Development of a nomogram for predicting the probability of postoperative delirium in patients undergoing free flap reconstruction for head and neck cancer

Determining the ground truth for the prediction of delirium in adult patients in acute care: a scoping review

Objective

Delirium is a severe condition, often underreported and linked to adverse outcomes such as increased mortality and prolonged hospitalization. Despite its significance, delirium prediction is often hindered by underreporting and inconsistent labeling, highlighting the need for models trained on reliably labeled data (ground truth). This review examines (i) practices for determining labels in delirium prediction models and (ii) how study designs affect label quality, aiming to identify key considerations for improving model reliability.

Materials and Methods

A search of Cochrane, PubMed, and IEEE identified 120 studies that met the inclusion criteria.

Results

To establish the ground truth, 40.8% of studies used routine data, while 42.5% used primary data. The Confusion Assessment Method (CAM) was the most widely used assessment tool (60. 0%). Label and data leakage occurred in 35.0% of studies. High Risk of Bias (RoB) was a recurring issue, with 31.7% of studies lacking sufficient reporting and 36.7% showing inadequate outcome determination. Studies using primary data had lower RoB, whereas those with unclear label sources displayed higher RoB.

Discussion

Our findings underscore the importance of careful planning in determining the ground truth frequently neglected in existing studies. To address these challenges, we provide a decision support flowchart to guide the development of more accurate and reliable prediction models.

Conclusion

This review uncovers significant variability in labeling methods and discusses how this may affect delirium prediction model reliability. Highlighting the importance of addressing underreporting bias and providing guidance for developing more robust models.

Risk Factors for Postoperative Delirium in Patients Undergoing Major Head and Neck Cancer Surgery: A Meta-analysis

BACKGROUND

Postoperative delirium (POD) is a common and serious complication after extensive surgery. Understanding the independent and potential modifiable risk factors leading to POD in patients with head and neck cancer (HNC) can provide information for future intervention trials aimed at reducing this risk.

OBJECTIVE

To systematically analyze influencing factors of POD in patients with HNC and identify high-risk individuals for delirium.

METHODS

PubMed, EMBASE, Scopus, OVID, and Cochrane Library were searched for publications prior to June 2023. Comparative studies in which POD risk factors were investigated were identified following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. The Newcastle-Ottawa Scale was used to evaluate the study quality. Pooled odds ratios or mean differences for individual risk factors were estimated using the Mantel-Haenszel and inverse-variance methods.

RESULTS

This review included 17 studies with a total of 4188 patients undergoing HNC surgery. The pooled prevalence of POD was 15.44%. Based on pooled analysis, 8 significant risk factors were identified including age older than 70 years, male sex, history of smoking, history of psychiatric disorder, American Society of Anesthesiologists score, albumin level, postoperative insomnia, and fluid intake.

CONCLUSION

In the present study, 8 factors that correlated with POD were identified: 6 preoperative, 1 intraoperative, and 1 postoperative.

IMPLICATIONS FOR PRACTICE

The influencing factors for POD in patients with HNC were identified that can provide a reference for improving the psychological state of the patient population and further development of effective treatment interventions.

The impact of preoperative psychiatric intervention for postoperative delirium after major oral and maxillofacial surgery with free flap reconstruction

BACKGROUND

Postoperative delirium (POD) is a severe complication associated with various adverse outcomes, especially in older patients. Although the incidence and risk factors for POD have been explored in general surgery, they have not been fully elucidated. Early identification of high-risk patients and active preoperative intervention are considered essential for the prevention of POD. Recently, psychiatric consultation intervention have been shown to prevent delirium. This study investigated the effect of preoperative psychiatric interventions on preventing POD in our specific surgical context.

MATERIALS AND METHODS

This retrospective, single-center observational study included 86 patients who underwent major oral and maxillofacial surgery with free flap reconstruction between 2016 and 2023. The effect of psychiatric intervention were compared between patients with and without delirium.

RESULTS

Preoperative psychiatric intervention did not reduce the incidence of POD. The incidence of POD was 29.1 %. Univariate analyses showed no significant associations between POD and any clinical variables.

CONCLUSION

There was no difference in the incidence of POD between patients who received preoperative psychiatric intervention and those who did not, and further investigation is needed to determine the efficacy of preoperative psychiatric intervention in the prevention of POD.

Recent developments in delirium after oral and maxillofacial free-flap reconstruction

Postoperative delirium (POD), a common complication following surgery and anesthesia, is particularly prone to occur after reconstruction surgery in the oral and maxillofacial region. The occurrence of POD seriously affects the incidence of postoperative complications, the survival of free flaps, the length of hospital stays, and brings great pain to patients and their families, and even increases perioperative mortality. Currently, a large number of studies have reported on the risk factors for POD after oral and maxillofacial free-flap reconstruction. Multiple independent risk factors have been identified, including age, history of excessive alcohol consumption, and perioperative nutritional status. This review summarizes the relevant literature and hopes to provide insights for the prevention of POD in high-risk patients.

Translation, cultural debugging, and validation of the Chinese version of the Sour Seven Questionnaire: a cross-sectional study

Introduction

Intensive care unit delirium (ICUD) is an acute cerebral dysfunction accompanied by a change of level of consciousness, disorientation, and cognitive dysfunction, typically occurring over a short duration ranging from hours to days and resulting from underlying medical causes. Family members may sometimes detect changes in consciousness earlier than medical staff. The Sour Seven Questionnaire is a tool to assist family members in screening for delirium, but there is currently no Chinese version. This study aimed to translate and cross-culturally debug the Sour Seven Questionnaire and test the effectiveness of the Chinese version in screening for ICUD by family members.

