Implementation of patient-reported outcome measures in appropriateness criteria of surgery for degenerative lumbar scoliosis

Impact of preoperative clinical state on 2-year clinical outcomes following degenerative lumbar scoliosis surgery

To assess the preoperative clinical state's impact on clinical outcomes after surgery for degenerative lumbar scoliosis (DLS) based on the minimal clinically important difference (MCID). Preoperative and follow-up (FU) scores in each Scoliosis Research Society-22 (SRS-22) domain were compared with age- and sex-matched normative references. At baseline, patients were classified by differences from normative values in four groups: Worst, Severe, Poor, and Moderate. At 2 years postoperative FU, patients were divided into four groups (Worst Severe Poor Asymptomatic) based on the difference in MCID between postoperative and normal values. The changes in MCID were considered as the criterion for surgical efficacy. In addition, we calculated the classification of preoperative and FU clinical symptom severity in each domain in same patient. The distinction among curve types was also performed based on the SRS-Schwab classification. A total of 123 patients were included. During FU, patients with more severe preoperative clinical symptoms were more likely to achieve clinical changes (>1 MCID, p < 0.05), but the rate of reaching "asymptomatic" was lower (p < 0.05). Kendall's tau-b correlation analysis found that preoperative clinical severity was correlated with clinical changes category in Activity (Tau-b = 0.252; p = 0.002), Pain (Tau-b = 0.230; p = 0.005), Appearance (Tau-b = 0.307; p < 0.001), and Mental (Tau-b = 0.199; p = 0.016), and it also was correlated with FU clinical severity in Activity (Tau-b = 0.173; p = 0.023), Pain (Tau-b = 0.280; p < 0.001), and Mental (Tau-b = 0.349; p < 0.001). There was a correlation between preoperative clinical severity and FU SRS-22 score outcomes. Patients with severe preoperative clinical symptoms can experience better treatment outcomes during FU, but it is also more difficult to recover to the normal reference.

Surgical appropriateness nudges: Developing behavioral science nudges to integrate appropriateness criteria into the decision making of spine surgeons

BACKGROUND

Substantial variation exists in surgeon decision making. In response, multiple specialty societies have established criteria for the appropriate use of spine surgery. Yet few strategies exist to facilitate routine use of appropriateness criteria by surgeons. Behavioral science nudges are increasingly used to enhance decision making by clinicians. We sought to design "surgical appropriateness nudges" to support routine use of appropriateness criteria for degenerative lumbar scoliosis and spondylolisthesis.

METHODS

The work reflected Stage I of the NIH Stage Model for Behavioral Intervention Development and involved an iterative, multi-method approach, emphasizing qualitative methods. Study sites included two large referral centers for spine surgery. We recruited spine surgeons from both sites for two rounds of focus groups. To produce preliminary nudge prototypes, we examined sources of variation in surgeon decision making (Focus Group 1) and synthesized existing knowledge of appropriateness criteria, behavioral science nudge frameworks, electronic tools, and the surgical workflow. We refined nudge prototypes via feedback from content experts, site leaders, and spine surgeons (Focus Group 2). Concurrently, we collected data on surgical practices and outcomes at study sites. We pilot tested the refined nudge prototypes among spine surgeons, and surveyed them about nudge applicability, acceptability, and feasibility (scale 1-5, 5 = strongly agree).

RESULTS

Fifteen surgeons participated in focus groups, giving substantive input and feedback on nudge design. Refined nudge prototypes included: individualized surgeon score cards (frameworks: descriptive social norms/peer comparison/feedback), online calculators embedded in the EHR (decision aid/mapping), a multispecialty case conference (injunctive norms/social influence), and a preoperative check (reminders/ salience of information/ accountable justification). Two nudges (score cards, preop checks) incorporated data on surgeon practices and outcomes. Six surgeons pilot tested the refined nudges, and five completed the survey (83%). The overall mean score was 4.0 (standard deviation [SD] 0.5), with scores of 3.9 (SD 0.5) for applicability, 4.1 (SD 0.5) for acceptability, and 4.0 (SD 0.5), for feasibility. Conferences had the highest scores 4.3 (SD 0.6) and calculators the lowest 3.9 (SD 0.4).

CONCLUSIONS

Behavioral science nudges might be a promising strategy for facilitating incorporation of appropriateness criteria into the surgical workflow of spine surgeons. Future stages in intervention development will test whether these surgical appropriateness nudges can be implemented in practice and influence surgical decision making.

The influence of preoperative Veterans RAND-12 physical composite score in patients undergoing anterior lumbar interbody fusion [Retrospective Review]

