Project Summary Post-surgical complications (PSCs) have been an increasing concern for hospitals, particularly in light of payment reform focusing on longer episodes and Medicare penalties for 30-day readmissions and adverse outcomes including deep or organ space surgical site infections (DOS-SSIs). Readmission due to post- discharge complications in particular has become a target for quality improvement since many of these events are considered preventable. The wide adoption of electronic health records (EHRs) has led to a number of clinical risk models for PSCs. These modeling efforts have primarily been targeted at the surgical specialty areas within which a large number of events occur (such as colorectal surgery) as well as applying sophisticated statistical modeling / machine learning to allow for missing data, interactions, and nonlinearities. However, there is still considerable room for improvement both in terms of accuracy and generalizability. In our current funding period, we have demonstrated the predictive value of clinical notes for PSCs. However, one glaring limitation of current models is that they are trained on high volume surgical specialties at large tertiary care institutions with high quality clinical data and use of advanced informatics approaches. The impetus of this proposal is essentially two-fold: (i) Accurate models can be created for lower volume institutions and specialties via transfer learning and leveraging more data via unconfirmed outcomes (i.e., those that mimic gold standard outcomes, but are less reliable) with proper accounting of reliability. (ii) Decision making can be significantly improved by leveraging time varying, real-time data such as labs, vitals, and clinical notes to provide the current risk of PSCs for patients using all information as it becomes available. We aim to i) develop and apply longitudinal risk models for PSCs to explicitly account for the time varying nature of some of the information (e.g., labs, vitals, clinical notes) as it becomes available in real-time so that it can be integrated into the clinician?s decision making; ii) develop and apply transfer learning to PSC risk models; iii) develop modeling approaches that allow for the use of more widely available unconfirmed outcomes, while explicitly accounting for the additional uncertainty and bias due to the use of such unconfirmed outcomes when compared to a less available gold standard; and iv) develop a widely applicable framework for model evaluation and monitoring. Models will often not perform in practice as they do in research for a variety of reasons. This framework will allow us to identify these issues and more efficiently translate and apply these complex predictive models into practice so that the research can have an immediate clinical impact. Successful development would open the door for next generation patient monitoring, alerts, and interventions for all surgical specialties and all institutions. We will make the relevant modeling results publicly available so that lower volume institutions can leverage the transfer learning approach developed here without the need for our actual data. This will ultimately lead to improved patient care and lower overall cost by identifying complications early and limiting readmission due to PSCs at Mayo Clinic and other institutions across the nation.
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