scholarly journals A nomogram to predict the risk of prolonged length of stay following primary total hip arthroplasty with an enhanced recovery after surgery program

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Haosheng Wang ◽  
Tingting Fan ◽  
Wenle Li ◽  
Bo Yang ◽  
Qiang Lin ◽  
...  
Keyword(s):  
Risk Factors ◽  
Regression Analysis ◽  
Total Hip Arthroplasty ◽  
Length Of Stay ◽  
Prediction Model ◽  
Enhanced Recovery ◽  
Enhanced Recovery After Surgery ◽  
Prolonged Length ◽  
Independent Risk Factors ◽  
Primary Total Hip Arthroplasty

Abstract Background The aim of this study was to identify the risk factors associated with prolonged length of stay (LOS) in patients undergoing primary total hip arthroplasty (THA) managed with an enhanced recovery after surgery (ERAS) program and develop a prediction model for improving the perioperative management of THA. Methods In this single-center retrospective study, patients who underwent primary THA in accordance with ERAS from May 2018 to December 2019 were enrolled in this study. The primary outcome was prolonged LOS (> 48 h) beyond the first postoperative day. We collected the clinical patient’s clinical characteristics, surgery-related parameters, and laboratory tests. A logistic regression analysis explored the independent risk factors for prolonged LOS. According to published literature and clinical experience, a series of variables were selected to develop a nomogram prediction model to predict the risk of prolonged LOS following primary THA with an ERAS program. Evaluation indicators of the prediction model, including the concordance index (C-index), the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis, were reported to assess the performance of the prediction model. The bootstrap method was conducted to validate the performance of the designed nomogram. Results A total of 392 patients were included in the study, of whom 189 (48.21%) had prolonged LOS. The logistics regression analysis demonstrated that age, sex, hip deformities, intraoperative blood loss, operation time, postoperative Day 1 (POD) hemoglobin (Hb), POD albumin (ALB), and POD interleukin-6 (IL-6) were independent risk factors for prolonged LOS. The C-index was 0.863 (95% CI 0.808 to 0.918) and 0.845 in the bootstrapping validation, respectively. According to the results of the calibration, ROC curve, and decision curve analyses, we found that the nomogram showed satisfactory performance for prolonged LOS in this study. Conclusions We explored the risk factors for prolonged LOS following primary THA with an ERAS program and developed a prediction model. The designed nomogram was expected to be a precise and personalized tool for predicting the risk and prognosis for prolonged LOS following primary THA with an ERAS program.

scholarly journals Development of Predictive Nomograms for Clinical Use to Quantify the Risk of Amputation in Patients with Diabetic Foot Ulcer

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Bocheng Peng ◽  
Rui Min ◽  
Yiqin Liao ◽  
Aixi Yu
Keyword(s):  
Risk Factors ◽  
Regression Analysis ◽  
Prediction Model ◽  
Predictive Factors ◽  
Diabetic Foot ◽  
Univariate Analysis ◽  
Foot Ulcer ◽  
Diabetic Foot Ulcer ◽  
Single Factor ◽  
Independent Risk Factors

