AIM To establish minimum clinically important difference(MCID) for measurements in an orthopaedic patient population with joint disorders.METHODS Adult patients aged 18 years and older seeking care for joint condition...AIM To establish minimum clinically important difference(MCID) for measurements in an orthopaedic patient population with joint disorders.METHODS Adult patients aged 18 years and older seeking care for joint conditions at an orthopaedic clinic took the Patient-Reported Outcomes Measurement Information System Physical Function(PROMIS~? PF) computerized adaptive test(CAT), hip disability and osteoarthritis outcome score for joint reconstruction(HOOS JR), and the knee injury and osteoarthritis outcome score for joint reconstruction(KOOS JR) from February 2014 to April 2017. MCIDs were calculated using anchorbased and distribution-based methods. Patient reports of meaningful change in function since their first clinic encounter were used as an anchor.RESULTS There were 2226 patients who participated with a mean age of 61.16(SD = 12.84) years, 41.6% male, and 89.7% Caucasian. Mean change ranged from 7.29 to 8.41 for the PROMIS~? PF CAT, from 14.81 to 19.68 for the HOOS JR, and from 14.51 to 18.85 for the KOOS JR. ROC cut-offs ranged from 1.97-8.18 for the PF CAT, 6.33-43.36 for the HOOS JR, and 2.21-8.16 for the KOOS JR. Distribution-based methods estimated MCID values ranging from 2.45 to 21.55 for the PROMIS~? PF CAT; from 3.90 to 43.61 for the HOOS JR, and from 3.98 to 40.67 for the KOOS JR. The median MCID value in the range was similar to the mean change score for each measure and was 7.9 for the PF CAT, 18.0 for the HOOS JR, and 15.1 for the KOOS JR.CONCLUSION This is the first comprehensive study providing a wide range of MCIDs for the PROMIS? PF, HOOS JR, and KOOS JR in orthopaedic patients with joint ailments.展开更多
BACKGROUND Oral cancer is the sixth most prevalent cancer worldwide.Public knowledge in oral cancer risk factors and survival is limited.AIM To come up with machine learning(ML)algorithms to predict the length of surv...BACKGROUND Oral cancer is the sixth most prevalent cancer worldwide.Public knowledge in oral cancer risk factors and survival is limited.AIM To come up with machine learning(ML)algorithms to predict the length of survival for individuals diagnosed with oral cancer,and to explore the most important factors that were responsible for shortening or lengthening oral cancer survival.METHODS We used the Surveillance,Epidemiology,and End Results database from the years 1975 to 2016 that consisted of a total of 257880 cases and 94 variables.Four ML techniques in the area of artificial intelligence were applied for model training and validation.Model accuracy was evaluated using mean absolute error(MAE),mean squared error(MSE),root mean squared error(RMSE),R2 and adjusted R2.RESULTS The most important factors predictive of oral cancer survival time were age at diagnosis,primary cancer site,tumor size and year of diagnosis.Year of diagnosis referred to the year when the tumor was first diagnosed,implying that individuals with tumors that were diagnosed in the modern era tend to have longer survival than those diagnosed in the past.The extreme gradient boosting ML algorithms showed the best performance,with the MAE equaled to 13.55,MSE 486.55 and RMSE 22.06.CONCLUSION Using artificial intelligence,we developed a tool that can be used for oral cancer survival prediction and for medical-decision making.The finding relating to the year of diagnosis represented an important new discovery in the literature.The results of this study have implications for cancer prevention and education for the public.展开更多
Objective: The purpose of this study is to investigate whether the LE CAT, PROMIS PF CAT, Depression CAT, or Pain CAT can be used as a proxy for the EQ-5D-5L. Background: Patient-reported out-come measures have become...Objective: The purpose of this study is to investigate whether the LE CAT, PROMIS PF CAT, Depression CAT, or Pain CAT can be used as a proxy for the EQ-5D-5L. Background: Patient-reported out-come measures have become vital tools for physicians to understand the effectiveness and value of treatment and care. Methods: This study was conducted in 2012 with 116 patients that took the EQ-5D-5L and a number of patient-reported outcome instruments in a university orthopaedic clinic. Regression analyses were conducted to predict EQ-5D-5L index scores from the LE CAT, PROMIS PF CAT, Depression CAT, and Pain CAT. Results: All predictors, separately or combined, significantly predicted the EQ-5D-5L index scores (p < 0.0001). The LE CAT was the best predictor;it alone accounted for 37% of the variability in the EQ-5D-5L. When combining patient-reported outcome measures, the best predicting model was the one consisting of the LE CAT, Depression CAT and Pain CAT;they explained for 43.9% of the variance in EQ-5D-5L. Conclusions: The findings provide encouraging news that the LE CAT, PF CAT, Depression CAT and Pain CAT can be used alone or in combination as a proxy for the EQ-5D-5L. Researchers have the options of using these patient-reported outcome measures for economic evaluations and medical intervention studies.展开更多
基金National Institute of Arthritis and Musculoskeletal and Skin Diseases of the National Institutes of Health,No.U01AR067138.
