Dynamic prediction of time to wound healing at routine wound care visits.

Objective Having a wound decreases patients' quality of life and brings uncertainty, especially if the wound does not show a healing tendency. The objective of this study was to develop and validate a model to dynamically predict time to wound healing at subsequent routine wound care visits. Approach A dynamic prediction model was developed in a cohort of wounds treated by nurse practitioners between 2017-2022. Potential predictors were selected based on literature, expert opinion, and availability in the routine care setting. To assess performance for future wound care visits, the model was validated in a new cohort of wounds visited in early 2023. Reporting followed TRIPOD guidelines. Results We analyzed data from 92,098 visits, corresponding to 14,248 wounds and 7,221 patients. At external validation, discriminative performance of our developed model was comparable to internal validation (c-statistic = 0.70 [95% CI 0.69, 0.71]) and the model remained well-calibrated. Strong predictors were wound-level characteristics and indicators of the healing process so far (e.g., wound surface area). Innovation Going beyond previous prediction studies in the field, the developed model dynamically predicts the remaining time to wound healing for many wound types at subsequent wound care visits, in line with the dynamic nature of wound care. In addition, the model was externally validated and showed stable performance. Conclusion: The developed model can potentially contribute to patient satisfaction and reduce uncertainty around wound healing times when implemented in practice. When the predicted time of wound healing remains high, practitioners can consider adapting their wound management.

Effective sample size: A measure of individual uncertainty in predictions.

Clinical prediction models are estimated using a sample of limited size from the target population, leading to uncertainty in predictions, even when the model is correctly specified. Generally, not all patient profiles are observed uniformly in model development. As a result, sampling uncertainty varies between individual patients' predictions. We aimed to develop an intuitive measure of individual prediction uncertainty. The variance of a patient's prediction can be equated to the variance of the sample mean outcome in n ∗ $$ {n}_{\ast } $$ hypothetical patients with the same predictor values. This hypothetical sample size n ∗ $$ {n}_{\ast } $$ can be interpreted as the number of similar patients n eff $$ {n}_{\mathrm{eff}} $$ that the prediction is effectively based on, given that the model is correct. For generalized linear models, we derived analytical expressions for the effective sample size. In addition, we illustrated the concept in patients with acute myocardial infarction. In model development, n eff $$ {n}_{\mathrm{eff}} $$ can be used to balance accuracy versus uncertainty of predictions. In a validation sample, the distribution of n eff $$ {n}_{\mathrm{eff}} $$ indicates which patients were more and less represented in the development data, and whether predictions might be too uncertain for some to be practically meaningful. In a clinical setting, the effective sample size may facilitate communication of uncertainty about predictions. We propose the effective sample size as a clinically interpretable measure of uncertainty in individual predictions. Its implications should be explored further for the development, validation and clinical implementation of prediction models.

Discontinuation of Anti-Tumour Necrosis Factor Therapy in Patients with Perianal Fistulizing Crohn's Disease: Individual Participant Data Meta-Analysis of 309 Patients from 12 Studies.

BACKGROUND: The risk of relapse after anti-tumour necrosis factor [TNF] therapy discontinuation in Crohn's disease patients with perianal fistulas [pCD] is unclear. We aimed to assess this risk. METHODS: A systematic literature search was conducted to identify cohort studies on the incidence of relapse following anti-TNF discontinuation in pCD patients. Individual participant data were requested from the original study cohorts. Inclusion criteria were age ≥16 years, pCD as a (co)indication for start of anti-TNF therapy, more than three doses, and remission of luminal and pCD at anti-TNF discontinuation. The primary outcome was the cumulative incidence of CD relapse using Kaplan-Meier estimates. Secondary outcomes included response to re-treatment and risk factors associated with relapse as assessed by Cox regression analysis. RESULTS: In total, 309 patients from 12 studies in ten countries were included. The median duration of anti-TNF treatment was 14 months [interquartile range 5.8-32.5]. Most patients were treated for pCD without active luminal disease [89%], received first-line anti-TNF therapy [87%], and continued immunomodulatory therapy following anti-TNF discontinuation [78%]. The overall cumulative incidence of relapse was 36% (95% confidence interval [CI] 25-48%) and 42% [95% CI 32-53%] at 1 and 2 years after anti-TNF discontinuation, respectively. Risk factors for relapse included smoking (hazard ratio [HR] 1.5 [1.0, 2.1]) and history of proctitis (HR 1.7 [1.1, 2.5]). The overall re-treatment response rate was 82%. CONCLUSIONS: This individual participant data meta-analysis, on predominantly patients with pCD without active luminal disease and first-line anti-TNF therapy, shows that over half of patients remain in remission 2 years after anti-TNF discontinuation. Therefore, anti-TNF discontinuation may be considered in this subgroup.

