Proposal of a novel cardiovascular risk prediction score in lupus nephritis.

Introduction: Patients with systemic lupus erythematosus are prone to develop cardiovascular disease (CVD), and have increased morbidity and mortality. Methods: We conducted a retrospective analysis on lupus nephritis patients to assess the occurrence and predictors of major adverse cardiovascular events (MACE). Data were collected from patients who underwent kidney biopsy between 2005 and 2020. Statistical analysis was performed to unveil correlations. Results: 91 patients were analyzed in this period, with a mean age of 37.3 ± 12.3 years and 86% being female. The mean follow-up time was 62 ± 48 months. 15.38% of the patients underwent at least one MACE. Two patients deceased of CVD. Increased age (35.81 ± 11.14 vs 45.5 ± 15.11 years, p=0.012) entailed a higher occurrence of MACEs. Neutrophil count (5.15 ± 2.83 vs 7.3 ± 2.99 Giga/L, p=0.001) was higher, whereas diastolic blood pressure (DBP) was lower (89.51 ± 10.96 vs 78.43 ± 6.9 mmHg, p<0.001) at the time of the biopsy in patients with MACE. Age, neutrophil count, and DBP proved to be independent predictors of MACEs. We propose a new model (CANDE - Cardiovascular risk based on Age, Neutrophil count, and Diastolic blood pressure Estimation score) calculated from these variables, which predicts the probability of MACE occurrence. Conclusion: This study underscores the importance of actively screening for cardiovascular risks in this vulnerable patient population. Age, neutrophil count, and diastolic blood pressure have been established as independent risk factors for MACE in lupus nephritis. The CANDE score derived from these parameters may serve as a prompt, cost-effective, and easily accessible estimation tool for assessing the likelihood of major adverse cardiovascular risk. These findings emphasize the necessity for comprehensive management strategies addressing both immune dysregulation and cardiovascular risk factors in systemic lupus erythematosus to mitigate adverse outcomes.

Development of predictive model for predicting postoperative BMI and optimize bariatric surgery: a single center pilot study.

BACKGROUND: The pilot study addresses the challenge of predicting postoperative outcomes, particularly body mass index (BMI) trajectories, following bariatric surgery. The complexity of this task makes preoperative personalized obesity treatment challenging. OBJECTIVES: To develop and validate sophisticated machine learning (ML) algorithms capable of accurately forecasting BMI reductions up to 5 years following bariatric surgery aiming to enhance planning and postoperative care. The secondary goal involves the creation of an accessible web-based calculator for healthcare professionals. This is the first article that compares these methods in BMI prediction. SETTING: The study was carried out from January 2012 to December 2021 at GZOAdipositas Surgery Center, Switzerland. Preoperatively, data for 1004 patients were available. Six months postoperatively, data for 1098 patients were available. For the time points 12 months, 18 months, 2 years, 3 years, 4 years, and 5 years the following number of follow-ups were available: 971, 898, 829, 693, 589, and 453. METHODS: We conducted a comprehensive retrospective review of adult patients who underwent bariatric surgery (Roux-en-Y gastric bypass or sleeve gastrectomy), focusing on individuals with preoperative and postoperative data. Patients with certain preoperative conditions and those lacking complete data sets were excluded. Additional exclusion criteria were patients with incomplete data or follow-up, pregnancy during the follow-up period, or preoperative BMI ≤30 kg/m2. RESULTS: This study analyzed 1104 patients, with 883 used for model training and 221 for final evaluation, the study achieved reliable predictive capabilities, as measured by root mean square error (RMSE). The RMSE values for three tasks were 2.17 (predicting next BMI value), 1.71 (predicting BMI at any future time point), and 3.49 (predicting the 5-year postoperative BMI curve). These results were showcased through a web application, enhancing clinical accessibility and decision-making. CONCLUSION: This study highlights the potential of ML to significantly improve bariatric surgical outcomes and overall healthcare efficiency through precise BMI predictions and personalized intervention strategies.

A dynamic online nomogram predicting post-traumatic arrhythmias: A retrospective cohort study.

