Development and Validation of a nomogram for Predicting Catheter Dysfunction in Patients Receiving Continuous Renal Replacement Therapy in Intensive Care Units: an observational Cohort Study

Development and Validation of a nomogram for Predicting Catheter Dysfunction in Patients Receiving Continuous Renal Replacement Therapy in Intensive Care Units: an observational Cohort Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Development and Validation of a nomogram for Predicting Catheter Dysfunction in Patients Receiving Continuous Renal Replacement Therapy in Intensive Care Units: an observational Cohort Study Jie-Ru Yang, Mo-Qi Li, Ying Yin, Yu Zou, Wei Guo, Wei Wang, Jia-Chuan Xiong, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6913829/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Continuous renal replacement therapy (CRRT) has become an essential treatment modality for renal replacement in the intensive care unit (ICU). As the primary vascular access for CRRT, hemodialysis catheters benefit patients significantly, yet inherently pose risks of complications like catheter dysfunction. This study aimed to develop and validate a predictive model for catheter dysfunction that can provide clinicians with early signs. Methods The study was conducted using convenience sampling to select 300 patients from a tertiary Grade A hospital in China between October 2022 and November 2024. These patients, who were receiving CRRT in the ICU, were using a hemodialysis catheter. Patients were grouped by hemodialysis catheter dysfunction occurrence, with variables first screened via univariate analysis, followed by multivariate logistic regression to identify independent predictors of catheter dysfunction. With these predictors, a nomogram was constructed, and its predictive capability was assessed via the area under the curve (AUC), calibration curve, Hosmer-Lemeshow test, and decision curve analysis (DCA). Results Six factors of hemodialysis catheter dysfunction were identified: age, history of catheter placement, mechanical ventilation, red blood cell count, pre-pump pressure ratio > 0.66, and catheter retention time. The corresponding odds ratios (ORs) were 1.03, 2.89, 3.36, 1.60, 3.72, and 1.05 (all P < 0.05). Developed with six factors, the nomogram demonstrated in internal validation an AUC of 0.82 (95% CI: 0.757–0.884), a well-fitting calibration curve (Hosmer-Lemeshow χ² = 3.223, P = 0.92). Conclusions The developed nomogram can reliably predict the risk of hemodialysis catheter dysfunction in critically ill patients undergoing CRRT. Trial registration: Not applicable. CRRT Prediction Model Hemodialysis Catheter Catheter Dysfunction Figures Figure 1 Figure 2 Figure 3 Background Continuous renal replacement therapy (CRRT)has become an essential treatment modality for renal replacement in the intensive care unit (ICU) [ 1 ]. As the primary vascular access for CRRT, hemodialysis catheters benefit patients significantly yet inherently pose risks of complications like catheter dysfunction. Catheter dysfunction manifests as ineffective blood flow through the hemodialysis catheter [ 2 ]. catheter dysfunction is reported incidence ranging from 10.33–56% [ 3 – 5 ], It not only reduces treatment efficacy (e.g., coagulation in filters and lines), but also increases the risk of serious complications, such as catheter-related bloodstream infections, which exacerbate patient morbidity and may be life-threatening. Therefore, timely identification and intervention to address catheter dysfunction are crucial to improving CRRT care quality and patient prognosis. Previous research has focused on factors associated with tunnelled-cuffed catheters (TCC) dysfunction among maintenance hemodialysis populations [ 6 – 7 ], including coagulation time, catheter insertion site, and diabetes. In the context of CRRT, studies on catheter dysfunction have primarily focused on access sites and tip design influence dysfunction rates [ 8 – 9 ]. Despite a recent study’s identification of risk factors for dialysis catheter dysfunction within 48 hours of CRRT in critically ill patients, early prediction of such dysfunction in CRRT-requiring ICU patients with substantial medical resource needs remains under-focused [ 10 ]. This research intends to detect risk factors for catheter dysfunction through logistic regression analysis and create a visual dynamic nomogram prediction model. This model will provide evidence-based support for the early identification of high-risk populations for hemodialysis catheter dysfunction among CRRT patients and for formulating prevention strategies. Materials and methods Study design and patients A study flow diagram is demonstrated in Fig. 1. Patients receiving CRRT in the ICU of a tertiary Grade A hospital in China were recruited via convenience sampling between October 2022 and November 2024. Inclusion criteria: Adult patients who underwent hemodialysis catheter insertion at this hospital and received CRRT and associated care services. Hemodialysis catheters were retained for 48 hours to 4 weeks. Exclusion Criteria: Patients with pre-existing vascular lesions before catheter placement. Patients with incomplete survey content or missing clinical data make practical analysis impossible. Sample size was determined using the 10–20 fold independent variable rule [ 11 ]. Ultimately, 300 patients were included in the study. We fully informed all participants or their family members about the study content, and they voluntarily agreed to participate. Hemodialysis Catheter Dysfunction Diagnosis Hemodialysis catheter dysfunction is diagnosed [ 12 ] when the catheter cannot maintain a blood flow velocity of 200 ml/min or when extracorporeal arterial pressure drops below − 250 mmHg while venous pressure exceeds 250 mmHg (1 mmHg equals 0.133 kPa).Based on these criteria, hemodialysis catheter dysfunction can be diagnosed if :(1) there is difficulty or complete inability to draw blood before treatment; (2) intervention such as swapping catheter arterial and venous ports or thrombolytic therapy is required during treatment; or (3) there is failure to draw blood smoothly during treatment while arterial pressure is below 250 mmHg and/or venous pressure is above 250 mmHg. [ 2 , 12 ]. Statistical collection Based on the literature review, a self-developed CRRT catheter dysfunction data survey questionnaire (see Supplementary Table 1) was created through expert consultation with five physicians and four CRRT specialists (all with intermediate professional titles or higher). The questionnaire comprised eighteen potential influencing factors and was divided into four sections: (1) Baseline-related Factors: age, Gender, body mass index, smoking, Hypertension, and Diabetes. (2) Catheter-related Factors: Catheter placement history, Catheter site, Catheter retention time. (3) Laboratory-related Factors: Red blood cell count, Haemoglobin count, Platelet Count, C-reactive Protein count, Fibrinogen concentration count. (4) Treatment-related Factors: Mechanical ventilation, Anticoagulation, Pre-pump pressure ratio (PPR) > 0.66, Unplanned Discontinuations incidence rate. Data were collected by four trained researchers via review of the electronic medical records system and nursing documentation. Laboratory values were extracted from pre-hospital diagnostic test results, while CRRT nurses evaluated and documented catheter function during treatment sessions. All collected data underwent double-entry verification and cross-validation, with inconsistencies resolved by referencing original records to ensure data integrity. Statistical analysis Using SPSS 27.0 and R 4.0.3, statistical analyses were conducted. Continuous variables with normal distribution were presented as mean ± SD, and intergroup comparisons were performed via independent samples t-tests. Non-normally distributed continuous variables were presented as median (25th, 75th percentiles) and analyzed using Wilcoxon rank-sum tests. Frequency and percentage were employed to characterize categorical variables, and chi-square tests or Fisher’s exact test were used for intergroup comparisons. Binary logistic regression identified independent risk factors for catheter dysfunction in CRRT patients. Based on these predictors, a nomogram prediction model was constructed. Model performance was evaluated via the area under the curve (AUC), bootstrap resampling, the