Investigating the inequalities in route to diagnosis amongst patients with diffuse large B-cell or follicular lymphoma in England

Introduction: Diagnostic delay is associated with lower chances of cancer survival. Underlying comorbidities are known to affect the timely diagnosis of cancer. Diffuse large B-cell (DLBCL) and follicular lymphomas (FL) are primarily diagnosed amongst older patients, who are more likely to have comorbidities. Characteristics of clinical commissioning groups (CCG) are also known to impact diagnostic delay. We assess the association between comorbidities and diagnostic delay amongst patients with DLBCL or FL in England during 2005–2013. Methods: Multivariable generalised linear mixed-effect models were used to assess the main association. Empirical Bayes estimates of the random effects were used to explore between-cluster variation. The latent normal joint modelling multiple imputation approach was used to account for partially observed variables. Results: We included 30,078 and 15,551 patients diagnosed with DLBCL or FL, respectively. Amongst patients from the same CCG, having multimorbidity was strongly associated with the emergency route to diagnosis (DLBCL: odds ratio 1.56, CI 1.40–1.73; FL: odds ratio 1.80, CI 1.45–2.23). Amongst DLBCL patients, the diagnostic delay was possibly correlated with CCGs that had higher population densities. Conclusions: Underlying comorbidity is associated with diagnostic delay amongst patients with DLBCL or FL. Results suggest a possible correlation between CCGs with higher population densities and diagnostic delay of aggressive lymphomas

Missing data is poorly handled and reported in prediction model studies using machine learning: a literature review

OBJECTIVES: Missing data is a common problem during the development, evaluation, and implementation of prediction models. Although machine learning (ML) methods are often said to be capable of circumventing missing data, it is unclear how these methods are used in medical research. We aim to find out if and how well prediction model studies using machine learning report on their handling of missing data. STUDY DESIGN AND SETTING: We systematically searched the literature on published papers between 2018 and 2019 about primary studies developing and/or validating clinical prediction models using any supervised ML methodology across medical fields. From the retrieved studies information about the amount and nature (e.g. missing completely at random, potential reasons for missingness) of missing data and the way they were handled were extracted. RESULTS: We identified 152 machine learning-based clinical prediction model studies. A substantial amount of these 152 papers did not report anything on missing data (n = 56/152). A majority (n = 96/152) reported details on the handling of missing data (e.g., methods used), though many of these (n = 46/96) did not report the amount of the missingness in the data. In these 96 papers the authors only sometimes reported possible reasons for missingness (n = 7/96) and information about missing data mechanisms (n = 8/96). The most common approach for handling missing data was deletion (n = 65/96), mostly via complete-case analysis (CCA) (n = 43/96). Very few studies used multiple imputation (n = 8/96) or built-in mechanisms such as surrogate splits (n = 7/96) that directly address missing data during the development, validation, or implementation of the prediction model. CONCLUSION: Though missing values are highly common in any type of medical research and certainly in the research based on routine healthcare data, a majority of the prediction model studies using machine learning does not report sufficient information on the presence and handling of missing data. Strategies in which patient data are simply omitted are unfortunately the most often used methods, even though it is generally advised against and well known that it likely causes bias and loss of analytical power in prediction model development and in the predictive accuracy estimates. Prediction model researchers should be much more aware of alternative methodologies to address missing data

Direct, indirect and buffering effects of support for mothers on children's socio-emotional adjustment

Support for mothers may improve children’s socioemotional adjustment, yet few studies have consideredthe benefits of formal support (from health and social work professionals) in addition to social support(from family and friends) or explored the mechanisms. These issues were addressed using a birth cohort(n�2,649) to explore how mothers’ perceptions of social and formal support when children were ages10–22 months predicted trajectories of children’s externalizing and internalizing problems from 58 to122 months. We tested mediating pathways from support to child adjustment via 3 family stressorsmeasured at 46–58 months (maternal distress, economic strain, and dysfunctional parenting) andexamined whether support buffered effects of stressors on child adjustment. Social and formal supportwere simultaneously associated with lower child externalizing and internalizing problem trajectoryintercepts at 90 months but did not predict trajectory slopes. Social support effects were mediated mainlyvia lower maternal distress, which then reduced children’s problems via lower dysfunctional parenting,or more directly. Additional indirect effects involved lower economic strain. Formal support effects weremediated to a lesser extent by reduced dysfunctional parenting. Two buffering effects were found: socialsupport reduced effects of economic strain on internalizing problems, and formal support reduced effectsof dysfunctional parenting on internalizing problems. Findings suggest measures promoting families’social integration should benefit children’s socioemotional adjustment via improved parental psycho-logical and economic resources and by buffering impacts of economic strain. Enhancing access to healthand welfare services through greater awareness and trust should benefit children’s adjustment, viaimproved parenting and by buffering impacts of dysfunctional parenting

