Prediction models for living organ transplantation are poorly developed, reported and validated: a systematic review

Clinical performance of the iPREDICTLIVING tool for the prediction of the post-transplant recipient and living donor outcomes in a European cohort

A living donor kidney transplant (LDKT) is the best treatment for ESRD. A prediction tool based on clinical and demographic data available pre-KT was developed in a Norwegian cohort with three different models to predict graft loss, recipient death, and donor candidate's risk of death, the iPREDICTLIVING tool. No external validations are yet available. We sought to evaluate its predictive performance in our cohort of 352 pairs LKDT submitted to KT from 1998 to 2019. The model for censored graft failure (CGF) showed the worse discriminative performance with Harrell's C of .665 and a time-dependent AUC of .566, with a calibration slope of .998. For recipient death, at 10 years, the model had a Harrell's C of .776, a time-dependent AUC of .773, and a calibration slope of 1.003. The models for donor death were reasonably discriminative, although with a poor calibration, particularly for 20 years of death, with a Harrell's C of .712 and AUC of .694 with a calibration slope of .955. These models have moderate discriminative and calibration performance in our population. The tool was validated in this Northern Portuguese cohort, Caucasian, with a low incidence of diabetes and other comorbidities. It can improve the informed decision-making process at the living donor consultation joining clinical and other relevant information.

Abdominal Organ Transplantation: Noteworthy Literature in 2022

This review highlights noteworthy literature published in 2022 pertinent to anesthesiologists and critical care physicians caring for patients undergoing abdominal organ transplantation. We begin by exploring the impacts that the COVID-19 pandemic has had across the field of abdominal organ transplantation, including the successful use of grafts procured from COVID-19-infected donors. In pancreatic transplantation, we highlight several studies on dexmedetomidine and ischemia-reperfusion injury, equity in transplantation, and medical management, as well as studies comparing pancreatic transplantation to islet cell transplantation. In our section on intestinal transplantation, we explore donor selection. Kidney transplantation topics include cardiovascular risk management, obesity, and intraoperative management, including fluid resuscitation, dexmedetomidine, and sugammadex. The liver transplantation section focuses on clinical trials, systematic reviews, and meta-analyses published in 2022 and covers a wide range of topics, including machine perfusion, cardiovascular issues, renal issues, and coagulation/transfusion.

Regression-based prognostic models for functional independence after postacute brain injury rehabilitation are not transportable: a systematic review

BACKGROUND AND OBJECTIVES

To identify and summarize validated multivariable prognostic models for the Functional Independence Measure® (FIM®) at discharge from post-acute inpatient rehabilitation in adults with acquired brain injury (ABI).

METHODS

This review was conducted based on the recommendations of the Cochrane Prognosis Methods Group and adheres to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Three databases were systematically searched in May 2021 and updated in April 2022. Main inclusion criteria were: a) adult patients with ABI, b) validated multivariable prognostic model, c) time of prognostication within 1-week of admission to post-acute rehabilitation, and d) outcome was the FIM® at discharge from post-acute rehabilitation.

RESULTS

The search yielded 3,169 unique articles. Three articles fulfilled the inclusion criteria, accounting for n = 6 internally and n = 2 externally validated prognostic models. Discrimination was estimated as an area under the curve between 0.76 and 0.89. Calibration was deemed to be assessed insufficiently. The included models were judged to be of high risk of bias.

CONCLUSION

Current prognostic models for the FIM® in post-acute rehabilitation for patients with ABI lack the methodological rigor to support clinical use outside the development setting. Future studies addressing functional independence should ensure appropriate model validation and conform to uniform reporting standards for prognosis research.

Reporting and risk of bias of prediction models based on machine learning methods in preterm birth: A systematic review

INTRODUCTION

There was limited evidence on the quality of reporting and methodological quality of prediction models using machine learning methods in preterm birth. This systematic review aimed to assess the reporting quality and risk of bias of a machine learning-based prediction model in preterm birth.

MATERIAL AND METHODS

We conducted a systematic review, searching the PubMed, Embase, the Cochrane Library, China National Knowledge Infrastructure, China Biology Medicine disk, VIP Database, and WanFang Data from inception to September 27, 2021. Studies that developed (validated) a prediction model using machine learning methods in preterm birth were included. We used the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement and Prediction model Risk of Bias Assessment Tool (PROBAST) to evaluate the reporting quality and the risk of bias of included studies, respectively. Findings were summarized using descriptive statistics and visual plots. The protocol was registered in PROSPERO (no. CRD 42022301623).

RESULTS

Twenty-nine studies met the inclusion criteria, with 24 development-only studies and 5 development-with-validation studies. Overall, TRIPOD adherence per study ranged from 17% to 79%, with a median adherence of 49%. The reporting of title, abstract, blinding of predictors, sample size justification, explanation of model, and model performance were mostly poor, with TRIPOD adherence ranging from 4% to 17%. For all included studies, 79% had a high overall risk of bias, and 21% had an unclear overall risk of bias. The analysis domain was most commonly rated as high risk of bias in included studies, mainly as a result of small effective sample size, selection of predictors based on univariable analysis, and lack of calibration evaluation.

CONCLUSIONS

Reporting and methodological quality of machine learning-based prediction models in preterm birth were poor. It is urgent to improve the design, conduct, and reporting of such studies to boost the application of machine learning-based prediction models in preterm birth in clinical practice.

Using Background Knowledge from Preceding Studies for Building a Random Forest Prediction Model: A Plasmode Simulation Study

There is an increasing interest in machine learning (ML) algorithms for predicting patient outcomes, as these methods are designed to automatically discover complex data patterns. For example, the random forest (RF) algorithm is designed to identify relevant predictor variables out of a large set of candidates. In addition, researchers may also use external information for variable selection to improve model interpretability and variable selection accuracy, thereby prediction quality. However, it is unclear to which extent, if at all, RF and ML methods may benefit from external information. In this paper, we examine the usefulness of external information from prior variable selection studies that used traditional statistical modeling approaches such as the Lasso, or suboptimal methods such as univariate selection. We conducted a plasmode simulation study based on subsampling a data set from a pharmacoepidemiologic study with nearly 200,000 individuals, two binary outcomes and 1152 candidate predictor (mainly sparse binary) variables. When the scope of candidate predictors was reduced based on external knowledge RF models achieved better calibration, that is, better agreement of predictions and observed outcome rates. However, prediction quality measured by cross-entropy, AUROC or the Brier score did not improve. We recommend appraising the methodological quality of studies that serve as an external information source for future prediction model development.