UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.

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BACKGROUND: Living donor kidney transplantation is the optimal treatment for end-stage kidney disease; however, few living donor candidates (LDCs) who begin evaluation actually donate. While some LDCs are deemed medically ineligible, others discontinue for potentially modifiable reasons.
METHODS: At five transplant centers, we conducted a prospective cohort study measuring LDCs\' clinical and psychosocial characteristics, educational preparation, readiness to donate, and social determinants of health. We followed LDCs for 12 months after evaluation to determine whether they donated a kidney, opted to discontinue, had modifiable reasons for discontinuing, were medically ineligible, or had other recipient-related reasons for discontinuing.
RESULTS: Among 2184 LDCs, 18.6% donated, 38.2% opted to or had modifiable reasons for discontinuing, and 43.2% were deemed ineligible due to medical or recipient-related reasons. Multivariable analyses comparing successful LDCs with those who did not complete donation for modifiable reasons (N = 1241) found that LDCs who discussed donation with the recipient before evaluation (OR, 2.31; 95% CI, 1.54-3.46), had completed high school (OR, 2.01; 95% CI, 1.21-3.35), or were a \"close relation\" to their recipient (OR, 1.89; 95% CI, 1.33-2.69) were more likely to donate. Conversely, LDCs who reported religion as important (OR, 0.55; 95% CI, 0.38-0.80), were Non-White (OR, 0.70; 95% CI, 0.49-1.00), or had overall higher anxiety scores (OR, 0.92; 95% CI, 0.86-0.99) were less likely to donate.
CONCLUSIONS: With fewer than a fifth of LDCs donating, developing programs to provide greater emotional support and facilitate open discussions between LDCs and recipients earlier may increase living donation rates.

UNASSIGNED: The deep inferior epigastric artery perforator (DIEP) free flap is the gold-standard for breast reconstruction but is technically demanding, resource intensive and time-consuming, making it a daunting task for the junior surgeon.
UNASSIGNED: To report the lessons learnt from the experience of a single surgeon performing their first 150 DIEP reconstructions as a guide for junior surgeons.
UNASSIGNED: Data regarding patient demographics and surgical outcomes from April 2021 to October 2022 were collected retrospectively from medical records. Surgical outcomes were analysed using Microsoft Excel.
UNASSIGNED: Over 17 months, 150 flaps were completed in 97 patients by the senior author (BS). Operative duration was negatively correlated with case number for unilateral DIEPs (r = -0.73, p < 0.05) and for bilateral DIEPs (r = -0.67, p = 0.14). Raise time and ischaemic time were also negatively correlated with case number (r = -0.82, p < 0.05 and r = -0.79, p < 0.05, respectively). There were 10 complications and no flap losses.
UNASSIGNED: The data demonstrate an expected improvement in surgical efficiency with increased experience. We describe the key factors contributing to efficiency in our series, such as preoperative CT angiography, surgical markings prior to the day of surgery, a two-team approach with three diathermy sets, flap raise using monopolar diathermy, preference towards choosing a single dominant perforator and early commitment to perforator choice. This case series acts as a guide for the junior plastic surgeon in achieving safe, aesthetic and efficient results when completing free DIEP flap breast reconstructions.

Precision medicine is transforming psychiatric treatment by tailoring personalized healthcare interventions based on clinical, genetic, environmental, and lifestyle factors to optimize medication management. This study investigates how artificial intelligence (AI) and machine learning (ML) can address key challenges in integrating pharmacogenomics (PGx) into psychiatric care. In this integration, AI analyzes vast genomic datasets to identify genetic markers linked to psychiatric conditions. AI-driven models integrating genomic, clinical, and demographic data demonstrated high accuracy in predicting treatment outcomes for major depressive disorder and bipolar disorder. This study also examines the pressing challenges and provides strategic directions for integrating AI and ML in genomic psychiatry, highlighting the importance of ethical considerations and the need for personalized treatment. Effective implementation of AI-driven clinical decision support systems within electronic health records is crucial for translating PGx into routine psychiatric care. Future research should focus on developing enhanced AI-driven predictive models, privacy-preserving data exchange, and robust informatics systems to optimize patient outcomes and advance precision medicine in psychiatry.

With the increasing rate of infections caused by multidrug-resistant organisms (MDRO), selecting appropriate empiric antibiotics has become challenging. We aimed to develop and externally validate a model for predicting the risk of MDRO infections in patients with cirrhosis.
METHODS: We included patients with cirrhosis and bacterial infections from two prospective studies: a transcontinental study was used for model development and internal validation (n = 1302), and a study from Argentina and Uruguay was used for external validation (n = 472). All predictors were measured at the time of infection. Both culture-positive and culture-negative infections were included. The model was developed using logistic regression with backward stepwise predictor selection. We externally validated the optimism-adjusted model using calibration and discrimination statistics and evaluated its clinical utility.
RESULTS: The prevalence of MDRO infections was 19% and 22% in the development and external validation datasets, respectively. The model\'s predictors were sex, prior antibiotic use, type and site of infection, MELD-Na, use of vasopressors, acute-on-chronic liver failure, and interaction terms. Upon external validation, the calibration slope was 77 (95% CI .48-1.05), and the area under the ROC curve was .68 (95% CI .61-.73). The application of the model significantly changed the post-test probability of having an MDRO infection, identifying patients with nosocomial infection at very low risk (8%) and patients with community-acquired infections at significant risk (36%).
CONCLUSIONS: This model achieved adequate performance and could be used to improve the selection of empiric antibiotics, aligning with other antibiotic stewardship program strategies.