BACKGROUND: A nomogram is a visualized clinical prediction models, which offer a scientific basis for clinical decision-making. There is a lack of reports on its use in predicting the risk of arrhythmias in trauma patients. This study aims to develop and validate a straightforward nomogram for predicting the risk of arrhythmias in trauma patients. METHODS: We retrospectively collected clinical data from 1119 acute trauma patients who were admitted to the Advanced Trauma Center of the Affiliated Hospital of Zunyi Medical University between January 2016 and May 2022. Data recorded included intra-hospital arrhythmia, ICU stay, and total hospitalization duration. Patients were classified into arrhythmia and non-arrhythmia groups. Data was summarized according to the occurrence and prognosis of post-traumatic arrhythmias, and randomly allocated into a training and validation sets at a ratio of 7:3. The nomogram was developed according to independent risk factors identified in the training set. Finally, the predictive performance of the nomogram model was validated. RESULTS: Arrhythmias were observed in 326 (29.1%) of the 1119 patients. Compared to the non-arrhythmia group, patients with arrhythmias had longer ICU and hospital stays and higher in-hospital mortality rates. Significant factors associated with post-traumatic arrhythmias included cardiovascular disease, catecholamine use, glasgow coma scale (GCS) score, abdominal abbreviated injury scale (AIS) score, injury severity score (ISS), blood glucose (GLU) levels, and international normalized ratio (INR). The area under the receiver operating characteristic curve (AUC) values for both the training and validation sets exceeded 0.7, indicating strong discriminatory power. The calibration curve showed good alignment between the predicted and actual probabilities of arrhythmias. Decision curve analysis (DCA) indicated a high net benefit for the model in predicting arrhythmias. The Hosmer-Lemeshow goodness-of-fit test confirmed the model's good fit. CONCLUSION: The nomogram developed in this study is a valuable tool for accurately predicting the risk of post-traumatic arrhythmias, offering a novel approach for physicians to tailor risk assessments to individual patients.

Self-Report Tool for Identification of Individuals With Coronary Atherosclerosis: The Swedish CardioPulmonary BioImage Study.

BACKGROUND: Coronary atherosclerosis detected by imaging is a marker of elevated cardiovascular risk. However, imaging involves large resources and exposure to radiation. The aim was, therefore, to test whether nonimaging data, specifically data that can be self-reported, could be used to identify individuals with moderate to severe coronary atherosclerosis. METHODS AND RESULTS: We used data from the population-based SCAPIS (Swedish CardioPulmonary BioImage Study) in individuals with coronary computed tomography angiography (n=25 182) and coronary artery calcification score (n=28 701), aged 50 to 64 years without previous ischemic heart disease. We developed a risk prediction tool using variables that could be assessed from home (self-report tool). For comparison, we also developed a tool using variables from laboratory tests, physical examinations, and self-report (clinical tool) and evaluated both models using receiver operating characteristic curve analysis, external validation, and benchmarked against factors in the pooled cohort equation. The self-report tool (n=14 variables) and the clinical tool (n=23 variables) showed high-to-excellent discriminative ability to identify a segment involvement score ≥4 (area under the curve 0.79 and 0.80, respectively) and significantly better than the pooled cohort equation (area under the curve 0.76, P<0.001). The tools showed a larger net benefit in clinical decision-making at relevant threshold probabilities. The self-report tool identified 65% of all individuals with a segment involvement score ≥4 in the top 30% of the highest-risk individuals. Tools developed for coronary artery calcification score ≥100 performed similarly. CONCLUSIONS: We have developed a self-report tool that effectively identifies individuals with moderate to severe coronary atherosclerosis. The self-report tool may serve as prescreening tool toward a cost-effective computed tomography-based screening program for high-risk individuals.

Comparison of Two Previously Validated Risk Assessment and Prediction Tool Models in Extended Length of Stay Patients Following total Hip Arthroplasty.

