Prediction models for risk assessment of surgical site infection after spinal surgery: A systematic review

Background

Spinal surgeries are a common procedure, but there is significant risk of adverse events following these operations. While the rate of adverse events ranges from 8% to 18%, surgical site infections (SSIs) alone occur in between 1% and 4% of spinal surgeries.

Methods

We completed a systematic review addressing factors that contribute to surgical site infection after spinal surgery. From the included studies, we separated the articles into groups based on whether they propose a clinical predictive tool or model. We then compared the prediction variables, model development, model validation, and model performance.

Results

About 47 articles were included in this study: 10 proposed a model and 5 validated a model. The models were developed from 7,720 participants in total and 210 participants with SSI. Only one of the proposed models was externally validated by an independent group. The other 4 validation papers examined the performance of the ACS NSQIP surgical risk calculator.

Conclusions

While some preoperative risk models have been validated, and even successfully implemented clinically, the significance of postoperative SSIs and the unique susceptibility of spine surgery patients merits the development of a spine-specific preoperative risk model. Additionally, comprehensive and stratified risk modeling for SSI would be of invaluable clinical utility and greatly improve the field of spine surgery.

Deep Learning Prediction of Cervical Spine Surgery Revision Outcomes Using Standard Laboratory and Operative Variables

BACKGROUND

Cervical spine procedures represent a major proportion of all spine surgery. Mitigating the revision rate following cervical procedures requires careful patient selection. While complication risk has successfully been predicted, revision risk has proven more challenging. This is likely due to the absence of granular variables in claims databases. The objective of this study was to develop a state-of-the-art model of revision prediction of cervical spine surgery using laboratory and operative variables.

METHODS

Using the Stanford Research Repository, patients undergoing a cervical spine procedure between 2016 and 2022 were identified (N = 3151), and recent laboratory values were collected. Patients were classified into separate cohorts by revision outcome and time frame. Machine and deep learning models were trained to predict each revision outcome from laboratory and operative variables.

RESULTS

Red blood cell count, hemoglobin, hematocrit, mean corpuscular hemoglobin concentration, red blood cell distribution width, platelet count, carbon dioxide, anion gap, and calcium all were significantly associated with ≥1 revision cohorts. For the prediction of 3-month revision, the deep neural network achieved an area under the receiver operating characteristic curve of 0.833. The model demonstrated increased performance for anterior versus posterior and arthrodesis versus decompression procedures.

CONCLUSIONS

Our deep learning approach successfully predicted 3-month revision outcomes from demographic variables, standard laboratory values, and operative variables in a cervical spine surgery cohort. This work used standard laboratory values and operative codes as meaningful predictive variables for revision outcome prediction. The increased performance on certain procedures evidences the need for careful development and validation of one-size-fits-all risk scores for spine procedures.

Impact of Unplanned Readmission on Patient-Reported Outcomes After Cervical Spine Surgery: A National Study of 13 355 Patients

BACKGROUND AND OBJECTIVES

Although risk factors for unplanned readmission after cervical spine surgery have been widely reported, less is known about how readmission itself affects patient-reported outcome measures (PROMs). Using the Quality Outcomes Database registry of patients undergoing elective cervical spine surgery, we sought to (1) determine the impact of unplanned readmission on PROMs and (2) compare the effect of specific readmission reasons on PROMs.

METHODS

An observational study was performed using a multi-institution, retrospective registry for patients undergoing cervical spine surgery. The occurrence of 90-day unplanned readmission classified into medical, surgical, pain only, and no readmissions was the exposure variable. Outcome variables included 12-month PROMs of Neck Disability Index (NDI), Numeric Rating Scale (NRS)-neck/arm pain, EuroQol-5D (EQ-5D), and patient dissatisfaction. Multivariable models predicting each PROM were built using readmission reasons controlling for demographics, clinical characteristics, and preoperative PROMs.

RESULTS

Data from 13 355 patients undergoing elective cervical spine surgery (82% anterior approach and 18% posterior approach) were analyzed. Unplanned readmission within 90 days of surgery occurred in 3.8% patients, including medical (1.6%), surgical (1.8%), and pain (0.3%). Besides medical reasons, wound infection/dehiscence was the most common reason for unplanned readmission for the total cohort (0.5%), dysphagia in the anterior approach (0.6%), and wound infection/dehiscence in the posterior approach (1.5%). Based on multivariable regression, surgical readmission was significantly associated with worse 12-month NDI, NRS-neck pain, NRS-arm pain, EQ-5D, and higher odds of dissatisfaction. Pain readmissions were associated with worse 12-month NDI and NRS-neck pain scores, and worse dissatisfaction. For specific readmission reasons, pain, surgical site infection/wound dehiscence, hematoma/seroma, revision surgery, deep vein thrombosis, and pulmonary embolism were significantly associated with worsened 12-month PROMs.

CONCLUSION

In patients undergoing elective cervical spine surgery, 90-day unplanned surgical and pain readmissions were associated with worse 12-month PROMs compared with patients with medical readmissions and no readmissions.

