scholarly journals Machine Learning Based on a Multiparametric and Multiregional Radiomics Signature Predicts Radiotherapeutic Response in Patients with Glioblastoma

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Zi-Qi Pan ◽  
Shu-Jun Zhang ◽  
Xiang-Lian Wang ◽  
Yu-Xin Jiao ◽  
Jian-Jian Qiu
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Cox Regression ◽  
Clinical Risk Factors ◽  
Cox Regression Analysis ◽  
Clinical Risk ◽  
Independent Test ◽  
Independent Test Dataset ◽  
Multiple Regions ◽  
Radiomics Signature

Background and Objective. Although radiotherapy has become one of the main treatment methods for cancer, there is no noninvasive method to predict the radiotherapeutic response of individual glioblastoma (GBM) patients before surgery. The purpose of this study is to develop and validate a machine learning-based radiomics signature to predict the radiotherapeutic response of GBM patients. Methods. The MRI images, genetic data, and clinical data of 152 patients with GBM were analyzed. 122 patients from the TCIA dataset (training set: n = 82 ; validation set: n = 40 ) and 30 patients from local hospitals were used as an independent test dataset. Radiomics features were extracted from multiple regions of multiparameter MRI. Kaplan-Meier survival analysis was used to verify the ability of the imaging signature to predict the response of GBM patients to radiotherapy before an operation. Multivariate Cox regression including radiomics signature and preoperative clinical risk factors was used to further improve the ability to predict the overall survival (OS) of individual GBM patients, which was presented in the form of a nomogram. Results. The radiomics signature was built by eight selected features. The C -index of the radiomics signature in the TCIA and independent test cohorts was 0.703 ( P < 0.001 ) and 0.757 ( P = 0.001 ), respectively. Multivariate Cox regression analysis confirmed that the radiomics signature (HR: 0.290, P < 0.001 ), age (HR: 1.023, P = 0.01 ), and KPS (HR: 0.968, P < 0.001 ) were independent risk factors for OS in GBM patients before surgery. When the radiomics signature and preoperative clinical risk factors were combined, the radiomics nomogram further improved the performance of OS prediction in individual patients ( C ‐ index = 0.764 and 0.758 in the TCIA and test cohorts, respectively). Conclusion. This study developed a radiomics signature that can predict the response of individual GBM patients to radiotherapy and may be a new supplement for precise GBM radiotherapy.

scholarly journals An Improvement of Survival Stratification in Glioblastoma Patients via Combining Subregional Radiomics Signatures

2021 ◽  
Vol 15 ◽  
Author(s):  
Yang Yang ◽  
Yu Han ◽  
Xintao Hu ◽  
Wen Wang ◽  
Guangbin Cui ◽  
...  
Keyword(s):  
Risk Factors ◽  
Cox Regression ◽  
Clinical Usefulness ◽  
Clinical Risk Factors ◽  
Survival Prediction ◽  
Clinical Risk ◽  
Single Region ◽  
Tumor Region ◽  
Selection Operator ◽  
Radiomics Signature

PurposeTo investigate whether combining multiple radiomics signatures derived from the subregions of glioblastoma (GBM) can improve survival prediction of patients with GBM.MethodsIn total, 129 patients were included in this study and split into training (n = 99) and test (n = 30) cohorts. Radiomics features were extracted from each tumor region then radiomics scores were obtained separately using least absolute shrinkage and selection operator (LASSO) COX regression. A clinical nomogram was also constructed using various clinical risk factors. Radiomics nomograms were constructed by combing a single radiomics signature from the whole tumor region with clinical risk factors or combining three radiomics signatures from three tumor subregions with clinical risk factors. The performance of these models was assessed by the discrimination, calibration and clinical usefulness metrics, and was compared with that of the clinical nomogram.ResultsIncorporating the three radiomics signatures, i.e., Radscores for ET, NET, and ED, into the radiomics-based nomogram improved the performance in estimating survival (C-index: training/test cohort: 0.717/0.655) compared with that of the clinical nomogram (C-index: training/test cohort: 0.633/0.560) and that of the radiomics nomogram based on single region radiomics signatures (C-index: training/test cohort: 0.656/0.535).ConclusionThe multiregional radiomics nomogram exhibited a favorable survival stratification accuracy.

