Prediction of risk factors for linezolid-induced thrombocytopenia based on neural network model
Ontology highlight
ABSTRACT:
SUBMITTER: Zhao X
PROVIDER: S-EPMC10915059 | biostudies-literature | 2024 Jan
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:The pharmacological mechanisms underlying the adverse effects of linezolid on thrombocytopenia have not been conclusively determined. This network pharmacology study aimed at investigating the potential pharmacological mechanisms of linezolid-induced adverse reactions in thrombocytopenia. In this study, target genes for linezolid and thrombocytopenia were compared and analyzed. Overlapping thrombocytopenia-associated targets and predicted targets of linezolid were imported to establish protein-protein interaction networks. Gene Ontology and the Kyoto Encyclopedia of Genes and Genome pathway enrichment analyses were performed to determine the enriched biological terms and pathways. The mechanisms involved in linezolid-induced thrombocytopenia were established to be associated with various biological processes, including T cell activation, peptidyl serine modification, and peptidyl serine phosphorylation. The top five relevant protein targets were obtained, including ALB, AKT1, EGFR, IL6, and MTOR. Enrichment analysis showed that the targets of linezolid were positively correlated with T cell activation responses. The mechanism of action of linezolid was positively correlated with the PI3K-AKT signaling pathway and negatively correlated with the Ras signaling pathway. We identified the important protein targets and signaling pathways involved in linezolid-induced thrombocytopenia in anti-infection therapy, providing new information for subsequent studies on the pathogenesis of drug-induced thrombocytopenia and potential therapeutic strategies for rational use of linezolid in clinical settings.
| S-EPMC9588367 | biostudies-literature
Project description:In order to solve the problem of low accuracy of traditional construction project risk prediction, a project risk prediction model based on EW-FAHP and 1D-CNN(One Dimensional Convolution Neural Network) is proposed. Firstly, the risk evaluation index value of construction project is selected by literature analysis method, and the comprehensive weight of risk index is obtained by combining entropy weight method (EW) and fuzzy analytic hierarchy process (FAHP). The risk weight is input into the 1D-CNN model for training and learning, and the prediction values of construction period risk and cost risk are output to realize the risk prediction. The experimental results show that the average absolute error of the construction period risk and cost risk of the risk prediction model proposed in this paper is below 0.1%, which can meet the risk prediction of construction projects with high accuracy.
| S-EPMC7872238 | biostudies-literature
Project description:Risk of hyperuricemia (HU) has been shown to be strongly associated with dietary factors. However, there is scarce evidence on prediction models incorporating dietary factors to estimate the risk of HU. The aim of this study was to develop a prediction model to predict the risk of HU in Chinese adults based on dietary information. Our study was based on a cross-sectional survey, which recruited 1,488 community residents aged 18 to 60 years in Beijing from October 2010 to January 2011. The eligible participants were randomly divided into a training set (n1 = 992) and a validation set (n2 = 496) in the ratio of 2:1. We developed the prediction model in three stages. We first used a logistic regression model (LRM) based on the training set to select a set of dietary risk factors which were related to the risk of HU. Artificial neural network (ANN) was then used to construct the prediction model using the training set. Finally, we used receiver operating characteristic (ROC) curve analysis to assess the accuracy of the prediction model using training and validation sets. In the training set, the mean age of participants with and without HU was 39.3 (standard deviation [SD]: 9.65) and 38.2 (SD: 9.38) years, respectively. Patients with HU consisted of 101 males (77.7%) and 29 females (22.3%). The LRM found that food frequency (vegetables [odds ratio (OR) = 0.73], meat [0.72], eggs [0.80], plant oil [0.78], tea [0.51], eating habits (breakfast [OR = 1.28]), and the salty cooking style (OR = 1.33) were associated with risk of HU. In the ANN analysis, we selected a three-layer back propagation neural network (BPNN) model with 14, 3, and 1 neuron in the input, hidden, and output layers, respectively, as the best prediction model. The areas under the ROC of the training and validation sets were 0.827 and 0.814, respectively. HU would occur when the incidence probability is greater than 0.128. The indicators of accuracy, sensitivity, specificity, and Yuden Index suggested that the ANN model in our study is successful and valuable. This study suggests that the ANN model could be used to predict the risk of HU in Chinese adults. Further prospective studies are needed to improve the accuracy and to generalize the use of model.
