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Background: Postoperative complications are the main consumers of technical, medical, and human resources. Especially in the field of elective joint replacement surgery, a specialized, easy-to-obtain, and cost-efficient preoperative stratification and risk-estimation model is missing., Methods: With preoperatively surveyed patient parameters, we identified the most relevant parameters to predict postoperative medical complications. We devised a prospective risk model, measuring the individual probability for intermediate care unit (IMC) or intensive care unit (ICU) admission. The study includes all patients (n = 649) treated with primary or revision total knee arthroplasty in our clinic from 2008 to 2012., Results: The association between general comorbidity scores and mortality risk is well known. Among different comorbidity scores, the Charlson Comorbidity Index is not only relevant for overall postoperative complications (odds ratios [OR] = 2.20) but also predictive of specific complications such as the postoperative need for blood transfusion (OR = 1.94) and unexpected adverse events (OR = 1.74). Considering adverse events, c-reactive protein and leukocyte levels are also highly relevant. Upon predicting a necessary postoperative transfer to an IMC or ICU, the preoperative hemoglobin level, the Charlson Comorbidity Index, and the Index of Coexistent Disease stood out. The latter indicates an increased rate for an IMC/ICU stay by 341% per point. Condensing the most influential predictors, the probability for postoperative IMC/ICU transfer can be calculated for each individual patient. Using the routinely assessed patient's variables, no steadier prediction is possible., Conclusion: The introduced risk-estimation model offers a specialized preoperative resource-stratification method in knee joint replacement surgery. It condenses the most influential, individual risk factors to avoid clinical test redundancy and improve resource efficiency and presurgical care planning. A prospective follow-up study could help validating the risk model in clinical routine., (Copyright © 2019 Elsevier Inc. All rights reserved.)