Machine learning analysis of multispectral imaging and clinical risk factors to predict amputation wound healing

2021 ◽  
Author(s):  
John J. Squiers ◽  
Jeffrey E. Thatcher ◽  
David Bastawros ◽  
Andrew J. Applewhite ◽  
Ronald D. Baxter ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Wound Healing ◽  
Multispectral Imaging ◽  
Clinical Risk Factors ◽  
Clinical Risk ◽  
Learning Analysis

Clinical Predictors of Venous Thromboembolism during Therapy for Childhood Acute Lymphoblastic Leukemia

Blood ◽  
2016 ◽  
Vol 128 (22) ◽  
pp. 1182-1182 ◽  
Author(s):  
Marion K Mateos ◽  
Toby N. Trahair ◽  
Chelsea Mayoh ◽  
Pasquale M Barbaro ◽  
Rosemary Sutton ◽  
...  
Keyword(s):  
Risk Factors ◽  
Risk Factor ◽  
Venous Thromboembolism ◽  
Cox Regression ◽  
Lymphoblastic Leukemia ◽  
Clinical Risk Factors ◽  
Clinical Factors ◽  
Clinical Risk ◽  
Cancer Project ◽  
Prophylactic Anticoagulation

Abstract Venous thromboembolism (VTE) is an unpredictable and life-threatening toxicity that occurs early in acute lymphoblastic leukemia (ALL) therapy. The incidence is approximately 5% in children diagnosed with ALL [Caruso et al. Blood. 2006;108(7):2216-22], which is higher than in other pediatric cancer types [Athale et al. Pediatric Blood & Cancer. 2008;51(6):792-7]. Clinical risk factors for VTE in children during ALL therapy include older age and the use of asparaginase. We hypothesized that there may be additional risk factors that can modify VTE risk, beyond those previously reported [Mitchell et al. Blood. 2010;115(24):4999-5004]. We sought to define early predictive clinical factors that could select a group of children at highest risk of VTE, with possible utility in an interventional trial of prophylactic anticoagulation. We conducted a retrospective study of 1021 Australian children, aged 1-18 years, treated between 1998-2013 on successive BFM-based ALL therapies. Patient records were reviewed to ascertain incidence of VTE; and to systematically document clinical variables present at diagnosis and during induction/consolidation phases of therapy. The CTCAE v4.03 system was used for grading of VTE events. Multivariate logistic and cox regression were used to determine significant clinical risk factors associated with VTE (SPSS v23.0). All P values were 2-tailed, significance level <.05. The incidence of on-treatment VTE was 5.09% [96% ≥Grade 2 (CTCAE v4.0)]. Age ≥10 years [P =.048, HR 1.96 (95% confidence interval= 1.01-3.82)], positive blood culture in induction/consolidation [ P =.009, HR 2.35 (1.24-4.46)], extreme weight at diagnosis <5th or >95th centile [ P =.028, HR 2.14 (1.09-4.20)] and elevated peak gamma-glutamyl transferase (GGT) >5 x upper limit normal in induction/consolidation [ P =.018, HR 2.24 (1.15-4.36)] were significantly associated with VTE in multivariate cox regression modeling. The cumulative incidence of VTE, if all 4 clinical risk factors in our model were present, was 33.33%, which is significantly greater than the incidence of VTE for a patient without any risk factors (1.62%, P <.001). These 4 clinical factors could be used as a basis for assigning thromboprophylaxis in children with ALL. Our model detected 80% (42/52) of all VTE events by incorporating one or more risk factors. An equal proportion of patients eventually developing VTE could be predicted by weight and age ≥10 years; or later bacteremia and elevated GGT. Bacteremia, when present as a risk factor, preceded VTE in 80% of cases (20/25 cases) at a median of 29 days before VTE (range 3-668 days). The negative predictive value (NPV), specificity and sensitivity for the 4 risk factor model were 98.38%, 98.70% and 28.57% respectively. If 3 specified risk factors were included in the algorithm, such as 2 baseline and one treatment-related variable, the incidence of VTE was ≥25%, NPV 98.38%, specificity ≥96.19% and sensitivity 80%. The high NPV and high specificity mean the model can successfully exclude children who are not at increased risk of VTE. The challenge is to balance unnecessary exposure to anticoagulation against the risk of development of VTE. We have identified novel clinical risk factors in induction/consolidation - positive blood culture, hepatic enzymatic elevation and extreme weight at diagnosis- that may highlight risk mechanisms related to VTE pathogenesis. Our predictive model can define a group at highest risk of VTE who may benefit from randomized trials of prophylactic anticoagulation in childhood ALL therapy. Acknowledgments: The authors acknowledge support from the Kids Cancer Alliance (a Translational Cancer Research Centre of Cancer Institute NSW), Cancer Institute New South Wales, Royal Australasian College of Physicians - Kids Cancer Project Research Entry Scholarship, Cancer Therapeutics CRC (CTx) PhD Clinician Research Top-Up Scholarship, The Kids Cancer Project, Australian and New Zealand Children's Haematology Oncology Group, ASSET study members, data managers and clinical research associates at each site. Disclosures No relevant conflicts of interest to declare.

