Long-term safety of unopposed estrogen used by women surviving myocardial infarction : 14 year follow-up of the ESPRIT randomised controlled trial

Peer reviewedPostprin

Rapid Prenatal Diagnosis and Exclusion of Epidermolysis Bullosa Using Novel Antibody Probes

Prenatal diagnosis of recessive dystrophic epidermolysis bullosa was successfully achieved at 19 weeks' gestation by indirect immunofluorescence examination of a fetal skin biopsy sample using the monoclonal antibody LH 7:2. The abortus displayed marked blistering and the diagnosis was confirmed by transmission electron microscopy (TEM). In 3 further pregnancies at risk for lethal junctional epidermolysis bullosa the diagnosis was excluded using the polyclonal antibody AA3. In all these studies the results were available within 4h of receiving the samples. These new techniques offer a quick and simple alternative to TEM for midtrimester prenatal diagnosis of 2 severe recessive forms of epidermolysis bullosa

Susceptibility to multiple cutaneous basal cell carcinomas: significant interactions between glutathione S-transferase GSTM1 genotypes, skin type and male gender.

The factors that determine development of single and multiple primary cutaneous basal cell carcinomas (BCCs) are unclear. We describe a case-control study firstly, to examine the influence of allelism at the glutathione S-transferase GSTM1 and GSTT1 and cytochrome P450 CYP2D6 loci on susceptibility to these tumours and, secondly, to identify interactions between genotypes and relevant individual characteristics, such as skin type and gender. Frequency distributions for GSTM1 genotypes in cases and controls were not different, although the frequency of GSTM1 A/B was significantly lower (P = 0.048) in the multiple BCCs than in controls. We found no significant differences in the frequencies of GSTT1 and CYP2D6 genotypes in cases and controls. Interactions between genotypes were studied by comparing multinomial frequency distributions in mutually exclusive groups. These identified no differences between cases and controls for combinations of the putatively high risk GSTM1 null, GSTT1 null, CYP2D6 EM genotypes. Interactions between GSTM1 A/B and the CYP2D6 PM and GSTT1-positive genotypes were also not different. Frequency distributions of GSTM1 A/B with CYP2D6 EM in controls and multiple BCCs were significantly different (P = 0.033). The proportion of males in the multiple BCC group (61.3%) was greater than in controls (47.0%) and single BCC (52.2%), and the frequency of the combination GSTM1 null/male gender was significantly greater in patients with multiple tumours (P = 0.002). Frequency distributions of GSTM1 null/skin type 1 were also significantly different (P = 0.029) and the proportion of subjects who were GSTM1 null with skin type 1 was greater (P = 0.009) in the multiple BCC group. We examined the data for interactions between GSTM1 null/skin type 1/male gender by comparing frequency distributions of these factors in the single and multiple BCC groups. The distributions were almost significantly different (exact P = 0.051). No significant interactions between GSTT1 null or CYP2D6 EM and skin type 1 were identified. Comparisons of frequency distributions of smoking with the GSTM1 null, GSTT1 null and CYP2D6 EM genotypes identified no differences between patients with single and multiple tumours

Advanced cardiovascular risk prediction in the emergency department: updating a clinical prediction model - a large database study protocol.

From Europe PMC via Jisc Publications RouterHistory: ppub 2021-10-01, epub 2021-10-07Publication status: PublishedFunder: Department of Health; Grant(s): NIHR300246Funder: national institute for health research; Grant(s): NIHR300246BackgroundPatients presenting with chest pain represent a large proportion of attendances to emergency departments. In these patients clinicians often consider the diagnosis of acute myocardial infarction (AMI), the timely recognition and treatment of which is clinically important. Clinical prediction models (CPMs) have been used to enhance early diagnosis of AMI. The Troponin-only Manchester Acute Coronary Syndromes (T-MACS) decision aid is currently in clinical use across Greater Manchester. CPMs have been shown to deteriorate over time through calibration drift. We aim to assess potential calibration drift with T-MACS and compare methods for updating the model.MethodsWe will use routinely collected electronic data from patients who were treated using TMACS at two large NHS hospitals. This is estimated to include approximately 14,000 patient episodes spanning June 2016 to October 2020. The primary outcome of acute myocardial infarction will be sourced from NHS Digital's admitted patient care dataset. We will assess the calibration drift of the existing model and the benefit of updating the CPM by model recalibration, model extension and dynamic updating. These models will be validated by bootstrapping and one step ahead prequential testing. We will evaluate predictive performance using calibrations plots and c-statistics. We will also examine the reclassification of predicted probability with the updated TMACS model.DiscussionCPMs are widely used in modern medicine, but are vulnerable to deteriorating calibration over time. Ongoing refinement using routinely collected electronic data will inevitably be more efficient than deriving and validating new models. In this analysis we will seek to exemplify methods for updating CPMs to protect the initial investment of time and effort. If successful, the updating methods could be used to continually refine the algorithm used within TMACS, maintaining or even improving predictive performance over time.Trial registrationISRCTN number: ISRCTN41008456