Hosmer-Lemeshow test, and decision curve analysis (DCA). All analyses used two-tailed tests at α = 0.05. Results Baseline demographics and clinical characteristics Among 311 enrolled patients, 300 were included in the final analysis after excluding invalid samples during data preprocessing, with 19 characteristic variables recorded: catheter function and 18 potential risk factors for catheter dysfunction. Patients had a median age of 61 years (interquartile range [IQR]: 48, 72.25), comprising 199 males and 101 females. Catheter dysfunction occurred in 55 patients, resulting in an incidence rate of 18.33%. Univariate Logistic Analysis of Hemodialysis Catheter Dysfunction in CRRT Patients Patients were divided into the catheter dysfunction and normal function groups according to whether catheter dysfunction occurred during CRRT treatment. Univariate logistic analysis was conducted between the two groups. Univariate logistic analysis (Table 1 ) showed that age, RBC count, haemoglobin level, catheter placement history, catheter retention duration, mechanical ventilation, and PPR > 0.66 during CRRT were significant risk factors for hemodialysis catheter dysfunction in CRRT patients (all P < 0.05) . Table 1 Baseline characteristics of included patients according to hemodialysis catheter dysfunction Variables Group Dysfunction (n = 55) Group Functional(n = 245) P-value Age, years 68 (56,78) 61 (47,71) 0.007 Male 36 (65.45%) 163 (66.53%) 0.879 BMI, kg/m2 22.9 (20.8,26) 23.6 (20.5,26.1) 0.916 Smoking 19 (34.55%) 63 (25.71%) 0.184 Hypertension 31 (56.36%) 130 (53.06%) 0.657 Diabetes 31 (56.36%) 130 (53.06%) 0.657 Catheter placement history 18 (32.73%) 32 (13.06%) < 0.001 RBC count, 10 12 /L 3.28 (2.47,3.97) 2.82 (2.32,3.44) 0.004 HB count, 10 9 /L 93 (76,112.5) 82 (66,100) 0.015 PLT count, 10 9 /L 160 (107,232) 165 (110,232) 0.776 CRP count, 10mg/L 28 (14.55,80.05) 22.9 (7.5,64.8) 0.156 FIB count, g/L 3.83 (3.09,4.44) 3.9 (3.27,4.75) 0.398 Catheter site 0.133 Femoral 38 (69.09%) 163 (66.53%) Internal Jugular 17 (30.91%) 82 (33.47%) Catheter retention time(days), per day 8 (4.5,15.5) 4 (2,8) < 0.001 Mechanical ventilation 22 (40%) 27 (11.02%) 0.66 33 (60%) 51 (20.82%) < 0.001 Unplanned Discontinuations incidence rate 0.33 (0,0.5) 0 (0,0.5) 0.085 Abbreviations: 1: BMI: Body mass index; 2: RBC: Red Blood Cell; 3: HB: Haemoglobin;4. CRP: C-reactive protein; 5: PLT: Platelet. 6: FIB: Fibrinogen; 7: PRP: pre-pump pressure ratio Multivariate Logistic Regression Analysis of Hemodialysis Catheter Dysfunction in Patients Receiving CRRT For multivariate logistic regression, variables with statistical significance from univariate analysis were selected. Independent variables coded as follows: Catheter placement history (0 = no history, 1 = history present); mechanical ventilation (0 = absent, 1 = present); PPR > 0.66 (0 = PPR ≤ 0.66, 1 = PPR > 0.66). Continuous variables included age, RBC, haemoglobin level, and catheter retention duration (days). Patient age, catheter placement history, RBC count, mechanical ventilation, PPR > 0.66, and catheter retention time were identified as significant predictors of catheter dysfunction in CRRT patients via multivariate logistic regression analysis (P < 0.05). The detailed results are presented in Table 2 . Table 2 Multivariate analysis of risk factors for hemodialysis catheter dysfunction Variables B OR [95%ci] P-value Age (years) 0.026 1.03[1,1.05] 0.026 Catheter placement history 1.062 2.89[1.29,6.49] 0.010 RBC count 0.473 1.60[1.12,2.3] 0.010 Mechanical ventilation 1.213 3.36[1.54,7.36] 0.002 PRP>0.66 1.313 3.72[1.84,7.49] < 0.001 Catheter retention time (days) 0.051 1.05[1.02,1.09] 0.002 Construction and validation of the Prediction Model. A multivariate logistic regression analysis was used to establish a predictive nomogram model for identifying hemodialysis catheter dysfunction in CRRT patients. Each risk factor shown in Fig. 1A was assigned a score on the nomogram based on its coefficient in the regression model. The probability of catheter dysfunction is determined by summarizing the scores for each factor, projecting the total score onto the total score axis, and drawing a straight line to the probability axis to calculate the total score. The predictive model's performance was comprehensively evaluated using four measures (discrimination, calibration performance, goodness of fit, and net clinical benefit). The model had an AUC of 0.82 (95% CI: 0.757–0.884)(shown in Fig. 1B), demonstrating excellent discriminative ability, while a maximum Youden index of 0.118, 65.3% sensitivity, and 85.5% specificity further validated its discrimination. Calibration curve analysis ((shown in Fig. 2 A) revealed that predicted probabilities were in strong agreement with observed outcomes. The Hosmer-Lemeshow test suggested a good fit of the model, as evidenced by a chi-square statistic of 3.223 (P = 0.92). DCA (shown in Fig. 2 B) showed that over a broad range of risk thresholds (0.03–0.95), the model provided better clinical net benefit than the "all treatment" or "no treatment" strategy. Collectively, these results validate the model's clinical utility for predicting catheter dysfunction in CRRT patients. Discussion The model demonstrated excellent discriminative performance (AUC = 0.82, 95% CI: 0.757–0.884), with 65.3% sensitivity and 85.5% specificity in effectively distinguishing patients with and without catheter dysfunction. The Hosmer-Lemeshow test confirmed good calibration, with a χ² statistic of 3.223 (P = 0.92) indicating agreement between predicted probabilities and observed outcomes. A dynamic nomogram tool was also developed based on the hospital's CRRT information system. Medical staff can access and operate remotely via hospital computers or mobile terminals. This significantly enhances the practicality of the predictive tool within clinical settings. The system's warning function promptly alerts medical staff to high-risk patients to formulate targeted interventions. In summary, the hemodialysis catheter dysfunction prediction model constructed in this study demonstrates excellent predictive efficacy and broad clinical applicability. Risk Factors of Hemodialysis Catheter Dysfunction A positive association was found between age and the risk of hemodialysis catheter dysfunction in CRRT patients (OR = 1.03, P < 0.05). Elderly patients (≥ 65 years) exhibit a higher risk of catheter dysfunction than younger counterparts (< 65 years), as reported in previous studies [ 13 ]. The elevated risk is associated with age-related physiological decline, characterised by dysregulation of calcium-phosphorus homeostasis, structural vascular wall modifications, and compromised valve function. Reducing physical activity in elderly patients exacerbates endothelial injury and increases coagulation factor levels, thereby inducing a hypercoagulable state that predisposes to catheter dysfunction [ 14 ]. To mitigate these risks, medical staff may enhance vascular status assessment and monitoring at the cannulation site in elderly patients, adopt individualised catheter maintenance protocols, and encourage patients to perform regular active/passive limb mobilisation to improve venous return and reduce catheter dysfunction risk. An increased risk of catheter dysfunction (OR = 2.89, P = 0.01) was observed in patients with a history of hemodialysis catheter placement. Repeated catheter placement can trigger inflammatory responses in adjacent vasculature and tissues, activating platelets and increasing venous thrombosis risk,, thus impairing catheter function [ 12 , 15 ]. This aligns with Huang et al. [ 16 ], who found that repeated punctures enhance venous wall permeability and disrupt post-catheterisation blood flow, significantly increasing thrombosis risk. Additionally, multiple CVC insertions triple central venous stenosis incidence [ 17 ]. Clinical guidelines [ 18 ] recommend avoiding repeated punctures to minimise catheter dysfunction. Medical staff should thoroughly assess patients' medical histories before CVC placement in CRRT patients. Puncturing the same anatomical site should be avoided when possible. For CRRT patients requiring repeated punctures at the same site, protective vascular agents (e.g., anti-inflammatory or antithrombotic drugs) may be promptly administered to mitigate recurrent vascular wall injury and reduce catheter dysfunction incidence. This study found