Analysis of Factors Influencing Downtime During Continuous Renal Replacement Therapy in Critically Ill Patients and Construction of a Prediction Model

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.

Authors

Institutions

Publication Details

Journal
Nursing in Critical Care
Published
2026-08-27
DOI
https://doi.org/10.1111/nicc.70664
Primary Topic
Acute Kidney Injury Research
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
article

Analysis of Factors Influencing Downtime During Continuous Renal Replacement Therapy in Critically Ill Patients and Construction of a Prediction Model

Bingbing Pang, Qiaoju Kang, Yanling Yin, Yanshuo Wu et al.
Nursing in Critical Care
Acute Kidney Injury Research
article

Analysis of Factors Influencing Downtime During Continuous Renal Replacement Therapy in Critically Ill Patients and Construction of a Prediction Model

Bingbing Pang, Qiaoju Kang, Yanling Yin, Yanshuo Wu, Kaihua Dong, Jie Zhang, Suzhi Guo
article en

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.

Nursing in Critical CareVol. 31(5)
Hebei Medical University (CN), Fourth Hospital of Hebei Medical University (CN)
Health Commission of Hebei Province
Good health and well-being
Openalex Percentile: Top 10%
Acute Kidney Injury Research
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.