Development and external validation of a clinical prediction model for functional impairment after intracranial tumor surgery

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

doi:10.3171/2020.4.jns20643

Cited by 13 publications

(9 citation statements)

References 36 publications

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“…Hence, it determines the personal quality of life and overall wellbeing. It was described that prediction of functional outcome after intracranial tumor surgery was very complex, even if machine learning algorithms were used [ 21 ]. On the other hand, frailty showed an association with complications and mortality, transfer to a higher level of care facility, length of hospital stay, re-operation and re-admission across other fields of neurological surgery [ 22 ].…”

Section: Discussionmentioning

confidence: 99%

The Clinical Frailty Scale as predictor of overall survival after resection of high-grade glioma

et al. 2022

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Background The Clinical Frailty Scale (CFS) describes the general level of fitness or frailty and is widely used in geriatric medicine, intensive care and orthopaedic surgery. This study was conducted to analyze, whether CFS could be used for patients with high-grade glioma. Methods Patients harboring high-grade gliomas, undergoing first resection at our center between 2015 and 2020 were retrospectively evaluated. Patients’ performance was assessed using the Rockwood Clinical Frailty Scale and the Karnofsky Performance Scale (KPS) preoperatively and 3–6 months postoperatively. Results 289 patients were included. Pre- as well as postoperative median frailty was 3 CFS points (IqR 2–4) corresponding to “managing well”. CFS strongly correlated with KPS preoperatively (r = − 0.85; p < 0.001) and at the 3–6 months follow-up (r = − 0.90; p < 0.001). The reduction of overall survival (OS) was 54% per point of CFS preoperatively (HR 1.54, CI 95% 1.38–1.70; p < 0.001) and 58% at the follow-up (HR 1.58, CI 95% 1.41–1.78; p < 0.001), comparable to KPS. Patients with IDH mutation showed significantly better preoperative and follow-up CFS and KPS (p < 0.05). Age and performance scores correlated only mildly with each other (r = 0.21…0.35; p < 0.01), but independently predicted OS (p < 0.001 each). Conclusion CFS seems to be a reliable tool for functional assessment of patients suffering from high-grade glioma. CFS includes non-cancer related aspects and therefore is a contemporary approach for patient evaluation. Its projection of survival can be equally estimated before and after surgery. IDH-mutation caused longer survival and higher functionality.

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Section: Discussionmentioning

confidence: 99%

The Clinical Frailty Scale as predictor of overall survival after resection of high-grade glioma

et al. 2022

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“…Hence, it determines the personal quality of life and overall wellbeing. It was described that prediction of functional outcome after intracranial tumor surgery was very complex, even if machine learning algorithms were used [19]. On the other hand, frailty showed an association with complications and mortality, transfer to a higher level of care facility, length of hospital stay, reoperation and re-admission across other elds of neurological surgery [20].…”

Section: Discussionmentioning

confidence: 99%

The Clinical Frailty Scale as Predictor of Overall Survival After Resection of High-grade Glioma

Klingenschmid

Krigers

Pinggera

et al. 2022

Preprint

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Background The Clinical Frailty Scale (CFS) describes the general level of fitness or frailty and is widely used in geriatric medicine, intensive care and orthopaedic surgery. This study was conducted to analyze, whether CFS could be used for patients with high-grade glioma. Methods Patients harboring high-grade gliomas, undergoing first resection at our center between 2015 and 2020 were retrospectively evaluated. Patients’ performance was assessed using the Rockwood Clinical Frailty Scale and the Karnofsky Performance Scale (KPS) preoperatively and 3–6 months postoperatively. Results 289 patients were included. Pre- as well as postoperative median frailty was 3 CFS points (IqR 2–4) corresponding to “managing well”. CFS strongly correlated with KPS preoperatively (r = -0.85; p < 0.001) and at the 3–6 months follow-up (r = -0.90; p < 0.001). The reduction of overall survival (OS) was 54% per point of CFS preoperatively (HR 1.54, CI95% 1.38–1.70; p < 0.001) and 58% at the follow-up (HR 1.58, CI95% 1.41–1.78; p < 0.001), comparable to KPS. Patients with IDH mutation showed significantly better preoperative and follow-up CFS and KPS (p < 0.05). Age and performance scores correlated only mildly with each other (r = 0.21…0.35; p < 0.01), but independently predicted OS (p < 0.001 each). Conclusion CFS seems to be a reliable tool for functional assessment of patients suffering from high-grade glioma. CFS includes non-cancer related aspects and therefore is a contemporary approach for patient evaluation. Its projection of survival can be equally estimated before and after surgery. IDH-mutation caused longer survival and higher functionality.

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“…ML methods can deduce a simple risk assessment model from relatively complex data [4,5]. For this reason, ML has been proven to aid in improved shared decision-making as well as enhanced patient care by modification of risk factors [7,27,28]. However, some factors cannot be taken into account by any model-prediction models will always remain just that: models of reality.…”

Section: Discussionmentioning

confidence: 99%

Machine learning-based clinical outcome prediction in surgery for acromegaly

et al. 2021

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Purpose Biochemical remission (BR), gross total resection (GTR), and intraoperative cerebrospinal fluid (CSF) leaks are important metrics in transsphenoidal surgery for acromegaly, and prediction of their likelihood using machine learning would be clinically advantageous. We aim to develop and externally validate clinical prediction models for outcomes after transsphenoidal surgery for acromegaly. Methods Using data from two registries, we develop and externally validate machine learning models for GTR, BR, and CSF leaks after endoscopic transsphenoidal surgery in acromegalic patients. For the model development a registry from Bologna, Italy was used. External validation was then performed using data from Zurich, Switzerland. Gender, age, prior surgery, as well as Hardy and Knosp classification were used as input features. Discrimination and calibration metrics were assessed. Results The derivation cohort consisted of 307 patients (43.3% male; mean [SD] age, 47.2 [12.7] years). GTR was achieved in 226 (73.6%) and BR in 245 (79.8%) patients. In the external validation cohort with 46 patients, 31 (75.6%) achieved GTR and 31 (77.5%) achieved BR. Area under the curve (AUC) at external validation was 0.75 (95% confidence interval: 0.59–0.88) for GTR, 0.63 (0.40–0.82) for BR, as well as 0.77 (0.62–0.91) for intraoperative CSF leaks. While prior surgery was the most important variable for prediction of GTR, age, and Hardy grading contributed most to the predictions of BR and CSF leaks, respectively. Conclusions Gross total resection, biochemical remission, and CSF leaks remain hard to predict, but machine learning offers potential in helping to tailor surgical therapy. We demonstrate the feasibility of developing and externally validating clinical prediction models for these outcomes after surgery for acromegaly and lay the groundwork for development of a multicenter model with more robust generalization.

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Development and external validation of a clinical prediction model for functional impairment after intracranial tumor surgery

Cited by 13 publications

References 36 publications

The Clinical Frailty Scale as predictor of overall survival after resection of high-grade glioma

The Clinical Frailty Scale as predictor of overall survival after resection of high-grade glioma

The Clinical Frailty Scale as Predictor of Overall Survival After Resection of High-grade Glioma

Machine learning-based clinical outcome prediction in surgery for acromegaly

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