Clinical and radiomics feature-based outcome analysis in lumbar disc herniation surgery

BACKGROUND

Low back pain is a widely prevalent symptom and the foremost cause of disability on a global scale. Although various degenerative imaging findings observed on magnetic resonance imaging (MRI) have been linked to low back pain and disc herniation, none of them can be considered pathognomonic for this condition, given the high prevalence of abnormal findings in asymptomatic individuals. Nevertheless, there is a lack of knowledge regarding whether radiomics features in MRI images combined with clinical features can be useful for prediction modeling of treatment success. The objective of this study was to explore the potential of radiomics feature analysis combined with clinical features and artificial intelligence-based techniques (machine learning/deep learning) in identifying MRI predictors for the prediction of outcomes after lumbar disc herniation surgery.

METHODS

We included n = 172 patients who underwent discectomy due to disc herniation with preoperative T2-weighted MRI examinations. Extracted clinical features included sex, age, alcohol and nicotine consumption, insurance type, hospital length of stay (LOS), complications, operation time, ASA score, preoperative CRP, surgical technique (microsurgical versus full-endoscopic), and information regarding the experience of the performing surgeon (years of experience with the surgical technique and the number of surgeries performed at the time of surgery). The present study employed a semiautomatic region-growing volumetric segmentation algorithm to segment herniated discs. In addition, 3D-radiomics features, which characterize phenotypic differences based on intensity, shape, and texture, were extracted from the computed magnetic resonance imaging (MRI) images. Selected features identified by feature importance analyses were utilized for both machine learning and deep learning models (n = 17 models).

RESULTS

The mean accuracy over all models for training and testing in the combined feature set was 93.31 ± 4.96 and 88.17 ± 2.58. The mean accuracy for training and testing in the clinical feature set was 91.28 ± 4.56 and 87.69 ± 3.62.

CONCLUSIONS

Our results suggest a minimal but detectable improvement in predictive tasks when radiomics features are included. However, the extent of this advantage should be considered with caution, emphasizing the potential of exploring multimodal data inputs in future predictive modeling.

Nomogram for predicting survival of patients with gastric cancer and multiple primary malignancies: a real-world retrospective analysis using the Surveillance, Epidemiology and End Results database

OBJECTIVES

Gastric cancer combined with multiple primary malignancies (GCM) is increasingly common. This study investigated GCM clinical features and survival time.

METHODS

Patients with GCM and GC only (GCO) were selected from the Surveillance, Epidemiology and End Results (SEER) database. Survival was compared between GCM and GCO groups using propensity score matching. Then, the GCM group was divided into a training cohort and a validation cohort. These cohorts were used to establish a nomogram for survival prediction in patients with GCM.

RESULTS

Survival time was significantly longer in the GCM group than in the GCO group. All-subsets regression was used to identify four variables for nomogram establishment: age, gastric cancer sequence, N stage, and surgery. The concordance index and time-dependent receiver operating characteristic curve indicated that the nomogram had favorable discriminative ability. Calibration plots of predicted and actual probabilities showed good consistency in both the training and validation cohorts. Decision curve analysis and risk stratification showed that the nomogram was clinically useful; it had favorable discriminative ability to recognize patients with different levels of risk.

CONCLUSIONS

Compared with GCO, GCM is a relatively indolent malignancy. The nomogram developed in this study can help clinicians to assess GCM prognosis.

Development of a machine-learning based model for predicting multidimensional outcome after surgery for degenerative disorders of the spine

BACKGROUND

It is clear that individual outcomes of spine surgery can be quite heterogeneous. When consenting a patient for surgery, it is important to be able to offer an individualized prediction regarding the likely outcome. This study used a comprehensive set of data collected over 12 years in an in-house registry to develop a parsimonious model to predict the multidimensional outcome of patients undergoing surgery for degenerative pathologies of the thoracic, lumbar or cervical spine.

METHODS

Data from 8374 patients (mean age 63.9 (14.9-96.3) y, 53.4% female) were used to develop a model to predict the 12-month scores for the Core Outcome Measures Index (COMI) and its subdomain scores. The data were split 80:20 into a training and test set. The top predictors were selected by applying recursive feature elimination based on LASSO cross validation models. Based on the 111 top predictors (contained within 20 variables), Ridge cross validation models were trained, validated, and tested for each of 9 outcome domains, for patients with either "Back" (thoracic/lumbar spine) or "Neck" (cervical spine) problems (total 18 models).

RESULTS

Among the strongest outcome predictors in most models were: preoperative scores for almost all COMI items (especially axial pain (back or neck) and peripheral pain (leg/buttock or arm/shoulder)), catastrophizing, fear avoidance beliefs, comorbidity, age, BMI, nationality, previous spine surgery, type and spinal level of intervention, number of affected levels, and surgeon seniority. The R² of the models on the validation/test sets averaged 0.16/0.13. A preliminary online tool was programmed to present the predicted outcomes for individual patients, based on their presenting characteristics. https://linkup.kws.ch/prognostictool .

CONCLUSION

The models provided estimates to enable a bespoke prediction of the outcome of surgery for individual patients with varying degenerative pathologies and baseline characteristics. The models form the basis of a simple, freely-available online prognostic tool developed to improve access to and usability of prognostic information in clinical practice. It is hoped that, following confirmation of its validity and practical utility, the tool will ultimately serve to facilitate decision-making and the management of patients' expectations.

Classification and Treatment for Cervical Spine Fracture with Ankylosing Spondylitis: A Clinical Nomogram Prediction Study

Objective

Through the follow-up analysis of cervical spine fracture cases with ankylosing spondylitis (AS), a treatment-oriented fracture classification method is introduced to evaluate the clinical efficacy guided by this classification method.

