Prevalence and risk factors of postoperative delirium after spinal surgery: a meta-analysis

Risk factors of postoperative delirium following spine surgery: A meta-analysis of 50 cohort studies with 1.1 million participants

Objectives

Postoperative delirium (POD) is considered to be a common complication of spine surgery. Although many studies have reported the risk factors associated with POD, the results remain unclear. Therefore, we performed a meta-analysis to identify risk factors for POD among patients following spinal surgery.

Methods

We systematically searched the PubMed, Embase and the Cochrane Library for relevant articles published from 2006 to February 1, 2023 that reported risk factors associated with the incidence of POD among patients undergoing spinal surgery. The Meta-Analysis of Observational Studies in Epidemiology (MOOSE) guidelines were followed, and random effects models were used to estimate pooled odds ratio (OR) estimates with 95 % confidence intervals (CIs) for each factor. The evidence from observational studies was classified according to Egger's P value, total sample size, and heterogeneity between studies.

Results

Of 11,329 citations screened, 50 cohort studies involving 1,182,719 participants met the inclusion criteria. High-quality evidence indicated that POD was associated with hypertension, diabetes mellitus, cardiovascular disease, pulmonary disease, older age (>65 years), patients experiencing substance use disorder (take drug ≥1 month), cerebrovascular disease, kidney disease, neurological disorder, parkinsonism, cervical surgery, surgical site infection, postoperative fever, postoperative urinary tract infection, and admission to the intensive care unit (ICU). Moderate-quality evidence indicated that POD was associated with depression, American Society of Anesthesiologists (ASA) fitness grade (>II), blood transfusion, abnormal potassium, electrolyte disorder, length of stay, inability to ambulate and intravenous fluid volume.

Conclusions

Conspicuous risk factors for POD were mainly patient- and surgery-related. These findings help clinicians identify high-risk patients with POD following spinal surgery and recognize the importance of early intervention.

Random forest algorithm for predicting postoperative delirium in older patients

Objective

In this study, we were aimed to identify important variables via machine learning algorithms and predict postoperative delirium (POD) occurrence in older patients.

Methods

This study was to make the secondary analysis of data from a randomized controlled trial. The Boruta function was used to screen relevant basic characteristic variables. Four models including Logistic Regression (LR), K-Nearest Neighbor (KNN), the Classification and Regression Tree (CART), and Random Forest (RF) were established from the data set using repeated cross validation, hyper-parameter optimization, and Smote technique (Synthetic minority over-sampling technique, Smote), with the calculation of confusion matrix parameters and the plotting of Receiver operating characteristic curve (ROC), Precision recall curve (PRC), and partial dependence graph for further analysis and evaluation.

Results

The basic characteristic variables resulting from Boruta screening included grouping, preoperative Mini-Mental State Examination(MMSE), CHARLSON score, preoperative HCT, preoperative serum creatinine, intraoperative bleeding volume, intraoperative urine volume, anesthesia duration, operation duration, postoperative morphine dosage, intensive care unit (ICU) duration, tracheal intubation duration, and 7-day postoperative rest and move pain score (median and max; VAS-Rest-M, VAS-Move-M, VAS-Rest-Max, and VAS-Move-Max). And Random Forest (RF) showed the best performance in the testing set among the 4 models with Accuracy: 0.9878; Matthews correlation coefficient (MCC): 0.8763; Area under ROC curve (AUC-ROC): 1.0; Area under the PRC Curve (AUC-PRC): 1.0.

Conclusion

A high-performance algorithm was established and verified in this study demonstrating the degree of POD risk changes in perioperative elderly patients. And the major risk factors for the development of POD were CREA and VAS-Move-Max.

Family Presence May Reduce Postoperative Delirium After Spinal Surgery

ABSTRACT

BACKGROUND: Delirium is associated with worse outcomes, but there is a gap in literature identifying nurse-led interventions to reduce delirium in postoperative (postop) surgical spine patients. Because family presence has been associated with a variety of beneficial effects, we aimed to examine whether family presence in the spine intensive care unit (ICU) during the night after surgery was associated with less confusion or delirium on postop day 1. METHODS: This is a prospective nonrandomized pilot clinical trial with pragmatic sampling. Group designation was assigned by natural history. The family-present group was designated as patients for whom a family member remained present during the first night after surgery. The unaccompanied group was designated as patients who did not have a family member stay the night. Data include the Richmond Agitation Sedation Scale, the Confusion Assessment Method for the ICU, the 4AT (Alertness, Attention, Abbreviated mental test, and Acute change) score, and confusion measured with the orientation item on the Glasgow Coma Scale. Baseline data were collected after admission to the spine ICU and compared with the same data collected in the morning of postop day 1. RESULTS: At baseline, 5 of 16 patients in the family-present group (31.3%) had at least 1 incidence of delirium or confusion. Similarly, 6 of 14 patients in the unaccompanied group (42.9%) had at least 1 incidence of delirium or confusion. There was a clinically relevant, but not statistically significant, reduction in postop day 1 delirium or confusion comparing the family-present (6.3%) and unaccompanied (21.4%) groups ( P = .23). CONCLUSION: Family presence may reduce delirium and confusion for patients after spine surgery. The results support continued research into examining nurse-led interventions to reduce delirium and improve outcomes for this population.

