Validation of prognostic models predicting mortality or ICU admission in patients with COVID-19 in low- and middle-income countries: a global individual participant data meta-analysis.

Background: We evaluated the performance of prognostic models for predicting mortality or ICU admission in hospitalized patients with COVID-19 in the World Health Organization (WHO) Global Clinical Platform, a repository of individual-level clinical data of patients hospitalized with COVID-19, including in low- and middle-income countries (LMICs).

Methods: We identified eligible multivariable prognostic models for predicting overall mortality and ICU admission during hospital stay in patients with confirmed or suspected COVID-19 from a living review of COVID-19 prediction models. These models were evaluated using data contributed to the WHO Global Clinical Platform for COVID-19 from nine LMICs (Burkina Faso, Cameroon, Democratic Republic of Congo, Guinea, India, Niger, Nigeria, Zambia, and Zimbabwe).

The definition of predictor and outcome variables in mortality prediction models: a scoping review and quality of reporting study.

Background And Objectives: Mortality prediction models are promising tools for guiding clinical decision-making and resource allocation in intensive care units (ICUs). Clearly specified predictor and outcome variables are necessary to enable external validation and safe clinical application of prediction models. The objective of this study was to identify the predictor and outcome variables used in different mortality prediction models in the ICU and investigate their reporting.

Boosting the accuracy of existing models by updating and extending: using a multicenter COVID-19 ICU cohort as a proxy.

Most published prediction models for Coronavirus Disease 2019 (COVID-19) were poorly reported, at high risk of bias, and heterogeneous in model performance. To tackle methodological challenges faced in previous prediction studies, we investigated whether model updating and extending improves mortality prediction, using the Intensive Care Unit (ICU) as a proxy. All COVID-19 patients admitted to seven ICUs in the Euregio-Meuse Rhine during the first pandemic wave were included.

Effectiveness of safety netting approaches for acutely ill children: a network meta-analysis.

Background: Safety netting advice (SNA) can help in the management of acutely ill children.

Aim: Assess the effectiveness of different SNA methods for acutely ill children on antibiotic prescription and consumption.

Design And Setting: Systematic review and network meta-analysis of randomised controlled trials, non-randomised trials of interventions, and controlled before-after studies in ambulatory care.

Development and validation of a risk prediction model for hospital admission in COVID-19 patients presenting to primary care.

Background: There is a paucity of prognostic models for COVID-19 that are usable for in-office patient assessment in general practice (GP).

Objectives: To develop and validate a risk prediction model for hospital admission with readily available predictors.

Methods: A retrospective cohort study linking GP records from 8 COVID-19 centres and 55 general practices in the Netherlands to hospital admission records.

TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods.

The TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) statement was published in 2015 to provide the minimum reporting recommendations for studies developing or evaluating the performance of a prediction model. Methodological advances in the field of prediction have since included the widespread use of artificial intelligence (AI) powered by machine learning methods to develop prediction models. An update to the TRIPOD statement is thus needed.

Table 0; documenting the steps to go from clinical database to research dataset.

Objectives: Data-driven decision support tools have been increasingly recognized to transform health care. However, such tools are often developed on predefined research datasets without adequate knowledge of the origin of this data and how it was selected. How a dataset is extracted from a clinical database can profoundly impact the validity, interpretability and interoperability of the dataset, and downstream analyses, yet is rarely reported.

ADNEX risk prediction model for diagnosis of ovarian cancer: systematic review and meta-analysis of external validation studies.

Objectives: To conduct a systematic review of studies externally validating the ADNEX (Assessment of Different Neoplasias in the adnexa) model for diagnosis of ovarian cancer and to present a meta-analysis of its performance.

Design: Systematic review and meta-analysis of external validation studies.

Data Sources: Medline, Embase, Web of Science, Scopus, and Europe PMC, from 15 October 2014 to 15 May 2023.

Factors associated with inappropriateness of antibiotic prescriptions for acutely ill children presenting to ambulatory care in high-income countries: a systematic review and meta-analysis.

Background: Acutely ill children are at risk of unwarranted antibiotic prescribing. Data on the appropriateness of antibiotic prescriptions provide insights into potential tailored interventions to promote antibiotic stewardship.

Objectives: To examine factors associated with the inappropriateness of antibiotic prescriptions for acutely ill children presenting to ambulatory care in high-income countries.

Systematic review finds risk of bias and applicability concerns for models predicting central line-associated bloodstream infection.

Objectives: To systematically review the risk of bias and applicability of published prediction models for risk of central line-associated bloodstream infection (CLA-BSI) in hospitalized patients.

