Interventions for unexplained infertility: a systematic review and network meta-analysis.

BACKGROUND: Clinical management for unexplained infertility includes expectant management as well as active treatments, including ovarian stimulation (OS), intrauterine insemination (IUI), OS-IUI,  and in vitro fertilisation (IVF) with or without intracytoplasmic sperm injection (ICSI).Existing systematic reviews have conducted head-to-head comparisons of these interventions using pairwise meta-analyses. As this approach allows only the comparison of two interventions at a time and is contingent on the availability of appropriate primary evaluative studies, it is difficult to identify the best intervention in terms of effectiveness and safety. Network meta-analysis compares multiple treatments simultaneously by using both direct and indirect evidence and provides a hierarchy of these treatments, which can potentially better inform clinical decision-making. OBJECTIVES: To evaluate the effectiveness and safety of different approaches to clinical management (expectant management, OS, IUI, OS-IUI, and IVF/ICSI) in couples with unexplained infertility. SEARCH METHODS: We performed a systematic review and network meta-analysis of relevant randomised controlled trials (RCTs). We searched electronic databases including the Cochrane Gynaecology and Fertility Group Specialised Register of Controlled Trials, the Cochrane Central Register of Studies Online, MEDLINE, Embase, PsycINFO and CINAHL, up to 6 September 2018, as well as reference lists, to identify eligible studies. We also searched trial registers for ongoing trials. SELECTION CRITERIA: We included RCTs comparing at least two of the following clinical management options in couples with unexplained infertility: expectant management, OS, IUI, OS-IUI, and IVF (or combined with ICSI). DATA COLLECTION AND ANALYSIS: Two review authors independently screened titles and abstracts identified by the search strategy. We obtained the full texts of potentially eligible studies to assess eligibility and extracted data using standardised forms. The primary effectiveness outcome was a composite of cumulative live birth or ongoing pregnancy, and the primary safety outcome was multiple pregnancy. We performed a network meta-analysis within a random-effects multi-variate meta-analysis model. We presented treatment effects by using odds ratios (ORs) and 95% confidence intervals (CIs). For the network meta-analysis, we used Confidence in Network Meta-analysis (CINeMA) to evaluate the overall certainty of evidence. MAIN RESULTS: We included 27 RCTs (4349 couples) in this systematic review and 24 RCTs (3983 couples) in a subsequent network meta-analysis. Overall, the certainty of evidence was low to moderate: the main limitations were imprecision and/or heterogeneity.Ten RCTs including 2725 couples reported on live birth. Evidence of differences between OS, IUI, OS-IUI, or IVF/ICSI versus expectant management was insufficient (OR 1.01, 95% CI 0.51 to 1.98; low-certainty evidence; OR 1.21, 95% CI 0.61 to 2.43; low-certainty evidence; OR 1.61, 95% CI 0.88 to 2.94; low-certainty evidence; OR 1.88, 95 CI 0.81 to 4.38; low-certainty evidence). This suggests that if the chance of live birth following expectant management is assumed to be 17%, the chance following OS, IUI, OS-IUI, and IVF would be 9% to 28%, 11% to 33%, 15% to 37%, and 14% to 47%, respectively. When only including couples with poor prognosis of natural conception (3 trials, 725 couples) we found OS-IUI and IVF/ICSI increased live birth rate compared to expectant management (OR 4.48, 95% CI 2.00 to 10.1; moderate-certainty evidence; OR 4.99, 95 CI 2.07 to 12.04; moderate-certainty evidence), while there was insufficient evidence of a difference between IVF/ICSI and OS-IUI (OR 1.11, 95% CI 0.78 to 1.60; low-certainty evidence).Eleven RCTs including 2564 couples reported on multiple pregnancy. Compared to expectant management/IUI, OS (OR 3.07, 95% CI 1.00 to 9.41; low-certainty evidence) and OS-IUI (OR 3.34 95% CI 1.09 to 10.29; moderate-certainty evidence) increased the odds of multiple pregnancy, and there was insufficient evidence of a difference between IVF/ICSI and expectant management/IUI (OR 2.66, 95% CI 0.68 to 10.43; low-certainty evidence). These findings suggest that if the chance of multiple pregnancy following expectant management or IUI is assumed to be 0.6%, the chance following OS, OS-IUI, and IVF/ICSI would be 0.6% to 5.0%, 0.6% to 5.4%, and 0.4% to 5.5%, respectively.Trial results show insufficient evidence of a difference between IVF/ICSI and OS-IUI for moderate/severe ovarian hyperstimulation syndrome (OHSS) (OR 2.50, 95% CI 0.92 to 6.76; 5 studies; 985 women; moderate-certainty evidence). This suggests that if the chance of moderate/severe OHSS following OS-IUI is assumed to be 1.1%, the chance following IVF/ICSI would be between 1.0% and 7.2%. AUTHORS' CONCLUSIONS: There is insufficient evidence of differences in live birth between expectant management and the other four interventions (OS, IUI, OS-IUI, and IVF/ICSI). Compared to expectant management/IUI, OS may increase the odds of multiple pregnancy, and OS-IUI probably increases the odds of multiple pregnancy. Evidence on differences between IVF/ICSI and expectant management for multiple pregnancy is insufficient, as is evidence of a difference for moderate or severe OHSS between IVF/ICSI and OS-IUI.

