A Systematic Literature Review of the Humanistic Burden of COPD

Background: Chronic obstructive pulmonary disease (COPD) is a leading cause of morbidity and mortality worldwide, causing substantial economic and social burden. Objective: This review assessed the patient-reported humanistic burden associated with moderate to very severe COPD, specifically the impact on health-related quality of life (HRQoL), symptoms, limitations in daily life, and emotional implications, through the use of HRQoL instruments. Methods: A systematic review was conducted to retrieve relevant clinical data from published literature using a representative sample of countries where healthcare systems provide wide availability of COPD medications and/or universal coverage includes respiratory medicines (Australia, Canada, China, France, Germany, Italy, Spain, the UK, and the USA). The primary inclusion criteria were patients with moderate to very severe COPD. HRQoL was quantified with non-disease-specific and disease-specific questionnaires. Results: In total, 82 studies from 95 publications presented HRQoL data from patients with moderate to very severe COPD. Patient-reported HRQoL declined with worsening airflow limitation, advancing GOLD group, and increasing exacerbation frequency. Both increasing frequency of hospitalization for COPD exacerbations and recurrent hospitalization adversely impacted HRQoL. Comorbidity incidence was higher in patients with increased airflow limitation. It was associated with a further decline in HRQoL and increased depression and anxiety, particularly as disease-associated pain worsened. Physical activity improved HRQoL over time. Conclusion: This review highlighted the impact of exacerbations and associated hospitalizations on the humanistic burden of COPD. These findings underline the importance of managing COPD actively, including prompt and appropriate use of pharmacological and non-pharmacological therapies that can improve symptoms and reduce the risk of exacerbations, thereby lessening the humanistic burden. Future reviews could consider a broader range of countries and publications to further assess the humanistic impact of COPD in low- and middle-income economies

A gene risk score using missense variants in SLCO1B1 is associated with earlier onset statin intolerance

Background and aims The efficacy of statin therapy is hindered by intolerance to the therapy, leading to discontinuation. Variants in SLCO1B1, which encodes the hepatic transporter OATB1B1, influence statin pharmacokinetics, resulting in altered plasma concentrations of the drug and its metabolites. Current pharmacogenetic guidelines require sequencing of the SLCO1B1 gene, which is more expensive and less accessible than genotyping. In this study, we aimed to develop an easy, clinically implementable functional gene risk score (GRS) of common variants in SLCO1B1 to identify patients at risk of statin intolerance. Methods and results A GRS was developed from four common variants in SLCO1B1. In statin users from Tayside, Scotland, UK, those with a high-risk GRS had increased odds across three phenotypes of statin intolerance [general statin intolerance (GSI): ORGSI 2.42; 95% confidence interval (CI): 1.29–4.31, P = 0.003; statin-related myopathy: ORSRM 2.51; 95% CI: 1.28–4.53, P = 0.004; statin-related suspected rhabdomyolysis: ORSRSR 2.85; 95% CI: 1.03–6.65, P = 0.02]. In contrast, using the Val174Ala genotype alone or the recommended OATP1B1 functional phenotypes produced weaker and less reliable results. A meta-analysis with results from adjudicated cases of statin-induced myopathy in the PREDICTION-ADR Consortium confirmed these findings (ORVal174Ala 1.99; 95% CI: 1.01–3.95, P = 0.048; ORGRS 1.76; 95% CI: 1.16–2.69, P = 0.008). For those requiring high-dose statin therapy, the high-risk GRS was more consistently associated with the time to onset of statin intolerance amongst the three phenotypes compared with Val174Ala (GSI: HRVal174Ala 2.49; 95% CI: 1.09–5.68, P = 0.03; HRGRS 2.44; 95% CI: 1.46–4.08, P < 0.001). Finally, sequence kernel association testing confirmed that rare variants in SLCO1B1 are associated with the risk of intolerance (P = 0.02). Conclusion We provide evidence that a GRS based on four common SLCO1B1 variants provides an easily implemented genetic tool that is more reliable than the current recommended practice in estimating the risk and predicting early-onset statin intolerance