INTRODUCTION: Previous studies have attempted to validate the risk assessment and prediction tool (RAPT) in primary total hip arthroplasty (THA) patients. The purpose of this study was to: (1) identify patients who had an extended length of stay (LOS) following THA; and (2) compare the accuracy of 2 previously validated RAPT models. METHODS: We retrospectively reviewed all primary THA patients from 2014 to 2021 who had a completed RAPT score. Youden's J computational analysis was used to determine the LOS where facility discharge was statistically more likely. Based on the cut-offs proposed by Oldmeadow and Dibra, patients were separated into high- (O: 1 to 5 versus D: 1 to 3), medium- (O: 6 to 9 versus D: 4 to 7), and low- (O: 10 to 12 versus D: 8 to 12) risk groups. RESULTS: We determined that an LOS of greater than 2 days resulted in a higher chance of facility discharge. In these patients (n = 717), the overall predictive accuracy (PA) of the RAPT was 79.8%. The Dibra model had a higher PA in the high-risk group (D: 68.2 versus O: 61.2% facility discharge). The Oldmeadow model had a higher PA in the medium-risk (O: 78.7 versus D: 61.4% home discharge) and low-risk (O: 97.0 versus D. 92.5% home discharge) groups. CONCLUSIONS: As institutions continue to optimize LOS, the RAPT may need to be defined in the context of a patient's hospital stay. In patients requiring an LOS of greater than 2 days, the originally established RAPT cut-offs may be more accurate in predicting discharge disposition. LEVEL OF EVIDENCE: III Retrospective Cohort Study.

Meningioma recurrence: Time for an online prediction tool?

Background: Meningioma, the most common brain tumor, traditionally considered benign, has a relatively high risk of recurrence over a patient's lifespan. In addition, with the emergence of several clinical, radiological, and molecular variables, it is becoming evident that existing grading criteria, including Simpson's and World Health Organization classification, may not be sufficient or accurate. As web-based tools for widespread accessibility and usage become commonplace, such as those for gene identification or other cancers, it is timely for meningioma care to take advantage of evolving new markers to help advance patient care. Methods: A scoping review of the meningioma literature was undertaken using the MEDLINE and Embase databases. We reviewed original studies and review articles from September 2022 to December 2023 that provided the most updated information on the demographic, clinical, radiographic, histopathological, molecular genetics, and management of meningiomas in the adult population. Results: Our scoping review reveals a large body of meningioma literature that has evaluated the determinants for recurrence and aggressive tumor biology, including older age, female sex, genetic abnormalities such as telomerase reverse transcriptase promoter mutation, CDKN2A deletion, subtotal resection, and higher grade. Despite a large body of evidence on meningiomas, however, we noted a lack of tools to aid the clinician in decision-making. We identified the need for an online, self-updating, and machine-learning-based dynamic model that can incorporate demographic, clinical, radiographic, histopathological, and genetic variables to predict the recurrence risk of meningiomas. Conclusion: Although a challenging endeavor, a recurrence prediction tool for meningioma would provide critical information for the meningioma patient and the clinician making decisions on long-term surveillance and management of meningiomas.

Risk factors for hypoglycaemia in non-critical hospitalised diabetic patients.

OBJECTIVE: To determine the risk factors for hypoglycaemia in patients with diabetes on general hospital wards based on a systematic review of the literature since 2013 and meta-analysis. METHODS: Systematic review of the literature focused on the conceptual and methodological aspects of the PRISMA Declaration. The search carried out in Pub Med, Web of Science, Medline, Scielo, Lilacs, OVID, grey literature and Google Academic focused on risk factors for hypoglycaemia in patients with diabetes on general hospital wards. The CASPe (Critical Appraisal Skills Programme Spanish) tool was applied for quality control. RESULTS: From 805 references, 70 potentially eligible articles were identified for review of abstracts and full text. Finally, according to inclusion and exclusion criteria, seven studies with 554,601 patients of Asian, European and North American ethnicity were selected. A meta-analysis performed using the random effects model found an association between the presence of hypoglycaemia and: the use of insulin (OR 2.89 [95% CI: 1.8-4.5]); the use of long-acting insulin (OR 2.27 [95% CI: 1.8-2.8]) or fast-acting insulin (OR 1.4 [95% CI: 1.18-1.85]); nasogastric tube feeding (OR 1.75 [95% CI: 1.33-2.3]); chronic kidney disease (OR 1.65 [95% CI: 1.14-2.38]); congestive heart failure (OR 1.36 [95% CI: 1.10-1.68]); and elevated levels of glycosylated haemoglobin (OR 1.59 [95% CI: 1.32-1.91]). CONCLUSION: The factors associated with the risk of hypoglycaemia in non-critically ill hospitalised patients with type 2 diabetes were: use of any insulin; nasogastric tube feeding; elevated glycosylated haemoglobin levels; history of congestive heart failure; and chronic kidney disease.

External validation of the 2-year mortality prediction tool in hemodialysis patients developed using a Bayesian network.

