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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. [ABSTRACT FROM AUTHOR]

Background: This study aimed to develop and validate an individualized nomogram to predict the risk of positive hidden blood loss (HBL) in patients with single-level thoracolumbar burst fracture (TBF) during the perioperative period. Methods: We conducted a retrospective investigation including 150 consecutive patients with TBL, and the corresponding patient data was extracted from March 2013 to March 2019. The independent risk factors for positive HBL were screened using univariate and multivariate logistic regression analyses. According to published literature and clinical experience, a series of variables were selected to develop a nomogram prediction model for positive HBL. The area under the receiver operating characteristic curves (AUC), C-index, calibration plot, and decision curve analysis (DCA) were used to evaluate the performance of the prediction model. Bootstrapping validation was performed to evaluate the performance of the model. Results: Among the 150 consecutive patients, 62 patients were positive for HBL (38.0%). The multivariate logistic regression analysis showed that the six risk factors of age, length of surgical incision, duration of operation, percentage of vertebral height restoration (P1%), preoperative total cholesterol, and preoperative fibrinogen were independent risk factors of positive HBL. The C-index was 0.831 (95% CI 0.740–0.889) and 0.845 in bootstrapping validation, respectively. The calibration curve showed that the predicted probability of the model was consistent with the actual probability. Decision curve analysis (DCA) showed that the nomogram had clinical utility. Conclusion: Overall, we explored the relationship between the positive HBL requirement and predictors. The individualized prediction model for patients with single-level TBF can accurately assess the risk of positive HBL and facilitate clinical decision making. However, external validation will be needed in the future. [ABSTRACT FROM AUTHOR]

Background: This study aimed to develop and validate a nomogram for predicting acute kidney injury (AKI) during the Intensive Care Unit (ICU) stay of patients with diabetic ketoacidosis (DKA). Methods: A total of 760 patients diagnosed with DKA from the Medical Information Mart for Intensive Care III (MIMIC-III) database were included and randomly divided into a training set (70%, n = 532) and a validation set (30%, n = 228). Clinical characteristics of the data set were utilized to establish a nomogram for the prediction of AKI during ICU stay. The least absolute shrinkage and selection operator (LASSO) regression was utilized to identified candidate predictors. Meanwhile, a multivariate logistic regression analysis was performed based on variables derived from LASSO regression, in which variables with P < 0.1 were included in the final model. Then, a nomogram was constructed applying these significant risk predictors based on a multivariate logistic regression model. The discriminatory ability of the model was determined by illustrating a receiver operating curve (ROC) and calculating the area under the curve (AUC). Moreover, the calibration plot and Hosmer-Lemeshow goodness-of-fit test (HL test) were conducted to evaluate the performance of our newly bullied nomogram. Decision curve analysis (DCA) was performed to evaluate the clinical net benefit. Results: A multivariable model that included type 2 diabetes mellitus (T2DM), microangiopathy, history of congestive heart failure (CHF), history of hypertension, diastolic blood pressure (DBP), urine output, Glasgow coma scale (GCS), and respiratory rate (RR) was represented as the nomogram. The predictive model demonstrated satisfied discrimination with an AUC of 0.747 (95% CI, 0.706–0.789) in the training dataset, and 0.712 (95% CI, 0.642–0.782) in the validation set. The nomogram showed well-calibrated according to the calibration plot and HL test (P > 0.05). DCA showed that our model was clinically useful. Conclusion: The nomogram predicted model for predicting AKI in patients with DKA was constructed. This predicted model can help clinical physicians to identify the patients with high risk earlier and prevent the occurrence of AKI and intervene timely to improve prognosis. [ABSTRACT FROM AUTHOR]

Background: This study aimed to predict C5 palsy (C5P) after posterior laminectomy and fusion (PLF) with cervical myelopathy (CM) from routinely available variables using a support vector machine (SVM) method. Methods: We conducted a retrospective investigation based on 184 consecutive patients with CM after PLF, and data were collected from March 2013 to December 2019. Clinical and imaging variables were obtained and imported into univariable and multivariable logistic regression analyses to identify risk factors for C5P. According to published reports and clinical experience, a series of variables was selected to develop an SVM machine learning model to predict C5P. The accuracy (ACC), area under the receiver operating characteristic curve (AUC), and confusion matrices were used to evaluate the performance of the prediction model. Results: Among the 184 consecutive patients, C5P occurred in 26 patients (14.13%). Multivariate analyses demonstrated the following 4 independent factors associated with C5P: abnormal electromyogram (odds ratio [OR] = 7.861), JOA recovery rate (OR = 1.412), modified Pavlov ratio (OR = 0.009), and presence of C4–C5 foraminal stenosis (OR = 15.492). The SVM model achieved an area under the receiver operating characteristic curve (AUC) of 0.923 and an ACC of 0.918. Additionally, the confusion matrix showed the classification results of the discriminant analysis. Conclusions: The designed SVM model presented satisfactory performance in predicting C5P from routinely available variables. However, future external validation is needed. [ABSTRACT FROM AUTHOR]