Competing risks analysis for neutrophil to lymphocyte ratio as a predictor of diabetic retinopathy incidence in the Scottish population

Background Diabetic retinopathy (DR) is a major sight-threatening microvascular complication in individuals with diabetes. Systemic inflammation combined with oxidative stress is thought to capture most of the complexities involved in the pathology of diabetic retinopathy. A high level of neutrophil–lymphocyte ratio (NLR) is an indicator of abnormal immune system activity. Current estimates of the association of NLR with diabetes and its complications are almost entirely derived from cross-sectional studies, suggesting that the nature of the reported association may be more diagnostic than prognostic. Therefore, in the present study, we examined the utility of NLR as a biomarker to predict the incidence of DR in the Scottish population. Methods The incidence of DR was defined as the time to the first diagnosis of R1 or above grade in the Scottish retinopathy grading scheme from type 2 diabetes diagnosis. The effect of NLR and its interactions were explored using a competing risks survival model adjusting for other risk factors and accounting for deaths. The Fine and Gray subdistribution hazard model (FGR) was used to predict the effect of NLR on the incidence of DR. Results We analysed data from 23,531 individuals with complete covariate information. At 10 years, 8416 (35.8%) had developed DR and 2989 (12.7%) were lost to competing events (death) without developing DR and 12,126 individuals did not have DR. The median (interquartile range) level of NLR was 2.04 (1.5 to 2.7). The optimal NLR cut-off value to predict retinopathy incidence was 3.04. After accounting for competing risks at 10 years, the cumulative incidence of DR and deaths without DR were 50.7% and 21.9%, respectively. NLR was associated with incident DR in both Cause-specific hazard (CSH = 1.63; 95% CI: 1.28–2.07) and FGR models the subdistribution hazard (sHR = 2.24; 95% CI: 1.70–2.94). Both age and HbA1c were found to modulate the association between NLR and the risk of DR. Conclusions The current study suggests that NLR has a promising potential to predict DR incidence in the Scottish population, especially in individuals less than 65 years and in those with well-controlled glycaemic status

Use of Advanced Flexible Modeling Approaches for Survival Extrapolation from Early Follow-up Data in two Nivolumab Trials in Advanced NSCLC with Extended Follow-up

Objectives: Immuno-oncology (IO) therapies are often associated with delayed responses that are deep and durable, manifesting as long-term survival benefits in patients with metastatic cancer. Complex hazard functions arising from IO treatments may limit the accuracy of extrapolations from standard parametric models (SPMs). We evaluated the ability of flexible parametric models (FPMs) to improve survival extrapolations using data from 2 trials involving patients with non–small-cell lung cancer (NSCLC). Methods: Our analyses used consecutive database locks (DBLs) at 2-, 3-, and 5-y minimum follow-up from trials evaluating nivolumab versus docetaxel in patients with pretreated metastatic squamous (CheckMate-017) and nonsquamous (CheckMate-057) NSCLC. For each DBL, SPMs, as well as 3 FPMs—landmark response models (LRMs), mixture cure models (MCMs), and Bayesian multiparameter evidence synthesis (B-MPES)—were estimated on nivolumab overall survival (OS). The performance of each parametric model was assessed by comparing milestone restricted mean survival times (RMSTs) and survival probabilities with results obtained from externally validated SPMs. Results: For the 2- and 3-y DBLs of both trials, all models tended to underestimate 5-y OS. Predictions from nonvalidated SPMs fitted to the 2-y DBLs were highly unreliable, whereas extrapolations from FPMs were much more consistent between models fitted to successive DBLs. For CheckMate-017, in which an apparent survival plateau emerges in the 3-y DBL, MCMs fitted to this DBL estimated 5-y OS most accurately (11.6% v. 12.3% observed), and long-term predictions were similar to those from the 5-y validated SPM (20-y RMST: 30.2 v. 30.5 mo). For CheckMate-057, where there is no clear evidence of a survival plateau in the early DBLs, only B-MPES was able to accurately predict 5-y OS (14.1% v. 14.0% observed [3-y DBL]). Conclusions: We demonstrate that the use of FPMs for modeling OS in NSCLC patients from early follow-up data can yield accurate estimates for RMST observed with longer follow-up and provide similar long-term extrapolations to externally validated SPMs based on later data cuts. B-MPES generated reasonable predictions even when fitted to the 2-y DBLs of the studies, whereas MCMs were more reliant on longer-term data to estimate a plateau and therefore performed better from 3 y. Generally, LRM extrapolations were less reliable than those from alternative FPMs and validated SPMs but remained superior to nonvalidated SPMs. Our work demonstrates the potential benefits of using advanced parametric models that incorporate external data sources, such as B-MPES and MCMs, to allow for accurate evaluation of treatment clinical and cost-effectiveness from trial data with limited follow-up. Flexible advanced parametric modeling methods can provide improved survival extrapolations for immuno-oncology cost-effectiveness in health technology assessments from early clinical trial data that better anticipate extended follow-up. Advantages include leveraging additional observable trial data, the systematic integration of external data, and more detailed modeling of underlying processes. Bayesian multiparameter evidence synthesis performed particularly well, with well-matched external data. Mixture cure models also performed well but may require relatively longer follow-up to identify an emergent plateau, depending on the specific setting. Landmark response models offered marginal benefits in this scenario and may require greater numbers in each response group and/or increased follow-up to support improved extrapolation within each subgroup

