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Genomic and biological risk profiling
Published in Ulrik Kihlbom, Mats G. Hansson, Silke Schicktanz, Ethical, Social and Psychological Impacts of Genomic Risk Communication, 2020
Mats G. Hansson, Karim Raza, Marie Falahee
Health risk information based on gene sequencing or the prevalence of different biomarkers is a kind of information for which questions about medicalization may be particularly pertinent. Risk profiling as part of medical practice fits very well within Zola’s remark about the very idea of primary prevention as providing health-related information to individuals in order to convince them to ‘do something now and perhaps at a time when the potential patient feels well or not especially troubled’ (op. cit., p. 493). Undoubtedly, it would be a negative type of medicalization if the information is not actionable and only leads to increased worry, or even wrong actions taken due to inaccurate information based on tests with too low sensitivity or specificity. However, when risk profiling provides opportunities for very early diagnosis with associated preventive treatment or effective changes in lifestyle it would be part of a positive type of medicalization. Potentially, it would even lead to the empowerment of individuals and families receiving this kind of information. However, this depends partly on how ‘empowerment’ is defined.
Post-Esophagectomy (for Esophageal Cancer) Neck Leak
Published in Savio George Barreto, Shailesh V. Shrikhande, Dilemmas in Abdominal Surgery, 2020
W.K. Ooi, S.M. Lagarde, B.P.L. Wijnhoven
Clinical features, together with a raised leukocyte and C-reactive protein level in the patient on day three to four after surgery, were suggestive of all complications including anastomotic leakage. Although the neck wound appeared normal and there were no clinical signs of pneumonia. Our initial assessment was focused on ruling out an anastomotic leak (with an intrathoracic manifestation) as this needs early treatment. The first diagnostic test we favor is a CT thorax with oral contrast. Although the sensitivity and specificity may be high, in our patient this appeared to be falsely negative. Hence an endoscopy was performed given the high index of suspicion for a leak. Endoscopy has better specificity and sensitivity compared to contrast swallow in detecting a leak and simultaneously permits assessment for conduit ischemia, which was negative in this patient. Despite that, the patient’s clinical condition did not improve and thus a reassessment by CT scan was carried out. This CT scan showed features of mediastinitis (the presence of mediastinal free air and fluid (Figure 7.1).
Identifying cases of disease: Clinimetrics and diagnosis
Published in Milos Jenicek, Foundations of Evidence-Based Medicine, 2019
Besides sensitivity, field specificity and predictive values of diagnoses, it is desirable to find tests that give as many true positive results as possible, with a minimum of false positives. The same applies to negative results. Two methods clarify this problem: likelihood ratio and the receiver-operating characteristics curve.
Characteristics of four-limb blood pressure and brachial-ankle pulse wave velocity in Chinese patients with Takayasu arteritis
Published in Blood Pressure, 2022
Yang Chen, Hui Dong, Hong-Wu Li, Yu-Bao Zou, Xiong-Jing Jiang
IBM SPSS Statistics, version 24.0 (IBM Corp., Armonk, NY, USA) was used to analyse the data. The measurement data were expressed as means ± standard deviations. The t-test or Mann–Whitney test was used to compare the measurement data between the two groups according to whether the normal distribution was satisfied. Data are expressed in frequency (percentage) form, and the X2 test was used to compare the counting data between the groups. Sensitivity and specificity define how effectively a test discriminates individuals with disease from those without disease. Sensitivity is the percentage of individuals with a disease who have abnormal test results. Specificity is the percentage of those without disease who have normal test results. The sensitivity and specificity of the test were calculated using the following formulas: Sensitivity = true positive/(true positive + false negative) × 100. Specificity = True negative/(True negative + False positive) × 100. Statistical significance was set at p < 0.05, and p < 0.01 was considered highly significant. The p-values were both bilateral.
Abdominal volume index is a better predictor of visceral fat in patients with type 2 diabetes: a cross-sectional study in Ho municipality, Ghana
Published in Alexandria Journal of Medicine, 2022
Sylvester Yao Lokpo, Wisdom Amenyega, Prosper Doe, James Osei-Yeboah, William KBA Owiredu, Christian Obirikorang, Evans Asamoah Adu, Percival Delali Agordoh, Emmanuel Ativi, Nii Korley Kortei, Samuel Ametepe, Verner Ndiduri Orish
The diagnostic capacities of the three adiposity indices evaluated in this study revealed AVI as the highest performing predictive index of visceral fat, regardless of gender. Thus, among men, the optimal threshold value for AVI, >15.56, demonstrated the highest sensitivity, 87.5% and specificity, 80.71% compared to CI and WHR while among women, the cutoff value for AVI, >18.49 produced the highest sensitivity, 77.05% and specificity, 85.29% (Table 2). Sensitivity and specificity values provide indications of the suitability of a diagnostic tool, and therefore are regarded as important indicators of test accuracy [21]. A highly sensitive test leads to a positive finding in an individual with a disease, while a highly specific test leads to a negative finding in an individual without a disease [22]. There is, however, an inverse relationship between sensitivity and specificity such that as sensitivity increases, specificity tends to decrease, and vice versa [23] but both must be considered to provide a holistic picture of a diagnostic test [24].
Commentary: statistical analysis of 2 × 2 tables in biomarker studies 2) study design and statistical tests
Published in Biomarkers, 2022
Any test is a trade-off between the FP and FN rates. As sensitivity increases, specificity decreases and vice versa (Figure 2, Article 1). The risk of false negatives could be reduced by declaring all cases positive giving a sensitivity of 100%, but with a high number of false positives. This would effectively be an extreme example of the ‘precautionary principle’. (Similarly, declaring all cases negative, would give a specificity of 100% but with a high number of false negatives). Receiver operating characteristic (ROC) curves can be used to identify cut-off points that optimize the sensitivity/specificity balance with quantitative endpoints. The ROC curve is drawn up by plotting the sensitivity (Ψ+, y axis) against the values of (1-specificity) or ((1 – Ψ–), x axis)) over a range of possible cut-off/points thresholds/for the quantitative biomarker.