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Purpose: Out-of-hospital cardiac arrest (OHCA) survivors face significant risks of complications and death from hypoxic–ischemic brain injury leading to withdrawal of life-sustaining treatment (WLST). Accurate multimodal neuroprognostication, including automated pupillometry, is essential to avoid inappropriate WLST. However, inconsistent study results hinder standardized threshold recommendations. We aimed to validate proposed pupillometry thresholds with no false predictions of unfavorable outcomes in comatose OHCA survivors. Methods: In the multi-center BOX-trial, quantitative measurements of automated pupillometry (quantitatively assessed pupillary light reflex [qPLR] and Neurological Pupil index [NPi]) were obtained at admission (0 h) and after 24, 48, and 72 h in comatose patients resuscitated from OHCA. We aimed to validate qPLR < 4% and NPi ≤ 2, predicting unfavorable neurological conditions defined as Cerebral Performance Category 3–5 at follow-up. Combined with 48-h neuron-specific enolase (NSE) > 60 μg/L, pupillometry was evaluated for multimodal neuroprognostication in comatose patients with Glasgow Motor Score (M) ≤ 3 at ≥ 72 h. Results: From March 2017 to December 2021, we consecutively enrolled 710 OHCA survivors (mean age: 63 ± 14 years; 82% males), and 266 (37%) patients had unfavorable neurological outcomes. An NPi ≤ 2 predicted outcome with 0% false-positive rate (FPR) at all time points (0–72 h), and qPLR < 4% at 24–72 h. In patients with M ≤ 3 at ≥ 72 h, pupillometry thresholds significantly increased the sensitivity of NSE, from 42% (35–51%) to 55% (47–63%) for qPLR and 50% (42–58%) for NPi, maintaining 0% (0–0%) FPR. Conclusion: Quantitative pupillometry thresholds predict unfavorable neurological outcomes in comatose OHCA survivors and increase the sensitivity of NSE in a multimodal approach at ≥ 72 h. [ABSTRACT FROM AUTHOR]

Purpose: Somatostatin receptor (SSTR) imaging features are predictive of treatment outcome for neuroendocrine tumor (NET) patients receiving peptide receptor radionuclide therapy (PRRT). However, comprehensive (all metastatic lesions), longitudinal (temporal variation), and lesion-level measured features have never been explored. Such features allow for capturing the heterogeneity in disease response to treatment. Furthermore, models combining these features are lacking. In this work we evaluated the predictive power of comprehensive, longitudinal, lesion-level 68GA-SSTR-PET features combined with a multivariate linear regression (MLR) model. Methods: This retrospective study enrolled NET patients treated with [177Lu]Lu-DOTA-TATE and imaged with [68Ga]Ga-DOTA-TATE at baseline and post-therapy. All lesions were segmented, anatomically labeled, and longitudinally matched. Lesion-level uptake and variation in uptake were measured. Patient-level features were engineered and selected for modeling of progression-free survival (PFS). The model was validated via concordance index, patient classification (ROC analysis), and survival analysis (Kaplan-Meier and Cox proportional hazards). The MLR was benchmarked against single feature predictions. Results: Thirty-six NET patients were enrolled and stratified into poor and good responders (PFS ≥ 25 months). Four patient-level features were selected, the MLR concordance index was 0.826, and the AUC was 0.88 (0.85 specificity, 0.81 sensitivity). Survival analysis led to significant patient stratification (p<.001) and hazard ratio (3⨯10-5). Lastly, in a benchmark study, the MLR modeling approach outperformed all the single feature predictors. Conclusion: Comprehensive, lesion-level, longitudinal 68GA-SSTR-PET analysis, combined with MLR modeling, leads to excellent predictions of PRRT outcome in NET patients, outperforming non-comprehensive, patient-level, and single time-point feature predictions. Message: Neuroendocrine tumor, peptide receptor radionuclide therapy, Somatostatin Receptor Imaging, Outcome Prediction, Treatment Response Assessment [ABSTRACT FROM AUTHOR]

Background Methods Results Conclusions Outcomes in alcohol‐associated liver disease (ALD) are influenced by several race and ethnic factors, yet its natural history across the continuum of patients in different stages of the disease is unknown.We conducted a retrospective cohort study of U.S. adults from 2011 to 2018, using three nationally representative databases to examine potential disparities in relevant outcomes among racial and ethnic groups. Our analysis included logistic and linear regressions, along with competing risk analysis.Black individuals had the highest daily alcohol consumption (12.6 g/day) while Hispanic participants had the largest prevalence of heavy episodic drinking (33.5%). In a multivariable‐adjusted model, Hispanic and Asian participants were independently associated with a higher ALD prevalence compared to Non‐Hispanic White interviewees (OR: 1.4, 95% CI: 1.1–1.8 and OR: 1.5 95% CI:1.1–2.0, respectively), while Blacks participants had a lower ALD prevalence (OR: .7 95% CI: .6–.9), and a lower risk of mortality during hospitalization due to ALD (OR: .83 95% CI: .73–.94). Finally, a multivariate competing‐risk analysis showed that Hispanic ethnicity had a decreased probability of liver transplantation if waitlisted for ALD (SHR: .7, 95% CI: .6–.8) along with female Asian population (HR: .40, 95% CI: .26–.62).After accounting for key social and biological health determinants, the Hispanic population showed an increased risk of ALD prevalence, even with lower alcohol consumption. Additionally, Hispanic and Asian female patients had reduced access to liver transplantation compared to other enlisted patients. [ABSTRACT FROM AUTHOR]

The variability in outcomes among adult cochlear implant (CI) users poses challenges for clinicians in accurately predicting the benefits of the implant for individual candidates. This study aimed to investigate the accuracy and confidence of clinicians in predicting speech perception outcomes for adult CI users one-year post-implantation. Participants were presented with comprehensive information on pre-implantation, one-month post-implantation, and six-month post-implantation data for 10 case studies. The cases encompassed a range of one-year post-implantation phoneme scores, from low performers (27%) to high performers (92%). Participants were tasked with predicting the speech perception outcomes for these cases one year after implantation. Forty-one clinicians completed the full outcome prediction survey. Our findings revealed a significant over-prediction of low performance by clinicians. Interestingly, clinicians tended to predict average performance (73–76% phoneme score) even when provided with information suggesting lower-than-average performance. Most clinicians expressed confidence in their predictions, irrespective of their accuracy. Identifying signs of low performance, particularly in the early post-implantation period, can enable clinicians to implement early interventions. Further research into accurate outcome prediction is essential for managing expectations, providing counselling, increasing CI adoption, and optimising clinical care for both high and low performers. [ABSTRACT FROM AUTHOR]

Background: Many children with severe traumatic brain injury (TBI) receive magnetic resonance imaging (MRI) during hospitalization. There are insufficient data on how different patterns of injury on early MRI inform outcomes. Methods: Children (3–17 years) admitted in 2010–2021 for severe TBI (Glasgow Coma Scale [GCS] score < 9) were identified using our site's trauma registry. We used multivariable modeling to determine whether the hemorrhagic diffuse axonal injury (DAI) grade and the number of regions with restricted diffusion (subcortical white matter, corpus callosum, deep gray matter, and brainstem) on MRI obtained within 7 days of injury were independently associated with time to follow commands and with Functional Independence Measure for Children (WeeFIM) scores at the time of discharge from inpatient rehabilitation. We controlled for the clinical variables age, preadmission cardiopulmonary resuscitation, pupil reactivity, motor GCS score, and fever (> 38 °C) in the first 12 h. Results: Of 260 patients, 136 (52%) underwent MRI within 7 days of injury at a median of 3 days (interquartile range [IQR] 2–4). Patients with early MRI were a median age of 11 years (IQR 7–14), 8 (6%) patients received cardiopulmonary resuscitation, 19 (14%) patients had bilateral unreactive pupils, the median motor GCS score was 1 (IQR 1–4), and 82 (60%) patients had fever. Grade 3 DAI was present in 46 (34%) patients, and restricted diffusion was noted in the corpus callosum in 75 (55%) patients, deep gray matter in 29 (21%) patients, subcortical white matter in 23 (17%) patients, and the brainstem in 20 (15%) patients. After controlling for clinical variables, an increased number of regions with restricted diffusion, but not hemorrhagic DAI grade, was independently associated with longer time to follow commands (hazard ratio 0.68, 95% confidence interval 0.53–0.89) and worse WeeFIM scores (estimate β − 4.67, 95% confidence interval − 8.33 to − 1.01). Conclusions: Regional restricted diffusion on early MRI is independently associated with short-term outcomes in children with severe TBI. Multicenter cohort studies are needed to validate these findings and elucidate the association of early MRI features with long-term outcomes in children with severe TBI. [ABSTRACT FROM AUTHOR]

