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PURPOSE: This systematic review and meta-analysis aimed to evaluate the safety of outpatient and inpatient Unicompartmental Knee Arthroplasty (UKA) based on the incidence of adverse events. METHOD: A systematic search of the literature was performed in October 2022 on PubMed, Web of Science, Cochrane library, and Embase. The Meta package for R was used to perform the meta-analysis. RESULT: Five studies with a total of 26,301 patients were included. 5813 patients (22.1%) were treated with outpatient UKA, and 20,488 patients (77.9%) were treated with inpatient UKA. There were no statistically significant differences in the incidence of total complications (RR = 1.36, 95% CI = 0.64-2.89, Z = 0.79, P = 0.43), readmission (RR = 1.02, 95% CI = 0.40-2.60, Z = 0.05, P = 0.96), and venous thrombosis (RR = 1.43, 95% CI = 0.96-2.11, Z = 1.78, P = 0.08). Incidence rates were lower in outpatient UKA regarding urinary tract infection (RR = 1.48, 95% CI = 1.07-2.04, Z = 2.40, P = 0.02), pulmonary embolus (RR = 7.48, 95% CI = 1.80-31.17, Z = 2.76, P < 0.01), and transfusion (RR = 2.77, 95% CI = 1.63-4.71, Z = 3.78, P < 0.01). CONCLUSION: In summary, outpatient UKA shows lower incidences of hospital-acquired complications such urinary tract infection, pulmonary embolus, and transfusion. It's worth noting that the incidences of total complications, readmission, and venous thrombosis in outpatient UKA were not higher than the incidences of inpatient UKA, suggestting that outpatient UKA can be considered a safe alternative to inpatient UKA.
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Artroplastia do Joelho , Complicações Pós-Operatórias , Humanos , Artroplastia do Joelho/métodos , Artroplastia do Joelho/efeitos adversos , Complicações Pós-Operatórias/epidemiologia , Procedimentos Cirúrgicos Ambulatórios/efeitos adversos , Procedimentos Cirúrgicos Ambulatórios/métodos , Segurança do Paciente , Incidência , Readmissão do Paciente/estatística & dados numéricosRESUMO
PURPOSE: We aim to present unsupervised machine learning-based analysis of clinical features, bone mineral density (BMD) features, and medical care costs of Rotator cuff tears (RCT). PATIENTS AND METHODS: Fifty-three patients with RCT were reviewed, the clinical features, BMD features, and medical care costs were collected and analyzed by descriptive statistics. Furtherly, unsupervised machine learning (UML) algorithm was used for dimensionality reduction and cluster analysis of the RCT data. RESULTS: There were 26 males and 27 females. The patients were divided into four subgroups using the UML algorithm. There were significant differences among four subgroups regarding trauma exposure, full-thickness supraspinatus tendon tears, infraspinatus tendon tear, subscapularis tendon tear, BMD distribution, medial row anchors, lateral row anchors, total medical care costs, and consumables costs. We observed the highest frequency of trauma exposure, infraspinatus tendon tear, subscapularis tendon tear, osteoporosis, the highest number of medial row anchors, lateral row anchors, total medical care costs, and consumables costs in subgroup II. CONCLUSION: The unsupervised machine learning-based analysis of RCT can provide clinically meaningful classification, which shows good interpretability and contribute to a better understanding of RCT. The significance of the results is limited due to the small number of samples, a larger follow-up study is needed to confirm the encouraging results.
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The purpose of the study was to investigate the relationship between postoperative bone marrow lesions (BMLs) and pain severity in patients undergoing open wedge high tibial osteotomy (OWHTO). We reviewed the patients undergoing OWHTO between April 2018 and April 2020. The demographic and clinical data of patients were collected. Clinically, VAS and Knee injury and Osteoarthritis Outcome Score (KOOS) were used to assess pain level and functional outcomes of patients. The MRI Osteoarthritis Knee Score (MOAKS) was used to assess the total BMLs size in medial tibiofemoral (MTF), lateral tibiofemoral (LTF), and patellofemoral (PF) joints. 98 patients were enrolled in the study, including 57 male and 41 female patients. The VAS scores improved significantly from 6.1 ± 0.8 to 1.5 ± 0.9 (p < 0.001), and all subscales of KOOS improved significantly after surgery (p < 0.001). There were no significant differences between the pre- and postoperative total BML size of PF and LTF joints (p > 0.05). We observed significant improvements in the total BML size of MTF joint (p < 0.001). The VAS scores and KOOS pain scores improved better in patients without postoperative MTF joint BMLs (p < 0.001). Postoperative MTF joint BMLs were correlated with postoperative VAS (p < 0.001) and KOOS pain (p < 0.001). Our study demonstrates that MTF joint BMLs improved significantly after OWTHO. We confirmed that the presence of postoperative MTF joint BMLs are strongly associated with pain severity. The greater the improvement in postoperative MTF joint BMLs, the less pain. Our findings provide valuable understandings of OWHTO in the treatment of knee osteoarthritis (KOA) and potential future directions for KOA treatment approaches.