scholarly journals Uncovering clinical risk factors and prediction of severe COVID-19: A machine learning approach based on UK Biobank data

2020 ◽  
Author(s):  
Kenneth C.Y. WONG ◽  
Hon-Cheong So
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Renal Function ◽  
Population Level ◽  
Clinical Risk Factors ◽  
Health Concern ◽  
Targeted Prevention ◽  
Uk Biobank ◽  
Clinical Risk ◽  
Glutamyl Transferase

Background: COVID-19 is a major public health concern. Given the extent of the pandemic, it is urgent to identify risk factors associated with severe disease. Accurate prediction of those at risk of developing severe infections is also important clinically. Methods: Based on the UK Biobank (UKBB data), we built machine learning(ML) models to predict the risk of developing severe or fatal infections, and to evaluate the major risk factors involved. We first restricted the analysis to infected subjects, then performed analysis at a population level, considering those with no known infections as controls. Hospitalization was used as a proxy for severity. Totally 93 clinical variables (collected prior to the COVID-19 outbreak) covering demographic variables, comorbidities, blood measurements (e.g. hematological/liver and renal function/metabolic parameters etc.), anthropometric measures and other risk factors (e.g. smoking/drinking habits) were included as predictors. XGboost (gradient boosted trees) was used for prediction and predictive performance was assessed by cross-validation. Variable importance was quantified by Shapley values and accuracy gain. Shapley dependency and interaction plots were used to evaluate the pattern of relationship between risk factors and outcomes. Results: A total of 1191 severe and 358 fatal cases were identified. For the analysis among infected individuals (N=1747), our prediction model achieved AUCs of 0.668 and 0.712 for severe and fatal infections respectively. Since only pre-diagnostic clinical data were available, the main objective of this analysis was to identify baseline risk factors. The top five contributing factors for severity were age, waist-hip ratio(WHR), HbA1c, number of drugs taken(cnt_tx) and gamma-glutamyl transferase levels. For prediction of mortality, the top features were age, systolic blood pressure, waist circumference (WC), urea and WHR. In subsequent analyses involving the whole UKBB population (N for controls=489987), the corresponding AUCs for severity and fatality were 0.669 and 0.749. The same top five risk factors were identified for both outcomes, namely age, cnt_tx, WC, WHR and cystatin C. We also uncovered other features of potential relevance, including testosterone, IGF-1 levels, red cell distribution width (RDW) and lymphocyte percentage. Conclusions: We identified a number of baseline clinical risk factors for severe/fatal infection by an ML approach. For example, age, central obesity, impaired renal function, multi-comorbidities and cardiometabolic abnormalities may predispose to poorer outcomes. The presented prediction models may be useful at a population level to help identify those susceptible to developing severe/fatal infections, hence facilitating targeted prevention strategies. Further replications in independent cohorts are required to verify our findings.

scholarly journals A novel urinary biomarker predicts 1-year mortality after discharge from intensive care

Critical Care ◽  
2020 ◽  
Vol 24 (1) ◽  
Author(s):  
Esther Nkuipou-Kenfack ◽  
◽  
Agnieszka Latosinska ◽  
Wen-Yi Yang ◽  
Marie-Céline Fournier ◽  
...  
Keyword(s):  
Risk Factors ◽  
Cox Regression ◽  
Fatal Outcome ◽  
Clinical Risk Factors ◽  
Risk Indicators ◽  
Support Vector ◽  
Molecular Pathways ◽  
Icu Mortality ◽  
Clinical Risk ◽  
Urinary Proteome