Publication bias in meta-analyses of the therapeutic efficacy of remdesivir interventions for patients with COVID-19

Purpose

No comprehensive statistical assessment of publication bias has been conducted in remdesivir-based intervention research for COVID-19 patients. This study aims to examine all meta-analyses of the efficacy of remdesivir interventions in COVID-19 patients and perform a statistical assessment of publication bias.

Design/methodology/approach

This is an analytic study conducted to assess the impact of publication bias on the results of meta-analyses of remdesivir-based interventions in patients infected with COVID-19. All English full-text meta-analyses published in peer-reviewed journals in 2019–2021 were included. A computerized search of PubMed and Web of Science electronic databases was performed on December 24, 2021. The trim-and-fill method calculated the number of missing studies and the adjusted cumulative effect sizes.

Findings

The final analysis comprised 21 studies with 88 outcomes. The investigation revealed missing studies in 46 outcomes (52%). Seventy-six missing studies were replaced in the outcomes using the trim-and-fill procedure. The adjusted recalculated effect sizes of the 27 outcomes increased by an average of 0.04. In comparison, the adjusted effect size of 18 outcomes fell by an average of 0.036. Only 14 out of 46 outcomes with publication bias were subjected to a gray literature search (30%). To discover related research, no gray literature search was conducted in most outcomes with publication bias (n = 32; 70%). In conclusion, the reported effect estimates regarding the effect of remdesivir in COVID-19 patients are only slightly affected by publication bias and can be considered authentic. Health-care decision-makers in COVID-19 should consider current research results when making clinical decisions.

Research limitations/implications

Most health decisions are based on the effect sizes revealed in meta-analyses. When deciding on remdesivir-based treatment for COVID-19 patients, therefore, the outcomes of this investigation may be of paramount importance to health policymakers, leading to better treatment strategies.

Practical implications

According to the results, no major publication bias and missing studies were detected on average. Therefore, the calculated effect sizes of remdesivir-based interventions on meta-analyses can be used as authentic and unbiased benchmarks by health-care decision-makers in treating patients with COVID-19.

Originality/value

This is the first study to examine the effect of publication bias and gray literature searches on the results of meta-analyses of treatment with COVID-19 (remdesivir).

Automated machine learning-based model for the prediction of delirium in patients after surgery for degenerative spinal disease

OBJECTIVE

This study used machine learning algorithms to identify critical variables and predict postoperative delirium (POD) in patients with degenerative spinal disease.

METHODS

We included 663 patients who underwent surgery for degenerative spinal disease and received general anesthesia. The LASSO method was used to screen essential features associated with POD. Clinical characteristics, preoperative laboratory parameters, and intraoperative variables were reviewed and were used to construct nine machine learning models including a training set and validation set (80% of participants), and were then evaluated in the rest of the study sample (20% of participants). The area under the receiver-operating characteristic curve (AUROC) and Brier scores were used to compare the prediction performances of different models. The eXtreme Gradient Boosting algorithms (XGBOOST) model was used to predict POD. The SHapley Additive exPlanations (SHAP) package was used to interpret the XGBOOST model. Data of 49 patients were prospectively collected for model validation.

RESULTS

The XGBOOST model outperformed the other classifier models in the training set (area under the curve [AUC]: 92.8%, 95% confidence interval [CI]: 90.7%-95.0%), validation set (AUC: 87.0%, 95% CI: 80.7%-93.3%). This model also achieved the lowest Brier Score. Twelve vital variables, including age, serum albumin, the admission-to-surgery time interval, C-reactive protein level, hypertension, intraoperative blood loss, intraoperative minimum blood pressure, cardiovascular-cerebrovascular disease, smoking, alcohol consumption, pulmonary disease, and admission-intraoperative maximum blood pressure difference, were selected. The XGBOOST model performed well in the prospective cohort (accuracy: 85.71%).

CONCLUSION

A machine learning model and a web predictor for delirium after surgery for the degenerative spinal disease were successfully developed to demonstrate the extent of POD risk during the perioperative period, which could guide appropriate preventive measures for high-risk patients.

Risk factors for delirium in elderly patients after lumbar spinal fusion

OBJECTIVE

To identify perioperative risk factors for postoperative delirium (POD) in patients aged 65 or older undergoing lumbar spinal fusion procedures.

PATIENTS AND METHODS

A retrospective cohort analysis was performed on patients undergoing lumbar spinal fusion over an approximately three-year period at a single institution. Demographic and perioperative data were obtained from electronic medical records. The primary outcome was the presence of postoperative delirium assayed by the Delirium Observation Screening Scale (DOSS) and Confusion Assessment Method for the ICU (CAM-ICU). Univariate and multivariate analyses were performed on the data.