Objective. To determine the novel proposed nomogram model accuracy in the prediction of the lower-extremity amputations (LEA) risk in diabetic foot ulcer (DFU). Methods and Materials. In this retrospective study, data of 125 patients with diabetic foot ulcer who met the research criteria in Zhongnan Hospital of Wuhan University from January 2015 to December 2019 were collected by filling in the clinical investigation case report form. Firstly, univariate analysis was used to find the primary predictive factors of amputation in patients with diabetic foot ulcer. Secondly, single factor and multiple factor logistic regression analysis were employed to screen the independent influencing factors of amputation introducing the primary predictive factors selected from the univariate analysis. Thirdly, the independent influencing factors were applied to build a prediction model of amputation risk in patients with diabetic foot ulcer by using R4.3; then, the nomogram was established according to the selected variables visually. Finally, the performance of the prediction model was evaluated and verified by receiver working characteristic (ROC) curve, corrected calibration curve, and clinical decision curve. Results. 7 primary predictive factors were selected by univariate analysis from 21 variables, including the course of diabetes, peripheral angiopathy of diabetic (PAD), glycosylated hemoglobin A1c (HbA1c), white blood cells (WBC), albumin (ALB), blood uric acid (BUA), and fibrinogen (FIB); single factor logistic regression analysis showed that albumin was a protective factor for amputation in patients with diabetic foot ulcer, and the other six factors were risk factors. Multivariate logical regression analysis illustrated that only five factors (the course of diabetes, PAD, HbA1c, WBC, and FIB) were independent risk factors for amputation in patients with diabetic foot ulcer. According to the area under curve (AUC) of ROC was 0.876 and corrected calibration curve of the nomogram displayed good fitting ability, the model established by these 5 independent risk factors exhibited good ability to predict the risk of amputation. The decision analysis curve (DCA) indicated that the nomogram model was more practical and accurate when the risk threshold was between 6% and 91%. Conclusion. Our novel proposed nomogram showed that the course of diabetes, PAD, HbA1c, WBC, and FIB are the independent risk factors of amputation in patients with DFU. This prediction model was well developed and behaved a great accurate value for LEA so as to provide a useful tool for screening LEA risk and preventing DFU from developing into amputation.

scholarly journals A Machine Learning Model to Predict Intravenous Immunoglobulin-Resistant Kawasaki Disease Patients: A Retrospective Study Based on the Chongqing Population

2021 ◽  
Vol 9 ◽  
Author(s):  
Jie Liu ◽  
Jian Zhang ◽  
Haodong Huang ◽  
Yunting Wang ◽  
Zuyue Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Retrospective Study ◽  
Kawasaki Disease ◽  
Prediction Model ◽  
Prediction Models ◽  
Ivig Resistance ◽  
Independent Risk Factors

Objective: We explored the risk factors for intravenous immunoglobulin (IVIG) resistance in children with Kawasaki disease (KD) and constructed a prediction model based on machine learning algorithms.Methods: A retrospective study including 1,398 KD patients hospitalized in 7 affiliated hospitals of Chongqing Medical University from January 2015 to August 2020 was conducted. All patients were divided into IVIG-responsive and IVIG-resistant groups, which were randomly divided into training and validation sets. The independent risk factors were determined using logistic regression analysis. Logistic regression nomograms, support vector machine (SVM), XGBoost and LightGBM prediction models were constructed and compared with the previous models.Results: In total, 1,240 out of 1,398 patients were IVIG responders, while 158 were resistant to IVIG. According to the results of logistic regression analysis of the training set, four independent risk factors were identified, including total bilirubin (TBIL) (OR = 1.115, 95% CI 1.067–1.165), procalcitonin (PCT) (OR = 1.511, 95% CI 1.270–1.798), alanine aminotransferase (ALT) (OR = 1.013, 95% CI 1.008–1.018) and platelet count (PLT) (OR = 0.998, 95% CI 0.996–1). Logistic regression nomogram, SVM, XGBoost, and LightGBM prediction models were constructed based on the above independent risk factors. The sensitivity was 0.617, 0.681, 0.638, and 0.702, the specificity was 0.712, 0.841, 0.967, and 0.903, and the area under curve (AUC) was 0.731, 0.814, 0.804, and 0.874, respectively. Among the prediction models, the LightGBM model displayed the best ability for comprehensive prediction, with an AUC of 0.874, which surpassed the previous classic models of Egami (AUC = 0.581), Kobayashi (AUC = 0.524), Sano (AUC = 0.519), Fu (AUC = 0.578), and Formosa (AUC = 0.575).Conclusion: The machine learning LightGBM prediction model for IVIG-resistant KD patients was superior to previous models. Our findings may help to accomplish early identification of the risk of IVIG resistance and improve their outcomes.

scholarly journals Primary Total Hip Arthroplasty in Hispanic/Latino Patients: An Updated Nationwide Analysis of Length of Stay, 30-Day Outcomes, and Risk Factors

