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To develop and validate decision trees using conditional probabilities to identify the predictors of mortality and morbidity deterioration in trauma patients.
A quasi-experimental longitudinal study conducted at a Level 1 Trauma Center in São Paulo, Brazil.
The study analysed 201 patient records using standardised nursing documentation (NANDA International and Nursing Outcomes Classification). Decision trees were constructed using the chi-squared automatic interaction detection (CHAID) algorithm and validated through K-fold cross-validation to ensure model reliability.
Decision trees identified key predictors of survival and mobility deterioration. Patients who did not require (NOC 0414) Cardiopulmonary Status but required (NOC 0210) Transfer Performance had a 97.4% survival rate. Conversely, those requiring (NOC 0414) Cardiopulmonary Status had a 25% risk of worsening mobility, compared to 9% for those who did not. K-fold cross-validation confirmed the model's predictive accuracy, reinforcing the robustness of the decision tree approach (Value).
Decision trees demonstrated strong predictive capabilities for mobility outcomes and mortality risk, offering a structured, data-driven framework for clinical decision-making. These findings underscore the importance of early mobilisation, tailored rehabilitation interventions and assistive devices in improving patient recovery. This study is among the first to apply decision trees in this context, highlighting its novelty and potential to enhance trauma critical care practices.
This study highlights the potential of decision trees, a supervised machine learning method, in nursing practice by providing clear, evidence-based guidance for clinical decision-making. By enabling early identification of high-risk patients, decision trees facilitate timely interventions, reduce complications and support personalised rehabilitation strategies that enhance patient safety and recovery.
This research addresses the challenge of improving outcomes for critically ill and trauma patients with impaired mobility by identifying effective strategies for early mobilisation and rehabilitation. The integration of artificial intelligence-driven decision trees strengthens evidence-based nursing practice, enhances patient education and informs scalable interventions that reduce trauma-related complications. These findings have implications for healthcare providers, rehabilitation specialists and policymakers seeking to optimise trauma care and improve long-term patient outcomes.
Patients provided authorisation for the collection of their clinical data from medical records during hospitalisation.
Palmoplantar pustulosis (PPP) is a rare, debilitating inflammatory skin disease involving painful pustules on the palms and soles. Janus kinase (JAK) inhibitors target pathways relevant to PPP disease biology but also confer a risk of major adverse cardiovascular events and malignancy in certain ‘at risk’ individuals; this includes those with PPP given prevalent smoking and cardiovascular risk factors in the PPP population. The feasibility of JAK inhibitor therapy for PPP requires assessment prior to a randomised controlled trial evaluation of drug efficacy and safety for this indication.
The ‘Janus kinase inhibitors in palmoplantar pustulosis: a mixed-methods feasibility’ trial is an open-label, single-centre, single-arm, mixed-methods feasibility trial of JAK inhibition in PPP (REC reference: 24/NE/0147; ISRCTN61751241). Participants (n=20) will receive 8 weeks of treatment with the JAK inhibitor upadacitinib (‘Rinvoq’, 30 mg, once daily). Qualitative semistructured interviews (up to n=40) will be undertaken with trial participants, trial decliners and healthcare professionals. The primary outcome will be a composite assessment of feasibility across three domains: recruitment, adherence and acceptability, using a mixed-methods analysis approach. Secondary objectives include the identification of trial recruitment optimisation strategies, using the ‘Quintet Recruitment Intervention’, and the generation of an indication of effect size on disease severity (measured using the Palmoplantar Pustulosis Psoriasis Area and Severity Index) to inform future sample size calculations. Historic placebo control data from the Anakinra for Pustular Psoriasis: Response in a Controlled Trial (National Institute of Health and Social Care reference: 13/50/17; Research Ethics Commitee reference: 16/LO/0436) will be used as the effect size comparator. Study recruitment will be undertaken over a 24-month period, commencing in November 2024.
This study has been approved by the Newcastle North Tyneside 2 Research Ethics Committee, 24/NE/0132. Our findings will inform the feasibility of a future adequately powered RCT evaluating the efficacy of JAK inhibitor therapy in PPP.
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