Measurement error and timing of predictor values for multivariable risk prediction models are poorly reported

External validation of the ARCH score in patients undergoing aortic arch reconstruction under circulatory arrest

BACKGROUND

Aortic arch surgery with hypothermic circulatory arrest (HCA) carries a higher risk of morbidity and mortality compared to routine cardiac surgical procedures. The newly developed ARCH (arch reconstruction under circulatory arrest with hypothermia) score has not been externally validated. We sought to externally validate this score in our local population.

METHODS

All consecutive open aortic arch surgeries with HCA performed between 2014 and 2023 were included. Univariable and multivariable analyses were performed. Model discrimination was assessed by the C-statistic with 95% confidence intervals as part of the receiver operating characteristic (ROC) curve analysis. Model performance was visualized by a calibration plot and quantified by the Brier score.

RESULTS

A total of 760 patients (38.3% females) were included. The mean age was 61 (±13.6) years, with 56.4% of patients' age >60 years. The procedures were carried out mostly emergently or urgently (59.6%). Total arch replacement was performed in 32.5% of the patients, and aortic root procedures were carried out in 74.6%. In-hospital death occurred in 64 patients (8.4%), and stroke occurred in 5.4%. The C-statistic revealed a low discriminatory ability for predicting in-hospital mortality (area under the ROC curve, 0.62; 95% confidence interval, 0.54-0.69; P = .002); however, model calibration was found to be excellent (Brier score of 0.07).

CONCLUSIONS

The ARCH score for in-hospital mortality showed low discriminatory ability in our local population, although with excellent ability for prediction of mortality.

Investigation of statistical methods used in prognostic prediction models for obstetric care: A 10 year-span cross-sectional study

INTRODUCTION

Obstetric care is a highly active area in the development and application of prognostic prediction models. The development and validation of these models often require the utilization of advanced statistical techniques. However, failure to adhere to rigorous methodological standards could greatly undermine the reliability and trustworthiness of the resultant models. Consequently, the aim of our study was to examine the current statistical practices employed in obstetric care and offer recommendations to enhance the utilization of statistical methods in the development of prognostic prediction models.

MATERIAL AND METHODS

We conducted a cross-sectional survey using a sample of studies developing or validating prognostic prediction models for obstetric care published in a 10-year span (2011-2020). A structured questionnaire was developed to investigate the statistical issues in five domains, including model derivation (predictor selection and algorithm development), model validation (internal and external), model performance, model presentation, and risk threshold setting. On the ground of survey results and existing guidelines, a list of recommendations for statistical methods in prognostic models was developed.

RESULTS

A total of 112 eligible studies were included, with 107 reporting model development and five exclusively reporting external validation. During model development, 58.9% of the studies did not include any form of validation. Of these, 46.4% used stepwise regression in a crude manner for predictor selection, while two-thirds made decisions on retaining or dropping candidate predictors solely based on p-values. Additionally, 26.2% transformed continuous predictors into categorical variables, and 80.4% did not consider nonlinear relationships between predictors and outcomes. Surprisingly, 94.4% of the studies did not examine the correlation between predictors. Moreover, 47.1% of the studies did not compare population characteristics between the development and external validation datasets, and only one-fifth evaluated both discrimination and calibration. Furthermore, 53.6% of the studies did not clearly present the model, and less than half established a risk threshold to define risk categories. In light of these findings, 10 recommendations were formulated to promote the appropriate use of statistical methods.

CONCLUSIONS

The use of statistical methods is not yet optimal. Ten recommendations were offered to assist the statistical methods of prognostic prediction models in obstetric care.

Prediction models in prehospital and emergency medicine research: How to derive and internally validate a clinical prediction model

Clinical prediction models are created to help clinicians with medical decision making, aid in risk stratification, and improve diagnosis and/or prognosis. With growing availability of both prehospital and in-hospital observational registries and electronic health records, there is an opportunity to develop, validate, and incorporate prediction models into clinical practice. However, many prediction models have high risk of bias due to poor methodology. Given that there are no methodological standards aimed at developing prediction models specifically in the prehospital setting, the objective of this paper is to describe the appropriate methodology for the derivation and validation of clinical prediction models in this setting. What follows can also be applied to the emergency medicine (EM) setting. There are eight steps that should be followed when developing and internally validating a prediction model: (1) problem definition, (2) coding of predictors, (3) addressing missing data, (4) ensuring adequate sample size, (5) variable selection, (6) evaluating model performance, (7) internal validation, and (8) model presentation. Subsequent steps include external validation, assessment of impact, and cost-effectiveness. By following these steps, researchers can develop a prediction model with the methodological rigor and quality required for prehospital and EM research.

