Implementing a pharmacogenomic-driven algorithm to guide antiplatelet therapy among Caribbean Hispanics: a non-randomised clinical trial.

OBJECTIVES: To assess whether genotype-guided selection of oral antiplatelet drugs using a clinical decision support (CDS) algorithm reduces the rate of major adverse cardiovascular and cerebrovascular events (MACCEs) among Caribbean Hispanic patients, after 6 months. DESIGN: An open-label, multicentre, non-randomised clinical trial. SETTING: Eight secondary and tertiary care hospitals (public and private) in Puerto Rico. PARTICIPANTS: 300 Caribbean Hispanic patients on clopidogrel, both genders, underwent percutaneous coronary intervention (PCI) for acute coronary syndromes, stable ischaemic heart disease and documented extracardiac vascular diseases. INTERVENTIONS: Patients were separated into standard-of-care (SoC) and genotype-guided (pharmacogenetic (PGx)-CDS) groups (150 each) and stratified by risk scores. Risk scores were calculated based on a previously developed CDS risk prediction algorithm designed to make actionable treatment recommendations for each patient. Individual platelet function, genotypes, clinical and demographic data were included. Ticagrelor was recommended for patients with a high-risk score ≥2 in the PGx-CDS group only, the rest were kept or de-escalated to clopidogrel. The intervention took place within 3-5 days after PCI. Adherence medication score was also measured. PRIMARY AND SECONDARY OUTCOMES: The occurrence rate of MACCEs (primary) and bleeding episodes (secondary). Statistical associations between patient time free of events and predictor variables (ie, treatment groups, risk scores) were tested using Kaplan-Meier survival analyses and Cox proportional-hazards regression models. RESULTS: The genotype-guided group had a clinically lower but not significantly different risk of MACCEs compared with the SoC group (8.7% vs 10.7%, p=0.56; HR=0.56). Among high-risk score patients, genotype-driven guidance of antiplatelet therapy showed superiority over SoC in reducing MACCE incidence 6 months postcoronary stenting (adjusted HR=0.104; p< 0.0001). CONCLUSIONS: The potential benefit of implementing our PGx-CDS algorithm to significantly reduce the incidence rate of MACCEs in post-PCI Caribbean Hispanic patients on clopidogrel was observed exclusively among high-risk patients, with apparently no evident effect in other patient groups. TRIAL REGISTRATION NUMBER: NCT03419325.

Application of Artificial Intelligence and Machine Learning Was Not Able to Reliably Predict Poor Outcomes in People With Hemophilia.

Background Artificial intelligence (AI) and machine learning (ML) are currently used in the clinical field to improve the outcome predictions on disease diagnosis and prognosis. However, to date, few AI/ML applications have been reported in rare diseases, such as hemophilia. In this study, taking advantage of the ATHNdataset, an extensive repository of hemostasis and thrombosis data, we aimed to demonstrate the application of AI/ML approaches to build predictive models to identify persons with hemophilia (PwH) who are at risk of poor outcome and to inform providers in clinical decision-making towards helping patients prevent long-term complications. Materials and methods This project was carried out in two steps. First, the data were mined from ATHN 7, a subset study of the ATHNdataset, to determine markers that defined "poor outcome." Second, we applied multiple AI/ML approaches on the ATHNdataset to validate our findings and to develop predictive models to identify PwH at risk of poor outcomes. The classical regression-based predictive model was used as a reference to evaluate the performance of various AI/ML models. Results Our models included features similarly distributed to response variables of interest, resulting in a limited ability to distinguish poor outcomes. Low recall (<53%) resulted in no single model reliably predicting poor outcomes out of all actual positive cases. Our results suggest that, to build a more useful AI/ML model, we may need a larger dataset size along with additional features. Furthermore, our results showed that most of the AI/ML models outperformed the classical logistic regression model in both model accuracy and precision. Conclusions Our AI and ML model showed limited ability to predict poor outcomes in people with hemophilia.

Prediction of medical admissions after psychiatric inpatient hospitalization in bipolar disorder: a retrospective cohort study.