文摘AIM To establish minimum clinically important difference(MCID) for measurements in an orthopaedic patient population with joint disorders.METHODS Adult patients aged 18 years and older seeking care for joint conditions at an orthopaedic clinic took the Patient-Reported Outcomes Measurement Information System Physical Function(PROMIS~? PF) computerized adaptive test(CAT), hip disability and osteoarthritis outcome score for joint reconstruction(HOOS JR), and the knee injury and osteoarthritis outcome score for joint reconstruction(KOOS JR) from February 2014 to April 2017. MCIDs were calculated using anchorbased and distribution-based methods. Patient reports of meaningful change in function since their first clinic encounter were used as an anchor.RESULTS There were 2226 patients who participated with a mean age of 61.16(SD = 12.84) years, 41.6% male, and 89.7% Caucasian. Mean change ranged from 7.29 to 8.41 for the PROMIS~? PF CAT, from 14.81 to 19.68 for the HOOS JR, and from 14.51 to 18.85 for the KOOS JR. ROC cut-offs ranged from 1.97-8.18 for the PF CAT, 6.33-43.36 for the HOOS JR, and 2.21-8.16 for the KOOS JR. Distribution-based methods estimated MCID values ranging from 2.45 to 21.55 for the PROMIS~? PF CAT; from 3.90 to 43.61 for the HOOS JR, and from 3.98 to 40.67 for the KOOS JR. The median MCID value in the range was similar to the mean change score for each measure and was 7.9 for the PF CAT, 18.0 for the HOOS JR, and 15.1 for the KOOS JR.CONCLUSION This is the first comprehensive study providing a wide range of MCIDs for the PROMIS? PF, HOOS JR, and KOOS JR in orthopaedic patients with joint ailments.
基金The authors sincerely thank the Clinical Outcomes Research and Education at Collegeof Dental Medicine, Roseman University of Health Sciences for supporting this study.
文摘BACKGROUND Oral cancer is the sixth most prevalent cancer worldwide.Public knowledge in oral cancer risk factors and survival is limited.AIM To come up with machine learning(ML)algorithms to predict the length of survival for individuals diagnosed with oral cancer,and to explore the most important factors that were responsible for shortening or lengthening oral cancer survival.METHODS We used the Surveillance,Epidemiology,and End Results database from the years 1975 to 2016 that consisted of a total of 257880 cases and 94 variables.Four ML techniques in the area of artificial intelligence were applied for model training and validation.Model accuracy was evaluated using mean absolute error(MAE),mean squared error(MSE),root mean squared error(RMSE),R2 and adjusted R2.RESULTS The most important factors predictive of oral cancer survival time were age at diagnosis,primary cancer site,tumor size and year of diagnosis.Year of diagnosis referred to the year when the tumor was first diagnosed,implying that individuals with tumors that were diagnosed in the modern era tend to have longer survival than those diagnosed in the past.The extreme gradient boosting ML algorithms showed the best performance,with the MAE equaled to 13.55,MSE 486.55 and RMSE 22.06.CONCLUSION Using artificial intelligence,we developed a tool that can be used for oral cancer survival prediction and for medical-decision making.The finding relating to the year of diagnosis represented an important new discovery in the literature.The results of this study have implications for cancer prevention and education for the public.
文摘Objective: The purpose of this study is to investigate whether the LE CAT, PROMIS PF CAT, Depression CAT, or Pain CAT can be used as a proxy for the EQ-5D-5L. Background: Patient-reported out-come measures have become vital tools for physicians to understand the effectiveness and value of treatment and care. Methods: This study was conducted in 2012 with 116 patients that took the EQ-5D-5L and a number of patient-reported outcome instruments in a university orthopaedic clinic. Regression analyses were conducted to predict EQ-5D-5L index scores from the LE CAT, PROMIS PF CAT, Depression CAT, and Pain CAT. Results: All predictors, separately or combined, significantly predicted the EQ-5D-5L index scores (p < 0.0001). The LE CAT was the best predictor;it alone accounted for 37% of the variability in the EQ-5D-5L. When combining patient-reported outcome measures, the best predicting model was the one consisting of the LE CAT, Depression CAT and Pain CAT;they explained for 43.9% of the variance in EQ-5D-5L. Conclusions: The findings provide encouraging news that the LE CAT, PF CAT, Depression CAT and Pain CAT can be used alone or in combination as a proxy for the EQ-5D-5L. Researchers have the options of using these patient-reported outcome measures for economic evaluations and medical intervention studies.