Simple Summary: Head and neck cancer is a serious health concern that affects millions of people across the globe. Predicting how patients will respond to therapy is critical for providing optimal care. To make these predictions, one may first manually identify tumour boundaries on medical imaging in order to obtain the necessary information. Manually establishing tumour borders, however, is both costly and time-consuming, and prone to error and disagreement. Our AI-based research provides a unique method for predicting patient outcome that eliminates the need for manual delineation steps. Instead, we collect information from the patient's whole head and neck area on PET scans as "looked upon" from a variety of angles. Our technique is faster, more consistent, and more precise than existing methods, with the potential to help doctors deliver better care to patients. We introduce an innovative, simple, effective segmentation-free approach for survival analysis of head and neck cancer (HNC) patients from PET/CT images. By harnessing deep learning-based feature extraction techniques and multi-angle maximum intensity projections (MA-MIPs) applied to Fluorodeoxyglucose Positron Emission Tomography (FDG-PET) images, our proposed method eliminates the need for manual segmentations of regions-of-interest (ROIs) such as primary tumors and involved lymph nodes. Instead, a state-of-the-art object detection model is trained utilizing the CT images to perform automatic cropping of the head and neck anatomical area, instead of only the lesions or involved lymph nodes on the PET volumes. A pre-trained deep convolutional neural network backbone is then utilized to extract deep features from MA-MIPs obtained from 72 multi-angel axial rotations of the cropped PET volumes. These deep features extracted from multiple projection views of the PET volumes are then aggregated and fused, and employed to perform recurrence-free survival analysis on a cohort of 489 HNC patients. The proposed approach outperforms the best performing method on the target dataset for the task of recurrence-free survival analysis. By circumventing the manual delineation of the malignancies on the FDG PET-CT images, our approach eliminates the dependency on subjective interpretations and highly enhances the reproducibility of the proposed survival analysis method. The code for this work is publicly released. [ABSTRACT FROM AUTHOR]

This article discusses the value of serial assessments and the challenges of observational research in the context of extracorporeal cardiopulmonary resuscitation (ECPR) for patients with out-of-hospital cardiac arrest (OHCA). It highlights three large randomized controlled trials that evaluated the effect of ECPR on survival, each with different findings. The article then introduces a new study that examines the added value of serial lactate measurements for outcome prediction among adult patients undergoing ECPR after OHCA. The study finds that increased lactate clearance is associated with higher survival rates and more favorable neurological outcomes. However, the study has limitations, such as not incorporating arterial oxygen tension (Pao2) into the analysis, which may have influenced the results. Overall, the study emphasizes the importance of serial assessments and post-resuscitation care in improving outcomes for ECPR patients. [Extracted from the article]

BackgroundSingle-cell transcriptomic studies have greatly improved organ-specific insights into macrophage polarization states are essential for the initiation and resolution of inflammation in all tissues; however, such insights are yet to translate into therapies that can predictably alter macrophage fate.MethodUsing machine learning algorithms on human macrophages, here we reveal the continuum of polarization states that is shared across diverse contexts. A path, comprised of 338 genes accurately identified both physiologic and pathologic spectra of "reactivity" and "tolerance", and remained relevant across tissues, organs, species, and immune cells (>12,500 diverse datasets).FindingsThis 338-gene signature identified macrophage polarization states at single-cell resolution, in physiology and across diverse human diseases, and in murine pre-clinical disease models. The signature consistently outperformed conventional signatures in the degree of transcriptome-proteome overlap, and in detecting disease states; it also prognosticated outcomes across diverse acute and chronic diseases, e.g., sepsis, liver fibrosis, aging, and cancers. Crowd-sourced genetic and pharmacologic studies confirmed that model-rationalized interventions trigger predictable macrophage fates.InterpretationThese findings provide a formal and universally relevant definition of macrophage states and a predictive framework (http://hegemon.ucsd.edu/SMaRT) for the scientific community to develop macrophage-targeted precision diagnostics and therapeutics.FundingThis work was supported by the National Institutes for Health (NIH) grant R01-AI155696 (to P.G, D.S and S.D). Other sources of support include: R01-GM138385 (to D.S), R01-AI141630 (to P.G), R01-DK107585 (to S.D), and UG3TR003355 (to D.S, S.D, and P.G). D.S was also supported by two Padres Pedal the Cause awards (Padres Pedal the Cause/RADY #PTC2017 and San Diego NCI Cancer Centers Council (C3) #PTC2017). S.S, G.D.K, and D.D were supported through The American Association of Immunologists (AAI) Intersect Fellowship Program for Computational Scientists and Immunologists. We also acknowledge support from the Padres Pedal the Cause #PTC2021 and the Torey Coast Foundation, La Jolla (P.G and D.S). D.S, P.G, and S.D were also supported by the Leona M. and Harry B. Helmsley Charitable Trust.

Abstract Digital data processing has revolutionized medical documentation and enabled the aggregation of patient data across hospitals. Initiatives such as those from the AO Foundation about fracture treatment (AO Sammelstudie, 1986), the Major Trauma Outcome Study (MTOS) about survival, and the Trauma Audit and Research Network (TARN) pioneered multi-hospital data collection. Large trauma registries, like the German Trauma Registry (TR-DGU) helped improve evidence levels but were still constrained by predefined data sets and limited physiological parameters. The improvement in the understanding of pathophysiological reactions substantiated that decision making about fracture care led to development of patient’s tailored dynamic approaches like the Safe Definitive Surgery algorithm. In the future, artificial intelligence (AI) may provide further steps by potentially transforming fracture recognition and/or outcome prediction. The evolution towards flexible decision making and AI-driven innovations may be of further help. The current manuscript summarizes the development of big data from local databases and subsequent trauma registries to AI-based algorithms, such as Parkland Trauma Mortality Index and the IBM Watson Pathway Explorer.

Abstract Background The frequency of anterior cervical discectomy and fusion (ACDF) has increased up to 400% since 2011, underscoring the need to preoperatively anticipate adverse postoperative outcomes given the procedure’s expanding use. Our study aims to accomplish two goals: firstly, to develop a suite of explainable machine learning (ML) models capable of predicting adverse postoperative outcomes following ACDF surgery, and secondly, to embed these models in a user-friendly web application, demonstrating their potential utility. Methods We utilized data from the National Surgical Quality Improvement Program database to identify patients who underwent ACDF surgery. The outcomes of interest were four short-term postoperative adverse outcomes: prolonged length of stay (LOS), non-home discharges, 30-day readmissions, and major complications. We utilized five ML algorithms - TabPFN, TabNET, XGBoost, LightGBM, and Random Forest - coupled with the Optuna optimization library for hyperparameter tuning. To bolster the interpretability of our models, we employed SHapley Additive exPlanations (SHAP) for evaluating predictor variables’ relative importance and used partial dependence plots to illustrate the impact of individual variables on the predictions generated by our top-performing models. We visualized model performance using receiver operating characteristic (ROC) curves and precision-recall curves (PRC). Quantitative metrics calculated were the area under the ROC curve (AUROC), balanced accuracy, weighted area under the PRC (AUPRC), weighted precision, and weighted recall. Models with the highest AUROC values were selected for inclusion in a web application. Results The analysis included 57,760 patients for prolonged LOS [11.1% with prolonged LOS], 57,780 for non-home discharges [3.3% non-home discharges], 57,790 for 30-day readmissions [2.9% readmitted], and 57,800 for major complications [1.4% with major complications]. The top-performing models, which were the ones built with the Random Forest algorithm, yielded mean AUROCs of 0.776, 0.846, 0.775, and 0.747 for predicting prolonged LOS, non-home discharges, readmissions, and complications, respectively. Conclusions Our study employs advanced ML methodologies to enhance the prediction of adverse postoperative outcomes following ACDF. We designed an accessible web application to integrate these models into clinical practice. Our findings affirm that ML tools serve as vital supplements in risk stratification, facilitating the prediction of diverse outcomes and enhancing patient counseling for ACDF.

