Hospital-based Health Technology Assessment of central dialysis fluid delivery system for hemodialysis patients

Hospital-based Health Technology Assessment of central dialysis fluid delivery system for hemodialysis patients | 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 Hospital-based Health Technology Assessment of central dialysis fluid delivery system for hemodialysis patients Wendi Cheng, Haiyin WANG, Jiangzi YUAN, Ying LI, Yashuang LUO, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6389072/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Oct, 2025 Read the published version in BMC Nephrology → Version 1 posted 17 You are reading this latest preprint version Abstract Background This study aims to evaluate the safety, effectiveness, and economic value of central dialysis fluid delivery system (CDDS) compared to single-patient dialysis fluid delivery system (SPDDS) for hemodialysis patients through hospital-based health technology assessment (HB-HTA). The findings provide a scientific basis for the hospital adoption and clinical application of CDDS. Methods Using the HB-HTA approach, we assessed the clinical value (safety and effectiveness) and economic value (cost and cost-effectiveness) of CDDS compared to SPDDS. Clinical value evaluation was based on evidence from a systematic literature review, while economic value was assessed through micro-costing from 3 hospitals. This included cost estimation for CDDS and SPDDS and an economic efficiency analysis under various scales of hemodialysis machine configurations. The analysis encompassed total costs, cost composition, cost savings, and their respective components to identify application scenarios where CDDS demonstrated economic value. Results Three studies compared the clinical effects of CDDS and SPDDS. Endotoxin Levels: Hassan et al. (2023) found significantly lower serum endotoxin levels in dialysis fluid in the CDDS group compared to SPDDS (0.05 vs. 0.11 EU/ml, P = 0.001). Ahmed et al. (2024) demonstrated significantly lower pre-dialysis (0.07 ± 0.05 vs. 0.20 ± 0.07 EU/ml, P < 0.001) and post-dialysis (0.04 ± 0.02 vs. 0.15 ± 0.03 EU/ml, P < 0.001) serum endotoxin levels in the CDDS group versus SPDDS. Inflammatory Markers: Hassan et al. (2023) showed a significant reduction in CRP levels at 3 months in the CDDS group (9.8 vs 4.7 mg/dL, P < 0.001), whereas no change was observed in the SPDDS group (9.6 vs 9.1 mg/dL, P = 0.54). Ni et al. (2024) reported a significant decrease in hs-CRP levels over time in the CDDS group (β CDDS = -0.793) compared to an increase in the SPDDS group (β SPDDS = 0.791), with a significant timegroup interaction effect (F Time*CDDS group = 13.389, P < 0.001). Anemia-Related Outcomes: Hassan et al. (2023) noted significant improvements in hemoglobin levels in the CDDS group(10.6 vs 12.3 mg/dL), whereas no change was observed in the SPDDS group (10.3 vs 10.0 mg/dL, P = 0.149). Hassan et al. (2023) showed a significant reduction in Erythropoietin Resistance Index (ERI) at 3 months in the CDDS group (9.7 vs 3.1, P<0.001), whereas significant improvements was observed in the SPDDS group (10.2 vs 12.3 mg/dL, P = 0.047). There are significant difference in ERI between the CDDS and SPDDS groups (P<0.001). Renal function and nutritional indicators: Ni et al. (2024) showed that no significant differences were observed in albumin or β2-microglobulin levels (β2-microglobulin: β = -0.658, F Time* CDDS group = 1.228, P = 0.269; albumin: β = 0.012, F Time* CDDS group = 1.429, P = 0.233). In terms of economic value, compared to SPDDS, CDDS reduced costs for a dialysis center equipped with 6 hemodialysis machines operating 2 shifts daily, serving 12 patients per day, achieving a 0.05% cost reduction. When the number of hemodialysis machines increased from 6 to 50, the cost reduction rate for CDDS increased from 0.05–21.08%, indicating greater economic benefits for larger-scale dialysis centers. For a dialysis center with 50 machines, CDDS saved approximately 11.61 USD per treatment session and 362,362 USD per year. Cost savings mainly arose from reductions in consumable costs (74%) and labor costs (24%). Conclusion Compared to SPDDS, CDDS improved the microinflammatory state and renal anemia outcomes in hemodialysis patients, potentially offering long-term clinical advantages. Under specific conditions, CDDS demonstrated economic value by reducing consumable and labor costs, making it cost-effective for dialysis centers with 6 or more machines serving at least 12 patients daily. The main sources of cost savings were reductions in consumable and labor costs, with clinical engineers playing an important role in implementation. In China, CDDS is suitable for promotion in dialysis centers of a certain scale. central dialysis fluid delivery system (CDDS) single-patient dialysis fluid delivery system (SPDDS) hemodialysis hospital-based health technology assessment (HB-HTA) clinical value economic value micro-costing Figures Figure 1 Figure 2 Figure 3 1 Background Hospital-based health technology assessment (HB-HTA) refers to the evaluation of various health technologies specifically tailored to a hospital's environment to support management decisions. HB-HTA provides hospital administrators with key evidence and analyses to assess the need for introducing new technologies, avoid the adoption of unsuitable technologies, and reduce the use of unnecessary ones, thereby enhancing the efficiency of hospital health resource allocation [ 1 ] . Compared to developing countries, HB-HTA receives more attention in developed nations, particularly in Europe and North America [ 2 – 11 ] . In recent years, interest in HB-HTA has increased in China, and related projects have provided scientific decision-making support for hospital adoption and management of new technologies. However, the number of researchers and institutions in this field remains limited, and the scope of research is narrow. In the future, more investment and encouragement for hospitals to conduct HB-HTA will be needed, along with the exploration of a system suitable for China's national conditions to enhance the scientific nature of hospital decision-making [ 12 ] . In March 2018, the Medical Administration Center under the National Health Commission initiated the first phase of HB-HTA pilot projects in seven hospitals, followed by a second phase in March 2019 involving 23 hospitals [ 13 ] , thereby creating a favorable policy environment for HB-HTA in China. In recent years, end-stage renal disease (ESRD) has emerged as a major global health challenge due to its high prevalence and mortality rates [ 14 – 15 ] . Hemodialysis (HD) remains the primary blood purification therapy for ESRD patients. China has the largest number of maintenance hemodialysis patients worldwide. At the 2024 Academic Annual Meeting of the Chinese Nephrology Association (CNA), academician Xiangmei Chen presented data from the China Research Data Services Platform. As of December 2023, there were 7,512 hemodialysis centers in China, with 916,600 patients, representing a prevalence rate of 635 per million population. Against this backdrop, China is urgently needed to reduce dialysis costs, improve efficiency, ensure high-quality dialysis, and lower overall healthcare expenditures. The quality of dialysate and the efficiency of dialysate delivery systems are critical for ensuring cost-effective and high-quality dialysis treatment [ 16 ] . Globally, there are three main types of dialysate delivery systems for hemodialysis: single-patient dialysis fluid delivery system (SPDDS), central dialysis concentrate supply system (CCDS), and central dialysis fluid delivery system (CDDS). SPDDS are widely used and considered the global standard for dialysis treatment [ 18 ] . CCDS have been implemented in most countries in Europe, the United States, and South Korea, while CDDS are primarily used in Japan [ 19 – 22 ] . In China, centralized dialysate delivery systems have been adopted relatively recently, but their clinical usage has been increasing annually, including both CCDS and CDDS. Currently, CCDS are more widely used. However, CCDS face several problems, including the high cost of new delivery equipment and the need for daily high-temperature disinfection. The resulting significant water and energy consumption and high operating costs are inconsistent with the principles of green dialysis. In contrast, CDDS offer high automation, safety, and efficiency. Their multi-filter design ensures ultrapure dialysate, and their one-touch dead-space-free disinfection is user-friendly [ 23 ] . CDDS have been developed over 50 years in Japan, with proven stability, safety, and dialysate purification [ 24 ] . A safe, effective, and cost-efficient dialysate delivery system is crucial for China's healthcare system and ESRD patients. This study aims to evaluate the safety, effectiveness, and economic value of CDDS compared to SPDDS in Chinese hemodialysis centers through HB-HTA. 2 Methods This study employed the HB-HTA approach to evaluate the clinical value (safety and effectiveness) and economic value (cost and cost-effectiveness) of CDDS versus SPDDS. Clinical value evaluation was based on evidence from a systematic literature review. Economic value was assessed through micro-costing from a hospital perspective, which included cost estimation for CDDS and SPDDS and an economic efficiency analysis under various scales of hemodialysis machine configurations. The analysis encompassed total costs, cost composition, cost savings, and their respective components to identify application scenarios where CDDS demonstrated economic value. 2.1 Systematic Literature Review A systematic literature review was conducted by searching the PubMed database using the free-text search terms "CDDS AND SPDDS". The search covered publications up to December 31, 2024. Inclusion criteria required original studies comparing the clinical value (safety and effectiveness) of CDDS and SPDDS. Studies with unrelated outcome measures were excluded. Two researchers independently performed the literature search and study selection. In cases of disagreement, senior researchers were consulted to resolve discrepancies. A self-designed data extraction form was used to collect basic information, primary outcome indicators, and other relevant data from the included studies. 2.2 Micro-Costing Analysis Micro-costing is an accurate method for estimating the costs of medical interventions and is particularly suited for comprehensive economic evaluations [ 25 ] . Among the primary methods of medical economic evaluation, micro-costing is especially applicable to studies involving labor-intensive services [ 26 – 27 ] . Globally, countries and regions such as the United Kingdom, Italy, Spain, Australia, Canada, Hong Kong (China), South Africa, and Malaysia have conducted micro-costing of ESRD-related services. These studies have covered topics such as hemodialysis versus peritoneal dialysis, rural versus urban dialysis settings, nocturnal dialysis, and hospital-based dialysis [ 28 – 36 ] . However, no micro-costing comparing CDDS and SPDDS dialysate delivery systems have been identified. 2.2.1 Study Subjects This study targeted the nephrology departments of hospitals. Field surveys and questionnaire-based interviews were conducted with management personnel and healthcare professionals from multiple medical institutions in Shanghai. The study clarified the cost components involved in using CDDS and SPDDS for hemodialysis, collected cost data, and calculated the total cost inputs. 