No association between exacerbation frequency and stroke in patients with COPD.

BACKGROUND: Patients with chronic obstructive pulmonary disease (COPD) have a higher risk of stroke than the general population. Chronic inflammation associated with COPD is thought to contribute to this risk. Exacerbations of COPD are associated with a rise in inflammation, suggesting that there may be an association between exacerbation frequency and the risk of stroke. This study examined that association. METHODS: Using the UK Clinical Practice Research Datalink, COPD patients with a first stroke between January 2004 and December 2013 were identified as cases and matched on age, sex, and general practice to controls with COPD but without a stroke (6,441 cases and 19,323 controls). Frequent exacerbators (FEs) were defined as COPD patients with ≥2 exacerbations, and infrequent exacerbators (IEs) have ≤1 exacerbation in the year prior to their stroke. Conditional logistic regression was used to estimate the association between exacerbation frequency and stroke overall, and by stroke subtype (hemorrhagic, ischemic, or transient ischemic attack). Exacerbations were also categorized into 0, 1, 2, or ≥3 exacerbations in the year prior to stroke. RESULTS: There was no evidence that FE had an increased odds of stroke compared to IE (OR [odds ratio] =0.95, 95% CI [confidence interval] =0.89-1.01). There was strong evidence that the risk of stroke decreased with each exacerbation of COPD experienced per year (P trend =0.003). In the subgroup analysis investigating stroke subtype, FE had 33% lower odds of hemorrhagic stroke than IE (OR =0.67, 95% CI =0.51-0.88, P=0.003). No association was found within other stroke types. CONCLUSION: This study found no evidence of a difference in the odds of stroke between IE and FE, suggesting that exacerbation frequency is unlikely to be the reason for increased stroke risk among COPD patients. Further research is needed to explore the association through investigation of stroke risk and the severity, duration, treatment of exacerbations, and concurrent treatment of cardiovascular risk factors

Identifying clinically important COPD sub-types using data-driven approaches in primary care population based electronic health records.

BACKGROUND: COPD is a highly heterogeneous disease composed of different phenotypes with different aetiological and prognostic profiles and current classification systems do not fully capture this heterogeneity. In this study we sought to discover, describe and validate COPD subtypes using cluster analysis on data derived from electronic health records. METHODS: We applied two unsupervised learning algorithms (k-means and hierarchical clustering) in 30,961 current and former smokers diagnosed with COPD, using linked national structured electronic health records in England available through the CALIBER resource. We used 15 clinical features, including risk factors and comorbidities and performed dimensionality reduction using multiple correspondence analysis. We compared the association between cluster membership and COPD exacerbations and respiratory and cardiovascular death with 10,736 deaths recorded over 146,466 person-years of follow-up. We also implemented and tested a process to assign unseen patients into clusters using a decision tree classifier. RESULTS: We identified and characterized five COPD patient clusters with distinct patient characteristics with respect to demographics, comorbidities, risk of death and exacerbations. The four subgroups were associated with 1) anxiety/depression; 2) severe airflow obstruction and frailty; 3) cardiovascular disease and diabetes and 4) obesity/atopy. A fifth cluster was associated with low prevalence of most comorbid conditions. CONCLUSIONS: COPD patients can be sub-classified into groups with differing risk factors, comorbidities, and prognosis, based on data included in their primary care records. The identified clusters confirm findings of previous clustering studies and draw attention to anxiety and depression as important drivers of the disease in young, female patients

Mortality after admission for heart failure in the UK compared with Japan

Objective Mortality amongst patients hospitalised for heart failure (HHF) in Western and Asian countries may differ, but this has not been investigated using individual patient-level data (IPLD). We sought to remedy this through rigorous statistical analysis of HHF registries and variable selection from a systematic literature review.Methods and results IPLD from registries of HHF in Japan (n=3781) and the UK (n=894) were obtained. A systematic literature review identified 23 models for predicting outcome of HHF. Five variables appearing in 10 or more reports were strongly related to prognosis (systolic blood pressure, serum sodium concentration, age, blood urea nitrogen and creatinine). To compare mortality in the UK and Japan, variables were imputed in a propensity model using inverse probability of treatment weighting (IPTW) and IPTW with logistic regression (doubly robust IPTW). Overall, patients in the UK were sicker and in-patient and post-discharge mortalities were greater, suggesting that the threshold for hospital admission was higher. Covariate-adjusted in-hospital mortality was similar in the UK and Japan (IPTW OR: 1.14, 95% CI 0.70 to 1.86), but 180-day postdischarge mortality was substantially higher in the UK (doubly robust IPTW OR: 2.33, 95% CI 1.58 to 3.43).Conclusions Despite robust methods to adjust for differences in patient characteristics and disease severity, HHF patients in the UK have roughly twice the mortality at 180 days compared with those in Japan. Similar analyses should be done using other data sets and in other countries to determine the consistency of these findings and identify factors that might inform healthcare policy and improve outcomes

Non-communicable diseases in sub-Saharan Africa: a scoping review of large cohort studies.

