Multivariate Prediction Research Articles

A Water Quality Prediction Method Based on Deep LSTM Network

Water quality parameters are key factors affecting marine ranching. The water quality parameters are not consistent, which are usually complex and variable. The traditional water quality prediction methods have problems such as low long-term prediction accuracy and weak generalization ability. In order to solve the above problems, this paper proposes a multivariate water quality prediction model based on WT-LSTM, which is compared with the BP neural network model for short-term prediction and long-term prediction of dissolved oxygen water quality parameters, and the experimental results show that the WT-LSTM model has good accuracy and generalization in both short-term and long-term prediction, and the short-term prediction accuracy can be up to 98.47% and the long-term prediction accuracy can be up to 98.28%.

Metamodeling Framework for Multivariate Probabilistic Prediction of Story-Drift Profile of Multistory Buildings

Metamodeling Framework for Multivariate Probabilistic Prediction of Story-Drift Profile of Multistory Buildings

Development and external validation of the eFalls tool: a multivariable prediction model for the risk of ED attendance or hospitalisation with a fall or fracture in older adults.

Falls are common in older adults and can devastate personal independence through injury such as fracture and fear of future falls. Methods to identify people for falls prevention interventions are currently limited, with high risks of bias in published prediction models. We have developed and externally validated the eFalls prediction model using routinely collected primary care electronic health records (EHR) to predict risk of emergency department attendance/hospitalisation with fall or fracture within 1 year. Data comprised two independent, retrospective cohorts of adults aged ≥65 years: the population of Wales, from the Secure Anonymised Information Linkage Databank (model development); the population of Bradford and Airedale, England, from Connected Bradford (external validation). Predictors included electronic frailty index components, supplemented with variables informed by literature reviews and clinical expertise. Fall/fracture risk was modelled using multivariable logistic regression with a Least Absolute Shrinkage and Selection Operator penalty. Predictive performance was assessed through calibration, discrimination and clinical utility. Apparent, internal-external cross-validation and external validation performance were assessed across general practices and in clinically relevant subgroups. The model's discrimination performance (c-statistic) was 0.72 (95% confidence interval, CI: 0.68 to 0.76) on internal-external cross-validation and 0.82 (95% CI: 0.80 to 0.83) on external validation. Calibration was variable across practices, with some over-prediction in the validation population (calibration-in-the-large, -0.87; 95% CI: -0.96 to -0.78). Clinical utility on external validation was improved after recalibration. The eFalls prediction model shows good performance and could support proactive stratification for falls prevention services if appropriately embedded into primary care EHR systems.

A Risk Model for 28-Day in-Hospital Mortality in 173 COVID-19 Patients Admission to ICU: A Retrospective Study.

The surge in the number of patients diagnosed with COVID-19 since China's open-door policy has placed a huge burden on the public healthcare system, especially the intensive care system. This study's objective was to discover possible clinical outcome predictors in COVID-19 patients treated in intensive care units (ICUs) and to provide useful information for future preventative efforts and therapies. This retrospective study included 173 COVID-19 critically ill patients and reviewed the 28-day survival outcome in the First Affiliated Hospital of Nanjing Medical University. Competing risk analysis was performed to predict the cumulative incidence function (CIF) of mortality in hospital. The independent prognostic factors were identified by applying the Fine-Gray proportional subdistribution hazard model. Receiver operating characteristic (ROC) curves were used to evaluate model efficacy, and calibration curves were used to validate the model. Finally, we compared the competing risk model with the traditional proportional hazards model (Cox regression model) using CIF. Of these 173 patients, 66 (38.2%) survived, 55 (31.8%) died, and 52 (30.0%) discharged. In univariate analysis, 12 variables were significantly correlated with mortality. In multivariate analysis, Age, Neutrophil ratio, Direct Bilirubin (DBIL) and Renal disease were independent predictors of 28-day outcome. The ROC curve of the multivariate prediction model showed an AUC (area under the curve) of 0.790. The results of the calibration curve and the concordance index (C-index) show that the model has good discriminatory power. The competing risk model we applied was more accurate than the Cox model. We presented a more accurate multivariate prediction model for 28-day in-hospital mortality for ICU COVID-19 patients using a competing risk model.

