A Study on the Evaluation of Comprehensive Interventions to Enhance Nutritional Management in Oncology Patients Based on Interrupted Time Series Analysis

A Study on the Evaluation of Comprehensive Interventions to Enhance Nutritional Management in Oncology Patients Based on Interrupted Time Series Analysis | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 9 January 2025 V1 Latest version Share on A Study on the Evaluation of Comprehensive Interventions to Enhance Nutritional Management in Oncology Patients Based on Interrupted Time Series Analysis Authors : Xiaojie Liu 0009-0003-9119-3230 , Xinwei Zhang , and Lu Chen [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.173640910.05714848/v1 202 views 159 downloads Contents Abstract Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract OBJECTIVE : This study aims to investigate the effectiveness of multi-level integrated management strategies designed to enhance nutritional screening and support for oncology patients. METHODS : An interrupted time series model was employed to evaluate changes in the levels and trends of nutritional screening and support for inpatients before (January 2021 to January 2023) and after (February 2023 to November 2024) the implementation of management measures. RESULTS: After a quality improvement project above the execution of extensive strategies to improve clinical nutrition services, there was a significant increase in the number of inpatients receiving nutritional screening (β3 = 125.17, P < 0.05), the rate of nutritional screening (β3 = 2.28, P < 0.05), and the nutritional treatment rate (β3 = 2.49, P < 0.05) among inpatients identified as at nutritional risk. Additionally, the number of inpatients receiving nutritional support (β2 = 481.45, P < 0.05) and the nutritional support rate (β2 = 5.09, P < 0.05) also significantly increased following the implementation of these improvement measures, with the results demonstrating statistical significance.Overall, these findings indicate a marked improvement in the nutrition screening rate, nutrition support rate, and nutritional treatment rate among hospitalized patients at nutritional risk after the policy intervention. governance strategies-including the establishment of a clinical nutrition management system, mandatory nutritional screening, adequate training, standardization of medical records documenting nutritional interventions, and the integration of clinical nutrition into multidisciplinary management to enhance clinical nutritional service-there was a significant increase in the number of inpatients receiving nutritional screening (β3 = 125.17, P < 0.05), the rate of nutritional screening (β3 = 2.28, P < 0.05), and the nutritional treatment rate (β3 = 2.49, P < 0.05) among inpatients identified as at nutritional risk.Furthermore, the number of inpatients receiving nutritional support (β2 = 481.45, P < 0.05) and the nutritional support rate (β2 = 5.09, P < 0.05) after these improvement measures were put into place, with the findings showing statistical significance.Overall, these findings indicate a marked improvement in the nutrition screening rate, nutrition support rate, and nutritional treatment rate among hospitalized patients at nutritional risk after the policy intervention. CONCLUSION: Through comprehensive management measures, nutritional screening and support for inpatients in specialized hospitals have significantly improved following comprehensive management measures. It is essential for healthcare institutions to increase awareness of malnutrition among cancer patients, prioritize early nutritional screening and assessment, and develop nutritional intervention plans aimed at improving the quality of survival for oncology patients. Type of the Paper (Article) A Study on the Evaluation of Comprehensive Interventions to Enhance Nutritional Management in Oncology Patients Based on Interrupted Time Series Analysis Xiaojie Liu 1 , Xinwei Zhang 2 , Lu Chen * affilliation: 1. [email protected] ; [email protected] ; Correspondence: [email protected] ; (Lu Chen; Tianjin Cancer Hospital Airport Hospital, Tianjin, China;) OBJECTIVE : This study aims to investigate the effectiveness of multi-level integrated management strategies designed to enhance nutritional screening and support for oncology patients. METHODS : An interrupted time series model was employed to evaluate changes in the levels and trends of nutritional screening and support for inpatients before (January 2021 to January 2023) and after (February 2023 to November 2024) the implementation of management measures. RESULTS: After a quality improvement project above the execution of extensive strategies to improve clinical nutrition services, there