Background: In recent years, a number of predictive models have appeared to predict the risk of medium-term mortality in hemodialysis patients, but only one, limited to patients aged over 70 years, has undergone sufficiently powerful external validation. Recently, using a national learning database and an innovative approach based on Bayesian networks and 14 carefully selected predictors, we have developed a clinical prediction tool to predict all-cause mortality at 2 years in all incident hemodialysis patients. In order to generalize the results of this tool and propose its use in routine clinical practice, we carried out an external validation using an independent external validation database. Methods: A regional, multicenter, observational, retrospective cohort study was conducted to externally validate the tool for predicting 2-year all-cause mortality in incident and prevalent hemodialysis patients. This study recruited a total of 142 incident and 697 prevalent adult hemodialysis patients followed up in one of the eight Association pour l'Utilisation du Rein Artificiel dans la région Lyonnaise (AURAL) Alsace dialysis centers. Results: In incident patients, the 2-year all-cause mortality prediction tool had an area under the receiver curve (AUC-ROC) of 0.73, an accuracy of 65%, a sensitivity of 71% and a specificity of 63%. In prevalent patients, the performance for the external validation were similar in terms of AUC-ROC, accuracy and specificity, but was lower in term of sensitivity. Conclusion: The tool for predicting all-cause mortality at 2 years, developed using a Bayesian network and 14 routinely available explanatory variables, obtained satisfactory external validation in incident patients, but sensitivity was insufficient in prevalent patients.

CD34+ cell yield among healthy donors: Large-scale model development and validation.

BACKGROUND: Successful engraftment in hematopoietic stem cell transplantation necessitates the collection of an adequate dose of CD34+ cells. Thus, the precise estimation of CD34+ cells harvested via apheresis is critical. Current CD34+ cell yield prediction models have limited reproducibility. This study aims to develop a more reliable and universally applicable model by utilizing a large dataset, enhancing yield predictions, optimizing the collection process, and improving clinical outcomes. MATERIALS AND METHODS: A secondary analysis was conducted using the Center for International Blood and Marrow Transplant Research database, involving data from over 17 000 healthy donors who underwent filgrastim-mobilized hematopoietic progenitor cell apheresis. Linear regression, gradient boosting regressor, and logistic regression classification models were employed to predict CD34+ cell yield. RESULTS: Key predictors identified include pre-apheresis CD34+ cell count, weight, age, sex, and blood volume processed. The linear regression model achieved a coefficient of determination (R2) value of 0.66 and a correlation coefficient (r) of 0.81. The gradient boosting regressor model demonstrated marginally improved results with an R2 value of 0.67 and an r value of 0.82. The logistic regression classification model achieved a predictive accuracy of 96% at the 200 × 106 CD34+ cell count threshold. At thresholds of 400, 600, 800, and 1000 × 106 CD34+ cell count, the accuracies were 88%, 83%, 83%, and 88%, respectively. The model demonstrated a high area under the receiver operator curve scores ranging from 0.90 to 0.93. CONCLUSION: This study introduces advanced predictive models for estimating CD34+ cell yield, with the logistic regression classification model demonstrating remarkable accuracy and practical utility.

Protocol for a systematic review of the application of the kidney failure risk equation and Oxford classification in estimating prognosis in IgA nephropathy.

BACKGROUND: IgA nephropathy (IgAN) is a common cause of chronic kidney disease (CKD) and end-stage renal disease (ESRD). Outcomes are highly variable and predicting risk of disease progression at an individual level is challenging. Accurate risk stratification is important to identify individuals most likely to benefit from treatment. The Kidney Failure Risk Equation (KFRE) has been extensively validated in CKD populations and predicts the risk of ESRD at 2 and 5 years using non-invasive tests; however, its predictive performance in IgAN is unknown. The Oxford classification (OC) describes pathological features demonstrated on renal biopsy that are associated with adverse clinical outcomes that may also inform prognosis. The objective of this systematic review is to compare the KFRE with the OC in determining prognosis in IgAN. METHODS: A systematic review will be conducted and reported in line with PRISMA guidelines (PRISMA-P checklist attached as Additional file 1). Inclusion criteria will be cohort studies that apply the KFRE or OC to determine the risk of CKD progression or ESRD in individuals with IgAN. Multiple databases will be searched in duplicate to identify relevant studies, which will be screened first by title, then by abstract and then by full-text analysis. Results will be collated for comparison. Risk of bias and confidence assessments will be conducted independently by two reviewers, with a third reviewer available if required. DISCUSSION: Identifying individuals at the highest risk of progression to ESRD is challenging in IgAN, due to the heterogeneity of clinical outcomes. Risk prediction tools have been developed to guide clinicians; however, it is imperative that these aids are accurate and reproducible. The OC is based on observations made by specialist renal pathologists and may be open to observer bias, therefore the utility of prediction models incorporating this classification may be diminished, particularly as in the future novel biomarkers may be incorporated into clinical practice. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42022364569.