Abstract Rationale The urinary proteome reflects molecular drivers of disease. Objectives To construct a urinary proteomic biomarker predicting 1-year post-ICU mortality. Methods In 1243 patients, the urinary proteome was measured on ICU admission, using capillary electrophoresis coupled with mass spectrometry along with clinical variables, circulating biomarkers (BNP, hsTnT, active ADM, and NGAL), and urinary albumin. Methods included support vector modeling to construct the classifier, Cox regression, the integrated discrimination (IDI), and net reclassification (NRI) improvement, and area under the curve (AUC) to assess predictive accuracy, and Proteasix and protein-proteome interactome analyses. Measurements and main results In the discovery (deaths/survivors, 70/299) and test (175/699) datasets, the new classifier ACM128, mainly consisting of collagen fragments, yielding AUCs of 0.755 (95% CI, 0.708–0.798) and 0.688 (0.656–0.719), respectively. While accounting for study site and clinical risk factors, hazard ratios in 1243 patients were 2.41 (2.00–2.91) for ACM128 (+ 1 SD), 1.24 (1.16–1.32) for the Charlson Comorbidity Index (+ 1 point), and ≥ 1.19 (P ≤ 0.022) for other biomarkers (+ 1 SD). ACM128 improved (P ≤ 0.0001) IDI (≥ + 0.50), NRI (≥ + 53.7), and AUC (≥ + 0.037) over and beyond clinical risk indicators and other biomarkers. Interactome mapping, using parental proteins derived from sequenced peptides included in ACM128 and in silico predicted proteases, including/excluding urinary collagen fragments (63/35 peptides), revealed as top molecular pathways protein digestion and absorption, lysosomal activity, and apoptosis. Conclusions The urinary proteomic classifier ACM128 predicts the 1-year post-ICU mortality over and beyond clinical risk factors and other biomarkers and revealed molecular pathways potentially contributing to a fatal outcome.

scholarly journals A methylation-based nomogram for predicting survival in patients with lung adenocarcinoma

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Xuelong Wang ◽  
Bin Zhou ◽  
Yuxin Xia ◽  
Jianxin Zuo ◽  
Yanchao Liu ◽  
...  
Keyword(s):  
Risk Factors ◽  
Dna Methylation ◽  
Overall Survival ◽  
Regression Analysis ◽  
Lung Adenocarcinoma ◽  
Risk Score ◽  
Cox Regression ◽  
Risk Group ◽  
Clinical Risk Factors ◽  
Cox Regression Analysis

Abstract Background DNA methylation alteration is frequently observed in Lung adenocarcinoma (LUAD) and may play important roles in carcinogenesis, diagnosis, and prognosis. Thus, this study aimed to construct a reliable methylation-based nomogram, guiding prognostic classification screening and personalized medicine for LUAD patients. Method The DNA methylation data, gene expression data and corresponding clinical information of lung adenocarcinoma samples were extracted from The Cancer Genome Atlas (TCGA) database. Differentially methylated sites (DMSs) and differentially expressed genes (DEGs) were obtained and then calculated correlation by pearson correlation coefficient. Functional enrichment analysis and Protein-protein interaction network were used to explore the biological roles of aberrant methylation genes. A prognostic risk score model was constructed using univariate Cox and LASSO analysis and was assessed in an independent cohort. A methylation-based nomogram that included the risk score and the clinical risk factors was developed, which was evaluated by concordance index and calibration curves. Result We identified a total of 1362 DMSs corresponding to 471 DEGs with significant negative correlation, including 752 hypermethylation sites and 610 hypomethylation sites. Univariate cox regression analysis showed that 59 DMSs were significantly associated with overall survival. Using LASSO method, we constructed a three-DMSs signature that was independent predictive of prognosis in the training cohort. Patients in high-risk group had a significant shorter overall survival than patients in low-risk group classified by three-DMSs signature (log-rank p = 1.9E-04). Multivariate cox regression analysis proved that the three-DMSs signature was an independent prognostic factor for LUAD in TCGA-LUAD cohort (HR = 2.29, 95%CI: 1.47–3.57, P = 2.36E-04) and GSE56044 cohort (HR = 2.16, 95%CI: 1.19–3.91, P = 0.011). Furthermore, a nomogram, combining the risk score with clinical risk factors, was developed with C-indexes of 0.71 and 0.70 in TCGA-LUAD and GSE56044 respectively. Conclusions The present study established a robust three-DMSs signature for the prediction of overall survival and further developed a nomogram that could be a clinically available guide for personalized treatment of LUAD patients.