Abstract The (re)hemorrhage in patients with sporadic cerebral cavernous malformations (CCM) was the primary aim for CCM management. However, accurately identifying the potential (re)hemorrhage among sporadic CCM patients in advance remains a challenge. This study aims to develop machine learning models to detect potential (re)hemorrhage in sporadic CCM patients. This study was based on a dataset of 731 sporadic CCM patients in open data platform Dryad. Sporadic CCM patients were followed up 5 years from January 2003 to December 2018. Support vector machine (SVM), stacked generalization, and extreme gradient boosting (XGBoost) were used to construct models. The performance of models was evaluated by area under receiver operating characteristic curves (AUROC), area under the precision-recall curve (PR-AUC) and other metrics. A total of 517 patients with sporadic CCM were included (330 female [63.8%], mean [SD] age at diagnosis, 42.1 [15.5] years). 76 (re)hemorrhage (14.7%) occurred during follow-up. Among 3 machine learning models, XGBoost model yielded the highest mean (SD) AUROC (0.87 [0.06]) in cross-validation. The top 4 features of XGBoost model were ranked with SHAP (SHapley Additive exPlanations). All-Elements XGBoost model achieved an AUROCs of 0.84 and PR-AUC of 0.49 in testing set, with a sensitivity of 0.86 and a specificity of 0.76. Importantly, 4-Elements XGBoost model developed using top 4 features got a AUROCs of 0.83 and PR-AUC of 0.40, a sensitivity of 0.79, and a specificity of 0.72 in testing set. Two machine learning-based models achieved accurate performance in identifying potential (re)hemorrhages within 5 years in sporadic CCM patients. These models may provide insights for clinical decision-making.

Machine learning (ML), a subset of artificial intelligence, is crucial for spine care and research due to its ability to improve treatment selection and outcomes, leveraging the vast amounts of data generated in health care for more accurate diagnoses and decision support. ML's potential in spine care is particularly notable in radiological image analysis, including the localization and labeling of anatomical structures, detection and classification of radiological findings, and prediction of clinical outcomes, thereby paving the way for personalized medicine. The manuscript discusses ML's application in spine care, detailing supervised and unsupervised learning, regression, classification, and clustering, and highlights the importance of both internal and external validation in assessing ML model performance. Several ML algorithms such as linear models, support vector machines, decision trees, neural networks, and deep convolutional neural networks, can be used in the spine domain to analyze diverse data types (visual, tabular, omics, and multimodal).

Background. The novel coronavirus disease 2019 (COVID-19), caused by a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has become an unprecedented challenge for the health care system and has taken a leading position among infectious diseases due to the rapid spread and development of complications from various body systems. The problem of cardiac comorbidity in COVID-19 has several aspects: the impact of concomitant cardiovascular diseases on the incidence of a new viral infection, its severity and risk of mortality, as well as possible side effects of a number of drugs traditionally used to treat certain types of cardiovascular diseases. SARS-CoV-2 is not only a pneumonia pathogen, but also causes serious systemic consequences. Patients with cardiovascular diseases and risk factors for cardiovascular complications, such as hypertension and coronary heart disease, are more exposed to fatal consequences of COVID-19. In addition, a significant proportion of patients develop COVID-associated myocardial injury, which significantly increases the risk of in-hospital mortality. The purpose of the study was to investigate the prognostic role of individual biomarkers in patients with COVID-19-associated pneumonia on the background of chronic coronary syndromes. Materials and methods. This study was defined as an open-label, non-randomized, controlled, comparative one in parallel groups. 124 patients with COVID-19-associated pneumonia were selected upon admission, including 92 participants with a history of chronic coronary syndromes. Results. The study showed that elevated levels of biomarkers such as proinflammatory cytokine interleukin-6, iron-storage protein ferritin, serum angiotensin-converting enzyme-2, ketone body beta-hydroxybutyric acid, and fibrin breakdown product D-dimer may be associated with a more severe course of pneumonia caused by COVID-19 infection. Thus, these biomarkers had good prognostic value in predicting severe disease and death in patients with chronic coronary syndromes during hospitalization. Conclusions. The study confirms that an increase in the level of proinflammatory biomarkers is a predictor for the development of a severe coronavirus infection.

Digital data processing has revolutionized medical documentation and enabled the aggregation of patient data across hospitals. Initiatives such as those from the AO Foundation about fracture treatment (AO Sammelstudie, 1986), the Major Trauma Outcome Study (MTOS) about survival, and the Trauma Audit and Research Network (TARN) pioneered multi-hospital data collection. Large trauma registries, like the German Trauma Registry (TR-DGU) helped improve evidence levels but were still constrained by predefined data sets and limited physiological parameters. The improvement in the understanding of pathophysiological reactions substantiated that decision making about fracture care led to development of patient's tailored dynamic approaches like the Safe Definitive Surgery algorithm. In the future, artificial intelligence (AI) may provide further steps by potentially transforming fracture recognition and/or outcome prediction. The evolution towards flexible decision making and AI-driven innovations may be of further help. The current manuscript summarizes the development of big data from local databases and subsequent trauma registries to AI-based algorithms, such as Parkland Trauma Mortality Index and the IBM Watson Pathway Explorer. [ABSTRACT FROM AUTHOR]

Pediatric Hodgkin and non-Hodgkin lymphomas differ from adult cases in biology and management, yet there is a lack of survival analysis tailored to pediatric lymphoma. We analyzed lymphoma data from 1975 to 2018, comparing survival trends between 7,871 pediatric and 226,211 adult patients, identified key risk factors for pediatric lymphoma survival, developed a predictive nomogram, and utilized machine learning to predict long-term lymphoma-specific mortality risk. Between 1975 and 2018, we observed substantial increases in 1-year (19.3%), 5-year (41.9%), and 10-year (48.8%) overall survival rates in pediatric patients with lymphoma. Prognostic factors such as age, sex, race, Ann Arbor stage, lymphoma subtypes, and radiotherapy were incorporated into the nomogram. The nomogram exhibited excellent predictive performance with area under the curve (AUC) values of 0.766, 0.724, and 0.703 for one-year, five-year, and ten-year survival, respectively, in the training cohort, and AUC values of 0.776, 0.712, and 0.696 in the validation cohort. Importantly, the nomogram outperformed the Ann Arbor staging system in survival prediction. Machine learning models achieved AUC values of approximately 0.75, surpassing the conventional method (AUC = ~ 0.70) in predicting the risk of lymphoma-specific death. We also observed that pediatric lymphoma survivors had a substantially reduced risk of lymphoma after ten years b,ut faced an increasing risk of non-lymphoma diseases. The study highlights substantial improvements in pediatric lymphoma survival, offers reliable predictive tools, and underscores the importance of long-term monitoring for non-lymphoma health issues in pediatric patients. [ABSTRACT FROM AUTHOR]

Predictive process monitoring (PPM) relies on predictive effectiveness, and to address the challenge of improving predictive performance in PPM, this paper proposed a novel approach called behavior profile matrix enhanced business process outcome prediction. Initially, this approach extracted the behavior profile matrix by analyzing the interactions among activities and incorporated it into the model along with event sequences. Then, it used convolutional neural networks (CNN) and long short-term memory networks (LSTM) to independently capture image features from the matrix and sequence features. Finally, this approach integrated an attention mechanism to seamlessly combine both image and sequence features for predictive purpo- ses. Validation using real event logs demonstrates that the proposed enhancement method significantly enhances predictive per- formance compared to the baseline LSTM prediction methods when forecasting event log outcomes, confirming the feasibility of this approach. This approach combines the behavior profile matrix to enhance the predictive model s understanding of relation- ships between behaviors in event logs, consequently leading to an improvement in predictive performance. [ABSTRACT FROM AUTHOR]