RESULTS

Of the 702 patients included in the study, 173 (24.6%) developed POD. Our analysis revealed that older age (p < 0.001), lower preoperative hemoglobin (p < 0.001), and higher ASA status (p < 0.001), were significant preoperative risk factors for developing POD. The only significant intraoperative risk factor was a higher number of spinal levels that were instrumented (p < 0.001). Higher pain scores on postoperative day 1 (p < 0.001), and lower postoperative hemoglobin (p < 0.001) were associated with increased POD; as were ICU admission (p < 0.001) and increased length of ICU stay (p < 0.001). Patients who developed POD had a longer hospital stay (p < 0.001) with lower rates of discharge to home as opposed to an inpatient facility (p < 0.001).

CONCLUSION

Risk factors for POD in older adults undergoing lumbar spinal fusion surgery include advanced age, diabetes, lower preoperative and postoperative hemoglobin, higher ASA grade, greater extent of surgery, and higher postoperative pain scores. Patients with delirium had a higher incidence of postoperative ICU admission, increased length of stay, decreased likelihood of discharge to home and increased mortality, all consistent with prior studies. Further studies may determine whether adequate management of anemia and pain lead to a reduction in the incidence of postoperative delirium in these patients.

Delirium in hospitalised adults with acute burns - A systematic review

INTRODUCTION

Delirium is a potentially modifiable, acutely altered mental state, commonly characterised as a hospital-acquired complication. Studies of adult inpatients with acute burns with and without delirium identify causative risks related to the injury or treatment and outcomes related to the patient and healthcare system. We compare patients with and without delirium, providing a high-level quantitative synthesis of delirium risks and outcomes to inform guidelines and future research.

METHODS

A systematic review, meta-analysis and GRADE evaluation of risks and outcomes associated with delirium in adults with acute burns was conducted using PRISMA guidelines and PROSPERO protocol CRD42021283055. The Newcastle-Ottawa Scale was used to assess quality.

RESULTS

Investigators reviewed ten studies. ASA score ≥ 3, Total Body Surface Area Percentage (TBSA)> 10%, surgery done, ICU admission, hospital and also Intensive Care Unit (ICU) lengths of stay all had statistically significant associations with delirium, with low-very low certainty on GRADE evaluation. Limitations were heterogeneous studies, review methodology and study bias.

CONCLUSION

Delirium represents a significant risk to comorbid patients with burns that are hospitalised, receive ICU care, and surgery. Further research is indicated to precisely categorise delirium along the clinical journey to identify modifiable factors, prevention, and proactive therapy.

Risk assessment of perioperative neurocognitive disorders, where are we now?

PURPOSE OF REVIEW

Perioperative neurocognitive disorders (PNDs) are among the most frequent complications after surgery and are associated with considerable morbidity and mortality. We analysed the recent literature regarding risk assessment of PND.

RECENT FINDINGS

Certain genetic variants of the cholinergic receptor muscarinic 2 and 4, as well as a marked degree of frailty but not the kind of anaesthesia (general or spinal) are associated with the risk to develop postoperative delirium (POD). Models predict POD with a discriminative power, for example, area under the receiver operating characteristics curve between 0.52 and 0.94.

SUMMARY

Advanced age as well as preexisting cognitive, functional and sensory deficits remain to be the main risk factors for the development of PND. Therefore, aged patients should be routinely examined for both preexisting and new developing deficits, as recommended in international guidelines. Appropriate tests should have a high discrimination rate, be feasible to be administered by staff that do not require excessive training, and only take a short time to be practical for a busy outpatient clinic. Models to predict PND, should be validated appropriately (and externally if possible) and should not contain a too large number of predictors to prevent overfitting of models.

Preoperative heart rate variability analysis is as a potential simple and easy measure for predicting perioperative delirium in esophageal surgery

Background

Delirium is one of the most common but severe perioperative complications. Autonomic activity evaluated by heart rate variability (HRV) has been recently reported as a useful tool for prediction and for early detection of delirium in acute care medicine, especially in postoperative intensive care unit (ICU) patients. We hypothesized that HRV, by 3-lead electrocardiogram (ECG), one day prior to surgery might correlate with the presence of postoperative delirium.

Materials and methods

This study was cohort prospective pilot study. We measured preoperative HRV and postoperative delirium in patients who underwent surgery for elective esophageal cancer. ECG of the participants was performed for 10 min 6–12 h preceding surgery. Postoperatively, patients were admitted to the ICU or critical care unit and stayed for at least 3 days. Delirium was diagnosed by psychiatrist rounds twice a day.

Results

Delirium was assessed for 3 days after surgery and 30 patients performed the study. Seven patients developed delirium during their ICU stay, while the remaining twenty-three did not. After HRV analysis, the preoperative high frequency power in delirium patients was significantly lower than that in non-delirium patient. Other parameters of HRV, including lower frequency power, total power and the ratio showed no statistically significant difference between the groups.

Conclusion

The results of current study demonstrated that preoperative measurement of HRV may be a useful predictor of delirium. Further investigation could pave the way to a non-invasive, minimally stressful method of predicting postoperative delirium.

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Delirium is one of the most common and severe postoperative complications.

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Delirium prediction can provide better treatment for patients.

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Heart rate variability analysis might predict delirium in esophageal cancer surgery.