Using Machine Learning Algorithms to Predict High-Risk Factors for Postoperative Delirium in Elderly Patients

Mitochondrial Dysfunction of Peripheral Platelets as a Predictive Biomarker for Postoperative Delirium in Elderly Patients

OBJECTIVE

To determine the association between the preoperative Bioenergetic Health Index (BHI) of platelets and the occurrence of postoperative delirium (POD) in elderly patients.

METHODS

Elderly patients scheduled for major abdominal surgery under general anesthesia were included. The presence of POD was assessed within the 3 days after surgery. Seahorse XF analysis and transmission electron microscopy were utilized to evaluate the mitochondrial metabolism and morphology of platelets.

RESULTS

A total of 20 out of 162 participants developed POD. Participants with POD showed lower preoperative Mini-Mental State Examination scores and total protein levels, fewer educational years, longer surgery duration, higher mean platelet volume, and lower platelet BHI compared with those without POD. Damaged mitochondria with swollen appearance and distorted cristae was detected in platelets from participants with POD. Preoperative platelet BHI was independently associated with the occurrence of POD after adjusting for age, education, preoperative Mini-Mental State Examination score, preoperative mean platelet volume and total protein levels, surgical type and duration, and lymphocyte counts on the first postoperative day (OR 0.11, 95% CI 0.03-0.37, p < 0.001). The areas under the receiver operating curves for predicting POD were 0.83 (95% CI 0.76-0.88) for platelet BHI. It showed a sensitivity of 85.00% and specificity of 73.24%, with an optimal cutoff value of 1.61. Using a serial combination (mean platelet volume followed by BHI) yielded a sensitivity of 80.00% and specificity of 82.39%.

INTERPRETATION

Preoperative platelet BHI was independently associated with the occurrence of POD in elderly patients and has the potential as a screening biomarker for POD risk. ANN NEUROL 2024;96:74-86.

Can admission Braden skin score predict delirium in older adults in the intensive care unit? Results from a multicenter study

AIMS AND OBJECTIVES

To investigate whether a low Braden Skin Score (BSS), reflecting an increased risk of pressure injury, could predict the risk of delirium in older patients in the intensive care unit (ICU).

BACKGROUND

Delirium, a common acute encephalopathy syndrome in older ICU patients, is associated with prolonged hospital stay, long-term cognitive impairment and increased mortality. However, few studies have explored the relationship between BSS and delirium.

DESIGN

Multicenter cohort study.

METHODS

The study included 24,123 older adults from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database and 1090 older adults from the eICU Collaborative Research Database (eICU-CRD), all of whom had a record of BSS on admission to the ICU. We used structured query language to extract relevant data from the electronic health records. Delirium, the primary outcome, was primarily diagnosed by the Confusion Assessment Method for the ICU or the Intensive Care Delirium Screening Checklist. Logistic regression models were used to validate the association between BSS and outcome. A STROBE checklist was the reporting guide for this study.

RESULTS

The median age within the MIMIC-IV and eICU-CRD databases was approximately 77 and 75 years, respectively, with 11,195 (46.4%) and 524 (48.1%) being female. The median BSS at enrollment in both databases was 15 (interquartile range: 13, 17). Multivariate logistic regression showed a negative association between BSS on ICU admission and the prevalence of delirium. Similar patterns were found in the eICU-CRD database.

CONCLUSIONS

This study found a significant negative relationship between ICU admission BSS and the prevalence of delirium in older patients.

RELEVANCE TO CLINICAL PRACTICE

The BSS, which is simple and accessible, may reflect the health and frailty of older patients. It is recommended that BSS assessment be included as an essential component of delirium management strategies for older patients in the ICU.

NO PATIENT OR PUBLIC CONTRIBUTION

This is a retrospective cohort study, and no patients or the public were involved in the design and conduct of the study.

Development of an individualized model for predicting postoperative delirium in elderly patients with hepatocellular carcinoma

Postoperative delirium (POD) is a common complication in older patients with hepatocellular carcinoma (HCC) that adversely impacts clinical outcomes. We aimed to evaluate the risk factors for POD and to construct a predictive nomogram. Data for a total of 1481 older patients (training set: n=1109; validation set: n=372) who received liver resection for HCC were retrospectively retrieved from two prospective databases. The receiver operating characteristic (ROC) curve, calibration plot, and decision curve analysis (DCA) were used to evaluate the performance. The rate of POD was 13.3% (148/1109) in the training set and 16.4% (61/372) in the validation set. Multivariate analysis of the training set revealed that factors including age, history of cerebrovascular disease, American Society of Anesthesiologists (ASA) classification, albumin level, and surgical approach had significant effects on POD. The area under the ROC curves (AUC) for the nomogram, incorporating the aforementioned predictors, was 0.798 (95% CI 0.752-0.843) and 0.808 (95% CI 0.754-0.861) for the training and validation sets, respectively. The calibration curves of both sets showed a degree of agreement between the nomogram and the actual probability. DCA demonstrated that the newly established nomogram was highly effective for clinical decision-making. We developed and validated a nomogram with high sensitivity to assist clinicians in estimating the individual risk of POD in older patients with HCC.

