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

Clinical prediction models provide individualized outcome predictions to inform patient counseling and clinical decision making. External validation is the process of examining a prediction model's performance in data independent to that used for model development. Current external validation studies often suffer from small sample sizes, and subsequently imprecise estimates of a model's predictive performance. To address this, we propose how to determine the minimum sample size needed for external validation of a clinical prediction model with a continuous outcome. Four criteria are proposed, that target precise estimates of (i) R2 (the proportion of variance explained), (ii) calibration-in-the-large (agreement between predicted and observed outcome values on average), (iii) calibration slope (agreement between predicted and observed values across the range of predicted values), and (iv) the variance of observed outcome values. Closed-form sample size solutions are derived for each criterion, which require the user to specify anticipated values of the model's performance (in particular R2 ) and the outcome variance in the external validation dataset. A sensible starting point is to base values on those for the model development study, as obtained from the publication or study authors. The largest sample size required to meet all four criteria is the recommended minimum sample size needed in the external validation dataset. The calculations can also be applied to estimate expected precision when an existing dataset with a fixed sample size is available, to help gauge if it is adequate. We illustrate the proposed methods on a case-study predicting fat-free mass in children

Transparent reporting of multivariable prediction models developed or validated using clustered data (TRIPOD-Cluster): explanation and elaboration.

The TRIPOD-Cluster (transparent reporting of multivariable prediction models developed or validated using clustered data) statement comprises a 19 item checklist, which aims to improve the reporting of studies developing or validating a prediction model in clustered data, such as individual participant data meta-analyses (clustering by study) and electronic health records (clustering by practice or hospital). This explanation and elaboration document describes the rationale; clarifies the meaning of each item; and discusses why transparent reporting is important, with a view to assessing risk of bias and clinical usefulness of the prediction model. Each checklist item of the TRIPOD-Cluster statement is explained in detail and accompanied by published examples of good reporting. The document also serves as a reference of factors to consider when designing, conducting, and analysing prediction model development or validation studies in clustered data. To aid the editorial process and help peer reviewers and, ultimately, readers and systematic reviewers of prediction model studies, authors are recommended to include a completed checklist in their submission

One-carbon metabolism in cancer

Cells require one-carbon units for nucleotide synthesis, methylation and reductive metabolism, and these pathways support the high proliferative rate of cancer cells. As such, anti-folates, drugs that target one-carbon metabolism, have long been used in the treatment of cancer. Amino acids, such as serine are a major one-carbon source, and cancer cells are particularly susceptible to deprivation of one-carbon units by serine restriction or inhibition of de novo serine synthesis. Recent work has also begun to decipher the specific pathways and sub-cellular compartments that are important for one-carbon metabolism in cancer cells. In this review we summarise the historical understanding of one-carbon metabolism in cancer, describe the recent findings regarding the generation and usage of one-carbon units and explore possible future therapeutics that could exploit the dependency of cancer cells on one-carbon metabolism

Minimum sample size calculations for external validation of a clinical prediction model with a time-to-event outcome.

Previous articles in Statistics in Medicine describe how to calculate the sample size required for external validation of prediction models with continuous and binary outcomes. The minimum sample size criteria aim to ensure precise estimation of key measures of a model's predictive performance, including measures of calibration, discrimination, and net benefit. Here, we extend the sample size guidance to prediction models with a time-to-event (survival) outcome, to cover external validation in datasets containing censoring. A simulation-based framework is proposed, which calculates the sample size required to target a particular confidence interval width for the calibration slope measuring the agreement between predicted risks (from the model) and observed risks (derived using pseudo-observations to account for censoring) on the log cumulative hazard scale. Precise estimation of calibration curves, discrimination, and net-benefit can also be checked in this framework. The process requires assumptions about the validation population in terms of the (i) distribution of the model's linear predictor and (ii) event and censoring distributions. Existing information can inform this; in particular, the linear predictor distribution can be approximated using the C-index or Royston's D statistic from the model development article, together with the overall event risk. We demonstrate how the approach can be used to calculate the sample size required to validate a prediction model for recurrent venous thromboembolism. Ideally the sample size should ensure precise calibration across the entire range of predicted risks, but must at least ensure adequate precision in regions important for clinical decision-making. Stata and R code are provided