Study Design And Setting: Systematic review of literature in PubMed, Embase, Web of Science Core Collection, and Scopus up to July 10, 2023. Two authors independently appraised risk models using CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS) and assessed their risk of bias and applicability using Prediction model Risk Of Bias ASsessment Tool (PROBAST).

Value-of-Information Analysis for External Validation of Risk Prediction Models.

Background: A previously developed risk prediction model needs to be validated before being used in a new population. The finite size of the validation sample entails that there is uncertainty around model performance. We apply value-of-information (VoI) methodology to quantify the consequence of uncertainty in terms of net benefit (NB).

Decision curve analysis: confidence intervals and hypothesis testing for net benefit.

Background: A number of recent papers have proposed methods to calculate confidence intervals and p values for net benefit used in decision curve analysis. These papers are sparse on the rationale for doing so. We aim to assess the relation between sampling variability, inference, and decision-analytic concepts.

What proportion of clinical prediction models make it to clinical practice? Protocol for a two-track follow-up study of prediction model development publications.

Introduction: It is known that only a limited proportion of developed clinical prediction models (CPMs) are implemented and/or used in clinical practice. This may result in a large amount of research waste, even when considering that some CPMs may demonstrate poor performance. Cross-sectional estimates of the numbers of CPMs that have been developed, validated, evaluated for impact or utilized in practice, have been made in specific medical fields, but studies across multiple fields and studies following up the fate of CPMs are lacking.

There is no such thing as a validated prediction model.

Background: Clinical prediction models should be validated before implementation in clinical practice. But is favorable performance at internal validation or one external validation sufficient to claim that a prediction model works well in the intended clinical context?

Main Body: We argue to the contrary because (1) patient populations vary, (2) measurement procedures vary, and (3) populations and measurements change over time. Hence, we have to expect heterogeneity in model performance between locations and settings, and across time.

Assessing Performance and Clinical Usefulness in Prediction Models With Survival Outcomes: Practical Guidance for Cox Proportional Hazards Models.

Risk prediction models need thorough validation to assess their performance. Validation of models for survival outcomes poses challenges due to the censoring of observations and the varying time horizon at which predictions can be made. This article describes measures to evaluate predictions and the potential improvement in decision making from survival models based on Cox proportional hazards regression.

Minimal reporting improvement after peer review in reports of COVID-19 prediction models: systematic review.

Objectives: To assess improvement in the completeness of reporting coronavirus (COVID-19) prediction models after the peer review process.

Study Design And Setting: Studies included in a living systematic review of COVID-19 prediction models, with both preprint and peer-reviewed published versions available, were assessed. The primary outcome was the change in percentage adherence to the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) reporting guidelines between pre-print and published manuscripts.

Predicting COVID-19 prognosis in the ICU remained challenging: external validation in a multinational regional cohort.

Objectives: Many prediction models for coronavirus disease 2019 (COVID-19) have been developed. External validation is mandatory before implementation in the intensive care unit (ICU). We selected and validated prognostic models in the Euregio Intensive Care COVID (EICC) cohort.

Benign descriptors and ADNEX in two-step strategy to estimate risk of malignancy in ovarian tumors: retrospective validation in IOTA5 multicenter cohort.

Objective: Previous work has suggested that the ultrasound-based benign simple descriptors (BDs) can reliably exclude malignancy in a large proportion of women presenting with an adnexal mass. This study aimed to validate a modified version of the BDs and to validate a two-step strategy to estimate the risk of malignancy, in which the modified BDs are followed by the Assessment of Different NEoplasias in the adneXa (ADNEX) model if modified BDs do not apply.

Methods: This was a retrospective analysis using data from the 2-year interim analysis of the International Ovarian Tumor Analysis (IOTA) Phase-5 study, in which consecutive patients with at least one adnexal mass were recruited irrespective of subsequent management (conservative or surgery).

Antibiotic use in ambulatory care for acutely ill children in high-income countries: a systematic review and meta-analysis.

Objective: To determine the rate and appropriateness of antibiotic prescribing for acutely ill children in ambulatory care in high-income countries.

Design: On 10 February 2021, we systematically searched articles published since 2000 in MEDLINE, Embase, CENTRAL, Web Of Science and grey literature databases. We included cross-sectional and longitudinal studies, time-series analyses, randomised controlled trials and non-randomised studies of interventions with acutely ill children up to and including 12 years of age in ambulatory care settings in high-income countries.

Clinical prediction models for mortality in patients with covid-19: external validation and individual participant data meta-analysis.

Objective: To externally validate various prognostic models and scoring rules for predicting short term mortality in patients admitted to hospital for covid-19.

Design: Two stage individual participant data meta-analysis.

Setting: Secondary and tertiary care.