Methods

To create the Chinese version of the questionnaire, the questionnaire was first translated and then culturally debugged through expert consultation and cognitive interviews. Patients and their family members admitted to three ICUs in a Chinese hospital were selected to test the Chinese version of the Sour Seven Questionnaire and the results were compared with those of the validated and recommended Confusion Assessment Method for the intensive care unit (CAM-ICU) assessment.

Results

A total of 190 ICU patients and their families were included in this study. Results of the CAM-ICU assessment showed that 73 (38.4%) patients developed ICUD compared to the 66 (34.7%) using the Chinese version of the Sour Seven Questionnaire, which had a Cohen's kappa coefficient of 0.853, a sensitivity of 0.863, and a specificity of 0.974. The positive predictive value was 0.954 and the negative predictive value was 0.919.

Discussion

The Chinese version of the Sour Seven Questionnaire is a valid assessment tool for helping families screen for ICUD, and it is effective in identifying altered consciousness in patients even during online visits.

Prevalence and risk factors of postoperative delirium in tumor patients after free flap reconstruction: A systematic review and meta-analysis of case-control studies

BACKGROUND

Postoperative delirium is a severe complication of flap transplantation surgery, adversely affecting surgical prognoses. The intricate pathophysiology of postoperative delirium renders the elucidation of its risk factors challenging. This research aims to delineate the prevalence and the specific risk factors of postoperative delirium in patients with cancer undergoing free flap reconstruction through a systematic review and meta-analysis to enlighten proactive prevention measures.

METHODS

The researchers systematically queried both the international and Chinese databases. Searches were performed for publications from inception until September 14, 2023, using the terms "free tissue flaps," "delirium," "neoplasms," and "risk factors." Data synthesis and statistical analysis were conducted using Stata SE (version 15.0) to calculate the combined effect size for identified risk factors. Reported outcomes included weighted mean differences or odds ratios with their respective 95% confidence intervals.

RESULTS

Twelve case-control studies were included (ntotal = 3,256). Among them, 515 patients developed postoperative delirium after free flap surgery, compared with 2,741 patients who did not. The outcomes suggest that the risk factors include but are not limited to age, male, late neoplasm staging, use of hypnotic or antipsychotic, history of background diseases, psychiatric review, tracheotomy, and impaired wound healing. In contrast, early neoplasm staging and others are the protective factors with statistical significance. Multivariate analysis further identified significant correlations between preoperative albumin, perioperative blood transfusion, sleep disturbance, postoperative visual analog scale, postoperative albumin, smoking, and the appearance of postoperative delirium.

CONCLUSION

The determined risk factors were grouped into preoperative, intraoperative, and postoperative categories substantiated by current data to present instructions for postoperative delirium prevention.

Postoperative delirium in oral and maxillofacial surgery: a scoping review

BACKGROUND

Postoperative delirium (POD) in the oral and maxillofacial settings has gained more attention in recent decades. Due to advances in medical technology, treatment possibilities have expanded treatment for elderly and frail patients. This scoping review explores the correlation between POD and oral and maxillofacial surgery, summarizing screening and management protocols and identifying risk factors in this surgical field.

METHODS

This review follows the Scoping Review extension of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA-ScR). A comprehensive literature search was performed using multiple databases, focusing on articles published from 2002 to 2023 that discuss delirium in oral and maxillofacial surgery settings. The review was registered beforehand in the Open Science Framework ( https://osf.io/r2ebc ).

RESULTS

From the initial 644 articles, 68 met the inclusion criteria. These studies highlighted the significant heterogeneity in POD diagnosis methods. The review identifies multiple risk factors across the preoperative, intraoperative, and postoperative phases that influence the occurrence of POD. Significant and independent risk factors in multiple regression analysis were highlighted, creating a clinical prediction list for the occurrence of POD.

CONCLUSION

It is crucial to preoperatively identify patients at risk for POD and actively modify these risks throughout the patient's hospital stay. Implementing nonpharmacological preventive measures for at-risk patients is recommended to decrease the incidence of POD. Future research should focus on creating standardized specialty-specific protocols incorporating validated assessment tools and addressing the full spectrum of risk factors associated with POD.

Predictive value of the Naples prognostic score on postoperative delirium in the elderly with gastrointestinal tumors: a retrospective cohort study

BACKGROUND

Postoperative delirium (POD) is a common complication among elderly patients after surgery. The Naples Prognostic Score (NPS), a novel prognostic marker based on immune-inflammatory and nutritional status, was widely used in the assessment of the prognosis of surgical patients. However, no study has evaluated the relationship between NPS and POD. The aim of this article was to investigate the association between NPS and POD and test the predictive efficacy of preoperative NPS for POD in elderly patients with gastrointestinal tumors.

MATERIALS AND METHODS

In the present study, we retrospectively collected perioperative data of 176 patients (≥ 60 years) who underwent elective gastrointestinal tumor surgery from June 2022 to September 2023. POD was defined according to the chart-based method and the NPS was calculated for each patient. We compared all the demographics and laboratory data between POD and non-POD groups. Univariate and multivariate logistic regression analysis was used to explore risk factors of POD. Moreover, the accuracy of NPS in predicting POD was further assessed by utilizing receiver operating characteristic (ROC) curves.

