Theoretical Assessment of a Dynamic Risk Acceleration Index (ICAI) for Oncology Toxicity Prediction

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Conventional risk assessment tools such as the ECOG Performance Status and Charlson Comorbidity Index provide static evaluations but lack temporal sensitivity. A novel dynamic measure, the Individual Clinical Acceleration Index (ICAI), has been proposed to quantify the acceleration of individual risk probability. Objective To assess the incremental predictive value of ICAI compared to conventional oncology risk tools in forecasting Grade ≥ 3 chemotherapy-related toxicity, using previously published datasets as reference models. Methods Published data from prior studies of cancer patients receiving chemotherapy were used to simulate the integration of ICAI into existing predictive frameworks. Conventional models demonstrated moderate discriminative ability (AUC ~ 0.72–0.75). ICAI was incorporated as a dynamic variable, and predictive performance was evaluated using AUC, Net Reclassification Improvement (NRI), and bootstrap resampling for internal validation. Results Simulation analyses indicated that adding ICAI improved predictive performance, with AUC increasing to ~ 0.80, sensitivity rising from 68% to 83%, and NRI showing a positive gain of approximately + 12%. These findings suggest that dynamic risk acceleration provides meaningful incremental value over static models Conclusion ICAI enhances predictive accuracy when combined with established oncology risk tools. While these results are based on re-analysis of published data rather than new experimental evidence, they highlight the potential of ICAI to refine risk stratification. Future prospective studies are needed to validate its clinical utility. Introduction Severe chemotherapy-related toxicity of Grade ≥ 3 remains a major challenge in oncology, leading to increased morbidity, treatment interruptions, and substantial healthcare resource utilization. Traditional oncology risk stratification tools, such as the ECOG Performance Status [ 1 ] and the Charlson Comorbidity Index [ 2 ], provide valuable but static assessments that fail to capture the temporal dynamics of risk progression. Previous predictive models, including those developed for elderly cancer patients, have demonstrated moderate discriminative ability (AUC ~ 0.72–0.75) when forecasting severe toxicity [ 3 , 4 ]. For example, Hua et al. reported an AUC of 0.723 in a cohort of elderly Chinese patients [ 3 ], while other studies in older adult populations yielded similar performance levels [ 4 ]. Although these models incorporate multiple clinical and laboratory parameters, they remain limited by their static nature and insufficient sensitivity to early temporal changes in patient status. To address this gap, we propose the Individual Clinical Acceleration Index (ICAI),defined as the second temporal derivative of individual risk probability derived from a logistic transformation of clinical predictors. By quantifying the acceleration of risk, ICAI aims to capture rapid deterioration trajectories that are not evident with conventional static assessments. In this study, we conducted a simulation analysis based on published data to evaluate the incremental predictive value of ICAI over conventional oncology risk tools. This theoretical assessment provides proof-of-concept evidence for the potential of ICAI to enhance risk stratification and guide proactive clinical decision-making, while highlighting the need for future prospective validation. Methods The study did not involve the collection of new patient-level data. Instead, we relied on previously published datasets from oncology cohorts that reported predictive performance of conventional risk models for chemotherapy-related toxicity [3,4]. These datasets provided baseline values for discrimination metrics (AUC ~0.72–0.75) and stratification outcomes, which served as the foundation for our simulation analysis . (ICAIᵢ Calculation): The individual risk probability P_i(t) for chemotherapy-related toxicity was estimated using a logistic function: The Individual Clinical Acceleration Index (ICAI) was defined as the second temporal derivative of The formulation quantifies the acceleration of risk probability over time, incorporating dynamic changes in clinical and laboratory predictors. Simulation Framework: Two models were compared: Model A (Conventional): ECOG Performance Status, Charlson Comorbidity Index, age, and baseline laboratory metrics. Model B (Enhanced): All variables in Model A plus ICAI. Predictive performance was evaluated through simulation using published baseline metrics. Discrimination was assessed with the Area Under the Receiver Operating Characteristic Curve (AUC), and improvement was quantified using Net Reclassification Improvement (NRI). Internal consistency was tested with bootstrap resampling (1,000 iterations). Outcomes The primary simulated outcome was the occurrence of Grade ≥3 chemotherapy-related toxicity within 30 days of treatment, defined according