was a significant increase in the number of inpatients receiving nutritional screening (β3 = 125.17, P < 0.05), the rate of nutritional screening (β3 = 2.28, P < 0.05), and the nutritional treatment rate (β3 = 2.49, P < 0.05) among inpatients identified as at nutritional risk. Additionally, the number of inpatients receiving nutritional support (β2 = 481.45, P < 0.05) and the nutritional support rate (β2 = 5.09, P < 0.05) also significantly increased following the implementation of these improvement measures, with the results demonstrating statistical significance.Overall, these findings indicate a marked improvement in the nutrition screening rate, nutrition support rate, and nutritional treatment rate among hospitalized patients at nutritional risk after the policy intervention. governance strategies-including the establishment of a clinical nutrition management system, mandatory nutritional screening, adequate training, standardization of medical records documenting nutritional interventions, and the integration of clinical nutrition into multidisciplinary management to enhance clinical nutritional service-there was a significant increase in the number of inpatients receiving nutritional screening (β3 = 125.17, P < 0.05), the rate of nutritional screening (β3 = 2.28, P < 0.05), and the nutritional treatment rate (β3 = 2.49, P < 0.05) among inpatients identified as at nutritional risk.Furthermore, the number of inpatients receiving nutritional support (β2 = 481.45, P < 0.05) and the nutritional support rate (β2 = 5.09, P < 0.05) after these improvement measures were put into place, with the findings showing statistical significance.Overall, these findings indicate a marked improvement in the nutrition screening rate, nutrition support rate, and nutritional treatment rate among hospitalized patients at nutritional risk after the policy intervention. CONCLUSION: Through comprehensive management measures, nutritional screening and support for inpatients in specialized hospitals have significantly improved following comprehensive management measures. It is essential for healthcare institutions to increase awareness of malnutrition among cancer patients, prioritize early nutritional screening and assessment, and develop nutritional intervention plans aimed at improving the quality of survival for oncology patients. Keywords: Comprehensive Intervention; Effect Evaluation; Interrupted Time Series; Nutritional Management Highlights: A framework for implementing integrated governance in quality improvement initiatives. An interrupted time series analysis is utilized to evaluate changes in trends prior to and following improvement. 1. Introduction The most recent statistics from the International Agency for Research on Cancer (IARC) indicate that in 2022, nearly 20 million new cancer cases were diagnosed, alongside approximately 9.7 million cancer-related deaths.[1]The prevalence of disease-related malnutrition (DRM) among cancer patients is significantly affected by inflammation and metabolic disturbances caused by the tumor itself, as well as by alterations in nutritional intake and requirements due to cancer therapies.[2,3] This condition not only increased the incidence of surgical complications[2]-but also prolongs hospital stays, heightens infection risks, imposes a significant economic burden on patients,[4] and severely reduces their quality of life.[5] Furthermore, DRM restricts treatment options, as these patients frequently exhibit poor tolerance to anti-tumor therapies and demonstrate inadequate responsiveness. [6]The guidelines crafted by the European Society for Clinical Nutrition and Metabolism (ESPEN) emphasize the crucial importance of nutritional management in the care of cancer patient.[3] Despite this, deficiencies in nutritional screening and evaluation for oncology patients persist even in developed countries. In Italy, a mere 27% of oncologists preform nutritional assessments during the diagnostic phase, and merely 16% of oncology departments utilize effective nutritional risk screening protocols.[7] In the United States,only 53% of outpatient cancer centers document the implementation of screenings for malnutrition risks, with merely 65% utilize validated screening methodologies.