RESULTS

20 had POD (11.4%) in a total of 176 patients, with a median age of 71 (65-76). The outcomes by univariate analysis pointed out that age, ASA status ≥ 3, creatinine, white blood cell count, fasting blood glucose (FBG), and NPS were associated with the risk of POD. Multivariate logistic regression analysis further showed that age, ASA grade ≥ 3, FBG and NPS were independent risk factors of POD. Additionally, the ROC curves revealed that NPS allowed better prognostic capacity for POD than other variables with the largest area under the curve (AUC) of 0.798, sensitivity of 0.800 and specificity of 0.667, respectively.

CONCLUSION

Age, ASA grade ≥ 3, and FBG were independent risk factors for POD in the elderly underwent gastrointestinal tumor surgery. Notably, the preoperative NPS was a more effective tool in predicting the incidence of POD, but prospective trials were still needed to further validate our conclusion.

TRIAL REGISTRATION

The registration information for the experiment was shown below. (date: 3rd January 2024; number: ChiCTR2400079459).

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.

Interpretable machine learning model for early prediction of delirium in elderly patients following intensive care unit admission: a derivation and validation study

Background and objective

Delirium is the most common neuropsychological complication among older adults admitted to the intensive care unit (ICU) and is often associated with a poor prognosis. This study aimed to construct and validate an interpretable machine learning (ML) for early delirium prediction in older ICU patients.

Methods

This was a retrospective observational cohort study and patient data were extracted from the Medical Information Mart for Intensive Care-IV database. Feature variables associated with delirium, including predisposing factors, disease-related factors, and iatrogenic and environmental factors, were selected using least absolute shrinkage and selection operator regression, and prediction models were built using logistic regression, decision trees, support vector machines, extreme gradient boosting (XGBoost), k-nearest neighbors and naive Bayes methods. Multiple metrics were used for evaluation of performance of the models, including the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, recall, F1 score, calibration plot, and decision curve analysis. SHapley Additive exPlanations (SHAP) were used to improve the interpretability of the final model.

Results

Nine thousand seven hundred forty-eight adults aged 65 years or older were included for analysis. Twenty-six features were selected to construct ML prediction models. Among the models compared, the XGBoost model demonstrated the best performance including the highest AUC (0.836), accuracy (0.765), sensitivity (0.713), recall (0.713), and F1 score (0.725) in the training set. It also exhibited excellent discrimination with AUC of 0.810, good calibration, and had the highest net benefit in the validation cohort. The SHAP summary analysis showed that Glasgow Coma Scale, mechanical ventilation, and sedation were the top three risk features for outcome prediction. The SHAP dependency plot and SHAP force analysis interpreted the model at both the factor level and individual level, respectively.

Conclusion

ML is a reliable tool for predicting the risk of critical delirium in elderly patients. By combining XGBoost and SHAP, it can provide clear explanations for personalized risk prediction and more intuitive understanding of the effect of key features in the model. The establishment of such a model would facilitate the early risk assessment and prompt intervention for delirium.

Temporal Generalizability of Machine Learning Models for Predicting Postoperative Delirium Using Electronic Health Record Data: Model Development and Validation Study

BACKGROUND

Although machine learning models demonstrate significant potential in predicting postoperative delirium, the advantages of their implementation in real-world settings remain unclear and require a comparison with conventional models in practical applications.

OBJECTIVE

The objective of this study was to validate the temporal generalizability of decision tree ensemble and sparse linear regression models for predicting delirium after surgery compared with that of the traditional logistic regression model.

METHODS

The health record data of patients hospitalized at an advanced emergency and critical care medical center in Kumamoto, Japan, were collected electronically. We developed a decision tree ensemble model using extreme gradient boosting (XGBoost) and a sparse linear regression model using least absolute shrinkage and selection operator (LASSO) regression. To evaluate the predictive performance of the model, we used the area under the receiver operating characteristic curve (AUROC) and the Matthews correlation coefficient (MCC) to measure discrimination and the slope and intercept of the regression between predicted and observed probabilities to measure calibration. The Brier score was evaluated as an overall performance metric. We included 11,863 consecutive patients who underwent surgery with general anesthesia between December 2017 and February 2022. The patients were divided into a derivation cohort before the COVID-19 pandemic and a validation cohort during the COVID-19 pandemic. Postoperative delirium was diagnosed according to the confusion assessment method.

RESULTS

A total of 6497 patients (68.5, SD 14.4 years, women n=2627, 40.4%) were included in the derivation cohort, and 5366 patients (67.8, SD 14.6 years, women n=2105, 39.2%) were included in the validation cohort. Regarding discrimination, the XGBoost model (AUROC 0.87-0.90 and MCC 0.34-0.44) did not significantly outperform the LASSO model (AUROC 0.86-0.89 and MCC 0.34-0.41). The logistic regression model (AUROC 0.84-0.88, MCC 0.33-0.40, slope 1.01-1.19, intercept -0.16 to 0.06, and Brier score 0.06-0.07), with 8 predictors (age, intensive care unit, neurosurgery, emergency admission, anesthesia time, BMI, blood loss during surgery, and use of an ambulance) achieved good predictive performance.

CONCLUSIONS

The XGBoost model did not significantly outperform the LASSO model in predicting postoperative delirium. Furthermore, a parsimonious logistic model with a few important predictors achieved comparable performance to machine learning models in predicting postoperative delirium.

Incidence and risk factors for postoperative delirium after head and neck cancer surgery: an updated meta-analysis

BACKGROUND

Postoperative delirium (POD) is a frequent neurologic dysfunction that often leads to more negative outcomes. Early identification of patients who are vulnerable to POD and early implementation of appropriate management strategies could decrease its occurrence and improve patient prognosis. Therefore, this meta-analysis comprehensively and quantitatively summarized the prevalence and related predictive factors of POD in head and neck cancer surgical patients.

