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Francesco Acerbi, Ignazio G. Vetrano, Tommaso Sattin, Jacopo Falco, Camilla de Laurentis, Costanza M. Zattra, Lorenzo Bosio, Zefferino Rossini, Morgan Broggi, Marco Schiariti, and Paolo Ferroli

OBJECTIVE

The best management of veins encountered during the neurosurgical approach is still a matter of debate. Even if venous sacrifice were to lead to devastating consequences, under certain circumstances, it might prove to be desirable, enlarging the surgical field or increasing the extent of resection in tumor surgery. In this study, the authors present a large series of patients with vascular or oncological entities, in which they used indocyanine green videoangiography (ICG-VA) with FLOW 800 analysis to study the patient-specific venous flow characteristics and the management workflow in cases in which a venous sacrifice was necessary.

METHODS

Between May 2011 and December 2017, 1972 patients were admitted to the authors’ division for tumor and/or neurovascular surgery. They retrospectively reviewed all cases in which ICG-VA and FLOW 800 were used intraoperatively with a specific target in the venous angiographic phase or for the management of venous sacrifice, and whose surgical videos and FLOW 800 analysis were available.

RESULTS

A total of 296 ICG-VA and FLOW 800 studies were performed intraoperatively. In all cases, the venous structures were clearly identifiable and were described according to the flow direction and speed. The authors therefore defined different patterns of presentation: arterialized veins, thrombosed veins, fast-draining veins with anterograde flow, slow-draining veins with anterograde flow, and slow-draining veins with retrograde flow. In 16 cases we also performed a temporary clipping test to predict the effect of the venous sacrifice by the identification of potential collateral circulation.

CONCLUSIONS

ICG-VA and FLOW 800 analysis can provide complete and real-time intraoperative information regarding patient-specific venous drainage pattern and can guide the decision-making process regarding venous sacrifice, with a possible impact on reduction of surgical complications.

Restricted access

Victor E. Staartjes, Costanza M. Zattra, Kevin Akeret, Nicolai Maldaner, Giovanni Muscas, Christiaan Hendrik Bas van Niftrik, Jorn Fierstra, Luca Regli, and Carlo Serra

OBJECTIVE

Although rates of postoperative morbidity and mortality have become relatively low in patients undergoing transnasal transsphenoidal surgery (TSS) for pituitary adenoma, cerebrospinal fluid (CSF) fistulas remain a major driver of postoperative morbidity. Persistent CSF fistulas harbor the potential for headache and meningitis. The aim of this study was to investigate whether neural network–based models can reliably identify patients at high risk for intraoperative CSF leakage.

METHODS

From a prospective registry, patients who underwent endoscopic TSS for pituitary adenoma were identified. Risk factors for intraoperative CSF leaks were identified using conventional statistical methods. Subsequently, the authors built a prediction model for intraoperative CSF leaks based on deep learning.

RESULTS

Intraoperative CSF leaks occurred in 45 (29%) of 154 patients. No risk factors for CSF leaks were identified using conventional statistical methods. The deep neural network–based prediction model classified 88% of patients in the test set correctly, with an area under the curve of 0.84. Sensitivity (83%) and specificity (89%) were high. The positive predictive value was 71%, negative predictive value was 94%, and F1 score was 0.77. High suprasellar Hardy grade, prior surgery, and older age contributed most to the predictions.

CONCLUSIONS

The authors trained and internally validated a robust deep neural network–based prediction model that identifies patients at high risk for intraoperative CSF. Machine learning algorithms may predict outcomes and adverse events that were previously nearly unpredictable, thus enabling safer and improved patient care and better patient counseling.

Free access

Francesco Doglietto, Marika Vezzoli, Antonio Biroli, Giorgio Saraceno, Luca Zanin, Marta Pertichetti, Stefano Calza, Edoardo Agosti, Jahard Mijail Aliaga Arias, Roberto Assietti, Silvio Bellocchi, Claudio Bernucci, Simona Bistazzoni, Daniele Bongetta, Andrea Fanti, Antonio Fioravanti, Alessandro Fiorindi, Alberto Franzin, Davide Locatelli, Raffaelino Pugliese, Elena Roca, Giovanni Marco Sicuri, Roberto Stefini, Martina Venturini, Oscar Vivaldi, Costanza Zattra, Cesare Zoia, and Marco Maria Fontanella

OBJECTIVE

The COVID-19 pandemic has forced many countries into lockdown and has led to the postponement of nonurgent neurosurgical procedures. Although stress has been investigated during this pandemic, there are no reports on anxiety in neurosurgical patients undergoing nonurgent surgical procedures.

