Objective: To determine whether implementation of a collaborative, evidence-based algorithm for care of pediatric parapneumonic effusion and empyema (PPE) can improve the quality of care delivered.
Study Design: Prospective cohort with retrospective control comparison of children aged 1 month to 18 years admitted with a clinical diagnosis of PPE. Quality improvement techniques were used to develop an algorithm, which was implemented September 2008. Primary outcome measures were decreased median and variability in length of stay (LOS), reduction in the use of chest computed tomography (CT), reduction in the total number of painful procedures, and increased initial use of effective drainage procedures when drainage was indicated.
Results: Compared with controls, algorithm implementation substantially reduced use of chest CT (0% vs. 41% of patients, P = 0.01) with no observed negative impact on LOS. Reductions in median LOS were not significant, but variability in LOS was reduced (P < 0.01 by F-test). Changes in number of procedures and use of effective drainage when indicated were in the predicted direction but not statistically significant.
Conclusions: Quality improvement techniques are an effective means for incorporating evidence-based medicine into pediatric care. PPE can be managed safely without the use of chest CT.
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http://dx.doi.org/10.1002/ppul.21429 | DOI Listing |
J Eval Clin Pract
February 2025
Department of Nursing, Trakya University Faculty of Health Sciences, Edirne, Turkey.
Objective: This study aims to assess the performance of machine learning (ML) techniques in optimising nurse staffing and evaluating the appropriateness of nursing care delivery models in hospital wards. The primary outcome measures include the adequacy of nurse staffing and the appropriateness of the nursing care delivery system.
Background: Historical and current healthcare challenges, such as nurse shortages and increasing patient acuity, necessitate innovative approaches to nursing care delivery.
Ann Intern Med
January 2025
959 Medical Operations Squadron, U.S. Air Force, Department of Neurology, Brooke Army Medical Center, San Antonio, Texas (T.K.).
Description: In July 2024, the U.S. Department of Veterans Affairs (VA) and U.
View Article and Find Full Text PDFJ Pak Med Assoc
January 2025
Department of Nephrology, Aga Khan University, Karachi, Pakistan.
Objective: To develop evidence-based local clinical practice guidelines and primary care referral pathways for general physicians to help streamline the management of glomerular diseases and diabetes in chronic kidney disease patients in Pakistan.
Methods: The study was conducted from October 2021 to February 2023 at the Centre for Clinical Best Practices, Aga Khan University Hospital, Karachi, in collaboration with the AKUH Section of Nephrology. Two source guidelines were selected by the local nephrologists after a thorough literature review on PubMed and Google Scholar.
Curr Gastroenterol Rep
January 2025
Division of Pediatric Gastroenterology, Hepatology and Nutrition, Department of Pediatrics, Columbia University Vagelos College of Physicians and Surgeons and New York- Presbyterian Morgan Stanley Children's Hospital, 630 West 168Th Street, New York, NY, PH17-105H10032, USA.
Purpose: To propose a gastrointestinal bleeding management algorithm that incorporates an endoscopic and imaging scoring system and specifies management of vascular complication from button battery ingestion.
Recent Findings: Button batteries (BB) are found in many electronic devices and ingestions are associated with serious complications especially in cases of unwitnessed ingestions, prolonged impaction, and in children less than 5 years of age. Gastrointestinal bleeding from BB related vascular injury is rare but often rapidly fatal, with a mortality rate as high as 81%.
PLoS One
January 2025
School of Optometry and Vision Science, UNSW Sydney, Sydney, New South Wales, Australia.
Purpose: In this study, we investigated the performance of deep learning (DL) models to differentiate between normal and glaucomatous visual fields (VFs) and classify glaucoma from early to the advanced stage to observe if the DL model can stage glaucoma as Mills criteria using only the pattern deviation (PD) plots. The DL model results were compared with a machine learning (ML) classifier trained on conventional VF parameters.
Methods: A total of 265 PD plots and 265 numerical datasets of Humphrey 24-2 VF images were collected from 119 normal and 146 glaucomatous eyes to train the DL models to classify the images into four groups: normal, early glaucoma, moderate glaucoma, and advanced glaucoma.
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