METHODS

PubMed, Embase, and Cochrane Library were searched for observational studies that reported the prevalence and risk factors for POD after head and neck cancer surgery and were published from their inception until December 31, 2022. Two reviewers independently selected qualified articles and extracted data. The qualities of related papers were assessed using the Newcastle-Ottawa scale (NOS). RevMan 5.3 and Stata 15.0 were applied to analysis the data and conduct the meta-analysis.

RESULTS

Sixteen observational studies with 3289 inpatients who underwent head and neck cancer surgery were included in this review. The occurrence of POD ranged from 4.2 to 36.9%, with a pooled incidence of 20% (95% CI 15-24%, I2 = 93.2%). The results of this pooled analysis demonstrated that the statistically significant risk factors for POD were increased age (OR: 1.05, 95% CI: 1.03-1.07, P < 0.001), age > 75 years (OR: 6.52, 95% CI: 3.07-13.87, P < 0.001), male sex (OR: 2.29, 95% CI: 1.06-4.97, P = 0.04), higher American Society of Anesthesiologists grade (OR: 2.19, 95% CI: 1.44-3.33, P < 0.001), diabetes mellitus (OR: 2.73, 95% CI: 1.24-6.01, P = 0.01), and history of smoking (OR: 2.74, 95% CI: 1.13-6.65, P = 0.03).

CONCLUSIONS

POD frequently occurs after head and neck cancer surgery. Several independent predictors for POD were identified, which might contribute to identifying patients at high risk for POD and play a prominent role in preventing POD in patients following head and neck cancer surgery.

Enhanced recovery after surgery, current, and future considerations in head and neck cancer

Objectives

Review of the current and relevant literature to develop a list of evidence-based recommendations that can be implemented in head and neck surgical practices. To provide rationale for the multiple aspects of comprehensive care for head and neck surgical patients. To improve postsurgical outcomes for head and neck surgical patients.

Methods

Extensive review of the medical literature was performed and relevant studies in both the head and neck surgery and other surgical specialties were considered for inclusion.

Results

A total of 18 aspects of perioperative care were included in this review. The literature search included 276 publications considered to be the most relevant and up to date evidence. Each topic is concluded with recommendation grade and quality of evidence for the recommendation.

Conclusion

Since it's conception, enhanced recovery after surgery (ERAS) protocols have continued to push for comprehensive and evidence based postsurgical care to improve patient outcomes. Head and neck oncology is one of the newest fields to develop a protocol. Due to the complexity of this patient population and their postsurgical needs, a multidisciplinary approach is needed to facilitate recovery while minimizing complications. Current and future advances in head and neck cancer research will serve to strengthen and add new principles to a comprehensive ERAS protocol.

Level of Evidence

2a.

Perioperative risk factors impact on intensive care unit length of stay (ICU length of stay) in oral squamous cell carcinoma

BACKGROUND

The trend in postoperative care for free flap patients is to deescalate from routine ICU admission into a specialty recovery unit. This study aims to investigate the predictive parameters in a routine perioperative clinical assessment that are expected to be directly correlated with prolonged ICU length of stay in at-risk patients who received oral reconstructive surgery for squamous cell carcinoma (OSCC).

METHODS

All patients who underwent ablative surgery for OSCC with free flap reconstruction and were managed in the ICU were included in this study. The primary outcome was ICU-length of stay. Perioperative, operative and postoperative parameters were analyzed using single test ( t-test, ANOVA analysis, correlation coefficients, effect size) and multivariate regression test. The P-value was set as < 0.005 to be considered statically significant.

RESULTS

The study included 136 homogeneous patients, with a mean ICU length of stay of 4.5 (± 4.43 day). Patients with pre-operative positive renal dysfunction (P = 0.004), peripheral vascular disease (P < 0.001), postoperative complications (P = 0.028) or positive heart failure class III (P < 0.001 ) were recognized as at-risk patients for a significantly longer ICU length of stay.

CONCLUSION

Patients with perioperative severe renal dysfunction, peripheral vascular disease, postoperative complication or high NYHA class are prone to have a significantly longer ICU length of stay. Several factors were considered as confounders contributing to increased ICU management time in combination with other variables. Additionally, in highly risk patient, the presence of the highly trained medical support, including the appropriate nursing care, is more critical than those patients without these risk factors.

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

Purpose

Postoperative delirium (POD) is a common postoperative complication in elderly patients, and it greatly affects the short-term and long-term prognosis of patients. The purpose of this study was to develop a machine learning model to identify preoperative, intraoperative and postoperative high-risk factors and predict the occurrence of delirium after nonbrain surgery in elderly patients.

Patients and Methods

A total of 950 elderly patients were included in the study, including 132 patients with POD. We collected 30 characteristic variables, including patient demographic characteristics, basic medical history, preoperative examination characteristics, type of surgery, and intraoperative information. Three machine learning algorithms, multilayer perceptron (MLP), extreme gradient boosting (XGBoost), and k-nearest neighbor algorithm (KNN), were applied to construct the model, and the k-fold cross-validation method, ROC curve, calibration curve, decision curve analysis (DCA) and external validation were used for model evaluation.

