BACKGROUND: Although medical intervention of periprosthetic bone loss in the immediate postoperative period was recommended, not all the patients experienced periprosthetic bone loss after total hip arthroplasty (THA). Prediction tools that enrolled all potential risk factors to calculate an individualized prediction of postoperative periprosthetic bone loss were strongly needed for clinical decision-making. METHODS: Data of the patients who underwent primary unilateral cementless THA between April 2015 and October 2017 in our center were retrospectively collected. Candidate variables included demographic data and bone mineral density (BMD) in spine, hip, and periprosthetic regions that measured 1 week after THA. Outcomes of interest included the risk of postoperative periprosthetic bone loss in Gruen zone 1, 7, and total zones in the 1st postoperative year. Nomograms were presented based on multiple logistic regressions via R language. One thousand Bootstraps were used for internal validation. RESULTS: Five hundred sixty-three patients met the inclusion criteria were enrolled, and the final analysis was performed in 427 patients (195 male and 232 female) after the exclusion. The mean BMD of Gruen zone 1, 7, and total were decreased by 4.1%, 6.4%, and 1.7% at the 1st year after THA, respectively. 61.1% of the patients (261/427) experienced bone loss in Gruen zone 1 at the 1st postoperative year, while there were 58.1% (248/427) in Gruen zone 7 and 63.0% (269/427) in Gruen zone total. Bias-corrected C-index for risk of postoperative bone loss in Gruen zone 1, 7, and total zones in the 1 postoperative year were 0.700, 0.785, and 0.696, respectively. The most highly influential factors for the postoperative periprosthetic bone loss were primary diagnosis and BMD in the corresponding Gruen zones at the baseline. CONCLUSIONS: To the best of our knowledge, our study represented the first time to use the nomograms in estimating the risk of postoperative periprosthetic bone loss with adequate predictive discrimination and calibration. Those predictive models would help surgeons to identify high-risk patients who may benefit from anti-bone-resorptive treatment in the early postoperative period effectively. It is also beneficial for patients, as they can choose the treatment options based on a reasonable expectation following surgery.