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J Neurosurg. 2020 Mar 20:1-11. doi: 10.3171/2020.1.JNS193186. [Epub ahead of print]

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.

Author information

1
1Department of Neurosurgery and.
2
2Institute of Medical Informatics, University Hospital Münster, Germany.

Abstract

OBJECTIVE:

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.

METHODS:

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.

RESULTS:

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).

CONCLUSIONS:

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.

KEYWORDS:

ACCI = age-adjusted CCI; AUC = area under the ROC curve; CCI = Charlson Comorbidity Index; CRP = C-reactive protein; ECOG = European Cooperative Oncology Group; INR = international normalized ratio; LEUCut = Leukocytosis, ECOG on admission, Urgency of surgery and Cutting-suture time of index surgery; LINC = Leukocytosis, length of stay in the ICU, Nursing care level, and CRP on admission; LOS = length of stay; NoInfECT = Nursing care level, length of stay on the ICU, Emergency admission, CRP on admission, and recurrenT diagnosis; ROC = receiver operating characteristic; brain tumor; infection; quality indicators; risk assessment; risk score

PMID:
32197255
DOI:
10.3171/2020.1.JNS193186

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