Influence of depression and interpersonal support on adherence to antiretroviral therapy among people living with HIV

BackgroundPoor adherence and under-utilization of antiretroviral therapy (ART) services have been major setbacks to achieving 95-95-95 policy goals in Sub-Saharan Africa. Social support and mental health challenges may serve as barriers to accessing and adhering to ART but are under-studied in low-income countries. The purpose of this study was to examine the association of interpersonal support and depression scores with adherence to ART among persons living with HIV (PLWH) in the Volta region of Ghana.MethodsWe conducted a cross-sectional survey among 181 PLWH 18 years or older who receive care at an ART clinic between November 2021 and March 2022. The questionnaire included a 6-item simplified ART adherence scale, the 20-item Center for Epidemiologic Studies Depression Scale (CES-D), and the 12-item Interpersonal Support Evaluation List-12 (ISEL-12). We first used a chi-squared or Fisher’s exact test to assess the association between these and additional demographic variables with ART adherence status. We then built a stepwise multivariable logistic regression model to explain ART adherence.ResultsART adherence was 34%. The threshold for depression was met by 23% of participants, but it was not significantly associated with adherence in multivariate analysis(p = 0.25). High social support was reported by 48.1%, and associated with adherence (p = 0.033, aOR = 3.45, 95% CI = 1.09–5.88). Other factors associated with adherence included in the multivariable model included not disclosing HIV status (p = 0.044, aOR = 2.17, 95% CI = 1.03–4.54) and not living in an urban area (p = 0.00037, aOR = 0.24, 95% CI = 0.11–0.52).ConclusionInterpersonal support, rural residence, and not disclosing HIV status were independent predictors of adherence to ART in the study area

External validation, update and development of prediction models for pre-eclampsia using an Individual Participant Data (IPD) meta-analysis: the International Prediction of Pregnancy Complication Network (IPPIC pre-eclampsia) protocol.

Background: Pre-eclampsia, a condition with raised blood pressure and proteinuria is associated with an increased risk of maternal and offspring mortality and morbidity. Early identification of mothers at risk is needed to target management. Methods/design: We aim to systematically review the existing literature to identify prediction models for pre-eclampsia. We have established the International Prediction of Pregnancy Complication Network (IPPIC), made up of 72 researchers from 21 countries who have carried out relevant primary studies or have access to existing registry databases, and collectively possess data from more than two million patients. We will use the individual participant data (IPD) from these studies to externally validate these existing prediction models and summarise model performance across studies using random-effects meta-analysis for any, late (after 34 weeks) and early (before 34 weeks) onset pre-eclampsia. If none of the models perform well, we will recalibrate (update), or develop and validate new prediction models using the IPD. We will assess the differential accuracy of the models in various settings and subgroups according to the risk status. We will also validate or develop prediction models based on clinical characteristics only; clinical and biochemical markers; clinical and ultrasound parameters; and clinical, biochemical and ultrasound tests. Discussion: Numerous systematic reviews with aggregate data meta-analysis have evaluated various risk factors separately or in combination for predicting pre-eclampsia, but these are affected by many limitations. Our large-scale collaborative IPD approach encourages consensus towards well developed, and validated prognostic models, rather than a number of competing non-validated ones. The large sample size from our IPD will also allow development and validation of multivariable prediction model for the relatively rare outcome of early onset pre-eclampsia. Trial registration: The project was registered on Prospero on the 27 November 2015 with ID: CRD42015029349

Association between prehospital end-tidal carbon dioxide levels and mortality in patients with suspected severe traumatic brain injury