Results

XGBoost showed the best performance among the three prediction models. The ROC curve results showed that XGBoost had a high area under the curve (AUC) value of 0.982 in the training set; the AUC value in the validation set was 0.924, and the prediction model was highly accurate. The k-fold cross-validation method was used for internal validation, and the XGBoost model was stable The calibration curve showed high predictive power of the XGBoost model. The DCA curve showed a higher benefit rate for patients who received interventional treatment under the XGBoost model. The AUC value for the external validation set was 0.88, indicating that the predictive model was extrapolative.

Conclusion

The prediction model of POD derived from the machine learning algorithm in this study has high prediction accuracy and clinical utility, which is beneficial for clinicians to diagnose and treat patients in a timely manner.

Risk factor stratification for postoperative delirium: A retrospective database study

METHODS

The EHR of 32734 patients >18 years of age undergoing surgery and had POD assessment were reviewed. Patient characteristics and study variables were summarized between delirium groups. We constructed univariate logistic regression models for POD using each study variable to estimate odds ratios (OR) and constructed a multivariable logistic regression model with stepwise variable selection. In order to create a clinically useful/implementable tool we created a nomogram to predict risk of delirium.

RESULTS

Overall, we found a rate of POD of 3.7% across our study population. The Model achieved an AUC of the ROC curve of 0.83 (95% CI 0.82-0.84). We found that age, increased American Society of Anesthesiologists (ASA) score (ASA 3-4 OR 2.81, CI 1.49-5.28, P < .001), depression (OR 1.28, CI 1.12-1.47, P < .001), postoperative benzodiazepine use (OR 3.52, CI 3.06-4.06, P < .001) and urgent cases (Urgent OR 3.51, CI 2.92-4.21, P < .001; Emergent OR 3.99, CI 3.21-4.96, P < .001; Critically Emergent OR 5.30, CI 3.53-7.96, P < .001) were associated with POD.

DISCUSSION

We were able to distinguish the contribution of individual risk factors to the development of POD. We created a clinically useful easy-to-use tool that has the potential to accurately identify those at high-risk of delirium, a first step to prevent POD.

Risk factors and a nomogram model for postoperative delirium in elderly gastric cancer patients after laparoscopic gastrectomy

Background

To evaluate the risk factors of postoperative delirium (POD) in elderly gastric cancer (GC) patients after laparoscopic gastrectomy and construct a predictive model.

Methods

Elderly GC patients undergoing laparoscopic gastrectomy were enrolled and grouped based on the status of POD development within postoperative 7 days. Independent risk factors were selected out by univariate and multivariate logistic regression analyses and then enrolled in the nomogram prediction model.

Results

A total of 270 elderly GC patients were enrolled, and POD occurred in 74 (27.4%) patients within postoperative 7 days. The results of multivariate regression analysis indicated that age (OR: 3.30, 95% CI: 1.41–6.85, P < 0.001), sleeping pills (OR: 1.87, 95% CI: 1.12–3.09, P = 0.012), duration of ICU stay (OR: 1.55, 95% CI: 1.02–2.37, P = 0.029), albumin/fibrinogen ratio (AFR) (OR: 1.74, 95% CI: 1.03–2.76, P = 0.019), and neutrophils/lymphocytes ratio (NLR) (OR: 2.12, 95% CI: 1.11–4.01, P = 0.016) were five independent risk factors for POD in elderly GC patients. The AUC of the constructed nomogram model based on these five factors was 0.807.

Conclusions

This study highlighted that age, AFR, NLR, sleeping pills taking, and duration of ICU stay were independent risk factors for POD, and the nomogram model based on these factors could effectively predict POD in elderly GC patients.

Early Ambulation to Prevent Delirium After Long-Time Head and Neck Cancer Surgery

Objective

Postoperative delirium is known to have various adverse effects on head and neck surgery patients. This study was designed to identify possible risk factors of delirium following long periods of head and neck cancer surgery and to help prevent postoperative delirium.

Methods

We enrolled 197 patients who underwent long-time (>6 h) head and neck surgery at the Asan Medical Center from January 2017 to December 2018 in this study. Clinical covariates that may be associated with delirium were analyzed retrospectively using univariate and multivariate analyses.

Results

Delirium occurred in 18 patients (9.1%). Within the first 7 days, 16 patients (88.9%) experienced delirium. Upon univariate analysis, delirium was associated with old age (≥75, p = 0.001), past neurological history (p = 0.019), time to ambulation (p = 0.014), and postoperative hospital day (p = 0.048). In multivariate analysis, old age (≥75, odds ratios (OR) 6.16, CI 2.00–19.00, p = 0.002), time to ambulation (OR 1.04, CI 1.01–1.07, p = 0.017), and past neurological history (OR 5.26, CI 1.09–25.37, p = 0.039) were significant risk factors associated with postoperative delirium.

Conclusions

Older patients or patients with neurologic history must be attended with care, especially early after surgery. Encouraging early ambulation might lower the incidence of postoperative delirium and, subsequently, reduce adverse effects. This result could benefit patients by helping them avoid undesirable outcomes.

Simple-to-use nomogram for predicting the risk of syphilis among MSM in Guangdong Province: results from a serial cross-sectional study

Background

The purpose of this study was to develop and validate a simple-to-use nomogram for the prediction of syphilis infection among men who have sex with men (MSM) in Guangdong Province.

Methods

A serial cross-sectional data of 2184 MSM from 2017 to 2019 was used to develop and validate the nomogram risk assessment model. The eligible MSM were randomly assigned to the training and validation dataset. Factors included in the nomogram were determined by multivariate logistic regression analysis based on the training dataset. The receiver operating characteristic (ROC) curves was used to assess its predictive accuracy and discriminative ability.