Objective: Bipolar Disorder (BD) is a severe mental illness associated with high rates of general medical comorbidity, reduced life expectancy, and premature mortality. Although BD has been associated with high medical hospitalization, the factors that contribute to this risk remain largely unexplored. We used baseline medical and psychiatric records to develop a supervised machine learning model to predict general medical admissions after discharge from psychiatric hospitalization. Methods: In this retrospective three-year cohort study of 71 patients diagnosed with BD (mean age=52.19 years, females=56.33%), lasso regression models combining medical and psychiatric records, as well as those using them separately, were fitted and their predictive power was estimated using a leave-one-out cross-validation procedure. Results: The proportion of medical admissions in patients with BD was higher compared with age- and sex-matched hospitalizations in the same region (25.4% vs. 8.48%). The lasso model fairly accurately predicted the outcome (area under the curve [AUC]=69.5%, 95%C.I.=55-84.1; sensitivity=61.1%, specificity=75.5%, balanced accuracy=68.3%). Notably, pre-existing cardiovascular, neurological, or osteomuscular diseases collectively accounted for more than 90% of the influence on the model. The accuracy of the model based on medical records was slightly inferior (AUC=68.7%, 95%C.I. = 54.6-82.9), while that of the model based on psychiatric records only was below chance (AUC=61.8%, 95%C.I.=46.2-77.4). Conclusion: Our findings support the need to monitor medical comorbidities during clinical decision-making to tailor and implement effective preventive measures in people with BD. Further research with larger sample sizes and prospective cohorts is warranted to replicate these findings and validate the predictive model.

Construction of a competency evaluation model for Clinical Research Coordinators in China: a study based on the Delphi method and questionnaire survey.

PURPOSE: As the number of clinical trials in China continues to grow, the assessment of competency of Clinical Research Coordinators (CRCs), who play a crucial role in clinical trials, has become an important and challenging topic. This study aims to construct a competency model for CRCs tailored to the Chinese context, in order to promote the standardisation and regulated development of the CRC industry. STUDY DESIGN AND SETTING: This study was conducted in China, engaging CRCs as the primary subjects. A competency evaluation model for CRCs was constructed through literature review, semi-structured interviews, Delphi expert consultation and the analytic hierarchy process. A questionnaire survey was distributed to a broad sample of CRCs across China to evaluate the model's reliability and validity. RESULTS: The final model encompasses 4 core competency dimensions and 37 indicators, tailored to assess the competencies of CRCs in China. The questionnaire yielded an effective response rate of 81.83%, with high internal consistency(Cronbach's α>0.7). Factor analysis confirmed the model's structure, indicating good reliability and validity. CONCLUSION: This study represents a pioneering effort in constructing a competency model specifically designed for Chinese CRCs, complemented by a robust and valid assessment scale. The findings bear significant implications for the recruitment, training, development and management of CRCs.

Clinical effectiveness of robotic versus laparoscopic and open surgery: an overview of systematic reviews.

OBJECTIVE: To undertake a review of systematic reviews on the clinical outcomes of robotic-assisted surgery across a mix of intracavity procedures, using evidence mapping to inform the decision makers on the best utilisation of robotic-assisted surgery. ELIGIBILITY CRITERIA: We included systematic reviews with randomised controlled trials and non-randomised controlled trials describing any clinical outcomes. DATA SOURCES: Ovid Medline, Embase and Cochrane Library from 2017 to 2023. DATA EXTRACTION AND SYNTHESIS: We first presented the number of systematic reviews distributed in different specialties. We then mapped the body of evidence across selected procedures and synthesised major findings of clinical outcomes. We used a measurement tool to assess systematic reviews to evaluate the quality of systematic reviews. The overlap of primary studies was managed by the corrected covered area method. RESULTS: Our search identified 165 systematic reviews published addressing clinical evidence of robotic-assisted surgery. We found that for all outcomes except operative time, the evidence was largely positive or neutral for robotic-assisted surgery versus both open and laparoscopic alternatives. Evidence was more positive versus open. The evidence for the operative time was mostly negative. We found that most systematic reviews were of low quality due to a failure to deal with the inherent bias in observational evidence. CONCLUSION: Robotic surgery has a strong clinical effectiveness evidence base to support the expanded use of robotic-assisted surgery in six common intracavity procedures, which may provide an opportunity to increase the proportion of minimally invasive surgeries. Given the high incremental cost of robotic-assisted surgery and longer operative time, future economic studies are required to determine the optimal use of robotic-assisted surgery capacity.

Logistical challenges of CAR T-cell therapy in non-Hodgkin lymphoma: a survey of healthcare professionals.