Risk factors for invasive pulmonary aspergillosis in patients with severe fever with thrombocytopenia syndrome: A multicenter retrospective study.

Invasive pulmonary aspergillosis (IPA) is a life-threatening complication in patients with severe fever with thrombocytopenia syndrome (SFTS), yet SFTS-associated IPA (SAPA)'s risk factors remain undefined. A multicenter retrospective cohort study across Hubei and Anhui provinces (May 2013-September 2022) utilized least absolute shrinkage and selection operator (LASSO) regression for variable selection. Multivariable logistic regression identified independent predictors of SAPA, Cox regression highlighted mortality-related risk factors. Of the 1775 screened SFTS patients, 1650 were included, with 169 developing IPA, leading to a 42-day mortality rate of 26.6% among SAPA patients. Multivariable logistic regression revealed SAPA risk factors including advanced age, petechia, hemoptysis, tremor, low albumin levels, elongated activated partial thromboplastin time (APTT), intensive care unit (ICU) admission, glucocorticoid usage, intravenous immunoglobulin (IVIG) and prolonged hospital stays. Cox regression identified predictors of 42-day mortality, including ecchymosis at venipuncture sites, absence of ICU admission, elongated prothrombin time (PT), vasopressor and glucocorticoid use, non-antifungals. Nomograms constructed on these predictors registered concordance indexes of 0.855 (95% CI: 0.826-0.884) and 0.778 (95% CI: 0.702-0.854) for SAPA onset and 42-day mortality, respectively. Lower survival rates for SAPA patients treated with glucocorticoids (p < 0.001) and improved 14-day survival with antifungal therapy (p = 0.036). Improving IPA management in SFTS-endemic areas is crucial, with effective predictive tool.

Machine learning-based preoperative analytics for the prediction of anastomotic leakage in colorectal surgery: a swiss pilot study.

BACKGROUND: Anastomotic leakage (AL), a severe complication following colorectal surgery, arises from defects at the anastomosis site. This study evaluates the feasibility of predicting AL using machine learning (ML) algorithms based on preoperative data. METHODS: We retrospectively analyzed data including 21 predictors from patients undergoing colorectal surgery with bowel anastomosis at four Swiss hospitals. Several ML algorithms were applied for binary classification into AL or non-AL groups, utilizing a five-fold cross-validation strategy with a 90% training and 10% validation split. Additionally, a holdout test set from an external hospital was employed to assess the models' robustness in external validation. RESULTS: Among 1244 patients, 112 (9.0%) suffered from AL. The Random Forest model showed an AUC-ROC of 0.78 (SD: ± 0.01) on the internal test set, which significantly decreased to 0.60 (SD: ± 0.05) on the external holdout test set comprising 198 patients, including 7 (3.5%) with AL. Conversely, the Logistic Regression model demonstrated more consistent AUC-ROC values of 0.69 (SD: ± 0.01) on the internal set and 0.61 (SD: ± 0.05) on the external set. Accuracy measures for Random Forest were 0.82 (SD: ± 0.04) internally and 0.87 (SD: ± 0.08) externally, while Logistic Regression achieved accuracies of 0.81 (SD: ± 0.10) and 0.88 (SD: ± 0.15). F1 Scores for Random Forest moved from 0.58 (SD: ± 0.03) internally to 0.51 (SD: ± 0.03) externally, with Logistic Regression maintaining more stable scores of 0.53 (SD: ± 0.04) and 0.51 (SD: ± 0.02). CONCLUSION: In this pilot study, we evaluated ML-based prediction models for AL post-colorectal surgery and identified ten patient-related risk factors associated with AL. Highlighting the need for multicenter data, external validation, and larger sample sizes, our findings emphasize the potential of ML in enhancing surgical outcomes and inform future development of a web-based application for broader clinical use.

