Nejvíce citovaný článek - PubMed ID 32067896
Gender Differences in Contribution of Smoking, Low Physical Activity, and High BMI to Increased Risk of Early Reoperation After TKA
Analysing complex datasets while maintaining the interpretability and explainability of outcomes for clinicians and patients is challenging, not only in viral infections. These datasets often include a variety of heterogeneous clinical, demographic, laboratory, and personal data, and it is not a single factor but a combination of multiple factors that contribute to patient characterisation and host response. Therefore, multivariate approaches are needed to analyse these complex patient datasets, which are impossible to analyse with univariate comparisons (e.g., one immune cell subset versus one clinical factor). Using a SARS-CoV-2 infection as an example, we employed a patient similarity network (PSN) approach to assess the relationship between host immune factors and the clinical course of infection and performed visualisation and data interpretation. A PSN analysis of ~85 immunological (cellular and humoral) and ~70 clinical factors in 250 recruited patients with coronavirus disease (COVID-19) who were sampled four to eight weeks after a PCR-confirmed SARS-CoV-2 infection identified a minimal immune signature, as well as clinical and laboratory factors strongly associated with disease severity. Our study demonstrates the benefits of implementing multivariate network approaches to identify relevant factors and visualise their relationships in a SARS-CoV-2 infection, but the model is generally applicable to any complex dataset.
- Klíčová slova
- COVID-19 severity, IgM and IgG levels, data visualisation, minimal immune signature, multivariate data analysis, patient similarity network,
- MeSH
- COVID-19 * MeSH
- lidé MeSH
- protilátky virové MeSH
- SARS-CoV-2 * MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- Názvy látek
- protilátky virové MeSH
Timely and accurate assessments of the factors influencing satisfaction, a key indicator of success in primary total knee arthroplasty (TKA), may help improve TKA outcomes. Here we performed the longitudinal trend analysis of relation between satisfaction and 12 postoperative factors, which positively or negatively influence the patient satisfaction 2 years after TKA. In a real-world registry cohort (women/men: 1121/650), we showed similarities and differences between women and men in the contribution of postoperative factors to satisfaction 2 years after TKA as assessed by odds-ratio-similarity network. In men, the strongest negative factors were pain and complications, followed by mechanical problems. In women, the strongest negative factors were the pain and knee instability, followed by other mechanical problems, complications and low levels of sports activity. In both sexes, physical activity and the Knee Society Score (general and functional) influenced positively satisfaction; long-distance walking was associated with satisfaction only in women. A trend analysis revealed a reduction in the strength of satisfaction-related factors over 2 years of check-ups, particularly in women. Our study demonstrates that the key check-up for assessing the evolution of satisfaction in the 2 years after TKA was at 3 months in both sexes.
BACKGROUND: The burden of chronic and societal diseases is affected by many risk factors that can change over time. The minimalisation of disease-associated risk factors may contribute to long-term health. Therefore, new data-driven health management should be used in clinical decision-making in order to minimise future individual risks of disease and adverse health effects. METHODS: We aimed to develop a health trajectories (HT) management methodology based on electronic health records (EHR) and analysing overlapping groups of patients who share a similar risk of developing a particular disease or experiencing specific adverse health effects. Formal concept analysis (FCA) was applied to identify and visualise overlapping patient groups, as well as for decision-making. To demonstrate its capabilities, the theoretical model presented uses genuine data from a local total knee arthroplasty (TKA) register (a total of 1885 patients) and shows the influence of step by step changes in five lifestyle factors (BMI, smoking, activity, sports and long-distance walking) on the risk of early reoperation after TKA. RESULTS: The theoretical model of HT management demonstrates the potential of using EHR data to make data-driven recommendations to support both patients' and physicians' decision-making. The model example developed from the TKA register acts as a clinical decision-making tool, built to show surgeons and patients the likelihood of early reoperation after TKA and how the likelihood changes when factors are modified. The presented data-driven tool suits an individualised approach to health management because it quantifies the impact of various combinations of factors on the early reoperation rate after TKA and shows alternative combinations of factors that may change the reoperation risk. CONCLUSION: This theoretical model introduces future HT management as an understandable way of conceiving patients' futures with a view to positively (or negatively) changing their behaviour. The model's ability to influence beneficial health care decision-making to improve patient outcomes should be proved using various real-world data from EHR datasets.
- Klíčová slova
- Clinical decision-making tool, Early reoperation, Electronic health record, Formal concept analysis, Health trajectory, Lifestyle factors, Precision health, Precision medicine, Revision rate, Total knee arthroplasty,
- MeSH
- individualizovaná medicína * MeSH
- klinické rozhodování MeSH
- lidé MeSH
- reoperace MeSH
- teoretické modely MeSH
- totální endoprotéza kolene * MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
The tissue microenvironment in chronic lymphocytic leukaemia (CLL) plays a key role in the pathogenesis of CLL, but the complex blood microenvironment in CLL has not yet been fully characterised. Therefore, immunophenotyping of circulating immune cells in 244 CLL patients and 52 healthy controls was performed using flow cytometry and analysed by multivariate Patient Similarity Networks (PSNs). Our study revealed high inter-individual heterogeneity in the distribution and activation of bystander immune cells in CLL, depending on the bulk of the CLL cells. High CLL counts were associated with low activation on circulating monocytes and T cells and vice versa. The highest activation of immune cells, particularly of intermediate and non-classical monocytes, was evident in patients treated with novel agents. PSNs revealed a low activation of immune cells in CLL progression, irrespective of IgHV status, Binet stage and TP53 disruption. Patients with high intermediate monocytes (> 5.4%) with low activation were 2.5 times more likely (95% confidence interval 1.421-4.403, P = 0.002) to had shorter time-to-treatment than those with low monocyte counts. Our study demonstrated the association between the activation of circulating immune cells and the bulk of CLL cells. The highest activation of bystander immune cells was detected in patients with slow disease course and in those treated with novel agents. The subset of intermediate monocytes showed predictive value for time-to-treatment in CLL.
- MeSH
- biologické modely MeSH
- chronická lymfatická leukemie krev imunologie patologie MeSH
- dospělí MeSH
- lidé středního věku MeSH
- lidé MeSH
- nádorové mikroprostředí imunologie MeSH
- senioři MeSH
- Check Tag
- dospělí MeSH
- lidé středního věku MeSH
- lidé MeSH
- mužské pohlaví MeSH
- senioři MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
