Predicting surgical site infection after spine surgery: a validated model using a prospective surgical registry

Lee, Michael J.; Cizik, Amy M.; Hamilton, Deven T.; Chapman, Jens R.

doi:10.1016/j.spinee.2013.12.026

Cited by 67 publications

(67 citation statements)

References 14 publications

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“…They are a significant advancement relative to models published by Lee and colleagues, which have lower ROC characteristics and only predict 2 outcomes-surgical-site infections and medical complications. 7,8 Our models' performance measures are similar, however, to that of recently published American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) models for complications after spine surgery. The ACS NSQIP models are generated from a much larger national data set, but they do not address any patient-reported outcomes.…”

Section: Model Developmentmentioning

confidence: 68%

Prediction model for outcome after low-back surgery: individualized likelihood of complication, hospital readmission, return to work, and 12-month improvement in functional disability

McGirt

Sivaganesan²,

Asher

et al. 2015

FOC

105

111

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OBJECT Lumbar spine surgery has been demonstrated to be efficacious for many degenerative spine conditions. However, there is wide variability in outcome after spine surgery at the individual patient level. All stakeholders in spine care will benefit from identification of the unique patient or disease subgroups that are least likely to benefit from surgery, are prone to costly complications, and have increased health care utilization. There remains a large demand for individual patient-level predictive analytics to guide decision support to optimize outcomes at the patient and population levels. METHODS One thousand eight hundred three consecutive patients undergoing spine surgery for various degenerative lumbar diagnoses were prospectively enrolled and followed for 1 year. A comprehensive patient interview and health assessment was performed at baseline and at 3 and 12 months after surgery. All predictive covariates were selected a priori. Eighty percent of the sample was randomly selected for model development, and 20% for model validation. Linear regression was performed with Bayesian model averaging to model 12-month ODI (Oswestry Disability Index). Logistic regression with Bayesian model averaging was used to model likelihood of complications, 30-day readmission, need for inpatient rehabilitation, and return to work. Goodness-of-fit was assessed via R2 for 12-month ODI and via the c-statistic, area under the receiver operating characteristic curve (AUC), for the categorical endpoints. Discrimination (predictive performance) was assessed, using R2 for the ODI model and the c-statistic for the categorical endpoint models. Calibration was assessed using a plot of predicted versus observed values for the ODI model and the Hosmer-Lemeshow test for the categorical endpoint models. RESULTS On average, all patient-reported outcomes (PROs) were improved after surgery (ODI baseline vs 12 month: 50.4 vs 29.5%, p < 0.001). Complications occurred in 121 patients (6.6%), 108 (5.9%) were readmitted within 30 days of surgery, 188 (10.3%) required discharge to inpatient rehabilitation, 1630 (88.9%) returned to work, and 449 (24.5%) experienced an unplanned outcome (no improvement in ODI, a complication, or readmission). There were 45 unique baseline variable inputs, derived from 39 clinical variables and 38 questionnaire items (ODI, SF-12, MSPQ, VAS-BP, VAS-LP, VAS-NP), included in each model. For prediction of 12-month ODI, R2 was 0.51 for development and 0.47 for the validation study. For prediction of a complication, readmission, inpatient rehabilitation, and return to work, AUC values ranged 0.72-0.84 for development and 0.79-0.84 for validation study. CONCLUSIONS A novel prediction model utilizing both clinical data and patient interview inputs explained the majority of variation in outcome observed after lumbar spine surgery and reliably predicted 12-month improvement in physical disability, return to work, major complications, readmission, and need for inpatient rehabilitation for individual patients. Application of these models may allow clinicians to offer spine surgery specifically to those who are most likely to benefit and least likely to incur complications and excess costs.

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Section: Model Developmentmentioning

confidence: 68%

Prediction model for outcome after low-back surgery: individualized likelihood of complication, hospital readmission, return to work, and 12-month improvement in functional disability

McGirt

Sivaganesan²,

Asher

et al. 2015

FOC

105

111

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“…[18][19][20] For instance, a recently developed spinal risk stratification tool, SpineSage (http://depts.washington.edu/ spinersk/), was partly derived from prospective research assessing postoperative SSI risk. 18,20 Therefore, thorough statistical analysis of large surgical registries can inform further studies on perioperative management tailored to patients' specific constellations of risk factors both to manage complications in high-risk patients but also reduce redundant care in low-risk patients. As a result, intensity of nursing care, frequency of laboratory testing, and anticipation of ancillary services could all be optimized, all of which would have a positive impact on the overall cost of care.…”

Section: Discussionmentioning

confidence: 99%

Risk Modeling Predicts Complication Rates for Spinal Surgery

Kimmell

Algattas

Joynt

et al. 2015

SPINE

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“…One study produced two models predicting medical complications and surgical site infections for spine surgery patients. They evaluated risk factors that could be used to estimate absolute risk in order to appropriately counsel patients and stratify them into risk categories for a pay-forperformance model [21,22].…”

Section: Quality/risk Stratificationmentioning

confidence: 99%

Pay-for-performance in orthopedics: how we got here and where we are going

Goldman

Kates

2017

Curr Rev Musculoskelet Med

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Purpose of the review Recent health laws have shifted from the traditional fee-for-service model toward a pay-forperformance model. In this changing climate, it is imperative that a provider understands these changes and recognizes the importance of health services research on medicine. Recent findings Increasing the value of care by improving quality and decreasing cost has been the focus of several projects. Preventing complications may be an effective way to increase value. Summary Patient risk stratification is a modifiable variable that will allow for improved patient selection. This in turn may reduce adverse events, thereby lessening the economic burden of complications, increased length of stay, and hospital readmission. Providers must partner with their hospitals to align their goals and maximize quality and efficiency in order to decrease costs.

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Predicting surgical site infection after spine surgery: a validated model using a prospective surgical registry

Cited by 67 publications

References 14 publications

Prediction model for outcome after low-back surgery: individualized likelihood of complication, hospital readmission, return to work, and 12-month improvement in functional disability

Prediction model for outcome after low-back surgery: individualized likelihood of complication, hospital readmission, return to work, and 12-month improvement in functional disability

Risk Modeling Predicts Complication Rates for Spinal Surgery

Pay-for-performance in orthopedics: how we got here and where we are going

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