Method

A retrospective analysis was performed on 128 AS patients who underwent comprehensive treatment in the Spine Surgery Department of Qingdao University Hospital from January 2009 to May 2018. Statistics of patient demographic data, distribution of different fractures corresponding to surgical methods, 3-year follow-up outcomes, and summary of objective fracture classification methods were analyzed. A prospective 5-year follow-up study of 90 patients with AS cervical spine fractures from June 2015 to August 2020 was also included. Statistical differences on the distribution of factors such as case information, cervical spine sagittal sequence parameters, and fracture classification were assessed. Correlations between surgical information, American Spinal Injuries Association grade (ASIA), modified Japanese Orthopaedic Association scores (mJOA), and other factors were analyzed to establish a nomogram predictive model for curative effect outcomes. Overall, three major types and the four subtypes of AS cervical spine fractures were evaluated based on the clinical efficacy of the classification and the selection of surgical treatment methods.

Result

The most common type of fracture was type II (30 cases, 33.33%), most of the subtypes were A (37 cases), followed by B (36 cases) and C (17 cases). Twenty-four of 28 patients with type I underwent anterior surgery, and 47 of 62 patients with type II and III underwent posterior surgery. The average follow-up time was 25.76 ± 11.80 months. The results of predicting clinical variables are different but include factors such as fracture type and subtype, type of operation, and age. The predictor variables include the above-mentioned similar variables, but survival is more affected by the fracture type of the patient.

Conclusion

This predictive model based on follow-up information delineation points out the impact of ankylosing spondylitis cervical spine fracture classification on surgical selection and clinical efficacy.

Deployment of Clinical Prediction Models: A Practical Guide to Nomograms and Online Calculators

The use of predictive models within neurosurgery is increasing and many models described in published journal articles are made available to readers in formats such as nomograms and online calculators. The present chapter details a step-by-step methodology with accompanying R code that may be used to implement models both in the form of traditional nomograms and as open-access, online calculators through RStudio's Shinyapps. The chapter assumes a basic understanding of predictive modeling in R and utilizes open-access files created by the Machine Intelligence in Clinical Neuroscience (MICN) Lab (Department of Neurosurgery and the Clinical Neuroscience Center of the University Hospital Zurich). When implemented correctly, tools such as nomograms and predictive calculators have the potential to improve user understanding of the underlying statistical models, facilitate broader adoption, and to streamline the eventual use of such models in clinical settings.

Prediction of outcome after spinal surgery-using The Dialogue Support based on the Swedish national quality register

PURPOSE

To evaluate the predictive precision of the Dialogue Support, a tool for additional help in shared decision-making before surgery of the degenerative spine.

METHODS

Data in Swespine (Swedish national quality registry) of patients operated between 2007 and 2019 found the development of prediction algorithms based on logistic regression analyses, where socio-demographic and baseline variables were included. The algorithms were tested in four diagnostic groups: lumbar disc herniation, lumbar spinal stenosis, degenerative disc disease and cervical radiculopathy. By random selection, 80% of the study population was used for the prediction of outcome and then tested against the actual outcome of the remaining 20%. Outcome measures were global assessment of pain (GA), and satisfaction with outcome.

RESULTS

Calibration plots demonstrated a high degree of concordance on a group level. On an individual level, ROC curves showed moderate predictive capacity with AUC (area under the curve) values 0.67-0.68 for global assessment and 0.6-0.67 for satisfaction.

CONCLUSION

The Dialogue Support can serve as an aid to both patient and surgeon when discussing and deciding on surgical treatment of degenerative conditions in the lumbar and cervical spine.

LEVEL OF EVIDENCE

I.

Web-Based Calculator Predicts Surgical-Site Infection After Thoracolumbar Spine Surgery

BACKGROUND

Surgical-site infection (SSI) after spine surgery leads to increased length of stay, reoperation, and worse patient quality of life. We sought to develop a web-based calculator that computes an individual's risk of a wound infection following thoracolumbar spine surgery.

METHODS

We performed a retrospective review of consecutive patients undergoing elective degenerative thoracolumbar spine surgery at a tertiary-care institution between January 2016 and December 2018. Patients who developed SSI requiring reoperation were identified. Regression analysis was performed and model performance was assessed using receiver operating curve analysis to derive an area under the curve. Bootstrapping was performed to check for overfitting, and a Hosmer-Lemeshow test was employed to evaluate goodness-of-fit and model calibration.

RESULTS

In total, 1259 patients were identified; 73% were index operations. The overall infection rate was 2.7%, and significant predictors of SSI included female sex (odds ratio [OR] 3.0), greater body mass index (OR 1.1), active smoking (OR 2.8), worse American Society of Anesthesiologists physical status (OR 2.1), and greater surgical invasiveness (OR 1.1). The prediction model had an optimism-corrected area under the curve of 0.81. A web-based calculator was created: https://jhuspine2.shinyapps.io/Wound_Infection_Calculator/.

CONCLUSIONS

In this pilot study, we developed a model and simple web-based calculator to predict a patient's individualized risk of SSI after thoracolumbar spine surgery. This tool has a predictive accuracy of 83%. Through further multi-institutional validation studies, this tool has the potential to alert both patients and providers of an individual's SSI risk to improve informed consent, mitigate risk factors, and ultimately drive down rates of SSIs.

