Published online Sep 15, 2024. doi: 10.4251/wjgo.v16.i9.3761
Revised: May 9, 2024
Accepted: June 3, 2024
Published online: September 15, 2024
Processing time: 173 Days and 11.7 Hours
Delirium, a complex neurocognitive syndrome, frequently emerges following surgery, presenting diverse manifestations and considerable obstacles, especially among the elderly. This editorial delves into the intricate phenomenon of pos
Core Tip: Postoperative delirium (POD) presents significant challenges in elderly patients, with no current gold standard for prevention. This editorial sheds light on a study that introduces a predictive model utilizing synthetic minority oversampling technique (SMOTE) to identify high-risk patients. Key risk factors include comorbidity index, anesthesia grade, and surgical duration. The editorial discusses that standardizing predictive models across surgical subspecialties is crucial for effective POD manage
- Citation: Lamprecht CB, Dagra A, Lucke-Wold B. Predictive modeling for post operative delirium in elderly. World J Gastrointest Oncol 2024; 16(9): 3761-3764
- URL: https://www.wjgnet.com/1948-5204/full/v16/i9/3761.htm
- DOI: https://dx.doi.org/10.4251/wjgo.v16.i9.3761
Delirium, a neurocognitive syndrome, arises from reversible neuronal disruption due to systemic perturbations and serves as an acute marker of brain dysfunction. The Diagnostic and Statistical Manual of Mental Disorders (DSM-5) offers diagnostic criteria for delirium, emphasizing disturbances in attention, cognition, and/or awareness that deviate from baseline function. Manifesting in hyperactive, hypoactive, or mixed forms, delirium can occur postoperatively, ranging from 10 minutes after anesthesia to 7 days post-op, with hypoactive delirium being particularly prevalent and identifiable in the Post-Anesthesia Care Unit. Postoperative delirium (POD) in elderly poses significant challenges due to its high incidence and associated adverse outcomes.
Like many other major bodily systems, the gastrointestinal and the central nervous system (CNS) are intricately linked, with disturbances in one system impacting the other's homeostasis and functionality[1]. The pathophysiology of delirium in the elderly post-operative period involves neurotransmitter imbalances, particularly acetylcholine, serotonin, dopa
POD stands out as one of the most common neurological consequences following surgery, with prevalence rates ranging from 5% to as high as 87.0%[6-8]. This highlights the importance of preoperative consideration and prevention strategies. The high prevalence of this condition along with the lack of a gold standard for pharmaceutical POD pre
The methodology employed in developing a delirium prediction model in this article utilizes the application of a synthetic minority oversampling technique (SMOTE), to improve predictive accuracy and aid in the early identification and mitigation of POD. The validation of this model relies on a selection of pivotal risk factors proven to be significant predictors of delirium. These factors encompass the Charlson comorbidity index, anesthesia grade, history of cerebrovascular disease, surgical duration, perioperative transfusion, and postoperative pain. This research study aims to address a crucial gap in the field by providing a more reliable method for predicting and potentially preventing POD in elderly patients with abdominal malignancies.
One evident consideration when analyzing this article is the specificity of the patient population recruited. All participants included in this study were elderly (65-75 years old) and diagnosed with colorectal cancer. While this specificity helps eliminate confounding variables and promotes a potentially effective intervention for this demographic, it may limit the generalizability of the predictive model to gastrointestinal surgical interventions addressing different pathologies or within various surgical subspecialties. Additionally, since the United States Preventive Services Task Force recommends colon cancer screening starting at the age of 45, the results of this predictive model may not be entirely applicable to all individuals with colon cancer[10]. This is particularly relevant considering that advanced age is identified as a risk factor for POD in other studies[6,7,11].
