Limitations of Using Population-Based Databases to Assess Trends in Spinal Stereotactic Radiosurgery

McClelland, Shearwood; Jalai, Cyrus M.; Ryu, Samuel; Passias, Peter G.

doi:10.1016/j.spinee.2016.07.406

Cited by 4 publications

(3 citation statements)

References 7 publications

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“…The NIS, although advantageous due to its quantity of data, was created to assess national trends and correlation and not to be used to potentially direct care at a statewide level. The database randomly samples 20% of hospitals nationwide, thus potentially leading to significant unintended bias as to where information is collected [21,23]. Furthermore, there are no weights or classifiers applied to each state to account for the difference in the number of metrics collected [22].…”

Section: Discussionmentioning

confidence: 99%

Comparable performance of machine learning algorithms in predicting readmission and complications following total joint arthroplasty with external validation

Shaikh,

Botros,

Ramirez

et al. 2023

Arthroplasty

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Background The purpose of the study was to use Machine Learning (ML) to construct a risk calculator for patients who undergo Total Joint Arthroplasty (TJA) on the basis of New York State Statewide Planning and Research Cooperative System (SPARCS) data and externally validate the calculator on a single TJA center. Methods Seven ML algorithms, i.e., logistic regression, adaptive boosting, gradient boosting (Xg Boost), random forest (RF) classifier, support vector machine, and single and a five-layered neural network were trained on the derivation cohort. Models were trained on 68% of data, validated on 15%, tested on 15%, and externally validated on 2% of the data from a single arthroplasty center. Results Validation of the models showed that the RF classifier performed best in terms of 30-d mortality AUROC (Area Under the Receiver Operating Characteristic) 0.78, 30-d readmission (AUROC 0.61) and 90-d composite complications (AUROC 0.73) amongst the test set. Additionally, Xg Boost was found to be the best predicting model for 90-d readmission and 90-d composite complications (AUC 0.73). External validation demonstrated that models achieved similar AUROCs to the test set although variation occurred in top model performance for 90-d composite complications and readmissions between our test and external validation set. Conclusion This was the first study to investigate the use of ML to create a predictive risk calculator from state-wide data and then externally validate it with data from a single arthroplasty center. Discrimination between best performing ML models and between the test set and the external validation set are comparable. Level of Evidence III.

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Section: Discussionmentioning

confidence: 99%

Comparable performance of machine learning algorithms in predicting readmission and complications following total joint arthroplasty with external validation

Shaikh,

Botros,

Ramirez

et al. 2023

Arthroplasty

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“…In this cross-sectional study of hospitalization records from the 2005–2014 AHRQ HCUP NIS database, we applied TMLE and Super Learner algorithms to compare distinct types of radiation therapies (SRS and/or non-SRS) on length of stay and discharge destination among US inpatients diagnosed with brain metastases. To date, SRS utilization and its outcomes have rarely been examined using the NIS—a nationally representative sample of hospitalized US patients—and most previously published studies were not focused on the treatment of brain metastases 9 , 16 – 20 . Study results suggested disparities in treatment selection according to patient and hospital characteristics, which did not fully explain the observed treatment outcome differences.…”

Section: Discussionmentioning

confidence: 99%

Hospitalization outcomes among brain metastasis patients receiving radiation therapy with or without stereotactic radiosurgery from the 2005–2014 Nationwide Inpatient Sample

Beydoun

Huang

et al. 2021

Sci Rep

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The purpose of this study was to compare hospitalization outcomes among US inpatients with brain metastases who received stereotactic radiosurgery (SRS) and/or non-SRS radiation therapies without neurosurgical intervention. A cross-sectional study was conducted whereby existing data on 35,199 hospitalization records (non-SRS alone: 32,981; SRS alone: 1035; SRS + non-SRS: 1183) from 2005 to 2014 Nationwide Inpatient Sample were analyzed. Targeted maximum likelihood estimation and Super Learner algorithms were applied to estimate average treatment effects (ATE), marginal odds ratios (MOR) and causal risk ratio (CRR) for three distinct types of radiation therapy in relation to hospitalization outcomes, including length of stay (‘ ≥ 7 days’ vs. ‘ < 7 days’) and discharge destination (‘non-routine’ vs. ‘routine’), controlling for patient and hospital characteristics. Recipients of SRS alone (ATE = − 0.071, CRR = 0.88, MOR = 0.75) or SRS + non-SRS (ATE = − 0.17, CRR = 0.70, MOR = 0.50) had shorter hospitalizations as compared to recipients of non-SRS alone. Recipients of SRS alone (ATE = − 0.13, CRR = 0.78, MOR = 0.59) or SRS + non-SRS (ATE = − 0.17, CRR = 0.72, MOR = 0.51) had reduced risks of non-routine discharge as compared to recipients of non-SRS alone. Similar analyses suggested recipients of SRS alone had shorter hospitalizations and similar risk of non-routine discharge when compared to recipients of SRS + non-SRS radiation therapies. SRS alone or in combination with non-SRS therapies may reduce the risks of prolonged hospitalization and non-routine discharge among hospitalized US patients with brain metastases who underwent radiation therapy without neurosurgical intervention.

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“…Administrative coding accuracy may be compromised by clerical error, omission due to perceived nonimportance, and lack of detail in specific codes. 37,38 The sample is large but including only ESPCN patients limits the generalizability of these results. Future studies may explore replicating this process in other settings.…”

Section: Limitationsmentioning

confidence: 99%

Transitions of care for hospital discharges in a primary care network

Schaub

Shpilkerman

Roland

et al. 2022

Healthc Manage Forum

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Transitions to and from primary care are a time of concern, especially for patients with chronic conditions and complex care needs. The Edmonton Southside Primary Care Network (ESPCN) developed a process for nurses to ensure timely post-discharge follow-up calls and physician appointments after hospitalization, assessing readmission risk with LACE and Clinical Frailty scores. Over 84% of eligible high-risk discharges received follow-up within 14 days. Of 7,400 index discharges, 1,464 had an emergency department revisit and 725 patients were readmitted within 30 days. Overall, ESPCN rates of readmission (9.8%) and rates of Family Practice Sensitive Conditions (FPSC) (5.7%) were significantly lower than national and provincial rates. FPSC rates for high-risk patients were significantly lower than low- or medium-risk groups. Consistent processes that support nursing involvement enable primary care teams to focus on those with highest risk for adverse outcomes and support patients to access the most appropriate place for the care they need.

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Limitations of Using Population-Based Databases to Assess Trends in Spinal Stereotactic Radiosurgery

Cited by 4 publications

References 7 publications

Comparable performance of machine learning algorithms in predicting readmission and complications following total joint arthroplasty with external validation

Comparable performance of machine learning algorithms in predicting readmission and complications following total joint arthroplasty with external validation

Hospitalization outcomes among brain metastasis patients receiving radiation therapy with or without stereotactic radiosurgery from the 2005–2014 Nationwide Inpatient Sample

Transitions of care for hospital discharges in a primary care network

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