Development of a brief bedside tool to screen women sexual assault survivors for risk of persistent posttraumatic stress six months after sexual assault.

This study aims to develop and validate a brief bedside tool to screen women survivors presenting for emergency care following sexual assault for risk of persistent elevated posttraumatic stress symptoms (PTSS) six months after assault. Participants were 547 cisgender women sexual assault survivors who presented to one of 13 sexual assault nurse examiner (SANE) programs for medical care within 72 h of a sexual assault and completed surveys one week and six months after the assault. Data on 222 potential predictors from the SANE visit and the week one survey spanning seven broadly-defined risk factor domains were candidates for inclusion in the screening tool. Elevated PTSS six months after assault were defined as PCL-5 > 38. LASSO logistic regression was applied to 20 randomly selected bootstrapped samples to evaluate variable importance. Logistic regression models comprised of the top 10, 20, and 30 candidate predictors were tested in 10 cross-validation samples drawn from 80% of the sample. The resulting instrument was validated in the remaining 20% of the sample. AUC of the finalized eight-item prediction tool was 0.77 and the Brier Score was 0.19. A raw score of 41 on the screener corresponds to a 70% risk of elevated PTSS at 6 months. Similar performance was observed for elevated PTSS at one year. This brief, eight-item risk stratification tool consists of easy-to-collect information and, if validated, may be useful for clinical trial enrichment and/or patient screening.

Predicting Extubation Readiness in Preterm Infants Utilizing Machine Learning: A Diagnostic Utility Study.

OBJECTIVE: The objective of this study was to predict extubation readiness in preterm infants using machine learning analysis of bedside pulse oximeter and ventilator data. STUDY DESIGN: This is an observational study with prospective recordings of oxygen saturation (SpO2) and ventilator data from infants <30 weeks of gestation age. Research pulse oximeters collected SpO2 (1 Hz sampling rate) to quantify intermittent hypoxemia (IH). Continuous ventilator metrics were collected (4-5-minute sampling) from bedside ventilators. Data modeling was completed using unbiased machine learning algorithms. Three model sets were created using the following data source combinations: (1) IH and ventilator (IH + SIMV), (2) IH, and (3) ventilator (SIMV). Infants were also analyzed separated by postnatal age (infants <2 or ≥2 weeks of age). Models were compared by area under the receiver operating characteristic curve (AUC). RESULTS: A total of 110 extubation events from 110 preterm infants were analyzed. Infants had a median gestation age and birth weight of 26 weeks and 825 g, respectively. Of the 3 models presented, the IH + SIMV model achieved the highest AUC of 0.77 for all infants. Separating infants by postnatal age increased accuracy further achieving AUC of 0.94 for <2 weeks of age group and AUC of 0.83 for ≥2 weeks group. CONCLUSIONS: Machine learning analysis has the potential to enhance prediction accuracy of extubation readiness in preterm infants while utilizing readily available data streams from bedside pulse oximeters and ventilators.

The Canadian Hypoglycemia During Hospitalization Score Is Externally Valid in the Australian Diabetes IN-hospital: Glucose & Outcomes (DINGO) Cohort.

OBJECTIVES: The Hypoglycemia During Hospitalization (HyDHo) score predicts hypoglycemia in a population of Canadian inpatients by assigning various weightings to 5 key clinical criteria known at the time of admission, in particular age, recent presentation to an emergency department, insulin use, use of oral hypoglycemic agents, and chronic kidney disease. Our aim in this study was to externally validate the HyDHo score by applying this risk calculator to an Australian population of inpatients with diabetes. METHODS: This study was a retrospective data analysis of a subset of the Diabetes IN-hospital: Glucose & Outcomes (DINGO) cohort. The HyDHo score was applied based on clinical information known at the time of admission to stratify risk of inpatient hypoglycemia. RESULTS: The HyDHo score was applied to 1,015 patients, generating a receiver-operating characteristic c-statistic of 0.607. A threshold of ≥9, as per the original study, generated a sensitivity of 83% and a specificity of 20%. A threshold of ≥10, to better suit this Australian population, generated a sensitivity of 90% and a specificity of 34%. The HyDHo score has been externally valid in a geographically different population; in fact, it outperformed the original study after accounting for local hypoglycemia rates. CONCLUSIONS: Our findings support the external validity of the HyDHo score in a geographically different population. Application of this simple and accessible tool can serve as an adjunct to predict an inpatient's risk of hypoglycemia and guide more appropriate glucose monitoring and diabetes management.