Aim: Characterize the logistical challenges faced by healthcare professionals (HCPs), patients and caregivers during the chimeric antigen receptor T-cell (CAR T) treatment process for non-Hodgkin lymphoma patients.Materials & methods: HCPs in the US and UK experienced with CAR T administration participated in interviews and completed a web-based survey.Results: A total of 133 (80 US, 53 UK) HCPs participated. Two or more logistical challenges were identified by ≥60% of respondents across all stages of the CAR T process. Commonly reported challenges were lengthy waiting periods, administrative and payer-related barriers, limited healthcare capacity, caregiver support and (particularly in the US) patient out-of-pocket costs.Conclusion: The CAR T treatment process presents numerous challenges, highlighting an unmet need for more convenient therapies.

Chimeric antigen receptor T-cell (CAR T) therapy is a new treatment for patients with non-Hodgkin lymphoma that have not responded to other types of treatment. CAR T therapy uses a person's own immune cells (T cells), which are modified in a laboratory to attack cancer cells. While CAR T therapy has the potential to be effective, there are challenges associated with the treatment process. In this study, we surveyed 133 healthcare professionals (HCPs) in the United States and United Kingdom to understand their experiences with logistical challenges involved in navigating the CAR T process. More than 60% of participants identified two or more logistical challenges at every stage of the CAR T treatment process. The most commonly reported challenges included long waiting periods, limited room at hospitals, availability of caregivers to support patients and issues related to out-of-pocket costs, travel and lodging for patients who are treated at specialized centers. In the United States, challenges related to insurance coverage and out-of-pocket costs for patients were highlighted. More than half of HCPs reported that patients' cancer getting worse while waiting to receive CAR T was a reason why patients may not proceed to treatment. While operational improvements might address some challenges in the CAR T treatment process, these findings highlight the need for more convenient, readily available and easily administered therapies for patients with non-Hodgkin lymphoma.

Nurses' perception of uncertainty in clinical decision-making: A qualitative study.

Background: Uncertainty is a common challenge for nurses in clinical decision-making, which can compromise patient care quality and safety. To address this issue, it is essential to understand how nurses perceive and cope with uncertainty in their practice. Aim: This study aimed to explore nurses' perceptions of uncertainty in clinical decision-making using a qualitative approach. Methods: This study was conducted with a qualitative approach and conventional content analysis in 2020. Participants consisted of 17 nurses from different wards of teaching hospitals in Northwestern Iran, recruited using the purposive sampling method. Data were collected through semi-structured interviews and analyzed simultaneously with data collection (June to December 2020). The data were analyzed using the content analysis approach suggested by Wildemuth. Data were managed with MAXQDA10 software. The analysis revealed four main themes and ten subthemes that described the nurses' experiences of uncertainty in clinical decision-making. Results: The main themes were: difficult choice, difficult situation, insufficient judgment, and emotional burden. Conclusions: The study participants defined uncertainty in clinical decision-making as a difficult choice that occurs in difficult situations, which influenced their clinical judgment and emotional well-being. These findings provide valuable insights for developing interventions to help nurses manage uncertainty and improve their decision-making skills and safety.

Barriers and facilitators to implementing imaging-based diagnostic artificial intelligence-assisted decision-making software in hospitals in China: a qualitative study using the updated Consolidated Framework for Implementation Research.

