Recognition of critical situations from time series of laboratory results by case-based reasoning

To develop a technique for recognizing critical situations based on laboratory results in settings in which a normal range cannot be defined, because what is "normal" differs widely from patient to patient. To assess the potential of this approach for kidney transplant recipients, where recognition of acute rejections is based on the pattern of changes in serum creatinine.

We developed a case-based reasoning algorithm using dynamic time-warping as the measure of similarity which allows comparison of series of infrequent measurements at irregular intervals for retrieval of the most similar historical cases for the assessment of a new situation.

The ability to recognize creatinine courses associated with an acute rejection was tested for a set of cases from a database of transplant patient records and compared with the diagnostic performance of experienced physicians. Tests were performed with case bases of various sizes.

The accuracy of the algorithm increased steadily with the size of the available case base. With the largest case bases, the case-based algorithm reached an accuracy of 78 +/- 2%, which is significantly higher than the performance of experienced physicians (69 +/- 5.3%) (p < 0.001).

The new case-based reasoning algorithm with dynamic time warping as the measure of similarity allows extension of the use of automatic laboratory alerting systems to conditions in which abnormal laboratory results are the norm and critical states can be detected only by recognition of pathological changes over time.