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IntroductionThis study considers the prediction of the time until two survival outcomes have both occurred. We compared a variety of analytical methods motivated by a typical clinical problem of multimorbidity prognosis.MethodsWe considered five methods: product (multiply marginal risks), dual-outcome (directly model the time until both events occur), multistate models (msm), and a range of copula and frailty models. We assessed calibration and discrimination under a variety of simulated data scenarios, varying outcome prevalence, and the amount of residual correlation. The simulation focused on model misspecification and statistical power. Using data from the Clinical Practice Research Datalink, we compared model performance when predicting the risk of cardiovascular disease and type 2 diabetes both occurring.ResultsDiscrimination was similar for all methods. The product method was poorly calibrated in the presence of residual correlation. The msm and dual-outcome models were the most robust to model misspecification but suffered a drop in performance at small sample sizes due to overfitting, which the copula and frailty model were less susceptible to. The copula and frailty model's performance were highly dependent on the underlying data structure. In the clinical example, the product method was poorly calibrated when adjusting for 8 major cardiovascular risk factors.DiscussionWe recommend the dual-outcome method for predicting the risk of two survival outcomes both occurring. It was the most robust to model misspecification, although was also the most prone to overfitting. The clinical example motivates the use of the methods considered in this study.

Background/Aims: The assessment of disease severity in people with diabetes enables identification of patients in need for intensive and targeted therapies, and could help optimise the allocation of healthcare resources. We aimed to identify and appraise measures that have used clinical data to establish systems for grading Type 2 diabetes severity. Methods: MEDLINE, Embase and PubMed were searched between inception-June 2018. Data from studies on diabetes-specific severity grading measures derived by medical data in adult patients with Type 2 diabetes were extracted. Studies reporting only on other diabetes forms were excluded. Detailed characteristics of the eligible severity measures, including design, severity domains, and the association between severity and health-related outcomes, were identified following independent screening. Results: Eighteen severity measures in 17 papers including 32,314 participants were included. Measures’ designs were: diabetes severity index (N = 8 measures); severity categories (N = 7 measures); complications’ count (N = 2 measures); or severity checklist (N = 1 measure). The included domains differed greatly across the identified measures. Glucose or HbA1c levels and/or diabetes-related complications were included in 89% of the measures. Two severity measures were validated in a separate study population. Higher levels of diabetes severity were associated with poorer cognitive function, higher healthcare expenses, and significantly increased risks for hospitalisation and death. Conclusion: Increasing diabetes severity is associated with greater risks for adverse outcomes. Despite that our findings demonstrate the suitability of health records to assess diabetes severity, the clinical uptake of existing measures is poor. The need to advance diabetes severity assessment is important to develop actionable measures that would help benchmark efficient clinical services.

Background: Healing of non-traumatic skin ulcers is often suboptimal. Prognostic tools that identify people at high risk of delayed healing within the context of routine ulcer assessments may improve this, but robust evidence on which factors to include is lacking. Therefore, we scoped the literature to identify which potentially prognostic factors may warrant future systematic reviews and meta-analyses.Methods: We conducted electronic searches in MEDLINE and Embase to identify studies in English published between 1997-2017 that tested the association between healing of the three most common non-traumatic skin ulcers encountered by healthcare professionals (venous leg, diabetic foot and pressure ulcers) and patient characteristics, ulcer characteristics and results from clinical investigations. Results: We included 42 studies that investigated factors that may be associated with healing of venous leg ulcers (n=17), diabetic foot ulcers (n=15), and pressure ulcers (n=10). Across ulcer types, ulcer characteristics were most commonly reported as potential prognostic factors for healing (n=37), including the size of the ulcer area (n=29) and ulcer duration at first assessment (n=16). Thirty-five studies investigated the prognostic value of patient characteristics (n=35), including age (n=31), sex (n=30), diabetes (n=22), smoking status (n=15) and history of deep vein thrombosis (n=13). Twenty three studies reported results from clinical investigations as potential prognostic factors, with the majority regarding vessel quality.Conclusion: Age, sex, diabetes, smoking status, history of deep vein thrombosis, ulcer area, and ulcer duration at time of first assessment warrant a systematic review and meta-analysis to quantify their prognostic value for delayed ulcer healing.

AimsTo identify and critically appraise measures that use clinical data to grade the severity of Type 2 diabetes.MethodsWe searched MEDLINE, Embase and PubMed between inception and June 2018. Studies reporting on clinical data‐based diabetes‐specific severity measures in adults with Type 2 diabetes were included. We excluded studies conducted solely in participants with other types of diabetes. After independent screening, the characteristics of the eligible measures including design and severity domains, the clinical utility of developed measures, and the relationship between severity levels and health‐related outcomes were assessed.ResultsWe identified 6798 studies, of which 17 studies reporting 18 different severity measures (32 314 participants in 17 countries) were included: a diabetes severity index (eight studies, 44%); severity categories (seven studies, 39%); complication count (two studies, 11%); and a severity checklist (one study, 6%). Nearly 89% of the measures included diabetes‐related complications and/or glycaemic control indicators. Two of the severity measures were validated in a separate study population. More severe diabetes was associated with increased healthcare costs, poorer cognitive function and significantly greater risks of hospitalization and mortality. The identified measures differed greatly in terms of the included domains. One study reported on the use of a severity measure prospectively.ConclusionsHealth records are suitable for assessment of diabetes severity; however, the clinical uptake of existing measures is limited. The need to advance this research area is fundamental as higher levels of diabetes severity are associated with greater risks of adverse outcomes. Diabetes severity assessment could help identify people requiring targeted and intensive therapies and provide a major benchmark for efficient healthcare services.

Background and objective: Baseline clinical and physiological features and changes in these parameters over time are known predictors of survival in patients with fibrotic idiopathic interstitial pneumonia (IIP). Pulmonary hypertension is common in advanced fibrotic IIP, and has a negative impact on survival. Serial pulmonary function profiles, indicative of increasing vascular impairment in patients with IIP, and in particular, selective reductions in gas transfer, have not been studied previously. Methods: Predictors of event-free survival time were investigated in a cohort of Dutch patients with IPF and fibrotic non-specific interstitial pneumonia (n = 117). Pulmonary function test data were prospectively collected from November 1993 to December 2005. Multivariate Cox regression models were developed to identify the prognostic relevance to survival of variables related to baseline demographics, histopathology, pulmonary function and the 6- and 12-month changes in pulmonary function parameters. Results: Different survival patterns were observed for patients with different histopathological diagnoses. At baseline, FVC was the most important prognostic factor. At the 6-month follow up, change in transfer coefficient (K-CO, DLCO/V-A), and at the 12-month follow up, age, baseline K-CO and 12-month change in FVC and K-CO, were independent predictors of event-free survival. Conclusions: Apart from histopathology, change in K-CO over time appeared to be the most consistent and powerful predictor of survival in these patients. The decline in K-CO may be indicative of increasing vascular impairment, which may have a major impact on survival, in patients with fibrotic IIP