OBJECTIVES: To identify the barriers and facilitators to the successful implementation of imaging-based diagnostic artificial intelligence (AI)-assisted decision-making software in China, using the updated Consolidated Framework for Implementation Research (CFIR) as a theoretical basis to develop strategies that promote effective implementation. DESIGN: This qualitative study involved semistructured interviews with key stakeholders from both clinical settings and industry. Interview guide development, coding, analysis and reporting of findings were thoroughly informed by the updated CFIR. SETTING: Four healthcare institutions in Beijing and Shanghai and two vendors of AI-assisted decision-making software for lung nodules detection and diabetic retinopathy screening were selected based on purposive sampling. PARTICIPANTS: A total of 23 healthcare practitioners, 6 hospital informatics specialists, 4 hospital administrators and 7 vendors of the selected AI-assisted decision-making software were included in the study. RESULTS: Within the 5 CFIR domains, 10 constructs were identified as barriers, 8 as facilitators and 3 as both barriers and facilitators. Major barriers included unsatisfactory clinical performance (Innovation); lack of collaborative network between primary and tertiary hospitals, lack of information security measures and certification (outer setting); suboptimal data quality, misalignment between software functions and goals of healthcare institutions (inner setting); unmet clinical needs (individuals). Key facilitators were strong empirical evidence of effectiveness, improved clinical efficiency (innovation); national guidelines related to AI, deployment of AI software in peer hospitals (outer setting); integration of AI software into existing hospital systems (inner setting) and involvement of clinicians (implementation process). CONCLUSIONS: The study findings contributed to the ongoing exploration of AI integration in healthcare from the perspective of China, emphasising the need for a comprehensive approach considering both innovation-specific factors and the broader organisational and contextual dynamics. As China and other developing countries continue to advance in adopting AI technologies, the derived insights could further inform healthcare practitioners, industry stakeholders and policy-makers, guiding policies and practices that promote the successful implementation of imaging-based diagnostic AI-assisted decision-making software in healthcare for optimal patient care.

Should AI models be explainable to clinicians?

In the high-stakes realm of critical care, where daily decisions are crucial and clear communication is paramount, comprehending the rationale behind Artificial Intelligence (AI)-driven decisions appears essential. While AI has the potential to improve decision-making, its complexity can hinder comprehension and adherence to its recommendations. "Explainable AI" (XAI) aims to bridge this gap, enhancing confidence among patients and doctors. It also helps to meet regulatory transparency requirements, offers actionable insights, and promotes fairness and safety. Yet, defining explainability and standardising assessments are ongoing challenges and balancing performance and explainability can be needed, even if XAI is a growing field.

Development of a Soft Tissue Knee Clinical Decision-Making Tool for Patients Presenting to Primary Point-of-Care Providers in Alberta, Canada.

Several barriers exist in Alberta, Canada to providing accurate and accessible diagnoses for patients presenting with acute knee injuries and chronic knee problems. In efforts to improve quality of care for these patients, an evidence-informed clinical decision-making tool was developed. Forty-five expert panelists were purposively chosen to represent stakeholder groups, various expertise, and each of Alberta Health Services' 5 geographical health regions. A systematic rapid review and modified Delphi approach were executed with the intention of developing standardized clinical decision-making processes for acute knee injuries, atraumatic/overuse conditions, knee arthritis, and degenerative meniscus. Standardized criteria for screening, history-taking, physical examination, diagnostic imaging, timelines, and treatment were developed. This tool standardizes and optimizes assessment and diagnosis of acute knee injuries and chronic knee problems in Alberta. This project was a highly collaborative, province-wide effort led by Alberta Health Services' Bone and Joint Health Strategic Clinical Network (BJH SCN) and the Alberta Bone and Joint Health Institute (ABJHI).

Proceedings of the 1st biannual bridging the gaps in lung cancer conference.

Lung cancer is the leading cause of cancer death in the US and globally. The mortality from lung cancer has been declining, due to a reduction in incidence and advances in treatment. Although recent success in developing targeted and immunotherapies for lung cancer has benefitted patients, it has also expanded the complexity of potential treatment options for health care providers. To aid in reducing such complexity, experts in oncology convened a conference (Bridging the Gaps in Lung Cancer) to identify current knowledge gaps and controversies in the diagnosis, treatment, and outcomes of various lung cancer scenarios, as described here. Such scenarios relate to biomarkers and testing in lung cancer, small cell lung cancer, EGFR mutations and targeted therapy in non-small cell lung cancer (NSCLC), early-stage NSCLC, KRAS/BRAF/MET and other genomic alterations in NSCLC, and immunotherapy in advanced NSCLC.

Clinician perspectives and recommendations regarding design of clinical prediction models for deteriorating patients in acute care.

