  Once integrated into clinical care, patient risk stratification models may
perform worse compared to their retrospective performance. To date, it is
widely accepted that performance will degrade over time due to changes in care
processes and patient populations. However, the extent to which this occurs is
poorly understood, in part because few researchers report prospective
validation performance. In this study, we compare the 2020-2021 ('20-'21)
prospective performance of a patient risk stratification model for predicting
healthcare-associated infections to a 2019-2020 ('19-'20) retrospective
validation of the same model. We define the difference in retrospective and
prospective performance as the performance gap. We estimate how i) "temporal
shift", i.e., changes in clinical workflows and patient populations, and ii)
"infrastructure shift", i.e., changes in access, extraction and transformation
of data, both contribute to the performance gap. Applied prospectively to
26,864 hospital encounters during a twelve-month period from July 2020 to June
2021, the model achieved an area under the receiver operating characteristic
curve (AUROC) of 0.767 (95% confidence interval (CI): 0.737, 0.801) and a Brier
score of 0.189 (95% CI: 0.186, 0.191). Prospective performance decreased
slightly compared to '19-'20 retrospective performance, in which the model
achieved an AUROC of 0.778 (95% CI: 0.744, 0.815) and a Brier score of 0.163
(95% CI: 0.161, 0.165). The resulting performance gap was primarily due to
infrastructure shift and not temporal shift. So long as we continue to develop
and validate models using data stored in large research data warehouses, we
must consider differences in how and when data are accessed, measure how these
differences may affect prospective performance, and work to mitigate those
differences.