Effect of disease related biases on the subjective assessment of social functioning in Alzheimer's disease and schizophrenia patients.

BACKGROUND: Questionnaires are the current hallmark for quantifying social functioning in human clinical research. In this study, we compared self- and proxy-rated (caregiver and researcher) assessments of social functioning in Schizophrenia (SZ) and Alzheimer's disease (AD) patients and evaluated if the discrepancy between the two assessments is mediated by disease-related factors such as symptom severity. METHODS: We selected five items from the WHO Disability Assessment Schedule 2.0 (WHODAS) to assess social functioning in 53 AD and 61 SZ patients. Caregiver- and researcher-rated assessments of social functioning were used to calculate the discrepancies between self-rated and proxy-rated assessments. Furthermore, we used the number of communication events via smartphones to compare the questionnaire outcomes with an objective measure of social behaviour. RESULTS: WHODAS results revealed that both AD (p < 0.001) and SZ (p < 0.004) patients significantly overestimate their social functioning relative to the assessment of their caregivers and/or researchers. This overestimation is mediated by the severity of cognitive impairments (MMSE; p = 0.019) in AD, and negative symptoms (PANSS; p = 0.028) in SZ. Subsequently, we showed that the proxy scores correlated more strongly with the smartphone communication events of the patient when compared to the patient-rated questionnaire scores (self; p = 0.076, caregiver; p < 0.001, researcher-rated; p = 0.046). CONCLUSION: Here we show that the observed overestimation of WHODAS social functioning scores in AD and SZ patients is partly driven by disease-related biases such as cognitive impairments and negative symptoms, respectively. Therefore, we postulate the development and implementation of objective measures of social functioning that may be less susceptible to such biases.

Sample size for binary logistic prediction models: Beyond events per variable criteria.

Binary logistic regression is one of the most frequently applied statistical approaches for developing clinical prediction models. Developers of such models often rely on an Events Per Variable criterion (EPV), notably EPV ≥10, to determine the minimal sample size required and the maximum number of candidate predictors that can be examined. We present an extensive simulation study in which we studied the influence of EPV, events fraction, number of candidate predictors, the correlations and distributions of candidate predictor variables, area under the ROC curve, and predictor effects on out-of-sample predictive performance of prediction models. The out-of-sample performance (calibration, discrimination and probability prediction error) of developed prediction models was studied before and after regression shrinkage and variable selection. The results indicate that EPV does not have a strong relation with metrics of predictive performance, and is not an appropriate criterion for (binary) prediction model development studies. We show that out-of-sample predictive performance can better be approximated by considering the number of predictors, the total sample size and the events fraction. We propose that the development of new sample size criteria for prediction models should be based on these three parameters, and provide suggestions for improving sample size determination.

A mild treatment strategy for in-vitro fertilisation: a randomised non-inferiority trial.

