Lifestyle index for mortality prediction using multiple ageing cohorts in the USA, UK and Europe

Current mortality prediction indexes are mainly based on functional morbidity and comorbidity, with limited information for risk prevention. This study aimed to develop and validate a modifiable lifestyle-based mortality predication index for older adults. Data from 51,688 participants (56% women) aged ≥50 years in 2002 Health and Retirement Study, 2002 English Longitudinal Study of Ageing and 2004 Survey of Health Ageing and Retirement in Europe were used to estimate coefficients of the index with cohort-stratified Cox regression. Models were validated across studies and compared to the Lee index (having comorbid and morbidity predictors). Over an average of 11-year follow-up, 10,240 participants died. The lifestyle index includes smoking, drinking, exercising, sleep quality, BMI, sex and age; showing adequate model performance in internal validation (C-statistic 0.79; D-statistic 1.94; calibration slope 1.13) and in all combinations of internal-external cross-validation. It outperformed Lee index (e.g. differences in C-statistic = 0.01, D-statistic = 0.17, P < 0.001) consistently across health status. The lifestyle index stratified participants into varying mortality risk groups, with those in the top quintile having 13.5% excess absolute mortality risk over 10 years than those in the bottom 50th centile. Our lifestyle index with easy-assessable behavioural factors and improved generalizability may maximize its usability for personalized risk management

Minimum sample size for external validation of a clinical prediction model with a binary outcome

In prediction model research, external validation is needed to examine an existing model's performance using data independent to that for model development. Current external validation studies often suffer from small sample sizes and consequently imprecise predictive performance estimates. To address this, we propose how to determine the minimum sample size needed for a new external validation study of a prediction model for a binary outcome. Our calculations aim to precisely estimate calibration (Observed/Expected and calibration slope), discrimination (C-statistic), and clinical utility (net benefit). For each measure, we propose closed-form and iterative solutions for calculating the minimum sample size required. These require specifying: (i) target SEs (confidence interval widths) for each estimate of interest, (ii) the anticipated outcome event proportion in the validation population, (iii) the prediction model's anticipated (mis)calibration and variance of linear predictor values in the validation population, and (iv) potential risk thresholds for clinical decision-making. The calculations can also be used to inform whether the sample size of an existing (already collected) dataset is adequate for external validation. We illustrate our proposal for external validation of a prediction model for mechanical heart valve failure with an expected outcome event proportion of 0.018. Calculations suggest at least 9835 participants (177 events) are required to precisely estimate the calibration and discrimination measures, with this number driven by the calibration slope criterion, which we anticipate will often be the case. Also, 6443 participants (116 events) are required to precisely estimate net benefit at a risk threshold of 8%. Software code is provided.</p

External validation, update and development of prediction models for pre-eclampsia using an Individual Participant Data (IPD) meta-analysis: the International Prediction of Pregnancy Complication Network (IPPIC pre-eclampsia) protocol.

Background: Pre-eclampsia, a condition with raised blood pressure and proteinuria is associated with an increased risk of maternal and offspring mortality and morbidity. Early identification of mothers at risk is needed to target management. Methods/design: We aim to systematically review the existing literature to identify prediction models for pre-eclampsia. We have established the International Prediction of Pregnancy Complication Network (IPPIC), made up of 72 researchers from 21 countries who have carried out relevant primary studies or have access to existing registry databases, and collectively possess data from more than two million patients. We will use the individual participant data (IPD) from these studies to externally validate these existing prediction models and summarise model performance across studies using random-effects meta-analysis for any, late (after 34 weeks) and early (before 34 weeks) onset pre-eclampsia. If none of the models perform well, we will recalibrate (update), or develop and validate new prediction models using the IPD. We will assess the differential accuracy of the models in various settings and subgroups according to the risk status. We will also validate or develop prediction models based on clinical characteristics only; clinical and biochemical markers; clinical and ultrasound parameters; and clinical, biochemical and ultrasound tests. Discussion: Numerous systematic reviews with aggregate data meta-analysis have evaluated various risk factors separately or in combination for predicting pre-eclampsia, but these are affected by many limitations. Our large-scale collaborative IPD approach encourages consensus towards well developed, and validated prognostic models, rather than a number of competing non-validated ones. The large sample size from our IPD will also allow development and validation of multivariable prediction model for the relatively rare outcome of early onset pre-eclampsia. Trial registration: The project was registered on Prospero on the 27 November 2015 with ID: CRD42015029349