2020 ◽  
Vol 6 (4) ◽  
pp. 721-725
Author(s):  
Christian Gronbeck ◽  
Antonio Cusano ◽  
Justin M. Cardenas ◽  
Melvyn A. Harrington ◽  
Mohamad J. Halawi
Keyword(s):  
Risk Factors ◽  
Total Hip Arthroplasty ◽  
Length Of Stay ◽  
Hip Arthroplasty ◽  
Total Hip ◽  
Primary Total Hip Arthroplasty

scholarly journals Nomogram for Predicting the Postoperative Venous Thromboembolism in Spinal Metastasis Tumor: A Multicenter Retrospective Study

2021 ◽  
Vol 11 ◽  
Author(s):  
Hao-ran Zhang ◽  
Ming-you Xu ◽  
Xiong-gang Yang ◽  
Feng Wang ◽  
Hao Zhang ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Venous Thromboembolism ◽  
Prediction Model ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Multivariate Logistic Regression Model ◽  
Multivariate Logistic Regression ◽  
Independent Risk Factors

IntroductionVenous thromboembolism can be divided into deep vein thrombosis and pulmonary embolism. These diseases are a major factor affecting the clinical prognosis of patients and can lead to the death of these patients. Unfortunately, the literature on the risk factors of venous thromboembolism after surgery for spine metastatic bone lesions are rare, and no predictive model has been established.MethodsWe retrospectively analyzed 411 cancer patients who underwent metastatic spinal tumor surgery at our institution between 2009 and 2019. The outcome variable of the current study is venous thromboembolism that occurred within 90 days of surgery. In order to identify the risk factors for venous thromboembolism, a univariate logistic regression analysis was performed first, and then variables significant at the P value less than 0.2 were included in a multivariate logistic regression analysis. Finally, a nomogram model was established using the independent risk factors.ResultsIn the multivariate logistic regression model, four independent risk factors for venous thromboembolism were further screened out, including preoperative Frankel score (OR=2.68, 95% CI 1.78-4.04, P=0.001), blood transfusion (OR=3.11, 95% CI 1.61-6.02, P=0.041), Charlson comorbidity index (OR=2.01, 95% CI 1.27-3.17, P=0.013; OR=2.29, 95% CI 1.25-4.20, P=0.017), and operative time (OR=1.36, 95% CI 1.14-1.63, P=0.001). On the basis of the four independent influencing factors screened out by multivariate logistic regression model, a nomogram prediction model was established. Both training sample and validation sample showed that the predicted probability of the nomogram had a strong correlation with the actual situation.ConclusionThe prediction model for postoperative VTE developed by our team provides clinicians with a simple method that can be used to calculate the VTE risk of patients at the bedside, and can help clinicians make evidence-based judgments on when to use intervention measures. In clinical practice, the simplicity of this predictive model has great practical value.

scholarly journals Limited Influence of Comorbidities on Length of Stay after Total Hip Arthroplasty: Experience of Enhanced Recovery after Surgery

2019 ◽  
Vol 12 (1) ◽  
pp. 153-161 ◽  
Author(s):  
Zi‐chuan Ding ◽  
Bing Xu ◽  
Zhi‐min Liang ◽  
Hao‐yang Wang ◽  
Ze‐yu Luo ◽  
...  
Keyword(s):  
Total Hip Arthroplasty ◽  
Length Of Stay ◽  
Hip Arthroplasty ◽  
Enhanced Recovery ◽  
Enhanced Recovery After Surgery ◽  
Total Hip

Analysis of factors associated with prolonged post-operative course after surgical repair of aortic coarctation

2020 ◽  
pp. 1-8
Author(s):  
Santosh Kaipa ◽  
Mouhammad Yabrodi ◽  
Brian D. Benneyworth ◽  
Eric S. Ebenroth ◽  
Christopher W. Mastropietro
Keyword(s):  
Risk Factors ◽  
Length Of Stay ◽  
Confidence Interval ◽  
Odds Ratio ◽  
Aortic Coarctation ◽  
Patient Characteristics ◽  
Oral Feeding ◽  
Prolonged Length ◽  
Independent Risk Factors ◽  
Neonates And Infants