Development of a nomogram to predict the prognosis of patients with secondary bone tumors in the intensive care unit: a retrospective analysis based on the MIMIC IV database.

PURPOSE: The present study aimed to develop a nomogram to predict the prognosis of patients with secondary bone tumors in the intensive care unit to facilitate risk stratification and treatment planning. METHODS: We used the MIMIC IV 2.0 (the Medical Information Mart for Intensive Care IV) to retrieve patients with secondary bone tumors as a study cohort. To evaluate the predictive ability of each characteristic on patient mortality, stepwise Cox regression was used to screen variables, and the selected variables were included in the final Cox proportional hazard model. Finally, the performance of the model was tested using the decision curve, calibration curve, and receiver operating characteristic (ROC) curve. RESULTS: A total of 1028 patients were enrolled after excluding cases with missing information. In the training cohort, albumin, APSIII (Acute Physiology Score III), chemotherapy, lactate, chloride, hepatic metastases, respiratory failure, SAPSII (Simplified Acute Physiology Score II), and total protein were identified as independent risk factors for patient death and then incorporated into the final model. The model showed good and robust prediction performance. CONCLUSION: We developed a nomogram prognostic model for patients with secondary bone tumors in the intensive care unit, which provides effective survival prediction information.

Multivariable prediction models for fetal macrosomia and large for gestational age: A systematic review.

BACKGROUND: The identification of large for gestational age (LGA) and macrosomic fetuses is essential for counselling and managing these pregnancies. OBJECTIVES: To systematically review the literature for multivariable prediction models for LGA and macrosomia, assessing the performance, quality and applicability of the included model in clinical practice. SEARCH STRATEGY: MEDLINE, EMBASE and Cochrane Library were searched until June 2022. SELECTION CRITERIA: We included observational and experimental studies reporting the development and/or validation of any multivariable prediction model for fetal macrosomia and/or LGA. We excluded studies that used a single variable or did not evaluate model performance. DATA COLLECTION AND ANALYSIS: Data were extracted using the Checklist for critical appraisal and data extraction for systematic reviews of prediction modelling studies checklist. The model performance measures discrimination, calibration and validation were extracted. The quality and completion of reporting within each study was assessed by its adherence to the TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) checklist. The risk of bias and applicability were measured using PROBAST (Prediction model Risk Of Bias Assessment Tool). MAIN RESULTS: A total of 8442 citations were identified, with 58 included in the analysis: 32/58 (55.2%) developed, 21/58 (36.2%) developed and internally validated and 2/58 (3.4%) developed and externally validated a model. Only three studies externally validated pre-existing models. Macrosomia and LGA were differentially defined by many studies. In total, 111 multivariable prediction models were developed using 112 different variables. Model discrimination was wide ranging area under the receiver operating characteristics curve (AUROC 0.56-0.96) and few studies reported calibration (11/58, 19.0%). Only 5/58 (8.6%) studies had a low risk of bias. CONCLUSIONS: There are currently no multivariable prediction models for macrosomia/LGA that are ready for clinical implementation.

External Validation of the International Prognosis Prediction Model of IgA Nephropathy.

BACKGROUND: The International IgA Nephropathy (IgAN) Network developed and validated two prognostic prediction models for IgAN, one incorporating a race parameter. These models could anticipate the risk of a 50% reduction in estimated glomerular filtration rate (eGFR) or progression to end-stage renal disease (ESRD) subsequent to an IgAN diagnosis via renal biopsy. This investigation aimed to validate the International IgA Nephropathy Prediction Tool (IIgANPT) within a contemporary Chinese cohort. METHODS: Within this study,185 patients diagnosed with IgAN via renal biopsy at the Center for Kidney Disease, Second Affiliated Hospital of Nanjing Medical University, between January 2012 and December 2021, were encompassed. Each patient's risk of progression was assessed utilizing the IIgANPT formula. The primary outcome, a 50% decline in eGFR or progression to ESRD, was examined. Two predictive models, one inclusive and the other exclusive of a race parameter, underwent evaluation via receiver-operating characteristic (ROC) curves, subgroup survival analyses, calibration plots, and decision curve analyses. RESULTS: The median follow-up duration within our cohort spanned 5.1 years, during which 18 patients encountered the primary outcome. The subgroup survival curves exhibited distinct separations, and the comparison of clinical and histological characteristics among the risk subgroups revealed significant differences. Both models demonstrated outstanding discrimination, evidenced by the areas under the ROC curve at five years: 0.882 and 0.878. Whether incorporating the race parameter or not, both prediction models exhibited acceptable calibration. Decision curve analysis affirmed the favorable clinical utility of both models. CONCLUSIONS: Both prognostic risk evaluation models for IgAN exhibited remarkable discrimination, sound calibration, and acceptable clinical utility.

