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Background: Heart failure (HF), affecting 1-4% of adults in industrialized countries, is a major public health priority. Several algorithms based on administrative health data (HAD) have been developed to detect patients with HF in a timely and inexpensive manner, in order to perform real-world studies at the population level. However, their reported diagnostic accuracy is highly variable.
Objective: To assess the diagnostic accuracy of validated HAD-based algorithms for detecting HF, compared to clinical diagnosis, and to investigate causes of heterogeneity.
Methods: We included all diagnostic accuracy studies that utilized HAD for the diagnosis of congestive HF in the general adult population, using clinical examination or chart review as the reference standard. A systematic search of MEDLINE (1946-2023) and Embase (1947-2023) was conducted, without restrictions. The QUADAS-2 tool was employed to assess the risk of bias and concerns regarding applicability. Due to low-quality issues of the primary studies, associated with both the index test and the reference standard definition and conduct, and to the high level of clinical heterogeneity, a quantitative synthesis was not performed. Measures of diagnostic accuracy of the included algorithms were summarized narratively and presented graphically, by population subgroups.
Results: We included 24 studies (161,524 patients) and extracted 36 algorithms. Algorithm selection was based on type of administrative data and DOR. Six studies (103,018 patients, 14 algorithms) were performed in the general outpatient population, with sensitivities ranging from 24.8 to 97.3% and specificities ranging from 35.6 to 99.5%. Eight studies (14,957 patients, 10 algorithms) included hospitalized patients with sensitivities ranging from 29.0 to 96.0% and specificities ranging from 65.8 to 99.2%. The remaining studies included subgroups of the general population or hospitalized patients with cardiologic conditions and were analyzed separately. Fourteen studies had one or more domains at high risk of bias, and there were concerns regarding applicability in 9 studies.
Discussion: The considerable percentage of studies with a high risk of bias, together with the high clinical heterogeneity among different studies, did not allow to generate a pooled estimate of diagnostic accuracy for HAD-based algorithms to be used in an unselected general adult population.
Systematic Review Registration: PROSPERO CRD42023487565.
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http://dx.doi.org/10.1186/s13643-024-02717-8 | DOI Listing |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11668106 | PMC |
Healthc Technol Lett
December 2024
Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems Ulster University, Magee campus Derry∼Londonderry Northern Ireland UK.
Missing Alzheimer's disease (AD) data is prevalent and poses significant challenges for AD diagnosis. Previous studies have explored various data imputation approaches on AD data, but the systematic evaluation of deep learning algorithms for imputing heterogeneous and comprehensive AD data is limited. This study investigates the efficacy of denoising autoencoder-based imputation of missing key features of heterogeneous data that comprised tau-PET, MRI, cognitive and functional assessments, genotype, sociodemographic, and medical history.
View Article and Find Full Text PDFThis paper develops a method for cancer classification from microRNA data using a convolutional neural network (CNN)-based model optimized by genetic algorithm. The convolutional neural network has performed well in various recognition and perception tasks. This paper contributes to the cancer classification using a union of two CNNs.
View Article and Find Full Text PDFInt J Womens Health
December 2024
Department of Ultrasound Imaging, The First Affiliated Hospital of Wenzhou Medical University, WenZhou, ZheJiang, 325000, People's Republic of China.
Objective: To analyse the parameters of shear wave elastography (SWE) and contrast-enhanced ultrasound (CEUS) in breast non-mass-like lesions (NMLs) and to evaluate the added diagnostic value of SWE and CEUS when combined with B-mode ultrasound (US) for differentiating NMLs.
Methods: A total of 118 NMLs from 115 patients underwent US, SWE, and CEUS examinations. The SWE parameter with the highest areas under the receiver operating characteristic (ROC) curves (Az) and independent variables of CEUS obtained by logistic regression were used to adjust the BI-RADS-US (Breast Imaging Reporting and Data System for Ultrasound) classification.
Tex Heart Inst J
December 2024
Department of Neurology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China.
Background: Cardiogenic stroke is associated with substantial morbidity and mortality, necessitating a better understanding of its clinical characteristics for improved patient outcomes. This study aimed to identify clinical characteristics influencing short-term functional prognosis in patients with cardiogenic stroke.
Methods: The study prospectively enrolled 212 patients with cardiogenic stroke, collecting their clinical data and laboratory results.
EClinicalMedicine
January 2025
Malawi-Liverpool Wellcome Programme, Blantyre, Malawi.
Background: Infections and sepsis are leading causes of morbidity and mortality in women during pregnancy and the post-pregnancy period. Using data from the 2017 WHO Global Maternal Sepsis Study, we explored the use of early warning systems (EWS) in women at risk of sepsis-related severe maternal outcomes.
Methods: On April 27, 2023, we searched the literature for EWS in clinical use or research in obstetric populations.
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