METHODS

Neurosurgical patients admitted to hospitals in eastern Lombardy for nonurgent surgery after the lockdown prospectively completed a pre- and postoperative structured questionnaire. Recorded data included demographics, pathology, time on surgical waiting list, anxiety related to COVID-19, primary pathology and surgery, safety perception during hospital admission before and after surgery, and surgical outcomes. Anxiety was measured with the State-Trait Anxiety Inventory. Descriptive statistics were computed on the different variables and data were stratified according to pathology (oncological vs nononcological). Three different models were used to investigate which variables had the greatest impact on anxiety, oncological patients, and safety perception, respectively. Because the variables (Xs) were of a different nature (qualitative and quantitative), mostly asymmetrical, and related to outcome (Y) by nonlinear relationships, a machine learning approach composed of three steps (1, random forest growing; 2, relative variable importance measure; and 3, partial dependence plots) was chosen.

RESULTS

One hundred twenty-three patients from 10 different hospitals were included in the study. None of the patients developed COVID-19 after surgery. State and trait anxiety were reported by 30.3% and 18.9% of patients, respectively. Higher values of state anxiety were documented in oncological compared to nononcological patients (46.7% vs 25%; p = 0.055). Anxiety was strongly associated with worry about primary pathology, surgery, disease worsening, and with stress during waiting time, as expected. Worry about positivity to SARS-CoV-2, however, was the strongest factor associated with anxiety, even though none of the patients were infected. Neuro-oncological disease was associated with state anxiety and with worry about surgery and COVID-19. Increased bed distance and availability of hand sanitizer were associated with a feeling of safety.

CONCLUSIONS

These data underline the importance of psychological support, especially for neuro-oncological patients, during a pandemic.

Restricted access

Victor E. Staartjes, Morgan Broggi, Costanza Maria Zattra, Flavio Vasella, Julia Velz, Silvia Schiavolin, Carlo Serra, Jiri Bartek Jr., Alexander Fletcher-Sandersjöö, Petter Förander, Darius Kalasauskas, Mirjam Renovanz, Florian Ringel, Konstantin R. Brawanski, Johannes Kerschbaumer, Christian F. Freyschlag, Asgeir S. Jakola, Kristin Sjåvik, Ole Solheim, Bawarjan Schatlo, Alexandra Sachkova, Hans Christoph Bock, Abdelhalim Hussein, Veit Rohde, Marike L. D. Broekman, Claudine O. Nogarede, Cynthia M. C. Lemmens, Julius M. Kernbach, Georg Neuloh, Oliver Bozinov, Niklaus Krayenbühl, Johannes Sarnthein, Paolo Ferroli, Luca Regli, Martin N. Stienen, and FEBNS

OBJECTIVE

Decision-making for intracranial tumor surgery requires balancing the oncological benefit against the risk for resection-related impairment. Risk estimates are commonly based on subjective experience and generalized numbers from the literature, but even experienced surgeons overestimate functional outcome after surgery. Today, there is no reliable and objective way to preoperatively predict an individual patient’s risk of experiencing any functional impairment.

METHODS

The authors developed a prediction model for functional impairment at 3 to 6 months after microsurgical resection, defined as a decrease in Karnofsky Performance Status of ≥ 10 points. Two prospective registries in Switzerland and Italy were used for development. External validation was performed in 7 cohorts from Sweden, Norway, Germany, Austria, and the Netherlands. Age, sex, prior surgery, tumor histology and maximum diameter, expected major brain vessel or cranial nerve manipulation, resection in eloquent areas and the posterior fossa, and surgical approach were recorded. Discrimination and calibration metrics were evaluated.

RESULTS

In the development (2437 patients, 48.2% male; mean age ± SD: 55 ± 15 years) and external validation (2427 patients, 42.4% male; mean age ± SD: 58 ± 13 years) cohorts, functional impairment rates were 21.5% and 28.5%, respectively. In the development cohort, area under the curve (AUC) values of 0.72 (95% CI 0.69–0.74) were observed. In the pooled external validation cohort, the AUC was 0.72 (95% CI 0.69–0.74), confirming generalizability. Calibration plots indicated fair calibration in both cohorts. The tool has been incorporated into a web-based application available at https://neurosurgery.shinyapps.io/impairment/.

CONCLUSIONS

Functional impairment after intracranial tumor surgery remains extraordinarily difficult to predict, although machine learning can help quantify risk. This externally validated prediction tool can serve as the basis for case-by-case discussions and risk-to-benefit estimation of surgical treatment in the individual patient.