Association between high or low-quality carbohydrate with depressive symptoms and socioeconomic-dietary factors model based on XGboost algorithm: From NHANES 2007-2018.

BACKGROUND: Depressive symptoms are a serious public mental health problem, and dietary intake is often considered to be associated with depressive symptoms. However, the relationship between the quality of dietary carbohydrates and depressive symptoms remains unclear. Therefore, this study aimed to investigate the relationship between high and low-quality carbohydrates and depressive symptoms and to attempt to construct an integrated model using machine learning to predict depressive symptoms. METHODS: A total of 4982 samples from the National Health and Nutrition Examination Survey (NHANES) were included in this study. Carbohydrate intake was assessed by a 24-h dietary review, and depressive symptoms were assessed using the Patient Health Questionnaire-9 (PHQ9). Variance inflation factor (VIF) and Relief-F algorithms were used for variable feature selection. RESULTS: The results of multivariate linear regression showed a negative association between high-quality carbohydrates and depressive symptoms (ß: -0.147, 95 % CI: -0.239, -0.056, p = 0.002) and a positive association between low-quality carbohydrates and depressive symptoms (ß: 0.018, 95 % CI: 0.007, 0.280, p = 0.001). Subsequently, we used the XGboost model to produce a comprehensive depressive symptom evaluation model and developed a corresponding online tool (http://8.130.128.194:5000/) to evaluate depressive symptoms clinically. LIMITATIONS: The cross-sectional study could not yield any conclusions regarding causality, and the model has not been validated with external data. CONCLUSIONS: Carbohydrate quality is associated with depressive symptoms, and machine learning models that combine diet with socioeconomic factors can be a tool for predicting depression severity.

Prognostic factors and clinical nomogram for in-hospital mortality in traumatic brain injury.

BACKGROUND: Traumatic brain injury (TBI) is a major cause of death and functional disability in the general population. The nomogram is a clinical prediction tool that has been researched for a wide range of medical conditions. The purpose of this study was to identify prognostic factors associated with in-hospital mortality. The secondary objective was to develop a clinical nomogram for TBI patients' in-hospital mortality based on prognostic factors. METHODS: A retrospective cohort study was conducted to analyze 14,075 TBI patients who were admitted to a tertiary hospital in southern Thailand. The total dataset was divided into the training and validation datasets. Several clinical characteristics and imaging findings were analyzed for in-hospital mortality in both univariate and multivariable analyses using the training dataset. Based on binary logistic regression, the nomogram was developed and internally validated using the final predictive model. Therefore, the predictive performances of the nomogram were estimated by the validation dataset. RESULTS: Prognostic factors associated with in-hospital mortality comprised age, hypotension, antiplatelet, Glasgow coma scale score, pupillary light reflex, basilar skull fracture, acute subdural hematoma, subarachnoid hemorrhage, midline shift, and basal cistern obliteration that were used for building nomogram. The predictive performance of the nomogram was estimated by the training dataset; the area under the receiver operating characteristic curve (AUC) was 0.981. In addition, the AUCs of bootstrapping and cross-validation methods were 0.980 and 0.981, respectively. For the temporal validation with an unseen dataset, the sensitivity, specificity, accuracy, and AUC of the nomogram were 0.90, 0.88, 0.88, and 0.89, respectively. CONCLUSION: A nomogram developed from prognostic factors had excellent performance; thus, the tool had the potential to serve as a screening tool for prognostication in TBI patients. Furthermore, future research should involve geographic validation to examine the predictive performances of the clinical prediction tool.