Faculty Bibliography

Sickle cell disease (SCD) is characterised by intermittent episodes of acute severe pain, related to vaso-occlusion. Environmental factors are thought to play an important role, and studies in tropical countries have suggested that cold and rainy seasons are associated with increased episodes of acute pain. We have studied retrospectively the number of admissions with acute pain and SCD to King's College Hospital, London, together with daily meteorological records collected locally. Data from 1400 d and 1047 separate admissions were analysed. Increased admissions were significantly associated with increased wind speed and low humidity, but showed no relationship to temperature, rainfall or barometric pressure. The strongest effect was for (maximum wind speed)/humidity, with 464 admissions on days in the lowest two quartiles of this parameter and 582 in the highest quartiles. The effect of high wind and low humidity is likely to be related to skin cooling.

In this paper we study queuing systems within the NHS. Recently imposed government performance targets lead NHS executives to investigate and instigate alternative management strategies, thereby imposing structural changes on the queues. Under such circumstances, it is most unlikely that such systems are in equilibrium. It is crucial, in our opinion, to recognise this state of affairs in order to make a balanced assessment of the role of queue management in the modern NHS. From a mathematical perspective it should be emphasised that measures of the state of a queue based upon the assumption of statistical equilibrium (a pervasive methodology in the study of queues) are simply wrong in the above scenario. To base strategic decisions around such ideas is therefore highly questionable and it is one of the purposes of this paper to offer alternatives: we present some (recent) research whose results generate performance measures and measures of risk, for example, of waiting-times growing unacceptably large; we emphasise that these results concern the transient behaviour of the queueing model-there is no asssumption of statistical equilibrium. We also demonstrate that our results are computationally tractable.

BACKGROUND: Thrombocytosis is one of several pre-analytical factors which contribute to spuriously high serum potassium concentrations, yet there is little published data to guide analysts in the selection of a specific platelet count threshold above which serum potassium results become unreliable. We have studied the sensitivity and specificity of blood platelet count as a predictor of false elevations in potassium. METHODS: Paired serum and plasma potassium measurements together with full blood count were performed for 300 patients. All samples were stored at room temperature and analysed within 4 h of collection. The difference between serum and plasma potassium was plotted against blood platelet count. RESULTS: When the difference (serum-plasma) in potassium concentration was plotted against platelet count, there was a direct linear relationship. Blood platelet counts of >500 x 10(9)/L will detect elevations in serum relative to plasma potassium of >0.5 mmol/L, with a sensitivity of 71% and a specificity of 89%. CONCLUSION: It is recommended that where blood platelet count is above 500 x 10(9)/L, potassium measurements should be repeated using lithium heparin plasma. When serum potassium results of >5.4 mmol/L are obtained, it is our policy to check the platelet count if a sample is available before reporting results. If available and above 500 x 10(9)/L, potassium results are withheld and plasma requested.

This paper describes a model that can forecast the daily number of occupied beds due to emergency admissions in an acute hospital. Out of sample forecasts 32 day days in advance. have an RMS error of 3% of the mean number of beds used for emergency admissions. We find that the number of occupied beds due to emergency admissions is related to both air temperature and PHLS data on influenza like illnesses. We find that a period of high volatility, indicated by GARCH errors, will result in an increase in waiting times in the A&E Department. Furthermore. volatility gives more warning of waiting times in A&E than total bed occupancy.