Single-arm studies involving patient-reported outcome data in oncology: a literature review on current practice.

Patient-reported outcomes (PROs) are increasingly used in single-arm cancer studies. We reviewed 60 papers published between 2018 and 2021 of single-arm studies of cancer treatment with PRO data for current practice on design, analysis, reporting, and interpretation. We further examined the studies' handling of potential bias and how they informed decision making. Most studies (58; 97%) analysed PROs without stating a predefined research hypothesis. 13 (22%) of the 60 studies used a PRO as a primary or co-primary endpoint. Definitions of PRO objectives, study population, endpoints, and missing data strategies varied widely. 23 studies (38%) compared the PRO data with external information, most often by using a clinically important difference value; one study used a historical control group. Appropriateness of methods to handle missing data and intercurrent events (including death) were seldom discussed. Most studies (51; 85%) concluded that PRO results supported treatment. Conducting and reporting of PROs in cancer single-arm studies need standards and a critical discussion of statistical methods and possible biases. These findings will guide the Setting International Standards in Analysing Patient-Reported Outcomes and Quality of Life Data in Cancer Clinical Trials-Innovative Medicines Initiative (SISAQOL-IMI) in developing recommendations for the use of PRO-measures in single-arm studies.

A Bayesian (meta-)regression model for treatment effects on the risk difference scale.

In clinical settings, the absolute risk reduction due to treatment that can be expected in a particular patient is of key interest. However, logistic regression, the default regression model for trials with a binary outcome, produces estimates of the effect of treatment measured as a difference in log odds. We explored options to estimate treatment effects directly as a difference in risk, specifically in the network meta-analysis setting. We propose a novel Bayesian (meta-)regression model for binary outcomes on the additive risk scale. The model allows treatment effects, covariate effects, interactions and variance parameters to be estimated directly on the linear scale of clinical interest. We compared effect estimates from this model to (1) a previously proposed additive risk model by Warn, Thompson and Spiegelhalter ("WTS model") and (2) backtransforming the predictions from a logistic model to the natural scale after regression. The models were compared in a network meta-analysis of 20 hepatitis C trials, as well as in the analysis of simulated single trial settings. The resulting estimates diverged, in particular for small sample sizes or true risks close to 0% or 100%. Researchers should be aware that modelling untransformed risk can yield very different results from default logistic models. The treatment effect in participants with such extreme predicted risks weighed more heavily on the overall treatment effect estimate from our proposed model compared to the WTS model. In our network meta-analysis, this sensitivity of our proposed model was needed to detect all information in the data.

Validation and update of a prediction model for risk of relapse after cessation of anti-TNF treatment in Crohn's disease.

BACKGROUND: Anti-tumor necrosis factor (TNF) therapy is effective for the treatment of Crohn's disease. Cessation may be considered in patients with a low risk of relapse. We aimed to externally validate and update our previously developed prediction model to estimate the risk of relapse after cessation of anti-TNF therapy. METHODS: We performed a retrospective cohort study in 17 Dutch hospitals. Crohn's disease patients in clinical, biochemical or endoscopic remission were included after anti-TNF cessation. Primary outcome was a relapse necessitating treatment. Discrimination and calibration of the previously developed model were assessed. After external validation, the model was updated. The performance of the updated prediction model was assessed in internal-external validation and by using decision curve analysis. RESULTS: 486 patients were included with a median follow-up of 1.7 years. Relapse rates were 35 and 54% after 1 and 2 years. At external validation, the discriminative ability of the prediction model was equal to that found at the development of the model [c-statistic 0.58 (95% confidence interval (CI) 0.54-0.62)], though the model was not well-calibrated on our cohort [calibration slope: 0.52 (0.28-0.76)]. After an update, a c-statistic of 0.60 (0.58-0.63) and calibration slope of 0.89 (0.69-1.09) were reported in internal-external validation. CONCLUSION: Our previously developed and updated prediction model for the risk of relapse after cessation of anti-TNF in Crohn's disease shows reasonable performance. The use of the model may support clinical decision-making to optimize patient selection in whom anti-TNF can be withdrawn. Clinical validation is ongoing in a prospective randomized trial.