[8] The substantial symptom burden [9]experienced by cancer patients, coupled with the imminent risk of malnutrition, underscores the indispensable need for systematic screening to assess malnutrition risks, thereby facilitating early detection and intervention.This study focuses on a specialized oncology hospital, with the objective of employing an interrupted time series model to evaluate the changes in both the level and trend of nutritional screening and support among inpatients following comprehensive management interventions. The findings are intended to provide foundational insights for improving the management of malnutrition in cancer patients. Materials and Methods 2.1. Study Design A quasi-experimental interrupted time series analysis (ITSA) approach has been implemented in this study to evaluate the nutritional screening and support practices for all inpatients at a specialized oncology hospital, covering the period from January 1, 2021, to November 30, 2024. 2.2. Data Sources This study performed with retrospective analysis of collected data-sourced from the hospital information system. Relevant information was extracted from the medical records of all hospitalized patients, including disease profiles, nutritional screening data, and details of nutritional interventions. The inpatient records prior to the initiation of comprehensive management (from January 1, 2021, to January 31, 2023) were designated as the control group, while records gathered subsequent to the management intervention (from February 1, 2023, to November 30, 2024) were categorized as the intervention group. 2.3. Research Methods 2.3.1. Intervention Strategies for Comprehensive Management (1) The establishment of a multidisciplinary collaborative management team aims to develop a robust clinical nutrition management system, ensuring implementation of reform at both the institutional and departmental levels. (2) The organization of comprehensive training initiatives involved the rollout of a systematic, continuous training program was rolled out across the hospital, employing a blend of online and offline formats, with concentrated in-person training sessions facilitated by nutritionists and nutritional pharmacists in critical departments such as Hematology and Gastroenterology. (3) The creation of an information technology platform incorporated the NRS-2002 nutritional risk screening tool into the admission evaluation framework, when moderate to high nutritional risk is detected, a pop-up warning prompts the necessity for a nutritionist’s assessment and the execution of a nutritional consultation. (4) The elevation of clinical nutrition service standards was achieved by incorporating the Nutrition Department into multidisciplinary treatment teams, advocating for the delivery of nutritional support therapies guided by nutritional risk assessments and the formulation of standardized protocols.2.3.2. Interrupted Time Series Analysis Interrupted time series analysis is characterized by its ability to quantify changes in levels and trends surrounding an intervention, its operational simplicity, and its capacity to present results in a clear and intuitive format. Consequently, it is regarded as one of the most robust quasi-experimental designs for evaluating longitudinal intervention effects.[10] Within this framework, data are systematically gathered at multiple time points preceding and following the intervention to determine whether the intervention exerts a more pronounced effect than any underlying long-term trends. In the present study, the intervention point was established on February 1, 2023, marking the formal rollout of comprehensive management. The data were divided into a pre-intervention group (from January 1, 2021, to February 2023) and a post-intervention group (from February 1, 2023, to November 30, 2024). A total of 25 months of data were collected prior to the intervention point, with an additional 22 months analyzed post-intervention. The dependent variables of the study included the number of nutritional screenings conducted among hospitalized patients, the nutritional screening rate within this population, the quantity of nutritional support interventions provided to inpatients, the nutritional support rate for inpatients, and the rate of nutritional therapy among those hospitalized patients identified as being at nutritional risk. A linear regression model was consequently formulated to estimate both the levels and trends of the dependent variables preceding the implementation of comprehensive management, as well as to delineate the resultant changes in levels and trends post-implementation. The model specifications, along with the related variables and coefficients, are detailed in Table 1.Yt =β0 + β1 *time + β2 * intervention + β3 *time after intervention+ε Variables and Coefficients Explanation Yt The observed metric for month t Time The duration in months from the start of the observation period to the final time point t in the series Intervention A binary time variable, coded as 0 prior to the intervention and 1 subsequent to the implementation of comprehensive management, initiated in February 2021 within this