Outcomes in Post-operative Delirium Following Bowel Resection: A Single Center Retrospective Review.

INTRODUCTION: Delirium is associated with adverse post-operative outcomes, long-term cognitive dysfunction, and prolonged hospitalization. Risk factors for its development include longer surgical duration, increased operative complexity and invasiveness, and medical comorbidities. This study aims to further evaluate the incidence of delirium and its impact on outcomes among patients undergoing both elective and emergency bowel resections. METHODS: This is a retrospective cohort study using an institutional patient registry. All patients undergoing bowel resection over a 3.5-year period were included. The study measured the incidence of post-operative delirium via the nursing confusion assessment method. This incidence was then compared to patient age, emergency versus elective admission, length of stay, mortality, discharge disposition, and hospital cost. RESULTS: A total of 1934 patients were included with an overall delirium incidence of 8.8%. Compared to patients without delirium, patients with delirium were more likely to have undergone emergency surgery, be greater than 70 y of age, have a longer length of stay, be discharged to a skilled nursing facility, and have a more expensive hospitalization. In addition, the overall mortality was 14% in patients experiencing delirium versus 0.1% in those that did not. Importantly, when broken down between elective and emergency groups, the mortality of those experiencing delirium was similar (11 versus 13%). CONCLUSIONS: The development of delirium following bowel resection is an important risk factor for worsened outcomes and mortality. Although the incidence of delirium is higher in the emergency surgery population, the development of delirium in the elective population infers a similar risk of mortality.

Prediction of Incident Delirium Using a Random Forest classifier.

Delirium is a serious medical complication associated with poor outcomes. Given the complexity of the syndrome, prevention and early detection are critical in mitigating its effects. We used Confusion Assessment Method (CAM) screening and Electronic Health Record (EHR) data for 64,038 inpatient visits to train and test a model predicting delirium arising in hospital. Incident delirium was defined as the first instance of a positive CAM occurring at least 48 h into a hospital stay. A Random Forest machine learning algorithm was used with demographic data, comorbidities, medications, procedures, and physiological measures. The data set was randomly partitioned 80% / 20% for training and validating the predictive model, respectively. Of the 51,240 patients in the training set, 2774 (5.4%) experienced delirium during their hospital stay; and of the 12,798 patients in the validation set, 701 (5.5%) experienced delirium. Under-sampling of the delirium negative population was used to address the class imbalance. The Random Forest predictive model yielded an area under the receiver operating characteristic curve (ROC AUC) of 0.909 (95% CI 0.898 to 0.921). Important variables in the model included previously identified predisposing and precipitating risk factors. This machine learning approach displayed a high degree of accuracy and has the potential to provide a clinically useful predictive model for earlier intervention in those patients at greatest risk of developing delirium.

Analysis of multi-dimensional contemporaneous EHR data to refine delirium assessments.

Delirium is a potentially lethal condition of altered mental status, attention, and level of consciousness with an acute onset and fluctuating course. Its causes are multi-factorial, and its pathophysiology is not well understood; therefore clinical focus has been on prevention strategies and early detection. One patient evaluation technique in routine use is the Confusion Assessment Method (CAM): a relatively simple test resulting in 'positive', 'negative' or 'unable-to-assess' (UTA) ratings. Hartford Hospital nursing staff use the CAM regularly on all non-critical care units, and a high frequency of UTA was observed after reviewing several years of records. In addition, patients with UTA ratings displayed poor outcomes such as in-hospital mortality, longer lengths of stay, and discharge to acute and long term care facilities. We sought to better understand the use of UTA, especially outside of critical care environments, in order to improve delirium detection throughout the hospital. An unsupervised clustering approach was used with additional, concurrent assessment data available in the EHR to categorize patient visits with UTA CAMs. The results yielded insights into the most common situations in which the UTA rating was used (e.g. impaired verbal communication, dementia), suggesting potentially inappropriate ratings that could be refined with further evaluation and remedied with updated clinical training. Analysis of the patient clusters also suggested that unrecognized delirium may contribute to the poor outcomes associated with the use of UTA. This method of using temporally related high dimensional EHR data to illuminate a dynamic medical condition could have wider applicability.