Machine learning-based model to predict delirium in patients with advanced cancer treated with palliative care: a multicenter, patient-based registry cohort

This study aimed to present a new approach to predict to delirium admitted to the acute palliative care unit. To achieve this, this study employed machine learning model to predict delirium in patients in palliative care and identified the significant features that influenced the model. A multicenter, patient-based registry cohort study in South Korea between January 1, 2019, and December 31, 2020. Delirium was identified by reviewing the medical records based on the criteria of the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition. The study dataset included 165 patients with delirium among 2314 patients with advanced cancer admitted to the acute palliative care unit. Seven machine learning models, including extreme gradient boosting, adaptive boosting, gradient boosting, light gradient boosting, logistic regression, support vector machine, and random forest, were evaluated to predict delirium in patients with advanced cancer admitted to the acute palliative care unit. An ensemble approach was adopted to determine the optimal model. For k-fold cross-validation, the combination of extreme gradient boosting and random forest provided the best performance, achieving the following accuracy metrics: 68.83% sensitivity, 70.85% specificity, 69.84% balanced accuracy, and 74.55% area under the receiver operating characteristic curve. The performance of the isolated testing dataset was also validated, and the machine learning model was successfully deployed on a public website ( http://ai-wm.khu.ac.kr/Delirium/ ) to provide public access to delirium prediction results in patients with advanced cancer. Furthermore, using feature importance analysis, sex was determined to be the top contributor in predicting delirium, followed by a history of delirium, chemotherapy, smoking status, alcohol consumption, and living with family. Based on a large-scale, multicenter, patient-based registry cohort, a machine learning prediction model for delirium in patients with advanced cancer was developed in South Korea. We believe that this model will assist healthcare providers in treating patients with delirium and advanced cancer.

[ChatGPT: curse or blessing in nursing care?]

Artificial intelligence (AI) has been used in healthcare for some years for risk detection, diagnostics, documentation, education and training and other purposes. A new open AI application is ChatGPT, which is accessible to everyone. The application of ChatGPT as AI in education, training or studies is currently being discussed from many perspectives. It is questionable whether ChatGPT can and should also support nursing professions in health care. The aim of this review article is to show and critically discuss possible areas of application of ChatGPT in theory and practice with a focus on nursing practice, pedagogy, nursing research and nursing development.

Icariin as a potential anticancer agent: a review of its biological effects on various cancers

Numerous chemical compounds used in cancer treatment have been isolated from natural herbs to address the ever-increasing cancer incidence worldwide. Therein is icariin, which has been extensively studied for its therapeutic potential due to its anti-inflammatory, antioxidant, antidepressant, and aphrodisiac properties. However, there is a lack of comprehensive and detailed review of studies on icariin in cancer treatment. Given this, this study reviews and examines the relevant literature on the chemopreventive and therapeutic potentials of icariin in cancer treatment and describes its mechanism of action. The review shows that icariin has the property of inhibiting cancer progression and reversing drug resistance. Therefore, icariin may be a valuable potential agent for the prevention and treatment of various cancers due to its natural origin, safety, and low cost compared to conventional anticancer drugs, while further research on this natural agent is needed.

Evaluating the performance of machine learning methods for risk estimation of delirium in patients hospitalized from the emergency department

INTRODUCTION

Delirium is a cerebral dysfunction seen commonly in the acute care setting. It is associated with increased mortality and morbidity and is frequently missed in the emergency department (ED) and inpatient care by clinical gestalt alone. Identifying those at risk of delirium may help prioritize screening and interventions in the hospital setting.

OBJECTIVE

Our objective was to leverage electronic health records to identify a clinically valuable risk estimation model for prevalent delirium in patients being transferred from the ED to inpatient units.

METHODS

This was a retrospective cohort study to develop and validate a risk model to detect delirium using patient data available from prior visits and ED encounter. Electronic health records were extracted for patients hospitalized from the ED between January 1, 2014, and December 31, 2020. Eligible patients were aged 65 or older, admitted to an inpatient unit from the emergency department, and had at least one DOSS assessment or CAM-ICU recorded within 72 h of hospitalization. Six machine learning models were developed to estimate the risk of delirium using clinical variables including demographic features, physiological measurements, medications administered, lab results, and diagnoses.

RESULTS

A total of 28,531 patients met the inclusion criteria with 8057 (28.4%) having a positive delirium screening within the outcome observation period. Machine learning models were compared using the area under the receiver operating curve (AUC). The gradient boosted machine achieved the best performance with an AUC of 0.839 (95% CI, 0.837-0.841). At a 90% sensitivity threshold, this model achieved a specificity of 53.5% (95% CI 53.0%-54.0%) a positive predictive value of 43.5% (95% CI 43.2%-43.9%), and a negative predictive value of 93.1% (95% CI 93.1%-93.2%). A random forest model and L1-penalized logistic regression also demonstrated notable performance with AUCs of 0.837 (95% CI, 0.835-0.838) and 0.831 (95% CI, 0.830-0.833) respectively.

CONCLUSION

This study demonstrated the use of machine learning algorithms to identify a combination of variables that enables an estimation of risk of positive delirium screens early in hospitalization to develop prevention or management protocols.