BACKGROUND: Mild in-vitro fertilisation (IVF) treatment might lessen both patients' discomfort and multiple births, with their associated risks. We aimed to test the hypothesis that mild IVF treatment can achieve the same chance of a pregnancy resulting in term livebirth within 1 year compared with standard treatment, and can also reduce patients' discomfort, multiple pregnancies, and costs. METHODS: We did a randomised, non-inferiority effectiveness trial. 404 patients were randomly assigned to undergo either mild treatment (mild ovarian stimulation with gonadotropin-releasing hormone [GnRH] antagonist co-treatment combined with single embryo transfer) or a standard treatment (stimulation with a GnRH agonist long-protocol and transfer of two embryos). Primary endpoints were proportion of cumulative pregnancies leading to term livebirth within 1 year after randomisation (with a non-inferiority threshold of -12.5%), total costs per couple up to 6 weeks after expected date of delivery, and overall discomfort for patients. Analysis was by intention to treat. This trial is registered as an International Standard Randomised Clinical Trial, number ISRCTN35766970. FINDINGS: The proportions of cumulative pregnancies that resulted in term livebirth after 1 year were 43.4% with mild treatment and 44.7% with standard treatment (absolute number of patients=86 for both groups). The lower limit of the one-sided 95% CI was -9.8%. The proportion of couples with multiple pregnancy outcomes was 0.5% with mild IVF treatment versus 13.1% (p<0.0001) with standard treatment, and mean total costs were 8333 euros and 10745 euros, respectively (difference 2412 euros, 95% CI 703-4131). There were no significant differences between the groups in the anxiety, depression, physical discomfort, or sleep quality of the mother. INTERPRETATION: Over 1 year of treatment, cumulative rates of term livebirths and patients' discomfort are much the same for mild ovarian stimulation with single embryos transferred and for standard stimulation with two embryos transferred. However, a mild IVF treatment protocol can substantially reduce multiple pregnancy rates and overall costs.

Comparing methods to combine functional loss and mortality in clinical trials for amyotrophic lateral sclerosis.

OBJECTIVE: Amyotrophic lateral sclerosis (ALS) clinical trials based on single end points only partially capture the full treatment effect when both function and mortality are affected, and may falsely dismiss efficacious drugs as futile. We aimed to investigate the statistical properties of several strategies for the simultaneous analysis of function and mortality in ALS clinical trials. METHODS: Based on the Pooled Resource Open-Access ALS Clinical Trials (PRO-ACT) database, we simulated longitudinal patterns of functional decline, defined by the revised amyotrophic lateral sclerosis functional rating scale (ALSFRS-R) and conditional survival time. Different treatment scenarios with varying effect sizes were simulated with follow-up ranging from 12 to 18 months. We considered the following analytical strategies: 1) Cox model; 2) linear mixed effects (LME) model; 3) omnibus test based on Cox and LME models; 4) composite time-to-6-point decrease or death; 5) combined assessment of function and survival (CAFS); and 6) test based on joint modeling framework. For each analytical strategy, we calculated the empirical power and sample size. RESULTS: Both Cox and LME models have increased false-negative rates when treatment exclusively affects either function or survival. The joint model has superior power compared to other strategies. The composite end point increases false-negative rates among all treatment scenarios. To detect a 15% reduction in ALSFRS-R decline and 34% decline in hazard with 80% power after 18 months, the Cox model requires 524 patients, the LME model 794 patients, the omnibus test 526 patients, the composite end point 1,274 patients, the CAFS 576 patients and the joint model 464 patients. CONCLUSION: Joint models have superior statistical power to analyze simultaneous effects on survival and function and may circumvent pitfalls encountered by other end points. Optimizing trial end points is essential, as selecting suboptimal outcomes may disguise important treatment clues.

Predictors of mortality in hospital survivors with type 2 diabetes mellitus and acute coronary syndromes.

AIM: To define the predictors of long-term mortality in patients with type 2 diabetes mellitus and recent acute coronary syndrome. METHODS AND RESULTS: A total of 7226 patients from a randomized trial, testing the effect on cardiovascular outcomes of the dual peroxisome proliferator-activated receptor agonist aleglitazar in patients with type 2 diabetes mellitus and recent acute coronary syndrome (AleCardio trial), were analysed. Median follow-up was 2 years. The independent mortality predictors were defined using Cox regression analysis. The predictive information provided by each variable was calculated as percent of total chi-square of the model. All-cause mortality was 4.0%, with cardiovascular death contributing for 73% of mortality. The mortality prediction model included N-terminal proB-type natriuretic peptide (adjusted hazard ratio = 1.68; 95% confidence interval = 1.51-1.88; 27% of prediction), lack of coronary revascularization (hazard ratio = 2.28; 95% confidence interval = 1.77-2.93; 18% of prediction), age (hazard ratio = 1.04; 95% confidence interval = 1.02-1.05; 15% of prediction), heart rate (hazard ratio = 1.02; 95% confidence interval = 1.01-1.03; 10% of prediction), glycated haemoglobin (hazard ratio = 1.11; 95% confidence interval = 1.03-1.19; 8% of prediction), haemoglobin (hazard ratio = 1.01; 95% confidence interval = 1.00-1.02; 8% of prediction), prior coronary artery bypass (hazard ratio = 1.61; 95% confidence interval = 1.11-2.32; 7% of prediction) and prior myocardial infarction (hazard ratio = 1.40; 95% confidence interval = 1.05-1.87; 6% of prediction). CONCLUSION: In patients with type 2 diabetes mellitus and recent acute coronary syndrome, mortality prediction is largely dominated by markers of cardiac, rather than metabolic, dysfunction.