Deep learning to automate the labelling of head MRI datasets for computer vision applications

OBJECTIVES: The purpose of this study was to build a deep learning model to derive labels from neuroradiology reports and assign these to the corresponding examinations, overcoming a bottleneck to computer vision model development. METHODS: Reference-standard labels were generated by a team of neuroradiologists for model training and evaluation. Three thousand examinations were labelled for the presence or absence of any abnormality by manually scrutinising the corresponding radiology reports ('reference-standard report labels'); a subset of these examinations (n = 250) were assigned 'reference-standard image labels' by interrogating the actual images. Separately, 2000 reports were labelled for the presence or absence of 7 specialised categories of abnormality (acute stroke, mass, atrophy, vascular abnormality, small vessel disease, white matter inflammation, encephalomalacia), with a subset of these examinations (n = 700) also assigned reference-standard image labels. A deep learning model was trained using labelled reports and validated in two ways: comparing predicted labels to (i) reference-standard report labels and (ii) reference-standard image labels. The area under the receiver operating characteristic curve (AUC-ROC) was used to quantify model performance. Accuracy, sensitivity, specificity, and F1 score were also calculated. RESULTS: Accurate classification (AUC-ROC > 0.95) was achieved for all categories when tested against reference-standard report labels. A drop in performance (ΔAUC-ROC > 0.02) was seen for three categories (atrophy, encephalomalacia, vascular) when tested against reference-standard image labels, highlighting discrepancies in the original reports. Once trained, the model assigned labels to 121,556 examinations in under 30 min. CONCLUSIONS: Our model accurately classifies head MRI examinations, enabling automated dataset labelling for downstream computer vision applications. KEY POINTS: • Deep learning is poised to revolutionise image recognition tasks in radiology; however, a barrier to clinical adoption is the difficulty of obtaining large labelled datasets for model training. • We demonstrate a deep learning model which can derive labels from neuroradiology reports and assign these to the corresponding examinations at scale, facilitating the development of downstream computer vision models. • We rigorously tested our model by comparing labels predicted on the basis of neuroradiology reports with two sets of reference-standard labels: (1) labels derived by manually scrutinising each radiology report and (2) labels derived by interrogating the actual images

Prostate-specific antigen and hormone receptor expression in male and female breast carcinoma

