Does time-incorporated admission severity improve stroke prognostication?

Introduction The dynamic relationship between time since symptom onset and severity on admission is poorly incorporated in stroke prognostic models. We sought to develop a novel approach and compare it with alternatives.

We developed admission stroke severity centiles to contextualise a patient’s stroke severity relative to others admitted at a similar time post ictus. We conducted a secondary data analysis to compare four prognostic models, including the novel severity centiles and other time-incorporated approaches. Models were evaluated using Pseudo-R squared, Bayesian Information Criterion, and Area Under the Curve.

Time since symptom onset not only predicts outcomes but also modifies the prognostic value of severity, with delayed admission amplifying its impact on functional dependency and mortality in acute ischaemic stroke (AIS) patients [Adjusted Odds Ratio (OR) (95% CI) = 1.02 (1.01 to 1.04); Adjusted OR (95% CI) = 1.03 (1.01 to 1.05), respectively]. Centile-based method offered a dynamic interpretation of stroke severity for both stroke types, demonstrating a prognostic signal comparable to traditional time-incorporated models. For AIS patients, each unit increase in centile corresponded to a 5% increase in the odds of functional dependency Adjusted OR (95% CI) = 1.05 (1.04 to 1.05) and a 5% increase in the odds of mortality Adjusted OR (95% CI) = 1.05 (1.04 to 1.07). Similar results were obtained for intracerebral haemorrhagic (ICH) patients. While covariate adjustment improved overall model performance, it did not alter the ranking of time-incorporated methods. Time-incorporated approaches did not enhance model performance for ICH patients, possibly due to a smaller sample.

We are the first to construct centiles for admission stroke severity. Severity centiles based on a reference population offer a graphical tool to support risk assessment and outcome prediction. While this approach provides clinically interpretable measures, our results suggest that explicitly incorporating time into prognostic models offers slightly better predictive performance. We recommend using statistical models that allow the effect of stroke severity to vary according to the time since symptom onset to assessment. Competing Interest Statement The authors have declared no competing interest. Funding Statement This study did not receive any funding Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: This study was approved by the Regional Ethics Committee for Medical and Health Research Ethics in Western Norway (approval no. 2012/1483 Norstroke). I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Data Availability All data produced in the present study are available upon reasonable request to the authors