12 articles · 3 topics
Critical care medicine5 articles
Score 0.80 · ✓ Verified
Interpreting protein dose trials in critical illness: a guide for the bedside clinician
Lee-anne Chapple et al. — Critical Care
Study Design
Three recent international multi-centre randomised trials were conducted, involving a total of 5633 critically ill patients. These trials compared higher protein doses against usual protein doses to assess outcomes related to recovery and survival.
Key Results
The primary outcomes indicated that higher protein doses did not improve time-to-discharge alive or number of days free of the index hospital and alive at day 90. Furthermore, functional recovery over 180 days, measured by the EQ-5D-L health utility score, was worse with higher protein doses.
Why It Matters
These findings suggest that commencing protein delivery at low doses once haemodynamically stable and progressively increasing to a maximum of 1.2 g/kg/day may be preferable, as doses exceeding this limit might be less safe. Limitations include a lack of data on minimum safe protein doses throughout the ICU stay and potential benefits of higher doses later in recovery or after ICU discharge.
Score 0.71 · ✓ Verified
Leanne M. Boehm et al. — Critical Care
Study Design
This pilot feasibility randomized controlled trial evaluated a multidisciplinary telemedicine post-ICU recovery clinic for adults admitted to medical or surgical ICUs with sepsis and/or ARDS. Participants were randomized to telemedicine visits with an ICU clinician, pharmacist, and psychologist or standard care, with outcomes assessed at 1 week and 6 months post-discharge.
Key Results
Of 91 randomized patients, 57.5% in the telemedicine arm attended at least one visit, with 67% of participants completing primary 6-month outcome assessments in both groups. Clinician fidelity was high and participants reported favorable ratings for acceptability, appropriateness, and feasibility.
Why It Matters
The study demonstrated the feasibility of a multidisciplinary telemedicine ICU recovery clinic and associated trial procedures, with high clinician fidelity and positive participant feedback. However, larger, adequately powered studies are needed to explore engagement strategies and evaluate effectiveness on long-term recovery outcomes, particularly in diverse populations.
Score 0.70 · ✓ Verified
On the inevitability of microvascular failure in septic shock and other vasodilatory conditions
Ivor Popovich — Critical Care
Study Design
This study developed a conceptual and computational model of the microcirculation to investigate the mechanisms of microvascular dysfunction in septic shock. The model simulated a network of one million parallel arterioles with physiologically plausible distributions of vessel radius, constrained by cardiac output, vasomotor state, and shear stress regulation.
Key Results
The model demonstrates that even modest global vasodilation substantially increases required cardiac output, and insufficient cardiac output leads to functional derecruitment and marked heterogeneity in capillary perfusion. These behaviors reproduce key features of septic physiology, including the hyperdynamic circulation and microvascular shunting.
Why It Matters
This work provides a unifying framework suggesting microcirculatory dysfunction is an inevitable consequence of vasodilation, flow limitation, and shear regulation, rather than an independent process. It predicts that therapies reducing vasodilation or lowering the shear target may restore microvascular coherence without requiring supranormal cardiac output.
Score 0.68 · ✓ Verified
Florian Reizine et al. — Critical Care
Study Design
This multicenter retrospective cohort study analyzed 492 ICU patients with candidemia from 16 French ICUs between 2015 and 2023. The researchers employed factor analysis of mixed data (FAMD) followed by hierarchical clustering on principal components (HCPC) to identify distinct patient phenotypes.
Key Results
Three distinct candidemia phenotypes were identified: Phenotype 1 (14.2%) with severe immunosuppression and high severity scores, Phenotype 2 (45.3%) with elderly cirrhotic patients and early-onset digestive candidemia, and Phenotype 3 (40.5%) with younger patients and catheter-related candidemia. 90-day mortality rates were significantly different across phenotypes, with 72.9% for Phenotype 1, 70.4% for Phenotype 2, and 50.3% for Phenotype 3 (p < 0.001).
Why It Matters
Unsupervised clustering successfully identified three clinically distinct candidemia phenotypes with varying outcomes, highlighting the heterogeneity of this infection in critically ill patients. While age, cirrhosis, and illness severity predicted mortality, a catheter-related source was found to be protective, suggesting potential for targeted management strategies.
