Model Internal Validation Research Articles

Development and internal validation of a metabolism-related model for predicting 30-day mortality in neonatal sepsis

ObjectiveNeonatal sepsis, a severe infectious disease associated with high mortality rates, is characterized by metabolic disturbances that play a crucial role in its progression. The aim of this study is to develop a metabolism-related model for assessing 30-day mortality in neonatal sepsis.MethodsThe clinical data of neonatal sepsis at Ganzhou Women and Children’s Health Care Hospital from January 2019 to December 2022 were retrospectively analyzed. Neonatal sepsis cases were divided into survival and non-survival groups. Multivariate logistic regression analysis was used to identify the independent risk factors for 30-day mortality. A nomogram model was developed based on these risk factors. Internal validation of the model was performed using 10-fold cross-validation. The predictive performance was evaluated through receiver operating characteristic (ROC) curves and calibration curve analyses. Decision curve analysis (DCA) was conducted to evaluate the clinical applicability of the developed model.ResultsThe study included a total of 156 cases of neonatal sepsis. Multivariate logistic regression analysis revealed that alanine(ALA), citrulline(CIT)), octadecanoylcarnitine(C18) and methionine(MET) were identified as independent risk factors for 30-day mortality of neonatal sepsis. The ROC curve showed an area under the curve of AUC = 0.866 (95% CI 0.796–0.936, P < 0.05). The calibration curve and DCA indicated excellent performance of the model.ConclusionThis study establishes a predictive model for neonatal sepsis-associated 30-day mortality, effectively capturing the perturbations in amino acid metabolism and fatty acid oxidation, thereby demonstrating robust predictive capabilities.

Development and internal validation of a diagnostic prediction model for life-threatening events in callers with shortness of breath: a cross-sectional study in out-of-hours primary care.

To develop and internally validate a model predicting life-threatening events for out-of-hours primary care callers with shortness of breath. This cross-sectional study includes data from 1,952 patients with shortness of breath who called out-of-hours primary care between September 2020 and August 2021. Four logistic regression models were developed with life-threatening events as the outcome. We started with a model of age and gender (model 1) and successively added call characteristics (calling at night and someone else calling on behalf of the patient; model 2), symptoms (cough, fever, inability to speak full sentences and wheezing; model 3), and medical history and medication use (cardiovascular and/or pulmonary; model 4). The models were internally validated using optimism correction via bootstrap with 1000 repetitions. Performance measures of discrimination (c-statistic) and calibration (calibration intercept and slope) were determined. Approximately 17% of callers with shortness of breath had a life-threatening event. Model 3 performed best. This model exhibited good discriminative ability (internal validation c-statistic of 0.764 (95% CI: 0.739 to 0.792)) and was well calibrated. All models had a high net benefit compared to using no model. Models 3 and 4 had a higher net benefit compared with models 1 and 2. As models 3 and 4 were similar in terms of net benefit, the model with fewer parameters (model 3) is preferred. A prediction model consisting of age, gender, call characteristics, and symptoms holds promise for improving telephone triage of callers to out-of-hours primary care with shortness of breath.

Preoperative prediction of tumor budding and lymphovascular invasion in colon cancer using dual-energy CT: a prospective study with internal model validation.