scholarly journals Clinical and Genetic Determinants of Heart Failure: Optimized by Machine Learning and Mendelian Randomization

2021 ◽  
Author(s):  
liao li zhen ◽  
chen zhi chong ◽  
li wei dong ◽  
liao xin xue ◽  
zhuang xiao dong
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Heart Failure ◽  
Blood Pressure ◽  
Regression Analysis ◽  
Mendelian Randomization ◽  
Cox Regression ◽  
Atherosclerosis Risk In Communities ◽  
Cox Regression Analysis ◽  
Potential Risk Factors

Abstract Background: Identifying unrecognized, potentially modifiable risk factors is essential for heart failure (HF) management.Methods: The Atherosclerosis Risk in Communities (ARIC) study was used for machine learning (ML) to establish the top 20 important variables as potential risk factors for HF. Multivariable Cox regression analysis was performed in an explorative manner to find independent factors for HF and Mendelian randomization (MR) analysis to address causality.Results: Of the 14,842 participants included in the ARIC analysis, 20.4% of participants (3,028) were identified as HF. The 20 variables with the highest importance selected by ML were creatinine, glucose, age, previous coronary artery disease (CAD), systolic blood pressure, fibrinogen, albumin, income, diabetes, magnesium, insulin, white blood cell, hemoglobin, sodium, education, phosphorus, diastolic blood pressure, protein-c, heart rate and body mass index (BMI). Cox regression analysis demonstrated 19 independently associated variables except sodium. MR analysis provided evidence supporting that genetically determined BMI, CAD, diabetes and education was causally associated with HF.Conclusions: The ML plus MR framework was useful in identifying important causal factors of HF. BMI, CAD, diabetes, and education not only served as excellent prognostic factors for HF, but therapeutics targeted at these factors were likely to prevent HF effectively.

scholarly journals A Methylation-based nomogram for predicting survival in patients with lung adenocarcinoma

2020 ◽  
Author(s):  
Xuelong Wang ◽  
Bin Zhou ◽  
Yuxin Xia ◽  
Jianxin Zuo ◽  
Yanchao Liu ◽  
...  
Keyword(s):  
Risk Factors ◽  
Dna Methylation ◽  
Overall Survival ◽  
Regression Analysis ◽  
Lung Adenocarcinoma ◽  
Risk Score ◽  
Cox Regression ◽  
Risk Group ◽  
Clinical Risk Factors ◽  
Cox Regression Analysis

Abstract Background DNA methylation alteration is frequently observed in Lung adenocarcinoma (LUAD) and may play important roles in carcinogenesis, diagnosis, and prognosis. Thus, this study aimed to construct a reliable methylation-based nomogram, guiding prognostic classification screening and personalized medicine for LUAD patients. Method: The DNA methylation data, gene expression data and corresponding clinical information of lung adenocarcinoma samples were extracted from The Cancer Genome Atlas (TCGA) database. Differentially methylated sites (DMSs) and differentially expressed genes (DEGs) were obtained and then calculated expression correlation by pearson correlation coefficient. Functional enrichment analysis and Protein-protein interaction network were used to explore the biological roles of aberrant methylation genes. A prognostic risk score model was constructed using univariate Cox and LASSO analysis and was assessed in an independent cohort. A methylation-based nomogram that included the risk score and the clinical risk factors was developed, which was evaluated by concordance index and calibration curves. Result We identified a total of 1362 DMSs corresponding to 471 DEGs with significant negative correlation, including 752 hypermethylation sites and 610 hypomethylation sites. Univariate cox regression analysis showed that 59 DMSs were significantly associated with overall survival. Using LASSO method, we constructed a three-DMSs signature that was independent predictive of prognosis in the training cohort. Patients in high-risk group had a significant shorter overall survival than patients in low-risk group classified by three-DMSs signature (log-rank p = 1.9E-04). Multivariate cox regression analysis proved that the three-DMSs signature was an independent prognostic factor for LUAD in TCGA-LUAD cohort (HR = 2.29, 95%CI: 1.47–3.57, P = 2.36E-04) and GSE56044 cohort (HR = 2.16, 95%CI: 1.19–3.91, P = 0.011). Furthermore, a nomogram, combining the risk score with clinical risk factors, was developed with C-indexes of 0.71 and 0.70 in TCGA-LUAD and GSE56044 respectively. Conclusions The present study established a robust three-DMSs signature for the prediction of overall survival and further developed a nomogram that could be a clinically available guide for personalized treatment of LUAD patients.

