Surgery versus conservative treatment for traumatic acute subdural haematoma: a prospective, multicentre, observational, comparative effectiveness study

Essen, Thomas A. van; Lingsma, Hester F.; Pisică, Dana; Singh, Ranjit D.; Volovici, Victor; Boogert, Hugo F. den; Younsi, Alexander; Peppel, Lianne D; Heijenbrok‐Kal, Majanka H.; Ribbers, Gerard M.; Walchenbach, R.; Menon, David; Hutchinson, Peter J.; Depreitere, Bart; Steyerberg, Ewout W.; Maas, Andrew I.R.; Ruiter, Godard C.W. de; Peul, Wilco C.; Åkerlund, Cecilia; Amrein, Krisztina; Anđelić, Nada; Andreassen, Lasse; Anke, Audny; Antoni, Anna; Audibert, Gérard; Azouvi, Philippe; Azzolini, Maria Luisa; Bartels, Ronald; Barzó, Pál; Beauvais, Romuald; Beer, Ronny; Bellander, Bo‐Michael; Belli, Antonio; Benali, Habib; Berardino, Maurizio; Beretta, Luigi; Blaabjerg, Morten; Bragge, Peter; Bražinová, Alexandra; Brinck, Vibeke; Brooker, Joanne; Brorsson, Camilla; Büki, András; Bullinger, Monika; Cabeleira, Manuel; Caccioppola, Alessio; Calappi, Emiliana; Calvi, Maria Rosa; Cameron, Peter; Lozano, Guillermo Carbayo; Carbonara, Marco; Castaño‐León, Ana M.; Cavallo, Simona; Chevallard, Giorgio; Chieregato, Arturo; Citerio, Giuseppe; Clusmann, Hans; Coburn, Mark; Coles, Jonathan P.; Cooper, Jamie D.; Correia, Marta; Čović, Amra; Curry, Nicola; Czeiter, Endre; Czosnyka, Marek; Dahyot‐Fizelier, Claire; Dark, Paul; Dawes, Helen; Keyser, Véronique De; Degos, Vincent; Corte, Francesco Della; Đilvesi, Đula; Dixit, Abhishek; Donoghue, Emma; Dreier, Jens P.; Dulière, Guy‐Loup; Ercole, Ari; Esser, Patrick; Ezer, Erzsébet; Fabricius, Martin; Feigin, Valery; Foks, Kelly; Frisvold, Shirin; Furmanov, Alex; Gagliardo, Pablo; Galanaud, Damien; Gantner, Dashiell; Gao, Guoyi; George, Pradeep; Ghuysen, Alexandre; Giga, Lelde; Glocker, Ben; Golubović, Jagoš; Gómez, Pedro A.; Gratz, Johannes; Gravesteijn, Benjamin; Grossi, Francesca; Gruen, Russell L.; Gupta, Deepak; Haagsma, Juanita A.; Haitsma, Iain; Helbok, Raimund; Helseth, Eirik; Horton, Lindsay; Huijben, Jilske; Jacobs, Bram; Jankowski, Stefan; Jarrett, Mike; Jiang, Jiyao; Johnson, Faye; Jones, Kelly; Karan, Mladen; Kolias, Angelos; Kompanje, Erwin J. O.; Kondziella, Daniel; Kornaropoulos, Evgenios N.; Koskinen, Lars‐Owe D.; Kovács, Noémi; Lagares, Alfonso; Lanyon, Linda; Laureys, Steven; Lecky, Fiona; Ledoux, Didier; Lefering, Rolf; Legrand, Valérie; Lejeune, Aurélie; Levi, Leon; Lightfoot, Roger; Maegele, Marc; Majdán, Marek; Manara, Alex; Manley, Geoffrey T.; Maréchal, Hugues; Martino, Costanza; Mattern, Julia; McMahon, Catherine; Melegh, Béla; Menovsky, Tomas; Mikolić, Ana; Benoît, Michel; Muraleedharan, Visakh; Murray, Lynnette; Nair, Nandesh; Negru, Ancuta; Nelson, David W.; Newcombe, Virginia; Nieboer, Daan; Nyirádi, József; Orešič, Matej; Ortolano, Fabrizio; Olubukola, Otesile; Palotie, Aarno; Parizel, Paul M.; Payen, Jean‐François; Perera, Natascha; Perlbarg, Vincent; Persona, Paolo; Piippo-Karjalainen, Anna; Pirinen, Matti; Pleș, Horia; Polinder, Suzanne; Pomposo, Iñigo; Posti, Jussi P.; Puybasset, Louis; Rădoi, Andreea; Ragauskas, Arminas; Raj, Rahul; Rambadagalla, Malinka; Rehorčíková, Veronika; Helmrich, Isabel Retel; Rhodes, Jonathan R.; Richardson, Sylvia; Richter, Sophie; Ripatti, Samuli; Ročka, Saulius; Røe, Cecilie; Røise, Olav; Rosand, Jonathan; Rosenfeld, Jeffrey V.; Rosenlund, Christina; Rosenthal, Guy; Rossaint, Rolf; Rossi, Sandra; Rueckert, Daniel; Rusňák, Martin; Sahuquillo, Juan; Sakowitz, Oliver; Sánchez-Porras, Renán; Sándor, János; Schäfer, Nadine; Schmidt, Silke; Schöechl, Herbert; Schoonman, Guus; Schou, Rico Frederik; Schwendenwein, Elisabeth; Sewalt, Charlie; Skandsen, Toril; Smielewski, Peter; Sorinola, Abayomi; Stamatakis, Emmanuel; Stanworth, Simon; Kowark, Ana; Stevens, Robert; Stewart, William Α.; Stocchetti, Nino; Sundström, Nina; Takala, Riikka; Tamás, Viktória; Tamošuitis, Tomas; Taylor, Mark; Ao, Braden Te; Tenovuo, Olli; Theadom, Alice; Thomas, Matt; Tibboel, Dick; Timmers, Marjolijn; Tolias, Christos M.; Trapani, Tony; Tudora, Cristina Maria; Unterberg, Andreas; Vajkoczy, Peter; Valeinis, Egils; Vallance, Shirley; Vámos, Zoltán; Jagt, Mathieu van der; Naalt, Joukje van der; Steen, Gregory Van der; Dijck, Jeroen T.J.M. van; Hecke, Wim Van; Heugten, Caroline van; Praag, Dominique Van; Veen, Ernest van; Wijk, R. Van; Vyvere, Thijs Vande; Vargiolu, Alessia; Vega, Emmanuel; Velt, Kimberley; Verheyden, Jan; Vespa, Paul; Vik, Anne; Vilcinis, Rimantas; Steinbüchel, Nicole von; Voormolen, Daphne; Vuleković, Petar; Wang, Kevin; Wiegers, Eveline; Williams, Guy; Wilson, Lindsay; Winzeck, Stefan; Wolf, Stefan; Yang, Zhihui; Ylén, Peter; Zeiler, Frederick A.; Ziverte, Agate; Zoerle, Tommaso