Patient-specific prediction model for clinical and quality-of-life outcomes after lumbar spine surgery

OBJECTIVE

Patient demographics, comorbidities, and baseline quality of life (QOL) are major contributors to postoperative outcomes. The frequency and cost of lumbar spine surgery has been increasing, with controversy revolving around optimal management strategies and outcome predictors. The goal of this study was to generate predictive nomograms and a clinical calculator for postoperative clinical and QOL outcomes following lumbar spine surgery for degenerative disease.

METHODS

Patients undergoing lumbar spine surgery for degenerative disease at a single tertiary care institution between June 2009 and December 2012 were retrospectively reviewed. Nomograms and an online calculator were modeled based on patient demographics, comorbidities, presenting symptoms and duration of symptoms, indication for surgery, type and levels of surgery, and baseline preoperative QOL scores. Outcomes included postoperative emergency department (ED) visit or readmission within 30 days, reoperation within 90 days, and 1-year changes in the EuroQOL-5D (EQ-5D) score. Bootstrapping was used for internal validation.

RESULTS

A total of 2996 lumbar surgeries were identified. Thirty-day ED visits were seen in 7%, 30-day readmission in 12%, 90-day reoperation in 3%, and improvement in EQ-5D at 1 year that exceeded the minimum clinically important difference in 56%. Concordance indices for the models predicting ED visits, readmission, reoperation, and dichotomous 1-year improvement in EQ-5D were 0.63, 0.66, 0.73, and 0.84, respectively. Important predictors of clinical outcomes included age, body mass index, Charlson Comorbidity Index, indication for surgery, preoperative duration of symptoms, and the type (and number of levels) of surgery. A web-based calculator was created, which can be accessed here: https://riskcalc.org/PatientsEligibleForLumbarSpineSurgery/.

CONCLUSIONS

The prediction tools derived from this study constitute important adjuncts to clinical decision-making that can offer patients undergoing lumbar spine surgery realistic and personalized expectations of postoperative outcome. They may also aid physicians in surgical planning, referrals, and counseling to ultimately lead to improved patient experience and outcomes.

Prediction Models in Degenerative Spine Surgery: A Systematic Review

STUDY DESIGN

Systematic review.

OBJECTIVES

To review the existing literature of prediction models in degenerative spinal surgery.

METHODS

Review of PubMed/Medline and Embase databases was conducted to identify articles between January 1, 2000 and March 1, 2020 that reported prediction model performance for outcomes following elective degenerative spine surgery.

RESULTS

Thirty-one articles were included. Twenty studies were of thoracolumbar, 5 were of cervical, and 6 included all spine patients. Five studies were externally validated. Prediction models were developed using machine learning (42%) and logistic regression (42%) as well as other techniques. Web-based calculators were included in 45% of published articles. Various outcomes were investigated, including complications, infection, length of stay, discharge disposition, reoperation, readmission, disability score, back pain, leg pain, return to work, and opioid dependence.

CONCLUSIONS

Significant heterogeneity exists in methods used to develop prediction models of postoperative outcomes after degenerative spine surgery. Most internally validate their scores, but a few have been externally validated. Areas under the curve for most models range from 0.6 to 0.9. Techniques for development are becoming increasingly sophisticated with different machine learning tools. With further external validation, these models can be deployed online for patient, physician, and administrative use, and have the potential to optimize outcomes and maximize value in spine surgery.

Predicting postoperative quality-of-life outcomes in patients with metastatic spine disease: who benefits?

Symptomatic spinal metastasis occurs in around 10% of all cancer patients, 5%-10% of whom will require operative management. While postoperative survival has been extensively evaluated, postoperative health-related quality-of-life (HRQOL) outcomes have remained relatively understudied. Available tools that measure HRQOL are heterogeneous and may emphasize different aspects of HRQOL. The authors of this paper recommend the use of the EQ-5D and Spine Oncology Study Group Outcomes Questionnaire (SOSGOQ), given their extensive validation, to capture the QOL effects of systemic disease and spine metastases. Recent studies have identified preoperative QOL, baseline functional status, and neurological function as potential predictors of postoperative QOL outcomes, but heterogeneity across studies limits the ability to derive meaningful conclusions from the data. Future development of a valid and reliable prognostic model will likely require the application of a standardized protocol in the context of a multicenter study design.

Development and validation of a nomogram for prediction of lymph node metastasis in early-stage breast cancer

Background

Lymph node status is an important factor in determining the prognosis of early-stage breast cancer. We endeavored to build and validate a simple nomogram to predict lymph node metastasis (LNM) in patients with early-stage breast cancer.

Methods

Patients with T1-2 and non-metastasis (M0) breast cancer registered in the Surveillance, Epidemiology, and End Results (SEER) database were enrolled. All patients were divided into primary cohort and validation cohort in a 2:1 ratio. In order to assess risk factors for LNM, we performed univariate and multivariate binary logistic regression, and based on results of multivariable analysis, we built the predictive nomogram model. The C-index, receiver operating characteristic (ROC) and calibration plots were applied to assess LNM model performance. Moreover, the nomogram efficiency was further validated through the validation cohort, part of which was from the First Affiliated Hospital of Nanjing Medical University database.