2.2.2 Cost Composition The consideration of costs varies depending on the analytical perspective [ 37 ] . Focusing on healthcare institutions in China, we examined direct and indirect costs through a bottom-up approach for detailed cost analysis [ 38 – 39 ] . The dialysis workflow was outlined based on the Standard Operating Procedures for Blood Purification (2021 Edition) and the Management Standards for Hemodialysis Rooms in Medical Institutions (2019 Revised Edition) . Field research was conducted in hospitals in Shanghai with extensive experience in operating CDDS, involving interviews with department directors, medical staff, and engineers. The cost inputs for CDDS and SPDDS were categorized into the following components: labor costs (e.g., equipment operation, consumable handling, waste liquid disposal, and disinfection), equipment costs (e.g., depreciation and maintenance costs), consumable costs (e.g., tubing, heparin, AB solution/powder, saline, dialyzers, ultrapure dialysate, endotoxin filters, disinfectants), and other costs (e.g., storage space for equipment and consumables, training, and costs associated with operational errors). Details are provided in Table 1 . Table 1 Cost composition of CDDS and SPDDS Cost categories Cost components Labor costs Equipment training, preparation of dialysis materials, self-checks until priming, blood drawing, intra-dialysis management, blood return, waste liquid disposal, disinfection, equipment maintenance, troubleshooting, disinfectant replenishment, dialysate handling, etc. Equipment costs Equipment purchase price, depreciation period, depreciation cost, failure rate, maintenance costs Consumable costs Tubing, heparin, AB powder/solution, saline, dialyzers, waste liquid bags, ultrapure dialysate, peracetic acid, sodium hypochlorite, citric acid, dialysate filters Other costs Storage costs for equipment, AB solution/powder, and disinfectants; costs from operational errors 2.2.3 Data Collection The micro-costing utilized multiple data collection methods [ 40 – 41 ] . Three hospitals in Shanghai with mature CDDS implementation for maintenance hemodialysis in ESRD patients were selected as sample sites: Baoshan Branch of Huashan Hospital Affiliated with Fudan University, Baoshan Branch of Renji Hospital Affiliated with Shanghai Jiao Tong University School of Medicine, and Jiading District Central Hospital. We mainly relied on administrative databases from these hospitals. Surveys and interviews included expert consultations and group discussions with nephrology department directors, head nurses, and engineers. The cost input investigation questionnaire was finalized through expert interviews and discussions. The questionnaire was divided into three categories: (1) Department Director Questionnaire: Addressed staff allocation, bed numbers, service volume, etc. (2) Engineer Questionnaire: Covered equipment costs, depreciation, consumable costs, and other expenses. (3) Nurse Questionnaire: Focused on basic labor costs, consumable costs, and other expenses. The questionnaire was designed and distributed via the online tool Wenjuanxing for quantitative data collection. Details were in Supplementary material-Cost Input Survey Questionnaire. Data analysis was conducted using Excel. A total of 45 questionnaires were collected: 3 from department directors, 3 from engineers, and 39 from nurses (3 head nurses, 36 staff nurses). Three researchers conducted direct observations in sample hospitals to verify the cost composition and input for CDDS and SPDDS, including staff time allocation. 2.2.4 Model Input Parameters Direct costs included labor costs, equipment costs, and consumable costs. In terms of labor costs, CDDS reduced time input by 11, 27, and 21 minutes for nurses, engineers, and workers, respectively. In terms of equipment costs, the average costs of single-pump and double-pump hemodialysis machines for SPDDS were considered. Depreciation costs were calculated using the straight-line method, assuming an average depreciation period of 7 years [ 42 ] . In terms of maintenance costs, two scenarios were considered: average data from the three sample hospitals (where equipment usage periods varied), and data from one hospital (with approximately 6 years of equipment usage). In terms of consumable costs, average procurement data from the three hospitals were used and cross-referenced with tender prices in the Yaozh Device Database to determine costs. Due to the sensitivity of hospital data, proportional relationships were used instead of raw values. Details are shown in Supplementary material Table 1. Other cost inputs included training, operational errors, storage space, personnel wages, working hours, and healthcare land costs. Compared to SPDDS, CDDS reduced equipment training time by 4 days, monthly operational errors by 0.11 incidents, and storage space requirements by 8 square meters. In terms of personnel wages, nurses had the highest wages, up to 240,000 RMB/year, while workers had the lowest, approximately 50,000 RMB/year. Working hours were set at 52 weeks/year, 6 days/week, and 8 hours/day. Details are shown in Supplementary material Table 2. Table 2 Basic information of studies included in the systematic literature review on clinical value Country Study population Sample size Intervention group (sample size) Control group (sample size) Outcome indicators Study design Ahmed 2024 [ 43 ] Egypt ESRD 80 CDDS SPDDS ① Serum endotoxin levels Cross-sectional study Ni Y 2024 [ 17 ] China ESRD 125 CDDS(n = 58) SPDDS(n = 67) ② hs-CRP levels; ③ Albumin levels; ④ β2-microglobulin levels Retrospective cohort study Hassan MS 2023 [ 18 ] Egypt ESRD 100 CDDS(n = 50) SPDDS(n = 50) ① Serum endotoxin levels; ⑤ CRP levels; ⑥ Hemoglobin levels; ⑦ Erythropoietin Resistance Index (ERI) ⑧ Erythropoiesis-stimulating agents (ESAs) doses Prospective cohort study 2.2.5 Statistical Analysis Methods A micro-cost calculation model was developed using Microsoft Excel 16.80 for Mac. Three scenarios were analyzed: Scenario 1. Baseline Analysis: the number of hemodialysis shifts per day was set to 2, the cost of hemodialysis machines was set to the average cost of single-pump and double-pump machines, and the equipment maintenance cost data was based on the average maintenance cost from three hospitals. Scenario 2: Compared to the baseline, the number of hemodialysis shifts per day was increased to 3, while all other parameters remained unchanged. Scenario 3: Due to differences in equipment usage durations across the three hospitals, scenario analysis was conducted for maintenance cost data. Compared to the baseline, the maintenance cost data was set based on the cost data of one sample hospital, where the equipment had the longest usage period (approximately 6 years). 3 Results 3.1 Clinical Value A total of three studies comparing the clinical value of CDDS and SPDDS were included in the systematic literature review [ 17 , 18 , 43 ] . These studies included retrospective cohort studies from China and prospective cohort studies from Egypt. The outcome indicators mainly addressed microinflammation, anemia, nutritional status, and dialysis adequacy in hemodialysis patients, with a total of eight key indicators. The basic information of the included studies is shown in Table 2 . Three studies compared the clinical effects of CDDS and SPDDS. Endotoxin Levels: Hassan et al. (2023) found significantly lower serum endotoxin levels in dialysis fluid in the CDDS group compared to SPDDS (0.05 vs. 0.11 EU/ml, P = 0.001). Ahmed et al. (2024) demonstrated significantly lower pre-dialysis (0.07 ± 0.05 vs. 0.20 ± 0.07 EU/ml, P < 0.001) and post-dialysis (0.04 ± 0.02 vs. 0.15 ± 0.03 EU/ml, P < 0.001) serum endotoxin levels in the CDDS group versus SPDDS. Inflammatory Markers: Hassan et al. (2023) showed a significant reduction in CRP levels at 3 months in the CDDS group (9.8 vs 4.7 mg/dL, P < 0.001), whereas no change was observed in the SPDDS group (9.6 vs 9.1 mg/dL, P = 0.54). Ni et al. (2024) reported a significant decrease in hs-CRP levels over time in the CDDS group (βCDDS = -0.793) compared to an increase in the SPDDS group (βSPDDS = 0.791), with a significant timegroup interaction effect (FTime*CDDS group = 13.389, P < 0.001). Anemia-Related Outcomes: Hassan et al. (2023) noted significant improvements in hemoglobin levels in the CDDS group(10.6 vs 12.3 mg/dL), whereas no change was observed in the SPDDS group (10.3 vs 10.0 mg/dL, P = 0.149). Hassan et al. (2023) showed a significant reduction in Erythropoietin Resistance Index (ERI) at 3 months in the CDDS group (9.7 vs 3.1, P<0.001), whereas significant improvements was observed in the SPDDS group (10.2 vs 12.3 mg/dL, P = 0.047). There are significant difference in ERI between the CDDS and SPDDS groups (P<0.001). Renal function and nutritional indicators: Ni et al. (2024) showed that no significant differences were observed in albumin or β2-microglobulin levels (β2-microglobulin: β = -0.658, FTime* CDDS group = 1.228, P = 0.269; albumin: β = 0.012, FTime* CDDS group = 1.429, P = 0.233). These findings are summarized in Table 3 . Compared to SPDDS, CDDS improved the occurrence of microinflammation in dialysis patients, including reductions in serum endotoxin levels, CRP levels, and hs-CRP levels, which may lead to long-term clinical advantages. CDDS also improved the occurrence of renal anemia in patients, including increased hemoglobin levels, decreased ERI, and reduced doses of ESAs. There were no significant differences between the CDDS and SPDDS groups regarding nutritional indicators (albumin levels) or dialysis adequacy indicators (β2-microglobulin levels, a middle-molecule toxin). Table 3 Outcome indicators results from the clinical value systematic literature review Study Intervention vs. Control Serum endotoxin levels (EU/ml) CRP levels (mg/dL) hs-CRP levels (mg/L) Hemoglobin levels (mg/dL) Erythropoietin resistance index (ERI) β2-microglobulin levels (mg/dL) Albumin levels (mg/L) Ahmed 2024 [ 43 ] CDDS 0.04 ± 0.02 / / / / / / SPDDS 0.15 ± 0.03 / / / / / / P P<0.001, significant difference between groups* / / / / / / Ni Y 2024 [ 17 ] CDDS / / 3 months 2.6 6 months 2.0 β CDDS =-0.793 decrease in follow-up period / / 3 months 2.6 6 months 2.0 3 months 38.3 6 months 38.15 SPDDS / / 3 months 3.3 6 months 4.17 β SPDDS = 0.791 increase in follow-up period / / 3 months 3.3 6 months 4.17 3 months 38.2 6 months 37.7 P / / 3 months P = 0.037 6 months P<0.001 β coefficient P < 0.001, signifcant difference in level changes between two groups* / / 3 months P = 0.060 6 months P = 0.050 β coefficient P = 0.269, no signifcant difference in level changes between two groups 3 months P = 0.639 6 months P = 0.998 β coefficient P = 0.233, no signifcant difference in level changes between two groups Hassan MS 2023 [ 18 ] CDDS 0.05 Baseline 9.8 1 month 5.6 2 months 4.7 3 months 4.7 (P<0.001) / Baseline 10.6 1 month 11.1 2 months 11.7 3 months 12.3 (P = 0.008, <0.001, and <0.001) Baseline 9.7 1 month 7.1 2 months 5.0 3 months 3.1 (P = 0.002, <0.001, and <0.001) / / SPDDS 0.11 Baseline 9.6 1 month 9.0 2 months 8.6 3 months 9.1 (P<0.001) / Baseline 10.3 1 month 10.3 2 months 10.1 3 months 10.0 (P = 0.70, 0.14, and 0.149 Baseline 10.2 1 month 11.6 2 months 11.9 3 months 12.3 (P = 0.039, 0.025, and 0.047) / / P P = 0.001, significant difference between groups* P<0.001, significant difference between groups* / P<0.001, significant difference between groups* P<0.001, significant difference between groups* / / 3.2 Economic Value CDDS had economies of scale. Scenario 1 results show that when dialysis centers equipped both CDDS and SPDDS groups with six or more hemodialysis machines, CDDS could reduce costs. As the number of hemodialysis machines increased from six to 50, the cost savings with CDDS became more significant. When the number of hemodialysis machines ranged from 6 to 50, the cost reduction rate per treatment session ranged from approximately 0.05–21.08%. Similar trends were observed in Scenario 2 and Scenario 3. In these scenarios, when equipped with 5 or 10 or more hemodialysis machines, CDDS reduced costs, with cost reduction rates ranging from 4.40–22.47% and 1.03–13.37%, respectively. Details are shown in Fig. 1 . The results of Scenario 1 show that when dialysis centers equipped both CDDS and SPDDS groups with 6 to 50 hemodialysis machines, the cost savings per treatment session for CDDS ranged from 0.03–11.61 USD. The daily cost savings for CDDS ranged from 0.32-1,161.42 USD, and the annual cost savings ranged from 99.75–362,362.15 USD. Similar results were observed in Scenario 2 and Scenario 3. When equipped with 5 to 50 or 10 to 50 hemodialysis machines, CDDS saved 2.31–11.77 USD per treatment session, 34.62-1,765.34 USD per day, and 10,802.01–550,785.57 USD annually. Details are shown in Fig. 2 . Taking a dialysis center equipped with 50 hemodialysis machines for example, the results of Scenario 1 show that the cost per treatment with CDDS was approximately 43.48 USD, while the cost per treatment with SPDDS was approximately 55.10 USD. Similar results were observed in Scenario 2 and Scenario 3. The detailed cost differences between CDDS and SPDDS are shown in Table 4 . Table 4 Cost Difference Between CDDS and SPDDS-50 Haemodialysis Machines (USD) Cost