Predicting major adverse cardiovascular events after orthotopic liver transplantation using a supervised machine learning model: A cohort study.

Liver transplant (LT) patients have become older and sicker. The rate of post-LT major adverse cardiovascular events (MACE) has increased, and this in turn raises 30-d post-LT mortality. Noninvasive cardiac stress testing loses accuracy when applied to pre-LT cirrhotic patients. To assess the feasibility and accuracy of a machine learning model used to predict post-LT MACE in a regional cohort. This retrospective cohort study involved 575 LT patients from a Southern Brazilian academic center. We developed a predictive model for post-LT MACE (defined as a composite outcome of stroke, new-onset heart failure, severe arrhythmia, and myocardial infarction) using the extreme gradient boosting (XGBoost) machine learning model. We addressed missing data (below 20%) for relevant variables using the k-nearest neighbor imputation method, calculating the mean from the ten nearest neighbors for each case. The modeling dataset included 83 features, encompassing patient and laboratory data, cirrhosis complications, and pre-LT cardiac assessments. Model performance was assessed using the area under the receiver operating characteristic curve (AUROC). We also employed Shapley additive explanations (SHAP) to interpret feature impacts. The dataset was split into training (75%) and testing (25%) sets. Calibration was evaluated using the Brier score. We followed Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis guidelines for reporting. Scikit-learn and SHAP in Python 3 were used for all analyses. The supplementary material includes code for model development and a user-friendly online MACE prediction calculator. Of the 537 included patients, 23 (4.46%) developed in-hospital MACE, with a mean age at transplantation of 52.9 years. The majority, 66.1%, were male. The XGBoost model achieved an impressive AUROC of 0.89 during the training stage. This model exhibited accuracy, precision, recall, and F1-score values of 0.84, 0.85, 0.80, and 0.79, respectively. Calibration, as assessed by the Brier score, indicated excellent model calibration with a score of 0.07. Furthermore, SHAP values highlighted the significance of certain variables in predicting postoperative MACE, with negative noninvasive cardiac stress testing, use of nonselective beta-blockers, direct bilirubin levels, blood type O, and dynamic alterations on myocardial perfusion scintigraphy being the most influential factors at the cohort-wide level. These results highlight the predictive capability of our XGBoost model in assessing the risk of post-LT MACE, making it a valuable tool for clinical practice. Our study successfully assessed the feasibility and accuracy of the XGBoost machine learning model in predicting post-LT MACE, using both cardiovascular and hepatic variables. The model demonstrated impressive performance, aligning with literature findings, and exhibited excellent calibration. Notably, our cautious approach to prevent overfitting and data leakage suggests the stability of results when applied to prospective data, reinforcing the model's value as a reliable tool for predicting post-LT MACE in clinical practice.

A tool to predict the risk of lower extremity amputation in patients starting dialysis.

Non-traumatic lower extremity amputation (LEA) is a severe complication during dialysis. To inform decision-making for physicians, we developed a multivariable prediction model for LEA after starting dialysis. Data from the Swedish Renal Registry (SNR) between 2010 and 2020 were geographically split into a development and validation cohort. Data from Netherlands Cooperative Study on the Adequacy of Dialysis (NECOSAD) between 1997 and 2009 were used for validation targeted at Dutch patients. Inclusion criteria were no previous LEA and kidney transplant and age ≥40 years at baseline. A Fine-Gray model was developed with LEA within 3 years after starting dialysis as the outcome of interest. Death and kidney transplant were treated as competing events. One coefficient, ordered by expected relevance, per 20 events was estimated. Performance was assessed with calibration and discrimination. SNR was split into an urban development cohort with 4771 individuals experiencing 201 (4.8%) events and a rural validation cohort with 4.876 individuals experiencing 155 (3.2%) events. NECOSAD contained 1658 individuals experiencing 61 (3.7%) events. Ten predictors were included: female sex, age, diabetes mellitus, peripheral artery disease, cardiovascular disease, congestive heart failure, obesity, albumin, haemoglobin and diabetic retinopathy. In SNR, calibration intercept and slope were -0.003 and 0.912, respectively. The C-index was estimated as 0.813 (0.783-0.843). In NECOSAD, calibration intercept and slope were 0.001 and 1.142 respectively. The C-index was estimated as 0.760 (0.697-0.824). Calibration plots showed good calibration. A newly developed model to predict LEA after starting dialysis showed good discriminatory performance and calibration. By identifying high-risk individuals this model could help select patients for preventive measures.

