A Predictive Model of Unfavorable Outcomes After Benign Intracranial Tumor Resection.
BACKGROUND
Benchmarking of outcomes and individualized risk prediction are central in patient-oriented shared decision making. We attempted to create a predictive model of complications in patients undergoing benign intracranial tumor resection.


METHODS
We performed a retrospective cohort study involving patients who underwent craniotomies for benign intracranial tumor resection during the period 2005-2011 and were registered in the National (Nationwide) Inpatient Sample database. A model for outcome prediction based on individual patient characteristics was developed.


RESULTS
There were 19,894 patients who underwent benign tumor resection. The respective inpatient postoperative incidences were 1.3% for death, 22.7% for unfavorable discharge, 4.2% for treated hydrocephalus, 1.1% for cardiac complications, 0.9% for respiratory complications, 0.5% for wound infection, 0.5% for deep venous thrombosis, 2.3% for pulmonary embolus, and 1.5% for acute renal failure. Multivariable analysis identified risk factors independently associated with the above-mentioned outcomes. A model for outcome prediction based on patient and hospital characteristics was developed and subsequently validated in a bootstrap sample. The models demonstrated good discrimination with areas under the curve of 0.85, 0.76, 0.72, 0.74, 0.72, 0.74, 0.76, 0.68, and 0.86 for postoperative risk of death, unfavorable discharge, hydrocephalus, cardiac complications, respiratory complications, wound infection, deep venous thrombosis, pulmonary embolus, and acute renal failure. The models also had good calibration, as assessed by the Hosmer-Lemeshow test.


CONCLUSIONS
Our models can provide individualized estimates of the risks of postoperative complications based on preoperative conditions and potentially can be used as an adjunct for decision making in benign intracranial tumor surgery.
Section of Neurosurgery, Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire, USA. Electronic address: kbekelis@gmail.com.
Kimon
K
Bekelis
Department of Neurosurgery, Louisiana State University Health Sciences, Shreveport, Louisiana, USA.
Piyush
P
Kalakoti
Department of Neurosurgery, Louisiana State University Health Sciences, Shreveport, Louisiana, USA.
Anil
A
Nanda
Department of Neurosurgery, Louisiana State University Health Sciences, Shreveport, Louisiana, USA.
Symeon
S
Missios
D058186:Acute Kidney Injury / Q000175:diagnosis; D000328:Adult; D000368:Aged; D001932:Brain Neoplasms / Q000601:surgery*; D003399:Craniotomy / Q000009:adverse effects*; D005260:Female; D006331:Heart Diseases / Q000175:diagnosis; D017052:Hospital Mortality; D006801:Humans; D006849:Hydrocephalus / Q000175:diagnosis; D015994:Incidence; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D019635:Neurosurgical Procedures / Q000009:adverse effects; D011183:Postoperative Complications / Q000175:diagnosis* / Q000453:epidemiology / Q000209:etiology / Q000517:prevention & control; D011237:Predictive Value of Tests; D011655:Pulmonary Embolism / Q000175:diagnosis; D015203:Reproducibility of Results; D012140:Respiratory Tract Diseases / Q000175:diagnosis; D012189:Retrospective Studies; D018570:Risk Assessment; D012307:Risk Factors; D016896:Treatment Outcome; D014481:United States / Q000453:epidemiology; D020246:Venous Thrombosis / Q000175:diagnosis
Benign intracranial tumors; Craniotomy; NIS; Risk prediction