that CRRT patients with higher RBC concentrations have an increased risk of catheter dysfunction (OR = 1.6, P = 0.01). Tefferi et al. [ 19 ] reported a 45% incidence of catheter malfunction in central venous catheters of patients with elevated red blood cell counts. Elevated erythrocyte counts and subsequent hyperviscosity in CRRT patients may impede blood flow, thereby increasing the risk of central venous catheter dysfunction. Prior studies have shown that standardised anticoagulation therapy effectively reduces the risk of catheter dysfunction in CRRT populations [ 20 ]. Medical staff should promptly monitor and identify patients with elevated erythrocyte cellularity during CRRT. Clinicians are advised to promptly adjust the anticoagulation regimen and tailor the selection of sealing solution and flushing frequency to individual patient characteristics to reduce catheter dysfunction risk. This study found that CRRT patients receiving mechanical ventilation have a significantly higher risk of catheter dysfunction (OR = 3.36, P < 0.01). The present findings are highly consistent with those reported by Malato et al. [ 21 ]. Mechanical ventilation elevates intrathoracic pressure, which decreases venous return driving force and volume, thereby reducing effective circulating blood volume and blood pressure. These physiological changes significantly increase the risk of catheter dysfunction risk [ 22 ]. Frequent coughing due to tracheal tube traction in mechanically ventilated patients [ 23 ], can lead to catheter displacement and dysfunction. It is recommended that airway management be enhanced, gentle suctioning techniques may minimise tracheal tube traction and reduce coughing. Positive-pressure connectors [ 24 ] are recommended to maintain intraluminal positive pressure and decrease dysfunction risk. Positive-pressure connectors should be promptly checked in patients with severe coughing or limb convulsions. Monitor catheter blood return and perform routine catheter care to prevent blood reflux-induced coagulation. For patients requiring prolonged mechanical ventilation, prone positioning strategies [ 25 ] may be considered to improve ventilation efficacy and reduce catheter dysfunction risk. The study found that CRRT patients with PPR > 0.66 have a significantly higher risk of hemodialysis catheter dysfunction (OR = 3.72, P < 0.01). The PPR [ 28 ] is the arterial pressure (mmHg)/blood flow velocity (ml/min) ratio, where arterial pressure reflects the pump’s blood-drawing force from the catheter’s arterial lumen and correlates with the dialysis machine’s preset flow rate. An increase in PPR is accompanied by an elevation in blood flow velocity. This implies that when the blood flow rate is constant, an increase in absolute arterial pressure leads to higher pump suction pressure required to draw blood from the catheter. Excessive negative pressure may cause the catheter to adhere to the blood vessel wall, increasing the risk of hemodialysis catheter dysfunction. A PPR > 0.66 was associated with arteriovenous fistula dysfunction in native fistula hemodialysis patients [ 26 ]. Zhang et al. [ 27 ] verified the utility of PPR in diagnosing catheter dysfunction in hemodialysis patients with tunneled cuffed central venous catheters (TCC). These findings highlight the clinical significance of monitoring dynamic changes in the PRP. Clinicians can continuously monitor PPR via an early warning system and proactively adjust CRRT protocols strategies. This approach may effectively reduce the incidence of hemodialysis catheter dysfunction and improve patient outcomes. This study revealed that CRRT patients with prolonged catheter retention time have a significantly elevated risk of catheter dysfunction (OR = 1.05, P < 0.01). This finding is consistent with the results reported by Parienti et al. [ 28 ]. Increased catheter retention time correlates with elevated risks of catheter-related complications. In CRRT patients, vascular endothelial damage occurs at catheter insertion and worsens gradually due to blood flow disturbances around the catheter during treatment. Irregular catheter surfaces enable platelet adhesion and intrinsic coagulation activation, driving fibrin sheath formation. Over time, this sheath migrates from the venous wall to the catheter tip with collagen deposition, increasing thrombosis and dysfunction risks. Therefore, avoiding prolonged catheter placement can reduce the risk of catheter dysfunction [ 29 ]. For patients requiring catheterization > 1 week, TCC is recommended as the vascular access for CRRT [ 30 ]. Limitations This study has several limitations. The exclusion of potential predictive factors related to catheter nursing from the analysis, attributable to missing data, might have undermined the model's comprehensiveness. The absence of multi-centre validation data restricts the generalizability of these findings. Investigations are recommended to gather data from multiple hospitals to validate and refine predictive factors within the model promptly in the future. Conclusions The predictive model demonstrated excellent performance in identifying high-risk patients for catheter dysfunction in ICU patients undergoing CRRT. The nomogram mainly incorporated six risk factors (e.g., age, catheter placement history, mechanical ventilation, PPR > 0.66, catheter retention time, and RBC count on admission). Furthermore, the model enables the construction of a dynamic interactive nomogram through the hospital's information system, which further enhances the model's accessibility and practicality, providing a foundation for early identification and intervention in catheter dysfunction. Abbreviations CRRT Continuous renal replacement therapy ICU Intensive care unit TCC Tunnelled-cuffed catheters AUC The area under the curve DCA Decision curve analysis OR Odds ratios RBC red blood cell PRP Pre-pump pressure ratio. Declarations Ethics approval and consent to participate The study was conducted according to the Declaration of Helsinki. The study protocol received approval from the Medical Ethics Committee of the Second Affiliated Hospital of Army Medical University, PLA (Approval Number: 2022-414-01). All participants were fully informed and signed written informed consent. Consent for publication Written informed consent for publication was obtained from patient. Availability of data and materials All data generated or analyzed during this study are included in this article. Further inquiries can be directed to the corresponding author. Competing interests The authors have no conflicts of interest to declare. Funding This research was partially funded by the Nursing Incubation Foundation of XinQiao Hospital, Army Medical University (2022HLPY002). Authors' contributions The final article has been approved by all authors, who have made substantial contributions to the review, are accountable for the presented work, and recognize their eligibility for authorship. Jie-Ru Yang: Conceptualisation, Formal analysis, Investigation, Writing- original draft preparation. Mo-Qi Li: Conceptualisation, Investigation, Writing-review & editing, Project administration. Ying Yin: Conceptualisation, Investigation, Resources, data curation, Writing-review & editing. Yu Zou: Conceptualisation, Investigation, Resources,Writing-review & editing. Wei Guo: Conceptualisation, Investigation, Resources, Writing-review & editing. Wei Wang: Conceptualisation, Investigation, Resources, Writing-review & editing. Jia-Chuan Xiong: Conceptualization, Methodology, Supervision, Writing-review & editing. Lu-Yu: Conceptualisation, Investigation, Supervision, Writing-review & editing. Wen-Chang He: Conceptualisation, Project administration, Supervision,Writing-review & editing. Quan-Chao Zhang: Methodology, Formal analysis, Writing-review & editing, Supervision. Acknowledgements The authors thank Zhi-Kai Yu for contributions to the study design and statistical advice. References Kovvuru K, Velez JCQ. Complications Associated with Continuous Renal Replacement Therapy. Semin Dial. 2021;34:489–94. https://doi.org/10.1111/sdi.12970 . Lok CE, Huber TS, Lee T, et al. 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J Vasc Surg. 2016;63:846. https://doi.org/10.1016/j.jvs.2016.01.007 . Massicotte MPatricia, Dix D, Monagle P, et al. Central Venous Catheter Related Thrombosis in children: Analysis of the Canadian Registry of Venous Thromboembolic Complications. J Pediatr. 