UNASSIGNED: Emotional intelligence is a crucial factor in managing stress and maintaining overall well-being. Emotions are essential for clinical care quality, as they drive clinical decision making.
UNASSIGNED: The purpose of this study was to assess the relationship between emotional intelligence and clinical decision making among fourth-year nursing students.
UNASSIGNED: The study was a cross-sectional study done on a convenience sample of 225 fourth-year nursing students. Data collection was performed by the \"Schutte Self Report Emotional Intelligence Test\" (SSEIT) and the \"clinical decision making scale\".
UNASSIGNED: The analysis revealed that the emotional intelligence mean was 151.3 ± 1.9 (ranging from 33 to 165), which is high. The analysis indicated that the clinical decision making of the participants was high (177.1.3 ± 9.8) (ranging from 40 to 200). Also, the analysis showed that there was a strong positive relationship between nursing students\' emotional intelligence and clinical decision making (r = .70, p = 0.001). Furthermore, the findings revealed that emotional intelligence was a predictor of clinical decision making.
UNASSIGNED: The emotional intelligence and clinical decision making among fourth-year nursing students were high. Also, clinical decision making was found to have strong positive relationship with emotional intelligence.

OBJECTIVE: 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.
METHODS: 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.
CONCLUSIONS: Early recognition and response to improvement enable acute care nurses to protect patients from risks of unnecessary treatment and promote recovery.
CONCLUSIONS: 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.

BACKGROUND: Markerless motion capture (MMC) uses video cameras or depth sensors for full body tracking and presents a promising approach for objectively and unobtrusively monitoring functional performance within community settings, to aid clinical decision-making in neurodegenerative diseases such as dementia.
OBJECTIVE: The primary objective of this systematic review was to investigate the application of MMC using full-body tracking, to quantify functional performance in people with dementia, mild cognitive impairment, and Parkinson disease.
METHODS: A systematic search of the Embase, MEDLINE, CINAHL, and Scopus databases was conducted between November 2022 and February 2023, which yielded a total of 1595 results. The inclusion criteria were MMC and full-body tracking. A total of 157 studies were included for full-text screening, out of which 26 eligible studies that met the selection criteria were included in the review. .
RESULTS: Primarily, the selected studies focused on gait analysis (n=24), while other functional tasks, such as sit to stand (n=5) and stepping in place (n=1), were also explored. However, activities of daily living were not evaluated in any of the included studies. MMC models varied across the studies, encompassing depth cameras (n=18) versus standard video cameras (n=5) or mobile phone cameras (n=2) with postprocessing using deep learning models. However, only 6 studies conducted rigorous comparisons with established gold-standard motion capture models.
CONCLUSIONS: Despite its potential as an effective tool for analyzing movement and posture in individuals with dementia, mild cognitive impairment, and Parkinson disease, further research is required to establish the clinical usefulness of MMC in quantifying mobility and functional performance in the real world.

UNASSIGNED: Mental state deterioration poses significant challenges in healthcare, impacting patients and providers. Symptoms like confusion and agitation can lead to prolonged hospital stays, increased costs, and the use of restrictive interventions. Despite its prevalence, there\'s a lack of consensus on effective practices for managing mental state deterioration in acute hospital settings. To address this gap, a rapid response team model has been proposed as a potential intervention, aiming to provide early identification and targeted interventions.
UNASSIGNED: Based on realist evaluation steps, first, initial program theories are formulated to understand the logic behind the intervention. Second, literature synthesis identifies empirical evidence on contexts, mechanisms, and outcomes elements, refining initial theories. During the third step, data will be collected using qualitative methods such as field observations and interviews, as well as quantitative methods such as surveys of the staff, audits of electronic medical records, and analysis of incident records of mental state deterioration. Analysing this data informs configurations of contexts, mechanisms, and outcomes. In the fifth step, the configurations are synthesised, presenting refined, evidence-informed program theories.
UNASSIGNED: This study addresses the knowledge gap by evaluating the rapid response model\'s effectiveness in managing mental state deterioration in acute hospital settings. Realist principles guide the exploration of causal mechanisms and their interaction with specific implementation contexts. The objective is to identify what works, for whom, and under what circumstances, aiming to manage deterioration, reduce restrictive interventions, and enhance the experience for patients and staff by implementing a proactive model of care. The findings contribute to evidence-based approaches for managing mental state deterioration in hospital settings, informing policy and practice in this crucial area of healthcare.