance elastography. J Hepatol 2023; 79:592-604. [PMID: 37121437 PMCID: PMC10623141 DOI: 10.1016/j.jhep.2023.04.025] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 03/23/2023] [Accepted: 04/12/2023] [Indexed: 05/02/2023]
Abstract
BACKGROUND & AIMS We conducted an individual patient data meta-analysis to establish stiffness cut-off values for magnetic resonance elastography (MRE) in staging liver fibrosis and to assess potential confounding factors. METHODS A systematic review of the literature identified studies reporting MRE data in patients with NAFLD. Data were obtained from the corresponding authors. The pooled diagnostic cut-off value for the various fibrosis stages was determined in a two-stage meta-analysis. Multilevel modelling methods were used to analyse potential confounding factors influencing the diagnostic accuracy of MRE in staging liver fibrosis. RESULTS Eight independent cohorts comprising 798 patients were included in the meta-analysis. The area under the receiver operating characteristic curve (AUROC) for MRE in detecting significant fibrosis was 0.92 (sensitivity, 79%; specificity, 89%). For advanced fibrosis, the AUROC was 0.92 (sensitivity, 87%; specificity, 88%). For cirrhosis, the AUROC was 0.94 (sensitivity, 88%, specificity, 89%). Cut-offs were defined to explore concordance between MRE and histopathology: ≥F2, 3.14 kPa (pretest probability, 39.4%); ≥F3, 3.53 kPa (pretest probability, 24.1%); and F4, 4.45 kPa (pretest probability, 8.7%). In generalized linear mixed model analysis, histological steatohepatitis with higher inflammatory activity (odds ratio 2.448, 95% CI 1.180-5.079, p <0.05) and high gamma-glutamyl transferase (GGT) concentration (>120U/L) (odds ratio 3.388, 95% CI 1.577-7.278, p <0.01] were significantly associated with elevated liver stiffness, and thus affecting accuracy in staging early fibrosis (F0-F1). Steatosis, as measured by magnetic resonance imaging proton density fat fraction, and body mass index(BMI) were not confounders. CONCLUSIONS MRE has excellent diagnostic performance for significant, advanced fibrosis and cirrhosis in patients with NAFLD. Elevated inflammatory activity and GGT level may lead to overestimation of early liver fibrosis, but anthropometric measures such as BMI or the degree of steatosis do not. IMPACT AND IMPLICATIONS This individual patient data meta-analysis of eight international cohorts, including 798 patients, demonstrated that MRE achieves excellent diagnostic accuracy for significant, advanced fibrosis and cirrhosis in patients with NAFLD. Cut-off values (significant fibrosis, 3.14 kPa; advanced fibrosis, 3.53 kPa; and cirrhosis, 4.45 kPa) were established. Elevated inflammatory activity and gamma-glutamyltransferase level may affect the diagnostic accuracy of MRE, leading to overestimation of liver fibrosis in early stages. We observed no impact of diabetes, obesity, or any other metabolic disorder on the diagnostic accuracy of MRE.
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Affiliation(s)
- Jia-Xu Liang
- Digestive Diseases Unit and CIBERehd, Virgen del Rocío University Hospital, Seville, Spain; Institute of Biomedicine of Seville (HUVR/CSIC/US), University of Seville, Seville, Spain; Department of Diagnostic Radiology, The Fifth Clinical Medical College of Henan University of Chinese Medicine (Zhengzhou People's Hospital), Zhengzhou, China
| | - Javier Ampuero
- Digestive Diseases Unit and CIBERehd, Virgen del Rocío University Hospital, Seville, Spain; Institute of Biomedicine of Seville (HUVR/CSIC/US), University of Seville, Seville, Spain
| | - Hao Niu
- Digestive System and Clinical Pharmacology Unit, Virgen de la Victoria University Hospital, Biomedical Research Institute of Malaga and Nanomedicine Platform-IBIMA (Plataforma BIONAND), University of Malaga, Málaga, Spain; Biomedical Research Network Center for Hepatic and Digestive Diseases (CIBERehd), Carlos III Health Institute, Madrid, Spain
| | - Kento Imajo
- Department of Gastroenterology, Yokohama City University Graduate School of Medicine; Yokohama, Japan
| | - Mazen Noureddin
- Fatty Liver Program, Division of Digestive and Liver Diseases, Comprehensive Transplant Program, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Jaideep Behari
- Department of Medicine, Division of Gastroenterology, Hepatology and Nutrition, Center for Liver Diseases, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Dae Ho Lee
- Department of Internal Medicine, Gachon University College of Medicine (Gachon University Gil Medical Center), Incheon, South Korea
| | - Richard L Ehman
- Department of Diagnostic Radiology, Mayo Clinic College of Medicine, Rochester, MN, USA
| | - Fredrik Rorsman
- Department of Medical Sciences, Section of Gastroenterology and Hepatology, Uppsala University, Uppsala, Sweden
| | - Johan Vessby
- Department of Medical Sciences, Section of Gastroenterology and Hepatology, Uppsala University, Uppsala, Sweden
| | - Juan R Lacalle
- Biostatistics Unit, Department of Preventive Medicine and Public Health, University of Seville, Seville, Spain
| | - Ferenc E Mózes
- Oxford Centre for Clinical Magnetic Resonance Research, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Michael Pavlides
- Oxford Centre for Clinical Magnetic Resonance Research, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK; Translational Gastroenterology Unit, University of Oxford, Oxford, UK
| | - Quentin M Anstee
- Translational and Clinical Research Institute; Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne, UK; Newcastle NIHR Biomedical Research Centre, Newcastle Upon Tyne Hospitals, NHS Trust, Newcastle Upon Tyne, UK
| | - Stephen A Harrison
- Oxford Centre for Clinical Magnetic Resonance Research, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Javier Castell
- Department of Radiology, Virgen del Rocío University Hospital, Seville, Spain
| | - Rohit Loomba
- Division of Epidemiology, Department of Family Medicine and Public Health, University of California San Diego, La Jolla, CA, USA; NAFLD Research Center, Division of Gastroenterology and Hepatology, Department of Medicine, University of California San Diego, La Jolla, CA, USA
| | - Manuel Romero-Gómez
- Digestive Diseases Unit and CIBERehd, Virgen del Rocío University Hospital, Seville, Spain; Institute of Biomedicine of Seville (HUVR/CSIC/US), University of Seville, Seville, Spain.