Purpose: Severe traumatic brain injury is a leading cause of mortality and morbidity, and these patients are frequently intubated in the prehospital setting. Cerebral perfusion and intracranial pressure are influenced by the arterial partial pressure of CO2 and derangements might induce further brain damage. We investigated which lower and upper limits of prehospital end-tidal CO2 levels are associated with increased mortality in patients with severe traumatic brain injury. Methods: The BRAIN-PROTECT study is an observational multicenter study. Patients with severe traumatic brain injury, treated by Dutch Helicopter Emergency Medical Services between February 2012 and December 2017, were included. Follow-up continued for 1 year after inclusion. End-tidal CO2 levels were measured during prehospital care and their association with 30-day mortality was analyzed with multivariable logistic regression. Results: A total of 1776 patients were eligible for analysis. An L-shaped association between end-tidal CO2 levels and 30-day mortality was observed (p = 0.01), with a sharp increase in mortality with values below 35 mmHg. End-tidal CO2 values between 35 and 45 mmHg were associated with better survival rates compared to &lt; 35 mmHg. No association between hypercapnia and mortality was observed. The odds ratio for the association between hypocapnia (&lt; 35 mmHg) and mortality was 1.89 (95% CI 1.53–2.34, p &lt; 0.001) and for hypercapnia (≥ 45 mmHg) 0.83 (0.62–1.11, p = 0.212). Conclusion: A safe zone of 35–45 mmHg for end-tidal CO2 guidance seems reasonable during prehospital care. Particularly, end-tidal partial pressures of less than 35 mmHg were associated with a significantly increased mortality.</p

Development of a multivariable prognostic PREdiction model for 1-year risk of FALLing in a cohort of community-dwelling older adults aged 75 years and above (PREFALL)

Abstract Background Falls are the leading cause of fatal and non-fatal injuries in older adults, and attention to falls prevention is imperative. Prognostic models identifying high-risk individuals could guide fall-preventive interventions in the rapidly growing older population. We aimed to develop a prognostic prediction model on falls rate in community-dwelling older adults. Methods Design: prospective cohort study with 12 months follow-up and participants recruited from June 14, 2018, to July 18, 2019. Setting: general population. Subjects: community-dwelling older adults aged 75+ years, without dementia or acute illness, and able to stand unsupported for one minute. Outcome: fall rate for 12 months. Statistical methods: candidate predictors were physical and cognitive tests along with self-report questionnaires. We developed a Poisson model using least absolute shrinkage and selection operator penalization, leave-one-out cross-validation, and bootstrap resampling with 1000 iterations. Results Sample size at study start and end was 241 and 198 (82%), respectively. The number of fallers was 87 (36%), and the fall rate was 0.94 falls per person-year. Predictors included in the final model were educational level, dizziness, alcohol consumption, prior falls, self-perceived falls risk, disability, and depressive symptoms. Mean absolute error (95% CI) was 0.88 falls (0.71–1.16). Conclusion We developed a falls prediction model for community-dwelling older adults in a general population setting. The model was developed by selecting predictors from among physical and cognitive tests along with self-report questionnaires. The final model included only the questionnaire-based predictors, and its predictions had an average imprecision of less than one fall, thereby making it appropriate for clinical practice. Future external validation is needed. Trial registration Clinicaltrials.gov ( NCT03608709 )

Development and validation of personalised risk prediction models for early detection and diagnosis of primary liver cancer among the English primary care population using the QResearch database: research protocol and statistical analysis plan

BACKGROUND AND RESEARCH AIM: The incidence and mortality of liver cancer have been increasing in the UK in recent years. However, liver cancer is still under-studied. The Early Detection of Hepatocellular Liver Cancer (DeLIVER-QResearch) project aims to address the research gap and generate new knowledge to improve early detection and diagnosis of primary liver cancer from general practice and at the population level. There are three research objectives: (1) to understand the current epidemiology of primary liver cancer in England, (2) to identify and quantify the symptoms and comorbidities associated with liver cancer, and (3) to develop and validate prediction models for early detection of liver cancer suitable for implementation in clinical settings. METHODS: This population-based study uses the QResearch® database (version 46) and includes adult patients aged 25–84 years old and without a diagnosis of liver cancer at the cohort entry (study period: 1 January 2008–30 June 2021). The team conducted a literature review (with additional clinical input) to inform the inclusion of variables for data extraction from the QResearch database. A wide range of statistical techniques will be used for the three research objectives, including descriptive statistics, multiple imputation for missing data, conditional logistic regression to investigate the association between the clinical features (symptoms and comorbidities) and the outcome, fractional polynomial terms to explore the non-linear relationship between continuous variables and the outcome, and Cox/competing risk regression for the prediction model. We have a specific focus on the 1-year, 5-year, and 10-year absolute risks of developing liver cancer, as risks at different time points have different clinical implications. The internal–external cross-validation approach will be used, and the discrimination and calibration of the prediction model will be evaluated. DISCUSSION: The DeLIVER-QResearch project uses large-scale representative population-based data to address the most relevant research questions for early detection and diagnosis of primary liver cancer in England. This project has great potential to inform the national cancer strategic plan and yield substantial public and societal benefits. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s41512-022-00133-x