This study evaluates the potential of dual-energy CT (DECT) for preoperative prediction of tumor budding (TB) and lymphovascular invasion (LVI) in colon cancer. This prospective study enrolled 153 patients (mean age 61.33 years ± 0.88) with pathologically confirmed colon cancer. All participants underwent arterial and venous phase DECT scans within one week before surgery. Two radiologists independently analyzed the images, assessing tumor location, clinical N stage (cN stage), iodine concentration (IC), effective atomic number (Z-eff), and dual-energy index (DEI). The normalized iodine concentration (nIC) was obtained by comparing measured IC to the abdominal aortic IC. Logistic regression identified independent risk factors for high-grade TB and LVI positivity. The Akaike Information Criterion guided model selection, and the area under the curve (AUC) was calculated. Bootstrap validation with 1000 iterations was used for internal validation. Tumor location and cN stage were identified as independent risk factors for high-grade TB, and nICA tumor and cN stage for LVI positivity. The optimal model for predicting high-grade TB included tumor location, cN stage, and DEIV tumor, with an AUC of 0.763 (sensitivity: 75.0%; specificity: 64.7%) and a mean AUC of 0.712. Similarly, the model for LVI positivity included nICA tumor, cN stage, and nICA peripheral fat, with an AUC of 0.811 (sensitivity: 71.7%; specificity: 76.6%) and a mean AUC of 0.814. DECT could consistently quantify colon cancer characteristics, and DECT-based models performed well in the preoperative prediction of TB and LVI.

Predicting response to PD-1 Inhibitors in head and neck squamous cell carcinomas using peripheral blood inflammatory markers

Immune checkpoint inhibitor (ICI) treatment has the potential to induce durable disease remission. However, the current combined positive score (CPS) is insufficient accurate for predicting which patients will benefit from it. In the present study, a real-world retrospective study was conducted on 56 patients of HNSCC who received ICI treatment. Then the treatment that patient received and levels of pre-treatment blood inflammatory markers (NLR, MLR and PLR) were identified to develop a model for predicting immunotherapy response. Notably, the model achieved an area under the curve (AUC) of 0.877 (95% CI 0.769-0.985) , providing a larger net benefit than the CPS marker (AUC=0.614, 95% CI 0.466-0.762). Furthermore, the internal validation of the prediction model showed a C-index of 0.835. Patients with high score of the model would get improved PFS than those with low score. Therefore, the prediction model for patients with local advanced or R/M HNSCC receiving ICI treatment, which represented an better efficient prediction of immunotherapy response than CPS marker.

Prediction models for outcomes in people with low back pain receiving conservative treatment: a systematic review

To identify, critically appraise and evaluate the performance measures of the available prediction models for outcomes in people with low back pain (LBP) receiving conservative treatment. In this systematic review, literature searches were conducted in Embase, Medline, CINAHL from their inception until February/2024. Studies containing follow-up assessment (e.g., prospective cohort studies, registry-based studies) investigating prediction models of outcomes (e.g., pain intensity and disability) for people with LBP receiving conservative treatment were included. Two independent reviewers performed the study selection, the data extraction using the Checklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS), and risk of bias assessment using the Prediction model Risk of Bias Assessment (PROBAST). Findings of individual studies were reported narratively taking into account the discrimination and calibration measures of the prediction models. Seventy-five studies developing or investigating the validity of 216 models were included in this review. Most prediction models investigated people receiving physiotherapy treatment and most models included socio-demographic variables, clinical features, and self-reported measures as predictors. The discriminatory capacity of the internal validity of the 27 prediction models for pain intensity varied greatly showing a c-statistic ranging from 0.48 to 0.94. Similarly, the discriminatory capacity for 31 models for disability had the same pattern showing a c-statistic ranging from 0.48 to 0.86. The calibration measures of the internal validity of the prediction models predicting pain intensity and disability showed to be adequate. Only one of three studies testing the external validity of models to predict pain intensity and disability and reported both discrimination and calibration measures, which showed to be inadequate. The prediction models predicting the secondary outcomes (e.g., self-reported recovery, quality of life, return to work) showed varied performance measures for internal validity, and only two studies tested the external validity of models although they did not provide performance the performance measures. Several prediction models have been developed for people with LBP receiving conservative treatment, however most show inadequate discriminatory validity. A few studies externally validated the prediction models and future studies should focus on testing this before implementing in clinical practice.

Development and internal validation of prognostic models for event-free survival in newly diagnosed Ewing sarcoma patients based on routinely collected clinical characteristics from the European randomised controlled trial, EE2012

IntroductionEwing sarcoma is a rare paediatric cancer. Currently, there is no way of accurately predicting these patients’ survival at diagnosis. Disease type (ie, localised disease, lung/pleuropulmonary metastases and other metastases)...

