Complications Predicting Perioperative Mortality in Patients Undergoing Elective Craniotomy: A Population-Based Study

Enhanced Recovery After Surgery for Craniotomies: A Systematic Review and Meta-analysis

The efficacy of the enhanced recovery after surgery (ERAS) protocols in neurosurgery has not yet been established. We performed a systematic review and meta-analysis of randomized controlled trials to compare the effects of ERAS protocols and conventional perioperative care on postoperative outcomes in patients undergoing craniotomy. The primary outcome was postoperative length of hospital stay. Secondary outcomes included postoperative pain visual analog pain scores, incidence of postoperative nausea and vomiting (PONV), postoperative complications, all-cause reoperation, readmission after discharge, and mortality. A literature search up to August 10, 2023, was conducted using PubMed, Embase, Cochrane Central Register of Controlled Trials, Web of Science, and Scopus databases. Five studies, including 871 patients, were identified for inclusion in this review. Compared with conventional perioperative care, ERAS protocols reduced the length of postoperative hospital stay (difference of medians, -1.52 days; 95% CI: -2.55 to -0.49); there was high heterogeneity across studies (I2, 74%). ERAS protocols were also associated with a lower risk of PONV (relative risk, 0.79; 95% CI: 0.69-0.90; I2, 99%) and postoperative pain with a visual analog scale score ≥4 at postoperative day 1 (relative risk, 0.37; 95% CI: 0.28-0.49; I2, 14%). Other outcomes, including postoperative complications, did not differ between ERAS and conventional care groups. ERAS protocols may be superior to conventional perioperative care in craniotomy patients in terms of lower length of hospital stay, lower incidence of PONV, and improved postoperative pain scores. Further randomized trials are required to identify the impact of ERAS protocols on the quality of recovery after craniotomy.

The frailty-driven predictive model for failure to rescue among patients who experienced a major complication following cervical decompression and fusion: an ACS-NSQIP analysis of 3,632 cases (2011-2020)

BACKGROUND CONTEXT

Preoperative risk stratification for patients considering cervical decompression and fusion (CDF) relies on established independent risk factors to predict the probability of complications and outcomes in order to help guide pre and perioperative decision-making.

PURPOSE

This study aims to determine frailty's impact on failure to rescue (FTR), or when a mortality occurs within 30 days following a major complication.

STUDY DESIGN/SETTING

Cross-sectional retrospective analysis of retrospective and nationally-representative data.

PATIENT SAMPLE

The American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database was queried for all CDF cases from 2011-2020.

OUTCOME MEASURES

CDF patients who experienced a major complication were identified and FTR was calculated as death or hospice disposition within 30 days of a major complication.

METHODS

Frailty was measured by the Risk Analysis Index-Revised (RAI-Rev). Baseline patient demographics and characteristics were compared for all FTR patients. Significant factors were assessed by univariate and multivariable regression for the development of a frailty-driven predictive model for FTR. The discriminative ability of the predictive model was assessed using a receiving operating characteristic (ROC) curve analysis.

RESULTS

There were 3632 CDF patients who suffered a major complication and 7.6% (277 patients) subsequently expired or dispositioned to hospice, the definition of FTR. Independent predictors of FTR were nonelective surgery, frailty, preoperative intubation, thrombosis or embolic complication, unplanned intubation, on ventilator for >48 hours, cardiac arrest, and septic shock. Frailty, and a combination of preoperative and postoperative risk factors in a predictive model for FTR, achieved outstanding discriminatory accuracy (C-statistic = 0.901, CI: 0.883-0.919).

CONCLUSION

Preoperative and postoperative risk factors, combined with frailty, yield a highly accurate predictive model for FTR in CDF patients. Our model may guide surgical management and/or prognostication regarding the likelihood of FTR after a major complication postoperatively with CDF patients. Future studies may determine the predictive ability of this model in other neurosurgical patient populations.

