Kym Ie Snell

Mortality and implant revision rates of hip arthroplasty in patients with osteoarthritis: registry based cohort study

Objectives To examine mortality and revision rates among patients with osteoarthritis undergoing hip arthroplasty and to compare these rates between patients undergoing cemented or uncemented procedures and to compare outcomes between men undergoing stemmed total hip replacements and Birmingham hip resurfacing. Design Cohort study. Setting National Joint Registry. Population About 275 000 patient records. Main outcome measures Hip arthroplasty procedures were linked to the time to any subsequent mortality or revision (implant failure). Flexible parametric survival analysis methods were used to analyse time to mortality and also time to revision. Comparisons between procedure groups were adjusted for age, sex, American Society of Anesthesiologists (ASA) grade, and complexity. Results As there were large baseline differences in the characteristics of patients receiving cemented, uncemented, or resurfacing procedures, unadjusted comparisons are inappropriate. Multivariable survival analyses identified a higher mortality rate for patients undergoing cemented compared with uncemented total hip replacement (adjusted hazard ratio 1.11, 95% confidence interval 1.07 to 1.16); conversely, there was a lower revision rate with cemented procedures (0.53, 0.50 to 0.57). These translate to small predicted differences in population averaged absolute survival probability at all time points. For example, compared with the uncemented group, at eight years after surgery the predicted probability of death in the cemented group was 0.013 higher (0.007 to 0.019) and the predicted probability of revision was 0.015 lower (0.012 to 0.017). In multivariable analyses restricted to men, there was a higher mortality rate in the cemented group and the uncemented group compared with the Birmingham hip resurfacing group. In terms of revision, the Birmingham hip resurfacings had a similar revision rate to uncemented total hip replacements. Both uncemented total hip replacements and Birmingham hip resurfacings had a higher revision rate than cemented total hip replacements. Conclusions There is a small but significant increased risk of revision with uncemented rather than cemented total hip replacement, and a small but significant increased risk of death with cemented procedures. It is not known whether these are causal relations or caused by residual confounding. Compared with uncemented and cemented total hip replacements, Birmingham hip resurfacing has a significantly lower risk of death in men of all ages. Previously, only adjusted analyses of hip implant revision rates have been used to recommend and justify use of cheaper cemented total hip implants. Our investigations additionally consider mortality rates and suggest a potentially higher mortality rate with cemented total hip replacements, which merits further investigation.

External validation of clinical prediction models using big datasets from e-health records or IPD meta-analysis : opportunities and challenges

Access to big datasets from e-health records and individual participant data (IPD) meta-analysis is signalling a new advent of external validation studies for clinical prediction models. In this article, the authors illustrate novel opportunities for external validation in big, combined datasets, while drawing attention to methodological challenges and reporting issues.

A guide to systematic review and meta-analysis of prediction model performance

Validation of prediction models is highly recommended and increasingly common in the literature. A systematic review of validation studies is therefore helpful, with meta-analysis needed to summarise the predictive performance of the model being validated across different settings and populations. This article provides guidance for researchers systematically reviewing and meta-analysing the existing evidence on a specific prediction model, discusses good practice when quantitatively summarising the predictive performance of the model across studies, and provides recommendations for interpreting meta-analysis estimates of model performance. We present key steps of the meta-analysis and illustrate each step in an example review, by summarising the discrimination and calibration performance of the EuroSCORE for predicting operative mortality in patients undergoing coronary artery bypass grafting.

Systematic review of prognostic models for recurrent venous thromboembolism (VTE) post-treatment of first unprovoked VTE

Objectives To review studies developing or validating a prognostic model for individual venous thromboembolism (VTE) recurrence risk following cessation of therapy for a first unprovoked VTE. Prediction of recurrence risk is crucial to informing patient prognosis and treatment decisions. The review aims to determine whether reliable prognostic models exist and, if not, what further research is needed within the field. Design Bibliographic databases (including MEDLINE, EMBASE and the Cochrane Library) were searched using index terms relating to the clinical field and prognosis. Screening of titles, abstracts and subsequently full texts was conducted by 2 reviewers independently using predefined criteria. Quality assessment and critical appraisal of included full texts was based on an early version of the PROBAST (Prediction study Risk Of Bias Assessment Tool) for risk of bias and applicability in prognostic model studies. Setting Studies in any setting were included. Primary and secondary outcome measures The primary outcome for the review was the predictive accuracy of identified prognostic models in relation to VTE recurrence risk. Results 3 unique prognostic models were identified including the HERDOO2 score, Vienna prediction model and DASH score. Quality assessment highlighted the Vienna, and DASH models were developed with generally strong methodology, but the HERDOO2 model had many methodological concerns. Further, all models were considered at least at moderate risk of bias, primarily due to the need for further external validation before use in practice. Conclusions Although the Vienna model shows the most promise (based on strong development methodology, applicability and having some external validation), none of the models can be considered ready for use until further, external and robust validation is performed in new data. Any new models should consider the inclusion of predictors found to be consistently important in existing models (sex, site of index event, D-dimer), and take heed of several methodological issues identified through this review. PROSPERO registration number CRD42013003494.