1998;133:770–6. https://doi.org/10.1016/s0022-3476(98)70149-0 . Ye C, Mao Z, Zhang P, et al. A Retrospective Study of Palindrome symmetrical-tip Catheters for Chronic Hemodialysis Access in China. Ren Fail. 2015;37:941–6. https://doi.org/10.3109/0886022x.2015.104033 . Additional Declarations No competing interests reported. Supplementary Files SupplementaryTable1.pdf Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6913829","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":485305656,"identity":"7ff596ee-1c94-4817-85cb-99d7193c3016","order_by":0,"name":"Jie-Ru Yang","email":"","orcid":"","institution":"Army Medical University, Third Military Medical University)","correspondingAuthor":false,"prefix":"","firstName":"Jie-Ru","middleName":"","lastName":"Yang","suffix":""},{"id":485305657,"identity":"c263c744-f08e-4089-ba48-ef6ef7ee4704","order_by":1,"name":"Mo-Qi Li","email":"","orcid":"","institution":"Army Medical University, Third Military Medical University)","correspondingAuthor":false,"prefix":"","firstName":"Mo-Qi","middleName":"","lastName":"Li","suffix":""},{"id":485305658,"identity":"0386bee0-66ca-4735-b5fb-cf08c4a0b540","order_by":2,"name":"Ying Yin","email":"","orcid":"","institution":"Army Medical University, Third Military Medical University)","correspondingAuthor":false,"prefix":"","firstName":"Ying","middleName":"","lastName":"Yin","suffix":""},{"id":485305659,"identity":"730640a2-be34-490e-a795-ff297c9dffbb","order_by":3,"name":"Yu Zou","email":"","orcid":"","institution":"Army Medical University, Third Military Medical University)","correspondingAuthor":false,"prefix":"","firstName":"Yu","middleName":"","lastName":"Zou","suffix":""},{"id":485305660,"identity":"1f1966e0-f1a9-4b2c-8cf3-455f8787c398","order_by":4,"name":"Wei Guo","email":"","orcid":"","institution":"Army Medical University, Third Military Medical University)","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Guo","suffix":""},{"id":485305661,"identity":"568db7be-8db5-4604-aaf5-29aa8bb3b453","order_by":5,"name":"Wei Wang","email":"","orcid":"","institution":"Army Medical University, Third Military Medical University)","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Wang","suffix":""},{"id":485305662,"identity":"cc665cba-c434-46c6-b889-6d2f218d8604","order_by":6,"name":"Jia-Chuan Xiong","email":"","orcid":"","institution":"Army Medical University, Third Military Medical University)","correspondingAuthor":false,"prefix":"","firstName":"Jia-Chuan","middleName":"","lastName":"Xiong","suffix":""},{"id":485305663,"identity":"fb03697d-436d-4f65-b838-916c7f739c8e","order_by":7,"name":"Lu Yu","email":"","orcid":"","institution":"Army Medical University, Third Military Medical University)","correspondingAuthor":false,"prefix":"","firstName":"Lu","middleName":"","lastName":"Yu","suffix":""},{"id":485305664,"identity":"41f5c3e9-a1fe-4c57-b3e8-aeedf741f4d8","order_by":8,"name":"Wen-Chang He","email":"","orcid":"","institution":"Army Medical University, Third Military Medical University)","correspondingAuthor":false,"prefix":"","firstName":"Wen-Chang","middleName":"","lastName":"He","suffix":""},{"id":485305665,"identity":"4481a453-c6cc-4515-8d44-84e15d02380b","order_by":9,"name":"Quan-Chao Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+klEQVRIiWNgGAWjYDACCQglx8bMfPABVMyAKC3G/OxtyQYHSNGSOLPnjJkEUVrkZzc/e/ilzI5xw40cs+qPOdsSG9ibt0kw1NzBqYVxzjFzY5lzycwGN9LKbhzcdjuxgedYmQTDsWc4tTBLJJhJS7YdYDO4kbwNokUix0yCseEwTi1sEunfQFp4DG4kmBWAtci/wa+FB2im5Me2AxKSPUfMGCC28ODXIiGRUybNcC7ZABTIEme33TZu40krtkg4hluL/Iz0bZI/yuzq24BR+aFy223ZfvbDG298qMGtBRwEPGzIvgMRCXg1AAP6BxsBFaNgFIyCUTCyAQDNY1kF6oEGdwAAAABJRU5ErkJggg==","orcid":"","institution":"Army Medical University, Third Military Medical University)","correspondingAuthor":true,"prefix":"","firstName":"Quan-Chao","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2025-06-17 11:23:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6913829/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6913829/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87033698,"identity":"afa2267c-ed9f-41e2-b30e-77e9b09c3cf9","added_by":"auto","created_at":"2025-07-18 13:07:53","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":176972,"visible":true,"origin":"","legend":"\u003cp\u003eFlow diagram of the study\u003c/p\u003e","description":"","filename":"fig.1..png","url":"https://assets-eu.researchsquare.com/files/rs-6913829/v1/f2e056e2f6b482c5d9d95290.png"},{"id":87035634,"identity":"1c848a09-e372-4a70-a29e-a9b96078a921","added_by":"auto","created_at":"2025-07-18 13:15:53","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":486459,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram(a) and ROC curves (b) of the prediction model.\u003c/p\u003e","description":"","filename":"fig.2..png","url":"https://assets-eu.researchsquare.com/files/rs-6913829/v1/2236b78d85a973d9f7cfc967.png"},{"id":87033703,"identity":"8358c6fc-086a-45d9-8c3a-c5aa6ff27d9f","added_by":"auto","created_at":"2025-07-18 13:07:53","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":360727,"visible":true,"origin":"","legend":"\u003cp\u003eThe calibration (c) and decision curves (d) of the prediction model.\u003c/p\u003e","description":"","filename":"fig.3..png","url":"https://assets-eu.researchsquare.com/files/rs-6913829/v1/e077b7494b7fa740e6f722c0.png"},{"id":87711069,"identity":"c6c9a4fe-0e51-428e-b273-6624d1c3ef19","added_by":"auto","created_at":"2025-07-28 08:39:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2138403,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6913829/v1/5fa6a1b7-0ab3-4f00-9f9e-b178f7e93180.pdf"},{"id":87033701,"identity":"73b56235-7572-400f-9956-1c8fb26c57a6","added_by":"auto","created_at":"2025-07-18 13:07:53","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":112750,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6913829/v1/ff43f63e0a3f04f2ea46c02b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development and Validation of a nomogram for Predicting Catheter Dysfunction in Patients Receiving Continuous Renal Replacement Therapy in Intensive Care Units: an observational Cohort Study","fulltext":[{"header":"Background","content":"\u003cp\u003eContinuous renal replacement therapy (CRRT)has become an essential treatment modality for renal replacement in the intensive care unit (ICU) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. As the primary vascular access for CRRT, hemodialysis catheters benefit patients significantly yet inherently pose risks of complications like catheter dysfunction. Catheter dysfunction manifests as ineffective blood flow through the hemodialysis catheter [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. catheter dysfunction is reported incidence ranging from 10.33\u0026ndash;56% [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], It not only reduces treatment efficacy (e.g., coagulation in filters and lines), but also increases the risk of serious complications, such as catheter-related bloodstream infections, which exacerbate patient morbidity and may be life-threatening. Therefore, timely identification and intervention to address catheter dysfunction are crucial to improving CRRT care quality and patient prognosis.\u003c/p\u003e\u003cp\u003ePrevious research has focused on factors associated with tunnelled-cuffed catheters (TCC) dysfunction among maintenance hemodialysis populations [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], including coagulation time, catheter insertion site, and diabetes. In the context of CRRT, studies on catheter dysfunction have primarily focused on access sites and tip design influence dysfunction rates [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Despite a recent study\u0026rsquo;s identification of risk factors for dialysis catheter dysfunction within 48 hours of CRRT in critically ill patients, early prediction of such dysfunction in CRRT-requiring ICU patients with substantial medical resource needs remains under-focused [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThis research intends to detect risk factors for catheter dysfunction through logistic regression analysis and create a visual dynamic nomogram prediction model. This model will provide evidence-based support for the early identification of high-risk populations for hemodialysis catheter dysfunction among CRRT patients and for formulating prevention strategies.