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M D Stevenson

Publications and source records attributed to M D Stevenson.

4 recordsLinked to original sources
PubMed2001

Triaging patients with serious head injury: results of a simulation evaluating strategies to bypass hospitals without neurosurgical facilities.

OBJECTIVES: to inform the debate on whether seriously head-injured adult patients should be transported directly to the regional neurosurgical unit or indirectly after evaluation and stabilisation at the nearest hospital. DESIGN: a simulation model was constructed to compare triage strategies and to identify those that predicted the maximum survivors. In each strategy, an estimate of the patient's condition in the field was used to determine the receiving hospital. The model used data from previous publications and local ambulance service and hospital databases. In the absence of valid data, expert clinical estimates were made and subjected to sensitivity analyses. SETTING: an area in the North West Midlands of UK, covered by six acute hospitals including one with a regional neurosurgical unit. OUTCOME MEASURE: the number of survivors predicted by each triage strategy. RESULTS: five strategies were identified which consistently predicted the highest number of survivors. Compared with current policy it was predicted that in the North West Midlands, ten lives per year could be saved (6 per million total population per year). The results from sensitivity analyses did not alter these successful policies. CONCLUSION: the successful strategies should be considered as potential improvements to be introduced into clinical practice.

Computer Simulation↗
PubMedDate not supplied

Gaussian process modeling in conjunction with individual patient simulation modeling: a case study describing the calculation of cost-effectiveness ratios for the treatment of established osteoporosis.

Individual patient-level models can simulate more complex disease processes than cohort-based approaches. However, large numbers of patients need to be simulated to reduce 1st-order uncertainty, increasing the computational time required and often resulting in the inability to perform extensive sensitivity analyses. A solution, employing Gaussian process techniques, is presented using a case study, evaluating the cost-effectiveness of a sample of treatments for established osteoporosis. The Gaussian process model accurately formulated a statistical relationship between the inputs to the individual patient model and its outputs. This model reduced the time required for future runs from 150 min to virtually-instantaneous, allowing probabilistic sensitivity analyses-to be undertaken. This reduction in computational time was achieved with minimal loss in accuracy. The authors believe that this case study demonstrates the value of this technique in handling 1st- and 2nd-order uncertainty in the context of health economic modeling, particularly when more widely used techniques are computationally expensive or are unable to accurately model patient histories.

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