Machine learning algorithms to predict outcomes in children and adolescents with COVID-19: A systematic review

Background and objectivesWe aimed to analyze the study designs, modeling approaches, and performance evaluation metrics in studies using machine learning techniques to develop clinical prediction models for children and adolescents with COVID-19. MethodsWe searched four databases for articles published between 01/01/2020 and 10/25/2023, describing the development of multivariable prediction models using any machine learning technique for predicting several outcomes in children and adolescents who had COVID-19. ResultsWe included ten articles, six (60 % [95 % confidence interval (CI) 0.31 - 0.83]) were predictive diagnostic models and four (40% [95 % CI 0.170.69]) were prognostic models. All models were developed to predict a binary outcome (n= 10/10, 100 % [95 % CI 0.72-1]). The most frequently predicted outcome was disease detection (n=3/10, 30% [95 % CI 0.11-0.60]). The most commonly used machine learning models in the studies were tree-based (n=12/33, 36.3% [95 % CI 0.17-0.47]) and neural networks (n=9/27, 33.2% [95% CI 0.15-0.44]). ConclusionOur review revealed that attention is required to address problems including small sample sizes, inconsistent reporting practices on data preparation, biases in data sources, lack of reporting metrics such as calibration and discrimination, hyperparameters and other aspects that allow reproducibility by other researchers and might improve the methodology.

Prognostic model for overall survival of head and neck cancer patients in the palliative phase

BackgroundPatients with head and neck squamous cell carcinoma (HNSCC) enter the palliative phase when cure is no longer possible or when they refuse curative treatment. The mean survival is five months, with a range of days until years. Realistic prognostic counseling enables patients to make well-considered end-of-life choices. However, physicians tend to overestimate survival. The aim of this study was to develop a prognostic model that calculates the overall survival (OS) probability of palliative HNSCC patients.MethodsPatients diagnosed with incurable HNSCC or patients who refused curative treatment for HNSCC between January 1st 2006 and June 3rd 2019 were included (n = 659). Three patients were lost to follow-up. Patients were considered to have incurable HNSCC due to tumor factors (e.g. inoperability with no other curative treatment options, distant metastasis) or patient factors (e.g. the presence of severe comorbidity and/or poor performance status).Tumor and patients factors accounted for 574 patients. An additional 82 patients refused curative treatment and were also considered palliative. The effect of 17 candidate predictors was estimated in the univariable cox proportional hazard regression model. Using backwards selection with a cut-off P-value < 0.10 resulted in a final multivariable prediction model. The C-statistic was calculated to determine the discriminative performance of the model. The final model was internally validated using bootstrapping techniques.ResultsA total of 647 patients (98.6%) died during follow-up. Median OS time was 15.0 weeks (95% CI: 13.5;16.6). Of the 17 candidate predictors, seven were included in the final model: the reason for entering the palliative phase, the number of previous HNSCC, cT, cN, cM, weight loss in the 6 months before diagnosis, and the WHO performance status. The internally validated C-statistic was 0.66 indicating moderate discriminative ability. The model showed some optimism, with a shrinkage factor of 0.89.ConclusionThis study enabled the development and internal validation of a prognostic model that predicts the OS probability in HNSCC patients in the palliative phase. This model facilitates personalized prognostic counseling in the palliative phase. External validation and qualitative research are necessary before widespread use in patient counseling and end-of-life care.

