scholarly journals A Prediction Model of Mortality in COVID-19 Pneumonia Based on CT Score and Lymphocyte Count

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Xie Y ◽  
◽  
Dong H ◽  
Liao Y ◽  
Zhang J ◽  
...  
Keyword(s):  
Risk Factors ◽  
Prediction Model ◽  
Lymphocyte Count ◽  
Goodness Of Fit ◽  
False Positive Rate ◽  
Laboratory Data ◽  
Absolute Lymphocyte Count ◽  
Youden Index ◽  
Goodness Of Fit Test ◽  
Independent Risk Factors

Background: COVID-19 nucleic acid swab tests have a high false positive rate; therefore, diagnosing COVID-19 pneumonia and predicting prognosis by CT scan are very important. Methods: In this retrospective single-centre study, we included consecutive suspected critical COVID-19 pneumonia cases in the intensive care unit of Wuhan Third Hospital from January 31, 2020, to March 16, 2020. 204 cases were confirmed by real-time RT-PCR, and all patients were evaluated with CT, cut-off values were obtained according to the Youden index and were divided into a high CT score group and a low CT score group. Epidemiological, demographic, clinical, and laboratory data were collected. Finally, Through multi-factor logistic regression model, a prediction model based on multiple prediction indicators was formed, and new joint predictive factors were calculated. The prediction model of mortality in COVID-19 pneumonia based on CT score and lymphocyte count was constructed through data processing analysis. Results: The major imaging feature of COVID-19 pneumonia is Ground Glass Opacities (GGOs). Multivariate regression analysis found that the CT score and absolute lymphocyte count were independent risk factors for death and that the CT score predicted mortality (AUC-ROC =0.7, cut-off=1.45). When the absolute lymphocyte count was lower, the patient’s CT score was also lower. Based on this, a prediction model was established. The prediction model was: In [P/(1-P)]=0.667*gender+0.057*age-0.086CT score-0.831 lymphocyte count-3.91, the goodness of fit test of the model was P=0.041, and the area under the curve of the ROC curve of the model was 0.779. Conclusion: CT score and absolute lymphocyte count are independent risk factors for mortality, and patients with a high CT score may have a worse prognosis. A lower absolute lymphocyte count may indicate that the patient’s CT score is also reduced. The model established by combining CT scores and lymphocyte count showed a good degree of calibration and differentiation.

scholarly journals Risk factors for peripheral venous catheter failure: A prospective cohort study of 5345 patients

2021 ◽  
pp. 112972982110150
Author(s):  
Ya-mei Chen ◽  
Xiao-wen Fan ◽  
Ming-hong Liu ◽  
Jie Wang ◽  
Yi-qun Yang ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Cohort Study ◽  
Goodness Of Fit ◽  
Insertion Site ◽  
Healthcare Providers ◽  
Venous Catheter ◽  
Goodness Of Fit Test ◽  
Peripheral Venous Catheter ◽  
Independent Risk Factors

Purpose: The objective of this study was to determine the independent risk factors associated with peripheral venous catheter (PVC) failure and develop a model that can predict PVC failure. Methods: This prospective, multicenter cohort study was carried out in nine tertiary hospitals in Suzhou, China between December 2017 and February 2018. Adult patients undergoing first-time insertion of a PVC were observed from catheter insertion to removal. Logistic regression was used to identify the independent risk factors predicting PVC failure. Results: This study included 5345 patients. The PVC failure rate was 54.05% ( n = 2889/5345), and the most common causes of PVC failure were phlebitis (16.3%) and infiltration/extravasation (13.8%). On multivariate analysis, age (45–59 years: OR, 1.295; 95% CI, 1.074–1.561; 60–74 years: OR, 1.375; 95% CI, 1.143–1.654; ⩾75 years: OR, 1.676; 95% CI, 1.355–2.073); department (surgery OR, 1.229; 95% CI, 1.062–1.423; emergency internal/surgical ward OR, 1.451; 95% CI, 1.082–1.945); history of venous puncture in the last week (OR, 1.298, 95% CI 1.130–1.491); insertion site, number of puncture attempts, irritant fluid infusion, daily infusion time, daily infusion volume, and type of sealing liquid were independent predictors of PVC failure. Receiver operating characteristic curve analysis indicated that a logistic regression model constructed using these variables had moderate accuracy for the prediction of PVC failure (area under the curve, 0.781). The Hosmer-Lemeshow goodness of fit test demonstrated that the model was correctly specified (χ2 = 2.514, p = 0.961). Conclusion: This study should raise awareness among healthcare providers of the risk factors for PVC failure. We recommend that healthcare providers use vascular access device selection tools to select a clinically appropriate device and for the timely detection of complications, and have a list of drugs classified as irritants or vesicants so they can monitor patients receiving fluid infusions containing these drugs more frequently.