per Treatment Cost per Day Cost per Year Scenario 1 CDDS SPDDS CDDS SPDDS CDDS SPDDS Labor Cost 2.51 5.27 251.44 526.78 78,449.33 164,354.68 Equipment Cost 8.11 8.17 810.55 816.89 252,890.70 254,868.91 Consumables Cost 32.77 41.37 3,276.62 4,136.81 1,022,306.07 1,290,684.12 Traing Cost 0.09 0.28 9.40 27.89 2,933.56 8,701.56 Misoperation Cost 0.00 0.00 0.07 0.30 23.07 92.85 Storage Space Cost 0.00 0.01 0.21 1.05 65.70 328.49 Total 43.48 55.10 4,348.30 5,509.71 1,356,668.43 1,719,030.61 Scenario 2 Labor Cost 18.02 37.81 2,703.66 5,671.97 843,541.67 1,769,656.14 Equipment Cost 38.79 39.09 5,818.27 5,863.78 1,815,300.00 4.00 Consumables Cost 233.94 296.95 35,091.64 44,542.25 10,948,592.09 13,897,183.13 Traing Cost 0.67 2.00 101.24 300.3 31,586.54 93,692.31 Misoperation Cost 0.01 0.02 0.8 3.2 248.44 999.69 Storage Space Cost 0.01 0.05 1.51 7.56 471.60 2,358.00 Total 291.45 375.93 43,717.12 56,389.07 13,639,740.34 17,593,389.27 Scenario 3 Labor Cost 18.05 37.81 1,804.89 3,781.32 563,125.00 1,179,770.76 Equipment Cost 58.32 44.49 5,831.57 4,449.25 1,819,450.00 1,388,166.67 Consumables Cost 235.2 276.03 23,520.25 27,603.17 7,338,317.43 8,612,188.75 Traing Cost 0.67 2.00 67.49 200.2 21,057.69 62,461.54 Misoperation Cost 0.01 0.02 0.53 2.14 165.63 666.46 Storage Space Cost 0.02 0.08 1.51 7.56 471.60 2,358.00 Total 312.26 360.44 31,226.24 36,043.63 9,742,587.34 11,245,612.18 Note: based on the exchange rate between RMB and USD on March 31, 2025, 1USD = 7.1782 CNY Consumable costs accounted for more than 70% of the costs for both CDDS and SPDDS. After analyzing the cost composition for CDDS and SPDDS equipped with 50 hemodialysis machines, it was learned that consumable costs accounted for over 70%, followed by equipment costs at over 15%, and labor costs at 5–10%. Consumable costs accounted for 74% of CDDS cost savings. For a dialysis center equipped with 50 hemodialysis machines, cost composition analysis shows that consumable costs accounted for 74% of the total cost savings, while labor costs accounted for 24%. This indicates that, compared to SPDDS, CDDS significantly reduced the costs of tubing, heparin, AB powder/solution, saline, dialyzers, waste liquid bags, ultrapure dialysate, peracetic acid disinfectant, sodium hypochlorite disinfectant, citric acid disinfectant, and dialysate filters. Moreover, CDDS significantly reduced costs related to nurses, engineers, and workers. Details are shown in Fig. 3 . 4 Discussion Based on HB-HTA, we confirmed the potential advantages of CDDS over SPDDS in clinical applications through a systematic literature review and micro-cost analysis. In terms of clinical value, the study shows that CDDS has advantages in improving the patient's microinflammatory state, manifested by reductions in CRP levels and serum endotoxin levels. Microinflammatory states are common in patients with moderate to advanced CKD, and they are closely related to the onset and progression of common complications in CKD patients, such as anemia and vascular calcification. These states are also positively correlated with the incidence of cardiovascular events and all-cause mortality [ 44 – 45 ] . Improving the purity of dialysate is critical to reducing microinflammation. Ultraclean dialysate can lower endotoxin levels, reduce inflammation, and potentially prevent complications in dialysis patients [ 17 ] . CDDS ensures a safe and stable endotoxin level of 0.001 EU/ml for patients [ 46 ] . Moreover, the study shows that CDDS has advantages in improving renal anemia, including reducing the ERI and decreasing the dose of ESAs. These improvements may be attributed to the positive effects of reduced microinflammation. No significant difference was found between the CDDS and SPDDS groups in terms of albumin levels. Serum prealbumin levels, an important indicator of nutritional status, when reduced, may indicate malnutrition in patients. No significant differences were found between the two groups in terms of dialysis adequacy indicators, such as β2-microglobulin levels, a middle-molecule toxin. β2-microglobulin is an important indicator for assessing dialysis membrane performance and biocompatibility, and it is a key parameter for evaluating dialysis effectiveness. Regular monitoring of β2-microglobulin levels and adjusting dialysis protocols when abnormalities are detected may help reduce the occurrence of dialysis-related amyloidosis. According to the Notice on Issuing the Medical Quality Control Indicators for Neurological and Renal Diseases (2020 Edition) issued by the National Health Commission, dialysis quality management indicators define the control rate of urea clearance index (Kt/V) and urea reduction ratio (URR) for hemodialysis patients (NEP-D-06). Currently, small-molecule toxin clearance indicators such as Kt/V have not been the focus of research. The methods for assessing hemodialysis adequacy include toxin clearance, with small-molecule toxins represented by urea nitrogen. The urea clearance fraction (Kt/V) and urea reduction ratio (URR) are measured before and after dialysis, with Kt/V achieving 1.2–1.4 and URR achieving 65%-70% being the standard for adequacy. Middle-molecule toxins are evaluated with β2-microglobulin as a reference. Safety indicators, such as dialysis hypotension events, have not been the subject of research. Furthermore, the assessment of dialysis adequacy should also consider electrolyte indicators such as serum phosphorus and potassium levels. Monitoring parathyroid hormone (PTH) levels is crucial for maintaining calcium and phosphorus balance. Monitoring potassium and sodium levels is equally important for maintaining cardiac electrophysiological function and fluid balance. Monitoring calcium and phosphorus levels is also essential for preventing bone and soft tissue complications. Our research group simultaneously conducted a clinical value comparison between CDDS and SPDDS, focusing on Kt/V, nutritional status, and dialysis hypotension events. In terms of economic value, micro-cost analysis revealed that CDDS holds economic value in certain specific scenarios. Compared to SPDDS, CDDS can save costs for a nephrology department equipped with six hemodialysis machines operating two shifts daily and serving 12 patients per day. CDDS brings greater economic benefits to dialysis centers with a larger scale of hemodialysis machines. For a dialysis center with 50 hemodialysis machines, CDDS can save approximately 362,362.15 USD per year, primarily from savings in consumable usage and labor costs, aligning with the concept of green dialysis. CDDS offers potential health benefits and cost savings, with clinical engineers playing a critical role [ 47 – 51 ] . The daily operation of a dialysis center requires close collaboration with clinical engineers. Employing dedicated hemodialysis clinical engineers who conduct regular maintenance and monitoring of dialysis equipment can effectively reduce equipment failures and ensure safe and orderly dialysis treatment. This study has some limitations. It did not include certain cost inputs that were the same for both the CDDS and SPDDS groups, such as the cost of measuring endotoxins, which means the cost measurement did not account for all costs. Besides, due to data availability restrictions, the study did not include the differences in hospital water and electricity costs, which may result in underestimating the cost and resource savings benefits brought by CDDS [ 53 – 54 ] . The data parameters in this study were sourced from Shanghai, one of the most economically developed regions in China, so the results are only applicable to cities and regions with similar economic development levels. If the experience is to be further promoted, it is recommended that the micro-cost analysis model developed in this study be updated with new parameters to obtain results that align with the actual conditions of different regions. 5 Conclusion Compared to SPDDS, CDDS improved the microinflammatory state and renal anemia outcomes in hemodialysis patients, potentially offering long-term clinical advantages. Under specific conditions, CDDS demonstrated economic value by reducing consumable and labor costs, making it cost-effective for dialysis centers with six or more hemodialysis machines serving at least 12 patients per day. The main sources of cost savings were reductions in consumable and labor costs, with clinical engineers playing an important role in its implementation. In China, CDDS is suitable for promotion in dialysis centers of a certain scale. Declarations Funding statement This research was supported by Shanghai Yintao Medical Devices Co., Ltd. Conflict of interest disclosure The authors declare no conflicts of interest regarding the publication of this manuscript. Ethics approval statement This study protocol was reviewed and approved by the Ethics Committee of the Shanghai Health Development Research Center (Shanghai Medical Information Center), approval number [2023002]. Patient consent statement This study was granted an exemption from requiring written informed consent by the Ethics Committee of the Shanghai Health Development Research Center (Shanghai Medical Information Center). The research did not involve any personal privacy or commercial interests. During data extraction by the hospitals, all individual patient privacy information was anonymized. Therefore, the requirement for written informed consent was waived. The study was conducted in accordance with ethical guidelines and the World Medical Association Declaration of Helsinki. Data Availability Statement The data supporting the findings of this study are available from the corresponding author upon reasonable request. Acknowledgements We would like to express our heartfelt gratitude to the management personnel, head nurses, nurses, and engineers from the Nephrology Departments of Huashan Hospital affiliated with Fudan University, Renji Hospital affiliated with Shanghai Jiao Tong University School of Medicine, the Baoshan Branch of Renji Hospital, and Shanghai Jiading District Central Hospital for their invaluable assistance in data collection during this study. Their dedication and support have been instrumental in the successful completion of our research, and we are sincerely appreciative of their contributions. Authors ’ contributions Wendi Cheng, Haiyin Wang, Jiangzi Yuan, and Ying Li contributed equally to this work as co-first authors. 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Supplementary Files SupplementarymaterialHospitalbasedHealthTechnologyAssessmentofCDDSandSPDDS.doc CDDSMicrocostingAnalysisModel.xlsx SurveyQuestionnaireCostInputSurvey.doc Cite Share Download PDF Status: Published Journal Publication published 27 Oct, 2025 Read the published version in BMC Nephrology → Version 1 posted Editorial decision: Revision requested 14 Jul, 2025 Reviewers agreed at journal 12 Jul, 2025 Reviews received at journal 11 Jul, 2025 Reviewers agreed at journal 11 Jul, 2025 Reviews received at journal 10 Jul, 2025 Reviewers agreed at journal 10 Jul, 2025 Reviewers agreed at journal 10 Jul, 2025 Reviewers agreed at journal 09 Jul, 2025 Reviews received at journal 20 Jun, 2025 Reviews received at journal 09 Jun, 2025 Reviewers agreed at journal 01 Jun, 2025 Reviewers agreed at journal 31 May, 2025 Reviewers agreed at journal 20 May, 2025 Reviewers invited by journal 30 Apr, 2025 Editor assigned by journal 30 Apr, 2025 Submission checks completed at journal 29 Apr, 2025 First submitted to journal 29 Apr, 2025 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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Background","content":"\u003cp\u003eHospital-based health technology assessment (HB-HTA) refers to the evaluation of various health technologies specifically tailored to a hospital's environment to support management decisions. HB-HTA provides hospital administrators with key evidence and analyses to assess the need for introducing new technologies, avoid the adoption of unsuitable technologies, and reduce the use of unnecessary ones, thereby enhancing the efficiency of hospital health resource allocation\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. Compared to developing countries, HB-HTA receives more attention in developed nations, particularly in Europe and North America\u003csup\u003e[\u003cspan additionalcitationids=\"CR3 CR4 CR5 CR6 CR7 CR8 CR9 CR10\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. In recent years, interest in HB-HTA has increased in China, and related projects have provided scientific decision-making support for hospital adoption and management of new technologies. However, the number of researchers and institutions in this field remains limited, and the scope of research is narrow. In the future, more investment and encouragement for hospitals to conduct HB-HTA will be needed, along with the exploration of a system suitable for China's national conditions to enhance the scientific nature of hospital decision-making\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn March 2018, the Medical Administration Center under the National Health Commission initiated the first phase of HB-HTA pilot projects in seven hospitals, followed by a second phase in March 2019 involving 23 hospitals\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e, thereby creating a favorable policy environment for HB-HTA in China.