Who benefit from adjuvant chemotherapy in stage I lung adenocarcinoma? A multi-dimensional model for candidate selection

BackgroundDespite promising overall survival of stage I lung adenocarcinoma (LUAD) patients, 10-25 % of them still went through recurrence after surgery. [1] While it is still disputable whether adjuvant chemotherapy is necessary for stage I patients. [2] IASLC grading system for non-mucinous LUAD shows that minor high-grade patterns are significant indicator of poor prognosis. [3] Other risk factors, such as, pleura invasion, lympho-vascular invasion, STAS, etc. are also related to poor prognosis. [4-6] There still lack evidence whether IASLC grade itself or together with other risk factors can guide the use of adjuvant therapy in stage I patients. In this article, we tried to establish a multi-variable recurrence prediction model for stage I LUAD patients that is able to identify candidates of adjuvant chemotherapy. MethodsWe retrospectively collected patients who underwent lung surgery from 2018.8.1 to 2018.12.31 at our institution and diagnosed with lung adenocarcinoma pT1-2aN0M0 (stage I). Clinical data, manifestation on CT scan, pathologic features, driver gene mutations and follow-up information were collected. Cox proportional hazards regression analyses were performed utilizing the non-adjuvant cohort to predict disease free survival (DFS) and a nomogram was constructed and applied to the total cohort. Kaplan-Meier method was used to compare DFS between groups. Statistical analysis was conducted by R version 3.6.3. FindingsA total of 913 stage I LUAD patients were included in this study. Median follow-up time is 48.1 months.4-year and 5-year DFS are 92.9 % and 89.6 % for the total cohort. 65 patient experienced recurrence or death. 4-year DFS are 97.0 %,94.6 % and 76.2 %, and 5-year DFS are 95.5 %, 90.0 % and 74.1 % in IASLC Grade1, 2 and 3, respectively(p < 0.0001). High-risk patients defined by single risk factors, such as, IASLC grade 3, pleura invasion, STAS, less LN resected could not benefit from adjuvant therapy. A LASSO-COX regression model was built and patients are divided into high-risk and low-risk groups. In the high-risk group, patients underwent adjuvant chemotherapy have longer DFS than those who did not (p = 0.024), while in the low-risk group, patients underwent adjuvant chemotherapy have inferior DFS than those who did not (p < 0.001). InterpretationIASLC grading is a significant indicator of DFS, however it could not guide adjuvant therapy in our stage I LUAD cohort. Growth patterns and T indicators together with other risk factors could identify high-risk patients that are potential candidate of adjuvant therapy, including some stage IA LUAD patients.

Prediction model protocols indicate better adherence to recommended guidelines for study conduct and reporting

Background and ObjectiveProtocols are invaluable documents for any research study, especially for prediction model studies. However, the mere existence of a protocol is insufficient if key details are omitted. We reviewed the reporting content and details of the proposed design and methods reported in published protocols for prediction model research. MethodsWe searched MEDLINE, Embase, and the Web of Science Core Collection for protocols for studies developing or validating a diagnostic or prognostic model using any modeling approach in any clinical area. We screened protocols published between Jan 1, 2022 and June 30, 2022. We used the abstract, introduction, methods, and discussion sections of The Transparent Reporting of a multivariable prediction model of Individual Prognosis Or Diagnosis (TRIPOD) statement to inform data extraction. ResultsWe identified 30 protocols, of which 28 were describing plans for model development and six for model validation. All protocols were open access, including a preprint. 15 protocols reported prospectively collecting data. 21 protocols planned to use clustered data, of which one-third planned methods to account for it. A planned sample size was reported for 93% development and 67% validation analyses. 16 protocols reported details of study registration, but all protocols reported a statement on ethics approval. Plans for data sharing were reported in 13 protocols. ConclusionProtocols for prediction model studies are uncommon, and few are made publicly available. Those that are available were reasonably well-reported and often described their methods following current prediction model research recommendations, likely leading to better reporting and methods in the actual study.