scholarly journals Prediction Model for Severe Mycoplasma Pneumoniae Pneumonia in Pediatric Patients by Admission Laboratory Indicators

2021 ◽  
Author(s):  
Qing Chang ◽  
Hong-Lin Chen ◽  
Neng-Shun Wu ◽  
Yan-Min Gao ◽  
Rong Yu ◽  
...  
Keyword(s):  
Prediction Model ◽  
Mycoplasma Pneumoniae ◽  
Eosinophil Count ◽  
Goodness Of Fit ◽  
Pediatric Patients ◽  
Community Acquired Pneumonia ◽  
Goodness Of Fit Test ◽  
Independent Risk Factors ◽  
Mycoplasma Pneumoniae Pneumonia ◽  
Actual Incidence

Abstract Objective The purpose of this study was to develop a model for predicting severe mycoplasma pneumoniae pneumonia (SMMP) in pediatric patients with MMP on admission by laboratory indicators. Methods Pediatric patients with MPP from January 2019 to December 2020 in our hospital were enrolled in this study. SMMP was diagnosed according to guideline for diagnosis and treatment of community acquired pneumonia in children (2019 version). Prediction model was developed according to the admission laboratory indicators. ROC curve and Goodness of fit test were analyzed for the predictive value. Results A total of 233 MMP patients were included in the study, with 121 males and 112 females, aged 4.541 (1–14) years. Among them, 84 (36.1%, 95% CI 29.9%-42.6%) pediatric patients were diagnosed as SMPP. Some admission laboratory indicators (IgM, eosinophil proportion, eosinophil count, hemoglobin, ESR, total protein, albumin and prealbumin) were found statistically different (P < 0.05) between non-SMMP group and SMMP group. Logistic regress analysis showed IgM, eosinophil proportion, eosinophil count, ESR, and prealbumin were independent risk factors for SMMP. According to these five admission laboratory indicators, Nomograph prediction model was developed. The AUC of the Nomograph prediction model was 0.777, and the goodness of fit test showed that the predicted incidence of the model was consistent with the actual incidence (χ2 = 244.51, P = 0.203). Conclusion We developed a model for predicting SMMP in pediatric patients by admission laboratory indicators. This model has good discrimination and calibration, which provides a basis for the early identification SMMP on admission.

scholarly journals Development and validation of a nomogram to predict perioperative blood transfusion for gastric cancer surgery

2020 ◽  
Author(s):  
Haoquan Huang ◽  
Zhixiao Han ◽  
Xia Liang ◽  
Zhongqi Liu ◽  
Shi Cheng ◽  
...  
Keyword(s):  
Risk Factors ◽  
Gastric Cancer ◽  
Blood Transfusion ◽  
Goodness Of Fit ◽  
Validation Cohort ◽  
Cancer Surgery ◽  
Goodness Of Fit Test ◽  
Gastric Cancer Surgery ◽  
Independent Risk Factors ◽  
Perioperative Blood Transfusion