BACKGROUND: Successful deployment of clinical prediction models for clinical deterioration relates not only to predictive performance but to integration into the decision making process. Models may demonstrate good discrimination and calibration, but fail to match the needs of practising acute care clinicians who receive, interpret, and act upon model outputs or alerts. We sought to understand how prediction models for clinical deterioration, also known as early warning scores (EWS), influence the decision-making of clinicians who regularly use them and elicit their perspectives on model design to guide future deterioration model development and implementation. METHODS: Nurses and doctors who regularly receive or respond to EWS alerts in two digital metropolitan hospitals were interviewed for up to one hour between February 2022 and March 2023 using semi-structured formats. We grouped interview data into sub-themes and then into general themes using reflexive thematic analysis. Themes were then mapped to a model of clinical decision making using deductive framework mapping to develop a set of practical recommendations for future deterioration model development and deployment. RESULTS: Fifteen nurses (n = 8) and doctors (n = 7) were interviewed for a mean duration of 42 min. Participants emphasised the importance of using predictive tools for supporting rather than supplanting critical thinking, avoiding over-protocolising care, incorporating important contextual information and focusing on how clinicians generate, test, and select diagnostic hypotheses when managing deteriorating patients. These themes were incorporated into a conceptual model which informed recommendations that clinical deterioration prediction models demonstrate transparency and interactivity, generate outputs tailored to the tasks and responsibilities of end-users, avoid priming clinicians with potential diagnoses before patients were physically assessed, and support the process of deciding upon subsequent management. CONCLUSIONS: Prediction models for deteriorating inpatients may be more impactful if they are designed in accordance with the decision-making processes of acute care clinicians. Models should produce actionable outputs that assist with, rather than supplant, critical thinking.

Enhanced labor pain monitoring using machine learning and ECG waveform analysis for uterine contraction-induced pain.

OBJECTIVES: This study aims to develop an innovative approach for monitoring and assessing labor pain through ECG waveform analysis, utilizing machine learning techniques to monitor pain resulting from uterine contractions. METHODS: The study was conducted at National Taiwan University Hospital between January and July 2020. We collected a dataset of 6010 ECG samples from women preparing for natural spontaneous delivery (NSD). The ECG data was used to develop an ECG waveform-based Nociception Monitoring Index (NoM). The dataset was divided into training (80%) and validation (20%) sets. Multiple machine learning models, including LightGBM, XGBoost, SnapLogisticRegression, and SnapDecisionTree, were developed and evaluated. Hyperparameter optimization was performed using grid search and five-fold cross-validation to enhance model performance. RESULTS: The LightGBM model demonstrated superior performance with an AUC of 0.96 and an accuracy of 90%, making it the optimal model for monitoring labor pain based on ECG data. Other models, such as XGBoost and SnapLogisticRegression, also showed strong performance, with AUC values ranging from 0.88 to 0.95. CONCLUSIONS: This study demonstrates that the integration of machine learning algorithms with ECG data significantly enhances the accuracy and reliability of labor pain monitoring. Specifically, the LightGBM model exhibits exceptional precision and robustness in continuous pain monitoring during labor, with potential applicability extending to broader healthcare settings. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT04461704.

Clinical decision making in the assessment and treatment of closed hand fractures: A scoping review.

Introduction: Closed hand fractures represent a significant proportion of emergency department attendances, result in substantial health service utilisation and have a detrimental effect on quality of life. Increasingly, hand therapists in the United Kingdom provide first line fracture treatment. However, the knowledge and skills required to work in such an extended scope capacity have not been elucidated or standardised. This literature review synthesises and reports evidence for the knowledge requisite of clinicians to make evidence-based treatment decisions for patients with hand fractures. Methods: A systematic search was undertaken, using Embase, MEDLINE, PsychInfo and CINAHL electronic databases. Inclusion criteria were English language, full research reports of studies assessing of the reliability or validity of the decision-making process in hand fracture treatment published between 2013 and 2023. Data were summarised narratively. Results: 15 studies met inclusion criteria; most assessed decision making for metacarpal fractures. Studies on imaging (n = 4) suggested the reliability of plain radiograph interpretation of hand fracture characteristics such as angulation is good and similar across various levels of experience. Agreement between surgeons and therapists in choosing surgical or nonsurgical treatment was generally good, but factors influencing decision making remained unclear. No evidence was identified that explored clinical assessment knowledge (subjective or objective patient factors) or the specific competencies required to treat hand fractures. Conclusions: There is limited evidence for the knowledge and skills required of clinicians for the competent assessment and treatment of hand fractures. Stakeholder consensus work is required to develop robust competencies and standardise practice.

A Multi-Machine Learning Consensus Model Based on Clinical Features Reveals That Interleukin-10 Derived from Monocytes Leads to a Poor Prognosis in Patients with Coronavirus Disease-2019.