Results

A total of 2184 MSM were recruited in this study. The prevalence of syphilis was 18.1% (396/2184). Multivariate logistic analysis found that age, the main venue used to find sexual partners, condom use in the past 6 months, commercial sex in the past 6 months, infection with sexually transmitted diseases (STD) in the past year were associated with syphilis infection using the training dataset. All these factors were included in the nomogram model that was well calibrated. The C-index was 0.80 (95% CI 0.76–0.84) in the training dataset, and 0.79 (95% CI 0.75–0.84) in the validation dataset.

Conclusions

A simple-to-use nomogram for predicting the risk of syphilis has been developed and validated among MSM in Guangdong Province. The proposed nomogram shows good assessment performance.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-021-06912-z.

Factors Associated With Postoperative Delirium in Patients Undergoing Complex Head and Neck Flap Surgery

PURPOSE

Postoperative delirium (PD) is a common and severe complication, following extensive surgery and prolonged stays in intensive care units (ICU). The study aimed to estimate the frequency of and identify risk factors for PD in a unified sample of head and neck surgery patients.

METHODS

The investigators implemented a retrospective cohort study composing of patients undergoing free flap reconstructive surgery over 1 year. The predictor variables were identified as 146 general and periprocedural parameters. The primary outcome variable was PD (+ or -). Descriptive and bivariate statistics were performed to identify existing correlations between the predictive factors and PD and the P-value was set at .05. A logistic regression model (LRM) was created to adjust for possible confounding factors and reveal possible independent prognostic factors for the onset of PD.

RESULTS

Hundred patients (18 with PD+, 82 with PD in patient history) undergoing microvascular free flap (65 males, 35 females, mean age = 65 [range 18 to 84 years]) surgery were recruited. The investigators identified 15 variables that were statistically associated with PD. In the LRM, after adjusting for age, diabetes status, and preoperative TSH, free fibula transplants (FT) as type of surgery was associated with an increased risk for PD (FT, Odds Ratio (OR) 6.3 (1.6 to 25.7, P = .01).

CONCLUSION

The investigators identified 15 variables associated with an increased risk of developing PD, 1 of which was also associated statistically significant after adjusting for other variables in an LRM. Future research efforts should be devoted to assessing the use of these variables for predicting PD further. Since the use of FT showed to be an independent prognostic parameter for the development of PD in this study, patients receiving FT should get special attention in the first days after surgery to prevent PD and associated complications such as increased mortality and prolonged hospital stays.

Effects of Pre-Operative Risk Factors on Intensive Care Unit Length of Stay (ICU-LOS) in Major Oral and Maxillofacial Cancer Surgery

OBJECTIVE

This study aimed to investigate the effect of certain pre-operative parameters directly on the post-operative intensive care unit (ICU)-length of stay (LOS), in order to identify at-risk patients that are expected to need prolonged intensive care management post-operatively.

MATERIAL AND METHODS

Retrospectively, patients managed in an ICU after undergoing major oral and maxillofacial surgery were analyzed. Inclusion criteria entailed: age 18-90 years, major primary oral cancer surgery including tumor resection, neck dissection and microvascular free flap reconstruction, minimum operation time of 8 h. Exclusion criteria were: benign/borderline tumors, primary radiation, other defect reconstruction than microvascular, treatment at other centers. Separate parameters used within the clinical routine were set in correlation with ICU-LOS, by applying single testing calculations (t-tests, variance analysis, correlation coefficients, effect sizes) and a valid univariate linear regression model. The primary outcome of interest was ICU-LOS.

RESULTS

This study included a homogenous cohort of 122 patients. Mean surgery time was 11.4 (±2.2) h, mean ICU-LOS was 3.6 (±2.6) days. Patients with pre-operative renal dysfunction (p < 0.001), peripheral vascular disease-PVD (p = 0.01), increasing heart failure-NYHA stage categories (p = 0.009) and higher-grade categories of post-operative complications (p = 0.023) were identified as at-risk patients for a significantly prolonged post-operative ICU-LOS.

CONCLUSIONS

At-risk patients are prone to need a significantly longer ICU-LOS than others. These patients are those with pre-operative severe renal dysfunction, PVD and/or high NYHA stage categories. Confounding parameters that contribute to a prolonged ICU-LOS in combination with other variables were identified as higher age, prolonged operative time, chronic obstructive pulmonary disease, and intra-operatively transfused blood.

Availability of preoperative neutrophil-lymphocyte ratio to predict postoperative delirium after head and neck free-flap reconstruction: A retrospective study

Postoperative delirium (POD) is a well-recognized postoperative complication and is associated with increased morbidity and mortality. We investigated whether the preoperative neutrophil-lymphocyte ratio (NLR) could be an effective predictor of POD after head and neck free-flap reconstruction. This was a single-center, retrospective, observational study. We analyzed the perioperative data of patients who had undergone elective head and neck free-flap reconstruction surgery. POD was assessed with the Intensive Care Delirium Screening Checklist (ICDSC) during admission to our intensive care unit (ICU). POD was defined as an ICDSC score ≥4. Risk factors for POD were evaluated by univariate and multivariate logistic regression analysis. We included 97 patients. The incidence of POD was 20.6% (20/97). Significantly longer ICU stays were observed in the patients with POD compared to those without POD (median [interquartile range]: 5 [4–6] vs. 4 [4–5], p = 0.031). Higher preoperative NLR values (3 <NLR ≤4 and 4 <NLR) were significantly associated with higher ICDSC scores compared to NLR ≤1 (4 [2–4] vs. 1 [1–1], p = 0.027 and 4 [1–4] vs. 1 [1–1], p = 0.038, respectively). The multivariable logistic regression analysis revealed that only a preoperative NLR >3.0 (adjusted Odds Ratio: 23.6, 95% Confidence Interval: 6.6–85.1; p<0.001) was independently associated with POD. The multivariate area under the receiver operator curve was significantly greater for the E-PRE-DELIRIC model with NLR compared to the E-PRE-DELIRIC model (0.87 vs. 0.60; p<0.001). The preoperative NLR may be a good predictor of POD in patients undergoing head and neck free-flap reconstruction.