A machine learning-based risk score for prediction of mechanical ventilation in children with dengue shock syndrome: A retrospective cohort study.

Patients with severe dengue who develop severe respiratory failure requiring mechanical ventilation (MV) support have significantly increased mortality rates. This study aimed to develop a robust machine learning-based risk score to predict the need for MV in children with dengue shock syndrome (DSS) who developed acute respiratory failure. This single-institution retrospective study was conducted at a tertiary pediatric hospital in Vietnam between 2013 and 2022. The primary outcome was severe respiratory failure requiring MV in the children with DSS. Key covariables were predetermined by the LASSO method, literature review, and clinical expertise, including age (< 5 years), female patients, early onset day of DSS (≤ day 4), large cumulative fluid infusion, higher colloid-to-crystalloid fluid infusion ratio, severe bleeding, severe transaminitis, low platelet counts (< 20 x 109/L), elevated hematocrit, and high vasoactive-inotropic score. These covariables were analyzed using supervised models, including Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), k-Nearest Neighbor (KNN), and eXtreme Gradient Boosting (XGBoost). Shapley Additive Explanations (SHAP) analysis was used to assess feature contribution. A total of 1,278 patients were included, with a median patient age of 8.1 years (IQR: 5.4-10.7). Among them, 170 patients (13.3%) with DSS required mechanical ventilation. A significantly higher fatality rate was observed in the MV group than that in the non-MV group (22.4% vs. 0.1%). The RF and SVM models showed the highest model discrimination. The SHAP model explained the significant predictors. Internal validation of the predictive model showed high consistency between the predicted and observed data, with a good slope calibration in training (test) sets 1.0 (0.934), and a low Brier score of 0.04. Complete-case analysis was used to construct the risk score. We developed a robust machine learning-based risk score to estimate the need for MV in hospitalized children with DSS.

Anemia Risk Prediction Model for Osteosarcoma Patients Post‐Chemotherapy Using Artificial Intelligence

ABSTRACTObjectiveThis study aimed to develop a machine learning model for predicting anemia post‐chemotherapy in osteosarcoma patients.MethodsClinical data from 631 osteosarcoma patients were collected, and after data filtering, a training set and validation set were created. Various statistical tests were conducted on the data, and single‐factor and multiple‐factor logistic regression analysis, random forest (RF), support vector machine (SVM), and least absolute shrinkage and selection operator (LASSO) were used to construct risk prediction models. A new model was created by intersecting the above models to identify common risk factors, and a nomogram was developed to display the new model. The model's performance was validated using the validation set.ResultsTwenty‐five risk factors were identified in the anemia group compared to the non‐anemia group (p &lt; 0.05). Single‐factor logistic regression analysis identified 22 risk factors (AUC 0.895), whereas multiple‐factor logistic regression analysis identified 8 risk factors (AUC 0.872), RF identified 7 risk factors (AUC 0.851), SVM identified 16 risk factors (AUC 0.851), and LASSO identified 19 risk factors (AUC 0.902). Five common risk factors (ALB, Ca, CREA, D‐dimer, and ESR) were identified through model intersection, yielding a new model with an AUC of 0.85. Internal validation of the new model showed an AUC of 0.802, indicating high predictive ability. A web model application was created (https://anemic‐prediction‐of‐osteosarcoma.shinyapps.io/DynNomapp/).ConclusionThe developed risk prediction model based on clinical and laboratory data can aid in individualized diagnosis and treatment of anemia in osteosarcoma patients post‐chemotherapy.