An Application of Sequential Meta-Analysis to Gene Expression Studies.

Most of the discoveries from gene expression data are driven by a study claiming an optimal subset of genes that play a key role in a specific disease. Meta-analysis of the available datasets can help in getting concordant results so that a real-life application may be more successful. Sequential meta-analysis (SMA) is an approach for combining studies in chronological order while preserving the type I error and pre-specifying the statistical power to detect a given effect size. We focus on the application of SMA to find gene expression signatures across experiments in acute myeloid leukemia. SMA of seven raw datasets is used to evaluate whether the accumulated samples show enough evidence or more experiments should be initiated. We found 313 differentially expressed genes, based on the cumulative information of the experiments. SMA offers an alternative to existing methods in generating a gene list by evaluating the adequacy of the cumulative information.

The influence of life events on first and recurrent admissions in bipolar disorder.

BACKGROUND: Life events play an important role in the onset and course of bipolar disorder. We will test the influence of life events on first and recurrent admissions in bipolar disorder and their interaction to test the kindling hypothesis. METHODS: We collected information about life events and admissions across the life span in 51 bipolar patients. We constructed four models to explore the decay of life event effects on admissions. To test their interaction, we used the Andersen-Gill model. RESULTS: The relationship between life events and admissions was best described with a model in which the effects of life events gradually decayed by 25% per year. Both life event load and recurrent admissions significantly increased the risk of both first and subsequent admissions. No significant interaction between life event load and number of admissions was found. CONCLUSIONS: Life events increase the risk of both first and recurrent admissions in bipolar disorder. We found no significant interaction between life events and admissions, but the effect of life events on admissions decreases after the first admission which is in line with the kindling hypothesis.

Behavioral signatures related to genetic disorders in autism.

BACKGROUND: Autism spectrum disorder (ASD) is well recognized to be genetically heterogeneous. It is assumed that the genetic risk factors give rise to a broad spectrum of indistinguishable behavioral presentations. METHODS: We tested this assumption by analyzing the Autism Diagnostic Interview-Revised (ADI-R) symptom profiles in samples comprising six genetic disorders that carry an increased risk for ASD (22q11.2 deletion, Down's syndrome, Prader-Willi, supernumerary marker chromosome 15, tuberous sclerosis complex and Klinefelter syndrome; total n = 322 cases, groups ranging in sample sizes from 21 to 90 cases). We mined the data to test the existence and specificity of ADI-R profiles using a multiclass extension of support vector machine (SVM) learning. We subsequently applied the SVM genetic disorder algorithm on idiopathic ASD profiles from the Autism Genetics Resource Exchange (AGRE). RESULTS: Genetic disorders were associated with behavioral specificity, indicated by the accuracy and certainty of SVM predictions; one-by-one genetic disorder stratifications were highly accurate leading to 63% accuracy of correct genotype prediction when all six genetic disorder groups were analyzed simultaneously. Application of the SVM algorithm to AGRE cases indicated that the algorithm could detect similarity of genetic behavioral signatures in idiopathic ASD subjects. Also, affected sib pairs in the AGRE were behaviorally more similar when they had been allocated to the same genetic disorder group. CONCLUSIONS: Our findings provide evidence for genotype-phenotype correlations in relation to autistic symptomatology. SVM algorithms may be used to stratify idiopathic cases of ASD according to behavioral signature patterns associated with genetic disorders. Together, the results suggest a new approach for disentangling the heterogeneity of ASD.