Derivation, Validation, Sustained Performance, and Clinical Impact of an Electronic Medical Record-Based Perioperative Delirium Risk Stratification Tool

BACKGROUND

Postoperative delirium is an important problem for surgical inpatients and was the target of a multidisciplinary quality improvement project at our institution. We developed and tested a semiautomated delirium risk stratification instrument, Age, WORLD backwards, Orientation, iLlness severity, Surgery-specific risk (AWOL-S), in 3 independent cohorts from our tertiary care hospital and describe its performance characteristics and impact on clinical care.

METHODS

The risk stratification instrument was derived with elective surgical patients who were admitted at least overnight and received at least 1 postoperative delirium screen (Nursing Delirium Screening Scale [NuDESC] or Confusion Assessment Method for the Intensive Care Unit [CAM-ICU]) and preoperative cognitive screening tests (orientation to place and ability to spell WORLD backward). Using data pragmatically collected between December 7, 2016, and June 15, 2017, we derived a logistic regression model predicting probability of delirium in the first 7 postoperative hospital days. A priori predictors included age, cognitive screening, illness severity or American Society of Anesthesiologists physical status, and surgical delirium risk. We applied model odds ratios to 2 subsequent cohorts ("validation" and "sustained performance") and assessed performance using area under the receiver operator characteristic curves (AUC-ROC). A post hoc sensitivity analysis assessed performance in emergency and preadmitted patients. Finally, we retrospectively evaluated the use of benzodiazepines and anticholinergic medications in patients who screened at high risk for delirium.

RESULTS

The logistic regression model used to derive odds ratios for the risk prediction tool included 2091 patients. Model AUC-ROC was 0.71 (0.67-0.75), compared with 0.65 (0.58-0.72) in the validation (n = 908) and 0.75 (0.71-0.78) in the sustained performance (n = 3168) cohorts. Sensitivity was approximately 75% in the derivation and sustained performance cohorts; specificity was approximately 59%. The AUC-ROC for emergency and preadmitted patients was 0.71 (0.67-0.75; n = 1301). After AWOL-S was implemented clinically, patients at high risk for delirium (n = 3630) had 21% (3%-36%) lower relative risk of receiving an anticholinergic medication perioperatively after controlling for secular trends.

CONCLUSIONS

The AWOL-S delirium risk stratification tool has moderate accuracy for delirium prediction in a cohort of elective surgical patients, and performance is largely unchanged in emergent/preadmitted surgical patients. Using AWOL-S risk stratification as a part of a multidisciplinary delirium reduction intervention was associated with significantly lower rates of perioperative anticholinergic but not benzodiazepine, medications in those at high risk for delirium. AWOL-S offers a feasible starting point for electronic medical record-based postoperative delirium risk stratification and may serve as a useful paradigm for other institutions.

Postoperative Delirium is Associated With Prolonged Head and Neck Resection and Reconstruction Surgery: An Institutional Study

PURPOSE

Delirium is a recognized complication of surgery. It has a deleterious effect on a patient's postoperative recovery and well-being. The purpose of this study was to estimate the frequency and identify the risk factors for the development of postoperative delirium (POD) in a cohort of patients who underwent extensive head and neck surgery (HNS) of greater than five hours duration.

MATERIALS AND METHODS

The authors undertook a retrospective cohort study of patients who underwent HNS of greater than five hours duration. The primary predictor variables comprised a set of risk factors (sociodemographic, disease-specific, duration of surgery, and duration of inpatient stay) that were thought to be associated with the development of POD. The primary outcome variable was the development of POD. Descriptive, bivariate, and multivariate statistical analysis was undertaken, and significance was set at P < .05.

RESULTS

One hundred and seventy patients were included in the study. There were 124 males and 46 females. Forty patients (23.53%) developed POD: 30 documented and 10 inferred. The mean age of the POD cohort was 65 years (SD 13), with a median age of 69 years. The occurrence of POD was statistically related to increased age, mental health status, American Society of Anesthesiologists (ASA) score, and drug dependence (either illicit or prescription). POD and operative duration were statistically associated. POD and length of stay were not statistically associated.

CONCLUSION

Delirium did occur postoperatively in 23.53% of our patients who underwent extensive and prolonged HNS. POD may go unrecognized by treating teams. As POD has a deleterious effect on the cognitive function, it is important to identify and aggressively treat episodes of POD that occur during a patient's postoperative recovery.