Combination protein biomarkers predict multiple sclerosis diagnosis and outcomes

Establishing biomarkers to predict multiple sclerosis diagnosis and prognosis has been challenging using a single biomarker approach. We hypothesised that a combination of biomarkers would increase the accuracy of prediction models to differentiate multiple sclerosis from other neurological disorders and enhance prognostication for people with multiple sclerosis. We measured 24 fluid biomarkers in the blood and cerebrospinal fluid of 77 people with multiple sclerosis and 80 people with other neurological disorders, using ELISA or Single Molecule Array assays. Primary outcomes were multiple sclerosis versus any other diagnosis, time to first relapse, and time to disability milestone (Expanded Disability Status Scale 6), adjusted for age and sex. Multivariate prediction models were calculated using the area under the curve value for diagnostic prediction, and concordance statistics (the percentage of each pair of events that are correctly ordered in time for each of the Cox regression models) for prognostic predictions. Predictions using combinations of biomarkers were considerably better than single biomarker predictions. The combination of cerebrospinal fluid [chitinase-3-like-1 + TNF-receptor-1 + CD27] and serum [osteopontin + MCP-1] had an area under the curve of 0.97 for diagnosis of multiple sclerosis, compared to the best discriminative single marker in blood (osteopontin: area under the curve 0.84) and in cerebrospinal fluid (chitinase-3-like-1 area under the curve 0.84). Prediction for time to next relapse was optimal with a combination of cerebrospinal fluid[vitamin D binding protein + Factor I + C1inhibitor] + serum[Factor B + Interleukin-4 + C1inhibitor] (concordance 0.80), and time to Expanded Disability Status Scale 6 with cerebrospinal fluid [C9 + Neurofilament-light] + serum[chitinase-3-like-1 + CCL27 + vitamin D binding protein + C1inhibitor] (concordance 0.98). A combination of fluid biomarkers has a higher accuracy to differentiate multiple sclerosis from other neurological disorders and significantly improved the prediction of the development of sustained disability in multiple sclerosis. Serum models rivalled those of cerebrospinal fluid, holding promise for a non-invasive approach. The utility of our biomarker models can only be established by robust validation in different and varied cohorts.

A new circular neural grey model and its application to CO2 emissions in China

The accurate prediction of China's CO2 emissions provides a reference and early warning for the implementation of the government's environmental strategy and has far-reaching significance for the world's environmental protection. The carbon emission system is a nonlinear dynamical system influenced by multiple factors with multiple data features. This paper establishes a novel grey differential dynamic multivariate prediction model with recurrent neural network. It uses the grey differential dynamic multivariate prediction model to describe the dynamic characteristics of the nonlinear system. And it uses recurrent neural network to extract data features. The new model is used to compensate for the shortcomings of the grey prediction model that use the least squares method to solve the model parameters affecting the accuracy of the model. The numerical solution of the whitening equations of the new model visualizes the hidden layer state function of the recurrent neural network. The optimal parameters of the new model are obtained through repeated training to improve the prediction accuracy of the model. Finally, the new model is applied to the prediction of China's CO2 emissions to verify the validity of the new model from four different perspectives. The results show that the simulation and prediction errors of the new model for data of different lengths are less than 5%. The average simulation error in the optimal scheme is 3.01%, and the average prediction error is 0.78%. The results of the new model are better than the results of the other models. This indicates that the new model has better prediction results. Applying the scenario in forecasting the amount of carbon dioxide in China from 2020 to 2026 yields a forecast that matches the actual trend, further illustrating the validity and feasibility of the model. Finally, the forecast results are analysed, and relevant policy recommendations are provided.