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e\u003cb\u003eStudy design and patients\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA study flow diagram is demonstrated in Fig.\u0026nbsp;1. Patients receiving CRRT in the ICU of a tertiary Grade A hospital in China were recruited via convenience sampling between October 2022 and November 2024. Inclusion criteria: Adult patients who underwent hemodialysis catheter insertion at this hospital and received CRRT and associated care services. Hemodialysis catheters were retained for 48 hours to 4 weeks. Exclusion Criteria: Patients with pre-existing vascular lesions before catheter placement. Patients with incomplete survey content or missing clinical data make practical analysis impossible. Sample size was determined using the 10\u0026ndash;20 fold independent variable rule [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Ultimately, 300 patients were included in the study. We fully informed all participants or their family members about the study content, and they voluntarily agreed to participate.\u003c/p\u003e\u003cp\u003e\u003cb\u003eHemodialysis Catheter Dysfunction Diagnosis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eHemodialysis catheter dysfunction is diagnosed [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] when the catheter cannot maintain a blood flow velocity of 200 ml/min or when extracorporeal arterial pressure drops below \u0026minus;\u0026thinsp;250 mmHg while venous pressure exceeds 250 mmHg (1 mmHg equals 0.133 kPa).Based on these criteria, hemodialysis catheter dysfunction can be diagnosed if :(1) there is difficulty or complete inability to draw blood before treatment; (2) intervention such as swapping catheter arterial and venous ports or thrombolytic therapy is required during treatment; or (3) there is failure to draw blood smoothly during treatment while arterial pressure is below 250 mmHg and/or venous pressure is above 250 mmHg. [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cb\u003eStatistical collection\u003c/b\u003e\u003c/p\u003e\u003cp\u003eBased on the literature review, a self-developed CRRT catheter dysfunction data survey questionnaire (see Supplementary Table\u0026nbsp;1) was created through expert consultation with five physicians and four CRRT specialists (all with intermediate professional titles or higher). The questionnaire comprised eighteen potential influencing factors and was divided into four sections: (1) Baseline-related Factors: age, Gender, body mass index, smoking, Hypertension, and Diabetes. (2) Catheter-related Factors: Catheter placement history, Catheter site, Catheter retention time. (3) Laboratory-related Factors: Red blood cell count, Haemoglobin count, Platelet Count, C-reactive Protein count, Fibrinogen concentration count. (4) Treatment-related Factors: Mechanical ventilation, Anticoagulation, Pre-pump pressure ratio (PPR)\u0026thinsp;\u0026gt;\u0026thinsp;0.66, Unplanned Discontinuations incidence rate.\u003c/p\u003e\u003cp\u003eData were collected by four trained researchers via review of the electronic medical records system and nursing documentation. Laboratory values were extracted from pre-hospital diagnostic test results, while CRRT nurses evaluated and documented catheter function during treatment sessions. All collected data underwent double-entry verification and cross-validation, with inconsistencies resolved by referencing original records to ensure data integrity.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eUsing SPSS 27.0 and R 4.0.3, statistical analyses were conducted. Continuous variables with normal distribution were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, and intergroup comparisons were performed via independent samples t-tests. Non-normally distributed continuous variables were presented as median (25th, 75th percentiles) and analyzed using Wilcoxon rank-sum tests. Frequency and percentage were employed to characterize categorical variables, and chi-square tests or Fisher\u0026rsquo;s exact test were used for intergroup comparisons. Binary logistic regression identified independent risk factors for catheter dysfunction in CRRT patients. Based on these predictors, a nomogram prediction model was constructed. Model performance was evaluated via the area under the curve (AUC), bootstrap resampling, the Hosmer-Lemeshow test, and decision curve analysis (DCA). All analyses used two-tailed tests at α\u0026thinsp;=\u0026thinsp;0.05.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eBaseline demographics and clinical characteristics\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAmong 311 enrolled patients, 300 were included in the final analysis after excluding invalid samples during data preprocessing, with 19 characteristic variables recorded: catheter function and 18 potential risk factors for catheter dysfunction. Patients had a median age of 61 years (interquartile range [IQR]: 48, 72.25), comprising 199 males and 101 females. Catheter dysfunction occurred in 55 patients, resulting in an incidence rate of 18.33%.\u003c/p\u003e\u003cp\u003e\u003cb\u003eUnivariate Logistic Analysis of Hemodialysis Catheter Dysfunction in CRRT Patients\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePatients were divided into the catheter dysfunction and normal function groups according to whether catheter dysfunction occurred during CRRT treatment. Univariate logistic analysis was conducted between the two groups. Univariate logistic analysis (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) showed that age, RBC count, haemoglobin level, catheter placement history, catheter retention duration, mechanical ventilation, and PPR\u0026thinsp;\u0026gt;\u0026thinsp;0.66 during CRRT were significant risk factors for hemodialysis catheter dysfunction in CRRT patients \u003cem\u003e(all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/em\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline characteristics of included patients according to hemodialysis catheter dysfunction\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGroup\u003c/p\u003e\u003cp\u003eDysfunction (n\u0026thinsp;=\u0026thinsp;55)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGroup\u003c/p\u003e\u003cp\u003eFunctional(n\u0026thinsp;=\u0026thinsp;245)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge, years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e68 (56,78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e61 (47,71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.007\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e36 (65.45%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e163 (66.53%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.879\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI, kg/m2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e22.9 (20.8,26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23.6 (20.5,26.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.916\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19 (34.55%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e63 (25.71%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.184\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e31 (56.36%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e130 (53.06%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.657\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e31 (56.36%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e130 (53.06%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.657\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCatheter placement history\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18 (32.73%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32 (13.06%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRBC count, 10\u003csup\u003e12\u003c/sup\u003e/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.28 (2.47,3.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.82 (2.32,3.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHB count, 10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e93 (76,112.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e82 (66,100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.015\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePLT count, 10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e160 (107,232)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e165 (110,232)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.776\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCRP count, 10mg/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e28 (14.55,80.