Development and validation of an international preoperative risk assessment model for postoperative delirium

Abstract

Background: Postoperative delirium (POD) is a frequent complication in older adults, characterised by disturbances in attention, awareness and cognition, and associated with prolonged hospitalisation, poor functional recovery, cognitive decline, long-term dementia and increased mortality. Early identification of patients at risk of POD can considerably aid prevention.

Methods: We have developed a preoperative POD risk prediction algorithm using data from eight studies identified during a systematic review and providing individual-level data. Ten-fold cross-validation was used for predictor selection and internal validation of the final penalised logistic regression model. The external validation used data from university hospitals in Switzerland and Germany.

Results: Development included 2,250 surgical (excluding cardiac and intracranial) patients 60 years of age or older, 444 of whom developed POD. The final model included age, body mass index, American Society of Anaesthesiologists (ASA) score, history of delirium, cognitive impairment, medications, optional C-reactive protein (CRP), surgical risk and whether the operation is a laparotomy/thoracotomy. At internal validation, the algorithm had an AUC of 0.80 (95% CI: 0.77-0.82) with CRP and 0.79 (95% CI: 0.77-0.82) without CRP. The external validation consisted of 359 patients, 87 of whom developed POD. The external validation yielded an AUC of 0.74 (95% CI: 0.68-0.80).

Conclusions: The algorithm is named PIPRA (Pre-Interventional Preventive Risk Assessment), has European conformity (ce) certification, is available at http://pipra.ch/ and is accepted for clinical use. It can be used to optimise patient care and prioritise interventions for vulnerable patients and presents an effective way to implement POD prevention strategies in clinical practice.

Keywords: algorithm; clinical practice; older people; postoperative delirium; risk prediction.

Graphical abstract

Figure 1

Exclusion criteria employed in the selection of patient data for training and validating the POD risk prediction algorithm. Numbers represent the number of patients excluded and remaining at each selection step. The algorithm was validated in an external dataset.

Figure 2

Performance of the model. (A,B) Classification plots of the models with and without CRP for development (A) and validation (B). (C) Calibration plots of the models with and without CRP using 10-fold cross-validation with the total training dataset. (D) Calibration plot of the models with and without CRP on the external validation dataset. Each datapoint represents 10% of the data presented as mean ± 95% confidence intervals. The diagonal white line represents the ideal calibration line with an intercept of 0 and a regression coefficient of 1. (E, F) Based on the risk scores provided by the algorithm without CRP, the patients were separated into four risk groups, and the proportion of patients in each group are displayed.

Figure 3

The PIPRA POD risk prediction algorithm clinical interface. Close-up images showing (A) the input screen and (B) the output screen of the web application. The impact of an individual risk factor on the overall risk is shown in comparison with the average (for continuous variables) or the most common (for categorical variables).