PDF(Pubmed)
Abstract：
UNASSIGNED: The purpose of this study is to establishment and validation of an early predictive model for severe acute pancreatitis (SAP).
UNASSIGNED: From January 2015 to August 2022, 2986 AP patients admitted to Changsha Central Hospital were enrolled in this study. They were randomly divided into a modeling group (n = 2112) and a validation group (n = 874). In the modeling group, identify risk factors through logistic regression models and draw column charts. Use internal validation method to verify the accuracy of column chart prediction. Apply calibration curves to evaluate the consistency between nomograms and ideal observations. Draw a DCA curve to evaluate the net benefits of the prediction model.
UNASSIGNED: Nine variables including respiratory rate, heart rate, WBC, PDW, PT, SCR, AMY, CK, and TG are the risk factors for SAP. The column chart risk prediction model which was constructed based on these 9 independent factors has high prediction accuracy (modeling group AUC = 0.788, validation group AUC = 7.789). The calibration curve analysis shows that the prediction probabilities of the modeling and validation groups are consistent with the observation probabilities. By drawing a DCA curve, it shows that the model has a wide threshold range (0.01-0.88).
UNASSIGNED: The study developed an intuitive nomogram containing readily available laboratory parameters to predict the incidence rate of SAP.
UNASSIGNED: From January 2015 to August 2022, 2986 AP patients admitted to Changsha Central Hospital were enrolled in this study. They were randomly divided into a modeling group (n = 2112) and a validation group (n = 874). In the modeling group, identify risk factors through logistic regression models and draw column charts. Use internal validation method to verify the accuracy of column chart prediction. Apply calibration curves to evaluate the consistency between nomograms and ideal observations. Draw a DCA curve to evaluate the net benefits of the prediction model.
UNASSIGNED: Nine variables including respiratory rate, heart rate, WBC, PDW, PT, SCR, AMY, CK, and TG are the risk factors for SAP. The column chart risk prediction model which was constructed based on these 9 independent factors has high prediction accuracy (modeling group AUC = 0.788, validation group AUC = 7.789). The calibration curve analysis shows that the prediction probabilities of the modeling and validation groups are consistent with the observation probabilities. By drawing a DCA curve, it shows that the model has a wide threshold range (0.01-0.88).
UNASSIGNED: The study developed an intuitive nomogram containing readily available laboratory parameters to predict the incidence rate of SAP.
摘要：
■本研究的目的是建立和验证重症急性胰腺炎（SAP）的早期预测模型。
■2015年1月至2022年8月，纳入长沙市中心医院收治的2986例AP患者。他们被随机分为建模组（n=2112）和验证组（n=874）。在建模组中，通过logistic回归模型识别风险因素并绘制柱状图。使用内部验证方法验证柱状图预测的准确性。应用校正曲线评估列线图和理想观测值之间的一致性。绘制DCA曲线，评估预测模型的净收益。
■九个变量，包括呼吸频率，心率,WBC,PDW,PT,SCR,艾米,CK,和TG是SAP的危险因素。基于这9个独立因素构建的柱状图风险预测模型具有较高的预测精度(建模组AUC=0.788,验证组AUC=7.789)。校准曲线分析表明，建模和验证组的预测概率与观察概率一致。通过绘制DCA曲线,表明该模型具有较宽的阈值范围(0.01-0.88)。
■该研究开发了一个直观的列线图，其中包含易于获得的实验室参数，以预测SAP的发生率。
■2015年1月至2022年8月，纳入长沙市中心医院收治的2986例AP患者。他们被随机分为建模组（n=2112）和验证组（n=874）。在建模组中，通过logistic回归模型识别风险因素并绘制柱状图。使用内部验证方法验证柱状图预测的准确性。应用校正曲线评估列线图和理想观测值之间的一致性。绘制DCA曲线，评估预测模型的净收益。
■九个变量，包括呼吸频率，心率,WBC,PDW,PT,SCR,艾米,CK,和TG是SAP的危险因素。基于这9个独立因素构建的柱状图风险预测模型具有较高的预测精度(建模组AUC=0.788,验证组AUC=7.789)。校准曲线分析表明，建模和验证组的预测概率与观察概率一致。通过绘制DCA曲线,表明该模型具有较宽的阈值范围(0.01-0.88)。
■该研究开发了一个直观的列线图，其中包含易于获得的实验室参数，以预测SAP的发生率。