Abstract Objective: We sought to describe patient characteristics associated with prolonged post-operative length of stay in a contemporary cohort of infants who underwent isolated repair of aortic coarctation. Methods: We reviewed patients less than 1 year of age who underwent isolated repair of aortic coarctation at our institution from 2009 to 2016. Prolonged post-operative length of stay was defined as length of stay within the upper tertile for the cohort. Bivariate and multi-variable analyses were performed to determine independent risk factors for prolonged length of stay. Results: We reviewed 95 consecutive patients who underwent isolated repair of aortic coarctation, of whom 71 were neonates at the time of diagnosis. The median post-operative length of stay was 6.5 days. The upper tertile for post-operative length of stay was greater than 10 days; 32 patients within this tertile and 1 patient who died at 8.5 days after surgery were analysed as having prolonged post-operative length of stay. In a multi-variable analysis, pre-maturity (odds ratio: 3.5, 95% confidence interval: 1.2, 10.7), genetic anomalies (odds ratio: 4.7, 95% confidence interval: 1.2, 18), absence of pre-operative oral feeding (odds ratio: 7.4, 95% confidence interval: 2.4, 22.3), and 12-hour vasoactive-ventilation-renal score greater than 25 (odds ratio: 7.4, 95% confidence interval: 1.9, 29) were independently associated with prolonged length of stay. Conclusions: In neonates and infants who underwent isolated repair of aortic coarctation, pre-maturity, genetic anomalies, lack of pre-operative oral feedings, and 12-hour vasoactive-ventilation-renal score more than 25 were independent risk factors for prolonged post-operative length of stay. Further study on the relationship between pre-operative oral feedings and post-operative length of stay should be pursued.

scholarly journals A Prognostic Model for Patients With Gastric Signet Ring Cell Carcinoma

2021 ◽  
Vol 20 ◽  
pp. 153303382110279
Author(s):  
Qinping Guo ◽  
Yinquan Wang ◽  
Jie An ◽  
Siben Wang ◽  
Xiushan Dong ◽  
...  
Keyword(s):  
Risk Factors ◽  
Regression Analysis ◽  
Cox Regression ◽  
Signet Ring Cell Carcinoma ◽  
Signet Ring Cell ◽  
Training Set ◽  
Cox Regression Analysis ◽  
Signet Ring ◽  
Ring Cell ◽  
Independent Risk Factors

Background: The aim of our study was to develop a nomogram model to predict overall survival (OS) and cancer-specific survival (CSS) in patients with gastric signet ring cell carcinoma (GSRC). Methods: GSRC patients from 2004 to 2015 were collected from the Surveillance, Epidemiology, and End Results (SEER) database and randomly assigned to the training and validation sets. Multivariate Cox regression analyses screened for OS and CSS independent risk factors and nomograms were constructed. Results: A total of 7,149 eligible GSRC patients were identified, including 4,766 in the training set and 2,383 in the validation set. Multivariate Cox regression analysis showed that gender, marital status, race, AJCC stage, TNM stage, surgery and chemotherapy were independent risk factors for both OS and CSS. Based on the results of the multivariate Cox regression analysis, prognostic nomograms were constructed for OS and CSS. In the training set, the C-index was 0.754 (95% CI = 0.746-0.762) for the OS nomogram and 0.762 (95% CI: 0.753-0.771) for the CSS nomogram. In the internal validation, the C-index for the OS nomogram was 0.758 (95% CI: 0.746-0.770), while the C-index for the CSS nomogram was 0.762 (95% CI: 0.749-0.775). Compared with TNM stage and SEER stage, the nomogram had better predictive ability. In addition, the calibration curves also showed good consistency between the predicted and actual 3-year and 5-year OS and CSS. Conclusion: The nomogram can effectively predict OS and CSS in patients with GSRC, which may help clinicians to personalize prognostic assessments and clinical decisions.

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