Machine learning-based diagnostic model for stroke in non-neurological intensive care unit patients with acute neurological manifestations

Stroke is a neurological complication that can occur in patients admitted to the intensive care unit (ICU) for non-neurological conditions, leading to increased mortality and prolonged hospital stays. The incidence of stroke in ICU settings is notably higher compared to the general population, and delays in diagnosis can lead to irreversible neurological damage. Early diagnosis of stroke is critical to protect brain tissue and treat neurological defects. Therefore, we developed a machine learning model to diagnose stroke in patients with acute neurological manifestations in the ICU. We retrospectively collected data on patients’ underlying diseases, blood coagulation tests, procedures, and medications before neurological symptom onset from 206 patients at the Chungbuk National University Hospital ICU (July 2020–July 2022) and 45 patients at Chungnam National University Hospital between (July 2020–March 2023). Using the Categorical Boosting (CatBoost) algorithm with Bayesian optimization for hyperparameter selection and k-fold cross-validation to mitigate overfitting, we analyzed model-feature relationships with SHapley Additive exPlanations (SHAP) values. Internal model validation yielded an average accuracy of 0.7560, sensitivity of 0.8959, specificity of 0.7000, and area under the receiver operating characteristic curve (AUROC) of 0.8201. External validation yielded an accuracy of 0.7778, sensitivity of 0.7500, specificity of 0.7931, and an AUROC of 0.7328. These results demonstrated the model’s effectiveness in diagnosing stroke in non-neurological ICU patients with acute neurological manifestations using their electronic health records, making it valuable for the early detection of stroke in ICU patients.

Predicting vaginal delivery after labor induction using machine learning: Development of a multivariable prediction model.

Induction of labor, often used for pregnancy termination, has globally rising rates, especially in high-income countries where pregnant women present with more comorbidities. Consequently, concerns on a potential rise in cesarean section (CS) rates after induction of labor (IOL) demand for improved counseling on delivery mode within this context. We aim to develop a prognostic model for predicting vaginal delivery after labor induction using computational learning. Secondary aims include elaborating a prognostic model for CS due to abnormal fetal heart rate and labor dystocia, and evaluation of these models' feature importance, using maternal clinical predictors at IOL admission. The best performing model was assessed in an independent validation data using the area under the receiver operating curve (AUROC). Internal model validation was performed using 10-fold cross-validation. Feature importance was calculated using SHAP (SHapley Additive exPlanation) values to interpret the importance of influential features. Our main outcome measures were mode of delivery after induction of labor, dichotomized as vaginal or cesarean delivery and CS indications, dichotomized as abnormal fetal heart rate and labor dystocia. Our sample comprised singleton term pregnant women (n = 2434) referred for IOL to a tertiary Obstetrics center between January 2018 and December 2021. Prediction of vaginal delivery obtained good discrimination in the independent validation data (AUROC = 0.794, 95% CI 0.783-0.805), showing high positive and negative predictive values (PPV and NPV) of 0.752 and 0.793, respectively, high specificity (0.910) and sensitivity (0.766). The CS model showed an AUROC of 0.590 (95% CI 0.565-0.615) and high specificity (0.893). Sensitivity, PPV and NVP values were 0.665, 0.617, and 0.7, respectively. Labor features associated with vaginal delivery were by order of importance: Bishop score, number of previous term deliveries, maternal height, interpregnancy time interval, and previous eutocic delivery. This prognostic model produced a 0.794 AUROC for predicting vaginal delivery. This, coupled with knowing the features influencing this outcome, may aid providers in assessing an individual's risk of CS after IOL and provide personalized counseling.

Derivation and internal validation of prediction models for pulmonary hypertension risk assessment in a cohort inhabiting Tibet, China.