Results

Totally, 184,531 female breast cancer with T1-2 tumor size from SEER database and 1,222 patients from the Chinese institutional data were included. There were 123,019 patients in the primary cohort and 62,734 patients in validation cohort. The LNM nomogram was composed of seven features including age at diagnosis, race, primary site, histologic type, grade, tumor size and subtype. The model showed good discrimination, with a C-index of 0.720 [95% confidence interval (CI): 0.717-0.723] and good calibration. Similar C-index was 0.718 (95% CI: 0.713-0.723) in validation cohort. Consistently, ROC curves presented good discrimination in the primary cohort [area under the curve (AUC) =0.720] and the validation set (AUC =0.718) for the LNM nomogram. Calibration curve of the nomogram demonstrated good agreement.

Conclusions

With the prediction of novel validated nomogram for women with early-stage breast cancer, doctors may distinguish patients with high possibility of LNM and devise individualize treatments.

Predicting High-Value Care Outcomes After Surgery for Skull Base Meningiomas

BACKGROUND

Although various predictors of adverse postoperative outcomes among patients with meningioma have been established, research has yet to develop a method for consolidating these findings to allow for predictions of adverse health care outcomes for patients diagnosed with skull base meningiomas. The objective of the present study was to develop 3 predictive algorithms that can be used to estimate an individual patient's probability of extended length of stay (LOS) in hospital, experiencing a nonroutine discharge disposition, or incurring high hospital charges after surgical resection of a skull base meningioma.

METHODS

The present study used data from patients who underwent surgical resection for skull base meningiomas at a single academic institution between 2017 and 2019. Multivariate logistic regression analysis was used to predict extended LOS, nonroutine discharge, and high hospital charges, and 2000 bootstrapped samples were used to calculate an optimism-corrected C-statistic. The Hosmer-Lemeshow test was used to assess model calibration, and P < 0.05 was considered statistically significant.

RESULTS

A total of 245 patients were included in our analysis. Our cohort was mostly female (77.6%) and white (62.4%). Our models predicting extended LOS, nonroutine discharge, and high hospital charges had optimism-corrected C-statistics of 0.768, 0.784, and 0.783, respectively. All models showed adequate calibration (P>0.05), and were deployed via an open-access, online calculator: https://neurooncsurgery3.shinyapps.io/high_value_skull_base_calc/.

CONCLUSIONS

After external validation, our predictive models have the potential to aid clinicians in providing patients with individualized risk estimation for health care outcomes after meningioma surgery.

Development and Validation of Cervical Prediction Models for Patient-Reported Outcomes at 1 Year After Cervical Spine Surgery for Radiculopathy and Myelopathy

STUDY DESIGN

Retrospective analysis of prospectively collected registry data.

OBJECTIVE

To develop and validate prediction models for 12-month patient-reported outcomes of disability, pain, and myelopathy in patients undergoing elective cervical spine surgery.

SUMMARY OF BACKGROUND DATA

Predictive models have the potential to be utilized preoperatively to set expectations, adjust modifiable characteristics, and provide a patient-centered model of care.

METHODS

This study was conducted using data from the cervical module of the Quality Outcomes Database. The outcomes of interest were disability (Neck Disability Index:), pain (Numeric Rating Scale), and modified Japanese Orthopaedic Association score for myelopathy. Multivariable proportional odds ordinal regression models were developed for patients with cervical radiculopathy and myelopathy. Patient demographic, clinical, and surgical covariates as well as baseline patient-reported outcomes scores were included in all models. The models were internally validated using bootstrap resampling to estimate the likely performance on a new sample of patients.

RESULTS

Four thousand nine hundred eighty-eight patients underwent surgery for radiculopathy and 2641 patients for myelopathy. The most important predictor of poor postoperative outcomes at 12-months was the baseline Neck Disability Index score for patients with radiculopathy and modified Japanese Orthopaedic Association score for patients with myelopathy. In addition, symptom duration, workers' compensation, age, employment, and ambulatory and smoking status had a statistically significant impact on all outcomes (P < 0.001). Clinical and surgical variables contributed very little to predictive models, with posterior approach being associated with higher odds of having worse 12-month outcome scores in both the radiculopathy and myelopathy cohorts (P < 0.001). The full models overall discriminative performance ranged from 0.654 to 0.725.

CONCLUSIONS

These predictive models provide individualized risk-adjusted estimates of 12-month disability, pain, and myelopathy outcomes for patients undergoing spine surgery for degenerative cervical disease. Predictive models have the potential to be used as a shared decision-making tool for evidence-based preoperative counselling.

LEVEL OF EVIDENCE

2.

Perioperative patient-specific factors-based nomograms predict short-term periprosthetic bone loss after total hip arthroplasty

BACKGROUND

Although medical intervention of periprosthetic bone loss in the immediate postoperative period was recommended, not all the patients experienced periprosthetic bone loss after total hip arthroplasty (THA). Prediction tools that enrolled all potential risk factors to calculate an individualized prediction of postoperative periprosthetic bone loss were strongly needed for clinical decision-making.

METHODS

Data of the patients who underwent primary unilateral cementless THA between April 2015 and October 2017 in our center were retrospectively collected. Candidate variables included demographic data and bone mineral density (BMD) in spine, hip, and periprosthetic regions that measured 1 week after THA. Outcomes of interest included the risk of postoperative periprosthetic bone loss in Gruen zone 1, 7, and total zones in the 1st postoperative year. Nomograms were presented based on multiple logistic regressions via R language. One thousand Bootstraps were used for internal validation.

