Development and validation of prediction scores for nosocomial infections, reoperations, and adverse events in the daily clinical setting of neurosurgical patients with cerebral and spinal tumors.


Journal

Journal of neurosurgery
ISSN: 1933-0693
Titre abrégé: J Neurosurg
Pays: United States
ID NLM: 0253357

Informations de publication

Date de publication:
20 Mar 2020
Historique:
received: 25 11 2019
accepted: 13 01 2020
pubmed: 21 3 2020
medline: 31 7 2021
entrez: 21 3 2020
Statut: epublish

Résumé

Various quality indicators are currently under investigation, aiming at measuring the quality of care in neurosurgery; however, the discipline currently lacks practical scoring systems for accurately assessing risk. The aim of this study was to develop three accurate, easy-to-use risk scoring systems for nosocomial infections, reoperations, and adverse events for patients with cerebral and spinal tumors. The authors developed a semiautomatic registry with administrative and clinical data and included all patients with spinal or cerebral tumors treated between September 2017 and May 2019. Patients were further divided into development and validation cohorts. Multivariable logistic regression models were used to develop risk scores by assigning points based on β coefficients, and internal validation of the scores was performed. In total, 1000 patients were included. An unplanned 30-day reoperation was observed in 6.8% of patients. Nosocomial infections were documented in 7.4% of cases and any adverse event in 14.5%. The risk scores comprise variables such as emergency admission, nursing care level, ECOG performance status, and inflammatory markers on admission. Three scoring systems, NoInfECT for predicting the incidence of nosocomial infections (low risk, 1.8%; intermediate risk, 8.1%; and high risk, 26.0% [p < 0.001]), LEUCut for 30-day unplanned reoperations (low risk, 2.2%; intermediate risk, 6.8%; and high risk, 13.5% [p < 0.001]), and LINC for any adverse events (low risk, 7.6%; intermediate risk, 15.7%; and high risk, 49.5% [p < 0.001]), showed satisfactory discrimination between the different outcome groups in receiver operating characteristic curve analysis (AUC ≥ 0.7). The proposed risk scores allow efficient prediction of the likelihood of adverse events, to compare quality of care between different providers, and further provide guidance to surgeons on how to allocate preoperative care.

Identifiants

pubmed: 32197255
doi: 10.3171/2020.1.JNS193186
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

1226-1236

Auteurs

Sebastian Lohmann (S)

1Department of Neurosurgery and.

Tobias Brix (T)

2Institute of Medical Informatics, University Hospital Münster, Germany.

Julian Varghese (J)

2Institute of Medical Informatics, University Hospital Münster, Germany.

Nils Warneke (N)

1Department of Neurosurgery and.

Michael Schwake (M)

1Department of Neurosurgery and.

Eric Suero Molina (E)

1Department of Neurosurgery and.

Markus Holling (M)

1Department of Neurosurgery and.

Walter Stummer (W)

1Department of Neurosurgery and.

Stephanie Schipmann (S)

1Department of Neurosurgery and.

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Classifications MeSH