dataset Time after interventiont A continuous variable indicating the number of months following the intervention, coded as 0 before the intervention and with sequential numbering assigned to the subsequent time periods β0 The baseline average level of the observational metric β1 The slope of the observational metric prior to the intervention, representative of the baseline trend β2 The immediate change in the observational metric at the point of intervention β3 The change in the slope of the observational metric transitioning from pre- to post-intervention, where β1 + β3 represents the trend following the intervention ε The error term for month t, signifying the unexplained random variability within the model Table 1 Names and descriptions of variables and coefficients 2.4. Outcome Indicators (1) The total count of nutritional screenings performed for hospitalized patients reflects the number of individuals assessed for nutritional risk by physicians utilizing the NRS-2002 scale within the system. (2) The nutritional screening rate for hospitalized patients is determined by dividing the total number of nutritional screenings conducted for these patients by the overall number of hospitalized patients during the corresponding timeframe. Subsequently, the rate was multiplied by a 100 for ease of interpretation.(3) The total number of instances of nutritional support provided for hospitalized patients includes those who received enteral nutritional support (both oral nutritional support and tube feeding) as well as those who underwent parenteral nutritional support (which encompasses single-bottle infusions of fat emulsion/amino acids, three-chamber bags, and total parenteral nutrition formulations). (4) The nutritional support rate among hospitalized patients is computed by dividing the total number of nutritional support instances by the overall number of hospitalized patients within the same timeframe, and then multiplying by 100. (5) The nutritional therapy rate for hospitalized patients presenting nutritional risk is calculated by dividing the number of hospitalized patients with nutritional risk who received nutritional therapy by the total number of hospitalized patients identified as being at nutritional risk during the same period, subsequently multiplied by 100 to be expressed as a percentage change. Nutritional risk is defined as those patients who were assessed by clinical practitioners as experiencing nutritional risk based on the Patient-Generated Subjective Global Assessment (PG-SGA). 2.5. Statistical Analysis and Data Processing In the present study, STATA 17.0 software was used to construct a segmented linear regression model[11] for analyzing time series data, with the objective of assessing alterations in the levels and trends of nutritional screening and nutritional support data surrounding the intervention measures of comprehensive governance. Categorical variables were expressed as counts and percentages, whereas continuous variables were expressed as means, standard deviations (SD), or medians and interquartile ranges. The analysis comparing independent samples was performed using the Mann-Whitney test. The Durbin-Watson test was conducted to evaluate the autocorrelation of the model. In instances where autocorrelation was identified, generalized least squares estimation was adopted, executed via the Prais-Winsten method.[12] The threshold for statistical significance was set at 0.05. Results 3.1. Comparative Analysis of Nutrition Screening Rates, Nutrition Support Rates, and Nutritional Treatment Rates Among Hospitalized Patients with Nutritional Risks A normality assessment of the nutrition screening rates, nutrition support rates, and nutritional treatment rates among hospitalized patients exhibiting nutritional risks indicated a skewed distribution. Therefore,a comparative analysis using the Mann-Whitney test was performed on multiple groups of independent samples. Following the implementation of comprehensive management strategies, a significant improvement was observed in the nutrition screening rates, nutrition support rates, and nutritional treatment rates among hospitalized patients identified as being at nutritional risk (p < 0.005).Additional details can be found in Table 2. Items Define Range Sample Size Median Std. err. Statistic P Cohen’s d Rate for NRS(%) Pre-intervention 25 28.28 9.55 31.44 <0.005 2.521 Post-intervention 22 82.67 23.25 Total 47 36.30 30.11 Rate for therapy(%) Pre-intervention 25 13.82 5.76 33.87 <0.005 3.625 Post-intervention 22 31.92 3.21 Total 47 23.48 9.73 Rate among treat for risk(%) Pre-intervention 25 2.64 2.64 33.79 <0.005 3.625 Post-intervention 22 19.60 19.86 Total 47 6.52 18.71 Table 2 Estimated level and trend changes of Nutrition Screening Rates, Nutrition Support Rates, and Nutritional Treatment Rates Among Hospitalized Patients with Nutritional Risks before and after the reform. 