RESULTS

Five hundred sixty-three patients met the inclusion criteria were enrolled, and the final analysis was performed in 427 patients (195 male and 232 female) after the exclusion. The mean BMD of Gruen zone 1, 7, and total were decreased by 4.1%, 6.4%, and 1.7% at the 1st year after THA, respectively. 61.1% of the patients (261/427) experienced bone loss in Gruen zone 1 at the 1st postoperative year, while there were 58.1% (248/427) in Gruen zone 7 and 63.0% (269/427) in Gruen zone total. Bias-corrected C-index for risk of postoperative bone loss in Gruen zone 1, 7, and total zones in the 1st postoperative year were 0.700, 0.785, and 0.696, respectively. The most highly influential factors for the postoperative periprosthetic bone loss were primary diagnosis and BMD in the corresponding Gruen zones at the baseline.

CONCLUSIONS

To the best of our knowledge, our study represented the first time to use the nomograms in estimating the risk of postoperative periprosthetic bone loss with adequate predictive discrimination and calibration. Those predictive models would help surgeons to identify high-risk patients who may benefit from anti-bone-resorptive treatment in the early postoperative period effectively. It is also beneficial for patients, as they can choose the treatment options based on a reasonable expectation following surgery.

Predictors of Nonroutine Discharge Disposition Among Patients with Parasagittal/Parafalcine Meningioma

OBJECTIVE

Discharge disposition is an important outcome for neurosurgeons to consider in the context of high-quality, value-based care. There has been limited research into how the unique anatomic considerations associated with parasagittal/parafalcine meningioma resection may influence discharge disposition. We investigated the effects of various predictors on discharge disposition within a cohort of patients with parasagittal/parafalcine meningioma.

METHODS

A total of 154 patients treated at a single institution were analyzed (2016-2019). Bivariate analysis was conducted using the Mann-Whitney U and Fisher exact tests. Multivariate analysis was conducted using logistic regression. An optimism-corrected C-statistic was calculated using 2000 bootstrap samples to assess logistic regression model performance.

RESULTS

Our cohort was mostly female (67.5%) and white (72.7%), with a mean age of 57.29 years. Most patients had tumors associated with the middle third of the superior sagittal sinus (SSS) (60.4%) and had tumors that were not fully occluding the SSS (74.0%). In multivariate analysis, independent predictors of nonroutine discharge disposition included 5-factor Modified Frailty Index score (odds ratio [OR], 2.06; P = 0.0088), Simpson grade IV resection (OR, 4.22; P = 0.0062), and occurrence of any postoperative complication (OR, 2.89; P = 0.031). The optimism-corrected C-statistic of our model was 0.757.

CONCLUSIONS

In our single-institution experience, neither extent of SSS invasion nor location along the SSS predicted nonroutine discharge, suggesting that tumor invasion and posterior location along the SSS are not necessarily contraindications to surgery. Our results also highlight the importance of frailty and tumor size in stratifying patients at risk of nonroutine discharge disposition.

Prediction calculator for nonroutine discharge and length of stay after spine surgery

BACKGROUND CONTEXT

Following spine surgery, delays in referral to rehabilitation facilities leads to increased length of hospital stay (LOS), increases costs, more risk of hospital acquired complications, and decreased patient satisfaction.

PURPOSE

We sought to create a prediction calculator to determine the expected LOS after spine surgery and identify patients most likely to need postoperative nonhome discharge. The goal would be to facilitate earlier referral to rehabilitation and thereby ultimately shorten LOS, reduce costs, and improve patient satisfaction.

STUDY DESIGN

Retrospective.

PATIENT SAMPLE

We retrospectively reviewed all adult patients who underwent spine surgery for all indications between January and June 2018.

OUTCOME MEASURES

Length of stay and discharge disposition.

METHODS

Demographic variables, insurance status, baseline comorbidities, narcotic use, operative characteristics, as well as postoperative length of stay and discharge disposition data were collected. Univariable and multivariable analyses were performed to identify independent predictors of LOS and discharge disposition.

RESULTS

Two hundred fifty-seven patients were included. Mean age was 59 years, 46% were females, and 52% had private insurance vs 7% with Medicaid and 41% with Medicare. The most commonly performed procedure was lumbar fusion (31.9%). Mean LOS after surgery was 4.8 days and 18% had prolonged LOS >7 days. Age, insurance type, marriage status, and surgical procedure were significantly associated with LOS and discharge disposition. The final model had an area under the curve of 89% with good discrimination. A web based calculator was developed: https://jhuspine1.shinyapps.io/RehabLOS/ CONCLUSIONS: This study established a novel pilot calculator to identify those patients most likely to be discharged to rehabilitation facilities and to predict LOS after spine surgery. Our calculator had a high predictive accuracy of 89% compared to others in the literature. With validation this tool may ultimately facilitate streamlining of the postoperative period to shorten LOS, optimize resource utilization, and improve patient care.

Predicting Patient-Centered Outcomes from Spine Surgery Using Risk Assessment Tools: a Systematic Review

PURPOSE OF REVIEW

The purpose of this systematic review is to evaluate the current literature in patients undergoing spine surgery in the cervical, thoracic, and lumbar spine to determine the available risk assessment tools to predict the patient-centered outcomes of pain, disability, physical function, quality of life, psychological disposition, and return to work after surgery.