Failure to rescue as a patient safety indicator for neurosurgical patients: are we there yet? A systematic review

Failure to rescue (FTR) is a standardized patient safety indicator (PSI-04) developed by the Agency for Healthcare Research and Quality (AHRQ) to assess the ability of a healthcare team to prevent mortality following a major complication. However, FTR rates vary and are impacted by non-modifiable individual patient characteristics such as baseline frailty. This raises concerns regarding the validity of FTR as an objective quality metric, as not all patients have the same baseline frailty level, or physiological reserve, to recover from major complications. Literature from other surgical specialties has identified flaws in FTR and called for risk-adjusted metrics. Currently, knowledge of factors influencing FTR and its subsequent implementation in neurosurgical patients are limited. The present review assesses trends in FTR utilization to assess how FTR performs as an objective neurosurgery quality metric. This review then proposes how FTR may be best modified to optimize use in neurosurgical patients. A PubMed search was performed to identify articles published until August 9, 2023. Studies that reported FTR as an outcome in patients undergoing neurosurgical procedures were included. A qualitative assessment was performed using the Newcastle Ottawa Scale (NOS). The initial search revealed 1232 citations. After a title and abstract screen, followed by a full text screen, 12 studies met criteria for inclusion. These articles measured FTR across a total of 764,349 patients undergoing neurosurgical procedures. Five studies analyzed FTR with regard to hospital characteristics, and three studies utilized patient characteristics to predict FTR. All studies were considered high quality based on the NOS. Modifications in criteria to measure FTR are necessary since FTR depends on patient characteristics like frailty. This would allow for the incorporation of risk-adjusted FTR metrics that would aid in clinical decision making in neurosurgical patients.

A data-driven medical knowledge discovery framework to predict the length of ICU stay for patients undergoing craniotomy based on electronic medical records

Craniotomy is an invasive operation with great trauma and many complications, and patients undergoing craniotomy should enter the ICU for monitoring and treatment. Based on electronic medical records (EMR), the discovery of high-risk multi-biomarkers rather than a single biomarker that may affect the length of ICU stay (LoICUS) can provide better decision-making or intervention suggestions for clinicians in ICU to reduce the high medical expenses of these patients and the medical burden as much as possible. The multi-biomarkers or medical decision rules can be discovered according to some interpretable predictive models, such as tree-based methods. Our study aimed to develop an interpretable framework based on real-world EMRs to predict the LoICUS and discover some high-risk medical rules of patients undergoing craniotomy. The EMR datasets of patients undergoing craniotomy in ICU were separated into preoperative and postoperative features. The paper proposes a framework called Rules-TabNet (RTN) based on the datasets. RTN is a rule-based classification model. High-risk medical rules can be discovered from RTN, and a risk analysis process is implemented to validate the rules discovered by RTN. The performance of the postoperative model was considerably better than that of the preoperative model. The postoperative RTN model had a better performance compared with the baseline model and achieved an accuracy of 0.76 and an AUC of 0.85 for the task. Twenty-four key decision rules that may have impact on the LoICUS of patients undergoing craniotomy are discovered and validated by our framework. The proposed postoperative RTN model in our framework can precisely predict whether the patients undergoing craniotomy are hospitalized for too long (more than 15 days) in the ICU. We also discovered and validated some key medical decision rules from our framework.

Myocardial infarction after craniotomy for asymptomatic meningioma

A man in his 40s with a history of coronary artery disease previously treated with a drug-eluting stent presented for elective craniotomy and resection of an asymptomatic but enlarging meningioma. During his craniotomy, he received desmopressin and tranexamic acid for surgical bleeding. Postoperatively, the patient developed chest pain and was found to have an ST-elevation myocardial infarction (MI). Because of the patient's recent neurosurgery, standard post-MI care was contraindicated and he was instead managed symptomatically in the intensive care unit. Echocardiogram on postoperative day 1 demonstrated no regional wall motion abnormalities and an ejection fraction of 60%. His presentation was consistent with thrombosis of his diagonal stent. He was transferred out of the intensive care unit on postoperative day 1 and discharged home on postoperative day 3.

Machine Learning Approaches-Driven for Mortality Prediction for Patients Undergoing Craniotomy in ICU

OBJECTIVES

We aimed to predict the mortality of patients with craniotomy in ICU by using predictive models to extract the high-risk factors leading to the death of patients from a retrospective a study.