| S-EPMC6983978 | biostudies-literature
Project description:BackgroundMetabolic syndrome (MetS) is a major public health concern due to its high prevalence and association with heart disease and diabetes. Artificial neural networks (ANN) are emerging as a reliable means of modelling relationships towards understanding complex illness situations such as MetS. Using ANN, this research sought to clarify predictors of metabolic syndrome (MetS) in a working age population.MethodsFour hundred sixty-eight employees of an oil refinery in Iran consented to providing anthropometric and biochemical measurements, and survey data pertaining to lifestyle, work-related stressors and sleep variables. National Cholesterol Education Programme Adult Treatment Panel ІІI criteria was used for determining MetS status. The Management Standards Indicator Tool and STOP-BANG questionnaire were used to measure work-related stress and obstructive sleep apnoea respectively. With 17 input variables, multilayer perceptron was used to develop ANNs in 16 rounds of learning. ANNs were compared to logistic regression models using the mean squared error criterion for validation.ResultsSex, age, exercise habit, smoking, high risk of obstructive sleep apnoea, and work-related stressors, particularly Role, all significantly affected the odds of MetS, but shiftworking did not. Prediction accuracy for an ANN using two hidden layers and all available input variables was 89%, compared to 72% for the logistic regression model. Sensitivity was 82.5% for ANN compared to 67.5% for the logistic regression, while specificities were 92.2 and 74% respectively.ConclusionsOur analyses indicate that ANN models which include psychosocial stressors and sleep variables as well as biomedical and clinical variables perform well in predicting MetS. The findings can be helpful in designing preventative strategies to reduce the cost of healthcare associated with MetS in the workplace.
| S-EPMC7659072 | biostudies-literature
Project description:BackgroundThis study aims to develop an oral microbiota-based model for gastric cancer (GC) risk stratification and prognosis prediction.MethodsOral microbial markers for GC prognosis and risk stratification were identified from 99 GC patients, and their predictive potential was validated on an external dataset of 111 GC patients. The identified bacterial markers were used to construct a Deep Neural Network (DNN) model, a Random Forest (RF) model, and a Support Vector Machine (SVM) model for predicting GC prognosis.ResultsGC patients with <3 years of survival showed a higher abundance of Aggregatibacter and diminished abundances of Filifactor and Moryella than those who survived ≥3 years. The Boruta algorithm unearthed Leptotrichia as another significant marker for GC prognosis. Consequently, a DNN model was constructed based on the relative abundances of these bacteria, predicting 3-year and 5-year survival in GC patients with Area Under Curve of 0.814 and 0.912, respectively. Notably, the DNN model outperformed the TNM staging system, SVM and RF models. The prognostic value of these bacterial markers was further reinforced by external validation.ConclusionThe oral microbiota-based DNN model may advance GC prognosis. The biological functions of these oral bacterial markers warrant further investigation from the perspective of GC progression.
