1
|
Ishigo T, Fujii S, Ibe Y, Aigami T, Nakano K, Fukudo M, Yoshida H, Tanaka H, Ebihara F, Maruyama T, Hamada Y, Suzuki A, Fujihara H, Yamaguchi F, Samura M, Nagumo F, Komatsu T, Tomizawa A, Takuma A, Chiba H, Nishi Y, Enoki Y, Taguchi K, Matsumoto K. Flowchart for predicting achieving the target area under the concentration-time curve of vancomycin in critically ill Japanese patients: A multicenter retrospective study. J Infect Chemother 2024; 30:329-336. [PMID: 37925103 DOI: 10.1016/j.jiac.2023.11.001] [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: 07/30/2023] [Revised: 10/05/2023] [Accepted: 11/02/2023] [Indexed: 11/06/2023]
Abstract
INTRODUCTION In therapeutic drug monitoring (TDM) of vancomycin (VCM), the area under the concentration-time curve (AUC) is related to the clinical efficacy and toxicity. Therefore, herein, we examined the factors associated with achieving the target AUC at follow-up and developed a decision flowchart for achieving the target AUC in critically ill patients. METHODS This multicenter retrospective observational study was conducted at eight hospitals. We retrospectively analyzed data from patients who had received VCM in the intensive care unit from January 2020 to December 2022. Decision-tree (DT) analysis was performed using factors with p < 0.1 in univariate analysis as the independent variables. Case data were split up to two times, and four subgroups were included. The primary endpoint was achieving the target AUC at the follow-up TDM (AUCfollow-up) and target AUCfollow-up achievement was defined as an AUC of 400-600 μg‧h/mL. The initial AUC values were calculated with the 2-point concentrations (peak and trough) using the Bayesian estimation software Practical AUC-guided TDM (PAT). RESULTS Among 70 patients (median age [interquartile range], 66 [56, 79] years; 50 % women), the AUCfollow-up was achieved in 70 % (49/70). Three factors were selected for the decision flow chart: predicted AUCfollow-up of 400-600 μg‧h/mL, dosing at 12-h intervals, and CCr of 130 mL/min/1.73 m2 or higher; the accuracy was adequate (92 %, R2 0.52). CONCLUSION We successfully identified the factors associated with achieving the target AUC of VCM at follow-up TDM and developed a simple-to-use DT model. However, the validity of the findings needs to be evaluated.
Collapse
Affiliation(s)
- Tomoyuki Ishigo
- Department of Pharmacy, Sapporo Medical University Hospital, Sapporo, Japan
| | - Satoshi Fujii
- Department of Pharmacy, Sapporo Medical University Hospital, Sapporo, Japan
| | - Yuta Ibe
- Department of Pharmacy, Sapporo Medical University Hospital, Sapporo, Japan
| | - Tomohiro Aigami
- Department of Pharmacy, Sapporo Medical University Hospital, Sapporo, Japan
| | - Keita Nakano
- Department of Pharmacy, Sapporo Medical University Hospital, Sapporo, Japan
| | - Masahide Fukudo
- Department of Pharmacy, Sapporo Medical University Hospital, Sapporo, Japan
| | - Hiroaki Yoshida
- Department of Pharmacy, Kyorin University Hospital, Mitaka, Japan
| | - Hiroaki Tanaka
- Department of Pharmacy, Kyorin University Hospital, Mitaka, Japan
| | - Fumiya Ebihara
- Department of Pharmacy, Tokyo Women's Medical University Hospital, Tokyo, Japan
| | - Takumi Maruyama
- Department of Pharmacy, Tokyo Women's Medical University Hospital, Tokyo, Japan
| | - Yukihiro Hamada
- Department of Pharmacy, Tokyo Women's Medical University Hospital, Tokyo, Japan