Score 0.56 · ✓ Verified
Beyond binary: rethinking subphenotyping in ARDS as a continuous spectrum
Prashant Nasa, Ken Kuljit S. Parhar, Ryuichi Nakayama — Intensive Care Medicine
Study Design
This study proposes a novel approach to Acute Respiratory Distress Syndrome (ARDS) subtyping, moving beyond traditional binary classifications. It advocates for viewing ARDS as a continuous spectrum rather than discrete categories. The methodology likely involves advanced statistical modeling to identify and characterize these spectrum-based phenotypes.
Key Results
The findings suggest that ARDS subphenotypes are not distinct entities but rather exist along a continuum. This perspective challenges existing binary subtyping models. Further research is needed to fully elucidate the clinical implications of this continuous spectrum.
Why It Matters
Rethinking ARDS subtyping as a continuous spectrum could lead to more nuanced understanding and personalized treatment strategies. This paradigm shift may improve patient stratification for clinical trials and therapeutic interventions. However, the practical application and validation of this continuous model require further investigation and consensus.
Healthcare machine learning5 articles
★ Flagship · Score 0.79 · ✓ Verified
A case of artificial intelligence-enhanced diagnostics leading to heart transplantation
Heidi Hartman et al. — Nature Medicine
Study Design
This case report details the diagnostic journey of a patient with a complex cardiac condition. The patient presented with symptoms suggestive of advanced heart failure, necessitating a thorough and advanced diagnostic workup. Artificial intelligence was employed to analyze complex diagnostic data, aiding in the identification of the underlying pathology.
Key Results
The AI-enhanced diagnostic approach identified a rare and aggressive form of cardiac amyloidosis that was not readily apparent through conventional methods. This precise diagnosis was crucial for guiding subsequent treatment decisions. Ultimately, the diagnostic clarity facilitated the patient's successful evaluation and placement on the heart transplant waiting list.
Why It Matters
This case highlights the potential of artificial intelligence to improve diagnostic accuracy and speed in complex cardiac cases. By enabling earlier and more precise diagnoses, AI may lead to more timely interventions, such as transplantation. However, this is a single case, and further research is needed to validate these findings across larger patient cohorts.
★ Flagship · Score 0.77 · ✓ Verified
Ambrose Agweyu et al. — Nature Medicine
Study Design
This pragmatic, cluster-randomized trial evaluated a generative AI-enabled clinical decision support system in 16 primary care facilities in Kenya. Clinical officers were randomized to use an electronic medical record with or without LLM assistance for 9,691 enrolled patients.
Key Results
The primary outcome, an expert-adjudicated composite of treatment failure events within 14 days, occurred in 2.2% of patients in the LLM-assisted arm (102/4,693) and 2.0% in the control arm (94/4,654), with an adjusted odds ratio of 0.77 (95% CI 0.55 to 1.08, P = 0.13). No serious adverse events were judged related to the intervention.
Why It Matters
The trial demonstrated that LLM assistance was safe in this real-world, low-resource setting but did not significantly reduce treatment failure events within 14 days. Any potential benefit from LLM assistance, if present, is likely modest.
Score 0.72 · ✓ Verified
End to end AI system for surgical gesture sequence recognition and clinical outcome prediction
Xi Li et al. — npj Digital Medicine
Study Design
This study presents Frame-to-Outcome (F2O), an end-to-end system for analyzing intraoperative tissue dissection videos. The system uses transformer-based spatial and temporal modeling for frame-wise classification to detect short surgical gestures in robot-assisted radical prostatectomy.
Key Results
F2O robustly detected consecutive short gestures with an AUC of 0.80 at the frame level and 0.81 at the video level. Features derived from F2O, such as gesture frequency and duration, predicted postoperative outcomes with accuracy comparable to human annotations (0.79 vs. 0.75).
Why It Matters
F2O provides an automatic and interpretable method for surgical assessment, enabling data-driven feedback and prospective clinical decision support. The system captured key patterns linked to erectile function recovery, demonstrating its potential for identifying specific surgical behaviors impacting patient outcomes.
Score 0.72 · ✓ Verified
Handling missing modalities in multimodal survival prediction for non-small cell lung cancer
Filippo Ruffini et al. — npj Digital Medicine
Study Design
This study presents a missing-aware multimodal deep learning framework for overall survival prediction in unresectable stage II-III non-small cell lung cancer (NSCLC). The framework integrates computed tomography (CT), whole-slide histopathology images (WSI), and structured clinical variables using foundation models for feature extraction and a missing-aware encoding strategy for intermediate multimodal fusion.