| S-EPMC11749243 | biostudies-literature
Project description:In this study we wished to determine the rank order of risk factors for endometrial cancer and calculate a pooled risk and percentage risk for each factor using a statistical meta-analysis approach. The next step was to design a neural network computer model to predict the overall increase or decreased risk of cancer for individual patients. This would help to determine whether this prediction could be used as a tool to decide if a patient should be considered for testing and to predict diagnosis, as well as to suggest prevention measures to patients. A meta-analysis of existing data was carried out to calculate relative risk, followed by design and implementation of a risk prediction computational model based on a neural network algorithm. Meta-analysis data were collated from various settings from around the world. Primary data to test the model were collected from a hospital clinic setting. Data from 40 patients notes currently suspected of having endometrial cancer and undergoing investigations and treatment were collected to test the software with their cancer diagnosis not revealed to the software developers. The forest plots allowed an overall relative risk and percentage risk to be calculated from all the risk data gathered from the studies. A neural network computational model to determine percentage risk for individual patients was developed, implemented, and evaluated. The results show that the greatest percentage increased risk was due to BMI being above 25, with the risk increasing as BMI increases. A BMI of 25 or over gave an increased risk of 2.01%, a BMI of 30 or over gave an increase of 5.24%, and a BMI of 40 or over led to an increase of 6.9%. PCOS was the second highest increased risk at 4.2%. Diabetes, which is incidentally also linked to an increased BMI, gave a significant increased risk along with null parity and noncontinuous HRT of 1.54%, 1.2%, and 0.56% respectively. Decreased risk due to contraception was greatest with IUD (intrauterine device) and IUPD (intrauterine progesterone device) at -1.34% compared to -0.9% with oral. Continuous HRT at -0.75% and parity at -0.9% also decreased the risk. Using open-source patient data to test our computational model to determine risk, our results showed that the model is 98.6% accurate with an algorithm sensitivity 75% on average. In this study, we successfully determined the rank order of risk factors for endometrial cancer and calculated a pooled risk and risk percentage for each factor using a statistical meta-analysis approach. Then, using a computer neural network model system, we were able to model the overall increase or decreased risk of cancer and predict the cancer diagnosis for particular patients to an accuracy of over 98%. The neural network model developed in this study was shown to be a potentially useful tool in determining the percentage risk and predicting the possibility of a given patient developing endometrial cancer. As such, it could be a useful tool for clinicians to use in conjunction with other biomarkers in determining which patients warrant further preventative interventions to avert progressing to endometrial cancer. This result would allow for a reduction in the number of unnecessary invasive tests on patients. The model may also be used to suggest interventions to decrease the risk for a particular patient. The sensitivity of the model limits it at this stage due to the small percentage of positive cases in the datasets; however, since this model utilizes a neural network machine learning algorithm, it can be further improved by providing the system with more and larger datasets to allow further refinement of the neural network.
| S-EPMC8345114 | biostudies-literature
Project description:Several studies have suggested the risk of thrombocytopenia with tedizolid, a second-in-class oxazolidinone antibiotic (approved June 2014), is less than that observed with linezolid (first-in-class oxazolidinone). Using data from the Food and Drug Administration adverse event reporting system (July 2014 through December 2016), we observed significantly increased risks of thrombocytopenia of similar magnitudes with both antibiotics: linezolid reporting odds ratio [ROR], 37.9 (95% confidence interval [CI], 20.78 to 69.17); tedizolid ROR, 34.0 (95% CI, 4.67 to 247.30).
| S-EPMC5740346 | biostudies-literature
Project description:A nomogram to estimate the risk of linezolid-induced thrombocytopenia in patients with renal impairment is not available. The aim of the study is to develop a nomogram for predicting linezolid-induced thrombocytopenia in patients with renal impairment and to investigate the incremental value of PNU-142300 concentration beyond clinical factors and linezolid trough concentration (Cmin) for risk prediction. Logistic regression was used to identify independent risk factors for linezolid-induced thrombocytopenia in patients with renal impairment and nomograms were established. The performance of the nomograms was assessed in terms of area under the receiver operating characteristic curve (AUROC), net reclassification improvement (NRI), integrated discrimination improvement (IDI) , decision curve analysis (DCA) and calibration. Internal validation and external validation of the nomograms were also performed. Four nomograms were created: nomogram A including total bilirubin, creatinine clearance and concomitant mannitol use; nomogram B containing linezolid Cmin additionally; nomogram C containing total bilirubin, concomitant mannitol use, linezolid Cmin, and PNU142300 concentration; nomogram D including total bilirubin, concomitant mannitol use, and PNU142300 concentration. Nomogram C improved the prediction performance than nomogram A (AUROC 0.881 vs. 0.749; NRI 0.290; IDI 0.226) and nomogram B (AUROC 0.881 vs. 0.812; NRI 0.152; IDI 0.130) in the training cohort. DCA analysis showed that nomogram C yielded a greater net benefit. Compared with nomogram A and nomogram B, nomogram C also showed superior discriminatory efficacy, good calibration and clinical usefulness in the external validation cohort. The nomogram containing PNU-142300 concentration and linezolid Cmin had better predictive capability than that containing linezolid Cmin for predicting linezolid-induced thrombocytopenia in patients with renal impairment.
