Development and validation of a dynamic prediction model for infective endocarditis patients based on platelet trajectories latent classes: a longitudinal retrospective study from multicenter databases

We read with great interest the article by Zheng et al, “Development and validation of a dynamic prediction model for infective endocarditis patients based on platelet (PLT) trajectories latent classes: a longitudinal retrospective study from multicenter databases”[1]. Their work leverages longitudinal PLT trajectories – a biomarker mechanistically linked to infection, immunity, coagulation, and frailty in infective endocarditis (IE) – to build a time-updated risk model for intensive care unit (ICU) outcomes[1]. In parallel, emerging guidance on transparent reporting of artificial intelligence and prediction tools, such as the TITAN guideline, has underscored the need to clearly describe model specification, calibration, updating, and clinical deployment[2]. Against this backdrop, the authors’ use of latent class growth mixture modelling and Bayesian joint models represents a timely contribution to the growing literature on dynamic laboratory trajectories in critical care, including PLT-based ICU mortality models, severe COVID-19 cohorts, and septic patients[3–5]. First, we are concerned that the very high headline performance of the dynamic joint model (area under the receiver operating characteristic curve around 0.71 with F1-scores reported as 0.92–0.97) may risk over-optimism from a clinical perspective, given the heterogeneity of IE and the moderate sample size[1]. For bedside decision-making, clinicians need not only point estimates but also robust internal validation (e.g., bootstrap resampling or internal–external cross-validation), confidence intervals around discrimination metrics, and explicit handling of repeated measurements and informative censoring[6,7]. Providing these details would allow readers to better gauge whether the dynamic model is likely to maintain performance outside the development datasets. Second, calibration and clinical usefulness could be reported more comprehensively. The abstract states that the model achieved “robust predictive performance,” yet only limited calibration information is shown, and no plots or statistics (such as calibration slope or calibration-in-the-large) are presented across different time horizons[1]. Modern prediction frameworks emphasize that even models with acceptable discrimination can mislead clinicians if they are miscalibrated –over- or under-estimating absolute risk in specific subgroups or risk ranges[7]. Similarly, decision-curve analysis and net-benefit plots are now widely recommended to quantify whether a model meaningfully improves decision-making across plausible threshold probabilities, instead of relying on accuracy or F1-scores alone[6,7]. Incorporating these elements would considerably strengthen the claim that the proposed dynamic tool is ready to inform high-stakes decisions in IE. Third, the incremental value of the complex Bayesian joint model over simpler approaches is not fully demonstrated. Previous work has shown that relatively parsimonious models using PLT trajectory patterns, nadirs, or early changes can already stratify mortality risk in ICU and septic populations[3–5]. In this context, it would be informative to compare the joint model against pragmatic competitors, for example, a Cox or logistic model using baseline characteristics plus a small number of handcrafted trajectory summaries, or simpler latent-class structures with fewer classes. Demonstrating clear, reproducible gains in discrimination, calibration, and net benefit over such simpler models would help justify the added implementation complexity of joint modeling in routine practice[6,7]. Looking forward, we see at least three avenues to maximize the translational impact of this important work. First, external validation across centere and calendar periods – ideally using internal–external cross-validation or temporal split-sample designs – would clarify how stable the identified PLT trajectory classes and model parameters are in different ICU case-mixes[3,5,7]. Second, future research might explicitly link the dynamic risk predictions to key management decisions in IE (for example, timing of surgery, intensity of hemodynamic monitoring, or early escalation of antimicrobial therapy), supported by decision-curve analyses that translate model performance into potential clinical benefit[6]. Third, aligning the reporting and open-science practices of this line of work with contemporary guidance such as TITAN – covering model specification, code availability, updating strategies, and fairness assessments – will facilitate independent validation and adaptation in diverse healthcare systems[2,7]. In summary, Zheng et al have advanced the field by integrating PLT trajectories with infection markers to dynamically stratify risk in patients with IE[1]. By further clarifying calibration, decision-analytic performance, and incremental value over simpler models – and by embracing emerging prediction-model reporting standards – we believe this dynamic framework could become a clinically interpretable and widely adopted tool for managing complex IE in the ICU[2,3,6,7].