Simulation Analysis and Multiobjective Optimization of Pulverization Process of Seed-Used Watermelon Peel Pulverizer Based on EDEM

To enhance the utilization of seed-used watermelon peel and mitigate environmental pollution, a hammer-blade seed-used watermelon peel crusher was designed and manufactured, and its structure and working parameters were optimized. Initially, the seed-used watermelon peel crusher and seed-used watermelon peel model were constructed, and the model’s parameters were calibrated. Subsequently, the discrete element method (EDEM2022) was employed to investigate the effects of spindle speed (MSS), the number of hammers (NCB), and feeding volume (FQ) on the pulverizing process. Multivariate nonlinear regression prediction models were developed for the percentage of pulverized particle size less than 8 mm (Psv), pulverizing efficiency (Ge), and power density (Ppd), followed by the analysis of influencing factors and prediction models using ANOVA. The multiobjective optimization of the prediction model utilizing the improved hybrid metacellular genetic algorithm CellDE resulted in solutions of 90.02%, 89.57%, and 8.35 × 10−3 t/(h-kw) for Psv-opt, Ge-opt, and Ppd-opt, respectively. The corresponding optimal interaction values of MSS, NCB, and FQ were determined to be 1500 r/min, 108, and 150 kg/min. Finally, a prototype test was conducted by combining the optimal factor interaction values, yielding statistically calculated values of 96.63%, 92.37%, and 7.76 × 10−3 t/(h-kw) for Psv-pr, Ge-pr, and Ppd-pr, respectively. The results indicate that the optimized values of Psv-opt, Ge-opt, and Ppd-opt models have an error of less than 8% compared to the statistically calculated values of the prototype test and outperform the values of Psv-ori, Ge-ori, and Ppd-ori obtained under the original parameters.

Circulating miR-129-3p in combination with clinical factors predicts vascular calcification in hemodialysis patients.

Vascular calcification (VC) commonly occurs and seriously increases the risk of cardiovascular events and mortality in patients with hemodialysis. For optimizing individual management, we will develop a diagnostic multivariable prediction model for evaluating the probability of VC. The study was conducted in four steps. First, identification of miRNAs regulating osteogenic differentiation of vascular smooth muscle cells (VSMCs) in calcified condition. Second, observing the role of miR-129-3p on VC in vitro and the association between circulating miR-129-3p and VC in hemodialysis patients. Third, collecting all indicators related to VC as candidate variables, screening predictors from the candidate variables by Lasso regression, developing the prediction model by logistic regression and showing it as a nomogram in training cohort. Last, verifying predictive performance of the model in validation cohort. In cell experiments, miR-129-3p was found to attenuate vascular calcification, and in human, serum miR-129-3p exhibited a negative correlation with vascular calcification, suggesting that miR-129-3p could be one of the candidate predictor variables. Regression analysis demonstrated that miR-129-3p, age, dialysis duration and smoking were valid factors to establish the prediction model and nomogram for VC. The area under receiver operating characteristic curve of the model was 0.8698. The calibration curve showed that predicted probability of the model was in good agreement with actual probability and decision curve analysis indicated better net benefit of the model. Furthermore, internal validation through bootstrap process and external validation by another independent cohort confirmed the stability of the model. We build a diagnostic prediction model and present it as an intuitive tool based on miR-129-3p and clinical indicators to evaluate the probability of VC in hemodialysis patients, facilitating risk stratification and effective decision, which may be of great importance for reducing the risk of serious cardiovascular events.

Patient outcomes following medical emergency team review on general wards: Development of predictive models.

To develop and internally validate risk prediction models for subsequent clinical deterioration, unplanned ICU admission and death among ward patients following medical emergency team (MET) review. A retrospective cohort study of 1500 patients who remained on a general ward following MET review at an Australian quaternary hospital. Logistic regression was used to model (1) subsequent MET review within 48 h, (2) unplanned ICU admission within 48 h and (3) hospital mortality. Models included demographic, clinical and illness severity variables. Model performance was evaluated using discrimination and calibration with optimism-corrected bootstrapped estimates. Findings are reported using the TRIPOD guideline for multivariable prediction models for prognosis or diagnosis. There was no patient or public involvement in the development and conduct of this study. Within 48 h of index MET review, 8.3% (n = 125) of patients had a subsequent MET review, 7.2% (n = 108) had an unplanned ICU admission and in-hospital mortality was 16% (n = 240). From clinically preselected predictors, models retained age, sex, comorbidity, resuscitation limitation, acuity-dependency profile, MET activation triggers and whether the patient was within 24 h of hospital admission, ICU discharge or surgery. Models for subsequent MET review, unplanned ICU admission, and death had adequate accuracy in development and bootstrapped validation samples. Patients requiring MET review demonstrate complex clinical characteristics and the majority remain on the ward after review for deterioration. A risk score could be used to identify patients at risk of poor outcomes after MET review and support general ward clinical decision-making. Our risk calculator estimates risk for patient outcomes following MET review using clinical data available at the bedside. Future validation and implementation could support evidence-informed team communication and patient placement decisions.