METHODS

Five machine-learning (ML) algorithms were applied for training on mortality predictive models with the data from a surgical intensive care unit (ICU) database of the Fujian Provincial Hospital in China. The accuracy, precision, recall, f1 score and the area under the receiver operator characteristic curve (AUC) were used to evaluate the performance of different models, and the calibration of the model was evaluated by brier score.

RESULTS

We demonstrated that eXtreme Gradient Boosting (XGBoost) was more suitable for the task, demonstrating a AUC of 0.84. We analyzed the feature importance with the Local Interpretable Model-agnostic Explanations (LIME) analysis and further identified the high-risk factors of mortality in ICU through this study.

CONCLUSIONS

This study established the mortality predictive model of patients who had undergone craniotomy in ICU. Identification of the factors that had great influence on mortality has the potential to provide auxiliary decision support for clinical medical staff on their practices.

Risk factors and associated complications with unplanned intubation in patients with craniotomy for brain tumor

Patients undergoing surgical resection of a brain tumor have the potential risk for beingintubated post-operatively, which may be associated with significant morbidity and/or mortality after surgery. This study was analyzed various preoperative patient characteristics, postoperative outcomes, and complications to identify risk factors for unplanned intubation (UI) in adult patients undergoing craniotomy for a brain tumor and created a risk score framework for that cohort. Patients undergoing surgery for a brain tumor were identified according to primary Current Procedural Terminology codes, and information found in The American College of Surgeons (ACS) National Surgical Quality Improvement Project (NSQIP) database from 2012 to 2015 was reviewed. A total of 18,642 adult brain tumor patients were included in the ACS-NSQIP. The rate of unplanned intubation in this cohort was 2.30% (4 2 8). The mortality rate of patients who underwent UI after surgical resection of brain tumor was 24.78% compared to an overall mortality of 2.46%. During the first 30 days after surgery, 33% of patients who underwent UI had an unplanned reoperation, compared to 4.76% of patients who did not undergo unplanned intubation. Bivariate and multivariate analyses identified several predictors and computed a risk score for UI. A risk score based on patient factors for those undergoing a craniotomy for a brain tumor predicts the postoperative UI rate. This could aid in surgical decision-making by identify patients at a higher risk of UI, while modifying perioperative management may help prevent UI.

Brain Tumor Surgery is Safe in Octogenarians and Nonagenarians: A Single-Surgeon 741 Patient Series

OBJECTIVES

Elderly patients with surgically accessible brain tumors are often not offered clinically indicated brain tumor surgery (BTS) because of to assumptions of greater risk for perioperative morbidity and mortality. Because brain tumor incidence is highest in the geriatric population, and because the global population is aging, accurate understanding of BTS risk in elderly patients is critical. We aimed to compare safety of BTS in elderly patients with younger counterparts to better understand the risk-benefit profile of BTS for elderly patients.

METHODS

Retrospective cohort study of young (20-29 years), senior (60-79 years), and elderly (80+ years) patients who underwent BTS with a single neurosurgeon. Differences between pre- and postoperative modified Rankin score (ΔmRS), length of hospitalization (LOH), complication rate, and 30-day readmission rates (30DRR) were recorded.

RESULTS

A total of 741 patients (83 elderly, 570 senior, and 88 young) were identified. No significant difference in preoperative mRS between different age groups, χ² = 0.269, P = 0.874. Elderly complication rate was 6.0%, not significantly different from young (4.5%, P = 0.667) or senior (7.2%, P = 0.696) complication rate. Elderly LOH was 1.93 ± SD 0.176 days; not significantly different from young (3.01 ± 0.384 days, P = 0.081) or senior (2.47 ± 0.144 days, P = 0.881). Statistical equivalence testing showed with 95% confidence that there was equivalence in ΔmRS among age groups.

CONCLUSIONS

Elderly patients did not have significantly different ΔmRS, LOH, 30DRR, or complication rates after BTS compared with younger counterparts. Therefore, in healthy patients, advanced age alone should not prevent patients from being offered BTS.