Predictive modeling of long-term opioid and benzodiazepine use after intradural tumor resection

Jin, Michael C.; Ho, Allen L.; Feng, Austin Y.; Zhang, Yi; Staartjes, Victor E.; Stienen, Martin N.; Han, Summer S.; Veeravagu, Anand; Ratliff, John K.; Desai, Atman

doi:10.1016/j.spinee.2020.10.010

Cited by 10 publications

(6 citation statements)

References 40 publications

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“…Data used in this study were derived from the IBM Watson Health MarketScan Database, a nationally sourced administrative claims database spanning 2007 to 2016 and encompassing more than 75 million enrollees covered by eligible health care plans that has been used to explore diverse spinal pathologies. 22 , 23 , 24 This study was approved by the Stanford University School of Medicine institutional review board and was conducted in accordance with Strengthening the Reporting of Observational Studies in Epidemiology ( STROBE ) reporting guidelines. Informed consent was not needed because the data were anonymous and publicly available, in accordance with 45 CFR §46.…”

Section: Methodsmentioning

confidence: 99%

“…Data used in this study were derived from the IBM Watson Health MarketScan Database, a nationally sourced administrative claims database spanning 2007 to 2016 and encompassing more than 75 million enrollees covered by eligible health care plans that has been used to explore diverse spinal pathologies. [22][23][24] This study was approved by the Stanford University School of Medicine institutional excluded. Those with a documented opioid prescription during the year before the index diagnosis date were excluded to differentiate acute neck pain from chronic pain syndromes.…”

Section: Cohort and Study Designmentioning

confidence: 99%

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Health Care Resource Utilization in Management of Opioid-Naive Patients With Newly Diagnosed Neck Pain

et al. 2022

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Key Points Question What are common health care utilization patterns for management of newly diagnosed acute neck pain and how are they associated with care delivery? Findings In this cross-sectional study of 679 030 adults conducted between 2008 and 2015, early imaging was common and often occurred before conservative treatment. However, early conservative therapy before imaging was associated with lower health care costs and reduced opioid use. Meaning These findings suggest a need for improved care standardization with prompt conservative therapy initiation to reduce treatment cost and increase effectiveness.

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Section: Methodsmentioning

confidence: 99%

“…Data used in this study were derived from the IBM Watson Health MarketScan Database, a nationally sourced administrative claims database spanning 2007 to 2016 and encompassing more than 75 million enrollees covered by eligible health care plans that has been used to explore diverse spinal pathologies. [22][23][24] This study was approved by the Stanford University School of Medicine institutional excluded. Those with a documented opioid prescription during the year before the index diagnosis date were excluded to differentiate acute neck pain from chronic pain syndromes.…”

Section: Cohort and Study Designmentioning

confidence: 99%

Health Care Resource Utilization in Management of Opioid-Naive Patients With Newly Diagnosed Neck Pain

et al. 2022

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“…Patient consent was not required as only deidentified data was used. Adult patients receiving spine surgery for resection of intradural tumors between 2007 and 2016 were identified in the IBM MarketScan Claims Database, which we have previously described [ 9 , 10 ]. Inclusion criteria required an inpatient procedure code indicating either laminectomy or corpectomy for intradural lesion removal concurrent with a diagnosis code indicating spinal neoplasm ( Supplementary Table 1 ).…”

Section: Methodsmentioning

confidence: 99%

Prediction of Discharge Status and Readmissions after Resection of Intradural Spinal Tumors