3.2. The Count of Nutritional Screenings for Hospitalized Patients Within the model assessing the number of nutritional screenings conducted among hospitalized patients, the baseline level prior to the intervention was recorded at 337 cases, indicating a trend of increase at a rate of 38.09 cases per month. During the intervention month, an observed increase of 486.72 cases in nutritional screenings. Post-intervention, a significant elevation in the number of nutritional screenings was noted (β3=125.17, P < 0.05). Compared to the pre-intervention period, there was an average monthly increment of 163 cases. The Durbin-Watson statistic was calculated at 2.10. Refer to Table 3 and Figure 1. Coefficient Std. err. t P>|t| [95% conf. interval] β0 337.25 395.78 0.85 0.399 -460.39 1134.89 β1 38.09 25.59 1.49 0.144 -13.50 89.67 β2 486.72 349.11 1.39 0.170 -216.87 1190.32 β3 125.17 44.38 2.82 0.007 35.72 214.63 Table 3 Multiple Regression Analysis of Segmented Regression for the Interrupted Time Series in Model 1: Regression Coefficients, Standard Errors, and P Values (with the Number of Nutritional Screenings as the Dependent Variable)Figure 1 The Variation Trend of Nutritional Screenings at a Specialized Oncology Hospital from January 2021 to November 2024.The vertical line indicates the time when the intervention commenced. 3.3. Nutritional Screening Rate Among Hospitalized Patients Within the model assessing the nutritional screening rate for hospitalized patients, the baseline level prior to the intervention was established at 18.71%,with a gradual increase of 0.63% per month. During the intervention month, the nutritional screening rate augmented by 8.15%. Post-intervention, a statistically significant upward trend was observed in the nutritional screening rate (β3=2.28, P < 0.05). In comparison to the pre-intervention period, an average monthly increment of 2.91% was noted. The Durbin-Watson statistic was calculated at 2.06.Refer toTable 4 and Figure 2. Coefficient Std. err. t P>|t| [95% conf. interval] β0 18.71 7.29 2.56 0.014 4.00 33.41 β1 0.63 0.49 1.28 0.207 -0.36 1.62 β2 8.15 8.28 0.98 0.330 -8.53 24.83 β3 2.28 0.81 2.80 0.008 0.64 3.93 Table 4 Multiple Regression Analysis of Segmented Regression for the Interrupted Time Series in Model 2: Regression Coefficients, Standard Errors, and P Values (with the Nutritional Screening Rate as the Dependent Variable)Figure 2 The Variation Trend of Nutritional Screening Rate at a Specialized Oncology Hospital from January 2021 to November 2024.The vertical line indicates the time when the intervention commenced. 3.4. Nutritional Support for Hospitalized Patients In the model assessing the number of nutritional support interventions for hospitalized patients, the baseline level before the intervention was established at 132 cases, demonstrating a trend of increase at a rate of 24.82 cases per month. Regarding the immediate effect of the intervention, there was a notable increase of 481.45 cases (β2=481.45, P < 0.05) in the number of patients receiving nutritional support during the intervention month. Following the intervention, there was an average monthly increment of 30.16 cases compared to the pre-intervention baseline. The Durbin-Watson statistic was calculated at 2.00.Refer to Table 5 and Figure 3. Coefficient Std. err. t P>|t| [95% conf. interval] β0 132.38 57.43 2.30 0.026 16.63 248.14 β1 24.82 4.08 6.08 0.000 16.59 33.04 β2 481.45 83.79 5.75 0.000 312.60 650.32 β3 5.34 6.23 0.86 0.396 -7.22 17.91 Table 5 Multiple Regression Analysis of Segmented Regression for the Interrupted Time Series in Model 3: Regression Coefficients, Standard Errors, and P Values (with the Number of Patients Receiving Nutritional Support as the Dependent Variable)Figure 3 The Trend of Variation in the Number of Patients Receiving Nutritional Support at a Specialized Oncology Hospital from January 2021 to November 2024. The vertical line indicates the time when the intervention commenced. 