Multivariable Prediction Model Research Articles

Prognostic value of multiparametric cardiac magnetic resonance after revascularized STEMI

Abstract Despite early revascularization of STEMI through angioplasty, the prognosis following STEMI remains poor for a non-negligible percentage of patients (1). In this setting, multiple imaging parameters have demonstrated their prognostic value. The aim of the present study is to evaluate which of these parameters are the most potent predictors of events following revascularized STEMI. Methods A prospective observational study was conducted, including a cohort of revascularized STEMI patients and who underwent cardiac MRI during the first week. The protocol included cardiac functional analysis and with b-SSFP sequences for evaluation of volumes, mass, ejection fraction and also circumferential and radial strain with feature tracking. STIR sequences were used to assess edema and salvaged myocardium, IR-FGE sequences were employed for evaluation of necrosis size and microvascular obstruction. Intra-infarct hemorrhage assessment was performed using T2* mapping sequences as previosly described (2,3,4) . A semi-automatic analysis was conducted, considering as microvascular obstruction segments with hypointensity in the infarct core and as segments with hemorrhage those with a T2* less than 20 ms. The association of imaging parameters with cardiovascular events in the first year following STEMI was studied using ROC curves. Variables with significant association were analyzed using a multivariate logistic regression model. Results Ninety-four patients with a mean age of 63 (SD 13) years were included. Nine of them had cardiovascular events in the first year and two of them died. The relative extent of necrosis and microvascular obstruction, minimal radial strain, left ventricular ejection fraction (LVEF) and indexed left ventricular end- systolic volume (ILVESV) showed significant association with cardiovascular events after STEMI. Variables with significant association were entered into a multivariate predictive model, revealing that the extent of microvascular obstruction (OR: 1.9, p=0.02) and the left ventricular end-systolic volume (OR: 1.04, p<0.01) are the only independent predictors of cardiovascular events following STEMI. Conclusion Although multiple imaging parameters have shown prognostic value after STEMI, the ILVESV and the extent of microvascular obstruction are the only independent predictors of events following STEMI.Multiparametric cardiac MR in STEMIAREA UNDER ROC CURVE OF CMR PARAMETERS

ABLE-SCORE: a simplified risk score for major adverse cardiovascular outcomes in patients with left ventricular noncompaction

Abstract Background Left ventricular noncompaction (LVNC) is a heterogeneous entity with life-threatening complications and variable prognosis [1,2]. However, there are limited prediction models available to identify individuals at high risk of adverse outcomes, and the current risk score in LVNC is comparatively complex for clinical practice [3,4]. Purpose To develop and validate a simplified risk score to predict major adverse cardiovascular events (MACE) in LVNC. Methods This multicenter longitudinal cohort study consecutively enrolled morphologically diagnosed LVNC patients between January 2009 and December 2020 at a single national-level medical center (derivation cohort n=300; internal validation cohort n=129), and between January 2014 and December 2022 at two national-level medical centers (external validation cohort n=95). The derivation/internal validation cohorts and the multicenter external validation cohort were followed annually until December 2022 and December 2023, respectively. MACE was defined as a composite of all-cause mortality, heart transplantation/left ventricular assist device implantation, cardiac resynchronization therapy, malignant ventricular arrhythmia, and thromboembolism. A simplified risk score, the ABLE-SCORE, was developed based on independent risk factors for MACE in the multivariable Cox regression predictive model, and underwent both internal and external validation to confirm its discrimination (Harrell’s C-index), calibration (calibration plots), and clinical applicability (decision curve analysis). Results A total of 524 LVNC patients (43.5 ± 16.6 years, 65.8% male) were included in the study. At the end of the follow-up, 97 (32.3%), 38 (29.5%), and 20 (21.1%) individuals in the derivation, internal validation, and external validation cohort experienced at least one endpoint of MACE. The ABLE-SCORE was established using four easily accessible clinical variables: age at diagnosis, N-terminal pro-brain natriuretic peptide levels, left atrium enlargement and left ventricular ejection fraction≤40% measured by echocardiography. The risk score showed excellent performance in discrimination, with Harrell’s C-index of 0.821 (95%CI 0.772-0.869), 0.786 (95%CI 0.703-0.869), and 0.750 (95%CI 0.644-0.856) in the derivation, internal validation, and external validation cohort, respectively. Calibration plots of the three datasets suggested accurate agreement between the predicted and observed 5-year risk of MACE in LVNC. According to decision curve analysis, the ABLE-SCORE displayed greater net benefit than the currently existing risk score for MACE in LVNC [3], indicating its strength in clinical applicability. Conclusions Derived from readily available laboratory and echocardiographic variables, a simplified and efficient risk score for MACE was developed and validated using a large LVNC cohort, making it a reliable and convenient tool for the risk stratification and clinical management of patients with LVNC.Development of the ABLE-SCOREValidation of the ABLE-SCORE