\u003c/p\u003e \u003cp\u003eIn recent years, end-stage renal disease (ESRD) has emerged as a major global health challenge due to its high prevalence and mortality rates\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. Hemodialysis (HD) remains the primary blood purification therapy for ESRD patients. China has the largest number of maintenance hemodialysis patients worldwide. At the 2024 Academic Annual Meeting of the Chinese Nephrology Association (CNA), academician Xiangmei Chen presented data from the China Research Data Services Platform. As of December 2023, there were 7,512 hemodialysis centers in China, with 916,600 patients, representing a prevalence rate of 635 per million population. Against this backdrop, China is urgently needed to reduce dialysis costs, improve efficiency, ensure high-quality dialysis, and lower overall healthcare expenditures.\u003c/p\u003e \u003cp\u003eThe quality of dialysate and the efficiency of dialysate delivery systems are critical for ensuring cost-effective and high-quality dialysis treatment\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. Globally, there are three main types of dialysate delivery systems for hemodialysis: single-patient dialysis fluid delivery system (SPDDS), central dialysis concentrate supply system (CCDS), and central dialysis fluid delivery system (CDDS). SPDDS are widely used and considered the global standard for dialysis treatment\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. CCDS have been implemented in most countries in Europe, the United States, and South Korea, while CDDS are primarily used in Japan\u003csup\u003e[\u003cspan additionalcitationids=\"CR20 CR21\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn China, centralized dialysate delivery systems have been adopted relatively recently, but their clinical usage has been increasing annually, including both CCDS and CDDS. Currently, CCDS are more widely used. However, CCDS face several problems, including the high cost of new delivery equipment and the need for daily high-temperature disinfection. The resulting significant water and energy consumption and high operating costs are inconsistent with the principles of green dialysis. In contrast, CDDS offer high automation, safety, and efficiency. Their multi-filter design ensures ultrapure dialysate, and their one-touch dead-space-free disinfection is user-friendly\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. CDDS have been developed over 50 years in Japan, with proven stability, safety, and dialysate purification\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eA safe, effective, and cost-efficient dialysate delivery system is crucial for China's healthcare system and ESRD patients. This study aims to evaluate the safety, effectiveness, and economic value of CDDS compared to SPDDS in Chinese hemodialysis centers through HB-HTA.\u003c/p\u003e"},{"header":"2 Methods","content":"\u003cp\u003eThis study employed the HB-HTA approach to evaluate the clinical value (safety and effectiveness) and economic value (cost and cost-effectiveness) of CDDS versus SPDDS. Clinical value evaluation was based on evidence from a systematic literature review. Economic value was assessed through micro-costing from a hospital perspective, which included cost estimation for CDDS and SPDDS and an economic efficiency analysis under various scales of hemodialysis machine configurations. The analysis encompassed total costs, cost composition, cost savings, and their respective components to identify application scenarios where CDDS demonstrated economic value.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Systematic Literature Review\u003c/h2\u003e \u003cp\u003eA systematic literature review was conducted by searching the PubMed database using the free-text search terms \"CDDS AND SPDDS\". The search covered publications up to December 31, 2024. Inclusion criteria required original studies comparing the clinical value (safety and effectiveness) of CDDS and SPDDS. Studies with unrelated outcome measures were excluded. Two researchers independently performed the literature search and study selection. In cases of disagreement, senior researchers were consulted to resolve discrepancies. A self-designed data extraction form was used to collect basic information, primary outcome indicators, and other relevant data from the included studies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Micro-Costing Analysis\u003c/h2\u003e \u003cp\u003eMicro-costing is an accurate method for estimating the costs of medical interventions and is particularly suited for comprehensive economic evaluations\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. Among the primary methods of medical economic evaluation, micro-costing is especially applicable to studies involving labor-intensive services\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. Globally, countries and regions such as the United Kingdom, Italy, Spain, Australia, Canada, Hong Kong (China), South Africa, and Malaysia have conducted micro-costing of ESRD-related services. These studies have covered topics such as hemodialysis versus peritoneal dialysis, rural versus urban dialysis settings, nocturnal dialysis, and hospital-based dialysis\u003csup\u003e[\u003cspan additionalcitationids=\"CR29 CR30 CR31 CR32 CR33 CR34 CR35\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e. However, no micro-costing comparing CDDS and SPDDS dialysate delivery systems have been identified.\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1 Study Subjects\u003c/h2\u003e \u003cp\u003eThis study targeted the nephrology departments of hospitals. Field surveys and questionnaire-based interviews were conducted with management personnel and healthcare professionals from multiple medical institutions in Shanghai. The study clarified the cost components involved in using CDDS and SPDDS for hemodialysis, collected cost data, and calculated the total cost inputs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2 Cost Composition\u003c/h2\u003e \u003cp\u003eThe consideration of costs varies depending on the analytical perspective\u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e. Focusing on healthcare institutions in China, we examined direct and indirect costs through a bottom-up approach for detailed cost analysis\u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e. The dialysis workflow was outlined based on the \u003cem\u003eStandard Operating Procedures for Blood Purification (2021 Edition)\u003c/em\u003e and the \u003cem\u003eManagement Standards for Hemodialysis Rooms in Medical Institutions (2019 Revised Edition)\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eField research was conducted in hospitals in Shanghai with extensive experience in operating CDDS, involving interviews with department directors, medical staff, and engineers. The cost inputs for CDDS and SPDDS were categorized into the following components: labor costs (e.g., equipment operation, consumable handling, waste liquid disposal, and disinfection), equipment costs (e.g., depreciation and maintenance costs), consumable costs (e.g., tubing, heparin, AB solution/powder, saline, dialyzers, ultrapure dialysate, endotoxin filters, disinfectants), and other costs (e.g., storage space for equipment and consumables, training, and costs associated with operational errors). Details are provided in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\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\u003eCost composition of CDDS and SPDDS\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCost categories\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCost components\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLabor costs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEquipment training, preparation of dialysis materials, self-checks until priming, blood drawing, intra-dialysis management, blood return, waste liquid disposal, disinfection, equipment maintenance, troubleshooting, disinfectant replenishment, dialysate handling, etc.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEquipment costs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEquipment purchase price, depreciation period, depreciation cost, failure rate, maintenance costs\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConsumable costs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTubing, heparin, AB powder/solution, saline, dialyzers, waste liquid bags, ultrapure dialysate, peracetic acid, sodium hypochlorite, citric acid, dialysate filters\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther costs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStorage costs for equipment, AB solution/powder, and disinfectants; costs from operational errors\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.2.3 Data Collection\u003c/h2\u003e \u003cp\u003eThe micro-costing utilized multiple data collection methods\u003csup\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e. Three hospitals in Shanghai with mature CDDS implementation for maintenance hemodialysis in ESRD patients were selected as sample sites: Baoshan Branch of Huashan Hospital Affiliated with Fudan University, Baoshan Branch of Renji Hospital Affiliated with Shanghai Jiao Tong University School of Medicine, and Jiading District Central Hospital. We mainly relied on administrative databases from these hospitals.\u003c/p\u003e \u003cp\u003eSurveys and interviews included expert consultations and group discussions with nephrology department directors, head nurses, and engineers. The cost input investigation questionnaire was finalized through expert interviews and discussions. The questionnaire was divided into three categories: (1) Department Director Questionnaire: Addressed staff allocation, bed numbers, service volume, etc. (2) Engineer Questionnaire: Covered equipment costs, depreciation, consumable costs, and other expenses. (3) Nurse Questionnaire: Focused on basic labor costs, consumable costs, and other expenses. The questionnaire was designed and distributed via the online tool Wenjuanxing for quantitative data collection. Details were in Supplementary material-Cost Input Survey Questionnaire.\u003c/p\u003e \u003cp\u003eData analysis was conducted using Excel. A total of 45 questionnaires were collected: 3 from department directors, 3 from engineers, and 39 from nurses (3 head nurses, 36 staff nurses).\u003c/p\u003e \u003cp\u003eThree researchers conducted direct observations in sample hospitals to verify the cost composition and input for CDDS and SPDDS, including staff time allocation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.2.4 Model Input Parameters\u003c/h2\u003e \u003cp\u003eDirect costs included labor costs, equipment costs, and consumable costs. In terms of labor costs, CDDS reduced time input by 11, 27, and 21 minutes for nurses, engineers, and workers, respectively. In terms of equipment costs, the average costs of single-pump and double-pump hemodialysis machines for SPDDS were considered. Depreciation costs were calculated using the straight-line method, assuming an average depreciation period of 7 years\u003csup\u003e[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/sup\u003e. In terms of maintenance costs, two scenarios were considered: average data from the three sample hospitals (where equipment usage periods varied), and data from one hospital (with approximately 6 years of equipment usage). In terms of consumable costs, average procurement data from the three hospitals were used and cross-referenced with tender prices in the Yaozh Device Database to determine costs. Due to the sensitivity of hospital data, proportional relationships were used instead of raw values. Details are shown in Supplementary material Table\u0026nbsp;1.