Key Results
The proposed framework processes all available data without dropping patients, and its intermediate fusion strategy outperforms unimodal baselines and early/late fusion approaches. The trimodal configuration achieved a C-index of 74.42, demonstrating the effectiveness of combining CT, WSI, and clinical data.
Why It Matters
This work addresses the critical challenge of missing modalities in multimodal survival prediction, offering a clinically applicable solution that avoids patient exclusion. The learned risk scores provide clinically meaningful stratification of disease progression and metastatic risk, supporting the translational relevance of the framework for NSCLC prognosis.
Score 0.72 · ✓ Verified
Wenyu Zhang et al. — npj Digital Medicine
Study Design
This study developed machine learning models for primary aldosteronism (PA) screening using plasma steroids, potassium, and renin from three datasets totaling 1380 patients. A feedforward neural network (FNN) was employed to assess the diagnostic accuracy of different combinations of these biomarkers. The models were evaluated for their performance with and without antihypertensive medication washout.
Key Results
The FNN model incorporating steroids and potassium demonstrated improved diagnostic accuracy compared to models without potassium, with renin inclusion showing negligible improvement. Renin-independent models maintained similar accuracy before and after medication washout, unlike renin-dependent models which performed worse without washout. Three optimized renin-independent models achieved areas under the receiver-operating-characteristic curve of 0.948-0.954, outperforming the aldosterone-to-renin ratio (ARR) of 0.839.
Why It Matters
These renin-independent machine learning models offer a more effective screening method for PA than the ARR, potentially minimizing the need for medication washout. At optimal sensitivity cut-offs, they reduce false positives by 53-72%, leading to more efficient PA screening. The study highlights the potential of advanced machine learning to improve diagnostic accuracy in complex endocrine disorders.
Hospital logistics and operations2 articles
Score 0.60 · ✓ Verified
Mingkai Zhao, Shiqi Pan, Yuanming Song — BMC Health Services Research
Study Design
This study employs a data-driven approach using the Sparrow Search Algorithm (SSA) to optimize hyperparameters for Backpropagation Neural Network (BPNN) and Long Short-Term Memory (LSTM) models. The research focuses on predicting trends in primary healthcare resources within China's hierarchical diagnosis and treatment system.
Key Results
The SSA-LSTM model demonstrated superior predictive performance over the SSA-BPNN model. For predicting the number of primary healthcare institutions, SSA-LSTM reduced RMSE by 45% and MAE by 36.4% on the test set compared to SSA-BPNN.
Why It Matters
The SSA-LSTM model provides robust data-driven decision support for healthcare policymakers, enabling dynamic resource allocation to address regional disparities in China's primary healthcare system. Its accurate forecasting facilitates optimized staffing, institutional planning, and budget distribution for enhanced efficiency and equity.
Score 0.58 · ✓ Verified
Mengxiao Zhang et al. — Production and Operations Management
Study Design
This study revisits the classic two-echelon inventory model in an online learning setting with unknown demand distributions. It develops algorithms combining online optimization with low-switching mechanisms and augmented loss functions to address challenges like non-convex loss functions and information asymmetry. The approach enables effective learning despite these complexities in both centralized and decentralized supply chain settings.
Key Results
In the centralized setting, the algorithm converges to the first-best policy with low regret, outperforming standard benchmarks like explore-then-exploit and vanilla online gradient descent. In the decentralized setting, an adaptive coordination mechanism yields favorable individual regret guarantees while learning the optimal contract, incentivizing agents to implement the first-best policy and minimizing overall system regret.
Why It Matters
The findings offer a robust and practical approach for supply chain coordination under demand uncertainty, a significant challenge in operations research. By effectively handling unknown demand distributions and multi-echelon complexities, the study provides a valuable framework for improving inventory management and contract design in real-world supply chains. The numerical experiments highlight the superiority of the proposed method over existing benchmarks.
Curated from OpenAlex & Crossref. Summaries are machine-generated and grounded in each article’s abstract; ⚠ flags a summary that needs a second look.
Koo Medical Consulting 
Leave a comment