Echocardiographic predictors of cardiovascular outcome in heart failure with preserved ejection fraction

Abstract Background The optimal echocardiographic predictors of cardiovascular outcome in heart failure with presereved ejection fraction (HFpEF) are unknown. Purpose We aimed to identify independent echocardiographic predictors of cardiovascular outcome in patients with HFpEF. Methods Systematic literature search of three electronic databases was conducted from date of inception until November 2022. Hazard ratios (HRs) and their 95% confidence intervals (CIs) for echocardiographic variables from multivariate prediction models for the composite primary endpoint of cardiovascular death and heart failure (HF) hospitalization were pooled using a random effects meta-analysis. Specific subgroup analyses were conducted for studies that enrolled patients with acute versus chronic HF, and for those studies that included E/e’, pulmonary artery systolic pressure (PASP), renal function, natriuretic peptides and diuretic use in multivariate models. Results Forty-six studies totalling 20,056 patients with HFpEF were included. Three echocardiographic parameters emerged as independent predictors in all subgroup analyses: Decreased left ventricular (LV) global longitudinal strain (HR 1.24, 95% CI 1.10-1.39 per 5% decrease), decreased left atrial (LA) reservoir strain (HR 1.30, 95% CI 1.13-1.1.50 per 5% decrease) and lower tricuspid annular plane systolic excursion (TAPSE) to PASP ratio (HR 1.17, 95% CI 1.07-1.25) per 0.1 unit decrease). Other independent echocardiographic predictors of the primary endpoint were a higher E/e’, moderate to severe tricuspid regurgitation, LV mass index and LA ejection fraction, although these variables were less robust. Conclusion Impaired LV global longitudinal strain, lower LA reservoir strain and lower TAPSE/PASP ratio predict cardiovascular death and HF hospitalization in HFpEF and are independent of filling pressures, clinical characteristics and natriuretic peptides. These echocardiographic parameters reflect key functional changes in HFpEF, and should be incorporated in future prospective risk prediction models.

Coconut Testa Flour Sub-Fractions: Correlation Between FTIR Spectral Data and α-Glucosidase Inhibitory Activities

Fourier-transform infrared spectroscopy (FTIR) serves as a rapid analytical technique to characterize food specimens chemically. The purpose of this study was to investigate the potential of FTIR combined with multivariate statistics to detect Alpha-glucosidase (Alpha-glu) inhibitory activities of a non-cereal flour-like coconut testa flour (CTF). CTF of five distinct local cultivars was sequentially extracted with hexane, ethyl acetate (EtOAc), and methanol (MeOH) to assay the Alpha-glu inhibitory activity. FTIR spectra of CTF extracts were obtained within the range of 4000–500 cm−1 and the prominent spectral peaks obtained for both hexane and EtOAc extracts were roughly similar but some additional peaks were observed in EtOAc extracts representing phenolic constituents. The major absorbance peaks found in MeOH extracts were primarily indicative of the occurrence of the hydroxyl group associated with carbohydrates and phenolic compounds. The multivariate predictive models developed using partial least squares (PLS) and orthogonal partial least squares (OPLS) regression analyses indicated a strong correlation between Alpha-glu inhibitory activity and spectral data. Models developed for the spectral regions 3700–2800 cm−1 and 1800–500 cm−1 exerted the highest regression coefficients with the lowest root mean square errors. In OPLS regression analysis, the model obtained with third-derivative spectral data was identified as the best, exhibiting the highest regression coefficients and the lowest root mean square errors. Both PLS and OPLS regression analyses indicated a potential correlation of Alpha-glu inhibitory activity with FTIR spectral regions. Notably, OPLS models offered enhanced interpretability of the model parameters. This study suggests that the application of multivariate regression analysis of FTIR spectral data on coconut-based products could help to detect Alpha-glu inhibitory activities.

A novel scoring system proposal to guide surgical treatment indications for high grade gliomas in elderly patients: DAK-75.