Addressing Measurement Error in Random Forests Using Quantitative Bias Analysis

Although variables are often measured with error, the impact of measurement error on machine-learning predictions is seldom quantified. The purpose of this study was to assess the impact of measurement error on the performance of random-forest models and variable importance. First, we assessed the impact of misclassification (i.e., measurement error of categorical variables) of predictors on random-forest model performance (e.g., accuracy, sensitivity) and variable importance (mean decrease in accuracy) using data from the National Comorbidity Survey Replication (2001-2003). Second, we created simulated data sets in which we knew the true model performance and variable importance measures and could verify that quantitative bias analysis was recovering the truth in misclassified versions of the data sets. Our findings showed that measurement error in the data used to construct random forests can distort model performance and variable importance measures and that bias analysis can recover the correct results. This study highlights the utility of applying quantitative bias analysis in machine learning to quantify the impact of measurement error on study results.

Design, methods, and reporting of impact studies of cardiovascular clinical prediction rules are suboptimal: a systematic review

OBJECTIVES

To evaluate design, methods, and reporting of impact studies of cardiovascular clinical prediction rules (CPRs).

STUDY DESIGN AND SETTING

We conducted a systematic review. Impact studies of cardiovascular CPRs were identified by forward citation and electronic database searches. We categorized the design of impact studies as appropriate for randomized and nonrandomized experiments, excluding uncontrolled before-after study. For impact studies with appropriate study design, we assessed the quality of methods and reporting. We compared the quality of methods and reporting between impact and matched control studies.

RESULTS

We found 110 impact studies of cardiovascular CPRs. Of these, 65 (59.1%) used inappropriate designs. Of 45 impact studies with appropriate design, 31 (68.9%) had substantial risk of bias. Mean number of reporting domains that impact studies with appropriate study design adhered to was 10.2 of 21 domains (95% confidence interval, 9.3 and 11.1). The quality of methods and reporting was not clearly different between impact and matched control studies.

CONCLUSION

We found most impact studies either used inappropriate study design, had substantial risk of bias, or poorly complied with reporting guidelines. This appears to be a common feature of complex interventions. Users of CPRs should critically evaluate evidence showing the effectiveness of CPRs.

Prediction of contralateral breast cancer: external validation of risk calculators in 20 international cohorts

BACKGROUND

Three tools are currently available to predict the risk of contralateral breast cancer (CBC). We aimed to compare the performance of the Manchester formula, CBCrisk, and PredictCBC in patients with invasive breast cancer (BC).

METHODS

We analyzed data of 132,756 patients (4682 CBC) from 20 international studies with a median follow-up of 8.8 years. Prediction performance included discrimination, quantified as a time-dependent Area-Under-the-Curve (AUC) at 5 and 10 years after diagnosis of primary BC, and calibration, quantified as the expected-observed (E/O) ratio at 5 and 10 years and the calibration slope.

RESULTS

The AUC at 10 years was: 0.58 (95% confidence intervals [CI] 0.57-0.59) for CBCrisk; 0.60 (95% CI 0.59-0.61) for the Manchester formula; 0.63 (95% CI 0.59-0.66) and 0.59 (95% CI 0.56-0.62) for PredictCBC-1A (for settings where BRCA1/2 mutation status is available) and PredictCBC-1B (for the general population), respectively. The E/O at 10 years: 0.82 (95% CI 0.51-1.32) for CBCrisk; 1.53 (95% CI 0.63-3.73) for the Manchester formula; 1.28 (95% CI 0.63-2.58) for PredictCBC-1A and 1.35 (95% CI 0.65-2.77) for PredictCBC-1B. The calibration slope was 1.26 (95% CI 1.01-1.50) for CBCrisk; 0.90 (95% CI 0.79-1.02) for PredictCBC-1A; 0.81 (95% CI 0.63-0.99) for PredictCBC-1B, and 0.39 (95% CI 0.34-0.43) for the Manchester formula.

CONCLUSIONS

Current CBC risk prediction tools provide only moderate discrimination and the Manchester formula was poorly calibrated. Better predictors and re-calibration are needed to improve CBC prediction and to identify low- and high-CBC risk patients for clinical decision-making.

The Added Value of Lactate and Lactate Clearance in Prediction of In-Hospital Mortality in Critically Ill Patients With Sepsis

Supplemental Digital Content is available in the text.

