Purpose: The incidence of oral cancers, particularly HPV-related oropharyngeal cancer, is steadily increasing worldwide, presenting a significant healthcare challenge. This study investigates trends and predictors of unplanned hospitalizations for oral cavity cancer (OCC) and oropharyngeal cancer (OPC) patients in the province of Alberta, Canada.
Methods: This retrospective, population-based, cohort study used administrative data collected from all hospitals in the province. Using the Alberta Cancer Registry (ACR), a cohort of adult patients diagnosed with a single primary OCC or OPC between January 2010 and December 2017 was identified. Linking this cohort with the Discharge Abstract Database (DAD), trends in hospitalizations, primary diagnoses, and predictors of unplanned hospitalization (UH) and 30-day unplanned readmission were analyzed.
Results: Of 1,721 patients included, 1,244 experienced 2,228 hospitalizations, with 48 % being categorized as UH. The UHs were significantly associated with a higher mortality rate, 18.5 % as compared to 4.6 % for planned, and influenced by sex, age groups, comorbidities, cancer types, stages, and treatment modalities. The rate of UH per patient decreased from 0.69 to 0.54 visits during the study period (P = 0.02). Common diagnoses for UH were palliative care and post-surgical convalescence, while surgery-related complications such as infection and hemorrhage were frequent in 30-day unplanned readmissions. Predictors of UH included cancer stage, material deprivation, and treatment, while cancer type and comorbidity predicted readmissions.
Conclusion: The rate of UHs showed a noteworthy decline in this study, which could be a result of enhanced care coordination. Furthermore, identified primary diagnosis and predictors associated with UHs and readmissions, provide valuable insights for enhancing the quality of care for cancer patients.
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http://dx.doi.org/10.1016/j.oraloncology.2024.106742 | DOI Listing |
JTCVS Open
December 2024
Division of Cardiovascular Anesthesia, Department of Anesthesiology, Perioperative and Pain Medicine, Texas Children's Hospital, Houston, Tex.
Objective: To describe intraoperative cardiac arrest in patients undergoing congenital heart surgery.
Methods: The Society of Thoracic Surgeons Congenital Heart Surgery Database was queried. Predictors of intraoperative cardiac arrest were assessed using univariate and multivariable analyses.
Acta Paediatr
January 2025
Hans Christian Andersen Children's Hospital, Odense University Hospital, Odense, Denmark.
Aim: Homecare for neonates has advanced, but combative analysis of contact methods remains unexplored. The aim was to identify predictors of readmission during homecare and to compare home visit, telemedicine or outpatient visit.
Methods: This retrospective study included infants receiving homecare from 1 January 2015 to 31 December 2022.
Clin Nephrol
December 2024
Dialysis initiation during an emergency hospital admission is associated with increased complications, more temporary access, and higher mortality. Even in patients known to nephrologists, more than one-third start dialysis in an unplanned fashion. This retrospective case-control study sought to identify features of the pre-dialysis period that are associated with unplanned dialysis initiation in patients known to nephrology services.
View Article and Find Full Text PDFPLoS One
December 2024
Department of Nursing, College of Medicine and Health Science, Woldia University, Woldia, Ethiopia.
Introduction: An unintended pregnancy refers to a situation where a pregnancy occurs either when there is no desire for a child (unwanted) or when it takes place at a time that was not anticipated (mistimed). Pregnant women infected with HIV face a two to tenfold increased risk of mortality during both pregnancy and the postpartum period compared to those who are not infected. A national level cohort study has identified that about 70 babies born HIV positive, 60% of them were from unplanned pregnancy.
View Article and Find Full Text PDFFront Public Health
December 2024
Department of Anesthesiology, Chengdu Fifth People's Hospital (The Second Clinical Medical College, Affiliated Fifth People's Hospital of Chengdu University of Traditional Chinese Medicine), Chengdu, China.
Background: Postoperative pneumonia, a prevalent form of hospital-acquired pneumonia, poses significant risks to patients' prognosis and even their lives. This study aimed to develop and validate a predictive model for postoperative pneumonia in surgical patients using nine machine learning methods.
Objective: Our study aims to develop and validate a predictive model for POP in surgical patients using nine machine learning algorithms.
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