scholarly journals Uncovering Clinical Risk Factors and Predicting Severe COVID-19 Cases Using UK Biobank Data: Machine Learning Approach (Preprint)

2021 ◽  
Author(s):  
Kenneth Chi-Yin Wong ◽  
Yong Xiang ◽  
Liangying Yin ◽  
Hon-Cheong So
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Renal Function ◽  
Prediction Model ◽  
Cystatin C ◽  
Population Level ◽  
Clinical Risk Factors ◽  
Health Concern ◽  
Uk Biobank ◽  
Clinical Risk

BACKGROUND COVID-19 is a major public health concern. Given the extent of the pandemic, it is urgent to identify risk factors associated with disease severity. More accurate prediction of those at risk of developing severe infections is of high clinical importance. OBJECTIVE Based on the UK Biobank (UKBB), we aimed to build machine learning models to predict the risk of developing severe or fatal infections, and uncover major risk factors involved. METHODS We first restricted the analysis to infected individuals (n=7846), then performed analysis at a population level, considering those with no known infection as controls (ncontrols=465,728). Hospitalization was used as a proxy for severity. A total of 97 clinical variables (collected prior to the COVID-19 outbreak) covering demographic variables, comorbidities, blood measurements (eg, hematological/liver/renal function/metabolic parameters), anthropometric measures, and other risk factors (eg, smoking/drinking) were included as predictors. We also constructed a simplified (lite) prediction model using 27 covariates that can be more easily obtained (demographic and comorbidity data). XGboost (gradient-boosted trees) was used for prediction and predictive performance was assessed by cross-validation. Variable importance was quantified by Shapley values (ShapVal), permutation importance (PermImp), and accuracy gain. Shapley dependency and interaction plots were used to evaluate the pattern of relationships between risk factors and outcomes. RESULTS A total of 2386 severe and 477 fatal cases were identified. For analyses within infected individuals (n=7846), our prediction model achieved area under the receiving-operating characteristic curve (AUC–ROC) of 0.723 (95% CI 0.711-0.736) and 0.814 (95% CI 0.791-0.838) for severe and fatal infections, respectively. The top 5 contributing factors (sorted by ShapVal) for severity were age, number of drugs taken (cnt_tx), cystatin C (reflecting renal function), waist-to-hip ratio (WHR), and Townsend deprivation index (TDI). For mortality, the top features were age, testosterone, cnt_tx, waist circumference (WC), and red cell distribution width. For analyses involving the whole UKBB population, AUCs for severity and fatality were 0.696 (95% CI 0.684-0.708) and 0.825 (95% CI 0.802-0.848), respectively. The same top 5 risk factors were identified for both outcomes, namely, age, cnt_tx, WC, WHR, and TDI. Apart from the above, age, cystatin C, TDI, and cnt_tx were among the top 10 across all 4 analyses. Other diseases top ranked by ShapVal or PermImp were type 2 diabetes mellitus (T2DM), coronary artery disease, atrial fibrillation, and dementia, among others. For the “lite” models, predictive performances were broadly similar, with estimated AUCs of 0.716, 0.818, 0.696, and 0.830, respectively. The top ranked variables were similar to above, including age, cnt_tx, WC, sex (male), and T2DM. CONCLUSIONS We identified numerous baseline clinical risk factors for severe/fatal infection by XGboost. For example, age, central obesity, impaired renal function, multiple comorbidities, and cardiometabolic abnormalities may predispose to poorer outcomes. The prediction models may be useful at a population level to identify those susceptible to developing severe/fatal infections, facilitating targeted prevention strategies. A risk-prediction tool is also available online. Further replications in independent cohorts are required to verify our findings.

Sign in / Sign up

Export Citation Format

Share Document