scholarly journals The risk factors and predictive nomogram of human albumin infusion during the perioperative period of posterior lumbar interbody fusion: a study based on 2015–2020 data from a local hospital

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Bo Liu ◽  
Junpeng Pan ◽  
Hui Zong ◽  
Zhijie Wang
Keyword(s):  
Risk Factors ◽  
Interbody Fusion ◽  
Posterior Lumbar Interbody Fusion ◽  
Perioperative Period ◽  
Training Group ◽  
Human Albumin ◽  
Validation Group ◽  
Multivariate Logistic Regression ◽  
Albumin Infusion ◽  
Independent Risk Factors

Abstract Background Perioperative hypoalbuminemia of the posterior lumbar interbody fusion (PLIF) can increase the risk of infection of the incision site, and it is challenging to accurately predict perioperative hypoproteinemia. The objective of this study was to create a clinical predictive nomogram and validate its accuracy by finding the independent risk factors for perioperative hypoalbuminemia of PLIF. Methods The patients who underwent PLIF at the Affiliated Hospital of Qingdao University between January 2015 and December 2020 were selected in this study. Besides, variables such as age, gender, BMI, current and past medical history, indications for surgery, surgery-related information, and results of preoperative blood routine tests were also collected from each patient. These patients were divided into injection group and non-injection group according to whether they were injected with human albumin. And they were also divided into training group and validation group, with the ratio of 4:1. Univariate and multivariate logistic regression analyses were performed in the training group to find the independent risk factors. The nomogram was developed based on these independent predictors. In addition, the area under the curve (AUC), the calibration curve and the decision curve analysis (DCA) were drawn in the training and validation groups to evaluate the prediction, calibration and clinical validity of the model. Finally, the nomograms in the training and validation groups and the receiver operating characteristic (ROC) curves of each independent risk factor were drawn to analyze the performance of this model. Results A total of 2482 patients who met our criteria were recruited in this study and 256 (10.31%) patients were injected with human albumin perioperatively. There were 1985 people in the training group and 497 in the validation group. Multivariate logistic regression analysis revealed 5 independent risk factors, including old age, accompanying T2DM, level of preoperative albumin, amount of intraoperative blood loss and fusion stage. We drew nomograms. The AUC of the nomograms in the training group and the validation group were 0.807, 95% CI 0.774–0.840 and 0.859, 95% CI 0.797–0.920, respectively. The calibration curve shows consistency between the prediction and observation results. DCA showed a high net benefit from using nomograms to predict the risk of perioperative injection of human albumin. The AUCs of nomograms in the training and the validation groups were significantly higher than those of five independent risk factors mentioned above (P < 0.001), suggesting that the model is strongly predictive. Conclusion Preoperative low protein, operative stage ≥ 3, a relatively large amount of intraoperative blood loss, old age and history of diabetes were independent predictors of albumin infusion after PLIF. A predictive model for the risk of albumin injection during the perioperative period of PLIF was created using the above 5 predictors, and then validated. The model can be used to assess the risk of albumin injection in patients during the perioperative period of PLIF. The model is highly predictive, so it can be clinically applied to reduce the incidence of perioperative hypoalbuminemia.

Risk Factor Analysis and Predictive Model of Human Albumin Infusion during the Perioperative Period of Posterior Lumbar Interbody Fusion: A Study Based on 2015-2020 Data from a Local Hospital

2021 ◽  
Author(s):  
Bo Liu ◽  
Junpeng Pan ◽  
Hui Zong ◽  
Zhijie Wang
Keyword(s):  
Risk Factors ◽  
Interbody Fusion ◽  
Posterior Lumbar Interbody Fusion ◽  
Perioperative Period ◽  
Training Group ◽  
Human Albumin ◽  
Validation Group ◽  
Multivariate Logistic Regression ◽  
Albumin Infusion ◽  
Independent Risk Factors