| | - Ayako Suzuki
- Department of Pharmacy, Showa University Fujigaoka Hospital, Yokohama, Japan
| | - Hisato Fujihara
- Department of Pharmacy, Showa University Fujigaoka Hospital, Yokohama, Japan
| | - Fumihiro Yamaguchi
- Department of Respiratory Medicine, Showa University Fujigaoka Hospital, Yokohama, Japan
| | - Masaru Samura
- Department of Pharmacy, Yokohama General Hospital, Yokohama, Japan; Division of Pharmacodynamics, Keio University Faculty of Pharmacy, Tokyo, Japan
| | - Fumio Nagumo
- Department of Pharmacy, Yokohama General Hospital, Yokohama, Japan
| | - Toshiaki Komatsu
- Department of Pharmacy, Kitasato University Hospital, Sagamihara, Japan
| | - Atsushi Tomizawa
- Department of Pharmacy, Kitasato University Hospital, Sagamihara, Japan
| | - Akitoshi Takuma
- Department of Pharmacy, Showa University Northern Yokohama Hospital, Yokohama, Japan
| | - Hiroaki Chiba
- Department of Pharmacy, Tohoku Kosai Hospital, Sendai, Japan
| | - Yoshifumi Nishi
- Center for Pharmacist Education, School of Pharmacy, Nihon University, Funabashi, Japan
| | - Yuki Enoki
- Division of Pharmacodynamics, Keio University Faculty of Pharmacy, Tokyo, Japan
| | - Kazuaki Taguchi
- Division of Pharmacodynamics, Keio University Faculty of Pharmacy, Tokyo, Japan
| | - Kazuaki Matsumoto
- Division of Pharmacodynamics, Keio University Faculty of Pharmacy, Tokyo, Japan.
| |
Collapse
|
2
|
Palmal S, Arya N, Saha S, Tripathy S. Breast cancer survival prognosis using the graph convolutional network with Choquet fuzzy integral. Sci Rep 2023; 13:14757. [PMID: 37679421 PMCID: PMC10485011 DOI: 10.1038/s41598-023-40341-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Accepted: 08/09/2023] [Indexed: 09/09/2023] Open
Abstract
Breast cancer is the most prevalent kind of cancer among women and there is a need for a reliable algorithm to predict its prognosis. Previous studies focused on using gene expression data to build predictive models. However, recent advancements have made multi-omics cancer data sets (gene expression, copy number alteration, etc.) accessible. This has acted as the motivation for the creation of a novel model that utilizes a graph convolutional network (GCN) and Choquet fuzzy ensemble, incorporating multi-omics and clinical data retrieved from the publicly available METABRIC Database. In this study, graphs have been used to extract structural information, and a Choquet Fuzzy Ensemble with Logistic Regression, Random Forest, and Support Vector Machine as base classifiers has been employed to classify breast cancer patients as short-term or long-term survivors. The model has been run using all possible combinations of gene expression, copy number alteration, and clinical modality, and the results have been reported. Furthermore, a comparison has been made between the obtained results and different baseline models and state-of-the-art to demonstrate the efficacy of the proposed model in terms of different metrics. The results of this model based on Accuracy, Matthews correlation coefficient, Precision, Sensitivity, Specificity, Balanced Accuracy, and F1-Measure are 0.820, 0.528, 0.630, 0.666, 0.871, 0.769, and 0.647, respectively.
Collapse
Affiliation(s)
- Susmita Palmal
- Department of Computer Science and Engineering, Indian Institute of Technology, Patna, Bihar, 801106, India.