High-grade gliomas are the most prevalent neurooncological desease in adults, their incidence increases with age, peaking in the seventh decade. This paper aims to address how to select patients for surgical resection by identifying pre-surgical predictors of 12-month mortality in newly diagnosed HGG patients aged ≥ 75years. A prognostic score will be proposed to guide surgical decisions based on expected survival. Retrospective observational single-center cohort study was carried out at the "Città della Salute e della Scienza-Molinette" University Hospital in Turin, Italy. All consecutive patients aged ≥ 75years newly diagnosed with HGG were included, regardless of whether they underwent surgical resection. Clinical, radiological, histological and molecular data were collected.Variables potentially available at the time of diagnosis were considered to develop a multivariable logistic regression predictive model, with 12-months overall survival as the dependent variable. 102 patients aged 75years or older received a new diagnosis of high-grade glioma, of whom 68 underwent surgical resection. Patients undergoing surgery were slightly younger (76.9 vs 79.0years, p = 0.007) and had better performance status (median KPS 80 vs 70). Most tumors undergoing surgery were localized in cortical or subcortical non-motor areas (p < 0.001) and less frequently deep-seated (p = 0.023) or multifocal (p < 0.001). A predictive model, the DAK-75 score, was developed: the AUROC of the final model was 0.822 (95% CI 0.741-0.902). The score includes clinical presentation, tumor location, and KPS, ranging from 0 to 20, categorizing risk scores into low-risk and high-risk groups (< or > 8). Higher scores corresponded to fewer surgical patients and higher one-year mortality rates (92.2% vs 47.1%, p < 0.001). DAK-75 score may represent a valuable tool in the decision-making process for neurosurgical intervention in elderly patients diagnosed with HGG. Further studies are needed to externally and prospectively validate the scoring system.

Subclassification of obesity for precision prediction of cardiometabolic diseases.

Obesity and cardiometabolic disease often, but not always, coincide. Distinguishing subpopulations within which cardiometabolic risk diverges from the risk expected for a given body mass index (BMI) may facilitate precision prevention of cardiometabolic diseases. Accordingly, we performed unsupervised clustering in four European population-based cohorts (N ≈ 173,000). We detected five discordant profiles consisting of individuals with cardiometabolic biomarkers higher or lower than expected given their BMI, which generally increases disease risk, in total representing ~20% of the total population. Persons with discordant profiles differed from concordant individuals in prevalence and future risk of major adverse cardiovascular events (MACE) and type 2 diabetes. Subtle BMI-discordances in biomarkers affected disease risk. For instance, a 10% higher probability of having a discordant lipid profile was associated with a 5% higher risk of MACE (hazard ratio in women 1.05, 95% confidence interval 1.03, 1.06, P = 4.19 × 10-10; hazard ratio in men 1.05, 95% confidence interval 1.04, 1.06, P = 9.33 × 10-14). Multivariate prediction models for MACE and type 2 diabetes performed better when incorporating discordant profile information (likelihood ratio test P < 0.001). This enhancement represents an additional net benefit of 4-15 additional correct interventions and 37-135 additional unnecessary interventions correctly avoided for every 10,000 individuals tested.

Effects of Stretch-Bending Straightening on the Tensile Properties of Cold Rolled Packaging Steel

Achieving perfect flatness, tension-free surfaces, and exceptional resistance to spring back are important characteristics of packaging steel, setting the standard for high-quality material performance. To guarantee these crucial parameters, the implementation of the stretch-bending process at the final stage of the production route is indispensable. Besides improving the flatness properties, the induced plastic deformation results in an accompanying change in the mechanical properties. This investigation focuses on understanding this change in mechanical properties due to different stretch-bending straightening process parameters. A multivariate predictive model is created to calculate a process window for achieving the desired flatness and also mechanical properties in the production of packaging steel. This model is validated by experiments with a laboratory facility.