BACKGROUND: Non-communicable diseases (NCDs) cause a large and growing burden of morbidity and mortality in sub-Saharan Africa. Prospective cohort studies are key to study multiple risk factors and chronic diseases and are crucial to our understanding of the burden, aetiology and prognosis of NCDs in SSA. We aimed to identify the level of research output on NCDs and their risk factors collected by cohorts in SSA. METHODS: We conducted a scoping review to map the extent of current NCDs research in SSA by identifying studies published after the year 2000 using prospectively collected cohort data on any of the six NCDs (cardiovascular diseases, diabetes, obesity, chronic kidney disease, chronic respiratory diseases, and cancers), ≥1 major risk factor (other than age and sex), set only within SSA, enrolled ≥500 participants, and ≥12 months of follow-up with ≥2 data collection points (or with plans to). We performed a systematic search of databases, a manual search of references lists from included articles and the INDEPTH network website, and study investigators from SSA were contacted for further articles. RESULTS: We identified 30 cohort studies from the 101 included articles. Eighteen countries distributed in West, Central, East and Southern Africa, were represented. The majority (27%) set in South Africa. There were three studies including children, twenty with adults, and seven with both. 53% of cohorts were sampled in general populations, 47% in clinical populations, and 1 occupational cohort study. Hypertension (n = 23) was most commonly reported, followed by obesity (n = 16), diabetes (n = 15), CKD (n = 6), COPD (n = 2), cervical cancer (n = 3), and breast cancer (n = 1). The majority (n = 22) reported data on at least one demographic/environmental, lifestyle, or physiological risk factor but these data varied greatly. CONCLUSIONS: Most studies collected data on a combination of hypertension, diabetes, and obesity and few studies collected data on respiratory diseases and cancer. Although most collected data on different risk factors the methodologies varied greatly. Several methodological limitations were found including low recruitment rate, low retention rate, and lack of validated and standardized data collection. Our results could guide potential collaborations and maximize impact to improve our global understanding of NCDs (and their risk factors) in SSA and also to inform future research, as well as policies

Intraoperative in vivo confocal laser endomicroscopy imaging at glioma margins: can we detect tumor infiltration?

OBJECTIVE Confocal laser endomicroscopy (CLE) is a US Food and Drug Administration-cleared intraoperative real-time fluorescence-based cellular resolution imaging technology that has been shown to image brain tumor histoarchitecture rapidly in vivo during neuro-oncological surgical procedures. An important goal for successful intraoperative implementation is in vivo use at the margins of infiltrating gliomas. However, CLE use at glioma margins has not been well studied. METHODS Matching in vivo CLE images and tissue biopsies acquired at glioma margin regions of interest (ROIs) were collected from 2 institutions. All images were reviewed by 4 neuropathologists experienced in CLE. A scoring system based on the pathological features was implemented to score CLE and H&E images from each ROI on a scale from 0 to 5. Based on the H&E scores, all ROIs were divided into a low tumor probability (LTP) group (scores 0-2) and a high tumor probability (HTP) group (scores 3-5). The concordance between CLE and H&E scores regarding tumor probability was determined. The intraclass correlation coefficient (ICC) and diagnostic performance were calculated. RESULTS Fifty-six glioma margin ROIs were included for analysis. Interrater reliability of the scoring system was excellent when used for H&E images (ICC [95% CI] 0.91 [0.86-0.94]) and moderate when used for CLE images (ICC [95% CI] 0.69 [0.40-0.83]). The ICCs (95% CIs) of the LTP group (0.68 [0.40-0.83]) and HTP group (0.68 [0.39-0.83]) did not differ significantly. The concordance between CLE and H&E scores was 61.6%. The sensitivity and specificity values of the scoring system were 79% and 37%. The positive predictive value (PPV) and negative predictive value were 65% and 53%, respectively. Concordance, sensitivity, and PPV were greater in the HTP group than in the LTP group. Specificity was higher in the newly diagnosed group than in the recurrent group. CONCLUSIONS CLE may detect tumor infiltration at glioma margins. However, it is not currently dependable, especially in scenarios where low probability of tumor infiltration is expected. The proposed scoring system has excellent intrinsic interrater reliability, but its interrater reliability is only moderate when used with CLE images. These results suggest that this technology requires further exploration as a method for consistent actionable intraoperative guidance with high dependability across the range of tumor margin scenarios. Specific-binding and/or tumor-specific fluorophores, a CLE image atlas, and a consensus guideline for image interpretation may help with the translational utility of CLE