\u003c/p\u003e \u003cp\u003eOther cost inputs included training, operational errors, storage space, personnel wages, working hours, and healthcare land costs. Compared to SPDDS, CDDS reduced equipment training time by 4 days, monthly operational errors by 0.11 incidents, and storage space requirements by 8 square meters. In terms of personnel wages, nurses had the highest wages, up to 240,000 RMB/year, while workers had the lowest, approximately 50,000 RMB/year. Working hours were set at 52 weeks/year, 6 days/week, and 8 hours/day. Details are shown in Supplementary material Table\u0026nbsp;2.\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\u003eBasic information of studies included in the systematic literature review on clinical value\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCountry\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStudy population\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSample size\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIntervention group (sample size)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eControl group (sample size)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOutcome indicators\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eStudy design\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAhmed 2024\u003csup\u003e[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEgypt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eESRD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCDDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSPDDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e① Serum endotoxin levels\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCross-sectional study\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNi Y 2024\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eESRD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCDDS(n\u0026thinsp;=\u0026thinsp;58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSPDDS(n\u0026thinsp;=\u0026thinsp;67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e② hs-CRP levels; ③ Albumin levels; ④ β2-microglobulin levels\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRetrospective cohort study\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHassan MS 2023\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEgypt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eESRD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCDDS(n\u0026thinsp;=\u0026thinsp;50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSPDDS(n\u0026thinsp;=\u0026thinsp;50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e① Serum endotoxin levels; ⑤ CRP levels; ⑥ Hemoglobin levels; ⑦ Erythropoietin Resistance Index (ERI) ⑧ Erythropoiesis-stimulating agents (ESAs) doses\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eProspective cohort study\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.2.5 Statistical Analysis Methods\u003c/h2\u003e \u003cp\u003eA micro-cost calculation model was developed using Microsoft Excel 16.80 for Mac. Three scenarios were analyzed:\u003c/p\u003e \u003cp\u003eScenario 1. Baseline Analysis: the number of hemodialysis shifts per day was set to 2, the cost of hemodialysis machines was set to the average cost of single-pump and double-pump machines, and the equipment maintenance cost data was based on the average maintenance cost from three hospitals.\u003c/p\u003e \u003cp\u003eScenario 2: Compared to the baseline, the number of hemodialysis shifts per day was increased to 3, while all other parameters remained unchanged.\u003c/p\u003e \u003cp\u003eScenario 3: Due to differences in equipment usage durations across the three hospitals, scenario analysis was conducted for maintenance cost data. Compared to the baseline, the maintenance cost data was set based on the cost data of one sample hospital, where the equipment had the longest usage period (approximately 6 years).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Clinical Value\u003c/h2\u003e \u003cp\u003eA total of three studies comparing the clinical value of CDDS and SPDDS were included in the systematic literature review\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/sup\u003e. These studies included retrospective cohort studies from China and prospective cohort studies from Egypt. The outcome indicators mainly addressed microinflammation, anemia, nutritional status, and dialysis adequacy in hemodialysis patients, with a total of eight key indicators. The basic information of the included studies is shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eThree studies compared the clinical effects of CDDS and SPDDS. Endotoxin Levels: Hassan et al. (2023) found significantly lower serum endotoxin levels in dialysis fluid in the CDDS group compared to SPDDS (0.05 vs. 0.11 EU/ml, P\u0026thinsp;=\u0026thinsp;0.001). Ahmed et al. (2024) demonstrated significantly lower pre-dialysis (0.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05 vs. 0.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07 EU/ml, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and post-dialysis (0.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02 vs. 0.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03 EU/ml, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) serum endotoxin levels in the CDDS group versus SPDDS. Inflammatory Markers: Hassan et al. (2023) showed a significant reduction in CRP levels at 3 months in the CDDS group (9.8 vs 4.7 mg/dL, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), whereas no change was observed in the SPDDS group (9.6 vs 9.1 mg/dL, P\u0026thinsp;=\u0026thinsp;0.54). Ni et al. (2024) reported a significant decrease in hs-CRP levels over time in the CDDS group (βCDDS = -0.793) compared to an increase in the SPDDS group (βSPDDS\u0026thinsp;=\u0026thinsp;0.791), with a significant timegroup interaction effect (FTime*CDDS group\u0026thinsp;=\u0026thinsp;13.389, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Anemia-Related Outcomes: Hassan et al. (2023) noted significant improvements in hemoglobin levels in the CDDS group(10.6 vs 12.3 mg/dL), whereas no change was observed in the SPDDS group (10.3 vs 10.0 mg/dL, P\u0026thinsp;=\u0026thinsp;0.149). Hassan et al. (2023) showed a significant reduction in Erythropoietin Resistance Index (ERI) at 3 months in the CDDS group (9.7 vs 3.1, P\u0026lt;0.001), whereas significant improvements was observed in the SPDDS group (10.2 vs 12.3 mg/dL, P\u0026thinsp;=\u0026thinsp;0.047). There are significant difference in ERI between the CDDS and SPDDS groups (P\u0026lt;0.001). Renal function and nutritional indicators: Ni et al. (2024) showed that no significant differences were observed in albumin or β2-microglobulin levels (β2-microglobulin: β = -0.658, FTime* CDDS group\u0026thinsp;=\u0026thinsp;1.228, P\u0026thinsp;=\u0026thinsp;0.269; albumin: β\u0026thinsp;=\u0026thinsp;0.012, FTime* CDDS group\u0026thinsp;=\u0026thinsp;1.429, P\u0026thinsp;=\u0026thinsp;0.233). These findings are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eCompared to SPDDS, CDDS improved the occurrence of microinflammation in dialysis patients, including reductions in serum endotoxin levels, CRP levels, and hs-CRP levels, which may lead to long-term clinical advantages. CDDS also improved the occurrence of renal anemia in patients, including increased hemoglobin levels, decreased ERI, and reduced doses of ESAs. There were no significant differences between the CDDS and SPDDS groups regarding nutritional indicators (albumin levels) or dialysis adequacy indicators (β2-microglobulin levels, a middle-molecule toxin).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOutcome indicators results from the clinical value systematic literature review\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntervention vs. Control\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSerum endotoxin levels (EU/ml)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCRP levels (mg/dL)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ehs-CRP levels (mg/L)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHemoglobin levels (mg/dL)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eErythropoietin resistance index (ERI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eβ2-microglobulin levels (mg/dL)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAlbumin levels (mg/L)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAhmed 2024\u003csup\u003e[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCDDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSPDDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u0026lt;0.001, significant difference between groups*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNi Y 2024\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCDDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 months 2.6\u003c/p\u003e \u003cp\u003e6 months 2.0\u003c/p\u003e \u003cp\u003eβ\u003csub\u003eCDDS\u003c/sub\u003e =-0.793 decrease in follow-up period\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3 months 2.6\u003c/p\u003e \u003cp\u003e6 months 2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3 months 38.3\u003c/p\u003e \u003cp\u003e6 months 38.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSPDDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 months 3.3\u003c/p\u003e \u003cp\u003e6 months 4.17\u003c/p\u003e \u003cp\u003eβ\u003csub\u003eSPDDS\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.791 increase in follow-up period\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3 months 3.3\u003c/p\u003e \u003cp\u003e6 months 4.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3 months 38.2\u003c/p\u003e \u003cp\u003e6 months 37.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 months P\u0026thinsp;=\u0026thinsp;0.037\u003c/p\u003e \u003cp\u003e6 months P\u0026lt;0.001\u003c/p\u003e \u003cp\u003eβ coefficient P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, signifcant difference in level changes between two groups*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3 months P\u0026thinsp;=\u0026thinsp;0.060\u003c/p\u003e \u003cp\u003e6 months P\u0026thinsp;=\u0026thinsp;0.050\u003c/p\u003e \u003cp\u003eβ coefficient P\u0026thinsp;=\u0026thinsp;0.269, no signifcant difference in level changes between two groups\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3 months P\u0026thinsp;=\u0026thinsp;0.639\u003c/p\u003e \u003cp\u003e6 months P\u0026thinsp;=\u0026thinsp;0.998\u003c/p\u003e \u003cp\u003eβ coefficient P\u0026thinsp;=\u0026thinsp;0.233, no signifcant difference in level changes between two groups\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHassan MS 2023\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCDDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBaseline 9.8\u003c/p\u003e \u003cp\u003e1 month 5.6\u003c/p\u003e \u003cp\u003e2 months 4.7\u003c/p\u003e \u003cp\u003e3 months 4.7\u003c/p\u003e \u003cp\u003e(P\u0026lt;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBaseline 10.6\u003c/p\u003e \u003cp\u003e1 month 11.1\u003c/p\u003e \u003cp\u003e2 months 11.7\u003c/p\u003e \u003cp\u003e3 months 12.3\u003c/p\u003e \u003cp\u003e(P\u0026thinsp;=\u0026thinsp;0.008, \u0026lt;0.001, and \u0026lt;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBaseline 9.7\u003c/p\u003e \u003cp\u003e1 month 7.1\u003c/p\u003e \u003cp\u003e2 months 5.0\u003c/p\u003e \u003cp\u003e3 months 3.1\u003c/p\u003e \u003cp\u003e(P\u0026thinsp;=\u0026thinsp;0.002, \u0026lt;0.001, and \u0026lt;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSPDDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBaseline 9.6\u003c/p\u003e \u003cp\u003e1 month 9.0\u003c/p\u003e \u003cp\u003e2 months 8.6\u003c/p\u003e \u003cp\u003e3 months 9.1\u003c/p\u003e \u003cp\u003e(P\u0026lt;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBaseline 10.3\u003c/p\u003e \u003cp\u003e1 month 10.3\u003c/p\u003e \u003cp\u003e2 months 10.1\u003c/p\u003e \u003cp\u003e3 months 10.0\u003c/p\u003e \u003cp\u003e(P\u0026thinsp;=\u0026thinsp;0.70, 0.14, and 0.149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBaseline 10.2\u003c/p\u003e \u003cp\u003e1 month 11.6\u003c/p\u003e \u003cp\u003e2 months 11.9\u003c/p\u003e \u003cp\u003e3 months 12.3\u003c/p\u003e \u003cp\u003e(P\u0026thinsp;=\u0026thinsp;0.039, 0.025, and 0.047)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.001, significant difference between groups*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u0026lt;0.001, significant difference between groups*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP\u0026lt;0.001, significant difference between groups*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP\u0026lt;0.001, significant difference between groups*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Economic Value\u003c/h2\u003e \u003cp\u003eCDDS had economies of scale. Scenario 1 results show that when dialysis centers equipped both CDDS and SPDDS groups with six or more hemodialysis machines, CDDS could reduce costs. As the number of hemodialysis machines increased from six to 50, the cost savings with CDDS became more significant. When the number of hemodialysis machines ranged from 6 to 50, the cost reduction rate per treatment session ranged from approximately 0.05\u0026ndash;21.08%. Similar trends were observed in Scenario 2 and Scenario 3. In these scenarios, when equipped with 5 or 10 or more hemodialysis machines, CDDS reduced costs, with cost reduction rates ranging from 4.40\u0026ndash;22.47% and 1.03\u0026ndash;13.37%, respectively. Details are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe results of Scenario 1 show that when dialysis centers equipped both CDDS and SPDDS groups with 6 to 50 hemodialysis machines, the cost savings per treatment session for CDDS ranged from 0.03\u0026ndash;11.61 USD. The daily cost savings for CDDS ranged from 0.32-1,161.42 USD, and the annual cost savings ranged from 99.75\u0026ndash;362,362.15 USD. Similar results were observed in Scenario 2 and Scenario 3. When equipped with 5 to 50 or 10 to 50 hemodialysis machines, CDDS saved 2.31\u0026ndash;11.77 USD per treatment session, 34.62-1,765.34 USD per day, and 10,802.01\u0026ndash;550,785.57 USD annually. Details are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTaking a dialysis center equipped with 50 hemodialysis machines for example, the results of Scenario 1 show that the cost per treatment with CDDS was approximately 43.48 USD, while the cost per treatment with SPDDS was approximately 55.10 USD. Similar results were observed in Scenario 2 and Scenario 3. The detailed cost differences between CDDS and SPDDS are shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCost Difference Between CDDS and SPDDS-50 Haemodialysis Machines (USD)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eCost per Treatment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eCost per Day\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eCost per Year\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScenario 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCDDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSPDDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCDDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSPDDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCDDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSPDDS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLabor Cost\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e251.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e526.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e78,449.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e164,354.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEquipment Cost\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e810.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e816.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e252,890.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e254,868.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConsumables Cost\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,276.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4,136.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,022,306.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1,290,684.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTraing Cost\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2,933.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8,701.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMisoperation Cost\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e92.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStorage Space Cost\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e65.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e328.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4,348.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5,509.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,356,668.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1,719,030.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScenario 2\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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLabor Cost\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,703.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5,671.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e843,541.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1,769,656.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEquipment Cost\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5,818.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5,863.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,815,300.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConsumables Cost\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e233.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e296.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35,091.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e44,542.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10,948,592.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13,897,183.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTraing Cost\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e101.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e300.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e31,586.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e93,692.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMisoperation Cost\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e248.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e999.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStorage Space Cost\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e471.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2,358.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e291.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e375.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43,717.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e56,389.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13,639,740.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e17,593,389.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScenario 3\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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLabor Cost\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,804.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3,781.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e563,125.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1,179,770.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEquipment Cost\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5,831.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4,449.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,819,450.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1,388,166.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConsumables Cost\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e235.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e276.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23,520.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27,603.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7,338,317.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8,612,188.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTraing Cost\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e67.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e200.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21,057.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e62,461.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMisoperation Cost\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e165.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e666.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStorage Space Cost\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e471.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2,358.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e312.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e360.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31,226.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36,043.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9,742,587.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11,245,612.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote: based on the exchange rate between RMB and USD on March 31, 2025, 1USD\u0026thinsp;=\u0026thinsp;7.1782 CNY\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eConsumable costs accounted for more than 70% of the costs for both CDDS and SPDDS. After analyzing the cost composition for CDDS and SPDDS equipped with 50 hemodialysis machines, it was learned that consumable costs accounted for over 70%, followed by equipment costs at over 15%, and labor costs at 5\u0026ndash;10%. Consumable costs accounted for 74% of CDDS cost savings. For a dialysis center equipped with 50 hemodialysis machines, cost composition analysis shows that consumable costs accounted for 74% of the total cost savings, while labor costs accounted for 24%. This indicates that, compared to SPDDS, CDDS significantly reduced the costs of tubing, heparin, AB powder/solution, saline, dialyzers, waste liquid bags, ultrapure dialysate, peracetic acid disinfectant, sodium hypochlorite disinfectant, citric acid disinfectant, and dialysate filters. Moreover, CDDS significantly reduced costs related to nurses, engineers, and workers. Details are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eBased on HB-HTA, we confirmed the potential advantages of CDDS over SPDDS in clinical applications through a systematic literature review and micro-cost analysis. In terms of clinical value, the study shows that CDDS has advantages in improving the patient's microinflammatory state, manifested by reductions in CRP levels and serum endotoxin levels. Microinflammatory states are common in patients with moderate to advanced CKD, and they are closely related to the onset and progression of common complications in CKD patients, such as anemia and vascular calcification. These states are also positively correlated with the incidence of cardiovascular events and all-cause mortality\u003csup\u003e[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]\u003c/sup\u003e. Improving the purity of dialysate is critical to reducing microinflammation. Ultraclean dialysate can lower endotoxin levels, reduce inflammation, and potentially prevent complications in dialysis patients\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. CDDS ensures a safe and stable endotoxin level of 0.001 EU/ml for patients\u003csup\u003e[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]\u003c/sup\u003e. Moreover, the study shows that CDDS has advantages in improving renal anemia, including reducing the ERI and decreasing the dose of ESAs. These improvements may be attributed to the positive effects of reduced microinflammation. No significant difference was found between the CDDS and SPDDS groups in terms of albumin levels. Serum prealbumin levels, an important indicator of nutritional status, when reduced, may indicate malnutrition in patients. No significant differences were found between the two groups in terms of dialysis adequacy indicators, such as β2-microglobulin levels, a middle-molecule toxin. β2-microglobulin is an important indicator for assessing dialysis membrane performance and biocompatibility, and it is a key parameter for evaluating dialysis effectiveness. Regular monitoring of β2-microglobulin levels and adjusting dialysis protocols when abnormalities are detected may help reduce the occurrence of dialysis-related amyloidosis.