Ignite Creation Date:
2024-05-06 @ 3:47 PM
Last Modification Date:
2024-10-26 @ 1:56 PM
Study NCT ID:
NCT04751838
Status:
UNKNOWN
Last Update Posted:
2021-02-12
First Post:
2021-02-03
Brief Title:
Development and Validation of a Simple-to-use Nomogram for Predicting In-hospital Mortality in Acute Heart Failure Patients Undergoing Continuous Renal Replacement Therapy
Sponsor:
Qilu Hospital of Shandong University
Organization:
Qilu Hospital of Shandong University
Study Overview
Official Title:
Development and Validation of a Simple-to-use Nomogram for Predicting In-hospital Mortality in Acute Heart Failure Patients Undergoing Continuous Renal Replacement Therapy
Status:
UNKNOWN
Status Verified Date:
2021-02
Last Known Status:
RECRUITING
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
Brief Summary:
Acute heart failure AHF is one of the most common causes of hospitalization and life-threatening medical condition around worldwide The AHF patients admitted to the intensive care unit ICU usually be critically ill with multiorgan failure in which the kidneys are most frequently involved The goals of treatment of AHF in ICU were to improve hemodynamic stability and organ perfusion alleviate symptoms and limit cardiac and renal damage which can be achieved by continuous renal replacement therapy CRRT a continuous extracorporeal blood purification CRRT can mimic urine output to slowly and continuously remove patients plasma water providing accurate volume control and hemodynamic stability
Acute Heart Failure Global Survey of Standard Treatment ALARM-HF study showed that hospital mortality of AHF patients was about 178 in the intensive care unit ICU But the patients undergoing CRRT the mortality up to 45-621 For this reason an early model or score to a screening of AHF patients undergoing CRRT who at high mortality risk is crucial which can help clinicians to rapidly intervene and ameliorate disease outcomes The most popular tools especially that can predict mortality for critically ill patients are the Acute Physiology Assessment and Chronic Health Evaluation II APACHE II scoring systems and Simplified Acute Physiologic Score II SAPS II But variables in these scoring systems are complex which was not convenient to assess at any time Modified Early Warning Score MEWS much more concise than APACHE II and SAPS II not only can be used for early warning of the onset of AHF in patients with the risk of heart failure but also has a positive correlation with mortality in these patients However up to our knowledge there was no scores or model to predict the in-hospital mortality of AHF patient undergoing CRRT
Based on the acute heart failure unit AHFU of Qilu Hospital and the medical information mart for intensive care III MIMIC III database the investigators collected the data of AHF adults undergoing CRRT The present study aimed to develop and validate a simple-to-use nomogram model comprised of independent prognostic variables for predicting in-hospital mortality in AHF adults undergoing CRRT by using multivariate logistic regression analysis With this model the investigators can guide the early screening of high-risk patients in in-hospital mortality
Acute Heart Failure Global Survey of Standard Treatment ALARM-HF study showed that hospital mortality of AHF patients was about 178 in the intensive care unit ICU But the patients undergoing CRRT the mortality up to 45-621 For this reason an early model or score to a screening of AHF patients undergoing CRRT who at high mortality risk is crucial which can help clinicians to rapidly intervene and ameliorate disease outcomes The most popular tools especially that can predict mortality for critically ill patients are the Acute Physiology Assessment and Chronic Health Evaluation II APACHE II scoring systems and Simplified Acute Physiologic Score II SAPS II But variables in these scoring systems are complex which was not convenient to assess at any time Modified Early Warning Score MEWS much more concise than APACHE II and SAPS II not only can be used for early warning of the onset of AHF in patients with the risk of heart failure but also has a positive correlation with mortality in these patients However up to our knowledge there was no scores or model to predict the in-hospital mortality of AHF patient undergoing CRRT
Based on the acute heart failure unit AHFU of Qilu Hospital and the medical information mart for intensive care III MIMIC III database the investigators collected the data of AHF adults undergoing CRRT The present study aimed to develop and validate a simple-to-use nomogram model comprised of independent prognostic variables for predicting in-hospital mortality in AHF adults undergoing CRRT by using multivariate logistic regression analysis With this model the investigators can guide the early screening of high-risk patients in in-hospital mortality
Detailed Description:
The eligible patients randomly into training cohort and validation cohort The univariate logistics regression analyses were performed to determine the independent risk characteristics in the training cohort of the presence of in-hospital all-cause death Odds Ratios ORs and 95 confidence intervals CIs of these variables were estimated to quantify the strength of these associations All variables that showed a univariate relationship with in-hospital mortality or that were considered clinically relevant were candidates for stepwise multivariate analysis in the training cohort A nomogram model producing by using the rms package was formulated based on the results of independent risk factors in multivariate logistic regression Based on the nomogram model the total scores and prediction of in-hospital mortality risk of each patient were added by each eligible variable and then were converted to predicted probabilities both in the training cohort and validation cohort
To evaluated the model for the prediction value of in-hospital mortality firstly the investigators calculated the calibration of the model was measured by calibration with 1000 bootstrap samples to decrease the overfit bias Model fitting was assessed using the Hosmer-Lemeshow test to evaluate the goodness of fit Secondly the Harrell concordance index C index and receiver operating characteristic curve ROC curve to evaluate the predictive performance and discrimination of the nomogram The ROC curve analysis was used to calculate the optimal cutoff values that were determined by maximizing the Youden index Third the clinical effectiveness of the resulting model was evaluated by decision curve analysis DCA which was a method for evaluating alternative diagnostic or prognostic tools that had advantages over others16 The increase in the discriminative value of MEWS and the resulting model for mortality was assessed by the net reclassification index NRI
To evaluated the model for the prediction value of in-hospital mortality firstly the investigators calculated the calibration of the model was measured by calibration with 1000 bootstrap samples to decrease the overfit bias Model fitting was assessed using the Hosmer-Lemeshow test to evaluate the goodness of fit Secondly the Harrell concordance index C index and receiver operating characteristic curve ROC curve to evaluate the predictive performance and discrimination of the nomogram The ROC curve analysis was used to calculate the optimal cutoff values that were determined by maximizing the Youden index Third the clinical effectiveness of the resulting model was evaluated by decision curve analysis DCA which was a method for evaluating alternative diagnostic or prognostic tools that had advantages over others16 The increase in the discriminative value of MEWS and the resulting model for mortality was assessed by the net reclassification index NRI
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None