A Systematic Review and Meta-Analysis of MRI Radiomics for Predicting Microvascular Invasion in Patients with Hepatocellular Carcinoma.

The prediction power of MRI radiomics for microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC) remains uncertain. To investigate the prediction performance of MRI radiomics for MVI in HCC. Original studies focusing on preoperative prediction performance of MRI radiomics for MVI in HCC, were systematically searched from databases of PubMed, Embase, Web of Science and Cochrane Library. Radiomics quality score (RQS) and risk of bias of involved studies were evaluated. Meta-analysis was carried out to demonstrate the value of MRI radiomics for MVI prediction in HCC. Influencing factors of the prediction performance of MRI radiomics were identified by subgroup analyses. 13 studies classified as type 2a or above according to the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis statement were eligible for this systematic review and meta-analysis. The studies achieved an average RQS of 14 (ranging from 11 to 17), accounting for 38.9% of the total points. MRI radiomics achieved a pooled sensitivity of 0.82 (95%CI: 0.78 - 0.86), specificity of 0.79 (95%CI: 0.76 - 0.83) and area under the summary receiver operator characteristic curve (AUC) of 0.88 (95%CI: 0.84 - 0.91) to predict MVI in HCC. Radiomics models combined with clinical features achieved superior performances compared to models without the combination (AUC: 0.90 vs 0.85, P < 0.05). MRI radiomics has the potential for preoperative prediction of MVI in HCC. Further studies with high methodological quality should be designed to improve the reliability and reproducibility of the radiomics models for clinical application. The systematic review and meta-analysis was registered prospectively in the International Prospective Register of Systematic Reviews (No. CRD42022333822).

Evaluation of risk stratification for acute kidney injury: a comparative analysis of EKFC, 2009 and 2021 CKD-EPI glomerular filtration estimating equations

BackgroundThe adoption of the 2021 CKD-EPIcr equation for glomerular filtration rate (GFR) estimation provided a race-free eGFR calculation. However, the discriminative performance for AKI risk has been rarely validated. We aimed to evaluate the differences in acute kidney injury (AKI) prediction or reclassification power according to the three eGFR equations.MethodsWe performed a retrospective observational study within a tertiary hospital from 2011 to 2021. Acute kidney injury was defined according to KDIGO serum creatinine criteria. Glomerular filtration rate estimates were calculated by three GFR estimating equations: 2009 and 2021 CKD-EPIcr, and EKFC. In three equations, AKI prediction performance was evaluated with area under receiver operator curves (AUROC) and reclassification power was evaluated with net reclassification improvement analysis.ResultsA total of 187,139 individuals, including 27,447 (14.7%) AKI and 159,692 (85.3%) controls, were enrolled. In the multivariable regression prediction model, the 2009 CKD-EPIcr model (continuous eGFR model 2, 0.7583 [0.755–0.7617]) showed superior performance in AKI prediction to the 2021 CKD-EPIcr (0.7564 [0.7531–0.7597], < 0.001) or EKFC model in AUROC (0.7577 [0.7543–0.761], < 0.001). Moreover, in reclassification of AKI, the 2021 CKD-EPIcr and EKFC models showed a worse classification performance than the 2009 CKD-EPIcr model. (− 7.24 [− 8.21–− 6.21], − 2.38 [− 2.72–− 1.97]).ConclusionRegarding AKI risk stratification, the 2009 CKD-EPIcr equation showed better discriminative performance compared to the 2021 CKD-EPIcr equation in the study population.Graphical abstract

