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Objectives: COVID-19 severity prediction scores need further validation due to evolving COVID-19 illness. We evaluated existing COVID-19 risk prediction scores in Aotearoa New Zealand, including for Māori and Pacific peoples who have been inequitably affected by COVID-19.
Methods: We conducted a multicenter retrospective cohort study in adults hospitalized with COVID-19 from January to May 2022, including all Māori and Pacific patients, and every second non-Māori, non-Pacific (NMNP) patient to achieve equal analytic power by ethnic grouping. We assessed the accuracy of existing severity scores (4C Mortality, CURB-65, PRIEST, and VACO) to predict death in the hospital or within 28 days.
Results: Of 2319 patients, 582 (25.1%) identified as Māori, 914 (39.4%) as Pacific, and 862 (37.2%) as NMNP. There were 146 (6.3%, 95% confidence interval 5.4-7.4%) deaths, with a predicted probability of death higher than observed mortality for VACO (10.4%), modified PRIEST (15.1%) and 4C mortality (15.5%) scores, but lower for CURB-65 (4.5%). C-statistics (95% CI) of severity scores were: 4C mortality: Māori 0.82 (0.75, 0.88), Pacific 0.87 (0.83, 0.90), NMNP 0.90 (0.86, 0.93); CURB-65: Māori 0.83 (0.69, 0.92), Pacific 0.87 (0.82, 0.91), NMNP 0.86 (0.80, 0.91); modified PRIEST: Māori 0.85 (0.79, 0.90), Pacific 0.81 (0.76, 0.86), NMNP 0.83 (0.78, 0.87); and VACO: Māori 0.79 (0.75, 0.83), Pacific 0.71 (0.58, 0.82), NMNP 0.78 (0.73, 0.83).
Conclusions: Following re-calibration, existing risk prediction scores accurately predicted mortality.
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http://dx.doi.org/10.1016/j.ijregi.2024.100424 | DOI Listing |
J Magn Reson Imaging
September 2025
Department of Neurology, Dell Medical School, University of Texas at Austin, Austin, Texas, USA.
Background: Cerebrovascular reactivity reflects changes in cerebral blood flow in response to an acute stimulus and is reflective of the brain's ability to match blood flow to demand. Functional MRI with a breath-hold task can be used to elicit this vasoactive response, but data validity hinges on subject compliance. Determining breath-hold compliance often requires external monitoring equipment.
View Article and Find Full Text PDFZhong Nan Da Xue Xue Bao Yi Xue Ban
May 2025
Department of Cardiovascular Medicine, Affiliated Changsha Hospital of Xiangya School of Medicine, Central South University, Changsha 410005.
Objectives: The Charlson comorbidity index reflects overall comorbidity burden and has been applied in cardiovascular medicine. However, its role in predicting in-hospital mortality in patients with acute myocardial infarction (AMI) complicated by ventricular arrhythmias (VA) remains unclear. This study aims to evaluate the predictive value of the Charlson comorbidity index in this setting and to construct a nomogram model for early risk identification and individualized management to improve outcomes.
View Article and Find Full Text PDFChem Biol Drug Des
September 2025
Laboratory of Biochemistry and Animal Toxins, Institute of Biotechnology, Federal University of Uberlandia, Uberlandia, MG, Brazil.
Leishmaniasis, a disease caused by Leishmania parasites, poses a significant health threat globally, particularly in Latin America and Brazil. Leishmania amazonensis is an important species because it is associated with both cutaneous leishmaniasis and an atypical visceral form. Current treatments are hindered by toxicity, resistance, and high cost, driving the need for new therapeutic targets and drugs.
View Article and Find Full Text PDFProteomics Clin Appl
September 2025
AIBioMed Research Group, Taipei Medical University, Taipei, Taiwan.
Background: Endometrial carcinoma (EC) represents a significant clinical challenge due to its pronounced molecular heterogeneity, directly influencing prognosis and therapeutic responses. Accurate classification of molecular subtypes (CNV-high, CNV-low, MSI-H, POLE) and precise tumor mutational burden (TMB) assessment is crucial for guiding personalized therapeutic interventions. Integrating proteomics data with advanced machine learning (ML) techniques offers a promising strategy for achieving precise, clinically actionable classification and biomarker discovery in EC.
View Article and Find Full Text PDFNan Fang Yi Ke Da Xue Xue Bao
August 2025
Department of Urology, Third Affiliated Hospital of Southern Medical University, Guangzhou 510000, China.
Objectives: To identify immunosuppressive neutrophil subsets in patients with prostate cancer (PCa) and construct a risk prediction model for prognosis and immunotherapy response of the patients based on these neutrophil subsets.
Methods: Single-cell and transcriptome data from PCa patients were collected from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). Neutrophil subsets in PCa were identified through unsupervised clustering, and their biological functions and effects on immune regulation were analyzed by functional enrichment, cell interaction, and pseudo-time series analyses.