Background: Despite ongoing interventions, SARS-CoV-2 continues to cause significant global morbidity and mortality. Early diagnosis and intervention are crucial for effective clinical management. However, prognostic features based on transcriptional data have shown limited effectiveness, highlighting the need for more precise biomarkers to improve COVID-19 treatment outcomes. Methods: We retrospectively analyzed 149 clinical features from 189 COVID-19 patients, identifying prognostic features via univariate Cox regression. The cohort was split into training and validation sets, and 77 prognostic models were developed using seven machine learning algorithms. Among these, the least absolute shrinkage and selection operator (Lasso) method was employed to refine the selection of prognostic variables by ten-fold cross-validation strategy, which were then integrated with random survival forests (RSF) to build a robust COVID-19-related prognostic model (CRM). Model accuracy was evaluated across training, validation, and entire cohorts. The diagnostic relevance of interleukin-10 (IL-10) was confirmed in bulk transcriptional data and validated at the single-cell level, where we also examined changes in cellular communication between mononuclear cells with differing IL-10 expression and other immune cells. Results: Univariate Cox regression identified 43 prognostic features. Among the 77 machine learning models, the combination of Lasso and RSF produced the most robust CRM. This model consistently performed well across training, validation, and entire cohorts. IL-10 emerged as a key prognostic feature within the CRM, validated by single-cell transcriptional data. Transcriptome analysis confirmed the stable diagnostic value of IL-10, with mononuclear cells identified as the primary IL-10 source. Moreover, differential IL-10 expression in these cells was linked to altered cellular communication in the COVID-19 immune microenvironment. Conclusion: The CRM provides accurate prognostic predictions for COVID-19 patients. Additionally, the study underscores the importance of early IL-10 level testing upon hospital admission, which could inform therapeutic strategies.

Views and perceptions of advanced life support practitioners on initiating, withholding and terminating resuscitation in out-of-hospital cardiac arrest in the Emergency Medical Services of South Africa.

Introduction: This study aimed to explore the views and perceptions of Advanced Life Support (ALS) practitioners in two South African provinces on initiating, withholding, and terminating resuscitation in OHCA. Methodology: Semi-structured one-on-one interviews were conducted with operational ALS practitioners working within the prehospital setting in the Western Cape and Free State provinces. Recorded interviews were transcribed and subjected to inductive-dominant, manifest content analysis. After familiarisation with the data, meaning units were condensed, codes were applied and collated into categories that were then assessed, reviewed, and refined repeatedly. Results: A total of 18 ALS providers were interviewed. Five main categories were developed from the data analysis: 1) assessment of prognosis, 2) internal factors affecting decision-making, 3) external factors affecting decision-making, 4) system challenges, and 5) ideas for improvement. Factors influencing the assessment of prognosis were history, clinical presentation, and response to resuscitation. Internal factors affecting decision-making were driven by emotion and contemplation. External factors affecting decision-making included family, safety, and disposition. System challenges relating to bystander response and resources were identified. Ideas for improvement in training and support were brought forward. Conclusion: Many factors influence OHCA decision-making in the Western Cape and Free State provinces, and numerous system challenges have been identified. The findings of this study can be used as a frame of reference for prehospital emergency care personnel and contribute to the development of context-specific guidelines.

Acute care nurses' decisions to recognise and respond to patient improvement: A qualitative study.

AIM: To explore and describe acute care nurses' decisions to recognise and respond to improvement in patients' clinical states as they occurred in the real-world clinical environment. DESIGN: A descriptive study. METHODS: Nine medical and eleven surgical nurses in a large Australian metropolitan hospital were individually observed during nurse-patient interactions and followed up in interview to describe their reasoning and clinical judgements behind observed decisions. Verbal description of observations and interviews were recorded and transcribed. Reflexive thematic analysis was used to analyse the data. RESULTS: The three themes constructed from the data were as follows: nurses checking in; nurses reaching judgements about improvements; and nurses deciding on the best person to respond. Acute care nurses made targeted assessment decisions based on predicted safety risks related to improvement in clinical states. Subjective and objective cues were used to assess for and make judgements about patient improvement. Acute care nurses' judgment of patient safety and a desire to promote patient centred care guided their decisions to select the appropriate person to manage improvement. CONCLUSIONS: The outcomes of this research have demonstrated that the proven safety benefits of acute care nurses' decision making in response to deterioration extend to improvement in patients' clinical states. In response to improvement, acute care nurses' decisions protect patients from harm and promote recovery. IMPLICATIONS FOR PATIENT CARE: Early recognition and response to improvement enable acute care nurses to protect patients from risks of unnecessary treatment and promote recovery. IMPACT: This study makes explicit nurses' essential safety role in recognising and responding to improvement in patients' clinical states. Healthcare policy and education must reflect the equal importance of assessment for and management of deterioration and improvement to ensure patients are protected and provided with safe care.

