Objective To construct and validate a nomogram prediction model of post-intensive care syndrome (PICS) risk.

Methods A total of 603 patients who were hospitalized and successfully transferred out of comprehensive ICU in 2 tertiary grade A hospitals in Bengbu city were selected and divided into 422 patients in the training set and 181 patients in the validation set.Risk factors were collected using general and clinical information questionnaires during the patients' stay in ICU, followed up at the time of transfer and one month after transfer and divided into PICS group and non-PICS group according to the results of the follow-up.Logistic regression was used to determine the influencing factors, and R language software was applied to construct a nomogram prediction model of PICS risk.The model was internally validated by re-sampling 1 000 times using the enhanced Bootstrap method, and the C-index and calibration curve were used to evaluate the predictive efficacy of the model.

Results The model variables included 10 variables including APACHEⅡ score, CPOT score, duration of invasive mechanical ventilation, age, number of invasive lines at transfer out, total time in ICU, CAM-ICU (delirium), infection in ICU, tracheotomy and vasoactive drugs, with an area under the receiver operating characteristic curve of 0.944 (95%CI: 0.921-0.966) and an area under the receiver operating characteristic curve of 0.944 (95%CI: 0.921-0.966).The best cut-off value was 0.243, with a sensitivity of 91.4% and specificity of 82.63%.The internal and external validation C-indexes were 0.944 (95%CI: 0.921-0.966) and 0.943 (95%CI: 0.913-0.974), respectively, and the calibration curves showed good fits.

Conclusions The prediction model has good discrimination and calibration, which can screen people at high risk of developing PICS after successful ICU treatment visually and effectively, and provide a reference foundation for the early clinical screening and intervention.