3.5. Nutritional Support Rate for Hospitalized Patients Within the model assessing the nutritional support rate for hospitalized patients, the baseline level recorded prior to the intervention was 6.82%, reflecting a gradual increase of 0.68% per month. The immediate impact of the intervention resulted in a significant enhancement of 5.09% (β2=5.09, P < 0.05) in the nutritional support rate was observed during the month of the intervention. Compared to the pre-intervention period, there was an average monthly increase of 0.34% following the intervention. The Durbin-Watson statistic was found to be 1.93. Additional details can be found in Table 6 and Figure 4. Coefficient Std. err. t P>|t| [95% conf. interval] β0 6.82 1.67 4.08 0.000 3.44 10.19 β1 0.67 0.11 5.79 0.000 0.44 0.91 β2 5.09 2.25 2.25 0.029 0.53 9.63 β3 -0.34 0.18 -1.82 0.075 -0.71 0.03 Table 6 Multiple Regression Analysis of Segmented Regression for the Interrupted Time Series in Model 4: Regression Coefficients, Standard Errors, and P Values (with Nutritional Support Rate as the Dependent Variable)Figure 4 The Trend of Variation in the Nutritional Support Rate at a Specialized Oncology Hospital from January 2021 to November 2024.The vertical line indicates the time when the intervention commenced. 3.6. Nutritional Therapy Rate for Hospitalized Patients with Nutritional Risk In the model evaluating the nutritional therapy rate for hospitalized patients at nutritional risk, the baseline level established prior to the intervention was 0.24%, reflecting a gradual monthly increase of 0.13%. Post-intervention, the nutritional therapy rate for hospitalized patients exhibiting nutritional risk demonstrated a statistically significant upward trend (β3=2.49, P < 0.05). When compared to the pre-intervention phase, an average monthly increase of 2.63% was observed following the intervention. The Durbin-Watson statistic was recorded at 2.23. Additional details can be found in Table 7 and Figure 5. Coefficient Std. err. t P>|t| [95% conf. interval] β0 0.24 4.49 0.05 0.957 -8.81 9.30 β1 0.13 0.28 0.47 0.639 -0.43 0.70 β2 -0.92 3.25 -0.29 0.777 -7.47 5.62 β3 2.49 0.50 5.01 0.000 1.49 3.50 Table 7 Multiple Regression Analysis of Segmented Regression for the Interrupted Time Series in Model 5: Regression Coefficients, Standard Errors, and P Values (with the Nutritional Therapy Rate for Hospitalized Patients at Nutritional Risk as the Dependent Variable)Figure 5 The Trend of Variation in the Nutritional Therapy Rate for Hospitalized Patients at Nutritional Risk in a Specialized Oncology Hospital from January 2021 to November 2024.The vertical line indicates the time when the intervention commenced. Discussion This study utilizes the Interrupted Time Series Analysis (ITSA) model to conduct a longitudinal evaluation of the effects of nutritional screening and improvements in nutritional support for hospitalized patients in specialized healthcare institutions operating under a comprehensive governance framework. The ITSA model effectively mitigates the influence of long-term trend variations in the pre-intervention time series by analyzing data collected from multiple observation time points, thereby facilitating an accurate assessment of the genuine impact of comprehensive governance measures on the observed outcomes. This methodology has garnered considerable application in the public health sector.[13-16] Subsequent to the implementation of comprehensive governance, a significant elevation in the number of nutritional screenings, nutritional screening rates, and nutritional therapy rates for hospitalized patients at nutritional risk was observed. Furthermore, both the quantity of nutritional support and the corresponding rates among hospitalized patients exhibited a marked increase in the month when improvement measures were enacted, with these differences reaching statistical significance.Some studies show that the prevalence of nutritional risk among hospitalized patients is 69.5%, with a significant 87.8% diagnosed with malnutrition.[17] Notably, approximately 20% of cancer patients suffer severe consequences attributable to Disease-Related Malnutrition (DRM),[18] rather than their primary underlying condition. Implementing nutritional assessment and subsequent optimization management-particularly in surgical oncology patients[19]-demonstrates a beneficial impact on reducing both the duration of hospitalization and the incidence of complications. Furthermore, baseline nutritional risk shows considerable promise as a prognostic factor in oncology.[20,21]Drawing upon the lessons learned from past experiences, through a quality improvement project above comprehensive governance framework, the study institution has documented a notable upward trend in both the volume of nutritional screenings and the corresponding screening rates among hospitalized patients. In the majority of healthcare institutions, nutritional screening is not incorporated into standard nursing admission protocols, nor are there provisions for standardized screening and support procedures[19]. To address this challenge, prior initiatives have involved the integration of nutritional screening tools within electronic health records, resulting in an increase in nutritional screening rates from 60% to 78%.