Development and construction of a cataract risk prediction model based on biochemical indices: the National Health and Nutrition Examination Survey, 2005–2008

PurposeThe aim of this study is to develop and validate a novel multivariable prediction model capable of accurately estimating the probability of cataract development, utilizing parameters such as blood biochemical markers and age.DesignThis population-based cross-sectional study comprised 9,566 participants drawn from the National Health and Nutrition Examination Survey (NHANES) across the 2005–2008 cycles.MethodsDemographic information and laboratory test results from the patients were collected and analyzed using LASSO regression and multivariate logistic regression to accurately capture the influence of biochemical indicators on the outcomes. The SHAP (Shapley Additive Explanations) scale was employed to assess the importance of each clinical feature, excluding age. A multivariate logistic regression model was then developed and visualized as a nomogram. To assess the model’s performance, its discrimination, calibration, and clinical utility were evaluated using receiver operating characteristic (ROC) curves, 10-fold cross-validation, Hosmer-Lemeshow calibration curves, and decision curve analysis (DCA), respectively.ResultsLogistic regression analysis identified age, erythrocyte folate (nmol/L), blood glucose (mmol/L), and blood urea nitrogen (mmol/L) as independent risk factors for cataract, and these variables were incorporated into a multivariate logistic regression-based nomogram for cataract risk prediction. The area under the receiver operating characteristic (ROC) curve (AUC) for cataract risk prediction was 0.917 (95% CI: 0.9067–0.9273) in the training cohort, and 0.9148 (95% CI: 0.8979–0.9316) in the validation cohort. The Hosmer-Lemeshow calibration curve demonstrated a good fit, indicating strong model calibration. Ten-fold cross-validation confirmed the logistic regression model’s robust predictive performance and stability during internal validation. Decision curve analysis (DCA) demonstrated that the nomogram prediction model provided greater clinical benefit for predicting cataract risk when the patient’s threshold probability ranged from 0.10 to 0.90.ConclusionThis study identified blood urea nitrogen (mmol/L), serum glucose (mmol/L), and erythrocyte folate (mmol/L) as significant risk factors for cataract. A risk prediction model was developed, demonstrating strong predictive accuracy and clinical utility, offering clinicians a reliable tool for early and effective diagnosis. Cataract development may be delayed by reducing levels of blood urea nitrogen, serum glucose, and erythrocyte folate through lifestyle improvements and dietary modifications.

Recommendations for prediction models in clinical practice guidelines for cardiovascular diseases are over-optimistic: a global survey utilizing a systematic literature search

BackgroundThis study aimed to synthesize the recommendations for prediction models in cardiovascular clinical practice guidelines (CPGs) and assess the methodological quality of the relevant primary modeling studies.MethodsWe performed a systematic literature search of all available cardiovascular CPGs published between 2018 and 2023 that presented specific recommendations (whether in support or non-support) for at least one multivariable clinical prediction model. For the guideline-recommended models, the assessment of the methodological quality of their primary modeling studies was conducted using the Prediction model Risk Of Bias ASsessment Tool (PROBAST).ResultsIn total, 46 qualified cardiovascular CPGs were included, with 69 prediction models and 80 specific recommendations. Of the 80 specific recommendations, 74 supported 57 models (53 were fully recommended and 4 were conditionally recommended) in cardiovascular practice with moderate to strong strength. Most of the guideline-recommended models were focused on predicting prognosis outcomes (53/57, 93%) in primary and tertiary prevention, focusing primarily on long-term risk stratification and prognosis management. A total of 10 conditions and 7 types of target population were involved in the 57 models, while heart failure (14/57, 25%) and a general population with or without cardiovascular risk factor(s) (12/57, 21%) received the most attention from the guidelines. The assessment of the methodological quality of 57 primary studies on the development of the guideline-recommended models revealed that only 40% of the modeling studies had a low risk of bias (ROB). The causes of high ROB were mainly in the analysis and participant domains.ConclusionsGlobal cardiovascular CPGs presented an unduly positive appraisal of the existing prediction models in terms of ROB, leading to stronger recommendations than were warranted. Future cardiovascular practice may benefit from well-established clinical prediction models with better methodological quality and extensive external validation.

Human serum albumin: prediction model and reference values for preterm and term neonates.

