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. 2023 Jul;32(13-14):3831-3839.
doi: 10.1111/jocn.16503. Epub 2022 Aug 22.

Construction and validation of postoperative hypothermia prediction model for patients undergoing joint replacement surgery

Leilei Li  1 , Yubing Lu  1 , Li Li Yang  2 , Wei Xu  1 , Jing Kai Yu  1
Affiliations

Construction and validation of postoperative hypothermia prediction model for patients undergoing joint replacement surgery

Leilei Li et al. J Clin Nurs. 2023 Jul.

Abstract

Aim: To construct and validate a postoperative hypothermia prediction model for patients undergoing joint replacement surgery.

Background: Postoperative hypothermia is one of the harmful perioperative complications in patients undergoing joint replacement surgery. The previous studies mainly focused on intraoperative hypothermia prediction models. The prediction model for postoperative hypothermia in patients with joint replacement surgery was understudied.

Design: Cohort study.

Methods: We collected data from 503 participants undergoing joint replacement surgery in a tertiary hospital from January 2019 to December 2021. Of those, 404 cases were assigned to the modelling and 99 to the validation groups. Logistic regression was used to construct the model. The AUC was used to test the predictive effect of the model. Finally, 99 cases were used to verify the application effect of the model. A TRIPOD checklist was used to guide the reporting of this study.

Results: The factors entered into the prediction model were age, intraoperative hypothermia, BMI, heat preservation measures and platelet (PLT). The model was constructed as follows: Logit (P) = .537 + 3.669 × 1 (intraoperative hypothermia) + .030 × age - .289 × BMI + 2.857 × 1 (intraoperative insulation measures) + .003 × PLT. Hosmer-Lemeshow test, p = .608, the area under the receiver operating characteristic curve (AUC) was .861. The Youden index was .530, the sensitivity was .599 and the specificity was .93. The incidence of postoperative hypothermia in the modelling group was 42.93% (173/404), and that in the verification group was 43.43% (43/99), χ2 = .012, p = .912. The correct practical application rate was 87.88%. This model has a good application effect.

Conclusion: The current prediction model provided a reference for clinical screening of patients with high-risk hypothermia after joint replacement surgery.

Relevance to clinical practice: Clinical nurses can use the developed prediction model to predict the occurrence of postoperative hypothermia and provide a reference for the preventive measure.

Keywords: hypothermia; joint replacement surgery; nurses; nursing; prediction model.

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