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

PAT4 levels control amino-acid sensitivity of rapamycin-resistant mTORC1 from the Golgi and affect clinical outcome in colorectal cancer

Tumour cells can use strategies that make them resistant to nutrient deprivation to outcompete their neighbours. A key integrator of the cell’s responses to starvation and other stresses is amino-acid-dependent mechanistic target of rapamycin complex 1 (mTORC1). Activation of mTORC1 on late endosomes and lysosomes is facilitated by amino-acid transporters within the solute-linked carrier 36 (SLC36) and SLC38 families. Here, we analyse the functions of SLC36 family member, SLC36A4, otherwise known as proton-assisted amino-acid transporter 4 (PAT4), in colorectal cancer. We show that independent of other major pathological factors, high PAT4 expression is associated with reduced relapse-free survival after colorectal cancer surgery. Consistent with this, PAT4 promotes HCT116 human colorectal cancer cell proliferation in culture and tumour growth in xenograft models. Inducible knockdown in HCT116 cells reveals that PAT4 regulates a form of mTORC1 with two distinct properties: first, it preferentially targets eukaryotic translation initiation factor 4E-binding protein 1 (4E-BP1), and second, it is resistant to rapamycin treatment. Furthermore, in HCT116 cells two non-essential amino acids, glutamine and serine, which are often rapidly metabolised by tumour cells, regulate rapamycin-resistant mTORC1 in a PAT4-dependent manner. Overexpressed PAT4 is also able to promote rapamycin resistance in human embryonic kidney-293 cells. PAT4 is predominantly associated with the Golgi apparatus in a range of cell types, and in situ proximity ligation analysis shows that PAT4 interacts with both mTORC1 and its regulator Rab1A on the Golgi. These findings, together with other studies, suggest that differentially localised intracellular amino-acid transporters contribute to the activation of alternate forms of mTORC1. Furthermore, our data predict that colorectal cancer cells with high PAT4 expression will be more resistant to depletion of serine and glutamine, allowing them to survive and outgrow neighbouring normal and tumorigenic cells, and potentially providing a new route for pharmacological intervention

Chronic kidney disease after liver, cardiac, lung, heart–lung, and hematopoietic stem cell transplant

Patient survival after cardiac, liver, and hematopoietic stem cell transplant (HSCT) is improving; however, this survival is limited by substantial pretransplant and treatment-related toxicities. A major cause of morbidity and mortality after transplant is chronic kidney disease (CKD). Although the majority of CKD after transplant is attributed to the use of calcineurin inhibitors, various other conditions such as thrombotic microangiopathy, nephrotic syndrome, and focal segmental glomerulosclerosis have been described. Though the immunosuppression used for each of the transplant types, cardiac, liver and HSCT is similar, the risk factors for developing CKD and the CKD severity described in patients after transplant vary. As the indications for transplant and the long-term survival improves for these children, so will the burden of CKD. Nephrologists should be involved early in the pretransplant workup of these patients. Transplant physicians and nephrologists will need to work together to identify those patients at risk of developing CKD early to prevent its development and progression to end-stage renal disease

Inappropriate asthma therapy-a tale of two countries:a parallel population-based cohort study

Funding was received by Respiratory Effectiveness Group and University Lyon 1.Peer reviewedPublisher PD

Universal DNA methylation age across mammalian tissues.

Aging, often considered a result of random cellular damage, can be accurately estimated using DNA methylation profiles, the foundation of pan-tissue epigenetic clocks. Here, we demonstrate the development of universal pan-mammalian clocks, using 11,754 methylation arrays from our Mammalian Methylation Consortium, which encompass 59 tissue types across 185 mammalian species. These predictive models estimate mammalian tissue age with high accuracy (r > 0.96). Age deviations correlate with human mortality risk, mouse somatotropic axis mutations and caloric restriction. We identified specific cytosines with methylation levels that change with age across numerous species. These sites, highly enriched in polycomb repressive complex 2-binding locations, are near genes implicated in mammalian development, cancer, obesity and longevity. Our findings offer new evidence suggesting that aging is evolutionarily conserved and intertwined with developmental processes across all mammals