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Biochemia Medica Oct 2023Diabetic kidney disease (DKD) is one of the most common microvascular complications of both type 1 and type 2 diabetes and the most common cause of the end-stage renal... (Review)
Review
Diabetic kidney disease (DKD) is one of the most common microvascular complications of both type 1 and type 2 diabetes and the most common cause of the end-stage renal disease (ESRD). It has been evidenced that targeted interventions at an early stage of DKD can efficiently prevent or delay the progression of kidney failure and improve patient outcomes. Therefore, regular screening for DKD has become one of the fundamental principles of diabetes care. Long-established biomarkers such as serum-creatinine-based estimates of glomerular filtration rate and albuminuria are currently the cornerstone of diagnosis and risk stratification in routine clinical practice. However, their immanent biological limitations and analytical variations may influence the clinical interpretation of the results. Recently proposed new predictive equations without the variable of race, together with the evidence on better accuracy of combined serum creatinine and cystatin C equations, and both race- and sex-free cystatin C-based equation, have enabled an improvement in the detection of DKD, but also require the harmonization of the recommended laboratory tests, wider availability of cystatin C testing and specific approach in various populations. Considering the complex pathophysiology of DKD, particularly in type 2 diabetes, a panel of biomarkers is needed to classify patients in terms of the rate of disease progression and/or response to specific interventions. With a personalized approach to diagnosis and treatment, in the future, it will be possible to respond to DKD better and enable improved outcomes for numerous patients worldwide.
Topics: Humans; Diabetic Nephropathies; Diabetes Mellitus, Type 2; Cystatin C; Glomerular Filtration Rate; Biomarkers
PubMed: 37545693
DOI: 10.11613/BM.2023.030501 -
The Lancet. Digital Health Mar 2024Ovarian cancer is the most lethal gynecological malignancy. Timely diagnosis of ovarian cancer is difficult due to the lack of effective biomarkers. Laboratory tests are...
BACKGROUND
Ovarian cancer is the most lethal gynecological malignancy. Timely diagnosis of ovarian cancer is difficult due to the lack of effective biomarkers. Laboratory tests are widely applied in clinical practice, and some have shown diagnostic and prognostic relevance to ovarian cancer. We aimed to systematically evaluate the value of routine laboratory tests on the prediction of ovarian cancer, and develop a robust and generalisable ensemble artificial intelligence (AI) model to assist in identifying patients with ovarian cancer.
METHODS
In this multicentre, retrospective cohort study, we collected 98 laboratory tests and clinical features of women with or without ovarian cancer admitted to three hospitals in China during Jan 1, 2012 and April 4, 2021. A multi-criteria decision making-based classification fusion (MCF) risk prediction framework was used to make a model that combined estimations from 20 AI classification models to reach an integrated prediction tool developed for ovarian cancer diagnosis. It was evaluated on an internal validation set (3007 individuals) and two external validation sets (5641 and 2344 individuals). The primary outcome was the prediction accuracy of the model in identifying ovarian cancer.
FINDINGS
Based on 52 features (51 laboratory tests and age), the MCF achieved an area under the receiver-operating characteristic curve (AUC) of 0·949 (95% CI 0·948-0·950) in the internal validation set, and AUCs of 0·882 (0·880-0·885) and 0·884 (0·882-0·887) in the two external validation sets. The model showed higher AUC and sensitivity compared with CA125 and HE4 in identifying ovarian cancer, especially in patients with early-stage ovarian cancer. The MCF also yielded acceptable prediction accuracy with the exclusion of highly ranked laboratory tests that indicate ovarian cancer, such as CA125 and other tumour markers, and outperformed state-of-the-art models in ovarian cancer prediction. The MCF was wrapped as an ovarian cancer prediction tool, and made publicly available to provide estimated probability of ovarian cancer with input laboratory test values.
INTERPRETATION
The MCF model consistently achieved satisfactory performance in ovarian cancer prediction when using laboratory tests from the three validation sets. This model offers a low-cost, easily accessible, and accurate diagnostic tool for ovarian cancer. The included laboratory tests, not only CA125 which was the highest ranked laboratory test in importance of diagnostic assistance, contributed to the characterisation of patients with ovarian cancer.
FUNDING
Ministry of Science and Technology of China; National Natural Science Foundation of China; Natural Science Foundation of Guangdong Province, China; and Science and Technology Project of Guangzhou, China.
TRANSLATION
For the Chinese translation of the abstract see Supplementary Materials section.
Topics: Humans; Female; Artificial Intelligence; Retrospective Studies; Ovarian Neoplasms; Prognosis; ROC Curve
PubMed: 38212232
DOI: 10.1016/S2589-7500(23)00245-5 -
Neurology and Therapy Aug 2023Alzheimer's disease (AD) is a disease continuum from pathophysiologic, biomarker and clinical perspectives. With the advent of advanced technologies, diagnosing and...
INTRODUCTION
Alzheimer's disease (AD) is a disease continuum from pathophysiologic, biomarker and clinical perspectives. With the advent of advanced technologies, diagnosing and managing patients is evolving.
METHODS
A systematic literature review (SLR) of practice guidelines for mild cognitive impairment (MCI) and AD dementia was performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). This systematic literature review (SLR) aimed to summarize current clinical practice guidelines for screening, testing, diagnosis, treatment and monitoring in the AD continuum. The results of this SLR were used to propose a way forward for practice guidelines given the possible introduction of biomarker-guided technology using blood- or plasma-based assays and disease-modifying treatments (DMTs) targeted for early disease.
RESULTS
53 clinical practice guidelines were identified, 15 of which were published since 2018. Screening for asymptomatic populations was not recommended. Biomarker testing was not included in routine diagnostic practice. There was no consensus on which neurocognitive tests to use to diagnose and monitor MCI or AD dementia. Pharmacologic therapies were not recommended for MCI, while cholinesterase inhibitors and memantine were recommended for AD treatment.
DISCUSSION
The pre-2018 and post-2018 practice guidelines share similar recommendations for screening, diagnosis and treatment. However, once DMTs are approved, clinicians will require guidance on the appropriate use of DMTs in a clinical setting. This guidance should include strategies for identifying eligible patients and evaluating the DMT benefit-to-risk profile to facilitate shared decision-making among physicians, patients and care partners.
CONCLUSION
Regular evidence-based updates of existing guidelines for the AD continuum are required over the coming decades to integrate rapidly evolving technologic and medical scientific advances and bring emerging approaches for management of early disease into clinical practice. This will pave the way toward biomarker-guided identification and targeted treatment and the realization of precision medicine for AD.
PubMed: 37261607
DOI: 10.1007/s40120-023-00504-6