---
title: "Predicting CRRT Downtime in ICU Patients: Risk Factors and a Bedside Nomogram"
id: "pubmed-42661410"
canonical_url: "https://medichelpline.com/clinical-feed/pubmed-42661410"
content_type: "clinical_feed_article"
specialty: "Critical Care"
source_name: "PubMed / NCBI"
source_url: "https://pubmed.ncbi.nlm.nih.gov/42661410/"
doi: "10.1111/nicc.70664"
published_at: "2026-09-01T00:00:00.000Z"
evidence_level: "Journal Article"
license: "CC-BY-NC-4.0 / Informational Use"
---
# Predicting CRRT Downtime in ICU Patients: Risk Factors and a Bedside Nomogram
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/pubmed-42661410
- **Specialty:** [Critical Care](https://medichelpline.com/clinical-feed/critical-care.md)
- **Primary Source:** PubMed / NCBI
- **Source URL:** [Original Journal Publication](https://pubmed.ncbi.nlm.nih.gov/42661410/)
- **DOI:** [10.1111/nicc.70664](https://doi.org/10.1111%2Fnicc.70664)
- **Published At:** 2026-09-01T00:00:00.000Z
- **Evidence Rating:** Journal Article
## Executive GIST (TL;DR)
- Prolonged **continuous renal replacement therapy (CRRT)** downtime reduces treatment efficacy and is linked to worse outcomes; daily downtime >20% relates to 28‑day mortality. - Aim: develop and validate a bedside-accessible **nomogram** to identify CRRT treatment days at risk for suboptimal downtime control (binary outcome: cumulative downtime >2.4 h per standardized treatment day). - Retrospective single‑center cohort from one Chinese ICU (January 2019–December 2023): 145 patients contributing 595 CRRT treatment days; total treatment time 14,280 h and cumulative downtime 1,725.07 h. - Mean daily downtime: 2.90 ± 2.72 h, representing 12.08% ± 11.35% of treatment time; 232 treatment days (39.0%) met criteria for suboptimal downtime control. - Candidate predictors included demographics, labs, CRRT circuit and treatment factors; generalized estimating equations (GEE) were used to identify independent predictors. - Five independent risk factors for suboptimal CRRT downtime control were identified: daily number of filter replacements (OR 2.629), agitation (OR 2.331), plasma exchange (OR 4.654), catheter dysfunction (OR 10.528) and out‑of‑unit transport for procedures (OR 7.563). - The nomogram based on these predictors showed good discrimination: AUCs 0.789 (training), 0.813 (internal validation) and 0.835 (external validation); calibration and decision curve analyses indicated satisfactory performance. - Clinical relevance: ICU nurses can apply the nomogram at CRRT initiation to identify high‑risk treatment days and prioritise bedside procedures, sedation strategies, catheter care and measures to extend filter life to reduce downtime. - The abstract reports no conflicts of interest. Details beyond the abstract (full model coefficients, thresholds, or operationalisation at bedside) are provided in the full text; if not included in the source abstract, those specifics were not reported here.