Individuals residing in plateau regions are susceptible to pulmonary hypertension (PH) and there is an urgent need for a prediction nomogram to assess the risk of PH in this population. A total of 6603 subjects were randomly divided into a derivation set and a validation set at a ratio of 7:3. Optimal predictive features were identified through the least absolute shrinkage and selection operator regression technique, and nomograms were constructed using multivariate logistic regression. The performance of these nomograms was evaluated and validated using the area under the curve (AUC), calibration curves, the Hosmer-Lemeshow test, and decision curve analysis. Comparisons between nomograms were conducted using the net reclassification improvement (NRI) and integrated discrimination improvement (IDI) indices. NomogramI was established based on independent risk factors, including gender, Tibetan ethnicity, age, incomplete right bundle branch block (IRBBB), atrial fibrillation (AF), sinus tachycardia (ST), and T wave changes (TC). The AUCs for NomogramI were 0.716 in the derivation set and 0.718 in the validation set. NomogramII was established based on independent risk factors, including Tibetan ethnicity, age, right axis deviation, high voltage in the right ventricle, IRBBB, AF, pulmonary P waves, ST, and TC. The AUCs for NomogramII were 0.844 in the derivation set and 0.801 in the validation set. Both nomograms demonstrated satisfactory clinical consistency. The IDI and NRI indices confirmed that NomogramII outperformed NomogramI. Therefore, the online dynamic NomogramII was established to predict the risks of PH in the plateau population.

Derivation and internal validation of prediction models for pulmonary hypertension risk assessment in a cohort inhabiting Tibet, China

Individuals residing in plateau regions are susceptible to pulmonary hypertension (PH) and there is an urgent need for a prediction nomogram to assess the risk of PH in this population. A total of 6603 subjects were randomly divided into a derivation set and a validation set at a ratio of 7:3. Optimal predictive features were identified through the least absolute shrinkage and selection operator regression technique, and nomograms were constructed using multivariate logistic regression. The performance of these nomograms was evaluated and validated using the area under the curve (AUC), calibration curves, the Hosmer–Lemeshow test, and decision curve analysis. Comparisons between nomograms were conducted using the net reclassification improvement (NRI) and integrated discrimination improvement (IDI) indices. NomogramI was established based on independent risk factors, including gender, Tibetan ethnicity, age, incomplete right bundle branch block (IRBBB), atrial fibrillation (AF), sinus tachycardia (ST), and T wave changes (TC). The AUCs for NomogramI were 0.716 in the derivation set and 0.718 in the validation set. NomogramII was established based on independent risk factors, including Tibetan ethnicity, age, right axis deviation, high voltage in the right ventricle, IRBBB, AF, pulmonary P waves, ST, and TC. The AUCs for NomogramII were 0.844 in the derivation set and 0.801 in the validation set. Both nomograms demonstrated satisfactory clinical consistency. The IDI and NRI indices confirmed that NomogramII outperformed NomogramI. Therefore, the online dynamic NomogramII was established to predict the risks of PH in the plateau population.

Development of a Nomogram Based on Transcriptional Signatures, IFN-γ Response and Neutrophils for Diagnosis of Tuberculosis.

Tuberculosis (TB) is a major global health threat and its diagnosis remains challenging. This study aimed to develop a nomogram that incorporated peripheral blood transcriptional signatures and other blood tests for the diagnosis of tuberculosis. Patients with TB, patients with other definite pulmonary diseases (OPD), individuals with latent tuberculosis infection (LTBI), and healthy controls (HC) were retrospectively enrolled between May 2017 and April 2018. The results of the interferon-γ release assay (IGRA) and blood counts were obtained from medical records, and the transcripts of 10 genes were detected using reverse transcription polymerase chain reaction (RT-PCR). Variable selection was performed using least absolute shrinkage and selection operator regression (LASSO) and multivariate logistic regression was performed for the optimal prediction model with backward direction. The model was displayed as a nomogram, and its performance was evaluated for discrimination ability, calibration ability, and clinical usefulness. Internal validation of the prediction model was conducted using bootstrap resampling. A total of 185 participants were enrolled, including 84 patients with TB and 101 controls. A prediction nomogram composed of IGRA, percentage of neutrophils, and expression levels of CD64, granzyme A (GZMA), and PR/SET domain 1 (PRDM1) was established. The nomogram demonstrated good discrimination, with an unadjusted area under the curve (AUC) of 0.914 (95% CI: 0.875-0.954) and a bootstrap-corrected AUC of 0.914 (95% CI: 0.874-0.947). With a cutoff value of 0.519, the sensitivity and specificity for discriminating PTB from controls were 0.81 and 0.871, respectively. The nomogram also showed good calibration with the Hosmer-Lemeshow test (P=0.58) and good clinical practicality displayed by the decision curve analysis. A nomogram composed of IGRA, percentage of neutrophils, and expression of CD64, GZMA, and PRDM1 was established. The nomogram demonstrated a sensitivity and specificity of 81% and 87%, respectively, for differentiating TB from controls.