| S-EPMC11525775 | biostudies-literature
Project description:BackgroundThrombocytopenia, a common complication of coronary artery bypass graft (CABG) surgery, is particularly prevalent among elderly individuals. This study developed a risk prediction model utilizing preoperative and intraoperative variables to identify high-risk elderly patients prone to developing thrombocytopenia.MethodsThe patients were retrospectively recruited from Beijing Anzhen Hospital between February 2019 and December 2020. Postoperative thrombocytopenia was defined as a postoperative platelet (PLT) count <100×109/L as measured within 7 days after surgery. The entire population was randomly split into derivation and validation sets in a 7:3 ratio. The derivation set underwent variable screen by the least absolute shrinkage and selection operator (LASSO) regression method. To evaluate the predictive ability of the model for thrombocytopenia, decision curve analysis (DCA) and receiver operating characteristic (ROC) curves were generated in the derivation and validation sets.ResultsA total of 1,773 patients were recruited in this study, with random assignment to either the derivation set (1,242 cases) or the validation set (531 cases). LASSO regression was utilized the risk factors associated with thrombocytopenia, resulting in selection of preoperative baseline variables: body mass index (BMI), estimated glomerular filtration rate (eGFR), B-type natriuretic peptide (BNP), preoperative PLT, and use of beta-blocker, and intraoperative variables: red blood cell (RBC) transfusion, plasma transfusion, use of intra-aortic balloon pump (IABP) and cardiopulmonary bypass (CPB), reoperation for bleeding, washed RBC transfusion volume, and use of epinephrine. The logistic regression was employed to establish the risk prediction. The area under the ROC curve (AUC) for the derivation set was 0.900 [95% confidence interval (CI): 0.880-0.920], while for the validation cohort, it was 0.897 (95% CI: 0.866-0.928).ConclusionsThe model incorporating significant preoperative and intraoperative variables exhibited good predictive performance for thrombocytopenia in elderly patients undergoing CABG surgery.
| S-EPMC10894372 | biostudies-literature
Project description:BackgroundLinezolid-induced anemia (LI-AN) is a severe adverse reaction, but risk factors of the LI-AN for elderly patients have not been established.ObjectivesThe objective of this study was to develop a nomogram capable of predicting LI-AN in elderly patients.DesignThis is a retrospective study to develop and validate a nomogram for anemia prediction in elderly patients treated with linezolid.MethodsWe retrospectively screened elderly patients treated with linezolid at Inner Mongolia People's Hospital from January 2020 to December 2023 and validated our findings using the MIMIC-IV 2.2 database. Anemia was defined as hemoglobin reduction to 75% of baseline value. Univariate and multivariable logistic regression models were used to identify predictors and construct the nomogram, which was evaluated using receiver operating characteristic (ROC) curve analysis, calibration plot, and decision curve analysis.ResultsA total of 231 patients were enrolled in this study. The training set comprised 151 individuals, and anemia occurred in 28 cases (18.54%). In the external validation set of 80 individuals, 26 (32.5%) were diagnosed with anemia. The predictors included duration of linezolid therapy, patient estimated glomerular filtration rate value, and sequential organ failure assessment score ⩾2. The ROC curve for the training set was 0.830 (95% CI: 0.750-0.910), while a similar ROC curve of 0.743 (95% CI: 0.621-0.865) was obtained for the validation set. The calibration curve demonstrated good correlation between predicted and observed results, indicating that this study effectively predicts risk factors associated with LI-AN in elderly patients.ConclusionThe developed prediction model can provide valuable guidance for clinicians to prevent anemia and facilitate rational linezolid use in elderly patients.
| S-EPMC11425751 | biostudies-literature