doi:10.1016/s1474-4422(22)00166-1

Cited by 44 publications

(15 citation statements)

References 42 publications

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“…This specificity may increase inaccuracy if the model is applied to datasets that rely on theoretical indication or a proxy. Furthermore, clinical practice patterns may vary across clinicians, hospitals or trauma systems [ 23 ]. For example, based to the Shapley values, TBI after firearm injury have less neurosurgical requirement than other mechanisms.…”

Section: Discussionmentioning

confidence: 99%

“…The median GCS score was 6 [3][4][5][6][7][8][9] and 36% had a pupillary abnormality. Injury severity score (ISS) was 29 [22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38] and 84% of the patients were on mechanical ventilation in the pre-hospital setting. The characteristics of the patients without imputation and after imputation are summarized in Table 1.…”

Section: Populationmentioning

confidence: 99%

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Machine learning-based prediction of emergency neurosurgery within 24 h after moderate to severe traumatic brain injury

et al. 2022

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Background Rapid referral of traumatic brain injury (TBI) patients requiring emergency neurosurgery to a specialized trauma center can significantly reduce morbidity and mortality. Currently, no model has been reported to predict the need for acute neurosurgery in severe to moderate TBI patients. This study aims to evaluate the performance of Machine Learning-based models to establish to predict the need for neurosurgery procedure within 24 h after moderate to severe TBI. Methods Retrospective multicenter cohort study using data from a national trauma registry (Traumabase®) from November 2011 to December 2020. Inclusion criteria correspond to patients over 18 years old with moderate or severe TBI (Glasgow coma score ≤ 12) during prehospital assessment. Patients who died within the first 24 h after hospital admission and secondary transfers were excluded. The population was divided into a train set (80% of patients) and a test set (20% of patients). Several approaches were used to define the best prognostic model (linear nearest neighbor or ensemble model). The Shapley Value was used to identify the most relevant pre-hospital variables for prediction. Results 2159 patients were included in the study. 914 patients (42%) required neurosurgical intervention within 24 h. The population was predominantly male (77%), young (median age 35 years [IQR 24–52]) with severe head injury (median GCS 6 [3–9]). Based on the evaluation of the predictive model on the test set, the logistic regression model had an AUC of 0.76. The best predictive model was obtained with the CatBoost technique (AUC 0.81). According to the Shapley values method, the most predictive variables in the CatBoost were a low initial Glasgow coma score, the regression of pupillary abnormality after osmotherapy, a high blood pressure and a low heart rate. Conclusion Machine learning-based models could predict the need for emergency neurosurgery within 24 h after moderate and severe head injury. Potential clinical benefits of such models as a decision-making tool deserve further assessment. The performance in real-life setting and the impact on clinical decision-making of the model requires workflow integration and prospective assessment.

