To compare the characteristics of patients with type 2 diabetes mellitus in general practice and those included in randomised controlled trials on which clinical practice guidelines are based.

Cross-sectional comparative study.

We asked 45 general practitioners from three French Departments to identify the 15 patients with type 2 diabetes mellitus they most recently saw in consultation. In parallel, we selected randomised controlled trials included in the Cochrane systematic review on which the clinical practice guidelines for type 2 diabetes mellitus were based.

We included 675 patients with type 2 diabetes mellitus, and data were collected from 23 randomised controlled trials, corresponding to 36 059 patients.

Characteristics of general-practice patients were extracted from medical records by a unique observer. The same baseline characteristics of patients included in randomised controlled trials from the Cochrane systematic review were extracted and meta-analysed. We assessed standardised differences between these two series of baseline characteristics. A difference greater than 0.10 in absolute value was considered meaningful.

General-practice patients were older than randomised controlled trial patients (mean (SD) 68.8 (1.1) vs 59.9 years (standardised difference 0.8)) and had a higher body mass index (mean (SD) 31.5 (6.9) vs 28.2 kg/m² (standardised difference 0.5)) but smoked less (11.0% vs 29.3% (standardised difference –0.6)). They more frequently used antihypertensive drugs (82.1% vs 37.5% (standardised difference 1.2)) but less frequently had a myocardial infarction (7.6% vs 23.1% (standardised difference –1.1)).

Patients with type 2 diabetes mellitus cared for in general practice differ in a number of important aspects from patients included in randomised controlled trials on which clinical practice guidelines are based. This situation hampers the applicability of these guidelines. Future randomised trials should include patients who better fit the ‘average’ general-practice patient with type 2 diabetes mellitus to help improve the translation of study findings in daily practice.

Conventional prediction models fail to integrate the constantly evolving nature of critical illness. Alternative modelling approaches to study dynamic changes in critical illness progression are needed. We compare static risk prediction models to dynamic probabilistic models in early critical illness.

We developed models to simulate disease trajectories of critically ill COVID-19 patients across different disease states. Eighty per cent of cases were randomly assigned to a training and 20% of the cases were used as a validation cohort. Conventional risk prediction models were developed to analyse different disease states for critically ill patients for the first 7 days of intensive care unit (ICU) stay. Daily disease state transitions were modelled using a series of multivariable, multinomial logistic regression models. A probabilistic dynamic systems modelling approach was used to predict disease trajectory over the first 7 days of an ICU admission. Forecast accuracy was assessed and simulated patient clinical trajectories were developed through our algorithm.

We retrospectively studied patients admitted to a Cleveland Clinic Healthcare System in Ohio, for the treatment of COVID-19 from March 2020 to December 2022.

5241 patients were included in the analysis. For ICU days 2–7, the static (conventional) modelling approach, the accuracy of the models steadily decreased as a function of time, with area under the curve (AUC) for each health state below 0.8. But the dynamic forecasting approach improved its ability to predict as a function of time. AUC for the dynamic forecasting approach were all above 0.90 for ICU days 4–7 for all states.

We demonstrated that modelling critical care outcomes as a dynamic system improved the forecasting accuracy of the disease state. Our model accurately identified different disease conditions and trajectories, with a