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Feng Y, Tian L. Flexible diagnostic measures and new cut-point selection methods under multiple ordered classes. Pharm Stat 2021; 21:220-240. [PMID: 34449107 DOI: 10.1002/pst.2166] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Revised: 07/21/2021] [Accepted: 08/01/2021] [Indexed: 11/08/2022]
Abstract
Medical diagnosis is essentially a classification problem and usually it is done with multiple ordered classes. For example, cancer diagnosis might be "non-malignant," "early stage," or "late stage." Therefore, appropriate measures are needed to assess the accuracy of diagnostic markers under multiple ordered classes. However, all existing measures fail to differentiate among some distinctly different biomarkers. This paper presents a multi-step procedure for evaluating biomarker accuracy under multiple ordered classes. This procedure leads to two new flexible overall measures as well as three new cut-point selection methods with great computational ease. The performance of proposed measures and cut-point selection methods are numerically explored via a simulation study. In the end, an ovarian cancer dataset from the Prostate, Lung, Colorectal, and Ovarian cancer study is analyzed. The proposed accuracy measures were estimated for markers CA125 and HE4, and cut-points were estimated for the risk of ovarian malignancy algorithm score.
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Affiliation(s)
- Yingdong Feng
- Department of Biostatistics, University at Buffalo, Buffalo, New York, USA
| | - Lili Tian
- Department of Biostatistics, University at Buffalo, Buffalo, New York, USA
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Hua J, Tian L. Combining multiple biomarkers to linearly maximize the diagnostic accuracy under ordered multi-class setting. Stat Methods Med Res 2021; 30:1101-1118. [PMID: 33522437 DOI: 10.1177/0962280220987587] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Either in clinical study or biomedical research, it is a common practice to combine multiple biomarkers to improve the overall diagnostic performance. Despite the fact there exist a large number of statistical methods for biomarker combination under binary classification, research on this topic under multi-class setting is sparse. The overall diagnostic accuracy, i.e. the sum of correct classification rates, directly measures the classification accuracy of the combined biomarkers. Hence the overall accuracy can serve as an important objective function for biomarker combination, especially when the combined biomarkers are used for the purpose of making medical diagnosis. In this paper, we address the problem of combining multiple biomarkers to directly maximize the overall diagnostic accuracy by presenting several grid search methods and derivation-based methods. A comprehensive simulation study was conducted to compare the performances of these methods. An ovarian cancer data set is analyzed in the end.
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Affiliation(s)
- Jia Hua
- Department of Biostatistics, School of Public Health and Health Professions, University at Buffalo, Buffalo, NY, USA
| | - Lili Tian
- Department of Biostatistics, School of Public Health and Health Professions, University at Buffalo, Buffalo, NY, USA
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Yang J, Kuan PF, Li J. Non-monotone transformation of biomarkers to improve diagnostic and screening accuracy in a DNA methylation study with trichotomous phenotypes. Stat Methods Med Res 2019; 29:2360-2389. [DOI: 10.1177/0962280219882047] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
We propose a non-monotone transformation to biomarkers in order to improve the diagnostic and screening accuracy. The proposed quadratic transformation only involves modeling the distribution means and variances of the biomarkers and is therefore easy to implement in practice. Mathematical justification was rigorously established to support the validity of the proposed transformation. We conducted extensive simulation studies to assess the performance of the proposed method and compared the new method with the traditional methods. Case studies on real biomedical and epigenetics data were provided to illustrate the proposed transformation. In particular, the proposed method improved the AUC values for a large number of markers in a DNA methylation study and consequently led to the identification of greater number of important biomarkers and biologically meaningful genetic pathways.
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Affiliation(s)
- Jianping Yang
- School of Science, Zhejiang Sci-Tech University, Hangzhou, Zhejiang, China
| | - Pei-Fen Kuan
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA
| | - Jialiang Li
- Department of Statistics and Applied Probability, National University of Singapore, Singapore
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