Personalized approach to malignant struma ovarii: Insights from a web-based machine learning tool.

OBJECTIVES: Malignant struma ovarii (MSO) is a rare ovarian tumor characterized by mature thyroid tissue. The diverse symptoms and uncommon nature of MSO can create difficulties in its diagnosis and treatment. This study aimed to analyze data and use machine learning methods to understand the prognostic factors and potential management strategies for MSO. METHODS: In this retrospective cohort, the Surveillance, Epidemiology, and End Results (SEER) database provided the data used for this study's analysis. To identify the prognostic variables, we conducted Cox regression analysis and constructed prognostic models using five machine learning algorithms to predict the 5-year survival. A validation method incorporating the area under the curve of the receiver operating characteristic curve was used to validate the accuracy and reliability of the machine learning models. We also investigated the role of multiple therapeutic options using the Kaplan-Meier survival analysis. RESULTS: The study population comprised 329 patients. Multivariate Cox regression analysis revealed that older age, unmarried status, chemotherapy, and the total number of tumors in patients were poor prognostic factors. Machine learning models revealed that the multilayer perceptron accurately predicted outcomes, followed by the random forest classifier, gradient boosting classifier, K-nearest neighbors, and logistic regression models. The factors that contributed the most were age, marital status, and the total number of tumors in the patients. CONCLUSION: The present study offers a comprehensive approach for the treatment and prognosis assessment of patients with MSO. The machine learning models we have developed serve as a practical, personalized tool to aid in clinical decision-making processes.

Perceptions of C-reactive Protein Measurement Among General Physicians: A Qualitative Study on Diagnostic Value, Clinical Dilemmas, and Professional Growth.

Introduction C-reactive protein (CRP) is a widely used laboratory test for assessing infections, inflammatory diseases, and malignancies, playing a critical role in clinical diagnosis and management. Despite its utility, CRP measurement practices vary among physicians, often influenced by training and clinical experience. This study explores general physicians' perceptions of CRP measurement in clinical practice, focusing on its diagnostic value, associated dilemmas, and impact on clinical growth and decision-making. Methods This qualitative study employed thematic analysis to examine the perceptions of general physicians at Unnan City Hospital, Unnan, Japan regarding CRP measurement. Sixteen general physicians were selected through purposive sampling and participated in one-on-one semi-structured interviews. The interviews were conducted in Japanese, recorded, transcribed verbatim, and analyzed inductively to identify themes. The analysis involved iterative coding and extensive discussion among the research team to ensure the reliability and validity of the findings. Results Three main themes emerged from the analysis: the usefulness of CRP for diagnosis and collaboration, dilemmas associated with CRP usage, and clinical growth through reconsideration of CRP's importance. Physicians highlighted CRP's value in distinguishing inflammatory from non-inflammatory diseases, predicting clinical courses, and facilitating communication with specialists. However, dilemmas arose from discrepancies between CRP levels and clinical symptoms, the influence of various non-specific factors, and habitual testing driven by training, leading to unnecessary tests and diminished clinical skills. Participants recognized the need to view CRP as one of many diagnostic tools, cultivate a habit of questioning its necessity, and reflect on its use to enhance clinical reasoning and professional growth. Conclusions CRP measurement is a valuable diagnostic tool, but effective use requires a balanced and critical approach. Discrepancies between CRP levels and clinical symptoms can lead to over-reliance on laboratory results and unnecessary testing. General physicians should integrate CRP within a broader diagnostic framework, combining it with patient history, physical examination, and other tests. Reflecting on the necessity and implications of CRP measurements can improve clinical reasoning and decision-making, ultimately enhancing patient care and resource management. Future research should explore similar perceptions in diverse healthcare settings and develop strategies to optimize CRP use in clinical practice.