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.

Outcome prediction in prolonged disorders of consciousness (DOC) remains challenging. This can result in either inappropriate withdrawal of treatment or unnecessary prolongation of treatment. Electroencephalography (EEG) is a cheap, portable, and non-invasive device with various opportunities for complex signal analysis. Computational EEG measures, such as EEG connectivity and network metrics, might be ideal candidates for the investigation of DOC, but their capacity in prognostication is still undisclosed. We conducted a meta-analysis aiming to compare the prognostic power of the widely used clinical scale, Coma Recovery Scale-Revised - CRS-R and EEG connectivity and network metrics. We found that the prognostic power of the CRS-R scale was moderate (AUC: 0.67 (0.60-0.75)), but EEG connectivity and network metrics predicted outcome with significantly (p = 0.0071) higher accuracy (AUC:0.78 (0.70-0.86)). We also estimated the prognostic capacity of EEG spectral power, which was not significantly (p = 0.3943) inferior to that of the EEG connectivity and graph-theory measures (AUC:0.75 (0.70-0.80)). Multivariate automated outcome prediction tools seemed to outperform clinical and EEG markers.

Serum ferritin has garnered considerable attention as a prognostic marker in intensive care units (ICUs), offering valuable insights into patient outcomes and clinical management strategies. This comprehensive review examines the role of serum ferritin in predicting outcomes among critically ill patients, with a particular focus on its implications for ischemic heart disease (IHD). Elevated serum ferritin levels have consistently been associated with adverse outcomes in ICU settings, including increased mortality, prolonged hospital stays, and higher morbidity rates. Furthermore, the relationship between serum ferritin levels and IHD underscores its potential as a biomarker for cardiovascular risk assessment in critically ill populations. The review synthesizes existing literature to highlight the predictive value of serum ferritin in assessing illness severity and guiding clinical decision-making in the ICUs. It also explores potential mechanisms linking serum ferritin to adverse outcomes and discusses implications for clinical practice. Integrating serum ferritin measurements into routine assessments could enhance prognostication and risk stratification in ICU patients, while further research is needed to elucidate optimal management strategies and therapeutic targets. Collaborative efforts between clinicians and researchers are essential to advance our understanding of serum ferritin\'s prognostic value in the ICUs and translate this knowledge into improved patient care and outcomes.

UNASSIGNED: A blood-based biomarker (BBBM) test could help to better stratify patients with traumatic brain injury (TBI), reduce unnecessary imaging, to detect and treat secondary insults, predict outcomes, and monitor treatment effects and quality of care.
UNASSIGNED: What evidence is available for clinical applications of BBBMs in TBI and how to advance this field?
UNASSIGNED: This narrative review discusses the potential clinical applications of core BBBMs in TBI. A literature search in PubMed, Scopus, and ISI Web of Knowledge focused on articles in English with the words \"traumatic brain injury\" together with the words \"blood biomarkers\", \"diagnostics\", \"outcome prediction\", \"extracranial injury\" and \"assay method\" alone-, or in combination.
UNASSIGNED: Glial fibrillary acidic protein (GFAP) combined with Ubiquitin C-terminal hydrolase-L1(UCH-L1) has received FDA clearance to aid computed tomography (CT)-detection of brain lesions in mild (m) TBI. Application of S100B led to reduction of head CT scans. GFAP may also predict magnetic resonance imaging (MRI) abnormalities in CT-negative cases of TBI. Further, UCH-L1, S100B, Neurofilament light (NF-L), and total tau showed value for predicting mortality or unfavourable outcome. Nevertheless, biomarkers have less role in outcome prediction in mTBI. S100B could serve as a tool in the multimodality monitoring of patients in the neurointensive care unit.
UNASSIGNED: Largescale systematic studies are required to explore the kinetics of BBBMs and their use in multiple clinical groups. Assay development/cross validation should advance the generalizability of those results which implicated GFAP, S100B and NF-L as most promising biomarkers in the diagnostics of TBI.

This systematic review presents clinical evidence on the association of heart rate variability with outcome prediction in intracerebral and subarachnoid hemorrhages. The literature search led to the retrieval of 19 significant studies. Outcome prediction included functional outcome, cardiovascular complications, secondary brain injury, and mortality. Various aspects of heart rate recording and analysis, based on linear time and frequency domains and a non-linear entropy approach, are reviewed. Heart rate variability was consistently associated with poor functional outcome and mortality, while controversial results were found regarding the association between heart rate variability and secondary brain injury and cardiovascular complications.

BACKGROUND: Classification and scoring systems can help both clinical management and audit the outcomes of routine care.
OBJECTIVE: 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.
CONCLUSIONS: Despite the limitations, this systematic review provided sufficient evidence to support recommendations on the use of six particular systems in specific clinical scenarios.