Abstract BackgroundPerioperative hypoalbuminemia of the Posterior Lumbar Interbody Fusion (PLIF) can increase the risk of infection of the incision site, and it is challenging to accurately predict perioperative hypoproteinemia. The objective of this study was to create a clinical predictive nomogram and validate its accuracy by finding the independent risk factors for perioperative hypoalbuminemia of PLIF.MethodsThe patients who underwent PLIF at The Affiliated Hospital of Qingdao University between January 2015 and December 2020 were selected in this study. Besides, variables such as age, gender, BMI, current and past medical history, indications for surgery, surgery-related information, and results of preoperative blood routine tests were also collected from each patient. These patients were divided into injection group and non-injection group according to whether they were injected with human albumin. And they were also divided into training group and validation group, with the ratio of 4:1. Univariate and multivariate logistic regression analyses were performed in the training group to find the independent risk factors. The nomogram was developed based on these independent predictors. In addition, the area under the curve (AUC), the calibration curve and the decision curve analysis (DCA) were drawn in the training and validation groups to evaluate the prediction, calibration and clinical validity of the model. Finally, the nomograms in the training and validation groups and the receiver operating characteristic (ROC) curves of each independent risk factor were drawn to analyze the performance of this model.ResultsA total of 2,482 patients who met our criteria were recruited in this study and 256 (10.31%) patients were injected with human albumin perioperatively. There were 1,985 people in the training group and 497 in the validation group. Multivariate logistic regression analysis revealed 5 independent risk factors, including old age, accompanying T2DM, level of preoperative albumin, amount of intraoperative blood loss and fusion stage. We drew nomograms. The AUC of the nomograms in the training group and the validation group were 0.807, 95%CI = 0.774-0.840 and 0.859, 95%CI=0.797-0.920, respectively. The calibration curve shows consistency between the prediction and observation results. DCA showed a high net benefit from using nomograms to predict the risk of perioperative injection of human albumin. The AUCs of nomograms in the training and the validation groups were significantly higher than those of five independent risk factors mentioned above (P< 0.001), suggesting that the model is strongly predictive. ConclusionPreoperative low protein, operative stage ≥3, a relatively large amount of intraoperative blood loss, old age and history of diabetes were independent predictors of albumin infusion after PLIF. A predictive model for the risk of albumin injection during the perioperative period of PLIF was created using the above 5 predictors, and then validated. The model can be used to assess the risk of albumin injection in patients during the perioperative period of PLIF. The model is highly predictive, so it can be clinically applied to reduce the incidence of perioperative hypoalbuminemia.

scholarly journals Development and Internal Validation of Supervised Machine Learning Algorithms for Predicting the Risk of Surgical Site Infection Following Minimally Invasive Transforaminal Lumbar Interbody Fusion Under Quadrant Channel

2021 ◽  
Author(s):  
Haosheng Wang ◽  
Tingting Fan ◽  
Bo Yang ◽  
Qiang Lin ◽  
Wenle Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Interbody Fusion ◽  
Clinical Decision Making ◽  
Transforaminal Lumbar Interbody Fusion ◽  
Clinical Decision ◽  
Lumbar Interbody Fusion ◽  
Regression Analyses ◽  
Multivariate Logistic Regression ◽  
Independent Risk Factors

Abstract Background: Machine Learning (ML) is rapidly growing in capability and is increasingly applied to model outcomes and complications in medicine. Surgical site infections (SSI) are a common postoperative complication in spinal surgery. This study aimed to develop and validate supervised ML algorithms for predicting the risk of SSI following minimally invasive transforaminal lumbar interbody fusion (MIS-TLIF) under the Quadrant channel.Methods: This single-central retrospective study included a total of 705 cases between May 2012 and October 2019. Data of patients who underwent MIS-TLIF under the Quadrant channel was extracted by the electronic medical record system. The patient’s clinical characteristics, surgery-related parameters, and routine laboratory tests were collected. Univariate and multivariate logistic regression analyses were used to screen and identify independent risk factors for SSI. Then, the independent risk factors were imported into six ML algorithms, including k-Nearest Neighbor (KNN), Decision Tree (DT), Support Vector Machine (SVM), Random Forest (RF), Multi-Layer Perceptron (MLP), and Naïve Bayes (NB), to develop a prediction model for predicting the risk of SSI following MIS-TLIF under Quadrant channel. During the training process, 10-fold cross-validation was used for validation. Indices like the area under the receiver operating characteristic (AUC), sensitivity, specificity, and accuracy (ACC) were reported to test the performance of ML models.Results: Among the 705 patients, SSI occurred in 33 patients (4.68%). The univariate and multivariate logistic regression analyses showed that preoperative glycated hemoglobin A1c (HbA1c), estimated blood loss (EBL), preoperative albumin, body mass index (BMI), and age were all independent predictors of SSI. In predicting SSI, six ML models posted an average AUC of 0.60-0.80 and an ACC of 0.80-0.95, with the NB model standing out, registering an average AUC and an ACC of 0.78 and 0.90. Then, the feature importance of the NB model was reported.Conclusions: ML algorithms are impressive tools in clinical decision-making, which can achieve satisfactory prediction of SSI with the NB model performing the best. The NB model may help access the risk of SSI following MIS-TLIF under the Quadrant channel and facilitate clinical decision-making. However, future external validation is needed.