Abstract Background This study aimed to construct and validate a nomogram composed of preoperative variables to predict perioperative blood transfusion for gastric cancer surgery. Methods 600 gastric cancer patients undergoing gastrectomy between January 2010 and December 2015 were selected as primary cohort. 399 patients from January 2016 to June 2019 were collected as validation cohort. In the primary cohort, univariate and multivariate analyses were performed to identify independent risk factors for blood transfusion. Using Akaike information criterion, selected variables were incorporated to construct a nomogram. Validations of the nomogram were performed in the primary and validation cohort. Discrimination of the nomogram was assessed by the concordance index (C-index) and calibration of the nomogram was assessed by calibration curve and Hosmer–Lemeshow goodness-of-fit test. Results The following independent risk factors for transfusion were identified: Charlson comorbidity index score over 3 (odds ratio (OR) 2.15), tumor location (diffuse vs upper: OR 1.50), pTNM stage (III vs I: OR 3.17), type of gastrectomy (subtotal vs total gastrectomy: OR 0.58), extragastric organ resection (OR 2.03) and preoperative hemoglobin less than 80 g/l (vs over 120 g/l: OR 66.03). C-index was 0.863 and 0.901 in the primary and validation cohort, respectively, indicating good discrimination of the nomogram. Both calibration curves and Hosmer–Lemeshow goodness-of-fit tests (P-value 0.716 and 0.935) demonstrated high agreement between prediction and actual outcome. Conclusion A nomogram composed of preoperative variables to predict blood transfusion for gastric cancer surgery was developed and validated. This nomogram could be used to improve utilization of packed red blood cells.

scholarly journals Malnutrition Contributes to Low Lymphocyte Count in Early-Stage Coronavirus Disease-2019

2022 ◽  
Vol 8 ◽  
Author(s):  
Kai Zhang ◽  
Weidong Qin ◽  
Yue Zheng ◽  
Jiaojiao Pang ◽  
Ning Zhong ◽  
...  
Keyword(s):  
Risk Factors ◽  
Lymphocyte Count ◽  
Clinical Symptoms ◽  
Early Stage ◽  
Laboratory Data ◽  
Total Lymphocyte Count ◽  
Subjective Global Assessment ◽  
Nutrition Risk ◽  
Failure Assessment ◽  
Independent Risk Factors

Background and Aim: Lymphocytes play an important role in fighting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections. Low total lymphocyte count (TLC), which contributes to poor clinical outcomes, is common in persons with coronavirus disease 2019 (COVID-19). The current explanation for the cause of low TLC is that it is directly related to the invasiveness of SARS-CoV-2, which attacks lymphocytes. We hypothesized that malnutrition contributes to the development of low TLC in early-stage COVID-19.Methods: We prospectively enrolled 101 patients with confirmed COVID-19. On their first day of hospitalization, we collected baseline and laboratory data, including clinical symptoms; the Sequential Organ Failure Assessment, Nutrition Risk Screening 2002 and Subjective Global Assessment were used to assess the malnutrition status of the patients. Multivariable logistic regression was used to identify independent risk factors for low TLC and severe COVID-19.Results: Malnutrition was associated with lower TLC in COVID-19. Fifty-nine (58.4%) of the patients showed low TLC, 41 (40.6%) were at risk for malnutrition, and 18 of them were malnourished. Low TLC was an independent risk factor for severe COVID-19. Compared to patients with normal TLC, those with low TLC more often presented with anorexia, malnutrition, higher SOFA scores (P &lt; 0.05) and comorbidities (diabetes and malignancies). Malnutrition (OR: 3.05, 95% CI: 1.5–6.19, P = 0.006) and SOFA scores (OR: 1.51, 95% CI: 1.04-2.43, P = 0.042) were identified as independent risk factors for low TLC.Conclusions: Malnutrition was common among our patients with early-stage COVID-19, and it contributed to the occurrence of low TLC.

scholarly journals Pulmonary infection after cardiopulmonary bypass surgery in children: a risk estimation model in China

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Chunnian Ren ◽  
Chun Wu ◽  
Zhengxia Pan ◽  
Quan Wang ◽  
Yonggang Li
Keyword(s):  
Risk Factors ◽  
Cardiopulmonary Bypass ◽  
Prediction Model ◽  
Congenital Heart ◽  
Bypass Surgery ◽  
Pulmonary Infection ◽  
Regression Analyses ◽  
Cardiopulmonary Bypass Surgery ◽  
Independent Risk Factors ◽  
Individualized Prediction