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22.9 (7.5,64.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.156\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFIB count, g/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.83 (3.09,4.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.9 (3.27,4.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.398\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCatheter site\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.133\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemoral\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e38 (69.09%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e163 (66.53%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInternal Jugular\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17 (30.91%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e82 (33.47%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCatheter retention time(days), per day\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8 (4.5,15.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 (2,8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMechanical ventilation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e22 (40%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27 (11.02%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnticoagulation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e43 (78.18%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e174 (71.02%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.283\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePRP\u0026thinsp;\u0026gt;\u0026thinsp;0.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e33 (60%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e51 (20.82%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnplanned Discontinuations incidence rate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.33 (0,0.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0,0.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.085\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003eAbbreviations: 1: BMI: Body mass index; 2: RBC: Red Blood Cell; 3: HB: Haemoglobin;4. CRP: C-reactive protein; 5: PLT: Platelet. 6: FIB: Fibrinogen; 7: PRP: pre-pump pressure ratio\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eMultivariate Logistic Regression Analysis of Hemodialysis Catheter Dysfunction in Patients Receiving CRRT\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFor multivariate logistic regression, variables with statistical significance from univariate analysis were selected. Independent variables coded as follows: Catheter placement history (0\u0026thinsp;=\u0026thinsp;no history, 1\u0026thinsp;=\u0026thinsp;history present); mechanical ventilation (0\u0026thinsp;=\u0026thinsp;absent, 1\u0026thinsp;=\u0026thinsp;present); PPR\u0026thinsp;\u0026gt;\u0026thinsp;0.66 (0\u0026thinsp;=\u0026thinsp;PPR\u0026thinsp;\u0026le;\u0026thinsp;0.66, 1\u0026thinsp;=\u0026thinsp;PPR\u0026thinsp;\u0026gt;\u0026thinsp;0.66). Continuous variables included age, RBC, haemoglobin level, and catheter retention duration (days). Patient age, catheter placement history, RBC count, mechanical ventilation, PPR\u0026thinsp;\u0026gt;\u0026thinsp;0.66, and catheter retention time were identified as significant predictors of catheter dysfunction in CRRT patients via multivariate logistic regression analysis (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The detailed results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMultivariate analysis of risk factors for hemodialysis catheter dysfunction\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eB\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOR [95%ci]\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.026\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.03[1,1.05]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.026\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCatheter placement history\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.062\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.89[1.29,6.49]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.010\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRBC count\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.473\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.60[1.12,2.3]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.010\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMechanical ventilation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.213\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.36[1.54,7.36]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePRP\u0026gt;0.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.313\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.72[1.84,7.49]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCatheter retention time (days)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.051\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.05[1.02,1.09]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eConstruction and validation of the Prediction Model.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA multivariate logistic regression analysis was used to establish a predictive nomogram model for identifying hemodialysis catheter dysfunction in CRRT patients. Each risk factor shown in Fig.\u0026nbsp;1A was assigned a score on the nomogram based on its coefficient in the regression model. The probability of catheter dysfunction is determined by summarizing the scores for each factor, projecting the total score onto the total score axis, and drawing a straight line to the probability axis to calculate the total score. The predictive model's performance was comprehensively evaluated using four measures (discrimination, calibration performance, goodness of fit, and net clinical benefit). The model had an AUC of 0.82 (95% CI: 0.757\u0026ndash;0.884)(shown in Fig.\u0026nbsp;1B), demonstrating excellent discriminative ability, while a maximum Youden index of 0.118, 65.3% sensitivity, and 85.5% specificity further validated its discrimination. Calibration curve analysis ((shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eA) revealed that predicted probabilities were in strong agreement with observed outcomes. The Hosmer-Lemeshow test suggested a good fit of the model, as evidenced by a chi-square statistic of 3.223 (P\u0026thinsp;=\u0026thinsp;0.92). DCA (shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eB) showed that over a broad range of risk thresholds (0.03\u0026ndash;0.95), the model provided better clinical net benefit than the \"all treatment\" or \"no treatment\" strategy. Collectively, these results validate the model's clinical utility for predicting catheter dysfunction in CRRT patients.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe model demonstrated excellent discriminative performance (AUC\u0026thinsp;=\u0026thinsp;0.82, 95% CI: 0.757\u0026ndash;0.884), with 65.3% sensitivity and 85.5% specificity in effectively distinguishing patients with and without catheter dysfunction. The Hosmer-Lemeshow test confirmed good calibration, with a χ\u0026sup2; statistic of 3.223 (P\u0026thinsp;=\u0026thinsp;0.92) indicating agreement between predicted probabilities and observed outcomes. A dynamic nomogram tool was also developed based on the hospital's CRRT information system. Medical staff can access and operate remotely via hospital computers or mobile terminals. This significantly enhances the practicality of the predictive tool within clinical settings. The system's warning function promptly alerts medical staff to high-risk patients to formulate targeted interventions. In summary, the hemodialysis catheter dysfunction prediction model constructed in this study demonstrates excellent predictive efficacy and broad clinical applicability.