Effects of Bariatric Surgery on Human Small Artery Function Evidence for Reduction in Perivascular Adipocyte Inflammation, and the Restoration of Normal Anticontractile Activity Despite Persistent Obesity

ObjectivesThe aim of this study was to investigate the effects of bariatric surgery on small artery function and the mechanisms underlying this.BackgroundIn lean healthy humans, perivascular adipose tissue (PVAT) exerts an anticontractile effect on adjacent small arteries, but this is lost in obesity-associated conditions such as the metabolic syndrome and type II diabetes where there is evidence of adipocyte inflammation and increased oxidative stress.MethodsSegments of small subcutaneous artery and perivascular fat were harvested from severely obese individuals before (n = 20) and 6 months after bariatric surgery (n = 15). Small artery contractile function was examined in vitro with wire myography, and perivascular adipose tissue (PVAT) morphology was assessed with immunohistochemistry.ResultsThe anticontractile activity of PVAT was lost in obese patients before surgery when compared with healthy volunteers and was restored 6 months after bariatric surgery. In vitro protocols with superoxide dismutase and catalase rescued PVAT anticontractile function in tissue from obese individuals before surgery. The improvement in anticontractile function after surgery was accompanied by improvements in insulin sensitivity, serum glycemic indexes, inflammatory cytokines, adipokine profile, and systolic blood pressure together with increased PVAT adiponectin and nitric oxide bioavailability and reduced macrophage infiltration and inflammation. These changes were observed despite the patients remaining severely obese.ConclusionsBariatric surgery and its attendant improvements in weight, blood pressure, inflammation, and metabolism collectively reverse the obesity-induced alteration to PVAT anticontractile function. This reversal is attributable to reductions in local adipose inflammation and oxidative stress with improved adiponectin and nitric oxide bioavailability

Comparison of generalized estimating equations and quadratic inference functions using data from the National Longitudinal Survey of Children and Youth (NLSCY) database

<p>Abstract</p> <p>Background</p> <p>The generalized estimating equations (GEE) technique is often used in longitudinal data modeling, where investigators are interested in population-averaged effects of covariates on responses of interest. GEE involves specifying a model relating covariates to outcomes and a plausible correlation structure between responses at different time periods. While GEE parameter estimates are consistent irrespective of the true underlying correlation structure, the method has some limitations that include challenges with model selection due to lack of absolute goodness-of-fit tests to aid comparisons among several plausible models. The quadratic inference functions (QIF) method extends the capabilities of GEE, while also addressing some GEE limitations.</p> <p>Methods</p> <p>We conducted a comparative study between GEE and QIF via an illustrative example, using data from the "National Longitudinal Survey of Children and Youth (NLSCY)" database. The NLSCY dataset consists of long-term, population based survey data collected since 1994, and is designed to evaluate the determinants of developmental outcomes in Canadian children. We modeled the relationship between hyperactivity-inattention and gender, age, family functioning, maternal depression symptoms, household income adequacy, maternal immigration status and maternal educational level using GEE and QIF. Basis for comparison include: (1) ease of model selection; (2) sensitivity of results to different working correlation matrices; and (3) efficiency of parameter estimates.</p> <p>Results</p> <p>The sample included 795, 858 respondents (50.3% male; 12% immigrant; 6% from dysfunctional families). QIF analysis reveals that gender (male) (odds ratio [OR] = 1.73; 95% confidence interval [CI] = 1.10 to 2.71), family dysfunctional (OR = 2.84, 95% CI of 1.58 to 5.11), and maternal depression (OR = 2.49, 95% CI of 1.60 to 2.60) are significantly associated with higher odds of hyperactivity-inattention. The results remained robust under GEE modeling. Model selection was facilitated in QIF using a goodness-of-fit statistic. Overall, estimates from QIF were more efficient than those from GEE using AR (1) and Exchangeable working correlation matrices (Relative efficiency = 1.1117; 1.3082 respectively).</p> <p>Conclusion</p> <p>QIF is useful for model selection and provides more efficient parameter estimates than GEE. QIF can help investigators obtain more reliable results when used in conjunction with GEE.</p

Siamese Survival Analysis with Competing Risks

Survival analysis in the presence of multiple possible adverse events, i.e., competing risks, is a pervasive problem in many industries (healthcare, finance, etc.). Since only one event is typically observed, the incidence of an event of interest is often obscured by other related competing events. This nonidentifiability, or inability to estimate true cause-specific survival curves from empirical data, further complicates competing risk survival analysis. We introduce Siamese Survival Prognosis Network (SSPN), a novel deep learning architecture for estimating personalized risk scores in the presence of competing risks. SSPN circumvents the nonidentifiability problem by avoiding the estimation of cause-specific survival curves and instead determines pairwise concordant time-dependent risks, where longer event times are assigned lower risks. Furthermore, SSPN is able to directly optimize an approximation to the C-discrimination index, rather than relying on well-known metrics which are unable to capture the unique requirements of survival analysis with competing risks