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The article presents a case study of a 71-year-old woman who reported with a history of slowly worsening leg and buttock pain while walking. The patient manifested clinical signs and symptoms of lumbar spinal stenosis (LSS). It notes the assessment and differential diagnosis of LSS and discusses the algorithmic method to the treatment of the patient suffering from the disease.

Background: Intraoperative vascular injury (VI) may be an unavoidable complication of anterior lumbar spine surgery; however, vascular injury has implications for quality and safety reporting as this intraoperative complication may result in serious bleeding, thrombosis, and postoperative stricture.Purpose: The purpose of this study was to (1) develop machine learning algorithms for preoperative prediction of VI and (2) develop natural language processing (NLP) algorithms for automated surveillance of intraoperative VI from free-text operative notes.Patient Sample: Adult patients, 18 years or age or older, undergoing anterior lumbar spine surgery at two academic and three community medical centers were included in this analysis.Outcome Measures: The primary outcome was unintended VI during anterior lumbar spine surgery.Methods: Manual review of free-text operative notes was used to identify patients who had unintended VI. The available population was split into training and testing cohorts. Five machine learning algorithms were developed for preoperative prediction of VI. An NLP algorithm was trained for automated detection of intraoperative VI from free-text operative notes. Performance of the NLP algorithm was compared to current procedural terminology and international classification of diseases codes.Results: In all, 1035 patients underwent anterior lumbar spine surgery and the rate of intraoperative VI was 7.2% (n=75). Variables used for preoperative prediction of VI were age, male sex, body mass index, diabetes, L4-L5 exposure, and surgery for infection (discitis, osteomyelitis). The best performing machine learning algorithm achieved c-statistic of 0.73 for preoperative prediction of VI (https://sorg-apps.shinyapps.io/lumbar_vascular_injury/). For automated detection of intraoperative VI from free-text notes, the NLP algorithm achieved c-statistic of 0.92. The NLP algorithm identified 18 of the 21 patients (sensitivity 0.86) who had a VI whereas current procedural terminologyand international classification of diseases codes identified 6 of the 21 (sensitivity 0.29) patients. At this threshold, the NLP algorithm had a specificity of 0.93, negative predictive value of 0.99, positive predictive value of 0.51, and F1-score of 0.64.Conclusion: Relying on administrative procedural and diagnosis codes may underestimate the rate of unintended intraoperative VI in anterior lumbar spine surgery. External and prospective validation of the algorithms presented here may improve quality and safety reporting. [ABSTRACT FROM AUTHOR]

Background: The ability to preoperatively predict which patients will achieve a minimal clinically important difference (MCID) after lumbar spine decompression surgery can help determine the appropriateness and timing of surgery. Patient-Reported Outcome Measurement Information System (PROMIS) scores are an increasingly popular outcome instrument.Purpose: The purpose of this study was to develop algorithms predictive of achieving MCID after primary lumbar decompression surgery.Patient Sample: This was a retrospective study at two academic medical centers and three community medical centers including adult patients 18 years or older undergoing one or two level posterior decompression for lumbar disc herniation or lumbar spinal stenosis between January 1, 2016 and April 1, 2019.Outcome Measures: The primary outcome, MCID, was defined using distribution-based methods as one half the standard deviation of postoperative patient-reported outcomes (PROMIS physical function, pain interference, pain intensity).Methods: Five machine learning algorithms were developed to predict MCID on these surveys and assessed by discrimination, calibration, Brier score, and decision curve analysis. The final model was incorporated into an open access digital application.Results: Overall, 906 patients completed at least one PROMs survey in the 90 days before surgery and at least one PROMs survey in the year after surgery. Attainment of MCID during the study period by PROMIS instrument was 74.3% for physical function, 75.8% for pain interference, and 79.2% for pain intensity. Factors identified for preoperative prediction of MCID attainment on these outcomes included preoperative PROs, percent unemployment in neighborhood of residence, comorbidities, body mass index, private insurance, preoperative opioid use, surgery for disc herniation, and federal poverty level in neighborhood of residence. The discrimination (c-statistic) of the final algorithms for these outcomes was 0.79 for physical function, 0.74 for pain interference, and 0.69 for pain intensity with good calibration. The open access digital application for these algorithms can be found here: https://sorg-apps.shinyapps.io/promis_pld_mcid/ CONCLUSION: Lower preoperative PROMIS scores, fewer comorbidities, and certain sociodemographic factors increase the likelihood of achieving MCID for PROMIS after lumbar spine decompression. [ABSTRACT FROM AUTHOR]