Multi-Institutional Model to Predict Intensive Care Unit Length of Stay after Cardiac Surgery

Intensive care unit length of stay (ICU LOS) accounts for a large percentage of inpatient cost after cardiac surgery. The Society of Thoracic Surgeons risk calculator predicts total LOS but does not discriminate between ICU and non-ICU time. We sought to develop a predictive model of prolonged ICU LOS. Adult patients undergoing Society of Thoracic Surgeons index operations within a regional collaborative (2014-2021) were included. Prolonged ICU LOS was defined as ICU care for ≥72hours postoperatively. A logistic regression model was used to develop a prediction model for the prolonged ICU LOS with prespecified risk factors identified from our previous single-center study. Internal prediction model validation was determined by bootstrapping resampling method. The prediction model performance was assessed by measures of discrimination and calibration. We identified 37,519 patients that met inclusion criteria with 11,801 (31.5%) patients experiencing prolonged ICU stay. From the logistic regression model, there were significant associations between prolonged ICU LOS and all pre-specified factors except sleep apnea (all P<.05). Model for End-Stage Liver Disease, preoperative intra-aortic balloon pump use, and procedure types were the most significant predictors of prolonged ICU LOS (all P<.0001). Our prediction model had not only a good discrimination power (bootstrapped-corrected C-index=0.71) but also excellent calibration (bootstrapped-corrected mean absolute error=0.005). Prolonged ICU stay after cardiac surgery can be predicted with good predictive accuracy using preoperative data and may aid in patient counseling and resource allocation. Through use of a state-wide database, the application of this model may extend to other practices.

Development and internal validation of prediction model for rebleeding within one year after endoscopic treatment of cirrhotic varices: consideration from organ-based CT radiomics signature

BackgroundRebleeding after endoscopic treatment for esophagogastric varices (EGVs) in cirrhotic patients remains a significant clinical challenge, with high mortality rates and limited predictive tools. Current methods, relying on clinical indicators, often lack precision and fail to provide personalized risk assessments. This study aims to develop and validate a novel, non-invasive prediction model based on CT radiomics to predict rebleeding risk within one year of treatment, integrating radiomic features from key organs and clinical data.Methods123 patients were enrolled and divided into rebleeding (n = 44) and non-bleeding group (n = 79) within 1 year after endoscopic treatment of EGVs. The liver, spleen, and the lower part of the esophagus were segmented and the extracted radiomics features were selected to construct liver/spleen/esophagus radiomics signatures based on logistic regression. Clinic-radiomics combined models and multi-organ combined radiomics models were constructed based on independent model scores using logistic regression. The model performance was evaluated by ROC analysis, calibration and decision curves. The continuous net reclassification improvement (NRI) and integrated discrimination improvement (IDI) indices were analyzed.ResultsThe clinical-liver combined model had the highest AUC of 0.931 (95% CI: 0.887–0.974), which was followed by the liver-based model with AUC of 0.891 (95% CI: 0.835–0.74). The decision curves also showed that the clinical-liver combined model afforded a greater net benefit compared to other models within the threshold probability of 0.45 to 0.80. Significant improvements in discrimination (IDI, P < 0.05) and reclassification (NRI, P < 0.05) were obtained for clinical-liver combined model compared with the independent ones.ConclusionThe independent and combined liver-based CT radiomics models performed well in predicting rebleeding within 1 year after endoscopic treatment of EGVs.