Proximity to overhead power lines and childhood leukaemia: an international pooled analysis

© 2018, Cancer Research UK. Background: Although studies have consistently found an association between childhood leukaemia risk and magnetic fields, the associations between childhood leukaemia and distance to overhead power lines have been inconsistent. We pooled data from multiple studies to assess the association with distance and evaluate whether it is due to magnetic fields or other factors associated with distance from lines. Methods: We present a pooled analysis combining individual-level data (29,049 cases and 68,231 controls) from 11 record-based studies. Results: There was no material association between childhood leukaemia and distance to nearest overhead power line of any voltage. Among children living < 50 m from 200 + kV power lines, the adjusted odds ratio for childhood leukaemia was 1.33 (95% CI: 0.92–1.93). The odds ratio was higher among children diagnosed before age 5 years. There was no association with calculated magnetic fields. Odds ratios remained unchanged with adjustment for potential confounders. Conclusions: In this first comprehensive pooled analysis of childhood leukaemia and distance to power lines, we found a small and imprecise risk for residences < 50 m of 200 + kV lines that was not explained by high magnetic fields. Reasons for the increased risk, found in this and many other studies, remains to be elucidated

The widening spectrum of C9ORF72-related disease; genotype/phenotype correlations and potential modifiers of clinical phenotype

The GGGGCC (G4C2) repeat expansion in C9ORF72 is the most common cause of familial amyotrophic lateral sclerosis (ALS), frontotemporal lobar dementia (FTLD) and ALS–FTLD, as well as contributing to sporadic forms of these diseases. Screening of large cohorts of ALS and FTLD cohorts has identified that C9ORF72-ALS is represented throughout the clinical spectrum of ALS phenotypes, though in comparison with other genetic subtypes, C9ORF72 carriers have a higher incidence of bulbar onset disease. In contrast, C9ORF72-FTLD is predominantly associated with behavioural variant FTD, which often presents with psychosis, most commonly in the form of hallucinations and delusions. However, C9ORF72 expansions are not restricted to these clinical phenotypes. There is a higher than expected incidence of parkinsonism in ALS patients with C9ORF72 expansions, and the G4C2 repeat has also been reported in other motor phenotypes, such as primary lateral sclerosis, progressive muscular atrophy, corticobasal syndrome and Huntington-like disorders. In addition, the expansion has been identified in non-motor phenotypes including Alzheimer’s disease and Lewy body dementia. It is not currently understood what is the basis of the clinical variation seen with the G4C2 repeat expansion. One potential explanation is repeat length. Sizing of the expansion by Southern blotting has established that there is somatic heterogeneity, with different expansion lengths in different tissues, even within the brain. To date, no correlation with expansion size and clinical phenotype has been established in ALS, whilst in FTLD only repeat size in the cerebellum was found to correlate with disease duration. Somatic heterogeneity suggests there is a degree of instability within the repeat and evidence of anticipation has been reported with reducing age of onset in subsequent generations. This variability/instability in expansion length, along with its interactions with environmental and genetic modifiers, such as TMEM106B, may be the basis of the differing clinical phenotypes arising from the mutation

The Spectrum of C9orf72-mediated Neurodegeneration and Amyotrophic Lateral Sclerosis