Human serum albumin (HSA) concentrations may alter HSA-bound drug distribution. This study aims to describe longitudinal real-world HSA trends, and to develop a prediction model for HSA concentrations using a large neonatal cohort. Patients admitted to the neonatal intensive care unit of the University Hospitals Leuven (postnatal age (PNA) ≤28days) were retrospectively included. Using linear mixed models, covariate effects on HSA were explored. A multivariable prediction model was developed (backward model selection procedure, 1% significance level). In total, 848 neonates were included [median(interquartile range) gestational age (GA) 35(32-38)weeks, birth weight (BW) 2400(1640-3130)grams]. Median HSA concentration was 32.3(28.7-35.6)g/L. Longitudinal analyses demonstrated increasing HSA concentrations with PNA and GA for most GA groups. Univariable analyses revealed significant associations of HSA with PNA, GA, BW, current weight, total and direct bilirubin, total plasma proteins, respiratory support, mechanical ventilation, sepsis, ibuprofen use, and C-reactive protein (p-values < 0.05). A high-performance (R2 = 76.3%) multivariable HSA prediction model was developed, and PNA- and GA-dependent HSA centiles were provided. Population-specific HSA centiles and an accurate neonatal HSA prediction model were developed, incorporating both maturational and non-maturational covariates. These results can enhance future clinical care and pharmacokinetic analyses to improve pharmacotherapy of HSA-bound drugs in neonates, respectively. To improve future pharmacokinetic modeling initiatives, a high-performance human serum albumin (HSA) prediction model was developed for (pre)term neonates, using a large, single-center cohort of real-world data. This prediction model integrates both maturational and non-maturational covariates, resulting in accurate HSA predictions in neonates. Additionally, HSA centiles based on postnatal and gestational age were developed, which can be easily applied in clinical practice when interpreting HSA concentrations of neonates. In general, unbound drug fractions are higher in neonates compared to older populations. To improve pharmacotherapy of HSA-bound drugs in neonates, the obtained results can be integrated in future pharmacokinetic-pharmacodynamic analyses.

The substantial generation of photochemically produced reactive intermediates (PPRIs) in algae-type zones from one large shallow lake promoted the removal of organic pollutants

Photochemically produced reactive intermediates (PPRIs) are ubiquitously present in aquatic systems and hold significant importance in biogeochemical cycles. The photochemical reaction of dissolved organic matter (DOM), known as photosensitizers upon irradiation, is the main pathway for PPRIs generation. However, the PPRIs produced by algal-derived organic matter (ADOM) and their environmental effects remains elusive. This study confirmed that substantial PPRIs were generated by ADOM in the algal-derived areas. UV absorption spectra, fluorescence spectra and Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) were then indicated a significant correlation between the molecular weight of DOM and the quantum yield of PPRIs, with lower molecular weight of DOM exhibiting a higher potential for PPRIs generation. Orthogonal partial least squares (OPLS) were used to build novel multivariate predictive models for indicating the PPRIs production in algae-type zone. Also, the higher concentrations of PPRIs could significantly removal different kinds of organic pollutants, such as bisphenol A (BPA), sulfadiazine (SDZ) and 17α-ethinylestradiol (EE2). Quenching experiments further elucidated that 3DOM⁎ was the key specie for pollutants degradation, serving as the precursor to generate a series of PPRIs. This study highlighted the importance of PPRIs generated from ADOM in the natural attenuation of pollutants and provided a new insight for understanding the self-purification in aquatic system.

Quantification of blood and CSF volume to predict outcome after aneurysmal subarachnoid hemorrhage

This study aimed to describe the relationship between blood and CSF volumes in different compartments on baseline CT after aSAH, assess if they independently predict long-term outcome, and explore their interaction with age. CT scans from patients participating in a prospective multicenter randomized controlled trial of patients with aSAH were segmented for blood and CSF volumes. The primary outcomes were the mRS, and the Subarachnoid Hemorrhage Outcome Tool (SAHOT) at day 28 and 180. Univariate regressions were conducted to identify significant predictors of poor outcomes, followed by principal component analysis to explore correlations between imaging variables and WFNS. A multivariate predictive model was then developed and optimized using stepwise regression. CT scans from 97 patients with a median delay from symptom onset of 271 min (131–547) were analyzed. Univariate analysis showed only WFNS, and total blood volume (TBV) were significant predictors of both short and long-term outcome with WFNS more predictive of mRS and TBV more predictive of SAHOT. Principal component analysis showed strong dependencies between the imaging predictors. Multivariate ordinal regression showed models with WFNS alone were most predictive of day 180 mRS and models with TBV alone were most predictive of SAHOT. TBV was the most significant measured imaging predictor of poor long-term outcome after aSAH. All these imaging predictors are correlated, however, and may have multiple complex interactions necessitating larger datasets to detect if they provide any additional predictive value for long-term outcome.

ABLE-SCORE, a simplified risk score for major adverse cardiovascular outcomes in left ventricular hypertrabeculation: a multicenter longitudinal cohort study.