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Medula Óssea/patologia , Osteotomia/efeitos adversos , Medição da Dor , Índice de Gravidade de Doença , Tíbia/cirurgia , Idoso , Medula Óssea/diagnóstico por imagem , Feminino , Humanos , Imageamento por Ressonância Magnética , Masculino , Período Pós-Operatório , Tíbia/diagnóstico por imagem , Resultado do Tratamento , Escala Visual AnalógicaRESUMO
PURPOSE: We aim to present an unsupervised machine learning application in anterior cruciate ligament (ACL) rupture and evaluate whether supervised machine learning-derived radiomics features enable prediction of ACL rupture accurately. PATIENTS AND METHODS: Sixty-eight patients were reviewed. Their demographic features were recorded, radiomics features were extracted, and the input dataset was defined as a collection of demographic features and radiomics features. The input dataset was automatically classified by the unsupervised machine learning algorithm. Then, we used a supervised machine learning algorithm to construct a radiomics model. The t-test and least absolute shrinkage and selection operator (LASSO) method were used for feature selection, random forest and support vector machine (SVM) were used as machine learning classifiers. For each model, the sensitivity, specificity, accuracy, and the area under the curve (AUC) of receiver operating characteristic (ROC) curves were calculated to evaluate model performance. RESULTS: In total, 5 demographic features were recorded and 106 radiomics features were extracted. By applying the unsupervised machine learning algorithm, patients were divided into 5 groups. Group 5 had the highest incidence of ACL rupture and left knee involvement. There were significant differences in left knee involvement among the groups. Forty-three radiomics features were extracted using t-test and 7 radiomics features were extracted using LASSO method. We found that the combination of LASSO selection method and random forest classifier has the highest sensitivity, specificity, accuracy, and AUC. The 7 radiomics features extracted by LASSO method were potential predictors for ACL rupture. CONCLUSION: We validated the clinical application of unsupervised machine learning involving ACL rupture. Moreover, we found 7 radiomics features which were potential predictors for ACL rupture. The study indicated that radiomics could be a valuable method in the prediction of ACL rupture.
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The purpose of this study was to investigate the efficacy of tranexamic acid (TXA) in patients undergoing open-wedge high tibial osteotomy (OWHTO). Patients from August 2018 to May 2020 were retrospectively studied. Clinical data were obtained including gender, age, height, weight, body mass index (BMI), smoking, alcohol consumption, hypertension, diabetes, history of aspirin, prepostoperative hematocrit (Hct) and hemoglobin (Hb), thrombotic events, blood transfusion requirement, hospital length of stay, size of osteotomy gap, and wound complications such as wound hematoma and infection. 52 patients were enrolled in the tranexamic acid group (TA group), and 48 patients were enrolled in the nontranexamic acid group (NTA group); there were no significant differences between both groups in terms of gender, age, BMI, preoperative Hb, size of osteotomy gap, incidence of smoking, alcohol consumption, hypertension, diabetes, history of aspirin, thrombotic events, blood transfusion requirement, and wound hematoma and infection. The mean hospital length of stay was 9.4 ± 1.0 days in the TA group and 11.0 ± 1.2 days in the NTA group (P < 0.001), the blood loss was 296.0 ± 128.7 ml in the TA group and 383.3 ± 181.3 ml in the NTA group (P < 0.05), and the postoperative Hb level was 120.8 ± 15.0 g/l in the TA group and 109.5 ± 13.8 g/l in the NTA group (P < 0.001). In conclusion, the administration of TXA is beneficial to patients undergoing OWHTO via decreasing hospital length of stay, reducing blood loss, and maintaining higher postoperative Hb levels.
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Antifibrinolíticos/uso terapêutico , Osteotomia/efeitos adversos , Osteotomia/métodos , Tíbia/cirurgia , Ácido Tranexâmico/uso terapêutico , Idoso , Antifibrinolíticos/farmacologia , Perda Sanguínea Cirúrgica/prevenção & controle , Feminino , Hematoma/etiologia , Hemoglobinas/análise , Humanos , Tempo de Internação , Masculino , Pessoa de Meia-Idade , Osteoartrite do Joelho/cirurgia , Complicações Pós-Operatórias/etiologia , Complicações Pós-Operatórias/prevenção & controle , Estudos Retrospectivos , Ácido Tranexâmico/farmacologia , Infecção dos Ferimentos/etiologiaRESUMO
The purpose of the study was to identify patient characteristics related to blood loss following high tibial osteotomy (HTO). We evaluated 48 patients undergoing HTO from August 2018 to August 2019. The data of 48 patients were collected, including gender, age, height, weight, body mass index (BMI), smoking, alcohol consumption, hypertension, diabetes, history of aspirin, and pre-postoperative hematocrit (Hct). Multiple linear regression analysis was used to analyze the risk factors related to blood loss in HTO. The mean age of patients was 56.6 ± 10.2 years, including 22 males and 26 females. The mean BMI was 28.5 ± 4.2 kg/m2, and the mean blood loss volume was 383.3 ± 181.3 mL, 13 patients with smoking (27.1%), 15 patients with alcohol consumption (31.3%), 23 patients with hypertension (47.9%), 10 patients with diabetes mellitus (20.8%), and 12 patients with history of aspirin (25.0%). Multiple linear regression model suggested alcohol consumption and BMI were associated with blood loss in HTO, R 2 = 0.451, F(9, 38) = 3.462 (P < 0.05). Our study indicates that alcohol consumption and BMI are important risk factors related to blood loss in HTO.