Evaluating methodological quality of Prognostic models Including Patient-reported HeAlth outcomes iN oncologY (EPIPHANY): a systematic review protocol

Evaluating methodological quality of prognostic prediction models on patient reported outcome measurements after total hip replacement and total knee replacement surgery: a systematic review protocol

BACKGROUND

Prediction models for poor patient-reported surgical outcomes after total hip replacement (THR) and total knee replacement (TKR) may provide a method for improving appropriate surgical care for hip and knee osteoarthritis. There are concerns about methodological issues and the risk of bias of studies producing prediction models. A critical evaluation of the methodological quality of prediction modelling studies in THR and TKR is needed to ensure their clinical usefulness. This systematic review aims to (1) evaluate and report the quality of risk stratification and prediction modelling studies that predict patient-reported outcomes after THR and TKR; (2) identify areas of methodological deficit and provide recommendations for future research; and (3) synthesise the evidence on prediction models associated with post-operative patient-reported outcomes after THR and TKR surgeries.

METHODS

MEDLINE, EMBASE, and CINAHL electronic databases will be searched to identify relevant studies. Title and abstract and full-text screening will be performed by two independent reviewers. We will include (1) prediction model development studies without external validation; (2) prediction model development studies with external validation of independent data; (3) external model validation studies; and (4) studies updating a previously developed prediction model. Data extraction spreadsheets will be developed based on the CHARMS checklist and TRIPOD statement and piloted on two relevant studies. Study quality and risk of bias will be assessed using the PROBAST tool. Prediction models will be summarised qualitatively. Meta-analyses on the predictive performance of included models will be conducted if appropriate. A narrative review will be used to synthesis the evidence if there are insufficient data to perform meta-analyses.

DISCUSSION

This systematic review will evaluate the methodological quality and usefulness of prediction models for poor outcomes after THR or TKR. This information is essential to provide evidence-based healthcare for end-stage hip and knee osteoarthritis. Findings of this review will contribute to the identification of key areas for improvement in conducting prognostic research in this field and facilitate the progress in evidence-based tailored treatments for hip and knee osteoarthritis.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO registration number CRD42021271828.

Patient reported outcomes in oncology: changing perspectives-a systematic review

In public health context, oncology is associated with severe negative impact on patients and on their relatives’ quality of life. Over the last decades, survival has remained at 50% worldwide for some tumor locations. Patient reported outcomes (PROs) assessment and, the corresponding use in clinical practice, help establishing patient individualized profiling involving caregivers. The purpose of this systematic review was to examine critical success factors for PROs assessment in daily clinical oncology practice. Additionally, we investigated how PROs collection can change oncology perspectives for patients and caregivers. According to PRISMA guidelines, 83 studies were included in this systematic review, whether related with implementation in daily clinical practice or associated with its use in oncology. PROs assessment gathers multi-professional teams, biomedical and clinical expertise, patients, families and caregivers. Institutional involvement, first line for caregiver’s adherence, team continuous formation, encompassing training and support, design of clear workflows, continuous monitoring, and data analysis are crucial for implementation. PROs measures are decisive in oncology. Several items were improved, including caregiver–patient–physician communication, patient risk groups identification, unmet problems and needs detection, disease course and treatment tracking, prognostic markers, cost-effectiveness measurement and comfort/support provision for both patients and caregivers. Routine assessment and implementation of PROs in clinical practice are a major challenge and a paradigm transformation for future.

Patient-Reported Outcomes as Independent Prognostic Factors for Survival in Oncology: Systematic Review and Meta-Analysis

OBJECTIVES

Assessment of patient-reported outcomes (PROs) in oncology is of critical importance because it provides unique information that may also predict clinical outcomes.

METHODS

We conducted a systematic review of prognostic factor studies to examine the prognostic value of PROs for survival in cancer. A systematic literature search was performed in PubMed for studies published between 2013 and 2018. We considered any study, regardless of the research design, that included at least 1 PRO domain in the final multivariable prognostic model. The protocol (EPIPHANY) was published and registered in the International Prospective Register of Systematic Reviews (CRD42018099160).