Further scrutiny of the study involves an examination of the diagnostic criteria used to identify delirium. Typically, DSM-5 serves as the "gold standard" for delirium diagnosis, yet it was not employed in this investigation[11]. Instead, the diagnostic framework relied on the Ambiguity Assessment Scale and the acute onset of specific symptoms such as difficulty concentrating, confused thinking, and altered consciousness. While the absence of the DSM-5 criteria may raise concerns about the diagnostic validity, it's essential to acknowledge that the Ambiguity Assessment Scale is a validated tool for delirium screening. Moreover, the criteria utilized in this study have been extensively elucidated, demonstrating high sensitivity and specificity levels of 94%-100% and 90%-95%, respectively. Furthermore, the diagnostic evaluation was conducted daily for 7 days post-surgery, aligning with the timeframe known for the peak onset of POD, as evidenced in previous literature[11]. Notably, in this study, these criteria yielded a POD incidence rate of 22.91%, falling within the predictive range observed in prior studies (5%-51%)[6,7].
This study highlights the importance of identifying key risk factors of POD. Utilizing Multivariate logistic regression analysis, key factors such as Charlson comorbidity index, American Society of Anesthesiologists classification, history of cerebrovascular diseases, surgical duration, and perioperative blood transfusion were identified as significant contributors to POD development. In contrast, other predictive models like intensive care unit (ICU)-POD utilize different risk factors such as Physiological and Operative Severity Score for Mortality and morbidity, acid–base disturbances, and medical history[8]. Comparing performance, both Multivariate logistic regression and SMOTE logistic models showed comparable results with an AUC of 0.862 and 0.856, respectively, similar to the ICU-POD model (0.852)[8]. Furthermore, other studies have found other risk factors like age, pre-existing dementia, Barthel Index score, preoperative infection, preoperative hematocrit, and preoperative opioid use to also significantly impact the development of POD[11]. These findings highlight the uncertainty surrounding the exact etiology of POD and its primary contributing factors.
Future meta-analyses are pivotal for identifying preventable risk factors that have the most significant impact on POD across various predictive models. Insights from these analyses could inform comprehensive pre- and post-operative protocols aimed at mitigating POD risk, thereby benefiting surgical, anesthesia, and geriatric/internal medicine teams. Furthermore, subsequent studies could explore variations in POD predictive models across surgical subspecialties to develop more standardized guidelines while also facilitating the design of more effective pharmaceutical and non-pharmaceutical interventions.
With numerous global studies proving the significance of differing risk factors on the development of POD, standardizing datasets can be quite challenging. The authors propose an alternative approach to address this issue by advocating for further advancements and refinements in SMOTE algorithms. The article demonstrates SMOTE's effectiveness in addressing the inherent complexity of unbalanced datasets prevalent in bariatric oncology research. SMOTE achieves this by generating synthetic minority samples from existing data, thus addressing the imbalance and streamlining the process of acquiring high-quality and accurate predictive models.
In conclusion, the intricate dynamics of delirium, particularly in elderly patients undergoing abdominal malignancy surgery, stem from the complex interplay between the gastrointestinal and CNSs. We have underscored the significance of neurotransmitter imbalances, inflammatory markers, and age-related changes in blood-brain barrier integrity in delirium development. The utilization of SMOTE has notably improved predictive accuracy, enabling the identification of crucial risk factors such as comorbidity index, anesthesia grade, and surgical duration. However, it is essential to acknowledge limitations, including the specificity of this patient population and deviation from DSM-5 diagnostic criteria. Looking ahead, standardizing predictive models and interventions across surgical subspecialties is paramount for effectively addressing POD. Given the ongoing global research efforts generating valuable data, there is an urgent need for methods to standardize scattered data scales. This standardization will facilitate the creation of comprehensive predictive models, ensuring that emerging research findings are leveraged efficiently for the development of robust predictive models in the field.