scholarly journals Development and Validation of a Nomogram for Prediction of the Risk of Positive Hidden Blood Loss in the Perioperative Period of Thoracolumbar Burst Fracture

2021 ◽  
Author(s):  
Haosheng Wang ◽  
Tingting Fan ◽  
Zhi-Ri Tang ◽  
Wenle Li ◽  
Linjing Liu ◽  
...  
Keyword(s):  
Risk Factors ◽  
Prediction Model ◽  
Burst Fracture ◽  
Perioperative Period ◽  
Multivariate Logistic Regression ◽  
Vertebral Height ◽  
Decision Curve Analysis ◽  
Hidden Blood Loss ◽  
Independent Risk Factors ◽  
Duration Of Operation

Abstract Background: This study aimed to develop and validate an individualized nomogram to predict the risk of positive hidden blood loss (HBL) in patients with thoracolumbar burst fracture (TBF) during the perioperative period.Methods: We conducted a retrospective investigation including 161 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 161 consecutive patients, 62 patients were negative for HBL (14.13%). 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 0.862 (95% CI 0.788–0.903) and 0.8884 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: age, duration from admission to surgery, duration of operation, percentages of vertebral height restoration (P1%), preoperative total cholesterol, and preoperative fibrinogen. The individualized prediction model for patients with TBF can accurately assess the risk of positive HBL and facilitate clinical decision making. However, external validation will be needed in the future.

scholarly journals Development and validation of a nomogram for prediction of the risk of positive hidden blood loss in the perioperative period of single-level thoracolumbar burst fracture

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Haosheng Wang ◽  
Tingting Fan ◽  
Zhi-Ri Tang ◽  
Wenle Li ◽  
Linjing Liu ◽  
...  
Keyword(s):  
Risk Factors ◽  
Prediction Model ◽  
Burst Fracture ◽  
Perioperative Period ◽  
Thoracolumbar Burst Fracture ◽  
Multivariate Logistic Regression ◽  
Decision Curve Analysis ◽  
Single Level ◽  
Hidden Blood Loss ◽  
Independent Risk Factors

Abstract 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.

scholarly journals Preoperative risk factors for postoperative blood transfusion after hip fracture surgery: establishment of a nomogram

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Fu Cheng Bian ◽  
Xiao Kang Cheng ◽  
Yong Sheng An
Keyword(s):  
Risk Factors ◽  
Regression Analysis ◽  
Hip Fracture ◽  
Blood Transfusion ◽  
Multivariate Logistic Regression ◽  
Preoperative Risk ◽  
Hip Fracture Surgery ◽  
Preoperative Risk Factors ◽  
Independent Risk Factors ◽  
Fracture Surgery

Abstract Background This study aimed to explore the preoperative risk factors related to blood transfusion after hip fracture operations and to establish a nomogram prediction model. The application of this model will likely reduce unnecessary transfusions and avoid wasting blood products. Methods This was a retrospective analysis of all patients undergoing hip fracture surgery from January 2013 to January 2020. Univariate and multivariate logistic regression analyses were used to evaluate the association between preoperative risk factors and blood transfusion after hip fracture operations. Finally, the risk factors obtained from the multivariate regression analysis were used to establish the nomogram model. The validation of the nomogram was assessed by the concordance index (C-index), the receiver operating characteristic (ROC) curve, decision curve analysis (DCA), and calibration curves. Results A total of 820 patients were included in the present study for evaluation. Multivariate logistic regression analysis demonstrated that low preoperative hemoglobin (Hb), general anesthesia (GA), non-use of tranexamic acid (TXA), and older age were independent risk factors for blood transfusion after hip fracture operation. The C-index of this model was 0.86 (95% CI, 0.83–0.89). Internal validation proved the nomogram model’s adequacy and accuracy, and the results showed that the predicted value agreed well with the actual values. Conclusions A nomogram model was developed based on independent risk factors for blood transfusion after hip fracture surgery. Preoperative intervention can effectively reduce the incidence of blood transfusion after hip fracture operations.