No study has evaluated the preoperative impact of Veterans RAND-12 Physical Composite Score (VR-12 PCS) on anterior lumbar interbody fusion (ALIF) patients. This study examines its influence on physical function, mental health, pain, and disability outcomes. Two cohorts of ALIF patients with preoperative VR-12 PCS scores were formed using a single-surgeon registry: VR-12 PCS < 30 and VR-12 PCS ≥ 30. Demographics, perioperative characteristics, and patient-reported outcome measures (PROMs) were collected. PROMs of VR-12 PCS/Mental Composite Score (MCS), Short Form-12 (SF-12) PCS/MCS, Patient-Reported Outcomes Measurement Information System-Physical Function (PROMIS-PF), Patient Health Questionnaire-9 (PHQ-9), Visual Analog Scale-Back/Leg Pain (VAS-BP/LP), and Oswestry Disability Index (ODI) were collected pre/postoperatively up to 2-years. Demographics, perioperative characteristics, and preoperative PROMs were compared. Intercohort postoperative 6-week/final PROMs and improvements were compared. Of 80 patients, there were 41 in the VR-12 PCS < 30 cohort. Besides VR-12 PCS, VR-12 PCS < 30 patients reported inferior preoperative VR-12 MCS/SF-12 PCS/PROMIS-PF/PHQ-9/ODI scores (p ≤ 0.003, all). At 6-weeks postoperatively, VR-12 PCS < 30 reported inferior VR-12 PCS/SF-12 PCS/PROMIS-PF/PHQ-9 (p ≤ 0.030, all). There was greater improvement up to 6-weeks postoperatively in VR-12 PCS < 30 for VR-12 PCS/MCS and SF-12 PCS (p ≤ 0.020, all). VR-12 PCS < 30 reported superior improvement by final follow-up in VR-12 PCS/SF-12 PCS/PHQ-9 (p ≤ 0.006, all). MCID achievement rates were higher in VR-12 PCS < 30 for PHQ-9 and ODI (p ≤ 0.013, both). VR-12 PCS < 30 patients reported inferior postoperative physical function, mental health, and disability, yet superior magnitude of improvement in physical function and mental health. Rates of clinically meaningful improvement for VR-12 PCS < 30 were greater in mental health and disability.

Patient Expectations and Satisfaction in Pediatric Orthopedics

PURPOSE OF REVIEW

The purpose of the current review is to analyze the current literature regarding the tools available to evaluate patient expectations and satisfaction. There have been an increasing number of tools that have been developed and validated for various orthopedic procedures. Despite the growing number of tools, there are a limited number of tools available for pediatric patients.

RECENT FINDINGS

Several tools have been developed in orthopedics to evaluate patient expectations. However, there are no tools that have been validated in the pediatric population. In addition, pediatric patient expectations should be collected in conjunction with parent/caregiver expectations. Although not specifically validated for pediatric patients, there are several tools available that may pertain to pediatric patients including the HSS ACL Expectations Survey, HSS Shoulder Expectations Survey, HSS Knee Surgery Expectations Survey, HSS Foot and Ankle Surgery Expectation Survey, Sunnybrook Surgery Expectations Survey, Musculoskeletal Outcomes Data Evaluation and Management System (MODEMS) Instruments, Quick DASH, and DASH. In terms of patient satisfaction, there are even fewer tools available. Several tools have been developed to evaluate patient satisfaction and five additional tools within orthopedics. Of these tools, there are two that have been validated for pediatric patients: The Swedish Parents Satisfaction Questionnaire and the Scoliosis Research Society-22. There are a growing number of tools to evaluate patient's expectations and satisfaction in the orthopedic literature. Given most of these tools pertain to adult patients, there is a need for further development of tools specifically validated for pediatric patients and their parents/caregivers. Through the measurement of expectations and satisfaction, medical professionals can hope to improve satisfaction and outcomes.

Artificial intelligence in spine surgery

The continuous progress of research and clinical trials has offered a wide variety of information concerning the spine and the treatment of the different spinal pathologies that may occur. Planning the best therapy for each patient could be a very difficult and challenging task as it often requires thorough processing of the patient's history and individual characteristics by the clinician. Clinicians and researchers also face problems when it comes to data availability due to patients' personal information protection policies. Artificial intelligence refers to the reproduction of human intelligence via special programs and computers that are trained in a way that simulates human cognitive functions. Artificial intelligence implementations to daily clinical practice such as surgical robots that facilitate spine surgery and reduce radiation dosage to medical staff, special algorithms that can predict the possible outcomes of conservative versus surgical treatment in patients with low back pain and disk herniations, and systems that create artificial populations with great resemblance and similar characteristics to real patients are considered to be a novel breakthrough in modern medicine. To enhance the body of the related literature and inform the readers on the clinical applications of artificial intelligence, we performed this review to discuss the contribution of artificial intelligence in spine surgery and pathology.

Artificial Intelligence Models Predict Operative Versus Nonoperative Management of Patients with Adult Spinal Deformity with 86% Accuracy

OBJECTIVE

Patients with ASD show complex and highly variable disease. The decision to manage patients operatively is largely subjective and varies based on surgeon training and experience. We sought to develop models capable of accurately discriminating between patients receiving operative versus nonoperative treatment based only on baseline radiographic and clinical data at enrollment.

METHODS

This study was a retrospective analysis of a multicenter consecutive cohort of patients with ASD. A total of 1503 patients were included, divided in a 70:30 split for training and testing. Patients receiving operative treatment were defined as those undergoing surgery up to 1 year after their baseline visit. Potential predictors included available demographics, past medical history, patient-reported outcome measures, and premeasured radiographic parameters from anteroposterior and lateral films. In total, 321 potential predictors were included. Random forest, elastic net regression, logistic regression, and support vector machines (SVMs) with radial and linear kernels were trained.

RESULTS

Of patients in the training and testing sets, 69.0% (n = 727) and 69.1% (n = 311), respectively, received operative management. On evaluation with the testing dataset, performance for SVM linear (area under the curve =0.910), elastic net (0.913), and SVM radial (0.914) models was excellent, and the logistic regression (0.896) and random forest (0.830) models performed very well for predicting operative management of patients with ASD. The SVM linear model showed 86% accuracy.

CONCLUSIONS

This study developed models showing excellent discrimination (area under the curve >0.9) between patients receiving operative versus nonoperative management, based solely on baseline study enrollment values. Future investigations may evaluate the implementation of such models for decision support in the clinical setting.