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Trauma contributes to the greatest loss of disability-adjusted life-years for adolescents and young adults worldwide. In the context of global abdominal trauma, the trauma laparotomy is the most commonly performed operation. Variation likely exists in how these patients are managed and their subsequent outcomes, yet very little global data on the topic currently exists. The objective of the GOAL-Trauma study is to evaluate both patient and injury factors for those undergoing trauma laparotomy, their clinical management and postoperative outcomes.
We describe a planned prospective multicentre observational cohort study of patients undergoing trauma laparotomy. We will include patients of all ages who present to hospital with a blunt or penetrating injury and undergo a trauma laparotomy within 5 days of presentation to the treating centre. The study will collect system, patient, process and outcome data, following patients up until 30 days postoperatively (or until discharge or death, whichever is first). Our sample size calculation suggests we will need to recruit 552 patients from approximately 150 recruiting centres.
The GOAL-Trauma study will provide a global snapshot of the current management and outcomes for patients undergoing a trauma laparotomy. It will also provide insight into the variation seen in the time delays for receiving care, the disease and patient factors present, and patient outcomes. For current standards of trauma care to be improved worldwide, a greater understanding of the current state of trauma laparotomy care is paramount if appropriate interventions and targets are to be identified and implemented.
Common goals for procedural sedation are to control pain and ensure the patient is not moving to an extent that is impeding safe progress or completion of the procedure. Clinicians perform regular assessments of the adequacy of procedural sedation in accordance with these goals to inform their decision-making around sedation titration and also for documentation of the care provided. Natural language processing could be applied to real-time transcriptions of audio recordings made during procedures in order to classify sedation states that involve movement and pain, which could then be integrated into clinical documentation systems. The aim of this study was to determine whether natural language processing algorithms will work with sufficient accuracy to detect sedation states during procedural sedation.
A prospective observational study was conducted.
Audio recordings from consenting participants undergoing elective procedures performed in the interventional radiology suite at a large academic hospital were transcribed using an automated speech recognition model. Sentences of transcribed text were used to train and evaluate several different NLP pipelines for a text classification task. The NLP pipelines we evaluated included a simple Bag-of-Words (BOW) model, an ensemble architecture combining a linear BOW model and a “token-to-vector” (Tok2Vec) component, and a transformer-based architecture using the RoBERTa pre-trained model.
A total of 15,936 sentences from transcriptions of 82 procedures was included in the analysis. The RoBERTa model achieved the highest performance among the three models with an area under the ROC curve (AUC-ROC) of 0.97, an F1 score of 0.87, a precision of 0.86, and a recall of 0.89. The Ensemble model showed a similarly high AUC-ROC of 0.96, but lower F1 score of 0.79, precision of 0.83, and recall of 0.77. The BOW approach achieved an AUC-ROC of 0.97 and the F1 score was 0.7, precision was 0.83 and recall was 0.66.
The transformer-based architecture using the RoBERTa pre-trained model achieved the best classification performance. Further research is required to confirm the that this natural language processing pipeline can accurately perform text classifications with real-time audio data to allow for automated sedation state assessments.
Automating sedation state assessments using natural language processing pipelines would allow for more timely documentation of the care received by sedated patients, and, at the same time, decrease documentation burden for clinicians. Downstream applications can also be generated from the classifications, including for example real-time visualizations of sedation state, which may facilitate improved communication of the adequacy of the sedation between clinicians, who may be performing supervision remotely. Also, accumulation of sedation state assessments from multiple procedures may reveal insights into the efficacy of particular sedative medications or identify procedures where the current approach for sedation and analgesia is not optimal (i.e. a significant amount of time spent in “pain” or “movement” sedation states).
by Josh Williams, Haavard Ahlqvist, Alexander Cunningham, Andrew Kirby, Ira Katz, John Fleming, Joy Conway, Steve Cunningham, Ali Ozel, Uwe Wolfram
For the one billion sufferers of respiratory disease, managing their disease with inhalers crucially influences their quality of life. Generic treatment plans could be improved with the aid of computational models that account for patient-specific features such as breathing pattern, lung pathology and morphology. Therefore, we aim to develop and validate an automated computational framework for patient-specific deposition modelling. To that end, an image processing approach is proposed that could produce 3D patient respiratory geometries from 2D chest X-rays and 3D CT images. We evaluated the airway and lung morphology produced by our image processing framework, and assessed deposition compared to in vivo data. The 2D-to-3D image processing reproduces airway diameter to 9% median error compared to ground truth segmentations, but is sensitive to outliers of up to 33% due to lung outline noise. Predicted regional deposition gave 5% median error compared to in vivo measurements. The proposed framework is capable of providing patient-specific deposition measurements for varying treatments, to determine which treatment would best satisfy the needs imposed by each patient (such as disease and lung/airway morphology). Integration of patient-specific modelling into clinical practice as an additional decision-making tool could optimise treatment plans and lower the burden of respiratory diseases.
As life expectancy increases, older people are living longer with multimorbidity (MM, co-occurrence of ≥2 chronic health conditions) and complex multimorbidity (CMM, ≥3 chronic conditions affecting ≥3 different body systems). We assessed the impacts of MM and CMM on healthcare service use in Australia, as little was known about this.
Population-based cross-sectional data linkage study.
New South Wales, Australia.
248 496 people aged ≥45 years who completed the Sax Institute’s 45 and Up Study baseline questionnaire.
High average annual healthcare service use (≥2 hospital admissions, ≥11 general practice visits and ≥2 emergency department (ED) visits) during the 3-year baseline period (year before, year of and year after recruitment).
Baseline questionnaire data were linked with hospital, Medicare claims and ED datasets. Poisson regression models were used to estimate adjusted and unadjusted prevalence ratios for high service use with 95% CIs. Using a count of chronic conditions (disease count) as an alternative morbidity metric was requested during peer review.
Prevalence of MM and CMM was 43.8% and 15.5%, respectively, and prevalence increased with age. Across three healthcare settings, MM was associated with a 2.02-fold to 2.26-fold, and CMM was associated with a 1.83-fold to 2.08-fold, increased risk of high service use. The association was higher in the youngest group (45–59 years) versus the oldest group (≥75 years), which was confirmed when disease count was used as the morbidity metric in sensitivity analysis.
When comparing impact using three categories with no overlap (no MM/CMM, MM with no CMM, and CMM), CMM had greater impact than MM across all settings.
Increased healthcare service use among older adults with MM and CMM impacts on the demand for primary care and hospital services. Which of MM or CMM has greater impact on risk of high healthcare service use depends on the analytic method used. Ageing populations living longer with increasing burdens of MM and CMM will require increased Medicare funding and provision of integrated care across the healthcare system to meet their complex needs.
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