Left ventricular hypertrabeculation (LVHT) is a heterogeneous entity with life-threatening complications and variable prognosis. However, there are limited prediction models available to identify individuals at high risk of adverse outcomes, and the current risk score in LVHT is comparatively complex for clinical practice. This study aimed to develop and validate a simplified risk score to predict major adverse cardiovascular events (MACE) in LVHT. This multicenter longitudinal cohort study consecutively enrolled morphologically diagnosed LVHT patients between January 2009 and December 2020 at Fuwai Hospital (derivation cohort, n = 300; internal validation cohort, n = 129), and between January 2014 and December 2022 at two national-level medical centers (external validation cohort, n = 95). The derivation/internal validation cohorts and the external validation cohort were followed annually until December 2022 and December 2023, respectively. MACE was defined as a composite of all-cause mortality, heart transplantation/left ventricular assist device implantation, cardiac resynchronization therapy, malignant ventricular arrhythmia, and thromboembolism. A simplified risk score, the ABLE-SCORE, was developed based on independent risk factors in the multivariable Cox regression predictive model for MACE, and underwent both internal and external validations to confirm its discrimination, calibration, and clinical applicability. A total of 524 LVHT patients (43.5 ± 16.6years, 65.8% male) were included in the study. The ABLE-SCORE was established using four easily accessible clinical variables: age at diagnosis, N-terminal pro-brain natriuretic peptide levels, left atrium enlargement, and left ventricular ejection fraction ≤ 40% measured by echocardiography. The risk score showed excellent performance in discrimination, with Harrell's C-index of 0.821 [95% confidence interval (CI), 0.772-0.869], 0.786 (95%CI, 0.703-0.869), and 0.750 (95%CI, 0.644-0.856) in the derivation, internal validation, and external validation cohort, respectively. Calibration plots of the three datasets suggested accurate agreement between the predicted and observed 5-year risk of MACE in LVHT. According to decision curve analysis, the ABLE-SCORE displayed greater net benefits than the existing risk score for LVHT, indicating its strength in clinical applicability. A simplified and efficient risk score for MACE was developed and validated using a large LVHT cohort, making it a reliable and convenient tool for the risk stratification and clinical management of patients with LVHT.

The utility of MRI radiological biomarkers in determining intracranial pressure

Intracranial pressure (ICP) is a physiological parameter that conventionally requires invasive monitoring for accurate measurement. Utilising multivariate predictive models, we sought to evaluate the utility of non-invasive, widely accessible MRI biomarkers in predicting ICP and their reversibility following cerebrospinal fluid (CSF) diversion. The retrospective study included 325 adult patients with suspected CSF dynamic disorders who underwent brain MRI scans within three months of elective 24-h ICP monitoring. Five MRI biomarkers were assessed: Yuh sella grade, optic nerve vertical tortuosity (VT), optic nerve sheath distension, posterior globe flattening and optic disc protrusion (ODP). The association between individual biomarkers and 24-h ICP was examined and reversibility of each following CSF diversion was assessed. Multivariate models incorporating these radiological biomarkers were utilised to predict 24-h median intracranial pressure. All five biomarkers were significantly associated with median 24-h ICP (p < 0.0001). Using a pair-wise approach, the presence of each abnormal biomarker was significantly associated with higher median 24-h ICP (p < 0.0001). On multivariate analysis, ICP was significantly and positively associated with Yuh sella grade (p < 0.0001), VT (p < 0.0001) and ODP (p = 0.003), after accounting for age and suspected diagnosis. The Bayesian multiple linear regression model predicted 24-h median ICP with a mean absolute error of 2.71 mmHg. Following CSF diversion, we found pituitary sella grade to show significant pairwise reversibility (p < 0.001). ICP was predicted with clinically useful precision utilising a compact Bayesian model, offering an easily interpretable tool using non-invasive MRI data. Brain MRI biomarkers are anticipated to play a more significant role in the screening, triaging, and referral of patients with suspected CSF dynamic disorders.

Predictive Value of CCQ and its Subdomains for Hospitalized Exacerbations in Chronic Obstructive Pulmonary Disease: A 3-Year Observational Prospective Study.