Development, validation, and visualization of a web-based nomogram for predicting chronic kidney disease incidence at health examination centers

Purpose To develop and validate a web-based nomogram for predicting new incident chronic kidney disease (CKD) within 4 years in a cohort undergoing routine physical examination from two health examination centers. Methods One center was utilized for training and internal validation of a nomogram model involving 6515 patients, while a separate center was employed for external validation with 3152 patients. Sixteen candidate predictors, including patient demographics, medical histories, physical examination, and laboratory test data, were included in this study to ascertain factors linked to new incident CKD. A nomogram was created to predict CKD risks using a logistic model. The nomogram’s performance was assessed using the area under the receiver operating characteristic curve (AUC), calibration plot, and decision curve analysis. Results Out of the 9667 healthy individuals included in the study with mean age of 46 years, sex ratio (male/female) of 1.69 (6075/3592), 118 (2.59%), 51 (2.61%), and 60 (1.90%) individuals developed CKD in the training (n = 4563), internal validation (n = 1952), and external validation (n = 3152) datasets, respectively. Age, history of diabetes mellitus, systolic blood pressure, serum creatinine, albumin, and triglyceride levels were used to build the nomogram, which yielded excellent discrimination ability (training cohort, AUC = 0.8806, 95% confidence interval [CI] 0.8472–0.9141; internal validation cohort, AUC = 0.8506, 95% CI 0.7856–0.9156; external validation cohort, AUC = 0.9183, 95% CI 0.8698–0.9669). We further developed a web-based calculator for convenient application (https://luochuxuan.shinyapps.io/dynnomapp/). Conclusion Our web-based nomogram accurately predicted CKD risks in Chinese health individuals and can be easily used in clinical settings.

Development and internal validation of prehospital prediction models for identifying intracerebral haemorrhage in suspected stroke patients

IntroductionDistinguishing patients with intracerebral haemorrhage (ICH) from other suspected stroke cases in the prehospital setting is crucial for determining the appropriate level of care and minimising the onset-to-treatment time, thereby...

Competence-based Educational Design Model for Medical Sciences Based on the Community of Inquiry

Introduction: Achieving competence as the main goal in medical education requires a dynamic and active learning process that includes the characteristics of a community of inquiry framework. This theoretical framework provides an overall and coherent structure of an interactive educational experience with a focus on the learning process. Therefore, the purpose of this research was to develop a competency-oriented educational model for medical sciences based on the framework of the community of inquiry. Methods: This was a qualitative and quantitative research. The qualitative part was conducted using qualitative content analysis of documents and focus group discussions, while the quantitative part involved internal validation of the educational design model through a descriptive survey method (based on experts' opinions) and a researcher-made questionnaire. The sampling method was purposeful, focusing on specific or unique cases continuing until theoretical saturation was reached. Results: Using qualitative content analysis (36 documents, 33 articles, and three books) and focus group discussions (with the participation of 14 experts in medical education), the dimensions of competence, principles of competency-based medical education, and principles of inquiry community were identified. Based on theoretical foundations, these were categorized into three domains: affective, cognitive-skills, and metacognitive, along with their subcategories. The alignment of these dimensions led to the formation of a competency-based medical education model, grounded in the inquiry community framework. Internal validation of the developed model was conducted using a researcher-made questionnaire and the participation of 14 experts in educational technology and medical education. According to the specialists' opinions, the developed model achieved a relatively high internal validity (T=14.52, P&lt;0.0001). Conclusion: In this study, the competency-based educational design model for medical education, based on the inquiry community, exhibited appropriate design and organization, flexibility, and adaptability to individual learning needs, even in unpredictable situations within medical education. Its design and organization can be continuously refined and improved throughout the educational experience. Given that it achieved a relatively good internal validity according to the opinions of specialists in medical education and educational technology, it can be introduced as a suitable model in the field of competency-based medical education.

