Development and Validation of a Simple-to-use Nomogram for Predicting In-hospital Mortality in Acute Heart Failure Patients Undergoing Continuous Renal Replacement Therapy

NCT ID: NCT04751838

Last Updated: 2021-02-12

Basic Information

• Recruitment Status: UNKNOWN
• Total Enrollment: 226 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2020-10-30
• Study Completion Date: 2021-03-30

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 patient's 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 17.8% in the intensive care unit (ICU). But the patients undergoing CRRT, the mortality up to 45%-62.1%. 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 others[16]. The increase in the discriminative value of MEWS and the resulting model for mortality was assessed by the net reclassification index (NRI).

Conditions

Heart Failure Acute

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: RETROSPECTIVE

Study Groups

Survivor cohort; Non-survivor Cohort

All patients were categorized according to the state of departure from the hospital, named survivor or non-survivor.

no intervention

Intervention Type: OTHER

no intervention

Training Cohort, Validation Cohort

the eligible patients randomly (7:3) into training cohort and validation cohort. The training cohort were used to build nomogram model, while the validation cohort validated the model.

no intervention

Intervention Type: OTHER

no intervention

Interventions

no intervention

no intervention

Intervention Type: OTHER

Eligibility Criteria

Inclusion Criteria

• Clinical diagnosis of acute heart failure
• undergoing CRRT

Exclusion Criteria

• died before CRRT

• Minimum Eligible Age: 15 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Qilu Hospital of Shandong University

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Locations

Qilu Hospital of Shandong University

Jinan, Shandong, China

Site Status: RECRUITING

Countries

China

Central Contacts

Luyao Gao

Role: CONTACT

15165110975@163.com

15165110975

Facility Contacts

Luyao Gao

Role: primary

15165110975@163.com

86-15165110975

References

Ponikowski P, Voors AA, Anker SD, Bueno H, Cleland JGF, Coats AJS, Falk V, Gonzalez-Juanatey JR, Harjola VP, Jankowska EA, Jessup M, Linde C, Nihoyannopoulos P, Parissis JT, Pieske B, Riley JP, Rosano GMC, Ruilope LM, Ruschitzka F, Rutten FH, van der Meer P; ESC Scientific Document Group. 2016 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure: The Task Force for the diagnosis and treatment of acute and chronic heart failure of the European Society of Cardiology (ESC)Developed with the special contribution of the Heart Failure Association (HFA) of the ESC. Eur Heart J. 2016 Jul 14;37(27):2129-2200. doi: 10.1093/eurheartj/ehw128. Epub 2016 May 20. No abstract available.

Reference Type: BACKGROUND

PMID: 27206819 (View on PubMed)

Schaubroeck HA, Gevaert S, Bagshaw SM, Kellum JA, Hoste EA. Acute cardiorenal syndrome in acute heart failure: focus on renal replacement therapy. Eur Heart J Acute Cardiovasc Care. 2020 Oct;9(7):802-811. doi: 10.1177/2048872620936371. Epub 2020 Jun 29.

Reference Type: BACKGROUND

PMID: 32597679 (View on PubMed)

Macedo E, Mehta RL. Continuous Dialysis Therapies: Core Curriculum 2016. Am J Kidney Dis. 2016 Oct;68(4):645-657. doi: 10.1053/j.ajkd.2016.03.427. Epub 2016 May 28. No abstract available.

Reference Type: BACKGROUND

PMID: 27241853 (View on PubMed)

Ronco C, Ricci Z. Renal replacement therapies: physiological review. Intensive Care Med. 2008 Dec;34(12):2139-46. doi: 10.1007/s00134-008-1258-6. Epub 2008 Sep 13.

Reference Type: BACKGROUND

PMID: 18791697 (View on PubMed)

Karkar A, Ronco C. Prescription of CRRT: a pathway to optimize therapy. Ann Intensive Care. 2020 Mar 6;10(1):32. doi: 10.1186/s13613-020-0648-y.

Reference Type: BACKGROUND

PMID: 32144519 (View on PubMed)

Other Identifiers

KYLL-202011-114

Identifier Type: -

Identifier Source: org_study_id