scholarly journals A Prediction Modeling Based on the Hospital for Special Surgery (HSS) Knee Score for Poor Postoperative Functional Prognosis of Elderly Patients with Patellar Fractures

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Chenting Ying ◽  
Chenyang Guo ◽  
Zhenlin Wang ◽  
Yiming Chen ◽  
Jiahui Sun ◽  
...  

Background. The main aim of this study was to develop a nomogram prediction model for poor functional prognosis after patellar fracture surgery in the elderly based on the hospital for special surgery (HSS) knee score. Methods. A retrospective analysis of 168 elderly patients with patellar fractures was performed to collect demographic data, knee imaging, and functional prognosis preoperatively and during the 6-month postoperative follow-up period. Good functional prognosis of knee joint was defined as the percentage of HSS knee scores on the injured side relative to the uninjured side ≥ 80 % at six-month postoperative review. Multifactorial linear regression analysis and logistic regression analysis were then used to identify risk factors of functional prognosis and develop the nomogram prediction model. Furthermore, the validity and accuracy of the prediction model were evaluated using C-index, area under the curve (AUC), and decision curve analyses. Results. The final screening from the 12 potential risk factors yielded three high-risk factors which were included in the nomogram prediction model: advanced age (OR 0.28 (95% CI 0.11-0.67), P = 0.005 ), sarcopenia (OR 0.11 (95% CI 0.05-0.26), P < 0.001 ), and low albumin level (OR 1.14 (95% CI 1.02-1.29), P = 0.025 ). The model had a good predictive ability with an AUC of 0.857 (95% CI (0.783-0.929)) for the training group and a C-index of 0.836 for the overall sample. In addition, the decision analysis curve indicated that the model had good clinical applicability. Conclusion. Our predictive model is effective in predicting the risk of poor functional prognosis after patellar fracture surgery in the elderly by assessing high-risk factors such as advanced age, sarcopenia, and serum albumin levels. This prediction model can help clinicians to make individualized risk prediction, early identification of patients at high risk for poor functional outcome, and appropriate interventions.

2020 ◽  
Author(s):  
Yuhan Gao ◽  
Shichong Jia ◽  
Chao Huang ◽  
Zhaowei Meng ◽  
Mei Yu ◽  
...  

Abstract Objectives The present study aimed to develop a random forest (RF) based prediction model for hyperuricemia (HUA) and estimate associated risk factors. Methods This cross-sectional study recruited 91,690 participants (52,607 males, 39,083 females). The prediction models were derived from training sets using RF learning machine. Performances of the prediction model were evaluated in validation datasets. Significant indicators were produced after comparing between true positive set and true negative set. Odds ratio was calculated by binary logistic regression models. Results The area under the receiver-operating curve was 0.732 in males and 0.837 in females in the RF prediction models. The sensitivity, specificity and negative predictive value of the models were 0.686, 0.656 and 0.882 in males, 0.786, 0.738 and 0.978 in females, respectively. According to the feature value of each index in RF, a total of 10 explanatory variables were selected for each gender. Triglyceride, creatinine, body mass index, waist circumference, alanine transaminase, age, weight and total cholesterol were high-risk factors for HUA in both genders. Conclusion RF demonstrated good stability and strong predictive power in predicting HUA in Chinese population. People with high risk factors should be encouraged to actively control the above factors to reduce the probability of developing HUA.


2012 ◽  
Vol 3 (4) ◽  
pp. 150-156 ◽  
Author(s):  
Cagatay Ulucay ◽  
Zehra Eren ◽  
Elif Cigdem Kaspar ◽  
Turhan Ozler ◽  
Korcan Yuksel ◽  
...  

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