A Delphi Process to Identify Relevant Outcomes That May Be Associated With a Predictive Analytic Tool to Detect Hemodynamic Deterioration in the Intensive Care Unit

Background The critical care literature has seen an increase in the development and validation of tools using artificial intelligence for early detection of patient events or disease onset in the intensive care unit (ICU). The hemodynamic stability index (HSI) was found to have an AUC of 0.82 in predicting the need for hemodynamic intervention in the ICU. Future studies using this tool may benefit from targeting those outcomes that are more relevant to clinicians and most achievable. Methods A three-round Delphi study was conducted with a panel of 10 critical care physicians and three nurses in the United States to identify outcomes that may be most relevant and achievable with the HSI when evaluated for use in the ICU. To achieve criteria for relevance, at least 65% of panelists had to rate an outcome as a 4 or 5 on a 5-point scale. Results Nineteen of 24 outcomes that may be associated with the HSI achieved consensus for relevance. The Kemeny-Young approach was used to develop a matrix depicting the distribution of outcomes considering both relevance and achievability. "Reduces time spent in hemodynamic instability" and "reduces times to recognition of hemodynamic instability" were the highest-ranking outcomes considering both relevance and achievability. Conclusion This Delphi study was a feasible method to identify relevant outcomes that may be associated with an appropriate predictive analytic tool in the ICU. These findings can provide insight to researchers looking to study such tools to impact outcomes relevant to critical care practitioners. Future studies should test these tools in the ICU that target the most clinically relevant and achievable outcomes, such as time spent hemodynamically unstable or time until actionable nursing assessment or treatment.

Evidence-Based Surgery: What Can Intra-Operative Images Contribute?

Evidence-based medicine integrates results from randomized controlled trials (RCTs) and meta-analyses, combining the best external evidence with individual clinical expertise and patients' preferences. However, RCTs of surgery differ from those of medicine in that surgical performance is often assumed to be consistent. Yet, evaluating whether each surgery is performed to the same standard is quite challenging. As a primary issue, the novelty of this review is to emphasize-with a focus on orthopedic trauma-the advantage of having complete intra-operative image documentation, allowing the direct evaluation of the quality of the intra-operative technical performance. The absence of complete intra-operative image documentation leads to the inhomogeneity of case series, yielding inconsistent results due to the impossibility of a secondary analysis. Thus, comparisons and the reproduction of studies are difficult. Access to complete intra-operative image data in surgical RCTs allows not only secondary analysis but also comparisons with similar cases. Such complete data can be included in electronic papers. Offering these data to peers-in an accessible link-when presenting papers facilitates the selection process and improves publications for readers. Additionally, having access to the full set of image data for all presented cases serves as a rich resource for learning. It enables the reader to sift through the information and pinpoint the details that are most relevant to their individual needs, allowing them to potentially incorporate this knowledge into daily practice. A broad use of the concept of complete intra-operative image documentation is pivotal for bridging the gap between clinical research findings and real-world applications. Enhancing the quality of surgical RCTs would facilitate the equalization of evidence acquisition in both internal medicine and surgery. Joint effort by surgeons, scientific societies, publishers, and healthcare authorities is needed to support the ideas, implement economic requirements, and overcome the mental obstacles to its realization.

Can Whole-Body Baseline CT Radiomics Add Information to the Prediction of Best Response, Progression-Free Survival, and Overall Survival of Stage IV Melanoma Patients Receiving First-Line Targeted Therapy: A Retrospective Register Study

BACKGROUND

The aim of this study was to investigate whether the combination of radiomics and clinical parameters in a machine-learning model offers additive information compared with the use of only clinical parameters in predicting the best response, progression-free survival after six months, as well as overall survival after six and twelve months in patients with stage IV malignant melanoma undergoing first-line targeted therapy.

METHODS

A baseline machine-learning model using clinical variables (demographic parameters and tumor markers) was compared with an extended model using clinical variables and radiomic features of the whole tumor burden, utilizing repeated five-fold cross-validation. Baseline CTs of 91 stage IV malignant melanoma patients, all treated in the same university hospital, were identified in the Central Malignant Melanoma Registry and all metastases were volumetrically segmented (n = 4727).

RESULTS

Compared with the baseline model, the extended radiomics model did not add significantly more information to the best-response prediction (AUC [95% CI] 0.548 (0.188, 0.808) vs. 0.487 (0.139, 0.743)), the prediction of PFS after six months (AUC [95% CI] 0.699 (0.436, 0.958) vs. 0.604 (0.373, 0.867)), or the overall survival prediction after six and twelve months (AUC [95% CI] 0.685 (0.188, 0.967) vs. 0.766 (0.433, 1.000) and AUC [95% CI] 0.554 (0.163, 0.781) vs. 0.616 (0.271, 1.000), respectively).

CONCLUSIONS

The results showed no additional value of baseline whole-body CT radiomics for best-response prediction, progression-free survival prediction for six months, or six-month and twelve-month overall survival prediction for stage IV melanoma patients receiving first-line targeted therapy. These results need to be validated in a larger cohort.

Simple Detection of Unstained Live Senescent Cells with Imaging Flow Cytometry

Cellular senescence is a hallmark of aging and a promising target for therapeutic approaches. The identification of senescent cells requires multiple biomarkers and complex experimental procedures, resulting in increased variability and reduced sensitivity. Here, we propose a simple and broadly applicable imaging flow cytometry (IFC) method. This method is based on measuring autofluorescence and morphological parameters and on applying recent artificial intelligence (AI) and machine learning (ML) tools. We show that the results of this method are superior to those obtained measuring the classical senescence marker, senescence-associated beta-galactosidase (SA-β-Gal). We provide evidence that this method has the potential for diagnostic or prognostic applications as it was able to detect senescence in cardiac pericytes isolated from the hearts of patients affected by end-stage heart failure. We additionally demonstrate that it can be used to quantify senescence “in vivo” and can be used to evaluate the effects of senolytic compounds. We conclude that this method can be used as a simple and fast senescence assay independently of the origin of the cells and the procedure to induce senescence.