The discovery that a hexanucleotide repeat expansion in C9orf72 is the most numerous genetic variant of both amyotrophic lateral sclerosis and frontotemporal dementia has opened a rapidly growing field, which may provide long hoped for advances in the understanding and treatment of these devastating diseases. In this review we describe the various phenotypes, clinical and pathological, associated with expansion of C9orf72, which go beyond amyotrophic lateral sclerosis and frontotemporal dementia to include neurodegeneration more broadly. Next we take a step back and summarize the current understanding of the C9orf72 expansion and its protein products at a molecular level. Three mechanisms are prominent: toxicity mediated directly by RNA transcribed from the repeat; toxicity mediated by dipeptide repeat proteins translated from the repeat sequence; and haploinsufficiency resulting from reduced transcription of the C9orf72 exonic sequence. A series of exciting advances have recently described how dipeptide repeat proteins might interfere with the normal role of the nucleolus in maturation of RNA binding proteins and in production of ribosomes. Importantly, these mechanisms are unlikely to be mutually exclusive. We draw attention to the fact that clinical and pathological similarities to other genetic variants without a repeat expansion must not be overlooked in ascribing a pathogenic mechanism to C9orf72-disease. Finally, with a view to impact on patient care, we discuss current practice with respect to genetic screening in patients with and without a family history of disease, and the most promising developments towards therapy that have been reported to date

Summarising and validating test accuracy results across multiple studies for use in clinical practice.

Following a meta-analysis of test accuracy studies, the translation of summary results into clinical practice is potentially problematic. The sensitivity, specificity and positive (PPV) and negative (NPV) predictive values of a test may differ substantially from the average meta-analysis findings, because of heterogeneity. Clinicians thus need more guidance: given the meta-analysis, is a test likely to be useful in new populations, and if so, how should test results inform the probability of existing disease (for a diagnostic test) or future adverse outcome (for a prognostic test)? We propose ways to address this. Firstly, following a meta-analysis, we suggest deriving prediction intervals and probability statements about the potential accuracy of a test in a new population. Secondly, we suggest strategies on how clinicians should derive post-test probabilities (PPV and NPV) in a new population based on existing meta-analysis results and propose a cross-validation approach for examining and comparing their calibration performance. Application is made to two clinical examples. In the first example, the joint probability that both sensitivity and specificity will be >80% in a new population is just 0.19, because of a low sensitivity. However, the summary PPV of 0.97 is high and calibrates well in new populations, with a probability of 0.78 that the true PPV will be at least 0.95. In the second example, post-test probabilities calibrate better when tailored to the prevalence in the new population, with cross-validation revealing a probability of 0.97 that the observed NPV will be within 10% of the predicted NPV

Multivariate meta-analysis of individual participant data helped externally validate the performance and implementation of a prediction model

OBJECTIVES: Our aim was to improve meta-analysis methods for summarizing a prediction model's performance when individual participant data are available from multiple studies for external validation. STUDY DESIGN AND SETTING: We suggest multivariate meta-analysis for jointly synthesizing calibration and discrimination performance, while accounting for their correlation. The approach estimates a prediction model's average performance, the heterogeneity in performance across populations, and the probability of "good" performance in new populations. This allows different implementation strategies (e.g., recalibration) to be compared. Application is made to a diagnostic model for deep vein thrombosis (DVT) and a prognostic model for breast cancer mortality. RESULTS: In both examples, multivariate meta-analysis reveals that calibration performance is excellent on average but highly heterogeneous across populations unless the model's intercept (baseline hazard) is recalibrated. For the cancer model, the probability of "good" performance (defined by C statistic ≥0.7 and calibration slope between 0.9 and 1.1) in a new population was 0.67 with recalibration but 0.22 without recalibration. For the DVT model, even with recalibration, there was only a 0.03 probability of "good" performance. CONCLUSION: Multivariate meta-analysis can be used to externally validate a prediction model's calibration and discrimination performance across multiple populations and to evaluate different implementation strategies