Background: The frequency of anterior cervical discectomy and fusion (ACDF) has increased up to 400% since 2011, underscoring the need to preoperatively anticipate adverse postoperative outcomes given the procedure's expanding use. Our study aims to accomplish two goals: firstly, to develop a suite of explainable machine learning (ML) models capable of predicting adverse postoperative outcomes following ACDF surgery, and secondly, to embed these models in a user-friendly web application, demonstrating their potential utility. Methods: We utilized data from the National Surgical Quality Improvement Program database to identify patients who underwent ACDF surgery. The outcomes of interest were four short-term postoperative adverse outcomes: prolonged length of stay (LOS), non-home discharges, 30-day readmissions, and major complications. We utilized five ML algorithms - TabPFN, TabNET, XGBoost, LightGBM, and Random Forest - coupled with the Optuna optimization library for hyperparameter tuning. To bolster the interpretability of our models, we employed SHapley Additive exPlanations (SHAP) for evaluating predictor variables' relative importance and used partial dependence plots to illustrate the impact of individual variables on the predictions generated by our top-performing models. We visualized model performance using receiver operating characteristic (ROC) curves and precision-recall curves (PRC). Quantitative metrics calculated were the area under the ROC curve (AUROC), balanced accuracy, weighted area under the PRC (AUPRC), weighted precision, and weighted recall. Models with the highest AUROC values were selected for inclusion in a web application. Results: The analysis included 57,760 patients for prolonged LOS [11.1% with prolonged LOS], 57,780 for non-home discharges [3.3% non-home discharges], 57,790 for 30-day readmissions [2.9% readmitted], and 57,800 for major complications [1.4% with major complications]. The top-performing models, which were the ones built with the Random Forest algorithm, yielded mean AUROCs of 0.776, 0.846, 0.775, and 0.747 for predicting prolonged LOS, non-home discharges, readmissions, and complications, respectively. Conclusions: Our study employs advanced ML methodologies to enhance the prediction of adverse postoperative outcomes following ACDF. We designed an accessible web application to integrate these models into clinical practice. Our findings affirm that ML tools serve as vital supplements in risk stratification, facilitating the prediction of diverse outcomes and enhancing patient counseling for ACDF. [ABSTRACT FROM AUTHOR]

The (re)hemorrhage in patients with sporadic cerebral cavernous malformations (CCM) was the primary aim for CCM management. However, accurately identifying the potential (re)hemorrhage among sporadic CCM patients in advance remains a challenge. This study aims to develop machine learning models to detect potential (re)hemorrhage in sporadic CCM patients. This study was based on a dataset of 731 sporadic CCM patients in open data platform Dryad. Sporadic CCM patients were followed up 5 years from January 2003 to December 2018. Support vector machine (SVM), stacked generalization, and extreme gradient boosting (XGBoost) were used to construct models. The performance of models was evaluated by area under receiver operating characteristic curves (AUROC), area under the precision-recall curve (PR-AUC) and other metrics. A total of 517 patients with sporadic CCM were included (330 female [63.8%], mean [SD] age at diagnosis, 42.1 [15.5] years). 76 (re)hemorrhage (14.7%) occurred during follow-up. Among 3 machine learning models, XGBoost model yielded the highest mean (SD) AUROC (0.87 [0.06]) in cross-validation. The top 4 features of XGBoost model were ranked with SHAP (SHapley Additive exPlanations). All-Elements XGBoost model achieved an AUROCs of 0.84 and PR-AUC of 0.49 in testing set, with a sensitivity of 0.86 and a specificity of 0.76. Importantly, 4-Elements XGBoost model developed using top 4 features got a AUROCs of 0.83 and PR-AUC of 0.40, a sensitivity of 0.79, and a specificity of 0.72 in testing set. Two machine learning-based models achieved accurate performance in identifying potential (re)hemorrhages within 5 years in sporadic CCM patients. These models may provide insights for clinical decision-making. [ABSTRACT FROM AUTHOR]

Objectives: This study proposed an outcome prediction method to improve the accuracy and efficacy of ischemic stroke outcome prediction based on the diversity of whole brain features, without using basic information about patients and image features in lesions. Design: In this study, we directly extracted dynamic radiomics features (DRFs) fromdynamic susceptibility contrast perfusion-weighted imaging (DSC-PWI) and further extracted static radiomics features (SRFs) and static encoding features (SEFs) from the minimum intensity projection (MinIP) map, which was generated fromthe time dimension of DSC-PWI images. After selecting whole brain features Ffuse from the combinations of DRFs, SRFs, and SEFs by the Lasso algorithm, various machine and deep learning models were used to evaluate the role of Ffuse in predicting stroke outcomes. Results: The experimental results show that the feature Ffuse generated from DRFs, SRFs, and SEFs (Resnet 18) outperformed other single and combination features and achieved the best mean score of 0.971 both on machine learning models and deep learning models and the 95% CI were (0.703, 0.877) and (0.92, 0.983), respectively. Besides, the deep learning models generally performed better than the machine learning models. Conclusion: Themethod used in our study can achieve an accurate assessment of stroke outcomes without segmentation of ischemic lesions, which is of great significance for rapid, efficient, and accurate clinical stroke treatment. [ABSTRACT FROM AUTHOR]

Introduction: This study aimed to identify CT-based imaging biomarkers for locoregional recurrence (LR) in Oral Cavity Squamous Cell Carcinoma (OSCC) patients. Methods: Computed tomography scans were collected from 78 patients with OSCC who underwent surgical treatment at a single medical center. We extracted 1,092 radiomic features from gross tumor volume in each patient's pre-treatment CT. Clinical characteristics were also obtained, including race, sex, age, tobacco and alcohol use, tumor staging, and treatment modality. A feature selection algorithm was used to eliminate the most redundant features, followed by a selection of the best subset of the Logistic regression model (LRM). The best LRM model was determined based on the best prediction accuracy in terms of the area under Receiver operating characteristic curve. Finally, significant radiomic features in the final LRM model were identified as imaging biomarkers. Results and discussion: Two radiomics biomarkers, Large Dependence Emphasis (LDE) of the Gray Level Dependence Matrix (GLDM) and Long Run Emphasis (LRE) of the Gray Level Run Length Matrix (GLRLM) of the 3D Laplacian of Gaussian (LoG σ=3), have demonstrated the capability to preoperatively distinguish patients with and without LR, exhibiting exceptional testing specificity (1.00) and sensitivity (0.82). The group with LRE > 2.99 showed a 3-year recurrence-free survival rate of 0.81, in contrast to 0.49 for the group with LRE = 2.99. Similarly, the group with LDE > 120 showed a rate of 0.82, compared to 0.49 for the group with LDE = 120. These biomarkers broaden our understanding of using radiomics to predict OSCC progression, enabling personalized treatment plans to enhance patient survival. [ABSTRACT FROM AUTHOR]

Significance: Current treatment for stage III colorectal cancer (CRC) patients involves surgery that may not be sufficient in many cases, requiring additional adjuvant systemic therapy. Identification of this latter cohort that is likely to recur following surgery is key to better personalized therapy selection, but there is a lack of proper quantitative assessment tools for potential clinical adoption. Aim: The purpose of this study is to employ Mueller matrix (MM) polarized light microscopy in combination with supervised machine learning (ML) to quantitatively analyze the prognostic value of peri-tumoral collagen in CRC in relation to 5-year local recurrence (LR). Approach: A simple MM microscope setup was used to image surgical resection samples acquired from stage III CRC patients. Various potential biomarkers of LR were derived from MM elements via decomposition and transformation operations. These were used as features by different supervised ML models to distinguish samples from patients that locally recurred 5 years later from those that did not. Results: Using the top five most prognostic polarimetric biomarkers ranked by their relevant feature importances, the best-performing XGBoost model achieved a patient-level accuracy of 86%. When the patient pool was further stratified, 96% accuracy was achieved within a tumor-stage-III sub-cohort. Conclusions: ML-aided polarimetric analysis of collagenous stroma may provide prognostic value toward improving the clinical management of CRC patients. [ABSTRACT FROM AUTHOR]