\u003c/p\u003e \u003cp\u003eAccording to the \u003cem\u003eNotice on Issuing the Medical Quality Control Indicators for Neurological and Renal Diseases (2020 Edition)\u003c/em\u003e issued by the National Health Commission, dialysis quality management indicators define the control rate of urea clearance index (Kt/V) and urea reduction ratio (URR) for hemodialysis patients (NEP-D-06). Currently, small-molecule toxin clearance indicators such as Kt/V have not been the focus of research. The methods for assessing hemodialysis adequacy include toxin clearance, with small-molecule toxins represented by urea nitrogen. The urea clearance fraction (Kt/V) and urea reduction ratio (URR) are measured before and after dialysis, with Kt/V achieving 1.2\u0026ndash;1.4 and URR achieving 65%-70% being the standard for adequacy. Middle-molecule toxins are evaluated with β2-microglobulin as a reference. Safety indicators, such as dialysis hypotension events, have not been the subject of research. Furthermore, the assessment of dialysis adequacy should also consider electrolyte indicators such as serum phosphorus and potassium levels. Monitoring parathyroid hormone (PTH) levels is crucial for maintaining calcium and phosphorus balance. Monitoring potassium and sodium levels is equally important for maintaining cardiac electrophysiological function and fluid balance. Monitoring calcium and phosphorus levels is also essential for preventing bone and soft tissue complications. Our research group simultaneously conducted a clinical value comparison between CDDS and SPDDS, focusing on Kt/V, nutritional status, and dialysis hypotension events.\u003c/p\u003e \u003cp\u003eIn terms of economic value, micro-cost analysis revealed that CDDS holds economic value in certain specific scenarios. Compared to SPDDS, CDDS can save costs for a nephrology department equipped with six hemodialysis machines operating two shifts daily and serving 12 patients per day. CDDS brings greater economic benefits to dialysis centers with a larger scale of hemodialysis machines. For a dialysis center with 50 hemodialysis machines, CDDS can save approximately 362,362.15 USD per year, primarily from savings in consumable usage and labor costs, aligning with the concept of green dialysis. CDDS offers potential health benefits and cost savings, with clinical engineers playing a critical role\u003csup\u003e[\u003cspan additionalcitationids=\"CR48 CR49 CR50\" citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]\u003c/sup\u003e. The daily operation of a dialysis center requires close collaboration with clinical engineers. Employing dedicated hemodialysis clinical engineers who conduct regular maintenance and monitoring of dialysis equipment can effectively reduce equipment failures and ensure safe and orderly dialysis treatment.\u003c/p\u003e \u003cp\u003eThis study has some limitations. It did not include certain cost inputs that were the same for both the CDDS and SPDDS groups, such as the cost of measuring endotoxins, which means the cost measurement did not account for all costs. Besides, due to data availability restrictions, the study did not include the differences in hospital water and electricity costs, which may result in underestimating the cost and resource savings benefits brought by CDDS\u003csup\u003e[\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]\u003c/sup\u003e. The data parameters in this study were sourced from Shanghai, one of the most economically developed regions in China, so the results are only applicable to cities and regions with similar economic development levels. If the experience is to be further promoted, it is recommended that the micro-cost analysis model developed in this study be updated with new parameters to obtain results that align with the actual conditions of different regions.\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eCompared to SPDDS, CDDS improved the microinflammatory state and renal anemia outcomes in hemodialysis patients, potentially offering long-term clinical advantages. Under specific conditions, CDDS demonstrated economic value by reducing consumable and labor costs, making it cost-effective for dialysis centers with six or more hemodialysis machines serving at least 12 patients per day. The main sources of cost savings were reductions in consumable and labor costs, with clinical engineers playing an important role in its implementation. In China, CDDS is suitable for promotion in dialysis centers of a certain scale.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by Shanghai Yintao Medical Devices Co., Ltd.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest disclosure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest regarding the publication of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study protocol was reviewed and approved by the Ethics Committee of the Shanghai Health Development Research Center (Shanghai Medical Information Center), approval number [2023002].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatient consent statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was granted an exemption from requiring written informed consent by the Ethics Committee of the Shanghai Health Development Research Center (Shanghai Medical Information Center). The research did not involve any personal privacy or commercial interests. During data extraction by the hospitals, all individual patient privacy information was anonymized. Therefore, the requirement for written informed consent was waived. The study was conducted in accordance with ethical guidelines and the World Medical Association Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data supporting the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to express our heartfelt gratitude to the management personnel, head nurses, nurses, and engineers from the Nephrology Departments of Huashan Hospital affiliated with Fudan University, Renji Hospital affiliated with Shanghai Jiao Tong University School of Medicine, the Baoshan Branch of Renji Hospital, and Shanghai Jiading District Central Hospital for their invaluable assistance in data collection during this study. Their dedication and support have been instrumental in the successful completion of our research, and we are sincerely appreciative of their contributions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u003c/strong\u003e\u003cstrong\u003e\u0026rsquo;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWendi Cheng, Haiyin Wang, Jiangzi Yuan, and Ying Li contributed equally to this work as co-first authors. They were involved in the conception and design of the study, data collection and analysis, interpretation of results, and drafting of the manuscript.\u003c/p\u003e\n\u003cp\u003eYashuang Luo, Yuyan Fu, Leyi Gu, and Jun Xue contributed to the data collection and analysis, as well as the critical revision of the manuscript for important intellectual content.\u003c/p\u003e\n\u003cp\u003eChunlin Jin and Yin Zheng contributed equally as co-corresponding authors. They provided supervision and guidance throughout the study, including the conceptualization, methodology, interpretation of results, and critical revision of the manuscript for important intellectual content.\u003c/p\u003e\n\u003cp\u003eAll authors approved the final version of the manuscript for submission and agree to be accountable for all aspects of the work.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eTang M, Zhang X, Ye Z, et al. The initiation, exploration, and development of hospital-based health technology assessment in China: 2005-2022. Biosci Trends. 2023;17(1):1-13. doi:10.5582/bst.2023.01013\u003c/li\u003e\n\u003cli\u003eMartelli N, van den Brink H, Denies F, et al. Hospital-based health technology assessment in France: how to proceed to evaluate innovative medical devices? Ann Pharm Fr. 2014;72(1):3-14. doi:10.1016/j.pharma.2013.09.002\u003c/li\u003e\n\u003cli\u003eDutot C, Mercier G, Borget I, et al. 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BMC Nephrol. 2024 Sep 19;25(1):313. doi: 10.1186/s12882-024-03751-3. PMID: 39300387; PMCID: PMC11414299.\u003c/li\u003e\n\u003cli\u003eHassan MS, Hebah HA, Mourad TAH, El-Sharabasy RM. Impact of Water Treatment on Anemia in Hemodialysis Patients. Indian J Nephrol. 2023 May-Jun;33(3):183-187. doi: 10.4103/ijn.ijn_300_21. Epub 2023 Mar 7. PMID: 37448903; PMCID: PMC10337217.\u003c/li\u003e\n\u003cli\u003eKawanishi H, Moriishi M, Sato T, et al. Fully automated dialysis system based on the central dialysis fluid delivery system[J]. Blood Purif, 2009, 27(S1):56-63.\u003c/li\u003e\n\u003cli\u003eSato T, Koga N. Centralized on- line hemodiafiltration system utilizing purified dialysate as substitution fluid[J]. Artif Organs, 2015, 22(4):285-290.\u003c/li\u003e\n\u003cli\u003eChihara Shinya. The history of hemodialysis and the progress of dialysis devices. Medical Progress, 253(6): 524-525, 2015.\u003c/li\u003e\n\u003cli\u003eKawanishi H, Tsuchiya Shinichiro, et al. 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Pharmacoeconomics. 2017;35(2):129-139. doi:10.1007/s40273-016-0465-1\u003c/li\u003e\n\u003cli\u003eXu, X., Lazar, C.M. \u0026amp; Ruger, J.P. Micro-costing in health and medicine: a critical appraisal . Health Econ Rev 11, 1 (2021). https://doi.org/10.1186/s13561-020-00298-5\u003c/li\u003e\n\u003cli\u003eTito F, Davidson-Peck D, Roettger AD, Borghetti F, Corbo M, Tozzi M. Hemodialysis patients and complications management costs in Italy: Plastic cannulae a potentially cost-effective strategy [published online ahead of print, 2022 Oct 16]. J Vasc Access. 2022;11297298221129898. doi:10.1177/11297298221129898\u003c/li\u003e\n\u003cli\u003eGarc\u0026iacute;a-Testal A, Mart\u0026iacute;nez-Olmos FJ, Gil-G\u0026oacute;mez JA, et al. Impact of an intradialysis virtual-reality-based exercise program on healthcare resources expenditure: a micro-costing analysis. BMC Nephrol. 2022;23(1):230. Published 2022 Jun 27. doi:10.1186/s12882-022-02859-8\u003c/li\u003e\n\u003cli\u003eRoberts G, Holmes J, Williams G, et al. Current costs of dialysis modalities: A comprehensive analysis within the United Kingdom. Perit Dial Int. 2022;42(6):578-584. doi:10.1177/08968608211061126\u003c/li\u003e\n\u003cli\u003eGorham G, Howard K, Zhao Y, et al. Cost of dialysis therapies in rural and remote Australia - a micro-costing analysis. BMC Nephrol. 2019;20(1):231. Published 2019 Jun 25. doi:10.1186/s12882-019-1421-z\u003c/li\u003e\n\u003cli\u003eMakhele L, Matlala M, Sibanda M, Martin AP, Godman B. A Cost Analysis of Haemodialysis and Peritoneal Dialysis for the Management of End-Stage Renal Failure At an Academic Hospital in Pretoria, South Africa [published correction appears in Pharmacoecon Open. 2019 Jun 26;:]. Pharmacoecon Open. 2019;3(4):631-641. doi:10.1007/s41669-019-0124-5\u003c/li\u003e\n\u003cli\u003eWong CKH, Chen J, Fung SKS, et al. Direct and indirect costs of end-stage renal disease patients in the first and second years after initiation of nocturnal home haemodialysis, hospital haemodialysis and peritoneal dialysis. Nephrol Dial Transplant. 2019;34(9):1565-1576. doi:10.1093/ndt/gfy395\u003c/li\u003e\n\u003cli\u003eKlarenbach SW, Tonelli M, Chui B, Manns BJ. Economic evaluation of dialysis therapies. Nat Rev Nephrol. 2014;10(11):644-652. doi:10.1038/nrneph.2014.145\u003c/li\u003e\n\u003cli\u003eWong B, Courtney M, Pauly RP, Jindal K, Klarenbach S. Cost analysis of in-centre nocturnal compared with conventional hemodialysis. Can J Kidney Health Dis. 2014;1:14. Published 2014 Jul 2. doi:10.1186/2054-3581-1-14\u003c/li\u003e\n\u003cli\u003eHooi LS, Lim TO, Goh A, et al. Economic evaluation of centre haemodialysis and continuous ambulatory peritoneal dialysis in Ministry of Health hospitals, Malaysia. Nephrology (Carlton). 