[22]Furthermore, healthcare institutions should undertake standardized and effective nutritional assessments based on the nutritional risk levels of patients and devise systematic intervention plans. Research indicates that nutritional interventions play a crucial role in maintaining the nutritional status of cancer patients, mitigating treatment-related toxicity, and enhancing the completion rates of chemotherapy and radiotherapy.[23-25] Distinct nutritional strategies may exert varying degrees of influence on patients’ nutritional status throughout the treatment process.[26] To ensure the provision of high-quality nutritional management for cancer patients, it is essential to utilize appropriate metrics for assessing the efficacy of nutritional diagnoses and therapeutic interventions. This study employs the nutritional support rate as an indicator of the institution’s overall nutritional intervention efforts, while the nutritional therapy rate among hospitalized patients with nutritional risk reflects the effectiveness and adherence to protocols in nutritional treatment delivered by the institution. Following the implementation of comprehensive governance, there has been a significant improvement in both the nutritional support rate and the nutritional therapy rate among hospitalized patients at nutritional risk, indicating that the adoption of comprehensive governance strategies significantly enhances the standardized management of nutritional interventions for oncology patients.This study does have certain limitations. Firstly, it is conducted within a specialized oncology hospital, which may restrict the generalizability of the findings to broader populations. Secondly, during the study period, the hospital’s compilation of the number of patients receiving nutritional support was based on a definition that encompassed a variety of nutritional strategies. In subsequent research, we aim to further delineate the various nutritional support protocols to provide additional evidence for the scientific enhancement of the standardization of nutritional support initiatives. Conclusion The implementation of a multi-faceted comprehensive governance strategy, which the establishment of a clinical nutrition management framework, the enforcement of mandatory nutritional screening, extensive training, the standardization of nutritional intervention documentation through information technology, and the integration of clinical nutrition into multidisciplinary management practices, significantly elevate the quality of clinical nutrition services. Consequently, nutritional screening and support for hospitalized patients within specialized oncology facilities have markedly improved. It is imperative for healthcare institutions to enhance healthcare providers’ awareness of malnutrition among cancer patients, conduct targeted early nutritional screenings and evaluations, and formulate tailored nutritional intervention plans to enhance the quality of life for patients with cancer. Acknowledgements The work was funded by Tianjin Medical University Hospital Management Innovation Research Project (2023YG18). Conflict of interest statement The authors declare no conflicts of interest. Ethic statement This project was determined to be a quality assurance project by the Tianjin Cancer Hospital Airport Hospital Institutional Review Board and did not require human subjects review oversight. References: 1. Bray, F.; Laversanne, M.; Sung, H.; Ferlay, J.; Siegel, R.L.; Soerjomataram, I.; Jemal, A. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 2024 , 74, 229-263, doi:10.3322/caac.21834. 2. Lorenzon, L.; Caccialanza, R.; Casalone, V.; Santoro, G.; Delrio, P.; Izzo, F.; Tonello, M.; Mele, M.C.; Pozzo, C.; Pedrazzoli, P., et al. 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Keywords comprehensive intervention effect evaluation interrupted time series nutritional management Authors Affiliations Xiaojie Liu 0009-0003-9119-3230 Tianjin Medical University Cancer Institute & Hospital View all articles by this author Xinwei Zhang Shanxi Datong University View all articles by this author Lu Chen [email protected] Tianjin Medical University Cancer Institute & Hospital View all articles by this author Metrics & Citations Metrics Article Usage 202 views 159 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Xiaojie Liu, Xinwei Zhang, Lu Chen. A Study on the Evaluation of Comprehensive Interventions to Enhance Nutritional Management in Oncology Patients Based on Interrupted Time Series Analysis. Authorea . 09 January 2025. DOI: https://doi.org/10.22541/au.173640910.05714848/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. 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