Perioperative Ischemic Optic Neuropathy after Cardiac Surgery: Development and Validation of a Preoperative Risk Prediction Model.

OBJECTIVE: Previous studies identified risk factors for ischemic optic neuropathy (ION) after cardiac surgery; however, there is no easy-to-use risk calculator for the physician to identify high-risk patients for ION before cardiac surgery. The authors sought to develop and validate a simple-to-use predictive model and calculator to assist with preoperative identification of risk and informed consent for this rare but serious complication. DESIGN: Retrospective case-control study. SETTING: Hospital discharge records. PATIENTS: A total of 5,561,177 discharges in the National Inpatient Sample >18 years of age, with procedure codes for coronary artery bypass grafting, heart valve repair/replacement, or left ventricular assist device insertion. INTERVENTIONS: All patients had undergone cardiac surgery. MEASUREMENTS AND MAIN RESULTS: Known preoperative risk factors for ION after cardiac surgery were assessed to develop a risk score and prediction model. This model was validated internally using the split-sample method. There were 771 cases of ION among 5,561,177 patients in the National Inpatient Sample. The risk factors for ION used in the model were carotid artery stenosis, cataract, diabetic retinopathy, macular degeneration, glaucoma, male sex, and prior stroke; whereas uncomplicated diabetes decreased risk. With the internal validation, the predictive model had an area under the receiver operating characteristic curve of 0.66. A risk score cutoff ≥3 had 98.4% specificity. CONCLUSIONS: This predictive model, based on previously identified preoperative factors, predicted risk of perioperative ION with a fair area under the receiver operating characteristic curve. This predictive model could enable screening to provide a more accurate risk assessment for ION, and consent process for cardiac surgery.

Predicting Risk of Perioperative Ischemic Optic Neuropathy in Spine Fusion Surgery: A Cohort Study Using the National Inpatient Sample.

BACKGROUND: Ischemic optic neuropathy (ION) is a rare complication of anesthesia and surgery that causes vision loss in spine fusion. We sought to develop a predictive model based on known preoperative risk factors for perioperative ION to guide patient and physician preoperative decision-making. METHODS: In the National Inpatient Sample (NIS) for 1998-2012, discharges for posterior thoracic, lumbar, and sacral spine fusion were identified and classified by ION status. Variables were selected without weighting via variable clustering using Principal Component Analysis of Mixed Data (PCA-MIX). Hierarchical clustering with 4 clusters was performed, and the variable with largest squared loading in each cluster was chosen. By splitting our sample into a training and testing data set, we developed and internally validated a predictive model. The final model using variables known preoperatively was constructed to allow determination of relative and absolute risk of developing perioperative ION and was tested for calibration and discrimination. RESULTS: The final predictive model based on hierarchical clustering contained 3 preoperative factors, age, male or female sex, and the presence of obstructive sleep apnea (OSA). The predictive model based on these factors had an area under the receiver operating characteristic curve (AUC) of 0.65 and good calibration. A score cutoff of >1 had 100% sensitivity, while score of 3 had 96.5% specificity. The highest estimated absolute risk (844.5/million) and relative risk of ION (46.40) was for a man, age 40-64 years, with OSA. CONCLUSIONS: The predictive model could enable screening for patients at higher risk of ION to provide more accurate risk assessment and surgical and anesthetic planning for perioperative ION in spine fusion.

Relationship Between Treatment Hours of Selected Disciplines And Change in Pain Impact During the Year Following Referral to an Interdisciplinary Pain Management Center: A Latent Class Analysis.

Introduction: Pain management clinics differ in treatments offered, and little evidence exists regarding which combinations of therapies result in best outcomes. This study analyzed clinical encounters and pain outcomes data for associations between treatment composition and outcomes. Methods: Retrospective observational study of 2,142 predominantly active-duty US service members referred to an interdisciplinary pain management center between 2014 and 2021. Latent class analysis was used to identify treatment groups with distinct outcome patterns during the year following initial assessment. The primary outcome measure was the National Institutes of Health Task Force on Research Standards for Chronic Low-Back Pain impact score. Results: Four distinct treatment groups were identified: 1 group engaged in conventional medical therapies alone (n = 726, median 3.5 treatment hours), and 3 groups used a combination of conventional, psychological, rehabilitative, and complementary therapies, with different levels of treatment hours: low (n = 814, median 15.7 h), medium (n = 177, median 40.1 h), and high (n = 425, median 72.5 h). All groups showed significant improvement in pain impact score from baseline up to 9 months but not at 12 months following initial assessment. At the 6-month time point, the group with high multimodal treatment hours had the most improvement in pain impact (-3.1 [95% CI -3.8, -2.4]) compared with the group using only conventional therapies (-1.0 points [95% CI -1.8, -0.1]) or with low multimodal treatment hours (-1.3 points [95% CI -1.9, -0.7]). There were no between-group differences at the 9- or 12-month time points. Conclusion: These results suggest that a combination of pain therapy approaches results in greater reduction in pain impact than the use of conventional medical treatment alone for up to 6 months after initiating therapy and that there may be a threshold of treatment hours that must be exceeded to achieve this benefit. Clinical trial registration: As this study is not a clinical trial, no registration was required.

