Heart Failure AcuteNCT04751838
Development and Validation of a Simple-to-use Nomogram for Predicting In-hospital Mortality in Acute Heart Failure Patients Undergoing Continuous Renal Replacement Therapy
An observational study of Heart Failure Acute, run by Qilu Hospital of Shandong University at 1 site in China.
Trial 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. Read more about this study
- Who is it for?
- Ages 15 years and older
- Conditions the study is for
- Heart Failure Acute
- How many people
- 226 participants
- Where
- 1 siteChina
- Study dates
- 30 Oct 2020to 30 Mar 2021
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 15 years and older
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
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.
Who may be able to join
- Clinical diagnosis of acute heart failure
- undergoing CRRT
Who cannot join
- died before CRRT
Trial locations and contacts
1 site in 1 country.
China1 site, 1 recruiting
- Recruiting
Qilu Hospital of Shandong University
Jinan, Shandong, 250012
Luyao Gao, 86-15165110975, 15165110975@163.com
Luyao Gao
About this study
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.
Read the 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
- Being tested
- no intervention
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
no interventionOther
no intervention
| Study group | Role | What they receive |
|---|---|---|
| Survivor cohort; Non-survivor Cohort | All patients were categorized according to the state of departure from the hospital, named survivor or non-survivor. Receives: 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. Receives: Other: no intervention |
What is being measured
The primary outcome is the main question the study is designed to answer.
in-hospital mortality
according to the state of departure from the hospital, if the patient died, named in-hospital mortality
Measured: During hospitalization, an average of 20 days
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted12 Feb 2021
- Study start30 Oct 2020
- Primary completion28 Feb 2021 (estimated)
- Study completion30 Mar 2021 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (5)Papers the sponsor has linked to this study.
- Other trials for Heart Failure AcuteStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 226 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Qilu Hospital of Shandong University
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04751838
- Sponsor's protocol ID
- KYLL-202011-114
- Status last verified
- Feb 2021
- First submitted
- 3 Feb 2021
- Last updated
- 12 Feb 2021
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Publications
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.
PubMed 27206819Schaubroeck 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.
PubMed 32597679Macedo 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.
PubMed 27241853Ronco 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.
PubMed 18791697Karkar 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.
PubMed 32144519
Study at a glance
From the registry record, last updated by the sponsor on 12 Feb 2021. Recruitment status is as the sponsor reported it: confirm with the study team.
- 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
- ClinicalTrials.gov ID
- NCT04751838
- Acronym / sponsor ID
- KYLL-202011-114
- Conditions
- Heart Failure Acute
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 226 participants (estimated)
- Lead sponsor
- Qilu Hospital of Shandong UniversityUniversity, hospital or other
- Study start
- 30 Oct 2020 (actual)
- Primary completion
- 28 Feb 2021 (estimated)
- Study completion
- 30 Mar 2021 (estimated)
- First posted
- 12 Feb 2021 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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).
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Survivor cohort; Non-survivor Cohort | – | Other: no interventionAll patients were categorized according to the state of departure from the hospital, named survivor or non-survivor. |
| Training Cohort, Validation Cohort | – | Other: no interventionthe 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. |
Who may qualify
- Ages
- 15 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- 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.
Key inclusion criteria (2 listed)
- Clinical diagnosis of acute heart failure
- undergoing CRRT
Key exclusion criteria (1 listed)
- died before CRRT
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| no interventionno intervention | Other | Survivor cohort; Non-survivor Cohort, Training Cohort, Validation Cohort |
Primary outcome
in-hospital mortality
Time frame: During hospitalization, an average of 20 days
according to the state of departure from the hospital, if the patient died, named in-hospital mortality
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 12 Feb 2021.
- Background: PubMed 27206819Ponikowski 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.
- Background: PubMed 32597679Schaubroeck 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.
- Background: PubMed 27241853Macedo 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.
- Background: PubMed 18791697Ronco 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.
- Background: PubMed 32144519Karkar 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.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Heart Failure Acute, run by Qilu Hospital of Shandong University at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 2 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 12 Feb 2021. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Luyao Gao (15165110975, 15165110975@163.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
in-hospital mortality (time frame: During hospitalization, an average of 20 days). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT04751838, which also lists 5 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 12 Feb 2021.