BackgroundMobile phones with operating systems and capable of running applications (smartphones) are increasingly being used in clinical settings. Medical calculating applications are popular mhealth apps for smartphones. These include, for example, apps that calculate the severity or likelihood of disease-based clinical scoring systems, such as determining the severity of liver disease, the likelihood of having a pulmonary embolism, and risk stratification in acute coronary syndrome. However, the accuracy of these apps has not been assessed.ObjectiveThe objective of this study was to evaluate the accuracy of smartphone-based medical calculation apps.MethodsA broad search on Google Play, BlackBerry World, and the iTunes App Store was conducted to find medical calculation apps for smartphones. The list of apps was narrowed down based on inclusion and exclusion criteria focusing on functions thought to be relevant by a panel of general internists (number of functions =13). Ten case values were inputted for each function and were compared to manual calculations. For each case, the correct answer was assigned a score of 1. A score for the 10 cases was calculated based on the accuracy of the results for each function on each app.ResultsWe tested 14 apps and 13 functions for each app if that function was available. We conducted 10 cases for each function for a total of 1240 tests. Most functions tested on the apps were accurate in their results with an overall accuracy of 98.6% (17 errors in 1240 tests). In all, 6 of 14 (43%) apps had 100% accuracy. Although 11 of 13 (85%) functions had perfect accuracy, there were issues with 2 functions: the Child-Pugh scores and Model for End-Stage Liver Disease (MELD) scores on 8 apps. Approximately half of the errors were clinically significant resulting in a significant change in prognosis (8/17, 47%).ConclusionsThe results suggest that most medical calculating apps provide accurate and reliable results. The free apps that were 100% accurate and contained the most functions desired by internists were CliniCalc, Calculate by QxMD, and Medscape. When using medical calculating apps, the answers will likely be accurate; however, it is important to be careful when calculating MELD scores or Child-Pugh scores on some apps. Despite the few errors found, greater scrutiny is warranted to ensure full accuracy of smartphone medical calculator apps.
IMPORTANCE Impaired skin barrier and aberrant immune function in atopic dermatitis (AD) may alter immune response to malignant cancer. Conflicting data exist on the risk of cancer in patients with AD.OBJECTIVE To assess the risk of noncutaneous and cutaneous cancers in patients with AD compared with the general population without AD. DATA SOURCES Studies identified from searches of MEDLINE and Embase that were published from 1946 and 1980, respectively, to January 3, 2019. The following search terms were used: [(exp NEOPLASMS/ OR neoplas*.tw. OR tumo*.tw. OR cancer*.tw. OR malignanc*.tw.) AND (exp Dermatitis, Atopic/ OR (atopic adj1 (dermatit* or neurodermatit*)).tw. OR eczema.tw. OR disseminated OR neurodermatit*.tw.)]. STUDY SELECTION Included were observational studies (cohort and case-control designs) reporting a risk estimate for cancer in patients with AD compared with a control group (general population or patients without AD).DATA EXTRACTION AND SYNTHESIS Two independent reviewers extracted data and assessed the risk of bias using the Risk of Bias in Nonrandomized Studies of Interventions (ROBINS-I) assessment tool, modified for observational exposure studies. Data were pooled using a random-effects model and expressed as standardized incidence ratios (SIRs) or odds ratios (ORs) with 95% CIs. Heterogeneity was assessed using the Cochrane Q statistic and the I 2 statistic. MAIN OUTCOMES AND MEASURESThe main outcome of the study was risk of cancer measured by SIRs or ORs. RESULTSThis systematic review and meta-analysis included 8 population-based cohort studies (n = 5 726 692 participants) and 48 case-control studies (n = 114 136 participants). Among cohort studies, a statistically significant association was found between AD and keratinocyte carcinoma (5 studies; pooled SIR, 1.46; 95% CI, 1.20-1.77) as well as cancers of the kidney (2 studies; pooled SIR, 1.86; 95% CI, 1.14-3.04), central nervous system (2 studies; pooled SIR, 1.81; 95% CI, 1.22-2.70), and pancreas (1 study; SIR, 1.90; 95% CI, 1.03-3.50). Among 48 case-control studies, pooled effects showed patients with AD had statistically significantly lower odds of central nervous system cancers (15 studies; pooled OR, 0.76; 95% CI, 0.70-0.82) and pancreatic cancer (5 studies; pooled OR, 0.81; 95% CI, 0.66-0.98), contrary to the higher incidence found in cohort studies. Case-control studies also demonstrated lower odds of lung and respiratory system cancers (4 studies; pooled OR, 0.61; 95% CI, 0.45-0.82). No evidence of association was found between AD and other cancer types, including melanoma. There was substantial heterogeneity between studies for many other cancers, which precluded pooling of data, and there was moderate to serious risk of bias among included studies. CONCLUSIONS AND RELEVANCEObservational evidence suggests potential associations between AD and increased risk of keratinocyte carcinoma and kidney cancer as well as lower odds of lung and respiratory system cancers. Further research is needed to address the heterogeneity and limitatio...
Obesity is associated with the production of inflammatory cytokines that are implicated in insulin resistance (IR), and if not addressed, can lead to type 2 diabetes (T2D). The role of the immune system in skeletal muscle (SM) inflammation and insulin sensitivity is not yet well characterized. As SM IR is an important determinant of glycaemia, it is critical that the muscle-immune phenotype is mapped to help design interventions to target T2D. This systematic review synthesized the evidence for SM macrophage content and phenotype in humans and murine models of obesity, and the association of muscle macrophage content and phenotype with IR. Results were synthesized narratively, as we were unable to conduct a meta-analysis. We included 28 studies (n=10 human, n=18 murine), and all studies detected macrophage markers in SM. Macrophage content was positively associated with IR. In humans and mice, there was variability in muscle macrophage content and phenotype in obesity.Overall certainty in the evidence was low due to heterogeneity in detection methods and incompleteness of data reporting. Macrophages are detected in human and murine SM in obesity and a positive association between macrophage content and IR is noted; however, the standardization of markers, detection methods, and reporting of study details is warranted to accurately characterize macrophages and improve the potential for creating specific and targeted immune-based therapies in obesity.
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