Development and internal validation of a predictive model for prolonged intensive care unit stays in patients with psychotropic drug poisoning

BackgroundSome patients with psychotropic drug poisoning need intensive care unit (ICU) admission, but risk prediction models for prolonged ICU stays are lacking. ObjectivesDevelop and evaluate a prediction model for prolonged ICU stays in patients with psychotropic drug poisoning. MethodsThe clinical data of patients with psychotropic drug poisoning were collected from the Medical Information Mart for Intensive Care (MIMIC)-Ⅳ 2.2 database. Patients were grouped by their ICU length of stay: non-prolonged (<2 days) and prolonged (≥2 days).Variable selection methods included LASSO and logistic regression. The selected variables were used to construct the model, which was subsequently evaluated for discrimination, calibration, and clinical utility. ResultsThe cohort included 413 patients with psychotropic drug poisoning, 49.4 % male, with a median age of 41 years. The variables stepwise selected for model construction through LASSO and logistic regression include sepsis, SAPS Ⅱ, heart rate, respiratory rate, and mechanical ventilation. The model showed good discrimination with an area under the receiver operating characteristic curve (AUC) of 0.785 (95 % CI: 0.736–0.833) and was validated well with bootstrap internal validation (AUC: 0.792, 95 % CI: 0.745–0.839). Calibration curves indicated good fit (χ2 = 4.148, P = 0.844), aligning observed and predicted rates of prolonged ICU stays. Decision curve analysis (DCA) showed positive net benefits across a threshold probability range of 0.07–0.85. ConclusionsThe model developed in this study may help predict the risk of prolonged ICU stays for patients with psychotropic drug poisoning.

Non-invasive assessment of severe liver fibrosis in patients with Fontan-associated liver disease: The VALDIG-EASL FONLIVER cohort

Fontan-type surgery is a palliative procedure for congenital heart disease with univentricular physiology that may, in the long term, lead to advanced chronic liver disease. Herein, we assessed the accuracy of conventional non-invasive models for assessing liver fibrosis in the context of Fontan circulation and developed a new risk score employing non-invasive tools. A prospective, cross-sectional, observational study was conducted across five European centers and encompassing all consecutive adult patients with Fontan circulation, liver biopsy and non-invasive tests (e.g. elastography, APRI, and FIB-4). The primary outcome was the identification of severe liver fibrosis on biopsy. Multivariable logistic regression identified non-invasive predictors of severe fibrosis, leading to the development and internal validation of a new scoring model named the FonLiver risk score. In total, 217 patients (mean [SD] age, 27.9 [8.9] years; 50.7% males) were included. Severe liver fibrosis was present in 47.9% (95% CI 41.2%-54.5%) and correlated with a lower functional class, protein-losing enteropathy, and compromised cardiopulmonary and systemic hemodynamics. The final FonLiver risk score incorporated liver stiffness measurement using transient elastography and platelet count and demonstrated strong discrimination and calibration (AUROC of 0.81). The FonLiver risk score outperformed conventional prediction models (e.g. APRI and FIB-4), which all exhibited worse performance in our cohort (AUROC <0.70 for all). Severe liver fibrosis is prevalent in adults following Fontan-type surgery and can be effectively estimated using the novel FonLiver risk score. This scoring system can be easily incorporated into the routine assessment of patients with Fontan circulation. Fontan-type surgery is used as a palliative procedure for congenital heart disease with univentricular physiology that may, in the long term, lead to advanced chronic liver disease. The severity of liver fibrosis progression has been proposed as a surrogate for failing Fontan hemodynamics as well as worse outcomes after heart transplantation. The development of FALD screening protocols would facilitate the early detection of advanced fibrosis and anticipate interventions to optimize the Fontan circulation, thereby improving outcomes. In our international series, we have developed the FonLiver risk score to predict severe fibrosis, that can be easily incorporated into the routine assessment of patients with Fontan circulation.