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 models 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 models 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 models 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 models calibration and discrimination performance across multiple populations and to evaluate different implementation strategies.

Patients with multiple myeloma have excellent long‐term outcomes after recovery from dialysis‐dependent acute kidney injury

The aim of this study was to report the long‐term outcomes in patients with multiple myeloma (MM) who receive dialysis treatment for acute kidney injury (AKI) due to myeloma cast nephropathy and subsequently recover renal function.

Meta-analysis of prediction model performance across multiple studies: Which scale helps ensure between-study normality for the C-statistic and calibration measures?:

If individual participant data are available from multiple studies or clusters, then a prediction model can be externally validated multiple times. This allows the model’s discrimination and calibration performance to be examined across different settings. Random-effects meta-analysis can then be used to quantify overall (average) performance and heterogeneity in performance. This typically assumes a normal distribution of ‘true’ performance across studies. We conducted a simulation study to examine this normality assumption for various performance measures relating to a logistic regression prediction model. We simulated data across multiple studies with varying degrees of variability in baseline risk or predictor effects and then evaluated the shape of the between-study distribution in the C-statistic, calibration slope, calibration-in-the-large, and E/O statistic, and possible transformations thereof. We found that a normal between-study distribution was usually reasonable for the calibration slope and calibration-in-the-large; however, the distributions of the C-statistic and E/O were often skewed across studies, particularly in settings with large variability in the predictor effects. Normality was vastly improved when using the logit transformation for the C-statistic and the log transformation for E/O, and therefore we recommend these scales to be used for meta-analysis. An illustrated example is given using a random-effects meta-analysis of the performance of QRISK2 across 25 general practices.

Exploring the roles of urinary HAI-1, EpCAM & EGFR in bladder cancer prognosis & risk stratification

Objectives To investigate whether elevated urinary HAI-1, EpCAM and EGFR are independent prognostic biomarkers within non-muscle-invasive bladder cancer (NMIBC) patients, and have utility for risk stratification to facilitate treatment decisions. Results After accounting for EAU risk group in NMIBC patients, the risk of BC-specific death was 2.14 times higher (95% CI: 1.08 to 4.24) if HAI-1 was elevated and 2.04 times higher (95% CI: 1.02 to 4.07) if EpCAM was elevated. The majority of events occurred in the high-risk NMIBC group and this is where the biggest difference is seen in the survival curves when plotted for EAU risk groups separately. In MIBC patients, being elevated for any of the three biomarkers was significantly associated with BC-specific mortality after accounting for other risk factors, HR = 4.30 (95% CI: 1.85 to 10.03). Patients and Methods Urinary levels of HAI-1, EpCAM and EGFR were measured by ELISA in 683 and 175 patients with newly-diagnosed NMIBC and MIBC, respectively, recruited to the Bladder Cancer Prognosis Programme. Associations between biomarkers and progression, BC-specific mortality and all-cause mortality were evaluated using univariable and multivariable Cox regression models, adjusted for European Association of Urology (EAU) NMIBC risk groups. The upper 25% of values for each biomarker within NMIBC patients were considered as elevated. Exploratory analyses in urine from MIBC patients were also undertaken. Conclusion Urinary HAI-1 and EpCAM are prognostic biomarkers for NMIBC patients. These biomarkers have potential to guide treatment decisions for high-risk NMIBC patients. Further analyses are required to define the roles of HAI-1, EpCAM and EGFR in MIBC patients.

Prediction of pre -eclampsia: Review of reviews

Primary studies and systematic reviews provide estimates of varying accuracy for different factors in the prediction of pre‐eclampsia. The aim of this study was to review published systematic reviews to collate evidence on the ability of available tests to predict pre‐eclampsia, to identify high‐value avenues for future research and to minimize future research waste in this field.

Minimum sample size for developing a multivariable prediction model: PART II - binary and time-to-event outcomes

When designing a study to develop a new prediction model with binary or time‐to‐event outcomes, researchers should ensure their sample size is adequate in terms of the number of participants (n) and outcome events (E) relative to the number of predictor parameters (p) considered for inclusion. We propose that the minimum values of n and E (and subsequently the minimum number of events per predictor parameter, EPP) should be calculated to meet the following three criteria: (i) small optimism in predictor effect estimates as defined by a global shrinkage factor of ≥0.9, (ii) small absolute difference of ≤ 0.05 in the models apparent and adjusted Nagelkerkes R2, and (iii) precise estimation of the overall risk in the population. Criteria (i) and (ii) aim to reduce overfitting conditional on a chosen p, and require prespecification of the models anticipated Cox‐Snell R2, which we show can be obtained from previous studies. The values of n and E that meet all three criteria provides the minimum sample size required for model development. Upon application of our approach, a new diagnostic model for Chagas disease requires an EPP of at least 4.8 and a new prognostic model for recurrent venous thromboembolism requires an EPP of at least 23. This reinforces why rules of thumb (eg, 10 EPP) should be avoided. Researchers might additionally ensure the sample size gives precise estimates of key predictor effects; this is especially important when key categorical predictors have few events in some categories, as this may substantially increase the numbers required.