Melanoma Epidemiology, Risk Factors, and Clinical Phenotypes

Osteoarthritis (OA) is a disorder of synovial joints and is characterized by the combination of focal hyaline cartilage loss and accompanying subchondral bone remodelling and marginal new bone formation (osteophyte). It has genetic, constitutional, and environmental risk factors and presents a spectrum of clinical phenotypes and outcomes. OA commonly affects just one region (e.g. knee OA, hip OA). However, multiple hand interphalangeal joint OA, usually accompanied by posterolateral firm swellings (nodes), is a marker for a tendency towards polyarticular ‘generalized nodal OA’.

Download Full-text

Risk Factors and Clinical Phenotypes Associated with Severity in Patients with COVID-19 in Northeast Mexico

Vector-Borne and Zoonotic Diseases ◽

10.1089/vbz.2021.0016 ◽

2021 ◽

Author(s):

Francisco Javier García-Alvarado ◽

Melisa Alejandra Muñoz-Hernández ◽

Elida Moran Guel ◽

Marisela del Rocío González-Martínez ◽

Maritza Argelia Macías Corral ◽

...

Keyword(s):

Risk Factors ◽

Clinical Phenotypes

Download Full-text

Deploying unsupervised clustering analysis to derive clinical phenotypes and risk factors associated with mortality risk in 2022 critically ill patients with COVID-19 in Spain

Critical Care ◽

10.1186/s13054-021-03487-8 ◽

2021 ◽

Vol 25 (1) ◽

Author(s):

Alejandro Rodríguez ◽

◽

Manuel Ruiz-Botella ◽

Ignacio Martín-Loeches ◽

María Jimenez Herrera ◽

...

Keyword(s):

Risk Factors ◽

Critically Ill ◽

Mortality Risk ◽

Critically Ill Patients ◽

Clustering Analysis ◽

Unsupervised Clustering ◽

Older Age ◽

Icu Mortality ◽

Factors Associated ◽

Clinical Phenotypes

Abstract Background The identification of factors associated with Intensive Care Unit (ICU) mortality and derived clinical phenotypes in COVID-19 patients could help for a more tailored approach to clinical decision-making that improves prognostic outcomes. Methods Prospective, multicenter, observational study of critically ill patients with confirmed COVID-19 disease and acute respiratory failure admitted from 63 ICUs in Spain. The objective was to utilize an unsupervised clustering analysis to derive clinical COVID-19 phenotypes and to analyze patient’s factors associated with mortality risk. Patient features including demographics and clinical data at ICU admission were analyzed. Generalized linear models were used to determine ICU morality risk factors. The prognostic models were validated and their performance was measured using accuracy test, sensitivity, specificity and ROC curves. Results The database included a total of 2022 patients (mean age 64 [IQR 5–71] years, 1423 (70.4%) male, median APACHE II score (13 [IQR 10–17]) and SOFA score (5 [IQR 3–7]) points. The ICU mortality rate was 32.6%. Of the 3 derived phenotypes, the A (mild) phenotype (537; 26.7%) included older age (< 65 years), fewer abnormal laboratory values and less development of complications, B (moderate) phenotype (623, 30.8%) had similar characteristics of A phenotype but were more likely to present shock. The C (severe) phenotype was the most common (857; 42.5%) and was characterized by the interplay of older age (> 65 years), high severity of illness and a higher likelihood of development shock. Crude ICU mortality was 20.3%, 25% and 45.4% for A, B and C phenotype respectively. The ICU mortality risk factors and model performance differed between whole population and phenotype classifications. Conclusion The presented machine learning model identified three clinical phenotypes that significantly correlated with host-response patterns and ICU mortality. Different risk factors across the whole population and clinical phenotypes were observed which may limit the application of a “one-size-fits-all” model in practice.

Download Full-text

Clinical phenotypes of atrial fibrillation and risk of mortality: a cluster analysis

European Heart Journal ◽

10.1093/ehjci/ehaa946.2893 ◽

2020 ◽

Vol 41 (Supplement_2) ◽

Author(s):

D Pastori ◽

E Antonucci ◽

A Milanese ◽

F Violi ◽

P Pignatelli ◽

...