Background Context: Patient-Reported Outcome Measurement Information System (PROMIS) scores are increasingly utilized in clinical care. However, it is unclear if PROMIS can discriminate surgeon performance on an individual level.Purpose: The purpose of this study was to examine surgeon-level variance in rates of achieving minimal clinically important difference (MCID) after lumbar decompression.Patient Sample: This is a prospective, observational cohort study performed across a healthcare enterprise (two academic medical centers and three community centers). Patients 18 years or older undergoing one- to two-level primary decompression for lumbar disc herniation (LDH) or lumbar spinal stenosis (LSS) were included.Outcome Measures: The primary outcome was achievement of MCID, using a distribution-based method, on paired PROMIS physical function scores.Methods: Descriptive statistics were generated to examine the baseline characteristics of the study cohort. Bivariate analyses were used to examine the impact of surgeon-level variance on rates of MCID. Multivariable analyses were used to examine the risk-adjusted impact of surgeon-level variance on rates of MCID.Results: Overall, 636 patients treated by nine surgeons were included. The median patient age was 58 [interquartile range (IQR): 46-70] and 62.3% (n=396) were female. Among all patients, 56.9% (n=362) underwent surgery for LDH. The overall rate of achieving MCID was 75.8% (n=482). Of the surgeons, the median years in practice were 12 (range 4-31) and 55.6% (n=5) were in academic practice settings. On bivariate analysis, patients treated by one of the surgeons had lower rates of achieving MICD (odds ratio=0.37, 95% confidence interval: 0.15-0.91, p=.03). However, on multivariable analysis adjusting for operative indication (LDH vs. LSS), body mass index, number of comorbidities, percent unemployment in patient zip code, and preoperative PROMIS physical function scores, all surgeons were equally likely to obtain MCID.Conclusions: In this cohort, variance in PROMIS scores after primary lumbar decompression is influenced by patient-related factors and not by individual surgeon. Adequate risk adjustment is needed if ascertaining clinical improvement on an individual surgeon basis.Level Of Evidence: 2. [ABSTRACT FROM AUTHOR]

Background: Surgical site infections are a major driver of morbidity and increased costs in the postoperative period after spine surgery. Current tools for surveillance of these adverse events rely on prospective clinical tracking, manual retrospective chart review, or administrative procedural and diagnosis codes.Purpose: The purpose of this study was to develop natural language processing (NLP) algorithms for automated reporting of postoperative wound infection requiring reoperation after lumbar discectomy.Patient Sample: Adult patients undergoing discectomy at two academic and three community medical centers between January 1, 2000 and July 31, 2019 for lumbar disc herniation.Outcome Measures: Reoperation for wound infection within 90 days after surgery METHODS: Free-text notes of patients who underwent surgery from January 1, 2000 to December 31, 2015 were used for algorithm training. Free-text notes of patients who underwent surgery after January 1, 2016 were used for algorithm testing. Manual chart review was used to label which patients had reoperation for wound infection. An extreme gradient-boosting NLP algorithm was developed to detect reoperation for postoperative wound infection.Results: Overall, 5,860 patients were included in this study and 62 (1.1%) had a reoperation for wound infection. In patients who underwent surgery after January 1, 2016 (n=1,377), the NLP algorithm detected 15 of the 16 patients (sensitivity=0.94) who had reoperation for infection. In comparison, current procedural terminology and international classification of disease codes detected 12 of these 16 patients (sensitivity=0.75). At a threshold of 0.05, the NLP algorithm had positive predictive value of 0.83 and F1-score of 0.88.Conclusion: Temporal validation of the algorithm developed in this study demonstrates a proof-of-concept application of NLP for automated reporting of adverse events after spine surgery. Adapting this methodology for other procedures and outcomes in spine and orthopedics has the potential to dramatically improve and automatize quality and safety reporting. [ABSTRACT FROM AUTHOR]