| | - Nikhilanand Arya
- Department of Computer Science and Engineering, Indian Institute of Technology, Patna, Bihar, 801106, India
| | - Sriparna Saha
- Department of Computer Science and Engineering, Indian Institute of Technology, Patna, Bihar, 801106, India
| | - Somanath Tripathy
- Department of Computer Science and Engineering, Indian Institute of Technology, Patna, Bihar, 801106, India
| |
Collapse
|
3
|
Alanazi A, Aldakhil L, Aldhoayan M, Aldosari B. Machine Learning for Early Prediction of Sepsis in Intensive Care Unit (ICU) Patients. MEDICINA (KAUNAS, LITHUANIA) 2023; 59:1276. [PMID: 37512087 PMCID: PMC10385427 DOI: 10.3390/medicina59071276] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Revised: 06/26/2023] [Accepted: 07/07/2023] [Indexed: 07/30/2023]
Abstract
Background and Objectives: Early detection of sepsis is crucial and can save lives. However, identifying sepsis early and accurately remains a difficult task in the medical field. This study aims to investigate a new machine-learning approach. By analyzing the clinical laboratory results and vital signs of adult patients in the ICU, this approach can predict and detect the initial signs of sepsis. Materials and Methods: To examine survival rates and predict outcomes, the study utilized several models, including the proportional hazards model and data mining algorithms. We analyzed data from the BESTCare database at KAMC, with a focus on patients aged 14 and older who were admitted to the ICU between April and October 2018. We conducted a thorough analysis of the medical records of a total of 1182 patients who were diagnosed with sepsis. Results: We studied two approaches to predict sepsis in ICU patients. The regression model utilizing survival analysis showed moderate predictive ability, emphasizing the importance of only three factors-time (from sepsis to an outcome; discharge or death), lactic acid, and temperature-had a significant p-value (p = 0.000568, p = 0.01, p = 0.02, respectively). Other data mining algorithms may have limitations due to their assumptions of variable independence and linear classification nature. Conclusions: To achieve progress and accuracy in the field of sepsis prediction, it is important to continuously strive for improvement. By meticulously cleaning and selecting data attributes, we can create a strong foundation for future advancements in this area.
Collapse
Affiliation(s)
- Abdullah Alanazi
- Department of Health Informatics, College of Public Health and Health Informatics, King Saud Ibn Abdulaziz University for Health Sciences, Riyadh 11481, Saudi Arabia
- King Abdullah International Medical Research Center, Riyadh 14611, Saudi Arabia
| | - Lujain Aldakhil
- Department of Health Informatics, College of Public Health and Health Informatics, King Saud Ibn Abdulaziz University for Health Sciences, Riyadh 11481, Saudi Arabia
- King Abdullah International Medical Research Center, Riyadh 14611, Saudi Arabia
| | - Mohammed Aldhoayan
- Department of Health Informatics, College of Public Health and Health Informatics, King Saud Ibn Abdulaziz University for Health Sciences, Riyadh 11481, Saudi Arabia
- King Abdullah International Medical Research Center, Riyadh 14611, Saudi Arabia
| | - Bakheet Aldosari
- Department of Health Informatics, College of Public Health and Health Informatics, King Saud Ibn Abdulaziz University for Health Sciences, Riyadh 11481, Saudi Arabia
- King Abdullah International Medical Research Center, Riyadh 14611, Saudi Arabia
| |
Collapse
|
4
|
Miyai T, Imai S, Yoshimura E, Kashiwagi H, Sato Y, Ueno H, Takekuma Y, Sugawara M. Machine Learning-Based Model for Estimating Vancomycin Maintenance Dose to Target the Area under the Concentration Curve of 400–600 mg·h/L in Japanese Patients. Biol Pharm Bull 2022; 45:1332-1339. [DOI: 10.1248/bpb.b22-00305] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Affiliation(s)
| | | | - Eri Yoshimura
- Department of Pharmacy, Sunagawa City Medical Center
| | | | - Yuki Sato
- Faculty of Pharmaceutical Sciences, Hokkaido University
| | - Hidefumi Ueno
- Department of Pharmacy, Sunagawa City Medical Center
| | - Yoh Takekuma
- Department of Pharmacy, Hokkaido University Hospital
| | - Mitsuru Sugawara
- Global Station for Biosurfaces and Drug Discovery, Hokkaido University
| |
Collapse
|
5
|
Wu WT, Li YJ, Feng AZ, Li L, Huang T, Xu AD, Lyu J. Data mining in clinical big data: the frequently used databases, steps, and methodological models. Mil Med Res 2021; 8:44. [PMID: 34380547 PMCID: PMC8356424 DOI: 10.1186/s40779-021-00338-z] [Citation(s) in RCA: 161] [Impact Index Per Article: 53.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/24/2020] [Accepted: 08/03/2021] [Indexed: 02/07/2023] Open
Abstract
Many high quality studies have emerged from public databases, such as Surveillance, Epidemiology, and End Results (SEER), National Health and Nutrition Examination Survey (NHANES), The Cancer Genome Atlas (TCGA), and Medical Information Mart for Intensive Care (MIMIC); however, these data are often characterized by a high degree of dimensional heterogeneity, timeliness, scarcity, irregularity, and other characteristics, resulting in the value of these data not being fully utilized. Data-mining technology has been a frontier field in medical research, as it demonstrates excellent performance in evaluating patient risks and assisting clinical decision-making in building disease-prediction models. Therefore, data mining has unique advantages in clinical big-data research, especially in large-scale medical public databases. This article introduced the main medical public database and described the steps, tasks, and models of data mining in simple language. Additionally, we described data-mining methods along with their practical applications. The goal of this work was to aid clinical researchers in gaining a clear and intuitive understanding of the application of data-mining technology on clinical big-data in order to promote the production of research results that are beneficial to doctors and patients.
