Predictive accuracy of risk prediction models for recurrence, metastasis and survival for early-stage cutaneous melanoma : a systematic review

OBJECTIVES: To identify prognostic models for melanoma survival, recurrence and metastasis among American Joint Committee on Cancer stage I and II patients postsurgery; and evaluate model performance, including overall survival (OS) prediction. DESIGN: Systematic review and narrative synthesis. DATA SOURCES: Searched MEDLINE, Embase, CINAHL, Cochrane Library, Science Citation Index and grey literature sources including cancer and guideline websites from 2000 to September 2021. ELIGIBILITY CRITERIA: Included studies on risk prediction models for stage I and II melanoma in adults ≥18 years. Outcomes included OS, recurrence, metastases and model performance. No language or country of publication restrictions were applied. DATA EXTRACTION AND SYNTHESIS: Two pairs of reviewers independently screened studies, extracted data and assessed the risk of bias using the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies checklist and the Prediction study Risk of Bias Assessment Tool. Heterogeneous predictors prevented statistical synthesis. RESULTS: From 28 967 records, 15 studies reporting 20 models were included; 8 (stage I), 2 (stage II), 7 (stages I-II) and 7 (stages not reported), but were clearly applicable to early stages. Clinicopathological predictors per model ranged from 3-10. The most common were: ulceration, Breslow thickness/depth, sociodemographic status and site. Where reported, discriminatory values were ≥0.7. Calibration measures showed good matches between predicted and observed rates. None of the studies assessed clinical usefulness of the models. Risk of bias was high in eight models, unclear in nine and low in three. Seven models were internally and externally cross-validated, six models were externally validated and eight models were internally validated. CONCLUSIONS: All models are effective in their predictive performance, however the low quality of the evidence raises concern as to whether current follow-up recommendations following surgical treatment is adequate. Future models should incorporate biomarkers for improved accuracy. PROSPERO REGISTRATION NUMBER: CRD42018086784

Effects of Digital Technologies on Older People’s Access to Health and Social Care: Umbrella Review

BACKGROUND: The 2020 COVID-19 pandemic prompted the rapid implementation of new and existing digital technologies to facilitate access to health and care services during physical distancing. Older people may be disadvantaged in that regard if they are unable to use or have access to smartphones, tablets, computers, or other technologies. OBJECTIVE: In this study, we synthesized evidence on the impact of digital technologies on older adults’ access to health and social services. METHODS: We conducted an umbrella review of systematic reviews published from January 2000 to October 2019 using comprehensive searches of 6 databases. We looked for reviews in a population of adults aged ≥65 years in any setting, reporting outcomes related to the impact of technologies on access to health and social care services. RESULTS: A total of 7 systematic reviews met the inclusion criteria, providing data from 77 randomized controlled trials and 50 observational studies. All of them synthesized findings from low-quality primary studies, 2 of which used robust review methods. Most of the reviews focused on digital technologies to facilitate remote delivery of care, including consultations and therapy. No studies examined technologies used for first contact access to care, such as online appointment scheduling. Overall, we found no reviews of technology to facilitate first contact access to health and social care such as online appointment booking systems for older populations. CONCLUSIONS: The impact of digital technologies on equitable access to services for older people is unclear. Research is urgently needed in order to understand the positive and negative consequences of digital technologies on health care access and to identify the groups most vulnerable to exclusion

Factors associated with unmet need for support to maintain independence in later life: a systematic review of quantitative and qualitative evidence

BACKGROUND: populations are considered to have an ‘unmet need’ when they could benefit from, but do not get, the necessary support. Policy efforts to achieve equitable access to long-term care require an understanding of patterns of unmet need. A systematic review was conducted to identify factors associated with unmet need for support to maintain independence in later life. METHODS: seven bibliographic databases and four non-bibliographic evidence sources were searched. Quantitative observational studies and qualitative systematic reviews were included if they reported factors associated with unmet need for support to maintain independence in populations aged 50+, in high-income countries. No limits to publication date were imposed. Studies were quality assessed and a narrative synthesis used, supported by forest plots to visualise data. FINDINGS: forty-three quantitative studies and 10 qualitative systematic reviews were included. Evidence across multiple studies suggests that being male, younger age, living alone, having lower levels of income, poor self-rated health, more functional limitations and greater severity of depression were linked to unmet need. Other factors that were reported in single studies were also identified. In the qualitative reviews, care eligibility criteria, the quality, adequacy and absence of care, and cultural and language barriers were implicated in unmet need. CONCLUSIONS: this review identifies which groups of older people may be most at risk of not accessing the support they need to maintain independence. Ongoing monitoring of unmet need is critical to support policy efforts to achieve equal ageing and equitable access to care

Predictive accuracy of risk prediction models for recurrence, metastasis and survival for early-stage cutaneous melanoma: a systematic review.

OBJECTIVES: To identify prognostic models for melanoma survival, recurrence and metastasis among American Joint Committee on Cancer stage I and II patients postsurgery; and evaluate model performance, including overall survival (OS) prediction. DESIGN: Systematic review and narrative synthesis. DATA SOURCES: Searched MEDLINE, Embase, CINAHL, Cochrane Library, Science Citation Index and grey literature sources including cancer and guideline websites from 2000 to September 2021. ELIGIBILITY CRITERIA: Included studies on risk prediction models for stage I and II melanoma in adults ≥18 years. Outcomes included OS, recurrence, metastases and model performance. No language or country of publication restrictions were applied. DATA EXTRACTION AND SYNTHESIS: Two pairs of reviewers independently screened studies, extracted data and assessed the risk of bias using the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies checklist and the Prediction study Risk of Bias Assessment Tool. Heterogeneous predictors prevented statistical synthesis. RESULTS: From 28 967 records, 15 studies reporting 20 models were included; 8 (stage I), 2 (stage II), 7 (stages I-II) and 7 (stages not reported), but were clearly applicable to early stages. Clinicopathological predictors per model ranged from 3-10. The most common were: ulceration, Breslow thickness/depth, sociodemographic status and site. Where reported, discriminatory values were ≥0.7. Calibration measures showed good matches between predicted and observed rates. None of the studies assessed clinical usefulness of the models. Risk of bias was high in eight models, unclear in nine and low in three. Seven models were internally and externally cross-validated, six models were externally validated and eight models were internally validated. CONCLUSIONS: All models are effective in their predictive performance, however the low quality of the evidence raises concern as to whether current follow-up recommendations following surgical treatment is adequate. Future models should incorporate biomarkers for improved accuracy. PROSPERO REGISTRATION NUMBER: CRD42018086784