Background: Gastro-entero-pancreatic neuroendocrine carcinomas (GEP-NECs) are an aggressive subgroup of neuroendocrine neoplasms (NENs). In patients affected with NEN, there is a growing body of evidence that increased C-X-C motif chemokine receptor (CXCR4) expression is linked to decreasing overall survival (OS) in an ex-vivo setting. Thus, we aimed to determine whether the in-vivo-derived CXCR4-directed whole-body PET signal can also determine GEP-NEC patients with shorter OS. Methods: We retrospectively included 16 patients with histologically proven GEP-NEC, who underwent CXCR4-directed PET/CT for staging and therapy planning. We assessed maximum, peak, and mean standardized uptake values as well as whole-body tumor volume (TV) and total-lesion uptake (TLU = SUVmean × TV) using a semi-automatic segmentation tool with a 50% threshold. Association of PET-based biomarkers and OS or radiographic progression-free survival (rPFS; according to RECIST 1.1 criteria) was analyzed using univariable and multivariable cox regression. Results: Median OS and rPFS was 7.5 and 7 months, respectively. A significant correlation between TV and TLU was found for OS (TV: hazard ratio (HR) 1.007 95% confidence interval (CI) 1.000–1.014, p = 0.0309; TLU: HR 1.002 95% CI 1.000–1.003, p = 0.0350) and rPFS (TV: HR 1.010 95% CI 1.002–1.021; p = 0.0275; TLU: HR 1.002 95% CI 1.000–1.004, p = 0.0329), respectively. No significant correlation with OS or rPFS was found for non-volumetric parameters (p > 0.4). TV remained a significant predictive marker for OS and rPFS in multivariable analysis (OS: HR 1.012 95%, CI 1.003–1.022, p = 0.0084; rPFS: HR 1.009, 95% CI 0.9999–1.019, p = 0.0491), whereas TLU remained only prognostic for OS (HR 1.009, 95% CI 0.9999–1.019, p = 0.0194) but narrowly failed significance for rPFS (p = 0.0559). Conclusion: In-vivo assessment of CXCR4 PET-derived volumetric parameters is predictive for outcome of patients with GEP-NEC and could be used as a risk stratification tool, which detects patients prone to early progression. [ABSTRACT FROM AUTHOR]

Though currently considered a 'black box,' machine learning (ML) has a promising future to ameliorate the health-care burden of stroke which is the second leading cause of mortality worldwide. Through this study, we sought to review the most influential articles on the applications of ML in stroke. Web of Sciences database was searched, and a list of the top 50 most cited articles, assessing the application of ML in stroke, was prepared by 2 authors, independently. Subsequently, a detailed analysis was performed to characterize the most impactful studies. The total number of citations to the top 50 articles were 2959 (range 35–243 citations) with a median of 47 citations. Highest number of articles were published in the journal Stroke and the United States was the major contributing country. The majority of the studies focused on the utilization of ML to improve stroke risk prediction, diagnosis, and outcome prediction. Statistical analysis revealed an insignificant association between the total and mean number of citations and the impact factor of the journal (P = 0.516 and 0.987, respectively). Recent years have witnessed a surge in the application of ML in stroke, with an enhancement in interest and funding over the years. ML has revolutionized the management of stroke and continues to aid in the neurosurgical decision-making and care in stroke patients. [ABSTRACT FROM AUTHOR]

The article discusses outcome prediction after cardiac arrest and the potential for self-fulfilling prophecies in medical care. It presents guidelines for neuroprognostication in unconscious cardiac arrest survivors and the findings of a retrospective study that reassessed the entry point for the algorithm. The study found a false-positive rate of 0% when applied to different patient populations but raises concerns about self-fulfilling prophecies and the need for further research. Another study found that over one-third of patients were alive and independent at the 6-month follow-up, indicating that therapeutic nihilism is misguided. The authors suggest implementing guideline-concordant neuroprognostication timed no earlier than 72 hours postarrest. [Extracted from the article]

Whether endovascular thrombectomy (EVT) improves functional outcome in patients with large-vessel occlusion (LVO) stroke that do not comply with inclusion criteria of randomized controlled trials (RCTs) but that are considered for EVT in clinical practice is uncertain. We aimed to systematically identify patients with LVO stroke underrepresented in RCTs who might benefit from EVT. Following the premises that (i) patients without reperfusion after EVT represent a non-treated control group and (ii) the level of reperfusion affects outcome in patients with benefit from EVT but not in patients without treatment benefit, we systematically assessed the importance of reperfusion level on functional outcome prediction using machine learning in patients with LVO stroke treated with EVT in clinical practice (N = 5235, German-Stroke-Registry) and in patients treated with EVT or best medical management from RCTs (N = 1488, Virtual-International-Stroke-Trials-Archive). The importance of reperfusion level on outcome prediction in an RCT-like real-world cohort equaled the importance of EVT treatment allocation for outcome prediction in RCT data and was higher compared to an unselected real-world population. The importance of reperfusion level was magnified in patient groups underrepresented in RCTs, including patients with lower NIHSS scores (0-10), M2 occlusions, and lower ASPECTS (0-5 and 6-8). Reperfusion level was equally important in patients with vertebrobasilar as with anterior LVO stroke. The importance of reperfusion level for outcome prediction identifies patient target groups who likely benefit from EVT, including vertebrobasilar stroke patients and among patients underrepresented in RCT patients with low NIHSS scores, low ASPECTS, and M2 occlusions.

IntroductionDespite advancements in stroke care, challenges persist in timely triage and treatment initiation to prevent the burden of stroke-related disabilities. Although nuclear medicine has shown promise, no imaging technique has yet provided a sufficiently rapid, precise, and cost-effective approach to routine stroke management. This study aims to review the clinical application of nuclear medicine in stroke diagnosis and treatment.MethodsA systematic search of the Cochrane, EU Clinical Trials Register, ISRCTN, the International Stroke Trial, and the ClinicalTrials.gov database was conducted to find all registered trials reporting nuclear medicine’s clinical applications in stroke up to June 07, 2024.ResultsAmong the 220 screened trials, 51 (36 interventional; 15 observational) met the eligibility criteria. Participants were older than 18 years old, with only six studies including pediatric under 17 years old, with a total of 11,262 stroke (9,232 ischemic; 2,030 haemorrhagic) participants. The bias risk varied across trials but remained mostly low to moderate.DiscussionThe review highlighted nuclear medicine’s significant contributions to stroke diagnosis and management, notably through mobile stroke units, pre-hospital acute stroke magnetic resonance image (MRI) based biomarkers, and MRI-based stroke mechanisms for 4D flow nuclear imaging. These advancements have generally reduced treatment delays and enhance clinical outcomes post-stroke. Specifically, radiopharmaceutical radiotracers can effectively discriminate between strokes and mimics, particularly in high-risk patients. Integrating novel positron emission tomography (PET) radiotracer 18F glycoprotein 1 and radionuclide angiography may improve sensitivity and specificity in thrombi detection for decisions regarding stenting or carotid endarterectomy, and the single-photon emission computed tomography and PET integration with ferumoxytol radiotracer-enhanced MRI enables functional imaging for evaluating cerebral perfusion, metabolic activity, and neuroinflammatory markers post-stroke. Overall, the integration of nuclear medicine into multimodal imaging equipment like computed-tomography PET and MRI-PET offers a more comprehensive picture of the brain. Nevertheless, further research is needed on novel stroke imaging techniques and standardization across stroke centers for optimal performance.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42024541680, identifier PROSPERO CRD(42024541680).

