Implementation and preliminary validation of a new score that predicts post-operative complications

Surgical Complexity and Complications: The Need for a Common Language

BACKGROUND

Quality measurement and outcome assessment have recently caught an attention of the neurosurgical community, but lack of standardized definitions and methodology significantly complicates these tasks.

OBJECTIVE

To identify a uniform definition of neurosurgical complications, to classify them according to etiology, and to evaluate them comprehensively in cases of intracranial tumor removal in order to establish a new, easy, and practical grading system capable of predicting the risk of postoperative clinical worsening of the patient condition.

METHODS

A retrospective analysis was conducted on all elective surgeries directed at removal of intracranial tumor in the authors' institution during 2-year study period. All sociodemographic, clinical, and surgical factors were extracted from prospectively compiled comprehensive patient registry. Data on all complications, defined as any deviation from the ideal postoperative course occurring within 30 days of the procedure, were collected with consideration of the required treatment and etiology. A logistic regression model was created for identification of independent factors associated with worsening of the Karnofsky Performance Scale (KPS) score at discharge after surgery in comparison with preoperative period. For each identified statistically significant independent predictor of the postoperative worsening, corresponding score was defined, and grading system, subsequently named Milan Complexity Scale (MCS), was formed.

RESULTS

Overall, 746 cases of surgeries for removal of intracranial tumor were analyzed. Postoperative complications of any kind were observed in 311 patients (41.7%). In 223 cases (29.9%), worsening of the KPS score at the time of discharge in comparison with preoperative period was noted. It was independently associated with 5 predictive factors-major brain vessel manipulation, surgery in the posterior fossa, cranial nerve manipulation, surgery in the eloquent area, tumor size >4 cm-which comprised MCS with a range of the total score from 0 to 8 (higher score indicates more complex clinical situations). Patients who demonstrated KPS worsening after surgery had significantly higher total MCS scores in comparison with individuals whose clinical status at discharge was improved or unchanged (3.24 ± 1.55 versus 1.47 ± 1.58; P < 0.001).

CONCLUSION

It is reasonable to define neurosurgical complication as any deviation from the ideal postoperative course occurring within 30 days of the procedure. Suggested MCS allows for standardized assessment of surgical complexity before intervention and for estimating the risk of clinical worsening after removal of intracranial tumor. Collection of data on surgical complexity, occurrence of complications, and postoperative outcomes, using standardized prospectively maintained comprehensive patient registries seems very important for quality measurement and should be attained in all neurosurgical centers.

The influence of psychological interventions on surgical outcomes: a systematic review

BACKGROUND

An amplified and/or prolonged surgical stress response might overcome the organs' functional reserve, thus leading to postoperative complications. The aim of this systematic literature review is to underline how specific psychological interventions may contribute to improve surgical outcomes through the positive modulation of the surgical stress response in surgical patients.

METHODS

We conducted a comprehensive literature search in the Cochrane Register of Controlled Trials, PubMed, EMBASE, Scopus, PsycINFO, and CINAHL databases. Only studies published in English from Jan 2000 to Apr 2022 and reporting pain and/or anxiety among outcome measures were included in the review. The following psychological interventions were considered: (1) relaxation techniques, (2) cognitive-behavioral therapies, (3) mindfulness, (4) narrative medicine, (5) hypnosis, and (6) coping strategies.

RESULTS

Among 3167 records identified in the literature, 5 papers were considered eligible for inclusion in this review because reporting the effects that psychological features have on neurochemical signaling during perioperative metabolic adaptation and those metabolic and clinical effects that the psychological interventions had on the observed population.

CONCLUSION

Our findings confirm that psychological interventions may contribute to improve surgical outcomes via the positive influence on patients' metabolic surgical stress response. A multidisciplinary approach integrating physical and non-physical therapies can be considered a good strategy to successfully improve surgical outcomes in the perioperative period.

The new SUMPOT to predict postoperative complications using an Artificial Neural Network

An accurate assessment of preoperative risk may improve use of hospital resources and reduce morbidity and mortality in high-risk surgical patients. This study aims at implementing an automated surgical risk calculator based on Artificial Neural Network technology to identify patients at risk for postoperative complications. We developed the new SUMPOT based on risk factors previously used in other scoring systems and tested it in a cohort of 560 surgical patients undergoing elective or emergency procedures and subsequently admitted to intensive care units, high-dependency units or standard wards. The whole dataset was divided into a training set, to train the predictive model, and a testing set, to assess generalization performance. The effectiveness of the Artificial Neural Network is a measure of the accuracy in detecting those patients who will develop postoperative complications. A total of 560 surgical patients entered the analysis. Among them, 77 patients (13.7%) suffered from one or more postoperative complications (PoCs), while 483 patients (86.3%) did not. The trained Artificial Neural Network returned an average classification accuracy of 90% in the testing set. Specifically, classification accuracy was 90.2% in the control group (46 patients out of 51 were correctly classified) and 88.9% in the PoC group (8 patients out of 9 were correctly classified). The Artificial Neural Network showed good performance in predicting presence/absence of postoperative complications, suggesting its potential value for perioperative management of surgical patients. Further clinical studies are required to confirm its applicability in routine clinical practice.

Predicting functional impairment in brain tumor surgery: the Big Five and the Milan Complexity Scale

OBJECT

The Milan Complexity Scale—a new practical grading scale designed to estimate the risk of neurological clinical worsening after performing surgery for tumor removal—is presented.

METHODS

A retrospective study was conducted on all elective consecutive surgical procedures for tumor resection between January 2012 and December 2014 at the Second Division of Neurosurgery at Fondazione IRCCS Istituto Neurologico Carlo Besta of Milan. A prospective database dedicated to reporting complications and all clinical and radiological data was retrospectively reviewed. The Karnofsky Performance Scale (KPS) was used to classify each patient’s health status. Complications were divided into major and minor and recorded based on etiology and required treatment. A logistic regression model was used to identify possible predictors of clinical worsening after surgery in terms of changes between the preoperative and discharge KPS scores. Statistically significant predictors were rated based on their odds ratios in order to build an ad hoc complexity scale. For each patient, a corresponding total score was calculated, and ANOVA was performed to compare the mean total scores between the improved/unchanged and worsened patients. Relative risk (RR) and chi-square statistics were employed to provide the risk of worsening after surgery for each total score.

RESULTS

The case series was composed of 746 patients (53.2% female; mean age 51.3 ± 17.1). The most common tumors were meningiomas (28.6%) and glioblastomas (24.1%). The mortality rate was 0.94%, the major complication rate was 9.1%, and the minor complication rate was 32.6%. Of 746 patients, 523 (70.1%) patients improved or remained unchanged, and 223 (29.9%) patients worsened. The following factors were found to be statistically significant predictors of the change in KPS scores: tumor size larger than 4 cm, cranial nerve manipulation, major brain vessel manipulation, posterior fossa location, and eloquent area involvement (Nagelkerke R2 = 0.286). A grading scale was obtained with scores ranging between 0 and 8. Worsened patients showed mean total scores that were significantly higher than the improved/unchanged scores (3.24 ± 1.55 vs 1.47 ± 1.58; p < 0.001). Finally, a grid was developed to show the risk of worsening after surgery for each total score: scores higher than 3 are suggestive of worse clinical outcome.

CONCLUSIONS

Through the evaluation of the 5 aforementioned parameters—the Big Five—the Milan Complexity Scale enables neurosurgeons to estimate the risk of a negative clinical course after brain tumor surgery and share these data with the patient. Furthermore, the Milan Complexity Scale could be used for research and educational purposes and better health system management.