RECENT FINDINGS

Risk assessment tools can assist surgeons and other healthcare providers in identifying the benefit-risk ratio of surgical candidates. These tools gather demographic, medical history, and other pertinent patient-reported measures to calculate a probability utilizing regression or machine learning statistical foundations. Currently, much is still unknown about the use of these tools to predict quality of life, disability, and other factors following spine surgery. A systematic review was conducted using PRISMA guidelines that identified risk assessment tools that utilized patient-reported outcome measures as part of the calculation. From 8128 identified studies, 13 articles met inclusion criteria and were accepted into this review. The range of c-index values reported in the studies was between 0.63 and 0.84, indicating fair to excellent model performance. Post-surgical patient-reported outcomes were identified in the following categories (n = total number of predictive models): return to work (n = 3), pain (n = 9), physical functioning and disability (n = 5), quality of life (QOL) (n = 6), and psychosocial disposition (n = 2). Our review has synthesized the available evidence on risk assessment tools for predicting patient-centered outcomes in patients undergoing spine surgery and described their findings and clinical utility.

Development of a Risk Prediction Model With Improved Clinical Utility in Elective Cervical and Lumbar Spine Surgery

STUDY DESIGN

Retrospective cohort.

OBJECTIVE

We present a universal model of risk prediction for patients undergoing elective cervical and lumbar spine surgery.

SUMMARY OF BACKGROUND DATA

Previous studies illustrate predictive risk models as possible tools to identify individuals at increased risk for postoperative complications and high resource utilization following spine surgery. Many are specific to one condition or procedure, cumbersome to calculate, or include subjective variables limiting applicability and utility.

METHODS

A retrospective cohort of 177,928 spine surgeries (lumbar (L) Ln = 129,800; cervical (C) Cn = 48,128) was constructed from the 2012 to 2016 American College of Surgeons National Surgical Quality Improvement Project (ACS-NSQIP) database. Cases were identified by Current Procedural Terminology (CPT) codes for cervical fusion, lumbar fusion, and lumbar decompression laminectomy. Significant preoperative risk factors for postoperative complications were identified and included in logistic regression. Sum of odds ratios from each factor was used to develop the Universal Spine Surgery (USS) score. Model performance was assessed using receiver-operating characteristic (ROC) curves and tested on 20% of the total sample.

RESULTS

Eighteen risk factors were identified, including sixteen found to be significant outcomes predictors. At least one complication was present among 11.1% of patients, the most common of which included bleeding requiring transfusion (4.86%), surgical site infection (1.54%), and urinary tract infection (1.08%). Complication rate increased as a function of the model score and ROC area under the curve analyses demonstrated fair predictive accuracy (lumbar = 0.741; cervical = 0.776). There were no significant deviations between score development and testing datasets.

CONCLUSION

We present the Universal Spine Surgery score as a robust, easily administered, and cross-validated instrument to quickly identify spine surgery candidates at increased risk for postoperative complications and high resource utilization without need for algorithmic software. This may serve as a useful adjunct in preoperative patient counseling and perioperative resource allocation.

LEVEL OF EVIDENCE

3.

Development and temporal validation of a prognostic model for 1-year clinical outcome after decompression surgery for lumbar disc herniation

PURPOSE

Surgeons need tools to provide individualised estimates of surgical outcomes and the uncertainty surrounding these, to convey realistic expectations to the patient. This study developed and validated prognostic models for patients undergoing surgical treatment of lumbar disc herniation, to predict outcomes 1 year after surgery, and implemented these models in an online prediction tool.

METHODS

Using the data of 1244 patients from a large spine unit, LASSO and linear regression models were fitted with 90% upper prediction limits, to predict scores on the Core Outcome Measures Index, and back and leg pain. Candidate predictors included sociodemographic factors, baseline symptoms, medical history, and surgeon characteristics. Temporal validation was conducted on 364 more recent patients at the same unit, by examining the proportion of observed outcomes exceeding the threshold of the 90% upper prediction limit (UPL), and by calculating mean bias and other calibration measures.

RESULTS

Poorer outcome was predicted by obesity, previous spine surgery, and having basic obligatory (rather than private) insurance. In the validation data, fewer than 12% of outcomes were above the 90% UPL. Calibration plots for the model validation showed values for mean bias < 0.5 score points and regression slopes close to 1.

CONCLUSION

While the model accuracy was good overall, the prediction intervals indicated considerable predictive uncertainty on the individual level. Implementation studies will assess the clinical usefulness of the online tool. Updating the models with additional predictors may improve the accuracy and precision of outcome predictions. These slides can be retrieved under Electronic Supplementary Material.

Evaluation of Postoperative Mental Health Outcomes in Patients Based on Patient-Reported Outcome Measurement Information System Physical Function Following Anterior Cervical Discectomy and Fusion

OBJECTIVE

To assess the relationship of preoperative physical function, as measured by Patient-Reported Outcome Measurement Information System Physical Function (PROMIS PF), to improvement in mental health, as evaluated by Short Form-12 Mental Component Summary (SF-12 MCS) following anterior cervical discectomy and fusion (ACDF).

METHODS

Patients undergoing primary ACDF were retrospectively reviewed and stratified based on preoperative PROMIS PF scores. PROMIS PF cohorts were tested for an association with demographic characteristics and perioperative variables using chi-square analysis and multivariate linear regression. Multivariate linear regression was utilized to determine the association between PROMIS PF cohorts and improvement in SF-12 MCS.