<p>Abstract</p> <p>Background</p> <p>Prostate carcinoma is among the most common solid tumors to secondarily involve the male breast. Prostate specific antigen (PSA) and prostate-specific acid phosphatase (PSAP) are expressed in benign and malignant prostatic tissue, and immunohistochemical staining for these markers is often used to confirm the prostatic origin of metastatic carcinoma. PSA expression has been reported in male and female breast carcinoma and in gynecomastia, raising concerns about the utility of PSA for differentiating prostate carcinoma metastasis to the male breast from primary breast carcinoma. This study examined the frequency of PSA, PSAP, and hormone receptor expression in male breast carcinoma (MBC), female breast carcinoma (FBC), and gynecomastia.</p> <p>Methods</p> <p>Immunohistochemical staining for PSA, PSAP, AR, ER, and PR was performed on tissue microarrays representing six cases of gynecomastia, thirty MBC, and fifty-six FBC.</p> <p>Results</p> <p>PSA was positive in two of fifty-six FBC (3.7%), focally positive in one of thirty MBC (3.3%), and negative in the five examined cases of gynecomastia. PSAP expression was absent in MBC, FBC, and gynecomastia. Hormone receptor expression was similar in males and females (AR 74.1% in MBC vs. 67.9% in FBC, p = 0.62; ER 85.2% vs. 68.5%, p = 0.18; and PR 51.9% vs. 48.2%, p = 0.82).</p> <p>Conclusions</p> <p>PSA and PSAP are useful markers to distinguish primary breast carcinoma from prostate carcinoma metastatic to the male breast. Although PSA expression appeared to correlate with hormone receptor expression, the incidence of PSA expression in our population was too low to draw significant conclusions about an association between PSA expression and hormone receptor status in breast lesions.</p

Awareness of islamic banking products among muslims: The case of Australia

© The Editor(s) (if applicable) and the Author(s) 2016. The concept of interest-free financing was practiced by Arabs prior to the advent of Islam, and was later adopted by Muslims as an acceptable form of trade financing. While the system had been used on a small scale for centuries, its commercial application began in the 1970s.1 Since then Islamic financing has experienced worldwide acceptance, and by early 2003 there were at least 176 Islamic banks around the world, with deposits in excess of $147bn

A priori postulated and real power in cluster randomized trials: mind the gap

BACKGROUND: Cluster randomization design is increasingly used for the evaluation of health-care, screening or educational interventions. The intraclass correlation coefficient (ICC) defines the clustering effect and be specified during planning. The aim of this work is to study the influence of the ICC on power in cluster randomized trials. METHODS: Power contour graphs were drawn to illustrate the loss in power induced by an underestimation of the ICC when planning trials. We also derived the maximum achievable power given a specified ICC. RESULTS: The magnitude of the ICC can have a major impact on power, and with low numbers of clusters, 80% power may not be achievable. CONCLUSION: Underestimating the ICC during planning cluster randomized trials can lead to a seriously underpowered trial. Publication of a priori postulated and a posteriori estimated ICCs is necessary for a more objective reading: negative trial results may be the consequence of a loss of power due to a mis-specification of the ICC

Population Attributable Risk of Unintentional Childhood Poisoning in Karachi Pakistan

Background: The percentage of unintentional childhood poisoning cases in a given population attributable to specific risk factors (i.e., the population attributable risk) which can be calculated, determination of such risk factors associated with potentially modifiable risk factors, are necessary to focus on the prevention strategies. Methods: We calculated PARs, using 120 cases with unintentional poisoning and 360 controls in a hospital based matched case- control study. The risk factors were accessibility to hazardous chemicals and medicines due to unsafe storage, child behavior reported as hyperactive, storage of kerosene and petroleum in soft drink bottles, low socioeconomic class, less education of the mother and the history of previous poisoning. Results: The Following Attrubuted Risks Were Observed: 12% (95% confidence interval [CI] = 8%-16%) for both chemicals and medicines stored unsafe, 19% (15%-23%) for child reported as hyperactive, 40% (38%-42%) for storage of kerosene and petroleum in soft drink bottles, 48% (42%-54%) for low socioeconomic status, 38% (32%-42%) for no formal mothers education and 5.8% (2%-10%) for history of previous poisoning. 48% of cases for overall study population which could be attributed to at least one of the six risk factors. Among girls, this proportion was 23% and 43% among boys. About half of the unintentional childhood poisoning cases in this Pakistani population could be avoided. Conclusion: Exposure to potentially modifiable risk indicators explained about half of the cases of unintentional poisoning among children under five years of age in this Pakistani population, indicating the theoretical scope for prevention of the disease