Background: Pulmonary contusions (PC) are common after blunt chest trauma and can be identified with computed tomography (CT). Complex scoring systems for grading PC exist, however recent scoring systems rely on computer-generated algorithms that are not readily available at all hospitals. We developed a scoring system for grading PC to predict the need for prolonged mechanical ventilation and initial hospital admission location.
Methods: A retrospective review was performed of adult blunt trauma patients with PC identified on initial chest CT during 2020. Data elements related to demographics, injury characteristics, disposition and healthcare utilization were extracted. The primary outcome was the need for mechanical ventilation for greater than 48 h. A novel scoring system, the Pulmonary Contusion Score (PCS) was developed. The maximum score was 10, with each lobe contributing up to 2 points. A score of 0 was given for no contusion present in the lobe, 1 for less than 50 % contusion, and 2 for greater than 50 % contusion. A PCS of 4 was hypothesized to correlate with need for mechanical ventilation for over 48 h. A confusion matrix of the scoring algorithm was created, and inter-rater concordance was calculated from a randomly selected 125 patients.
Results: A total of 217 patients were identified. 118 patients (54 %) were admitted to the ICU, but only 23 patients (19 %) were intubated, and only 17 patients (8 %) required mechanical ventilation > 48 h. Sensitivity of the scoring system was 20 %, while specificity was 93 %. Negative predictive value was 93 %. Inter-rater agreement was 77 %.
Conclusion: The PCS is a scoring system with high specificity and negative predictive value that can be used to evaluate the need for mechanical ventilation after sustaining blunt PC and can help properly allocate hospital resources.
Level Of Evidence: IV - diagnostic criteria.
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http://dx.doi.org/10.1016/j.sipas.2024.100247 | DOI Listing |
Neurogastroenterol Motil
January 2025
Division of Gastroenterology, Rabin Medical Center, Beilinson Campus, Petah Tikva, Israel.
Background: Proton pump inhibitors (PPI) for gastroesophageal reflux disease (GERD) are associated with a high failure rate. Our uncontrolled feasibility study aimed determining the effect of a transcutaneous electrical stimulation system (TESS) on GERD symptoms and acid exposure time (AET).
Methods: Recruited patients with heartburn and regurgitation.
Circ Genom Precis Med
January 2025
Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT (A.A., L.S.D., E.K.O., R.K.).
Background: While universal screening for Lp(a; lipoprotein[a]) is increasingly recommended, <0.5% of patients undergo Lp(a) testing. Here, we assessed the feasibility of deploying Algorithmic Risk Inspection for Screening Elevated Lp(a; ARISE), a validated machine learning tool, to health system electronic health records to increase the yield of Lp(a) testing.
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January 2025
Department of Medicine, Division of Cardiology (M.P., N.J.P., N.P.S.), Duke University, Durham, NC.
Background: Established risk models may not be applicable to patients at higher cardiovascular risk with a measured Lp(a) (lipoprotein[a]) level, a causal risk factor for atherosclerotic cardiovascular disease.
Methods: This was a model development study. The data source was the Nashville Biosciences Lp(a) data set, which includes clinical data from the Vanderbilt University Health System.
BJU Int
January 2025
Department of Urology, University of Alabama, Birmingham, AL, USA.
Objectives: To identify associations between 24-h urine abnormalities and clinical risk factors for recurrent stone formers.
Patients And Methods: The Registry for Stones of the Kidney and Ureter was queried for all patients who underwent 24-h urine studies. Patients were categorised by the number of clinical risk factors for recurrent stone disease.
Eur Heart J Digit Health
January 2025
Massachusetts General Hospital, 55 Fruit St, Boston, MA 02114, USA.
Aims: Accurate prediction of clinical outcomes following percutaneous coronary intervention (PCI) is essential for mitigating risk and peri-procedural planning. Traditional risk models have demonstrated a modest predictive value. Machine learning (ML) models offer an alternative risk stratification that may provide improved predictive accuracy.
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