RESULTS

A total of 129 one- to 3-level ACDF patients were included: 73 had PROMIS PF < 40 ("low PROMIS") and 56 had PROMIS PF ≥ 40 ("high PROMIS"). The low PROMIS cohort reported worse mental health preoperatively and at all postoperative timepoints except for 1 year. Both cohorts had similar changes in mental health from baseline through the 6-month follow-up. However, at 1 year. postoperatively, the low PROMIS cohort had a statistically greater change in mental health score.

CONCLUSION

Patients with worse preoperative physical function reported significantly worse preoperative and postoperative mental health. However, patients with worse preoperative physical function made significantly greater improvements in mental health from baseline. This suggests that patients with worse preoperative physical function can still expect significant improvements in mental health following surgery.

A predictive model and nomogram for predicting return to work at 3 months after cervical spine surgery: an analysis from the Quality Outcomes Database

OBJECTIVEBack pain and neck pain are two of the most common causes of work loss due to disability, which poses an economic burden on society. Due to recent changes in healthcare policies, patient-centered outcomes including return to work have been increasingly prioritized by physicians and hospitals to optimize healthcare delivery. In this study, the authors used a national spine registry to identify clinical factors associated with return to work at 3 months among patients undergoing a cervical spine surgery.METHODSThe authors queried the Quality Outcomes Database registry for information collected from April 2013 through March 2017 for preoperatively employed patients undergoing cervical spine surgery for degenerative spine disease. Covariates included demographic, clinical, and operative variables, and baseline patient-reported outcomes. Multiple imputations were used for missing values and multivariable logistic regression analysis was used to identify factors associated with higher odds of returning to work. Bootstrap resampling (200 iterations) was used to assess the validity of the model. A nomogram was constructed using the results of the multivariable model.RESULTSA total of 4689 patients were analyzed, of whom 82.2% (n = 3854) returned to work at 3 months postoperatively. Among previously employed and working patients, 89.3% (n = 3443) returned to work compared to 52.3% (n = 411) among those who were employed but not working (e.g., were on a leave) at the time of surgery (p < 0.001). On multivariable logistic regression the authors found that patients who were less likely to return to work were older (age > 56-65 years: OR 0.69, 95% CI 0.57-0.85, p < 0.001; age > 65 years: OR 0.65, 95% CI 0.43-0.97, p = 0.02); were employed but not working (OR 0.24, 95% CI 0.20-0.29, p < 0.001); were employed part time (OR 0.56, 95% CI 0.42-0.76, p < 0.001); had a heavy-intensity (OR 0.42, 95% CI 0.32-0.54, p < 0.001) or medium-intensity (OR 0.59, 95% CI 0.46-0.76, p < 0.001) occupation compared to a sedentary occupation type; had workers' compensation (OR 0.38, 95% CI 0.28-0.53, p < 0.001); had a higher Neck Disability Index score at baseline (OR 0.60, 95% CI 0.51-0.70, p = 0.017); were more likely to present with myelopathy (OR 0.52, 95% CI 0.42-0.63, p < 0.001); and had more levels fused (3-5 levels: OR 0.46, 95% CI 0.35-0.61, p < 0.001). Using the multivariable analysis, the authors then constructed a nomogram to predict return to work, which was found to have an area under the curve of 0.812 and good validity.CONCLUSIONSReturn to work is a crucial outcome that is being increasingly prioritized for employed patients undergoing spine surgery. The results from this study could help surgeons identify at-risk patients so that preoperative expectations could be discussed more comprehensively.

Determinants of Pain and Predictors of Pain Relief after Ulnar Shortening Osteotomy for Ulnar Impaction Syndrome

Background  The purpose of this study is to characterize patient- and surgery-specific factors associated with perioperative pain level in patients undergoing ulnar shortening osteotomy (USO) for ulnar impaction syndrome (UIS). We hypothesize that preoperative opiate consumption, tobacco utilization, and severity of ulnar variance will be associated with less postoperative pain relief. Methods  All cases of USO between January 2010 and December 2016 for management of UIS were retrospectively reviewed. Patient demographics, smoking status, type of labor, and opioid utilization before surgery were recorded. Radiographic measurements for ulnar variance, radial tilt and inclination, as well as triangular fibrocartilage complex and distal radial-ulnar joint (DRUJ) morphology were assessed. Pre- and postoperative pain score were recorded. Regression analysis was performed to determine predictors of pain scores. Results  A total of 69 patients were included for the final analysis with a mean age of 44 years (range 17-73 years). Seventeen patients reported use of daily opioid medications at the time of surgery (25%). Patients who used opioid analgesics daily, active laborers, smokers, and patients involved in worker compensation claims had significantly less pain relief after surgery. Patients with osteotomy performed at the metaphysis had significantly more pain relief than patients that had diaphyseal osteotomy. Regression analysis identified tobacco utilization and anatomic site of osteotomy as independent predictors of postoperative pain. Conclusion  The results from this study identified smoking and location of osteotomy as independent predictors of postoperative pain relief. While smoking cessation is paramount to prevent delayed/nonunion it may also help improve pain relief following USO. The potential to achieve greater shortening with a metaphyseal osteotomy suggests that in addition to the mechanical unloading the carpus, pain relief after USO may also stem from tensioning the ulnar collateral ligaments of the wrist, the ECU subsheath, and the radioulnar ligaments. Level of Evidence  This is a Level III, therapeutic study.