Keyword(s):

Risk Factors ◽

Heart Failure ◽

Cluster Analysis ◽

Risk Scores ◽

Peripheral Artery ◽

Clinical Phenotypes ◽

Risk Of Mortality ◽

All Cause Mortality ◽

Artery Disease ◽

Cluster 2

Abstract Background Patients with atrial fibrillation (AF) experience a high mortality rate despite optimal antithrombotic treatment. Characteristics of AF patients at higher mortality risk have been barely described so far and no risk score has been specifically developed at this aim. Furthermore, a clinical approach based on risk scores present some limits such as to not consider some important risk factors for mortality, and many available scores have poor predictive value. Cluster analysis may play a role in overcoming limitations of risk scores, especially in the case of overlapping risk factors. Purpose To identify of clinical phenotypes by using an unbiased statistical approach, such as the cluster analysis. Methods Cluster analysis was used to identify clinical phenotypes of AF patients associated with all-cause mortality in 5,171 AF patients from the START registry. Clinical variables used for the analysis were age, sex, diabetes, previous cerebrovascular events, previous cardiovascular events, heart failure, peripheral artery disease, use of non-vitamin K oral anticoagulants, cancer, pulmonary disease, smoking habit, previous major bleeding. The risk of all-cause mortality in each cluster was analyzed. Results We identified 4 clusters (Figure 1). Cluster 1 was composed by youngest patients, with obesity and paroxysmal AF; Cluster 2 by patients with low cardiovascular risk factors and high proportion of cancer; Cluster 3 by men with diabetes and coronary and peripheral artery disease, a high proportion of thrombocytopenia, and a high use of aspirin, proton pump inhibitors, and statins; Cluster 4 included the oldest patients, mainly women, with previous cerebrovascular disease, persistent/ permanent AF, heart failure, kidney disease and anemia. In this cluster there was the highest use of digoxin and NOACs. During 9856,84 patient/years of observation, 386 deaths (3.92%/year) occurred. Mortality rates significantly increased across clusters: 0.42%/year (cluster 1, reference group), 2.12%/year (cluster 2, adjusted hazard ratio [aHR] 3.306, 95% confidence interval [CI] 1.204–9.077, p=0.020), 4.41%/year (cluster 3, aHR 6.702, 95% CI 2.433–18.461, p<0.001) and 8.71%/year (cluster 4, aHR 8.927, 95% CI 3.238–24.605, p<0.001). Conclusions We identified different clinical phenotypes of AF patients by cluster analysis which were specifically associated with mortality. This approach may help identify patients at higher risk of mortality. Figure 1 Funding Acknowledgement Type of funding source: None

Download Full-text

Epidemiology and Risk Factors of Melanoma: A Review

Dermatology Practical & Conceptual ◽

10.5826/dpc.11s1a161s ◽

2021 ◽

pp. 2021161S

Author(s):

Claudio Conforti ◽

Iris Zalaudek

Keyword(s):

Risk Factors ◽

Positive Impact ◽

Age Groups ◽

Pediatric Age ◽

Familial Melanoma ◽

Dysplastic Nevus ◽

Factors Associated ◽

Dysplastic Nevus Syndrome ◽

Increased Risk ◽

Melanoma Epidemiology

We are currently witnessing a worldwide increase in the incidence of melanoma. Incidence in Europe is about 25 cases per 100,000 population, while in Australia it reaches a rate of 60 new cases per 100,000. While the epidemiological curves of the 1980's and 1990's suggested an increase in the incidence of melanoma across all age groups, the last 10 years’ data indicates a 5% reduction in the incidence of thin melanoma in young individuals aged between 15 and 24. This suggests a positive impact of primary prevention campaigns [1-2]. The risk factors associated with melanoma are different and multifactorial: on one hand, there is a genetic predisposition, as evidenced by the increased risk in patients with dysplastic nevus syndrome, with familial melanoma or familial melanoma syndromes; on the other hand, the unprotected interaction between UV rays and phototypes I-II increases the risk of developing melanoma, especially in case of sunburns in pediatric age. This review aims to summarize melanoma epidemiology and risk factors.