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Section: Discussionmentioning

confidence: 99%

Section: Populationmentioning

confidence: 99%

Machine learning-based prediction of emergency neurosurgery within 24 h after moderate to severe traumatic brain injury

et al. 2022

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“…For each patient, we retrieved the age, sex, Charlson Comorbidity Index (CCI), history of arterial hypertension, use of antithrombotic drugs, the need for an urgent coagulopathy correction at A&E admission, mechanism of injury, neurological status measured by the Glasgow Coma Scale (GCS) at admission and during the entire preoperative period, pupillary size and light reaction, neurological deficits, and seizures. Patients were divided in three GCS level groups, both at arrival and soon before surgery: mild (13)(14)(15), moderate (9)(10)(11)(12), and severe (3)(4)(5)(6)(7)(8).…”

Section: Clinical and Radiological Data Collectionmentioning

confidence: 99%

Has the Timing of the Surgery a Major Role in Influencing Outcome in Elderly Patients with Acute Subdural Hematomas?

Trevisi¹,

Scerrati²,

Rustemi³

et al. 2022

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Background: The incidence of traumatic acute subdural hematomas (ASDH) in elderly is increasing. Despite surgical evacuation, these patients have poor survival and low rate of functional outcome, and surgical timing plays a no clear role as predictor. We investigated if the timing of surgery has a major role in influencing outcome in these patients.Methods: We retrospectively retrieved clinical and radiological data of all patients ≥70 years operated on for post-traumatic ASDH in a 3 years period in 5 Italian Hospitals. Patients were divided in 3 surgical timing groups from hospital arrival: ultra-early (within 6h); early (6-24h); delayed (after 24h). Outcome was measured at discharge using two endpoints: survival (alive/dead) and functional outcome at Glasgow Outcome Scale (GOS). Univariate and multivariate predictor models were constructed.Results: We included 136 patients. About 33% died for consequences of ASDH and among the survivors only 24% were in good functional outcome at discharge. Surgical timing groups appeared different according to presenting GCS, which was on average lower in ultra-early surgery group, and radiological findings, which appeared worse in the same group. Delayed surgery was more frequent in patients with subacute clinical deterioration. Surgical timing appeared associated neither with survival nor with functional outcome also after stratification for preoperative GCS. Preoperative midline shift was the strongest outcome predictor. Conclusions: An earlier surgery was offered to patients with worse clinical-radiological findings. Also after stratification for GCS it was not associated with better outcome. Among the radiological markers, preoperative midline shift was the strongest outcome predictor.

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“…Limited evidence as to the optimal timing of surgical management for acute subdural haematoma (SDH) means that treatment decisions are often based on clinician judgement, leading to different operative thresholds between centres. This prospective multicentre observational study exploited these practice variations to compare the effectiveness of acute surgical evacuation compared with initial conservative management 3. The primary end point was functional outcome at 6 months measured by the Glasgow Outcome Scale Extended.…”

Section: Surgery Versus Conservative Treatment For Traumatic Acute Su...mentioning

confidence: 99%

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et al. 2022

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Surgery versus conservative treatment for traumatic acute subdural haematoma: a prospective, multicentre, observational, comparative effectiveness study

Cited by 44 publications

References 42 publications

Machine learning-based prediction of emergency neurosurgery within 24 h after moderate to severe traumatic brain injury

Machine learning-based prediction of emergency neurosurgery within 24 h after moderate to severe traumatic brain injury

Has the Timing of the Surgery a Major Role in Influencing Outcome in Elderly Patients with Acute Subdural Hematomas?

Journal update monthly top five

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