Abstract Objectives The occurrence of pulmonary infection after congenital heart disease (CHD) surgery can lead to significant increases in intensive care in cardiac intensive care unit (CICU) retention time, medical expenses, and risk of death risk. We hypothesized that patients with a high risk of pulmonary infection could be screened out as early after surgery. Hence, we developed and validated the first risk prediction model to verify our hypothesis. Methods Patients who underwent CHD surgery from October 2012 to December 2017 in the Children’s Hospital of Chongqing Medical University were included in the development group, while patients who underwent CHD surgery from December 2017 to October 2018 were included in the validation group. The independent risk factors associated with pulmonary infection following CHD surgery were screened using univariable and multivariable logistic regression analyses. The corresponding nomogram prediction model was constructed according to the regression coefficients. Model discrimination was evaluated by the area under the receiver operating characteristic curve (ROC) (AUC), and model calibration was conducted with the Hosmer-Lemeshow test. Results The univariate and multivariate logistic regression analyses identified the following six independent risk factors of pulmonary infection after cardiac surgery: age, weight, preoperative hospital stay, risk-adjusted classification for congenital heart surgery (RACHS)-1 score, cardiopulmonary bypass time and intraoperative blood transfusion. We established an individualized prediction model of pulmonary infection following cardiopulmonary bypass surgery for CHD in children. The model displayed accuracy and reliability and was evaluated by discrimination and calibration analyses. The AUCs for the development and validation groups were 0.900 and 0.908, respectively, and the P-values of the calibration tests were 0.999 and 0.452 respectively. Therefore, the predicted probability of the model was consistent with the actual probability. Conclusions Identified the independent risk factors of pulmonary infection after cardiopulmonary bypass surgery. An individualized prediction model was developed to evaluate the pulmonary infection of patients after surgery. For high-risk patients, after surgery, targeted interventions can reduce the risk of pulmonary infection.

scholarly journals Basic Predictive Risk Factors for Cytokine Storms in COVID-19 Patients

2021 ◽  
Vol 12 ◽  
Author(s):  
Sergey G. Shcherbak ◽  
Anna Yu Anisenkova ◽  
Sergei V. Mosenko ◽  
Oleg S. Glotov ◽  
Alexander N. Chernov ◽  
...  
Keyword(s):  
Risk Factors ◽  
Lymphocyte Count ◽  
Absolute Lymphocyte Count ◽  
Early Warning Score ◽  
Positive Test ◽  
City Hospital ◽  
Cytokine Storm ◽  
Positive Test Result ◽  
Test Result ◽  
D Dimer

ObjectiveA critical role in coronavirus disease 2019 (COVID-19) pathogenesis is played by immune dysregulation that leads to a generalized uncontrolled multisystem inflammatory response, caused by overproduction of proinflammatory cytokines, known as “a cytokine storm” (CS), strongly associated with a severe course of disease. The aim of this study is to identify prognostic biomarkers for CS development in COVID-19 patients and integrate them into a prognostic score for CS-associated risk applicable to routine clinical practice.Materials and MethodsThe authors performed a review of 458 medical records from COVID-19 patients (241 men and 217 women aged 60.0 ± 10.0) who received treatment in the St. Petersburg State Budgetary Institution of Healthcare City Hospital 40 (City Hospital 40, St. Petersburg), from Apr. 18, 2020 to Nov. 21, 2020. The patients were split in two groups: one group included 100 patients with moderate disease symptoms; the other group included 358 patients with progressive moderately severe, severe, and extremely severe disease. The National Early Warning Score (NEWS) score was used alongside with clinical assessment, chest computed tomographic (CT) scans, electrocardiography (ECG), and lab tests, like ferritin, C-reactive protein (CRP), interleukin (IL)-6, lactate dehydrogenase (LDH), and D-dimer.ResultsThe basic risk factors for cytokine storms in COVID-19 patients are male gender, age over 40 years, positive test result for replicative severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA, absolute lymphocyte count, dynamics in the NEWS score, as well as LDH, D-dimer, ferritin, and IL-6 levels. These clinical and instrumental findings can be also used as laboratory biomarkers for diagnosis and dynamic monitoring of cytokine storms. The suggested prognostic scale (including the NEWS score dynamics; serum IL-6 greater than 23 pg/ml; serum CRP 50 mg/L or greater; absolute lymphocyte count less than 0.72 × 109/L; positive test result for replicative coronavirus (SARS-CoV-2) RNA; age 40 years and over) is a useful tool to identify patients at a high risk for cytokine storm, requiring an early onset of anti-inflammatory therapy.

