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

NCT04751838 | Unknown

• 1 other identifier: KYLL-202011-114
• Study Type: observational
• Target: 226
• Locations: 1 country
• Sites: 1

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.

Conditions

Heart Failure Acute

Outcome Measures

Primary Outcomes (1)

• in-hospital mortality

  according to the state of departure from the hospital, if the patient died, named in-hospital mortality

  During hospitalization, an average of 20 days

Study Arms (2)

Survivor cohort; Non-survivor Cohort

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

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.

Other: no intervention

Interventions

no intervention OTHER

no intervention

Survivor cohort; Non-survivor Cohort; Training Cohort, Validation Cohort

Eligibility Criteria

• Age: 15 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

All data of patients were derived from two databases. One was CRRT databases collected the data of patients between November 9, 2011, to August 1, 2020, in AHFU of Qilu Hospital. Another was the MIMIC III database (version 1.4), a single-center, free, large online international database.

You may qualify if:

• Clinical diagnosis of acute heart failure
• undergoing CRRT

You may not qualify if:

• died before CRRT

Sponsors & Collaborators

• Qilu Hospital of Shandong University: lead

Study Sites (1)

Qilu Hospital of Shandong University

Jinan, Shandong, 250012, China

RECRUITING

Related Publications (5)

• 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.

  PMID: 27206819 BACKGROUND

• 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.

  PMID: 32597679 BACKGROUND

• 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.

  PMID: 27241853 BACKGROUND

• 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.

  PMID: 18791697 BACKGROUND

• 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.

  PMID: 32144519 BACKGROUND

Central Study Contacts

Luyao Gao

CONTACT

15165110975; 15165110975@163.com

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: February 3, 2021
• First Posted: February 12, 2021
• Study Start: October 30, 2020
• Primary Completion: February 28, 2021
• Study Completion: March 30, 2021
• Last Updated: February 12, 2021

Record last verified: 2021-02

Locations

CN (1)