Predictive model of ischemic optic neuropathy in spinal fusion surgery using a longitudinal medical claims database.

BACKGROUND CONTEXT: Perioperative ischemic optic neuropathy (ION) is a devastating complication of spinal fusion surgery. PURPOSE: To develop predictive models of this blinding condition using a longitudinal medical administrative claims database, which provides temporal sequence of perioperative ischemic optic neuropathy and potential risk factors. DESIGN: Nested case control study. PATIENT SAMPLE: Participants in Cliniformatics Data Mart medical claims database (2007-2017) with hospitalization involving lumbar or thoracic spinal fusion surgery and no history of ION. OUTCOME MEASURES: Perioperative ION (or not) during hospitalization for lumbar or thoracic spinal fusion surgery. METHODS: Sixty-five ION cases and 106,871 controls were identified. Matched controls (n=211) were selected based on year of surgery and zip code. Chronic and perioperative variables were assigned based on medical claims codes. Least absolute shrinkage and selection (LASSO) penalized conditional logistic regression with 10-fold cross validation was used to select variables for the optimal predictive model from the subset of variables with p<.15 between cases and matched controls (unadjusted conditional logistic regression). Receiver operating characteristic (ROC) curves were generated for the strata-independent matched and full sample. RESULTS: The predictive model included age 57-65 years, male gender, diabetes with and without complications, chronic anemia, hypertension, heart failure, carotid stenosis, perioperative hemorrhage and perioperative organ damage. Area under ROC curve was 0.75 (95% confidence interval [CI]: 0.68, 0.82) for the matched sample and 0.72 (95% CI: 0.66, 0.78) for the full sample. CONCLUSIONS: This predictive model for ION in spine fusion considering chronic conditions and perioperative conditions is unique to date in its use of longitudinal medical claims data, inclusion of International Classification of Disease-10 codes and study of ophthalmic conditions as risk factors. Similar to other studies of this condition the multivariable model included age, male gender, perioperative organ damage and perioperative hemorrhage. Hypertension, chronic anemia and carotid artery stenosis were new predictive factors identified by this study.

Incidence of and factors associated with acute kidney injury after scoliosis surgery in pediatric patients.

PURPOSE: We sought to identify the national incidence of acute kidney injury (AKI) associated with pediatric posterior spinal fusion (PSF) surgery for scoliosis, and to determine factors that increase risk. METHODS: The 1998-2014 National Inpatient Sample (NIS), a large United States hospital discharge database, was queried for discharges aged 0-17 years with International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes for scoliosis undergoing PSF for the outcome of AKI. Discharges were divided into those with AKI and unaffected. We fit adjusted logistic regression models to yield point estimates, odds ratios, 95% confidence intervals, and p values for the weighted, national population sample with postulated risk factors. The fit of the multivariable regression model was tested using the Hosmer-Lemeshow test, and collinearity using the variance inflation factor. RESULTS: The NIS contained 103,270 weighted discharges meeting inclusion criteria. AKI incidence was 0.1%. Multivariable logistic regression model showed significantly increased odds ratios with thrombocytopenia, rhabdomyolysis, chronic kidney disease, abnormal coagulation, and male sex. AKI increased both hospital stay and cost by threefold compared to unaffected children. CONCLUSION: This study suggests that AKI after pediatric PSF is rare. It is associated with abnormal coagulation, chronic kidney disease, and rhabdomyolysis, but not with the number of vertebral levels fused. Female sex appears to be protective. The retrospective nature of study and reliance on ICD-9-CM codes may under-represent the incidence of AKI in pediatric PSF patients.

Systems-Based Training in Graduate Medical Education for Service Learning in the State Legislature in the United States: Pilot Study.

BACKGROUND: There is a dearth of advocacy training in graduate medical education in the United States. To address this void, the Legislative Education and Advocacy Development (LEAD) course was developed as an interprofessional experience, partnering a cohort of pediatrics residents, fourth-year medical students, and public health students to be trained in evidence-informed health policy making. OBJECTIVE: The objective of our study was to evaluate the usefulness and acceptability of a service-based legislative advocacy course. METHODS: We conducted a pilot study using a single-arm pre-post study design with 10 participants in the LEAD course. The course's didactic portion taught learners how to define policy problems, research the background of the situation, brainstorm solutions, determine evaluation criteria, develop communication strategies, and formulate policy recommendations for state legislators. Learners worked in teams to create and present policy briefs addressing issues submitted by participating Illinois State legislators. We compared knowledge and attitudes of learners from pre- and postcourse surveys. We obtained qualitative feedback from legislators and pediatric residency directors. RESULTS: Self-reported understanding of the health care system increased (mean score from 4 to 3.3, P=.01), with answers scored from 1=highly agree to 5=completely disagree. Mean knowledge-based scores improved (6.8/15 to 12.0/15 correct). Pediatric residency program directors and state legislators provided positive feedback about the LEAD course. CONCLUSIONS: Promising results were demonstrated for the LEAD approach to incorporate advocacy training into graduate medical education.