The prediction capacity of the Clinical COPD Questionnaire (CCQ) and its functional, symptom, and mental subdomain for COPD hospitalized exacerbation were rarely studied. To examine the prognostic capacity of the total CCQ and its three subdomains for 3-year COPD hospitalized exacerbations. We analyzed the predictive ability of total CCQ score and its subdomains for hospitalized exacerbations in an observational cohort of 987 subjects with stable COPD from the RealDTC, an ongoing multicenter prospective study. Hospitalized exacerbations were prospectively collected every 6month for a maximum of 3years. The total CCQ and its functional and symptom domain, but not the mental domain, were significantly associated with 3-year hospitalized exacerbations by multivariate Cox regression analysis. The predictive capacity of functional domain was similar to that of the total CCQ, but significantly stronger than the symptom and mental domain by ROC analysis (P < 0.05). ROC curves also showed that the AUC of exacerbation history combined with CCQ functional domain was larger than that of exacerbation history alone (P < 0.0001). Additionally, the predictive value of multivariable models that contains CCQ functional domain was significantly better than the corresponding model without CCQ functional domain (P < 0.05). The total CCQ and its functional and symptom domain were independent risk factors of 3-year hospitalized exacerbations. The prognostic capacity of the functional domain was similar to that of total CCQ, and was significantly stronger than the symptom and mental domain. The CCQ functional domain was able to increase the predictive power of exacerbation history and other multivariable prediction models, indicating it may have an important role in the multivariable prediction tool for hospitalized exacerbation, and its combination with other clinical variables might be used as a low-cost approach for assessments of the disease severity and severe exacerbation in COPD patients in the future.

Incorporating Inflammation Biomarker-Driven Multivariate Predictive Model for Coronary Microcirculatory Dysfunction in Acute Myocardial Infarction Following Emergency Percutaneous Coronary Intervention.

Despite patients with successful revascularization as evidenced by angiographic findings, inadequate clinical management of coronary microcirculatory dysfunction (CMD) may result in preventable adverse outcomes. Therefore, it is imperative to use a multimodal data‑driven predictive model for the occurrence of CMD in patients with acute myocardial infarction (AMI) following emergency percutaneous coronary intervention (PCI). A prospective case-control analysis was conducted on a cohort of 77 patients with AMI who underwent PCI. The most informative predictors were selected for the predictive model through the application of LASSO analysis and multi-factor logistic regression. The diagnosis of CMD is based on findings from cardiac magnetic resonance (CMR). Based on the findings from LASSO analysis and multi-factor logistic regression, variables including sex, neutrophil-to-lymphocyte ratio (NLR), Gensini score, and diabetes mellitus were identified as independent predictors for the development of CMD in AMI patients who underwent emergency PCI. The predictive model was evaluated using bootstrap self-sampling 500 times. The resulting predictive model demonstrated an AUC value of 0.897 (95% CI: 0.827-0.958). The calibration curves exhibited good concordance between the predictions generated by the model and the CMR analysis. Furthermore, decision curve analysis revealed that the predictive model provided valuable clinical benefit in predicting CMD. The multivariate predictive model, constructed using readily available clinical variables in patients with AMI who underwent PCI, demonstrates satisfactory predictability for identifying comorbid CMD, thereby facilitating the identification of high-risk patients.

Predictors of Durable Remission After Successful Surgery for Cushing Disease: Results From the Multicenter RAPID Registry.

Cushing disease (CD) affects mortality and quality of life along with limited long-term remission, underscoring the need to better identify recurrence risk. The identification of surgical or imaging predictors for CD remission after transsphenoidal surgery has yielded some inconsistent results and has been limited by single-center, single-surgeon, or meta-analyses studies. We sought to evaluate the multicenter Registry of Adenomas of the Pituitary and Related Disorders (RAPID) database of academic US pituitary centers to assess whether robust nonhormonal recurrence predictors could be elucidated. Patients with treated CD from 2011 to 2023 were included. The perioperative and long-term characteristics of CD patients with and without recurrence were assessed using univariable and multivariable analyses. Of 383 patients with CD from 26 surgeons achieving postoperative remission, 288 (75.2%) maintained remission at last follow-up while 95 (24.8%) showed recurrence (median time to recurrence 9.99 ± 1.34 years). Patients with recurrence required longer postoperative hospital stays (5 ± 3 vs 4 ± 2 days, P = .002), had larger average tumor volumes (1.76 ± 2.53 cm 3 vs 0.49 ± 1.17 cm 3 , P = .0001), and more often previously failed prior treatment (31.1% vs 14.9%, P = .001) mostly being prior surgery. Multivariable hazard prediction models for tumor recurrence found younger age (odds ratio [OR] = 0.95, P = .002) and Knosp grade of 0 (OR = 0.09, reference Knosp grade 4, P = .03) to be protective against recurrence. Comparison of Knosp grade 0 to 2 vs 3 to 4 showed that lower grades had reduced risk of recurrence (OR = 0.27, P = .04). Other factors such as length of stay, surgeon experience, prior tumor treatment, and Knosp grades 1, 2, or 3 failed to reach levels of statistical significance in multivariable analysis. This multicenter study centers suggests that the strongest predictors of recurrence include tumor size/invasion and age. This insight can help with patient counseling and prognostication. Long-term follow-up is necessary for patients, and early treatment of small tumors may improve outcomes.