Multiple outcome prediction models have been developed for Head and Neck Squamous Cell Carcinoma (HNSCC). This systematic review aimed to identify HNSCC outcome prediction model studies, assess their methodological quality and identify those with potential utility for clinical practice. Inclusion criteria were mucosal HNSCC prognostic prediction model studies (development or validation) incorporating clinically available variables accessible at time of treatment decision making and predicting tumour-related outcomes. Eligible publications were identified from PubMed and Embase. Methodological quality and risk of bias were assessed using the checklist for critical appraisal and data extraction for systematic reviews of prediction modelling studies (CHARMS) and prediction model risk of bias assessment tool (PROBAST). Eligible publications were categorised by study type for reporting. 64 eligible publications were identified; 55 reported model development, 37 external validations, with 28 reporting both. CHARMS checklist items relating to participants, predictors, outcomes, handling of missing data, and some model development and evaluation procedures were generally well-reported. Less well-reported were measures accounting for model overfitting and model performance measures, especially model calibration. Full model information was poorly reported (3/55 model developments), specifically model intercept, baseline survival or full model code. Most publications (54/55 model developments, 28/37 external validations) were found to have high risk of bias, predominantly due to methodological issues in the PROBAST analysis domain. The identified methodological issues may affect prediction model accuracy in heterogeneous populations. Independent external validation studies in the local population and demonstration of clinical impact are essential for the clinical implementation of outcome prediction models.

Cancer metastasis is the major cause of cancer mortality and accounts for about 90% of cancer death. Although radiation therapy has been considered to reduce the localized cancer burden, emerging evidence that radiation can potentially turn tumors into an in situ vaccine has raised significant interest in combining radiation with immunotherapy. However, the combination approach might be limited by the radiation-induced immunosuppression. Assessment of radiation effects on the immune system at the patient level is critical to maximize the systemic antitumor response of radiation. In this review, we summarize the developed solutions in three different categories for systemic radiation therapy: blood dose, radiation-induced lymphopenia, and tumor control. Furthermore, we address how they could be combined to optimize radiotherapy regimens and maximize their synergy with immunotherapy.

BACKGROUND: Artificial intelligence (AI) is emerging in healthcare in various forms, including AI-based clinical decision support systems, machine learning, computer vision, natural language processing, big data analytics and AI-enhanced robotics. Given their potential impact on clinical processes and decision-making, AI-based health technologies (AIHT) are now seen to have a transformative effect on the nursing and medical professions, and on advanced nursing practice in particular.
OBJECTIVE: While nurse practitioners (NPs) are increasingly called upon to play a crucial role in improving the healthcare provided to the population, little is known about the nature, extent and outcomes of their involvement and experience with AIHT. This study\'s research objectives are twofold. First, it aims to characterize NPs\' involvement and experience with AIHT in terms of the functional and clinical attributes of the AIHT-based systems and applications that have emerged in advanced nursing care settings, and of the clinical tasks of NPs targeted for support by these systems and applications. Second, it aims to characterize this involvement and experience with AIHT in terms of its expected impacts on the clinical activities and performance of NPs, and of its potential outcomes for NPs\' patients and for the general population.
METHODS: We thus contribute to advanced practice nursing research by carrying out an initial evaluation of the role played by NPs in the emergence of these technologies, by means of a systematic review of the literature.
RESULTS: This review demonstrates that NPs, acting alone or in collaboration with physicians and other healthcare professionals, participate in the development and evaluation of various AI-based decision-making and predictive tools in primary, hospital and emergency care settings. This participation involves NPs as diagnostic and therapeutic experts whose clinical activities, decision-making and performance can be significantly impacted by their adoption and assimilation of AIHT.

BACKGROUND: Racial bias is a key concern regarding the development, validation, and implementation of machine learning (ML) models in clinical settings. Despite the potential of bias to propagate health disparities, racial bias in clinical ML has yet to be thoroughly examined and best practices for bias mitigation remain unclear.
OBJECTIVE: Our objective was to perform a scoping review to characterize the methods by which the racial bias of ML has been assessed and describe strategies that may be used to enhance algorithmic fairness in clinical ML.
METHODS: A scoping review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) Extension for Scoping Reviews. A literature search using PubMed, Scopus, and Embase databases, as well as Google Scholar, identified 635 records, of which 12 studies were included.
RESULTS: Applications of ML were varied and involved diagnosis, outcome prediction, and clinical score prediction performed on data sets including images, diagnostic studies, clinical text, and clinical variables. Of the 12 studies, 1 (8%) described a model in routine clinical use, 2 (17%) examined prospectively validated clinical models, and the remaining 9 (75%) described internally validated models. In addition, 8 (67%) studies concluded that racial bias was present, 2 (17%) concluded that it was not, and 2 (17%) assessed the implementation of bias mitigation strategies without comparison to a baseline model. Fairness metrics used to assess algorithmic racial bias were inconsistent. The most commonly observed metrics were equal opportunity difference (5/12, 42%), accuracy (4/12, 25%), and disparate impact (2/12, 17%). All 8 (67%) studies that implemented methods for mitigation of racial bias successfully increased fairness, as measured by the authors\' chosen metrics. Preprocessing methods of bias mitigation were most commonly used across all studies that implemented them.
CONCLUSIONS: The broad scope of medical ML applications and potential patient harms demand an increased emphasis on evaluation and mitigation of racial bias in clinical ML. However, the adoption of algorithmic fairness principles in medicine remains inconsistent and is limited by poor data availability and ML model reporting. We recommend that researchers and journal editors emphasize standardized reporting and data availability in medical ML studies to improve transparency and facilitate evaluation for racial bias.