Risk Factors for Poor Patient-Reported Quality of Life Outcomes After Posterior Lumbar Interbody Fusion

Spine ◽  
2017 ◽  
Vol 42 (19) ◽  
pp. 1502-1510 ◽  
Author(s):  
Takahiro Makino ◽  
Takashi Kaito ◽  
Hiroyasu Fujiwara ◽  
Hirotsugu Honda ◽  
Yusuke Sakai ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Risk Factors ◽  
Interbody Fusion ◽  
Posterior Lumbar Interbody Fusion ◽  
Lumbar Interbody Fusion ◽  
Life Outcomes ◽  
Poor Patient ◽  
Quality Of Life Outcomes ◽  
Patient Reported

Multifocal Papillary Thyroid Carcinoma，Hashimoto's Thyroiditis and Lymph Node Metastasis：A Retrospective Cohort Study

2021 ◽  
Author(s):  
Denghui Wang ◽  
Jiang Zhu ◽  
Chang Deng ◽  
Zhixin Yang ◽  
Daixing Hu ◽  
...  
Keyword(s):  
Risk Factors ◽  
Lymph Node ◽  
Lymph Node Metastases ◽  
Protective Factor ◽  
Roc Curves ◽  
Multivariate Logistic Regression ◽  
Lateral Lymph Node ◽  
Node Metastases ◽  
Independent Risk Factors ◽  
Central Lymph

Abstract Objective: Few studies have evaluated the influence of HT and Multifocality on central lymph node metastases(CLNM) and lateral lymph node metastases(LLNM) of PTC. The present study focused on risk factors for lymph node metastasis in PTC according to the presence of HT or multifocality. Materials and methods:1413 patients were identified.The relationship between HT or multifocality and lymph nodemetastasis was analyzed by univariate and multivariate logistic regression, ROC curves were constructed to show the predictive effect of each variable on the target outcome.Results: The PTCs with HT were more likely to be multifocal.(40.0% versus 17.5%,P ＜0.001). Compared to MPTC without HT, MPTC with HT showed a lower number of metastatic CLNs and LLNs (P < 0.05). HT was identifified as an independent protective factor for CLNM in all PTC patients (OR, 0.480; 95% CI, 0.359-0.643; P< .001) and in MPTC patients (OR, 0.094; 95% CI, 0.044-0.204; P < 0.001), the multicocality was independent risk factors for CLNM(OR, 2.316; 95% CI, 1.667-3.217; P< 0.001) and LLNM(OR, 2.004; 95% CI, 1.469-2.733; P< 0.001).The variables concluded HT or MPTC were screened to predict CLNM in all patients, CLNM in patients with MPTC and LLNM in all patients (AUCs: 0.731, 0.843 and 0.696, respectively, P < 0.0001). The two type of diseases existed concurrently may result in the decrease of CLNM and LLNM, AUCs of ROC to predict CLNM and LLNM are 0.696 and 0.63(P<0.0001). Conclusions: Our study identified multifocality as an independent risk factor predicting CLNM and LLNM in PTC patients. HT was proven to be a protective factor that reduced the CLNM risk in all patients and in patients with MPTC. The existence of both type of diseases can result in the reduction of CLNM and LLNM.

scholarly journals Risk Factors and Outcomes of Hospital Acquired Pneumonia in Young Bangladeshi Children

Life ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 1030
Author(s):  
Abu Sadat Mohammad Sayeem Bin Shahid ◽  
Tahmina Alam ◽  
Lubaba Shahrin ◽  
K. M. Shahunja ◽  
Md. Tanveer Faruk ◽  
...  
Keyword(s):  
Risk Factors ◽  
Severe Acute Malnutrition ◽  
Multivariate Logistic Regression Analysis ◽  
Acute Malnutrition ◽  
Persistent Diarrhea ◽  
Multivariate Logistic Regression ◽  
Resource Limited ◽  
Hospital Acquired Pneumonia ◽  
Independent Risk Factors ◽  
Hospital Acquired

Hospital acquired pneumonia (HAP) is common and often associated with high mortality in children aged five or less. We sought to evaluate the risk factors and outcome of HAP in such children. We compared demographic, clinical, and laboratory characteristics in children <5 years using a case control design during the period of August 2013 and December 2017, where children with HAP were constituted as cases (n = 281) and twice as many randomly selected children without HAP were constituted as controls (n = 562). HAP was defined as a child developing a new episode of pneumonia both clinically and radiologically after at least 48 h of hospitalization. A total of 4101 children were treated during the study period. The mortality was significantly higher among the cases than the controls (8% vs. 4%, p = 0.014). In multivariate logistic regression analysis, after adjusting for potential confounders, it was found that persistent diarrhea (95% CI = 1.32–5.79; p = 0.007), severe acute malnutrition (95% CI = 1.46–3.27; p < 0.001), bacteremia (95% CI = 1.16–3.49; p = 0.013), and prolonged hospitalization of >5 days (95% CI = 3.01–8.02; p < 0.001) were identified as independent risk factors for HAP. Early identification of these risk factors and their prompt management may help to reduce HAP-related fatal consequences, especially in resource limited settings.

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