## Clinical Analysis & Structured Key Points
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Affiliations Expand ### Affiliation * 1 Department of Intensive Care Medicine, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China. * PMID: **42661410** * PMCID: [ PMC13522733 ](https://pmc.ncbi.nlm.nih.gov/articles/PMC13522733/) * DOI: [ 10.1111/nicc.70664 ](https://doi.org/10.1111/nicc.70664) Item in Clipboard # Analysis of Factors Influencing Downtime During Continuous Renal Replacement Therapy in Critically Ill Patients and Construction of a Prediction Model Bingbing Pang et al. Nurs Crit Care. 2026 Sep. Show details Display options Display options Format Abstract PubMed PMID Nurs Crit Care Actions * [ Search in PubMed ](https://pubmed.ncbi.nlm.nih.gov/?term=%22Nurs+Crit+Care%22%5Bjour%5D&sort=date&sort_order=desc) * [ Search in NLM Catalog ](https://www.ncbi.nlm.nih.gov/nlmcatalog?term=%22Nurs+Crit+Care%22%5BTitle+Abbreviation%5D) * [ Add to Search ](https://pubmed.ncbi.nlm.nih.gov/42661410/) . 2026 Sep;31(5):e70664. doi: 10.1111/nicc.70664. ### Authors [Bingbing Pang](https://pubmed.ncbi.nlm.nih.gov/?term=Pang+B&cauthor_id=42661410)[ 1 ](https://pubmed.ncbi.nlm.nih.gov/42661410/#short-view-affiliation-1 "Department of Intensive Care Medicine, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China."), [Jie Zhang](https://pubmed.ncbi.nlm.nih.gov/?term=Zhang+J&cauthor_id=42661410)[ 1 ](https://pubmed.ncbi.nlm.nih.gov/42661410/#short-view-affiliation-1 "Department of Intensive Care Medicine, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China."), [Yanshuo Wu](https://pubmed.ncbi.nlm.nih.gov/?term=Wu+Y&cauthor_id=42661410)[ 1 ](https://pubmed.ncbi.nlm.nih.gov/42661410/#short-view-affiliation-1 "Department of Intensive Care Medicine, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China."), [Qiaoju Kang](https://pubmed.ncbi.nlm.nih.gov/?term=Kang+Q&cauthor_id=42661410)[ 1 ](https://pubmed.ncbi.nlm.nih.gov/42661410/#short-view-affiliation-1 "Department of Intensive Care Medicine, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China."), [Kaihua Dong](https://pubmed.ncbi.nlm.nih.gov/?term=Dong+K&cauthor_id=42661410)[ 1 ](https://pubmed.ncbi.nlm.nih.gov/42661410/#short-view-affiliation-1 "Department of Intensive Care Medicine, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China."), [Suzhi Guo](https://pubmed.ncbi.nlm.nih.gov/?term=Guo+S&cauthor_id=42661410)[ 1 ](https://pubmed.ncbi.nlm.nih.gov/42661410/#short-view-affiliation-1 "Department of Intensive Care Medicine, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China."), [Yanling Yin](https://pubmed.ncbi.nlm.nih.gov/?term=Yin+Y&cauthor_id=42661410)[ 1 ](https://pubmed.ncbi.nlm.nih.gov/42661410/#short-view-affiliation-1 "Department of Intensive Care Medicine, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China.") ### Affiliation * 1 Department of Intensive Care Medicine, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China. * PMID: **42661410** * PMCID: [ PMC13522733 ](https://pmc.ncbi.nlm.nih.gov/articles/PMC13522733/) * DOI: [ 10.1111/nicc.70664 ](https://doi.org/10.1111/nicc.70664) Item in Clipboard Full text links Cite Display options Display options Format Abstract PubMed PMID ## Abstract **Background:** Prolonged CRRT downtime compromises treatment efficacy and worsens outcomes. Daily downtime > 20% exacerbates acidosis and is associated with 28-day mortality. **Aims:** This study aimed to develop and validate a bedside-accessible nomogram for early identification of treatment days at risk of suboptimal CRRT downtime control in critically ill patients. **Study design:** This retrospective cohort study was conducted in one Chinese ICU (January 2019-December 2023). The binary outcome was