The use of routine laboratory data to predict in-hospital death in medical admissions.

The ability to predict clinical outcomes in the early phase of a patient's hospital admission could facilitate the optimal use of resources, might allow focused surveillance of high-risk patients and might permit early therapy. We investigated the hypothesis that the risk of in-hospital death of general medical patients can be modelled using a small number of commonly used laboratory and administrative items available within the first few hours of hospital admission. Matched administrative and laboratory data from 9497 adult hospital discharges, with a hospital discharge specialty of general medicine, were divided into two subsets. The dataset was split into a single development set, Q(1) (n=2257), and three validation sets, Q(2), Q(3) and Q(4) (n(1)=2335, n(2)=2361, n(3)=2544). Hospital outcome (survival/non-survival) was obtained for all discharges. An outcome model was constructed from binary logistic regression of the development set data. The goodness-of-fit of the model for the validation sets was tested using receiver-operating characteristics curves (c-index) and Hosmer-Lemeshow statistics. Application of the model to the validation sets produced c-indices of 0.779 (Q(2)), 0.764 (Q(3)) and 0.757 (Q(4)), respectively, indicating good discrimination. Hosmer-Lemeshow analysis gave chi(2)=9.43 (Q(2)), chi(2)=7.39 (Q(3)) and chi(2)=8.00 (Q(4)) (p-values of 0.307, 0.495 and 0.433) for 8 degrees of freedom, indicating good calibration. The finding that the risk of hospital death can be predicted with routinely available data very early on after hospital admission has several potential uses. It raises the possibility that the surveillance and treatment of patients might be categorised by risk assessment means. Such a system might also be used to assess clinical performance, to evaluate the benefits of introducing acute care interventions or to investigate differences between acute care systems.

Towards a national clinical minimum data set for general surgery.

BACKGROUND: Measurement and comparison of surgical performance is accepted as necessary and inevitable. Risk-stratified (case-mix adjusted) models of clinical outcomes form a metric with which to assess performance, but require accurate data. Collecting such data in the clinical environment is time consuming and difficult. This study aimed to construct effective models, for operative and non-operative admissions, from routine clinical data residing in hospital computers, so minimizing data collection and quality problems, and facilitating national implementation. METHODS: Data for 3181 non-operative emergency, 5039 elective and 3043 emergency operative admissions for the 2 years beginning 1 August 1997 were used to generate logistic regression equations for risk of death, which were applied prospectively to the following 3 years' data. RESULTS: The models use urea, haemoglobin, white blood cell count, sodium, potassium, age on admission, sex, British United Provident Association (BUPA) Operative Severity Score (for operative admissions) and, implicitly, mode of admission and mortality at discharge. All three models successfully stratified risk into five or more bands. CONCLUSION: Effective models of mortality, applicable to all general surgical admissions, can be constructed from existing routine clinical data, largely obtained from a single venesection. The data set is a candidate national clinical minimum data set.