Jin

Feng

et al. 2022

Neurospine

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Objective: Intradural spinal tumors are uncommon and while associations between clinical characteristics and surgical outcomes have been explored, there remains a paucity of literature unifying diverse predictors into an integrated risk model. To predict postresection outcomes for patients with spinal tumors.Methods: IBM MarketScan Claims Database was queried for adult patients receiving surgery for intradural tumors between 2007 and 2016. Primary outcomes-of-interest were nonhome discharge and 90-day postdischarge readmissions. Secondary outcomes included hospitalization duration and postoperative complications. Risk modeling was developed using a regularized logistic regression framework (LASSO, least absolute shrinkage and selection operator) and validated in a withheld subset.Results: A total of 5,060 adult patients were included. Most surgeries utilized a posterior approach (n = 5,023, 99.3%) and tumors were most commonly found in the thoracic region (n = 1,941, 38.4%), followed by the lumbar (n = 1,781, 35.2%) and cervical (n = 1,294, 25.6%) regions. Compared to models using only tumor-specific or patient-specific features, our integrated models demonstrated better discrimination (area under the curve [AUC] [nonhome discharge] = 0.786; AUC [90-day readmissions] = 0.693) and accuracy (Brier score [nonhome discharge] = 0.155; Brier score [90-day readmissions] = 0.093). Compared to those predicted to be lowest risk, patients predicted to be highest-risk for nonhome discharge required continued care 16.3 times more frequently (64.5% vs. 3.9%). Similarly, patients predicted to be at highest risk for postdischarge readmissions were readmitted 7.3 times as often as those predicted to be at lowest risk (32.6% vs. 4.4%).Conclusion: Using a diverse set of clinical characteristics spanning tumor-, patient-, and hospitalization-derived data, we developed and validated risk models integrating diverse clinical data for predicting nonhome discharge and postdischarge readmissions.

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“…Most big data and ML approaches to opioid use have relied on population-level databases and EHR information to form the models. 3,[8][9][10][11][12][13][14][15][16][17][18] Although patient-reported information has become increasingly important in surgical care as a metric for quality ratings, reimbursement, and patient experience, 4,6,20,21 the current ML-based opioid models focused on surgical patients lacking PRD. Because PRD provide unique, patient-specific information not captured in EHR data, we integrated these data into our models to evaluate postsurgical opioid use behavior under the belief that these data would provide unique insights into preintervention mental and social health and other aspects of the patient's well-being that would ultimately affect the pain experience and associated pain management with opioids.…”

Section: Plastic and Reconstructive Surgery • August 2023mentioning

confidence: 99%

Patient-Reported Data Augment Prediction Models of Persistent Opioid Use after Elective Upper Extremity Surgery

Giladi

Shipp

Sanghavi

et al. 2023

Plastic &Amp; Reconstructive Surgery

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Background: Opioids play a role in pain management after surgery, but prolonged use contributes to developing opioid use disorder. Identifying patients at risk of prolonged use is critical for deploying interventions that reduce or avoid opioids; however, available predictive models do not incorporate patientreported data (PRD), and it remains unclear whether PRD can predict postoperative use behavior. The authors used a machine learning approach leveraging preoperative PRD and electronic health record data to predict persistent opioid use after upper extremity surgery. Methods: Included patients underwent upper extremity surgery, completed preoperative PRD questionnaires, and were prescribed opioids after surgery. The authors trained models using a 2018 cohort and tested in a 2019 cohort. Opioid use was determined by patient report and filled prescriptions up to 6 months after surgery. The authors assessed model performance using area under the receiver operating characteristic, sensitivity, specificity, and Brier score. Results: Among 1656 patients, 19% still used opioids at 6 weeks, 11% at 3 months, and 9% at 6 months. The XGBoost model trained on PRD plus electronic health record data achieved area under the receiver operating characteristic 0.73 at 6 months. Factors predictive of prolonged opioid use included income; education; tobacco, drug, or alcohol abuse; cancer; depression; and race. Protective factors included preoperative Patient-Reported Outcomes Measurement Information System Global Physical Health and Upper Extremity scores. Conclusions: This opioid use prediction model using preintervention data had good discriminative performance. PRD variables augmented electronic health record-based machine learning algorithms in predicting postsurgical use behaviors and were some of the strongest predictors. PRD should be used in future efforts to guide proper opioid stewardship.

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Predictive modeling of long-term opioid and benzodiazepine use after intradural tumor resection

Cited by 10 publications

References 40 publications

Health Care Resource Utilization in Management of Opioid-Naive Patients With Newly Diagnosed Neck Pain

Health Care Resource Utilization in Management of Opioid-Naive Patients With Newly Diagnosed Neck Pain

Prediction of Discharge Status and Readmissions after Resection of Intradural Spinal Tumors

Patient-Reported Data Augment Prediction Models of Persistent Opioid Use after Elective Upper Extremity Surgery

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