Data-based predictions of individual Cognitive Behavioral Therapy (CBT) treatment response are a fundamental step towards precision medicine. Past studies demonstrated only moderate prediction accuracy (i.e. ability to discriminate between responders and non-responders of a given treatment) when using clinical routine data such as demographic and questionnaire data, while neuroimaging data achieved superior prediction accuracy. However, these studies may be considerably biased due to very limited sample sizes and bias-prone methodology. Adequately powered and cross-validated samples are a prerequisite to evaluate predictive performance and to identify the most promising predictors. We therefore analyzed resting state functional magnet resonance imaging (rs-fMRI) data from two large clinical trials to test whether functional neuroimaging data continues to provide good prediction accuracy in much larger samples. Data came from two distinct German multicenter studies on exposure-based CBT for anxiety disorders, the Protect-AD and SpiderVR studies. We separately and independently preprocessed baseline rs-fMRI data from n = 220 patients (Protect-AD) and n = 190 patients (SpiderVR) and extracted a variety of features, including ROI-to-ROI and edge-functional connectivity, sliding-windows, and graph measures. Including these features in sophisticated machine learning pipelines, we found that predictions of individual outcomes never significantly differed from chance level, even when conducting a range of exploratory post-hoc analyses. Moreover, resting state data never provided prediction accuracy beyond the sociodemographic and clinical data. The analyses were independent of each other in terms of selecting methods to process resting state data for prediction input as well as in the used parameters of the machine learning pipelines, corroborating the external validity of the results. These similar findings in two independent studies, analyzed separately, urge caution regarding the interpretation of promising prediction results based on neuroimaging data from small samples and emphasizes that some of the prediction accuracies from previous studies may result from overestimation due to homogeneous data and weak cross-validation schemes. The promise of resting-state neuroimaging data to play an important role in the prediction of CBT treatment outcomes in patients with anxiety disorders remains yet to be delivered.

Abstract Background Forkhead-box protein P1 (FOXP1) has been proposed to have both oncogenic and tumor-suppressive properties, depending on tumor heterogeneity. However, the role of FOXP1 in intrahepatic cholangiocarcinoma (ICC) has not been previously reported. Methods Immunohistochemistry was performed to detect FOXP1 expression in ICC and normal liver tissues. The relationship between FOXP1 levels and the clinicopathological characteristics of patients with ICC was evaluated. Finally, in vitro and in vivo experiments were conducted to examine the regulatory role of FOXP1 in ICC cells. Results FOXP1 was significantly downregulated in the ICC compared to their peritumoral tissues (p

Objective: In a previous pilot study, we have shown that intraarticular gold micro-particles can reduce knee osteoarthritis (KOA) pain at two years follow-up. Proteomic changes in serum and synovial fluid within eight weeks were associated with multiple inflammatory and immunological processes. The relation between the different biomarkers and the outcome measures is not known. We hypothesized that improvement in pain and function were associated with specific groups of biomarkers. We present the integrative analyses between proteomic biomarkers and outcomes. Design: A cohort of 30 patients, with moderate KOA, were included. Using the patients’ synovial fluid as the carrier, 20 ​mg gold microparticles were injected intraarticularly. Clinical outcome measures at inclusion, 8 weeks, and 2 years, were the PainDetect questionnaire, WOMAC pain, stiffness, and function. In addition, Quantitative Sensory Testing, Pain Pressure Threshold, Temporal Summation, Conditioned Pain Modulation, and pain diary were assessed at inclusion and after 8 weeks. Proteomic analysis was performed on SF and blood samples before and after 8 weeks of treatment. Results: Linear combinations of serum or synovial biomarkers changed significantly alongside the effect measures and PainDetect scores following gold micro-particle treatment. Of particular interest was identifying multiple members of a molecular complex that is suggestive of neural tissue regeneration and modulation following gold micro-particle treatment. Conclusions: Gold microparticles are a possible future option for the treatment of knee osteoarthritis. The treatment triggers putative regenerative and inflammation-modulating molecular mechanisms.

Background: Most clinical machine learning applications use a supervised learning approach using labeled variables. In contrast, unsupervised learning enables pattern detection without a prespecified outcome. Purpose/Hypothesis: The purpose of this study was to apply unsupervised learning to the combined Danish and Norwegian knee ligament register (KLR) with the goal of detecting distinct subgroups. It was hypothesized that resulting groups would have differing rates of subsequent anterior cruciate ligament reconstruction (ACLR) revision. Study Design: Cohort study; Level of evidence, 3. Methods: K-prototypes clustering was performed on the complete case KLR data. After performing the unsupervised learning analysis, the authors defined clinically relevant characteristics of each cluster using variable summaries, surgeons' domain knowledge, and Shapley Additive exPlanations analysis. Results: Five clusters were identified. Cluster 1 (revision rate, 9.9%) patients were young (mean age, 22 years; SD, 6 years), received hamstring tendon (HT) autograft (91%), and had lower baseline Knee injury and Osteoarthritis Outcome Score (KOOS) Sport and Recreation (Sports) scores (mean, 25.0; SD, 15.6). Cluster 2 (revision rate, 6.9%) patients received HT autograft (89%) and had higher baseline KOOS Sports scores (mean, 67.2; SD, 16.5). Cluster 3 (revision rate, 4.7%) patients received bone–patellar tendon–bone (BPTB) or quadriceps tendon (QT) autograft (94%) and had higher baseline KOOS Sports scores (mean, 65.8; SD, 16.4). Cluster 4 (revision rate, 4.1%) patients received BPTB or QT autograft (88%) and had low baseline KOOS Sports scores (mean, 20.5; SD, 14.0). Cluster 5 (revision rate, 3.1%) patients were older (mean age, 42 years; SD, 7 years), received HT autograft (89%), and had low baseline KOOS Sports scores (mean, 23.4; SD, 17.6). Conclusion: Unsupervised learning identified 5 distinct KLR patient subgroups and each grouping was associated with a unique ACLR revision rate. Patients can be approximately classified into 1 of the 5 clusters based on only 3 variables: age, graft choice (HT, BPTB, or QT autograft), and preoperative KOOS Sports subscale score. If externally validated, the resulting groupings may enable quick risk stratification for future patients undergoing ACLR in the clinical setting. Patients in cluster 1 are considered high risk (9.9%), cluster 2 patients medium risk (6.9%), and patients in clusters 3 to 5 low risk (3.1%-4.7%) for revision ACLR. [ABSTRACT FROM AUTHOR]

Temporal lobe epilepsy is the most common reason behind drug-resistant seizures and temporal lobectomy (TL) is performed after all other efforts have been taken for a Temporal lobe epilepsy. Our study aims to develop multiple machine learning (ML) models capable of predicting postoperative outcomes following TL surgery. Data from the American College of Surgeons National Surgical Quality Improvement Program database identified patients who underwent TL surgery. We focused on 3 outcomes: prolonged length of stay (LOS), nonhome discharges, and 30-day readmissions. Six ML algorithms, TabPFN, XGBoost, LightGBM, Support Vector Machine, Random Forest, and Logistic Regression, coupled with the Optuna optimization library for hyperparameter tuning, were tested. Models with the highest area under the receiver operating characteristic (AUROC) values were included in the web application. SHapley Additive exPlanations was used to evaluate importance of predictor variables. Our analysis included 423 patients. Of these patients, 111 (26.2%) experienced prolonged LOS, 33 (7.8%) had nonhome discharges, and 29 (6.9%) encountered 30-day readmissions. The top-performing models for each outcome were those built with the Random Forest algorithm. The Random Forest models yielded AUROCs of 0.868, 0.804, and 0.742 in predicting prolonged LOS, nonhome discharges, and 30-day readmissions, respectively. Our study uses ML to forecast adverse postoperative outcomes following TL. We developed accessible predictive models that enhance prognosis prediction for TL surgery. Making ML models available for this purpose represents a significant advancement in shifting toward a more patient-centric, data-driven paradigm. [ABSTRACT FROM AUTHOR]

Introduction Sepsis and septic shock represent severe pathological states, characterized by the systemic response to infection, which can lead to organ dysfunction and high mortality. Early diagnosis and rapid intervention are crucial for improving survival chances. However, the diagnosis of sepsis is complex due to its nonspecific symptoms and the variability of patient responses to infections. Methods The objective of this research was to analyze the implications of using artificial intelligence (AI) in the diagnosis of sepsis and septic shock. The research method applied in the analysis of the implications of using artificial intelligence (AI) in the diagnosis of sepsis and septic shock is the literature review. Results Among the benefits of using AI in the diagnosis of sepsis, it is noted that artificial intelligence can rapidly analyze large volumes of clinical data to identify early signs of sepsis, sometimes even before symptoms become evident to medical staff. AI models can use predictive algorithms to assess the risk of sepsis in patients, allowing for early interventions that can save lives. AI can contribute to the development of personalized treatment plans, adapting to the specific needs of each patient based on their medical history and response to treatment. The use of patient data to train AI models raises concerns regarding data privacy and security. Conclusions Artificial intelligence has the potential to revolutionize the diagnosis and treatment of sepsis, offering powerful tools for early identification and management of this critical condition. However, to realize this potential, close collaboration between researchers, clinicians, and technology developers is necessary, as well as addressing ethical and implementation challenges. [ABSTRACT FROM AUTHOR]