cumulative downtime > 2.4 h per standardised treatment day. Variables included demographics, laboratory parameters, CRRT circuit and other treatment factors. GEE identified predictors; the model was displayed as a nomogram and assessed by AUC, calibration and decision curve analysis. **Results:** A total of 145 patients contributing 595 CRRT treatment days were included. Total treatment time was 14 280 h and cumulative downtime was 1725.07 h. Mean daily downtime was 2.90 ± 2.72 h (12.08% ± 11.35% of treatment time). Suboptimal CRRT downtime control occurred on 232 treatment days (39.0%). Multivariable GEE analysis identified five independent risk factors for suboptimal downtime control: daily number of filter replacements (OR = 2.629, 95% CI 1.822-3.794, p < 0.001), agitation (OR = 2.331, 95% CI 1.041-5.218, p = 0.040), plasma exchange (OR = 4.654, 95% CI 1.042-20.782, p = 0.044), catheter dysfunction (OR = 10.528, 95% CI 2.939-37.710, p < 0.001) and out-of-unit transport for procedures (OR = 7.563, 95% CI 2.663-21.484, p < 0.001). The nomogram yielded AUCs of 0.789 (95% CI 0.726-0.841), 0.813 (95% CI 0.725-0.897) and 0.835 (95% CI 0.768-0.896) in the training, internal validation and external validation sets, respectively. **Conclusions:** Daily number of filter replacements, agitation, plasma exchange, catheter dysfunction and out-of-unit transport are independent risk factors for suboptimal CRRT downtime control. This nomogram showed satisfactory discrimination, calibration and net clinical benefit in internal and external validation, offering ICU nurses a rapid bedside risk assessment tool. **Relevance to clinical practice:** ICU nurses can use this nomogram at CRRT initiation to identify high-risk patients and prioritise bedside procedures, sedation, catheter care and filter longevity to reduce downtime. **Keywords:** continuous renal replacement therapy; critical care nursing; downtime; influencing factors; prediction model. © 2026 The Author(s). Nursing in Critical Care published by John Wiley & Sons Ltd on behalf of British Association of Critical Care Nurses. [PubMed Disclaimer](https://pubmed.ncbi.nlm.nih.gov/disclaimer/) ## Conflict of interest statement The authors declare no conflicts of interest. ## Figures [ ![FIGURE 1](https://cdn.ncbi.nlm.nih.gov/pmc/blobs/53cf/13522733/6cb042264103/NICC-31-0-g003.gif) ](https://cdn.ncbi.nlm.nih.gov/pmc/blobs/53cf/13522733/572a610507e2/NICC-31-0-g003.webp) ** FIGURE 1 ** Nomogram for predicting suboptimal CRRT… ** FIGURE 1 ** Nomogram for predicting suboptimal CRRT downtime control. **FIGURE 1** Nomogram for predicting suboptimal CRRT downtime control. [ ![FIGURE 2](https://cdn.ncbi.nlm.nih.gov/pmc/blobs/53cf/13522733/988c739d258d/NICC-31-0-g010.gif) ](https://cdn.ncbi.nlm.nih.gov/pmc/blobs/53cf/13522733/eed1d978e14e/NICC-31-0-g010.webp) ** FIGURE 2 ** AUC of the training set. ** FIGURE 2 ** AUC of the training set. **FIGURE 2** AUC of the training set. [ ![FIGURE 3](https://cdn.ncbi.nlm.nih.gov/pmc/blobs/53cf/13522733/4a4a0f258084/NICC-31-0-g008.gif) ](https://cdn.ncbi.nlm.nih.gov/pmc/blobs/53cf/13522733/0e170f8b3c37/NICC-31-0-g008.webp) ** FIGURE 3 ** AUC of the internal validation… ** FIGURE 3 ** AUC of the internal validation set. **FIGURE 3** AUC of the internal validation set. [ ![FIGURE 4](https://cdn.ncbi.nlm.nih.gov/pmc/blobs/53cf/13522733/4bdbf19b7313/NICC-31-0-g005.gif) ](https://cdn.ncbi.nlm.nih.gov/pmc/blobs/53cf/13522733/ff971522a862/NICC-31-0-g005.webp) ** FIGURE 4 ** AUC of the external validation… ** FIGURE 4 ** AUC of the external validation set. **FIGURE 4** AUC of the external validation set. [ ![FIGURE 5](https://cdn.ncbi.nlm.nih.gov/pmc/blobs/53cf/13522733/3920dded40ff/NICC-31-0-g007.gif) ](https://cdn.ncbi.nlm.nih.gov/pmc/blobs/53cf/13522733/4c7d8a9010e3/NICC-31-0-g007.webp) ** FIGURE 