We investigated the added predictive value of lactate and lactate clearance to the Acute Physiology and Chronic Health Evaluation IV model for predicting in-hospital mortality in critically ill patients with sepsis.

Methodological standards for the development and evaluation of clinical prediction rules: a review of the literature

Clinical prediction rules (CPRs) that predict the absolute risk of a clinical condition or future outcome for individual patients are abundant in the medical literature; however, systematic reviews have demonstrated shortcomings in the methodological quality and reporting of prediction studies. To maximise the potential and clinical usefulness of CPRs, they must be rigorously developed and validated, and their impact on clinical practice and patient outcomes must be evaluated. This review aims to present a comprehensive overview of the stages involved in the development, validation and evaluation of CPRs, and to describe in detail the methodological standards required at each stage, illustrated with examples where appropriate. Important features of the study design, statistical analysis, modelling strategy, data collection, performance assessment, CPR presentation and reporting are discussed, in addition to other, often overlooked aspects such as the acceptability, cost-effectiveness and longer-term implementation of CPRs, and their comparison with clinical judgement. Although the development and evaluation of a robust, clinically useful CPR is anything but straightforward, adherence to the plethora of methodological standards, recommendations and frameworks at each stage will assist in the development of a rigorous CPR that has the potential to contribute usefully to clinical practice and decision-making and have a positive impact on patient care.

The use of rigorous methods was strongly warranted among prognostic prediction models for obstetric care

OBJECTIVE

The objective of the study was to examine methodological characteristics about the design and conduct in prognostic prediction models used for obstetric care.

STUDY DESIGN AND SETTING

We searched PubMed for studies on prognostic prediction models for obstetric care, published in top general medicine or major specialty journals between January 2011 and February 2018. Teams of method-trained investigators independently screened titles and abstracts and collected data using a prespecified, pilot-tested, structured questionnaire.

RESULTS

In total, 91 studies were eligible, of which two were published in top general medicine journals, 20 (22.0%) involved an epidemiologist or statistician, 18 (19.4%) published study protocols, 53 (58.2%) did not include any model validation, 20 (22.0%) did not clearly state the intended timing of use, 23 (25.3%) had no eligibility criteria, 15 (16.5%) did not use clear criteria for ascertaining outcome, and 69 (75.82%) did not apply blinding to outcome assessment. Among those models, 11 (12.1%) included participants fewer than 200 events, 41 (48.8%) had fewer than 100 events, and 19 (24.7%) had fewer than 10 events per variable.

CONCLUSION

The prognostic prediction models have important limitations in design and conduct. Substantial efforts are needed to strengthen the production of reliable prognostic prediction models for obstetric care.

Evidence synthesis in prognosis research

Over the past few years, evidence synthesis has become essential to investigate and improve the generalizability of medical research findings. This strategy often involves a meta-analysis to formally summarize quantities of interest, such as relative treatment effect estimates. The use of meta-analysis methods is, however, less straightforward in prognosis research because substantial variation exists in research objectives, analysis methods and the level of reported evidence. We present a gentle overview of statistical methods that can be used to summarize data of prognostic factor and prognostic model studies. We discuss how aggregate data, individual participant data, or a combination thereof can be combined through meta-analysis methods. Recent examples are provided throughout to illustrate the various methods.

Impact of predictor measurement heterogeneity across settings on the performance of prediction models: A measurement error perspective

It is widely acknowledged that the predictive performance of clinical prediction models should be studied in patients that were not part of the data in which the model was derived. Out‐of‐sample performance can be hampered when predictors are measured differently at derivation and external validation. This may occur, for instance, when predictors are measured using different measurement protocols or when tests are produced by different manufacturers. Although such heterogeneity in predictor measurement between derivation and validation data is common, the impact on the out‐of‐sample performance is not well studied. Using analytical and simulation approaches, we examined out‐of‐sample performance of prediction models under various scenarios of heterogeneous predictor measurement. These scenarios were defined and clarified using an established taxonomy of measurement error models. The results of our simulations indicate that predictor measurement heterogeneity can induce miscalibration of prediction and affects discrimination and overall predictive accuracy, to extents that the prediction model may no longer be considered clinically useful. The measurement error taxonomy was found to be helpful in identifying and predicting effects of heterogeneous predictor measurements between settings of prediction model derivation and validation. Our work indicates that homogeneity of measurement strategies across settings is of paramount importance in prediction research.