FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3157577198> ?p ?o ?g. }

• W3157577198 endingPage "E5" @default.
• W3157577198 startingPage "E5" @default.
• W3157577198 abstract "OBJECTIVE Frailty is recognized as an important consideration in patients with cancer who are undergoing therapies, including spine surgery. The definition of frailty in the context of spinal metastases is unclear, and few have studied such markers and their association with postoperative outcomes and survival. Using national databases, the metastatic spinal tumor frailty index (MSTFI) was developed as a tool to predict outcomes in this specific patient population and has not been tested with external data. The purpose of this study was to test the performance of the MSTFI with institutional data and determine whether machine learning methods could better identify measures of frailty as predictors of outcomes. METHODS Electronic health record data from 479 adult patients admitted to the Massachusetts General Hospital for metastatic spinal tumor surgery from 2010 to 2019 formed a validation cohort for the MSTFI to predict major complications, in-hospital mortality, and length of stay (LOS). The 9 parameters of the MSTFI were modeled in 3 machine learning algorithms (lasso regularization logistic regression, random forest, and gradient-boosted decision tree) to assess clinical outcome prediction and determine variable importance. Prediction performance of the models was measured by computing areas under the receiver operating characteristic curve (AUROCs), calibration, and confusion matrix metrics (positive predictive value, sensitivity, and specificity) and was subjected to internal bootstrap validation. RESULTS Of 479 patients (median age 64 years [IQR 55–71 years]; 58.7% male), 28.4% had complications after spine surgery. The in-hospital mortality rate was 1.9%, and the mean LOS was 7.8 days. The MSTFI demonstrated poor discrimination for predicting complications (AUROC 0.56, 95% CI 0.50–0.62) and in-hospital mortality (AUROC 0.69, 95% CI 0.54–0.85) in the validation cohort. For postoperative complications, machine learning approaches showed a greater advantage over the logistic regression model used to develop the MSTFI (AUROC 0.62, 95% CI 0.56–0.68 for random forest vs AUROC 0.56, 95% CI 0.50–0.62 for logistic regression). The random forest model had the highest positive predictive value (0.53, 95% CI 0.43–0.64) and the highest negative predictive value (0.77, 95% CI 0.72–0.81), with chronic lung disease, coagulopathy, anemia, and malnutrition identified as the most important predictors of postoperative complications. CONCLUSIONS This study highlights the challenges of defining and quantifying frailty in the metastatic spine tumor population. Further study is required to improve the determination of surgical frailty in this specific cohort." @default.
• W3157577198 created "2021-05-10" @default.
• W3157577198 creator A5004513411 @default.
• W3157577198 creator A5010878662 @default.
• W3157577198 creator A5015603174 @default.
• W3157577198 creator A5021118626 @default.
• W3157577198 creator A5033876943 @default.
• W3157577198 creator A5035149869 @default.
• W3157577198 creator A5044426279 @default.
• W3157577198 creator A5069488782 @default.
• W3157577198 creator A5087075714 @default.
• W3157577198 date "2021-05-01" @default.
• W3157577198 modified "2023-10-17" @default.
• W3157577198 title "Performance assessment of the metastatic spinal tumor frailty index using machine learning algorithms: limitations and future directions" @default.
• W3157577198 cites W1125372527 @default.
• W3157577198 cites W1501619562 @default.
• W3157577198 cites W1970031607 @default.
• W3157577198 cites W1970952154 @default.
• W3157577198 cites W2000363665 @default.
• W3157577198 cites W2000463634 @default.
• W3157577198 cites W2009202244 @default.
• W3157577198 cites W2034565972 @default.
• W3157577198 cites W2059876839 @default.
• W3157577198 cites W2077794023 @default.
• W3157577198 cites W2078271269 @default.
• W3157577198 cites W2092170177 @default.
• W3157577198 cites W2098485862 @default.
• W3157577198 cites W2104302044 @default.
• W3157577198 cites W2105092978 @default.
• W3157577198 cites W2107640783 @default.
• W3157577198 cites W2129700906 @default.
• W3157577198 cites W2129940374 @default.
• W3157577198 cites W2139520621 @default.
• W3157577198 cites W2164186760 @default.
• W3157577198 cites W2167600867 @default.
• W3157577198 cites W2281478651 @default.
• W3157577198 cites W2345968422 @default.
• W3157577198 cites W2411435368 @default.
• W3157577198 cites W2466867856 @default.
• W3157577198 cites W2469359254 @default.
• W3157577198 cites W2512537768 @default.
• W3157577198 cites W2520821811 @default.
• W3157577198 cites W2551696023 @default.
• W3157577198 cites W2735784458 @default.
• W3157577198 cites W2737994538 @default.
• W3157577198 cites W2765823291 @default.
• W3157577198 cites W2771483181 @default.
• W3157577198 cites W2779842586 @default.
• W3157577198 cites W2791984756 @default.
• W3157577198 cites W2802583260 @default.
• W3157577198 cites W2817503133 @default.
• W3157577198 cites W2884189910 @default.
• W3157577198 cites W2884772002 @default.
• W3157577198 cites W2897513125 @default.
• W3157577198 cites W2907340920 @default.
• W3157577198 cites W2918133041 @default.
• W3157577198 cites W2921236195 @default.
• W3157577198 cites W2944426028 @default.
• W3157577198 cites W2946147355 @default.
• W3157577198 cites W2946691371 @default.
• W3157577198 cites W2970490904 @default.
• W3157577198 cites W2988929899 @default.
• W3157577198 cites W2997621365 @default.
• W3157577198 cites W2999698967 @default.
• W3157577198 cites W3005103197 @default.
• W3157577198 cites W3008768638 @default.
• W3157577198 cites W3010041731 @default.
• W3157577198 cites W3022378533 @default.
• W3157577198 cites W3025739397 @default.
• W3157577198 cites W3033272482 @default.
• W3157577198 cites W3037562358 @default.
• W3157577198 cites W3080137061 @default.
• W3157577198 cites W3085798259 @default.
• W3157577198 cites W3088082328 @default.
• W3157577198 cites W3094670355 @default.
• W3157577198 cites W595868238 @default.
• W3157577198 doi "https://doi.org/10.3171/2021.2.focus201113" @default.
• W3157577198 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/33932935" @default.
• W3157577198 hasPublicationYear "2021" @default.
• W3157577198 type Work @default.
• W3157577198 sameAs 3157577198 @default.
• W3157577198 citedByCount "20" @default.
• W3157577198 countsByYear W31575771982021 @default.
• W3157577198 countsByYear W31575771982022 @default.
• W3157577198 countsByYear W31575771982023 @default.
• W3157577198 crossrefType "journal-article" @default.
• W3157577198 hasAuthorship W3157577198A5004513411 @default.
• W3157577198 hasAuthorship W3157577198A5010878662 @default.
• W3157577198 hasAuthorship W3157577198A5015603174 @default.
• W3157577198 hasAuthorship W3157577198A5021118626 @default.
• W3157577198 hasAuthorship W3157577198A5033876943 @default.
• W3157577198 hasAuthorship W3157577198A5035149869 @default.
• W3157577198 hasAuthorship W3157577198A5044426279 @default.
• W3157577198 hasAuthorship W3157577198A5069488782 @default.
• W3157577198 hasAuthorship W3157577198A5087075714 @default.
• W3157577198 hasBestOaLocation W31575771981 @default.
• W3157577198 hasConcept C11413529 @default.
• W3157577198 hasConcept C119857082 @default.

• next