Background: This publication represents a scheduled update of the 2019 guidelines of the International Working Group of the Diabetic Foot (IWGDF) addressing the use of systems to classify foot ulcers in people with diabetes in routine clinical practice. The guidelines are based on a systematic review of the available literature that identified 28 classifications addressed in 149 articles and, subsequently, expert opinion using the GRADE methodology. Methods: First, we have developed a list of classification systems considered as being potentially adequate for use in a clinical setting, through the summary of judgements for diagnostic tests, focussing on the usability, accuracy and reliability of each system to predict ulcer‐related complications as well as use of resources. Second, we have determined, following group debate and consensus, which of them should be used in specific clinical scenarios. Following this process, in a person with diabetes and a foot ulcer we recommend:(a)for communication among healthcare professionals: to use the SINBAD (Site, Ischaemia, Bacterial infection, Area and Depth) system (first option) or consider using WIfI (Wound, Ischaemia, foot Infection) system (alternative option, when the required equipment and level of expertise is available and it is considered feasible) and in each case the individual variables that compose the systems should be described rather than a total score;(b)for predicting the outcome of an ulcer in a specific individual: no existing system could be recommended;(c)for characterising a person with an infected ulcer: the use of the IDSA/IWGDF classification (first option) or consider using the WIfI system (alternative option, when the required equipment and level of expertise is available and it is considered as feasible);(d)for characterising a person with peripheral artery disease: consider using the WIfI system as a means to stratify healing likelihood and amputation risk;(e)for the audit of outcome(s) of populations: the use of the SINBAD score. Conclusions: For all recommendations made using GRADE, the certainty of evidence was judged, at best, as being low. Nevertheless, based on the rational application of current data this approach allowed the proposal of recommendations, which are likely to have clinical utility. [ABSTRACT FROM AUTHOR]

Background: Classification and scoring systems can help both clinical management and audit the outcomes of routine care. Aim: This study aimed to assess published systems used to characterise ulcers in people with diabetes to determine which should be recommended to (a) aid communication between health professionals, (b) predict clinical outcome of individual ulcers, (c) characterise people with infection and/or peripheral arterial disease, and (d) audit to compare outcomes in different populations. This systematic review is part of the process of developing the 2023 guidelines to classify foot ulcers from the International Working Group on Diabetic Foot. Methods: We searched PubMed, Scopus and Web of Science for articles published up to December 2021 which evaluated the association, accuracy or reliability of systems used to classify ulcers in people with diabetes. Published classifications had to have been validated in populations of >80% of people with diabetes and a foot ulcer. Results: We found 28 systems addressed in 149 studies. Overall, the certainty of the evidence for each classification was low or very low, with 19 (68%) of the classifications being assessed by ≤ 3 studies. The most frequently validated system was the one from Meggitt‐Wagner, but the articles validating this system focused mainly on the association between the different grades and amputation. Clinical outcomes were not standardized but included ulcer‐free survival, ulcer healing, hospitalisation, limb amputation, mortality, and cost. Conclusion: Despite the limitations, this systematic review provided sufficient evidence to support recommendations on the use of six particular systems in specific clinical scenarios. [ABSTRACT FROM AUTHOR]

Meningiomas display diverse biological traits and clinical behaviors, complicating patient outcome prediction. This heterogeneity, along with varying prognoses, underscores the need for a precise, personalized evaluation of postoperative outcomes. Data from the American College of Surgeons National Surgical Quality Improvement Program database identified patients who underwent intracranial meningioma resections from 2014 to 2020. We focused on 5 outcomes: prolonged LOS, nonhome discharges, 30-day readmissions, unplanned reoperations, and major complications. Six machine learning algorithms, including TabPFN, TabNet, XGBoost, LightGBM, Random Forest, and Logistic Regression, coupled with the Optuna optimization library for hyperparameter tuning, were tested. Models with the highest area under the receiver operating characteristic (AUROC) values were included in the web application. SHapley Additive exPlanations were used to evaluate the importance of predictor variables. Our analysis included 7000 patients. Of these patients, 1658 (23.7%) had prolonged LOS, 1266 (18.1%) had nonhome discharges, 573 (8.2%) had 30-day readmission, 253 (3.6%) had unplanned reoperation, and 888 (12.7%) had major complications. Performance evaluation indicated that the top-performing models for each outcome were the models built with LightGBM and Random Forest algorithms. The LightGBM models yielded AUROCs of 0.842 and 0.846 in predicting prolonged LOS and nonhome discharges, respectively. The Random Forest models yielded AUROCs of 0.717, 0.76, and 0.805 in predicting 30-day readmissions, unplanned reoperations, and major complications, respectively. The study successfully demonstrated the potential of machine learning models in predicting short-term adverse postoperative outcomes after meningioma resections. This approach represents a significant step forward in personalizing the information provided to meningioma patients. [ABSTRACT FROM AUTHOR]

Mechanical thrombectomy (MT) improves outcomes in patients with LVO but many still experience mortality or severe disability. We sought to develop machine learning (ML) models that predict 90-day outcomes after MT for LVO. Consecutive patients who underwent MT for LVO between 2015–2021 at a Comprehensive Stroke Center were reviewed. Outcomes included 90-day favorable functional status (mRS 0–2), severe disability (mRS 4–6), and mortality. ML models were trained for each outcome using prethrombectomy data (pre) and with thrombectomy data (post). Three hundred and fifty seven patients met the inclusion criteria. After model screening and hyperparameter tuning the top performing ML model for each outcome and timepoint was random forest (RF). Using only prethrombectomy features, the AUCs for the RF pre models were 0.73 (95% CI 0.62–0.85) for favorable functional status, 0.77 (95% CI 0.65–0.86) for severe disability, and 0.78 (95% CI 0.64–0.88) for mortality. All of these were better than a standard statistical model except for favorable functional status. Each RF model outperformed Pre, SPAN-100, THRIVE, and HIAT scores (P < 0.0001 for all). The most predictive features were premorbid mRS, age, and NIHSS. Incorporating MT data, the AUCs for the RF post models were 0.80 (95% CI 0.67–0.90) for favorable functional status, 0.82 (95% CI 0.69–0.91) for severe disability, and 0.71 (95% CI 0.55–0.84) for mortality. RF models accurately predicted 90-day outcomes after MT and performed better than standard statistical and clinical prediction models. [ABSTRACT FROM AUTHOR]

The goal of this study is to implement machine learning (ML) algorithms to predict mortality, non-home discharge, prolonged length of stay (LOS), prolonged length of intensive care unit stay (ICU-LOS), and major complications in patients diagnosed with thoracolumbar spinal cord injury, while creating a publicly accessible online tool. The American College of Surgeons Trauma Quality Program database was used to identify patients with thoracolumbar spinal cord injury. Feature selection was performed with the Least Absolute Shrinkage and Selection Operator algorithm. Five ML algorithms, including TabPFN, TabNet, XGBoost, LightGBM, and Random Forest, were used along with the Optuna optimization library for hyperparameter tuning. A total of 147,819 patients were included in the analysis. For each outcome, we determined the best model for deployment in our web application based on the area under the receiver operating characteristic (AUROC) values. The top performing algorithms were as follows: LightGBM for mortality with an AUROC of 0.885, TabPFN for non-home discharge with an AUROC of 0.801, LightGBM for prolonged LOS with an AUROC of 0.673, Random Forest for prolonged ICU-LOS with an AUROC of 0.664, and LightGBM for major complications with an AUROC of 0.73. ML models demonstrate good predictive ability for in-hospital mortality and non-home discharge, fair predictive ability for major complications and prolonged ICU-LOS, but poor predictive ability for prolonged LOS. We have developed a web application that allows these models to be accessed. [ABSTRACT FROM AUTHOR]