1. | Panther EJ, Dodd W, Clark A, Lucke-Wold B. Gastrointestinal Microbiome and Neurologic Injury. Biomedicines. 2022;10. [PubMed] [DOI] [Cited in This Article: ] [Cited by in Crossref: 10] [Cited by in F6Publishing: 26] [Article Influence: 13.0] [Reference Citation Analysis (0)] |
2. | Maldonado JR. Neuropathogenesis of delirium: review of current etiologic theories and common pathways. Am J Geriatr Psychiatry. 2013;21:1190-1222. [PubMed] [DOI] [Cited in This Article: ] [Cited by in Crossref: 403] [Cited by in F6Publishing: 406] [Article Influence: 36.9] [Reference Citation Analysis (0)] |
3. | Austin CA, Yi J, Lin FC, Pandharipande P, Ely EW, Busby-Whitehead J, Carson SS. The Association of Selective Serotonin Reuptake Inhibitors With Delirium in Critically Ill Adults: A Secondary Analysis of the Bringing to Light the Risk Factors and Incidence of Neuropsychologic Dysfunction in ICU Survivors ICU Study. Crit Care Explor. 2022;4:e0740. [PubMed] [DOI] [Cited in This Article: ] [Reference Citation Analysis (0)] |
4. | Ali S, Patel M, Jabeen S, Bailey RK, Patel T, Shahid M, Riley WJ, Arain A. Insight into delirium. Innov Clin Neurosci. 2011;8:25-34. [PubMed] [Cited in This Article: ] |
5. | van der Mast RC, Fekkes D. Serotonin and amino acids: partners in delirium pathophysiology? Semin Clin Neuropsychiatry. 2000;5:125-131. [PubMed] [DOI] [Cited in This Article: ] [Cited by in F6Publishing: 14] [Reference Citation Analysis (0)] |
6. | Dasgupta M, Dumbrell AC. Preoperative risk assessment for delirium after noncardiac surgery: a systematic review. J Am Geriatr Soc. 2006;54:1578-1589. [PubMed] [DOI] [Cited in This Article: ] [Cited by in Crossref: 303] [Cited by in F6Publishing: 301] [Article Influence: 16.7] [Reference Citation Analysis (0)] |
7. | Inouye SK, Westendorp RG, Saczynski JS. Delirium in elderly people. Lancet. 2014;383:911-922. [PubMed] [DOI] [Cited in This Article: ] [Cited by in Crossref: 1954] [Cited by in F6Publishing: 2049] [Article Influence: 204.9] [Reference Citation Analysis (0)] |
8. | Xing H, Zhou W, Fan Y, Wen T, Wang X, Chang G. Development and validation of a postoperative delirium prediction model for patients admitted to an intensive care unit in China: a prospective study. BMJ Open. 2019;9:e030733. [PubMed] [DOI] [Cited in This Article: ] [Cited by in Crossref: 11] [Cited by in F6Publishing: 10] [Article Influence: 2.0] [Reference Citation Analysis (0)] |
9. | Hu WJ, Bai G, Wang Y, Hong DM, Jiang JH, Li JX, Hua Y, Wang XY, Chen Y. Predictive modeling for postoperative delirium in elderly patients with abdominal malignancies using synthetic minority oversampling technique. World J Gastrointest Oncol. 2024;16:1227-1235. [PubMed] [DOI] [Cited in This Article: ] [Reference Citation Analysis (0)] |
10. | US Preventive Services Task Force. Colorectal Cancer: Screening. 2021. Available from: https://www.uspreventiveservicestaskforce.org/uspstf/recommendation/colorectal-cancer-screening#fullrecommendationstart. [Cited in This Article: ] |
11. | Iamaroon A, Wongviriyawong T, Sura-Arunsumrit P, Wiwatnodom N, Rewuri N, Chaiwat O. Incidence of and risk factors for postoperative delirium in older adult patients undergoing noncardiac surgery: a prospective study. BMC Geriatr. 2020;20:40. [PubMed] [DOI] [Cited in This Article: ] [Cited by in Crossref: 21] [Cited by in F6Publishing: 47] [Article Influence: 11.8] [Reference Citation Analysis (0)] |