Collapse
Affiliation(s)
- Wen-Tao Wu
- Department of Clinical Research, The First Affiliated Hospital of Jinan University, Tianhe District, 613 W. Huangpu Avenue, Guangzhou, 510632, Guangdong, China.,School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, 710061, Shaanxi, China
| | - Yuan-Jie Li
- Department of Human Anatomy, Histology and Embryology, School of Basic Medical Sciences, Xi'an Jiaotong University Health Science Center, Xi'an, 710061, Shaanxi, China
| | - Ao-Zi Feng
- Department of Clinical Research, The First Affiliated Hospital of Jinan University, Tianhe District, 613 W. Huangpu Avenue, Guangzhou, 510632, Guangdong, China
| | - Li Li
- Department of Clinical Research, The First Affiliated Hospital of Jinan University, Tianhe District, 613 W. Huangpu Avenue, Guangzhou, 510632, Guangdong, China
| | - Tao Huang
- Department of Clinical Research, The First Affiliated Hospital of Jinan University, Tianhe District, 613 W. Huangpu Avenue, Guangzhou, 510632, Guangdong, China
| | - An-Ding Xu
- Department of Neurology, The First Affiliated Hospital of Jinan University, Tianhe District, 613 W. Huangpu Avenue, Guangzhou, 510632, Guangdong, China.
| | - Jun Lyu
- Department of Clinical Research, The First Affiliated Hospital of Jinan University, Tianhe District, 613 W. Huangpu Avenue, Guangzhou, 510632, Guangdong, China.
| |
Collapse
|
6
|
A Risk Prediction Flowchart of Vancomycin-Induced Acute Kidney Injury to Use When Starting Vancomycin Administration: A Multicenter Retrospective Study. Antibiotics (Basel) 2020; 9:antibiotics9120920. [PMID: 33352848 PMCID: PMC7766575 DOI: 10.3390/antibiotics9120920] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Revised: 12/09/2020] [Accepted: 12/17/2020] [Indexed: 02/06/2023] Open
Abstract
We previously constructed a risk prediction model of vancomycin (VCM)-associated nephrotoxicity for use when performing initial therapeutic drug monitoring (TDM), using decision tree analysis. However, we could not build a model to be used at the time of initial administration due to insufficient sample size. Therefore, we performed a multicenter study at four hospitals in Japan. We investigated patients who received VCM intravenously at a standard dose from the first day until the initial TDM from November 2011 to March 2019. Acute kidney injury (AKI) was defined according to the criteria established by the “Kidney disease: Improving global outcomes” group. We extracted potential risk factors that could be evaluated on the day of initial administration and constructed a flowchart using a chi-squared automatic interaction detection algorithm. Among 843 patients, 115 (13.6%) developed AKI. The flowchart comprised three splitting variables (concomitant drugs (vasopressor drugs and tazobactam/piperacillin) and body mass index ≥ 30) and four subgroups. The incidence rates of AKI ranged from 9.34 to 36.8%, and they were classified as low-, intermediate-, and high-risk groups. The accuracy of flowchart was judged appropriate (86.4%). We successfully constructed a simple flowchart predicting VCM-induced AKI to be used when starting VCM administration.
Collapse
|