5 ** Calibration curve of the training… ** FIGURE 5 ** Calibration curve of the training set. **FIGURE 5** Calibration curve of the training set. [ ![FIGURE 6](https://cdn.ncbi.nlm.nih.gov/pmc/blobs/53cf/13522733/b4cf2db41ac2/NICC-31-0-g009.gif) ](https://cdn.ncbi.nlm.nih.gov/pmc/blobs/53cf/13522733/07e6dcc21836/NICC-31-0-g009.webp) ** FIGURE 6 ** Calibration curve of the internal… ** FIGURE 6 ** Calibration curve of the internal validation set. **FIGURE 6** Calibration curve of the internal validation set. [ ![FIGURE 7](https://cdn.ncbi.nlm.nih.gov/pmc/blobs/53cf/13522733/d418da2cc083/NICC-31-0-g002.gif) ](https://cdn.ncbi.nlm.nih.gov/pmc/blobs/53cf/13522733/3e0fa1bf9e1f/NICC-31-0-g002.webp) ** FIGURE 7 ** Calibration curve of the external… ** FIGURE 7 ** Calibration curve of the external validation set. **FIGURE 7** Calibration curve of the external validation set. [ ![FIGURE 8](https://cdn.ncbi.nlm.nih.gov/pmc/blobs/53cf/13522733/bd30ea48995f/NICC-31-0-g001.gif) ](https://cdn.ncbi.nlm.nih.gov/pmc/blobs/53cf/13522733/775d943aa7b0/NICC-31-0-g001.webp) ** FIGURE 8 ** Decision curve of the training… ** FIGURE 8 ** Decision curve of the training set. **FIGURE 8** Decision curve of the training set. [ ![FIGURE 9](https://cdn.ncbi.nlm.nih.gov/pmc/blobs/53cf/13522733/7a72bfd8c0ff/NICC-31-0-g004.gif) ](https://cdn.ncbi.nlm.nih.gov/pmc/blobs/53cf/13522733/422a038839c1/NICC-31-0-g004.webp) ** FIGURE 9 ** Decision curve of the internal… ** FIGURE 9 ** Decision curve of the internal validation set. **FIGURE 9** Decision curve of the internal validation set. [ ![FIGURE 10](https://cdn.ncbi.nlm.nih.gov/pmc/blobs/53cf/13522733/2bfd044b58e6/NICC-31-0-g006.gif) ](https://cdn.ncbi.nlm.nih.gov/pmc/blobs/53cf/13522733/a932b04f1ea3/NICC-31-0-g006.webp) ** FIGURE 10 ** Decision curve of the external… ** FIGURE 10 ** Decision curve of the external validation set. **FIGURE 10** Decision curve of the external validation set. All figures (10) [See this image and copyright information in PMC](https://pubmed.ncbi.nlm.nih.gov/42661410/) ## Similar articles * [ Development and validation of a prediction model for the risk of citrate accumulation in critically ill patients with citrate anticoagulation for continuous renal replacement therapy: a retrospective cohort study based on MIMIC-IV database. ](https://pubmed.ncbi.nlm.nih.gov/40205353/) Hu ZQ, Ye ZL, Zou H, Liu SX, Mei CQ.Hu ZQ, et al.BMC Nephrol. 2025 Apr 9;26(1):183. doi: 10.1186/s12882-025-04106-2.BMC Nephrol. 2025.PMID: 40205353Free PMC article. * [ Construction and evaluation of a mortality prediction model for patients with acute kidney injury undergoing continuous renal replacement therapy based on machine learning algorithms. ](https://pubmed.ncbi.nlm.nih.gov/39155811/) Wang Y, Sun X, Lu J, Zhong L, Yang Z.Wang Y, et al.Ann Med. 2024 Dec;56(1):2388709. doi: 10.1080/07853890.2024.2388709. Epub 2024 Aug 19.Ann Med. 2024.PMID: 39155811Free PMC article. * [ Development and validation of a nomogram for circuit lifespan of regional citrate anticoagulation-continuous renal replacement therapy in intensive care patients with acute kidney injury. ](https://pubmed.ncbi.nlm.nih.gov/39511929/) Chen Z, Pan L, Zhang J, Chen Y, Liu Y, Jia P, Liu S, Wang B, Zheng P, Chen F, Zeng B, Zhang W, Yang Q, Huang X, Xie C.Chen Z, et al.Nurs Crit Care. 2025 Jul;30(4):e13196. doi: 10.1111/nicc.13196. Epub 2024 Nov 7.Nurs Crit Care. 2025.PMID: 39511929Free PMC article. * [ Interruptions and downtime of continuous renal replacement therapy in critically ill adults: A retrospective observational study. ](https://pubmed.ncbi.nlm.nih.gov/40022503/) Z
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