scholarly journals Development and Validation of Nomogram to Preoperatively Predict Intraoperative Cerebrospinal Fluid Leakage in Endoscopic Pituitary Surgery: A Retrospective Cohort Study

2021 ◽  
Vol 11 ◽  
Author(s):  
Xiangming Cai ◽  
Junhao Zhu ◽  
Jin Yang ◽  
Chao Tang ◽  
Feng Yuan ◽  
...  
Keyword(s):  
Risk Factors ◽  
Cerebrospinal Fluid ◽  
Prediction Model ◽  
Calibration Curve ◽  
Validation Cohort ◽  
Laboratory Data ◽  
Pituitary Surgery ◽  
Cerebrospinal Fluid Leakage ◽  
Training Cohort ◽  
Preoperative Prediction

BackgroundPituitary adenomas (PAs) are the most common tumor of the sellar region. PA resection is the preferred treatment for patients with clear indications for surgery. Intraoperative cerebrospinal fluid (iCSF) leakage is a major complication of PA resection surgery. Risk factors for iCSF leakage have been studied previously, but a predictive nomogram has not yet been developed. We constructed a nomogram for preoperative prediction of iCSF leakage in endoscopic pituitary surgery.MethodsA total of 232 patients who underwent endoscopic PA resection at the Department of Neurosurgery in Jinling Hospital between January of 2018 and October of 2020 were enrolled in this retrospective study. Patients treated by a board-certified neurosurgeon were randomly classified into a training cohort or a validation cohort 1. Patients treated by other qualified neurosurgeons were included in validation cohort 2. A range of demographic, clinical, radiological, and laboratory data were acquired from the medical records. The Least Absolute Shrinkage and Selection Operator (LASSO) algorithm and uni- and multivariate logistic regression were utilized to analyze these features and develop a nomogram model. We used a receiver operating characteristic (ROC) curve and calibration curve to evaluate the predictive performance of the nomogram model.ResultsVariables were comparable between the training cohort and validation cohort 1. Tumor height and albumin were included in the final prediction model. The area under the curve (AUC) of the nomogram model was 0.733, 0.643, and 0.644 in training, validation 1, and validation 2 cohorts, respectively. The calibration curve showed satisfactory homogeneity between the predicted probability and actual observations. Nomogram performance was stable in the subgroup analysis.ConclusionsTumor height and albumin were the independent risk factors for iCSF leakage. The prediction model developed in this study is the first nomogram developed as a practical and effective tool to facilitate the preoperative prediction of iCSF leakage in endoscopic pituitary surgery, thus optimizing treatment decisions.

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 Risk factors and clinical features of deterioration in COVID-19 patients in Zhejiang, China: a single- centre, retrospective study

2020 ◽  
Author(s):  
Ping Yi ◽  
Xiang Yang ◽  
Cheng Ding ◽  
Yanfei Chen ◽  
Kaijin Xu ◽  
...  
Keyword(s):  
Risk Factors ◽  
T Lymphocyte ◽  
Lymphocyte Count ◽  
Clinical Characteristics ◽  
B Lymphocyte ◽  
Blood Cell Count ◽  
Single Centre ◽  
Multiple Factors ◽  
Independent Risk Factors ◽  
Severity Of The Disease