\u003c/p\u003e\u003cp\u003e\u003cb\u003eRisk Factors of Hemodialysis Catheter Dysfunction\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA positive association was found between age and the risk of hemodialysis catheter dysfunction in CRRT patients (OR\u0026thinsp;=\u0026thinsp;1.03, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Elderly patients (\u0026ge;\u0026thinsp;65 years) exhibit a higher risk of catheter dysfunction than younger counterparts (\u0026lt;\u0026thinsp;65 years), as reported in previous studies [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The elevated risk is associated with age-related physiological decline, characterised by dysregulation of calcium-phosphorus homeostasis, structural vascular wall modifications, and compromised valve function. Reducing physical activity in elderly patients exacerbates endothelial injury and increases coagulation factor levels, thereby inducing a hypercoagulable state that predisposes to catheter dysfunction [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. To mitigate these risks, medical staff may enhance vascular status assessment and monitoring at the cannulation site in elderly patients, adopt individualised catheter maintenance protocols, and encourage patients to perform regular active/passive limb mobilisation to improve venous return and reduce catheter dysfunction risk.\u003c/p\u003e\u003cp\u003eAn increased risk of catheter dysfunction (OR\u0026thinsp;=\u0026thinsp;2.89, P\u0026thinsp;=\u0026thinsp;0.01) was observed in patients with a history of hemodialysis catheter placement. Repeated catheter placement can trigger inflammatory responses in adjacent vasculature and tissues, activating platelets and increasing venous thrombosis risk,, thus impairing catheter function [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. This aligns with Huang et al. [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], who found that repeated punctures enhance venous wall permeability and disrupt post-catheterisation blood flow, significantly increasing thrombosis risk. Additionally, multiple CVC insertions triple central venous stenosis incidence [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Clinical guidelines [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] recommend avoiding repeated punctures to minimise catheter dysfunction. Medical staff should thoroughly assess patients' medical histories before CVC placement in CRRT patients. Puncturing the same anatomical site should be avoided when possible. For CRRT patients requiring repeated punctures at the same site, protective vascular agents (e.g., anti-inflammatory or antithrombotic drugs) may be promptly administered to mitigate recurrent vascular wall injury and reduce catheter dysfunction incidence.\u003c/p\u003e\u003cp\u003eThis study found that CRRT patients with higher RBC concentrations have an increased risk of catheter dysfunction (OR\u0026thinsp;=\u0026thinsp;1.6, P\u0026thinsp;=\u0026thinsp;0.01). Tefferi et al. [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] reported a 45% incidence of catheter malfunction in central venous catheters of patients with elevated red blood cell counts. Elevated erythrocyte counts and subsequent hyperviscosity in CRRT patients may impede blood flow, thereby increasing the risk of central venous catheter dysfunction. Prior studies have shown that standardised anticoagulation therapy effectively reduces the risk of catheter dysfunction in CRRT populations [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Medical staff should promptly monitor and identify patients with elevated erythrocyte cellularity during CRRT. Clinicians are advised to promptly adjust the anticoagulation regimen and tailor the selection of sealing solution and flushing frequency to individual patient characteristics to reduce catheter dysfunction risk.\u003c/p\u003e\u003cp\u003eThis study found that CRRT patients receiving mechanical ventilation have a significantly higher risk of catheter dysfunction (OR\u0026thinsp;=\u0026thinsp;3.36, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). The present findings are highly consistent with those reported by Malato et al. [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Mechanical ventilation elevates intrathoracic pressure, which decreases venous return driving force and volume, thereby reducing effective circulating blood volume and blood pressure. These physiological changes significantly increase the risk of catheter dysfunction risk [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Frequent coughing due to tracheal tube traction in mechanically ventilated patients [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], can lead to catheter displacement and dysfunction. It is recommended that airway management be enhanced, gentle suctioning techniques may minimise tracheal tube traction and reduce coughing. Positive-pressure connectors [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] are recommended to maintain intraluminal positive pressure and decrease dysfunction risk. Positive-pressure connectors should be promptly checked in patients with severe coughing or limb convulsions. Monitor catheter blood return and perform routine catheter care to prevent blood reflux-induced coagulation. For patients requiring prolonged mechanical ventilation, prone positioning strategies [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] may be considered to improve ventilation efficacy and reduce catheter dysfunction risk.\u003c/p\u003e\u003cp\u003eThe study found that CRRT patients with PPR\u0026thinsp;\u0026gt;\u0026thinsp;0.66 have a significantly higher risk of hemodialysis catheter dysfunction (OR\u0026thinsp;=\u0026thinsp;3.72, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). The PPR [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] is the arterial pressure (mmHg)/blood flow velocity (ml/min) ratio, where arterial pressure reflects the pump\u0026rsquo;s blood-drawing force from the catheter\u0026rsquo;s arterial lumen and correlates with the dialysis machine\u0026rsquo;s preset flow rate. An increase in PPR is accompanied by an elevation in blood flow velocity. This implies that when the blood flow rate is constant, an increase in absolute arterial pressure leads to higher pump suction pressure required to draw blood from the catheter. Excessive negative pressure may cause the catheter to adhere to the blood vessel wall, increasing the risk of hemodialysis catheter dysfunction. A PPR\u0026thinsp;\u0026gt;\u0026thinsp;0.66 was associated with arteriovenous fistula dysfunction in native fistula hemodialysis patients [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Zhang et al. [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] verified the utility of PPR in diagnosing catheter dysfunction in hemodialysis patients with tunneled cuffed central venous catheters (TCC). These findings highlight the clinical significance of monitoring dynamic changes in the PRP. Clinicians can continuously monitor PPR via an early warning system and proactively adjust CRRT protocols strategies. This approach may effectively reduce the incidence of hemodialysis catheter dysfunction and improve patient outcomes.\u003c/p\u003e\u003cp\u003eThis study revealed that CRRT patients with prolonged catheter retention time have a significantly elevated risk of catheter dysfunction (OR\u0026thinsp;=\u0026thinsp;1.05, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). This finding is consistent with the results reported by Parienti et al. [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Increased catheter retention time correlates with elevated risks of catheter-related complications. In CRRT patients, vascular endothelial damage occurs at catheter insertion and worsens gradually due to blood flow disturbances around the catheter during treatment. Irregular catheter surfaces enable platelet adhesion and intrinsic coagulation activation, driving fibrin sheath formation. Over time, this sheath migrates from the venous wall to the catheter tip with collagen deposition, increasing thrombosis and dysfunction risks. Therefore, avoiding prolonged catheter placement can reduce the risk of catheter dysfunction [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. For patients requiring catheterization\u0026thinsp;\u0026gt;\u0026thinsp;1 week, TCC is recommended as the vascular access for CRRT [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cb\u003eLimitations\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study has several limitations. The exclusion of potential predictive factors related to catheter nursing from the analysis, attributable to missing data, might have undermined the model's comprehensiveness. The absence of multi-centre validation data restricts the generalizability of these findings. Investigations are recommended to gather data from multiple hospitals to validate and refine predictive factors within the model promptly in the future.