A novel fractional order variable structure multivariable grey prediction model with optimal differential background-value coefficients and its performance comparison analysis

PurposeTraditional multivariable grey prediction models define the background-value coefficients of the dependent and independent variables uniformly, ignoring the differences between their physical properties, which in turn affects the stability and reliability of the model performance.Design/methodology/approachA novel multivariable grey prediction model is constructed with different background-value coefficients of the dependent and independent variables, and a one-to-one correspondence between the variables and the background-value coefficients to improve the smoothing effect of the background-value coefficients on the sequences. Furthermore, the fractional order accumulating operator is introduced to the new model weaken the randomness of the raw sequence. The particle swarm optimization (PSO) algorithm is used to optimize the background-value coefficients and the order of the model to improve model performance.FindingsThe new model structure has good variability and compatibility, which can achieve compatibility with current mainstream grey prediction models. The performance of the new model is compared and analyzed with three typical cases, and the results show that the new model outperforms the other two similar grey prediction models.Originality/valueThis study has positive implications for enriching the method system of multivariable grey prediction model.

Simulation of emission reduction path under the path of differentiated energy transformation in China's industrial cities: a case study of Shanghai.

The formulation of long-term step-by-step emission reduction plan is an important step for effective scientific emission reduction. This paper takes Shanghai as the research object, constructs PSO-LSTM model on the basis of STIRPAT model, and further constructs three dynamic policy scenarios combined with China's actual situation and makes short-, medium-, and long-term multivariate predictions for them. The study finds that only the improvement of energy consumption structure has a promotion effect on carbon emission reduction, and urbanization, industrial structure, technology level, population, and economic level all have an increasing effect, and secondly, the carbon emission reduction path of Shanghai basically achieves the core objective of steady decrease under the three scenarios. Secondly, under the three scenarios, Shanghai's carbon emission reduction path basically achieves the core objective of steady decline, but the decline in the GU scenario is more significant. It is recommended that Shanghai further adjusts its industrial structure, optimizes its energy consumption structure, promotes technological innovation and progress, and promotes the development of the circular economy model.

A study protocol for a predictive model to assess population-based avoidable hospitalization risk: Avoidable Hospitalization Population Risk Prediction Tool (AvHPoRT)

IntroductionAvoidable hospitalizations are considered preventable given effective and timely primary care management and are an important indicator of health system performance. The ability to predict avoidable hospitalizations at the population level represents a significant advantage for health system decision-makers that could facilitate proactive intervention for ambulatory care-sensitive conditions (ACSCs). The aim of this study is to develop and validate the Avoidable Hospitalization Population Risk Tool (AvHPoRT) that will predict the 5-year risk of first avoidable hospitalization for seven ACSCs using self-reported, routinely collected population health survey data.Methods and analysisThe derivation cohort will consist of respondents to the first 3 cycles (2000/01, 2003/04, 2005/06) of the Canadian Community Health Survey (CCHS) who are 18–74 years of age at survey administration and a hold-out data set will be used for external validation. Outcome information on avoidable hospitalizations for 5 years following the CCHS interview will be assessed through data linkage to the Discharge Database (1999/2000–2017/2018) for an estimated sample size of 394,600. Candidate predictor variables will include demographic characteristics, socioeconomic status, self-perceived health measures, health behaviors, chronic conditions, and area-based measures. Sex-specific algorithms will be developed using Weibull accelerated failure time survival models. The model will be validated both using split set cross-validation and external temporal validation split using cycles 2000–2006 compared to 2007–2012. We will assess measures of overall predictive performance (Nagelkerke R2), calibration (calibration plots), and discrimination (Harrell’s concordance statistic). Development of the model will be informed by the Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD) statement.Ethics and disseminationThis study was approved by the University of Toronto Research Ethics Board. The predictive algorithm and findings from this work will be disseminated at scientific meetings and in peer-reviewed publications.