Abstract BackgroundSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection swept through Wuhan and spread across China and overseas beginning in December 2019. To identify predictors associated with disease progression, we evaluated clinical risk factors for exacerbation of SARS-CoV-2 infection.MethodsA retrospective analysis was used for PCR-confirmed COVID-19 (coronavirus disease 2019)-diagnosed hospitalized cases between January 19, 2020, and February 19, 2020, in Zhejiang, China. We systematically analysed the clinical characteristics of the patients and predictors of clinical deterioration.ResultsOne hundred patients with COVID-19, with a median age of 54 years, were included. Among them, 49 patients (49%) had severe and critical disease. Age ([36-58] vs [51-70], P=0.0001); sex (49% vs 77.6%, P=0.0031); Body Mass Index (BMI ) ([21.53-25.51] vs [23.28-27.01], P=0.0339); hypertension (17.6% vs 57.1%, P<0.0001); IL-6 ([6.42-30.46] vs [16.2-81.71], P=0.0001); IL-10 ([2.16-5.82] vs [4.35-9.63], P<0.0001); T lymphocyte count ([305- 1178] vs [167.5-440], P=0.0001); B lymphocyte count ([91-213] vs [54.5-163.5], P=0.0001); white blood cell count ([3.9-7.6] vs [5.5-13.6], P=0.0002); D2 dimer ([172-836] vs [408-953], P=0.005), PCT ([0.03-0.07] vs [0.04-0.15], P=0.0039); CRP ([3.8-27.9] vs [17.3-58.9], P<0.0001); AST ([16, 29] vs [18, 42], P=0.0484); artificial liver therapy (2% vs 16.3%, P=0.0148); and glucocorticoid therapy (64.7% vs 98%, P<0.0001) were associated with the severity of the disease. Age and weight were independent risk factors for disease severity.ConclusionDeterioration among COVID-19-infected patients occurred rapidly after hospital admission. In our cohort, we found that multiple factors were associated with the severity of COVID19. Early detection and monitoring of these indicators may reduce the progression of the disease. Removing these factors may halt the progression of the disease. In addition, Oxygen support, early treatment with low doses of glucocorticoids and liver therapy, when necessary, may help reduce mortality in critically ill patients.

Transarterial embolization for postpartum hemorrhage: lessons learned

2018 ◽  
Vol 59 (12) ◽  
pp. 1451-1457
Author(s):  
Chen-Ju Fu ◽  
Wiwan Irama ◽  
Yon-Cheong Wong ◽  
Hsiao-Jung Tseng ◽  
Li-Jen Wang ◽  
...  
Keyword(s):  
Risk Factors ◽  
Postpartum Hemorrhage ◽  
Transarterial Embolization ◽  
Laboratory Data ◽  
Vital Signs ◽  
International Normalized Ratio ◽  
Lessons Learned ◽  
Clinical Failure ◽  
Shock Index ◽  
Independent Risk Factors

Background Although transarterial embolization (TAE) can powerfully control postpartum hemorrhage (PPH), clinical failure of TAE is not uncommon. Purpose To discover whether any parameters could predict timely clinical failure of TAE, then whether a supplementary intervention could be promptly initiated. Material and Methods We retrospectively analyzed 118 TAE procedures in 113 patients with PPH performed at our institution between January 2012 and May 2015. The patients were divided into the following groups: clinically successful TAE and failed TAE. Successful TAE was defined as obviation of supplementary embolization or surgical intervention for hemostasis. Gestational conditions, angiographic factors, maternal vital signs, and laboratory data were compared between the two groups. Results In total, 100 (84.8%) TAEs were clinically successful. Multivariate logistic regression analyses revealed independent risk factors of TAE clinical failure, including the requirement for augmented embolic agents, placental retention, and international normalized ratio > 1.3 ( P = 0.009, 0.001, and 0.005, respectively). The post-TAE shock index was significantly associated with TAE failure, using a cut-off value of 0.8. Conclusion The discovered independent risk factors of TAE clinical failure existed before or during the TAE procedure and could not reflect the post-TAE conditions. Although the post-TAE shock index was not an independent factor, it reflected the conditions after TAE and could indicate TAE clinical failure timely.

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