An Advanced Practice Provider Initiative for Delirium Management in the ICU

Delirium is the most common neuropsychiatric challenge in cancer patients, particularly in the critically ill population. Without a screening method and constant vigilance by providers, delirium is often misdiagnosed. The purpose of our pilot study was to determine if an educational program targeting critical care medicine advanced practice providers (APPs) and fellows in an oncologic intensive care unit would increase APP knowledge of delirium and their level of comfort with delirium screening and management. Thirty-one APPs and fellows participated in the program. Scores on knowledge-based delirium assessments increased significantly after the intervention and at the 3-month follow-up (p < .0001 and p < .0225, respectively). Providers’ comfort with delirium screening and management also improved after the intervention (p = .0020 and p < .0001, respectively) and decreased slightly at the 3-month follow-up (p = .1764 and p = .9840, respectively). A brief and focused APP-led educational initiative successfully improved knowledge of delirium and comfort with screening and management.

Risk Factors Associated With Postoperative Delirium in Patients Undergoing Head and Neck Free Flap Reconstruction

Importance

Postoperative delirium (POD) is associated with an increased rate of adverse events, higher health care costs, and longer hospital stays. At present, limited data are available regarding the risk factors for developing POD in patients undergoing head and neck free flap reconstruction. Identification of patients at high risk of developing POD will allow implementation of risk-mitigation strategies.

Objective

To determine the frequency of and risk factors associated with POD in patients undergoing free flap reconstruction secondary to head and neck disease.

Design, Setting, and Participants

This retrospective cohort study included 515 patients undergoing free flap reconstruction from January 1, 2006, through December 31, 2012, at the James Cancer Hospital and Solove Research Institute, The Ohio State University Comprehensive Care Center, a tertiary care cancer hospital. Preoperative, intraoperative, and postoperative data were collected retrospectively. Data from January 1, 2006, through December 31, 2012, were analyzed, and the final date of data analysis was January 8, 2018.

Interventions

Head and neck free flap reconstruction.

Main Outcomes and Measures

The primary outcome was the development of POD as defined by the Diagnostic and Statistical Manual of Mental Disorders (Fourth Edition). Univariable and multivariable logistic regression were used to identify risk factors associated with POD.

Results

Five hundred fifteen patients underwent free flap reconstruction during the study period (66.2% male; mean [SD] age, 60.1 [12.8] years). Of these, 56 patients (10.9%) developed POD. On multivariable analysis, risk factors associated with POD included increased age (odds ratio [OR], 1.06; 95% CI, 1.02-1.11), male sex (OR, 5.02; 95% CI, 1.47-17.20), increased operative time (OR for each 1-minute increase, 1.004 [95% CI, 1.001-1.006]; OR for each 1-hour increase, 1.26 [95% CI, 1.08-1.46]), advanced nodal disease (OR, 3.00; 95% CI, 1.39-6.46), and tobacco use (OR, 7.23; 95% CI, 1.43-36.60). Preoperative abstinence from alcohol was identified as a protective factor (OR, 0.24; 95% CI, 0.12-0.51).

Conclusions and Relevance

This study identified variables associated with a higher risk of developing POD. Although many of these risk factors are nonmodifiable, they provide a target population for quality improvement initiatives. Furthermore, preoperative alcohol abstinence may be useful in preventing POD.

Do Risk Prediction Models for Postoperative Delirium Consider Patients' Preoperative Medication Use?

BACKGROUND

Medicines are potentially modifiable risk factors for postoperative delirium. However, the extent to which preoperative medicines are included in risk prediction models (RPMs) is unknown.

OBJECTIVE

This systematic review aimed to assess the extent of inclusion of preoperative medications in RPMs for postoperative delirium.

METHODS

Articles were systematically searched from MEDLINE, EMBASE and CINAHL using Medical Subject Headings (MeSH) where possible and keywords for postoperative delirium and prediction model. Studies published until May 2017 with a primary outcome of postoperative delirium that developed an RPM containing preoperative patient information were considered. Where a study had two cohorts, a derivation and a validation cohort, findings from the derivation cohort were extracted and reported.

RESULTS

Eighteen prospective and one retrospective cohort studies were included for review. Of the 19 studies, only nine considered preoperative medication data, with medications appearing as predictor variables in five models. There was wide variability in the factors included in the final models, with the most frequent predictors being age and cognitive impairment, appearing in 13 (68%) and 11 (58%) RPMs, respectively.

CONCLUSION

While medications are commonly cited risk factors for delirium, they are not adequately considered when developing RPMs. Future studies aiming to develop an RPM for postoperative delirium should include preoperative medication data as a potential predictor variable because of the modifiable nature of medication use and its impact on other factors commonly in models, such as cognition.

Risk factors for postoperative delirium in patients undergoing free flap reconstruction for oral cancer

The aim of this study was to investigate risk factors for postoperative delirium in patients undergoing free flap reconstruction for defects after oral cancer resection. This was a non-randomized, retrospective cohort study involving 102 patients who underwent oral cancer resection and free flap reconstruction. Data were collected from the medical records. Postoperative delirium occurred in 34 patients (33.3%), of whom 27 were male and seven were female. High preoperative total protein and albumin, diabetes mellitus, history of smoking, use of hypnotics or antipsychotics, time until getting out of bed after surgery, and postoperative insomnia were significantly related to delirium in the univariate analysis (P<0.05). In a multiple logistic regression model, high preoperative albumin (odds ratio 4.45), postoperative insomnia (odds ratio 10.72), and history of smoking (odds ratio 2.91) were significant risk factors for delirium (P<0.05). The analysis of laboratory data before and after surgery showed greater decreases in albumin, total protein, and haemoglobin after surgery in patients with postoperative delirium than in those without this condition. These results show that the perioperative maintenance of nutritional status and early postoperative management of the sleep cycle are important to prevent delirium after oral cancer resection and free flap reconstruction.