RESULTS

Eligibility criteria selected 138 studies including 158 127 patients, of which 43 studies were randomized, controlled trials. Overall, 120 (87%) studies reported at least 1 PRO to be statistically significantly prognostic for overall survival. Lung (n = 41, 29.7%) and genitourinary (n = 27, 19.6%) cancers were most commonly investigated. The prognostic value of PROs was investigated in secondary data analyses in 101 (73.2%) studies. The EORTC QLQ-C30 questionnaire was the most frequently used measure, and its physical functioning scale (range 0-100) the most frequent independent prognostic PRO, with a pooled hazard ratio estimate of 0.88 per 10-point increase (95% CI 0.84-0.92).

CONCLUSIONS

There is convincing evidence that PROs provide independent prognostic information for overall survival across cancer populations and disease stages. Further research is needed to translate current evidence-based data into prognostic tools to aid in clinical decision making.

Reporting of methodological studies in health research: a protocol for the development of the MethodologIcal STudy reportIng Checklist (MISTIC)

INTRODUCTION

Methodological studies (ie, studies that evaluate the design, conduct, analysis or reporting of other studies in health research) address various facets of health research including, for instance, data collection techniques, differences in approaches to analyses, reporting quality, adherence to guidelines or publication bias. As a result, methodological studies can help to identify knowledge gaps in the methodology of health research and strategies for improvement in research practices. Differences in methodological study names and a lack of reporting guidance contribute to lack of comparability across studies and difficulties in identifying relevant previous methodological studies. This paper outlines the methods we will use to develop an evidence-based tool-the MethodologIcal STudy reportIng Checklist-to harmonise naming conventions and improve the reporting of methodological studies.

METHODS AND ANALYSIS

We will search for methodological studies in the Cumulative Index to Nursing and Allied Health Literature, Cochrane Library, Embase, MEDLINE, Web of Science, check reference lists and contact experts in the field. We will extract and summarise data on the study names, design and reporting features of the included methodological studies. Consensus on study terms and recommended reporting items will be achieved via video conference meetings with a panel of experts including researchers who have published methodological studies.

ETHICS AND DISSEMINATION

The consensus study has been exempt from ethics review by the Hamilton Integrated Research Ethics Board. The results of the review and the reporting guideline will be disseminated in stakeholder meetings, conferences, peer-reviewed publications, in requests to journal editors (to endorse or make the guideline a requirement for authors), and on the Enhancing the QUAlity and Transparency Of health Research (EQUATOR) Network and reporting guideline websites.

REGISTRATION

We have registered the development of the reporting guideline with the EQUATOR Network and publicly posted this project on the Open Science Framework (www.osf.io/9hgbq).

Prognosis in pathology: Are we "prognosticating" or only establishing correlations between independent variables and survival? A study with various analytics cautions about the overinterpretation of statistical results

Survival data from 225 patients with resected pulmonary typical carcinoids were analyzed with Kaplan-Meier statistics (K-M) and "deep learning" methods to illustrate the difference between establishing "correlations" and "prognostications". Cases were stratified into G1 and G2 classes using a ≤5% Ki-67% cut-point. Overall survival, number of patients at risk and 95% confidence intervals (CI) were estimated for the two classes. Seven neural network models (NN) were developed with GMDH Shell 3.8.2 and Statgraphics Centurion 18.1 software, using variable prior probabilities and different numbers of training vs testing cases. The NNs used age, sex, and pTNM, G1 and G2 as input neurons and "alive" and "dead" as output neurons. Areas under the curve (AUC) and other performance measures were evaluated for all models. Log-rank test showed a significant difference in overall survival between G1 and G2 (p < 0.001). However, 95% CI estimates showed considerable variability in survival at different time intervals. Including the number of patients at risk at different time intervals showed that most G2 patients had been censored by 100 weeks. The NN models provided variable "prognostications", with AUC ranging from 0.5 to 1 and variability in the sensitivity, specificity, and other performance measures. The results illustrate the limitations of survival statistics and NNs in predicting the prognosis of individual patients. The need for pathologists not to overinterpret the finding of significant correlations as "prognostic" or "predictive" for individual patients is discussed.