Download Full-text

Skin melanoma: Epidemiology, etiology, risk factors, prevention

Zdravstvena zastita ◽

10.5937/zz1204053p ◽

2012 ◽

Vol 41 (6) ◽

pp. 53-60

Author(s):

Marko Panajotović ◽

Rade Panajotović ◽

Ljubomir Panajotović

Keyword(s):

Risk Factors ◽

Skin Melanoma ◽

Melanoma Epidemiology

Download Full-text

Risk Factors for Clinical Phenotypes of Crohn's Disease of the Ileal Pouch

The American Journal of Gastroenterology ◽

10.1111/j.1572-0241.2006.00838.x ◽

2006 ◽

Vol 101 (12) ◽

pp. 2760-2768 ◽

Cited By ~ 85

Author(s):

Bo Shen ◽

Victor W. Fazio ◽

Feza H. Remzi ◽

Ana E. Bennett ◽

Aaron Brzezinski ◽

...

Keyword(s):

Risk Factors ◽

Crohn’S Disease ◽

Crohn's Disease ◽

Ileal Pouch ◽

Clinical Phenotypes

Download Full-text

Deploying unsupervised clustering analysis to derive clinical phenotypes and risk factors associated with mortality risk in 2,022 critically ill patients with COVID-19 in Spain

10.21203/rs.3.rs-125422/v2 ◽

2021 ◽

Author(s):

Alejandro Rodríguez ◽

Manuel Ruiz Botella ◽

Ignacio Matín-Loeches ◽

María Jiménez Herrera ◽

Jordi Solé-Violan ◽

...

Keyword(s):

Risk Factors ◽

Intensive Care ◽

Critically Ill ◽

Mortality Risk ◽

Critically Ill Patients ◽

Clustering Analysis ◽

Older Age ◽

Icu Mortality ◽

Factors Associated ◽

Clinical Phenotypes

Abstract Background: The identification of factors associated with Intensive Care Unit (ICU) mortality and derived clinical phenotypes in COVID-19 patients could help for a more tailored approach to clinical decision-making that improves prognostic outcomes. Methods: Prospective, multicenter, observational study of critically ill patients with confirmed COVID-19 disease and acute respiratory failure admitted from 63 Intensive Care Units(ICU) in Spain. The objective was to utilize an unsupervised clustering analysis to derive clinical COVID-19 phenotypes and to analyze patient’s factors associated with mortality risk. Patient features including demographics and clinical data at ICU admission were analyzed. Generalized linear models were used to determine ICU morality risk factors. The prognostic models were validated and their performance was measured using accuracy test, sensitivity, specificity and ROC curves. Results: The database included a total of 2,022 patients (mean age 64[IQR5-71] years, 1423(70.4%) male, median APACHE II score (13[IQR10-17]) and SOFA score (5[IQR3-7]) points. The ICU mortality rate was 32.6%. Of the 3 derived phenotypes, the A(mild) phenotype (537;26.7%) included older age (<65 years), fewer abnormal laboratory values and less development of complications, B (moderate) phenotype (623,30.8%) had similar characteristics of A phenotype but were more likely to present shock. The C(severe) phenotype was the most common (857;42.5%) and was characterized by the interplay of older age (>65 years), high severity of illness and a higher likelihood of development shock. Crude ICU mortality was 20.3%, 25% and 45.4% for A, B and C phenotype respectively. The ICU mortality risk factors and model performance differed between whole population and phenotype classifications.Conclusion: The presented machine learning model identified three clinical phenotypes that significantly correlated with host-response patterns and ICU mortality. Different risk factors across the whole population and clinical phenotypes were observed which may limit the application of a “one-size-fits-all” model in practice.

Download Full-text