2005;10(1):25-32. doi:10.1111/j.1440-1797.2005.00360.x\u003c/li\u003e\n\u003cli\u003e\u0026Scaron;pac\u0026iacute;rov\u0026aacute; Z, Epstein D, Garc\u0026iacute;a-Moch\u0026oacute;n L, Rovira J, Olry de Labry Lima A, Esp\u0026iacute;n J. A general framework for classifying costing methods for economic evaluation of health care [published correction appears in Eur J Health Econ. 2021 Jul;22(5):847]. Eur J Health Econ. 2020;21(4):529-542. doi:10.1007/s10198-019-01157-9\u003c/li\u003e\n\u003cli\u003eWordsworth, S., Ludbrook, A., Caskey, F., Macleod, A.: Collecting unit cost data in multicentre studies. Eur. J. Health Econ. 50, 38\u0026ndash;44 (2005)\u003c/li\u003e\n\u003cli\u003eOlsson TM. Comparing top-down and bottom-up costing approaches for economic evaluation within social welfare. Eur J Health Econ. 2011;12(5):445-453. doi:10.1007/s10198-010-0257-z\u003c/li\u003e\n\u003cli\u003eFrick KD. Microcosting quantity data collection methods. Med Care. 2009;47(7 Suppl 1):S76-S81. doi:10.1097/MLR.0b013e31819bc064\u003c/li\u003e\n\u003cli\u003eChapel JM, Wang G. Understanding cost data collection tools to improve economic evaluations of health interventions. Stroke Vasc Neurol. 2019;4(4):214-222. Published 2019 Dec 1. doi:10.1136/svn-2019-000301\u003c/li\u003e\n\u003cli\u003eYang X. Equipment depreciation calculation method. Information Technology, 2011, 35(07):189-191\u003c/li\u003e\n\u003cli\u003eAhmed Mohamed Tawfik AMT, Howaida Abd ELhamid Elshinnawy HAEE, Hesham Mohamed ElSayed HME, Heba Wahid El Said HWES, Hoda Mohamed Hammoda HMH, Marwa Shaban Abd El Samea MSAES. [Effect of Central Dialysis Fluid Delivery System (CDDS) using high flux dialyzer versus Regular water treatment stations on endotoxemia and inflammatory markers among prevalent patients on Regular Hemodialysis]. Urologiia. 2024 Nov;(5):90-96. Russian. PMID: 39564850.\u003c/li\u003e\n\u003cli\u003eJovanovic, D. B., Stosović, M. D., Gojakovic, B. M., et al. Inflammatory markers as mortality predictors in continuous ambulatory peritoneal dialysis patients. Renal Failure, 2014; 37(2), 230\u0026ndash;236. https://doi.org/10.3109/0886022X.2014.982478\u003c/li\u003e\n\u003cli\u003eOleh M. Akchurin, Frederick Kaskel; Update on Inflammation in Chronic Kidney Disease. Blood Purif 1 May 2015; 39 (1-3): 84\u0026ndash;92. https://doi.org/10.1159/000368940\u003c/li\u003e\n\u003cli\u003eOhno F, Makinose S, Tange Y. Effectiveness of combination of heat water disinfection, continuous water circulation, and minimalized dead space for dialysis piping in maintaining ultrapure dialysis fluid and preventing biofilm formation in a central dialysis fluid delivery system. J Artif Organs. 2023;26(3):220\u0026ndash;5.\u003c/li\u003e\n\u003cli\u003eKawanishi H. A Perspective on the Current Status of Home Hemodialysis. Contrib Nephrol. 2018;196:171-177. doi:10.1159/000485718\u003c/li\u003e\n\u003cli\u003eKawanishi H, Moriishi M, Takahashi N, Tsuchiya S. Preparation and Quality Management of Fluids for Hemodialysis. Contrib Nephrol. 2017;189:153-159. doi:10.1159/000450772\u003c/li\u003e\n\u003cli\u003eOgawa T, Matsuda A, Yamaguchi Y, et al. Dialysate purification after introduction of automated hot water disinfection system to central dialysis fluid delivery system. ASAIO J. 2012;58(2):127-131. doi:10.1097/MAT.0b013e318241f506\u003c/li\u003e\n\u003cli\u003eUchino J, Kawasaki T. Purification of dialysis water in the central dialysis fluid delivery system in Japan: a prospective observation study. Blood Purif. 2009;27 Suppl 1:64-69. doi:10.1159/000213500\u003c/li\u003e\n\u003cli\u003eShibata M. Safety Management of Dialysis Fluid in Japan: Important Duties and Responsibilities of Clinical Engineers. Blood Purif. 2023;52(5):401-406. doi:10.1159/000512349\u003c/li\u003e\n\u003cli\u003eHuang H, Chen Z, Ke X et al. Research on maintenance training and responsibilities of related equipment for hemodialysis clinical engineers. China Medical Equipment, 2021, 18(03): 154-157. \u003c/li\u003e\n\u003cli\u003eZawierucha J, Marcinkowski W, Prystacki T, et al. Green Dialysis: Let Us Talk about Dialysis Fluid. Kidney Blood Press Res. 2023;48(1):385-391. doi:10.1159/000530439\u003c/li\u003e\n\u003cli\u003eBen Hmida M, Mechichi T, Piccoli GB, Ksibi M. Water implications in dialysis therapy, threats and opportunities to reduce water consumption: a call for the planet. Kidney Int. 2023;104(1):46-52. doi:10.1016/j.kint.2023.04.008\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-nephrology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bnep","sideBox":"Learn more about [BMC Nephrology](http://bmcnephrol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bnep/default.aspx","title":"BMC Nephrology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"central dialysis fluid delivery system (CDDS), single-patient dialysis fluid delivery system (SPDDS), hemodialysis, hospital-based health technology assessment (HB-HTA), clinical value, economic value, micro-costing","lastPublishedDoi":"10.21203/rs.3.rs-6389072/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6389072/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThis study aims to evaluate the safety, effectiveness, and economic value of central dialysis fluid delivery system (CDDS) compared to single-patient dialysis fluid delivery system (SPDDS) for hemodialysis patients through hospital-based health technology assessment (HB-HTA). The findings provide a scientific basis for the hospital adoption and clinical application of CDDS.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eUsing the HB-HTA approach, we assessed the clinical value (safety and effectiveness) and economic value (cost and cost-effectiveness) of CDDS compared to SPDDS. Clinical value evaluation was based on evidence from a systematic literature review, while economic value was assessed through micro-costing from 3 hospitals. This included cost estimation for CDDS and SPDDS and an economic efficiency analysis under various scales of hemodialysis machine configurations. The analysis encompassed total costs, cost composition, cost savings, and their respective components to identify application scenarios where CDDS demonstrated economic value.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThree studies compared the clinical effects of CDDS and SPDDS. Endotoxin Levels: Hassan et al. (2023) found significantly lower serum endotoxin levels in dialysis fluid in the CDDS group compared to SPDDS (0.05 vs. 0.11 EU/ml, P\u0026thinsp;=\u0026thinsp;0.001). Ahmed et al. (2024) demonstrated significantly lower pre-dialysis (0.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05 vs. 0.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07 EU/ml, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and post-dialysis (0.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02 vs. 0.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03 EU/ml, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) serum endotoxin levels in the CDDS group versus SPDDS. Inflammatory Markers: Hassan et al. (2023) showed a significant reduction in CRP levels at 3 months in the CDDS group (9.8 vs 4.7 mg/dL, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), whereas no change was observed in the SPDDS group (9.6 vs 9.1 mg/dL, P\u0026thinsp;=\u0026thinsp;0.54). Ni et al. (2024) reported a significant decrease in hs-CRP levels over time in the CDDS group (β\u003csub\u003eCDDS\u003c/sub\u003e = -0.793) compared to an increase in the SPDDS group (β\u003csub\u003eSPDDS\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.791), with a significant timegroup interaction effect (F\u003csub\u003eTime*CDDS group\u003c/sub\u003e = 13.389, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Anemia-Related Outcomes: Hassan et al. (2023) noted significant improvements in hemoglobin levels in the CDDS group(10.6 vs 12.3 mg/dL), whereas no change was observed in the SPDDS group (10.3 vs 10.0 mg/dL, P\u0026thinsp;=\u0026thinsp;0.149). Hassan et al. (2023) showed a significant reduction in Erythropoietin Resistance Index (ERI) at 3 months in the CDDS group (9.7 vs 3.1, P\u0026lt;0.001), whereas significant improvements was observed in the SPDDS group (10.2 vs 12.3 mg/dL, P\u0026thinsp;=\u0026thinsp;0.047). There are significant difference in ERI between the CDDS and SPDDS groups (P\u0026lt;0.001). Renal function and nutritional indicators: Ni et al. (2024) showed that no significant differences were observed in albumin or β2-microglobulin levels (β2-microglobulin: β = -0.658, F\u003csub\u003eTime* CDDS group\u003c/sub\u003e = 1.228, P\u0026thinsp;=\u0026thinsp;0.269; albumin: β\u0026thinsp;=\u0026thinsp;0.012, F\u003csub\u003eTime* CDDS group\u003c/sub\u003e = 1.429, P\u0026thinsp;=\u0026thinsp;0.233). In terms of economic value, compared to SPDDS, CDDS reduced costs for a dialysis center equipped with 6 hemodialysis machines operating 2 shifts daily, serving 12 patients per day, achieving a 0.05% cost reduction. When the number of hemodialysis machines increased from 6 to 50, the cost reduction rate for CDDS increased from 0.05\u0026ndash;21.08%, indicating greater economic benefits for larger-scale dialysis centers. For a dialysis center with 50 machines, CDDS saved approximately 11.61 USD per treatment session and 362,362 USD per year. Cost savings mainly arose from reductions in consumable costs (74%) and labor costs (24%).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eCompared to SPDDS, CDDS improved the microinflammatory state and renal anemia outcomes in hemodialysis patients, potentially offering long-term clinical advantages. Under specific conditions, CDDS demonstrated economic value by reducing consumable and labor costs, making it cost-effective for dialysis centers with 6 or more machines serving at least 12 patients daily. The main sources of cost savings were reductions in consumable and labor costs, with clinical engineers playing an important role in implementation. In China, CDDS is suitable for promotion in dialysis centers of a certain scale.\u003c/p\u003e","manuscriptTitle":"Hospital-based Health Technology Assessment of central dialysis fluid delivery system for hemodialysis patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-08 14:23:48","doi":"10.21203/rs.3.rs-6389072/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-14T17:39:16+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"70038307259356428671511873441653386117","date":"2025-07-12T07:13:04+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-11T11:09:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"277492851934906938057954830239987734555","date":"2025-07-11T10:45:08+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-10T16:39:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"187963528598897384301145110228739549451","date":"2025-07-10T15:16:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"19566935937492307550893365317159643874","date":"2025-07-10T05:59:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"231653361229108131262918540739729131084","date":"2025-07-09T23:50:28+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-21T02:12:43+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-09T23:30:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"248254100559211964774715891370440277430","date":"2025-06-01T18:10:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"721981408845489956336218804381963864","date":"2025-05-31T20:45:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"107295181909110226397204114796673605283","date":"2025-05-20T22:35:31+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-30T08:46:51+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-30T07:49:04+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-29T04:21:55+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Nephrology","date":"2025-04-29T04:20:51+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-nephrology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bnep","sideBox":"Learn more about [BMC Nephrology](http://bmcnephrol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bnep/default.aspx","title":"BMC Nephrology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"47b4564b-2c7b-4022-8b24-dc4f3add4f58","owner":[],"postedDate":"May 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-11-03T16:00:26+00:00","versionOfRecord":{"articleIdentity":"rs-6389072","link":"https://doi.org/10.1186/s12882-025-04484-7","journal":{"identity":"bmc-nephrology","isVorOnly":false,"title":"BMC Nephrology"},"publishedOn":"2025-10-27 15:57:22","publishedOnDateReadable":"October 27th, 2025"},"versionCreatedAt":"2025-05-08 14:23:48","video":"","vorDoi":"10.1186/s12882-025-04484-7","vorDoiUrl":"https://doi.org/10.1186/s12882-025-04484-7","workflowStages":[]},"version":"v1","identity":"rs-6389072","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6389072","identity":"rs-6389072","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}