FAFTransformer: Multivariate time series prediction method based on multi‐period feature recombination

AbstractMultivariate time series forecasting is widely used in various fields in real life. Many time series prediction models have been proposed. The current forecasting model lacks the mining of correlation between sequences based on different periods and correlation of periodical features between different periods when dealing with data. In this paper, we propose a multivariate data prediction model FAFTransformer based on the reorganization of multi‐periodic features, which first extracts the multi‐periodic information of the time series using the method of frequency domain analysis. The temporal dependencies within sequences are then captured using convolution based on different periods, and the correlations between sequences are learned by combining the multivariate attention mechanism to obtain the intra‐sequence and inter‐sequence correlations under the same period. Finally, period fusion is proposed to capture the correlation of period characteristics between different periods. The experimental results show that the model achieves the best results on multiple datasets compared to the latest seven predictive models for time‐series data.

Multivariate Analysis and Prediction Model Construction for Dermatoscope Use by Chinese Dermatologists via an Online Survey.

The use of dermatoscopes is constantly increasing globally, but to date, there are no studies on the use of dermatoscopes by Chinese dermatologists. To determine factors influencing the use of dermatoscopes in China. A web-based questionnaire was designed by the Department of Dermatology at Huashan Hospital affiliated with Fudan University and was published online via the Shanghai Wheat Color Intelligent Technology Company, China. In 2019 and 2022, 1581 and 1507 dermatologists, respectively, were recruited and completed the questionnaire online. In China, the application rate of dermatoscopy is higher in eastern provinces than in western and remote areas. The proportion of doctors from public tertiary hospitals is the highest, with females being the majority. The age range of 30-40 years has the highest proportion, the proportion of doctors with professional titles of attending physician or above is the highest, and the proportion of doctors with a bachelor's degree or above is the highest. By improving the education and professional standards of doctors, providing more training opportunities, simplifying access, and promoting dermatoscopy in grassroots hospitals, we can increase the confidence of dermatologists in the use of dermatoscopy.

Risk Prediction Models for Adverse Drug Reactions and Adverse Drug Events in Older Adults – A Systematic Review and Meta-analysis

Abstract Background Adverse drug reactions (ADRs) and adverse drug events (ADEs) are common and result in significant morbidity, mortality and healthcare costs. As the world’s population ages, the consumption of medications and healthcare will increase. Models predicting ADR risks in older adults have previously been found to lack reliability and validity. The aim of this systematic review and meta-analysis is to provide an updated, comprehensive quality assessment and analysis of ADR-risk prediction tools in older adults. Methods Standard computerised-databases and citations were searched (2012 to 2023) to identify relevant peer-reviewed studies. Studies which developed and/or validated an ADR/ADE prediction model for use in older adults were included. Four studies from a previous systematic review were also included. The TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) guidelines were used to evaluate each of the included studies. Random effects models were used to derive a pooled discrimination estimate (area under the receiver operation curve; AUROC) for model development studies and model validation studies. The prediction model risk of bias assessment tool (PROBAST) was applied to evaluate bias. Results Five out of 11,481 titles, plus four previous studies, met all inclusion criteria and underwent TRIPOD evaluation. Meta-analysis resulted in a favourable pooled AUROC 0.75 (95% CI 0.57, 0.87; I-squared= 96.65%) for model development studies and 0.70 (95% CI 0.57, 0.80; I-squared= 90.53%) for model validation studies but there was substantial heterogeneity. Studies had poor adherence (range 32-50%; median 44%; IQR 11.5%) to TRIPOD guidelines. The overall risk of bias was high for all included studies. Conclusion Seven risk prediction models were identified, all exhibiting poor adherence to TRIPOD guidelines which may question the investigational rigor of the studies and their usability. This underscores the need for the development of a validated, robust and reliable tool worthy of implementation and testing in a real-world setting.