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe predictive model demonstrated excellent performance in identifying high-risk patients for catheter dysfunction in ICU patients undergoing CRRT. The nomogram mainly incorporated six risk factors (e.g., age, catheter placement history, mechanical ventilation, PPR\u0026thinsp;\u0026gt;\u0026thinsp;0.66, catheter retention time, and RBC count on admission). Furthermore, the model enables the construction of a dynamic interactive nomogram through the hospital's information system, which further enhances the model's accessibility and practicality, providing a foundation for early identification and intervention in catheter dysfunction.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCRRT\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eContinuous renal replacement therapy\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eICU\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eIntensive care unit\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTCC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eTunnelled-cuffed catheters\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAUC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eThe area under the curve\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eDCA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eDecision curve analysis\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eOR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eOdds ratios\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eRBC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ered blood cell\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePRP\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePre-pump pressure ratio.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted according to the Declaration of Helsinki. The study protocol received approval from the Medical Ethics Committee of the Second Affiliated Hospital of Army Medical University, PLA (Approval Number: 2022-414-01). All participants were fully informed and signed written informed consent.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Written informed consent for publication was obtained from patient.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analyzed during this study are included in this article. Further inquiries can be directed to the corresponding author.\u003c/p\u003e\n\u003cp id=\"_Toc472330565\"\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors have no conflicts of interest to declare.\u003c/p\u003e\n\u003cp id=\"_Toc472330566\"\u003eFunding\u0026nbsp;\u003c/p\u003e\n\u003cp id=\"_Toc472330568\"\u003eThis research was partially funded by the Nursing Incubation Foundation of XinQiao Hospital, Army Medical University (2022HLPY002).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAuthors\u0026apos; contributions\u003c/p\u003e\n\u003cp\u003eThe final article has been approved by all authors, who have made substantial contributions to the review, are accountable for the presented work, and recognize their eligibility for authorship. \u003cem\u003eJie-Ru Yang:\u003c/em\u003e Conceptualisation, Formal analysis, Investigation, Writing- original draft preparation. \u003cem\u003eMo-Qi Li:\u0026nbsp;\u003c/em\u003eConceptualisation, Investigation, Writing-review \u0026amp; editing, Project administration. \u003cem\u003eYing Yin:\u0026nbsp;\u003c/em\u003eConceptualisation, Investigation, Resources, data curation, Writing-review \u0026amp; editing. \u003cem\u003eYu Zou:\u0026nbsp;\u003c/em\u003eConceptualisation, Investigation, Resources,Writing-review \u0026amp; editing. \u003cem\u003eWei Guo:\u0026nbsp;\u003c/em\u003eConceptualisation, Investigation, Resources, Writing-review \u0026amp; editing. \u003cem\u003eWei Wang:\u003c/em\u003e Conceptualisation, Investigation, Resources, Writing-review \u0026amp; editing.\u003cem\u003e\u0026nbsp;Jia-Chuan Xiong:\u003c/em\u003e Conceptualization, Methodology, Supervision, Writing-review \u0026amp; editing. \u003cem\u003eLu-Yu:\u003c/em\u003e Conceptualisation, Investigation, Supervision, Writing-review \u0026amp; editing.\u003cem\u003e\u0026nbsp;Wen-Chang He:\u0026nbsp;\u003c/em\u003eConceptualisation, Project administration, Supervision,Writing-review \u0026amp; editing.\u003cem\u003e\u0026nbsp;Quan-Chao Zhang:\u0026nbsp;\u003c/em\u003eMethodology, Formal analysis, Writing-review \u0026amp; editing, Supervision.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank Zhi-Kai Yu for contributions to the study design and statistical advice.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKovvuru K, Velez JCQ. 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Ren Fail. 2015;37:941\u0026ndash;6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3109/0886022x.2015.104033\u003c/span\u003e\u003cspan address=\"10.3109/0886022x.2015.104033\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"CRRT, Prediction Model, Hemodialysis Catheter, Catheter Dysfunction","lastPublishedDoi":"10.21203/rs.3.rs-6913829/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6913829/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e\u003cp\u003eContinuous renal replacement therapy (CRRT) has become an essential treatment modality for renal replacement in the intensive care unit (ICU). As the primary vascular access for CRRT, hemodialysis catheters benefit patients significantly, yet inherently pose risks of complications like catheter dysfunction. This study aimed to develop and validate a predictive model for catheter dysfunction that can provide clinicians with early signs.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe study was conducted using convenience sampling to select 300 patients from a tertiary Grade A hospital in China between October 2022 and November 2024. These patients, who were receiving CRRT in the ICU, were using a hemodialysis catheter. Patients were grouped by hemodialysis catheter dysfunction occurrence, with variables first screened via univariate analysis, followed by multivariate logistic regression to identify independent predictors of catheter dysfunction. With these predictors, a nomogram was constructed, and its predictive capability was assessed via the area under the curve (AUC), calibration curve, Hosmer-Lemeshow test, and decision curve analysis (DCA).\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e\u003cp\u003eSix factors of hemodialysis catheter dysfunction were identified: age, history of catheter placement, mechanical ventilation, red blood cell count, pre-pump pressure ratio\u0026thinsp;\u0026gt;\u0026thinsp;0.66, and catheter retention time. The corresponding odds ratios (ORs) were 1.03, 2.89, 3.36, 1.60, 3.72, and 1.05 (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Developed with six factors, the nomogram demonstrated in internal validation an AUC of 0.82 (95% CI: 0.757\u0026ndash;0.884), a well-fitting calibration curve (Hosmer-Lemeshow χ\u0026sup2; = 3.223, P\u0026thinsp;=\u0026thinsp;0.92).\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusions\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe developed nomogram can reliably predict the risk of hemodialysis catheter dysfunction in critically ill patients undergoing CRRT.\u003c/p\u003e\u003cp\u003e\u003cb\u003eTrial registration:\u003c/b\u003e\u003c/p\u003e\u003cp\u003eNot applicable.\u003c/p\u003e","manuscriptTitle":"Development and Validation of a nomogram for Predicting Catheter Dysfunction in Patients Receiving Continuous Renal Replacement Therapy in Intensive Care Units: an observational Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-18 13:07:48","doi":"10.21203/rs.3.rs-6913829/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"14d05a28-ab36-460b-8ce9-30560acff067","owner":[],"postedDate":"July 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-07-28T08:38:30+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-18 13:07:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6913829","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6913829","identity":"rs-6913829","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}