Purpose: A machine learning‐based anterior cruciate ligament (ACL) revision prediction model has been developed using Norwegian Knee Ligament Register (NKLR) data, but lacks external validation outside Scandinavia. This study aimed to assess the external validity of the NKLR model (https://swastvedt.shinyapps.io/calculator%5frev/) using the STABILITY 1 randomized clinical trial (RCT) data set. The hypothesis was that model performance would be similar. Methods: The NKLR Cox Lasso model was selected for external validation owing to its superior performance in the original study. STABILITY 1 patients with all five predictors required by the Cox Lasso model were included. The STABILITY 1 RCT was a prospective study which randomized patients to receive either a hamstring tendon autograft (HT) alone or HT plus a lateral extra‐articular tenodesis (LET). Since all patients in the STABILITY 1 trial received HT ± LET, three configurations were tested: 1: all patients coded as HT, 2: HT + LET group coded as bone‐patellar tendon‐bone (BPTB) autograft, 3: HT + LET group coded as unknown/other graft choice. Model performance was assessed via concordance and calibration. Results: In total, 591/618 (95.6%) STABILITY 1 patients were eligible for inclusion, with 39 undergoing revisions within 2 years (6.6%). Model performance was best when patients receiving HT + LET were coded as BPTB. Concordance was similar to the original NKLR prediction model for 1‐ and 2‐year revision prediction (STABILITY: 0.71; NKLR: 0.68–0.69). Concordance 95% confidence interval (CI) ranged from 0.63 to 0.79. The model was well calibrated for 1‐year prediction while the 2‐year prediction demonstrated evidence of miscalibration. Conclusion: When patients in STABILITY 1 who received HT + LET were coded as BPTB in the NKLR prediction model, concordance was similar to the index study. However, due to a wide 95% CI, the true performance of the prediction model with this Canadian and European cohort is unclear and a larger data set is required to definitively determine the external validity. Further, better calibration for 1‐year predictions aligns with general prediction modelling challenges over longer periods. While not a large enough sample size to elicit the true accuracy and external validity of the prediction model when applied to North American patients, this analysis provides more support for the notion that HT plus LET performs similarly to BPTB reconstruction. In addition, despite the wide confidence interval, this study suggests optimism regarding the accuracy of the model when applied outside of Scandinavia. Level of Evidence: Level 3, cohort study. [ABSTRACT FROM AUTHOR]

Background: We aimed to develop a simple scoring table for predicting probability of death within 1‐year after admission to an intensive care unit. We analysed data on emergency admissions from the nationwide Finnish intensive care quality registry. Methods: We included first admissions of adult patients with data available on 1‐year vital status (dead or alive) and all five variables included in a premorbid functional status score, which is the number of activities the person can manage independently of the following five: get out of bed, move indoors, dress, climb stairs and walk 400 m. We analysed data on patient characteristics and admission‐associated factors from 2012 to 2014 to find predictors of 1‐year mortality and to develop a score for predicting probability of death. We tested the performance of this score in data from 2015. We assessed the 1‐year functional status score of survivors with data available. Results: Out of 25,261 patients, 20,628 (81.7%) patients were able to perform all five functional activities independently prior to the intensive care unit admission. At 1‐year post admission, 19,625 (77.7%) patients were alive. 1‐year functional status score was known for 11,011 patients and 8970 (81.5%) patients achieved functional status score 5, managing all five activities independently. The score based on age, sex, preceding functional status, type of intensive care unit admission, severity of acute illness and the most significant diagnoses predicted 1‐year mortality with an area under the receiver operating characteristic curve 0.78 (95% CI, 0.76–0.79). The calibration of our prediction model was good, with calibration intercept −0.01 (−0.07 to 0.05) and calibration slope 0.96 (0.90 to 1.02). Conclusion: Our score based on data available at intensive care unit admission predicted 1‐year mortality with fairly good discrimination. Most survivors achieved good functional recovery. [ABSTRACT FROM AUTHOR]

The timely and precise prediction of flooding progression and its eventual outcome in ships with breached hulls can lead to dramatic improvements in maritime safety through improved guidance for both emergency response and ship design. The traditional approach to assessing damage-induced flooding in both these stages, which also fully complies with statutory rules, is through static calculations. On the other hand, the application of models that simulate the flooding progression and the behaviour of flooded ships from, or close to, first principles allows for increased accuracy of the modelling of the phenomenon. This increase in accuracy can then be used to support advanced design for safety procedures. Furthermore, it can considerably enhance a ship's capability for damage identification and inference-based logic for emergency decision support systems and marine accident response in general. This paper conducts a review of selected state-of-the-art methods, procedures, and case studies in recent years which aimed to model progressive flooding and damage ship behaviour and provide some explanations of fundamentals. Applications related to damage identification, the prediction of outcome/situation awareness, and flooding emergency response are also briefly discussed. The paper concludes with a brief reflection on salient gaps in the context of accelerating the development of these methods and their applicability. [ABSTRACT FROM AUTHOR]

Background: Forkhead-box protein P1 (FOXP1) has been proposed to have both oncogenic and tumor-suppressive properties, depending on tumor heterogeneity. However, the role of FOXP1 in intrahepatic cholangiocarcinoma (ICC) has not been previously reported. Methods: Immunohistochemistry was performed to detect FOXP1 expression in ICC and normal liver tissues. The relationship between FOXP1 levels and the clinicopathological characteristics of patients with ICC was evaluated. Finally, in vitro and in vivo experiments were conducted to examine the regulatory role of FOXP1 in ICC cells. Results: FOXP1 was significantly downregulated in the ICC compared to their peritumoral tissues (p < 0.01). The positive rates of FOXP1 were significantly lower in patients with poor differentiation, lymph node metastasis, invasion into surrounding organs, and advanced stages (p < 0.05). Notably, patients with FOXP1 positivity had better outcomes (overall survival) than those with FOXP1 negativity (p < 0.05), as revealed by Kaplan–Meier survival analysis. Moreover, Cox multivariate analysis showed that negative FOXP1 expression, advanced TNM stages, invasion, and lymph node metastasis were independent prognostic risk factors in patients with ICC. Lastly, overexpression of FOXP1 inhibited the proliferation, migration, and invasion of ICC cells and promoted apoptosis, whereas knockdown of FOXP1 had the opposite role. Conclusion: Our findings suggest that FOXP1 may serve as a novel outcome predictor for ICC as well as a tumor suppressor that may contribute to cancer treatment. [ABSTRACT FROM AUTHOR]

Traumatic brain injury (TBI) affects 69 million people worldwide each year, and acute traumatic epidural hematoma (atEDH) is a frequent and severe consequence of TBI. The aim of the study is to use machine learning (ML) algorithms to predict in-hospital death, non-home discharges, prolonged length of stay (LOS), prolonged length of intensive care unit stay (ICU-LOS), and major complications in patients with atEDH and incorporate the resulting ML models into a user-friendly web application for use in the clinical settings. The American College of Surgeons (ACS) Trauma Quality Program (TQP) database was used to identify patients with atEDH. Four ML algorithms (XGBoost, LightGBM, CatBoost, and Random Forest) were utilized, and the best performing models were incorporated into an open-access web application to predict the outcomes of interest. The study found that the ML algorithms had high area under the receiver operating characteristic curve (AUROC) values in predicting outcomes for patients with atEDH. In particular, the algorithms had an AUROC value range of between 0.874 to 0.956 for in-hospital mortality, 0.776 to 0.798 for non-home discharges, 0.737 to 0.758 for prolonged LOS, 0.712 to 0.774 for prolonged ICU-LOS, and 0.674 to 0.733 for major complications. The following link will take users to the open-access web application designed to generate predictions for individual patients based on their characteristics: huggingface.co/spaces/MSHS-Neurosurgery-Research/TQP-atEDH. This study aimed to improve the prognostication of patients with atEDH using ML algorithms and developed a web application for easy integration in clinical practice. It found that ML algorithms can aid in risk stratification and have significant potential for predicting in-hospital outcomes. Results demonstrated excellent performance for predicting in-hospital death and fair performance for non-home discharges, prolonged LOS and ICU-LOS, and poor performance for major complications. [ABSTRACT FROM AUTHOR]