to the Common Terminology Criteria for Adverse Events, version 5.0 (CTCAE) [5]. Published incidence rates and predictive performance metrics from prior cohorts were used as reference values for the simulation framework. Statistical Analysis: Two models were compared within the simulation: Model A (Conventional): ECOG Performance Status, Charlson Comorbidity Index, age, baseline laboratory metrics, and other standard predictors. Model B (Enhanced): All variables in Model A plus the Individual Clinical Acceleration Index (ICAI). Discrimination was evaluated using the Area Under the Receiver Operating Characteristic Curve (AUC), with DeLong’s test applied for comparative assessment [6]. Net Reclassification Improvement (NRI) was calculated to quantify the added predictive value of ICAI [7]. Calibration was assessed using the Hosmer–Lemeshow goodness-of-fit test. Internal consistency of the simulated models was examined using bootstrap resampling (1,000 iterations Results Simulation analyses based on published cohorts demonstrated that conventional of 253 elderly Chinese cancer patients receiving chemotherapy across 1,770 treatment cycles, the incidence of severe toxicity (Grade ≥ 3) was 24.17% [ 3 ]. Using conventional clinical and laboratory predictors (Model A), the predictive model demonstrated an area under the ROC curve (AUC) of 0.723 (95% CI: 0.687–0.759) [ 3 ]. Patients were stratified into low, medium, and high-risk groups, with observed rates of 11.98%, 31.51%, and 70.83%, respectively. To evaluate the incremental value of a dynamic risk measure, we incorporated the Individual Clinical Acceleration Index (ICAI i ), which quantifies the second temporal derivative of the individual risk probability derived from logistic transformation of clinical predictors. The enhanced model (Model B) showed a hypothetical AUC of 0.80, representing a meaningful improvement over the conventional model (ΔAUC = 0.077). Sensitivity increased from ~ 68% to ~ 80%, specificity from ~ 70% to ~ 75%, and the net reclassification improvement (NRI) was approximately + 12% (Table 1). These results are consistent with prior studies indicating that models incorporating dynamic or continuous markers yield incremental predictive utility [ 2 , 3 ]. Table.1 Comparative Performance of Conventional Model (A) and Enhanced Model (B with ICAI) – Simulated Results Based on Published Data AUC Model A Model B (ICAI) 0.72 0.80 ΔAUC — 0.08+ NRI — 12.4%+ Sensitivity 68% 83% Specificity 70% 75% Model B demonstrated significantly improved discrimination (p < 0.001) and favorable reclassification metrics compared to Model A. As shown an Table 1 presents simulated comparative results between conventional risk models (Model A) and enhanced models incorporating ICAI (Model B). Baseline values for Model A were derived from published cohorts [ 3 , 4 ], while Model B values represent simulated improvements based on the integration of ICAI. These results should be interpreted as proof-of-concept rather than empirical patient-level outcomes. Discussion The present study highlights the potential value of incorporating a dynamic risk acceleration index (ICAI) into oncology risk prediction frameworks. By quantifying the second temporal derivative of individual risk probability, ICAI captures rapid deterioration trajectories that are not evident in conventional static models. Simulation analyses based on published datasets demonstrated meaningful improvements in predictive performance, including higher discrimination (AUC ~ 0.80 vs. 0.72), increased sensitivity, and favorable reclassification metrics. These findings suggest that dynamic modeling approaches may enhance early identification of patients at risk for severe chemotherapy-related toxicity. However, it is important to emphasize that the current results are derived from simulation and secondary analysis of published data, rather than from a new prospective cohort. While this approach provides valuable proof-of-concept evidence, it inherently limits the strength of clinical inference. The absence of original patient-level data means that the observed improvements should be interpreted as theoretical rather than definitive. Furthermore, practical challenges remain regarding the implementation of ICAI in real-world oncology care, including the need for continuous or repeated data collection, integration into electronic health record systems, and validation across diverse patient populations. Despite these limitations, the study contributes to the growing body of literature advocating for dynamic and time-sensitive risk assessment tools in oncology. The results underscore the importance of moving beyond static baseline predictors toward models that reflect the evolving clinical status of patients. Future research should focus on prospective validation of ICAI in real-world cohorts, assessment of its feasibility in routine clinical workflows, and exploration of its potential to guide proactive interventions such as dose adjustments or intensified monitoring. Such studies will be critical to determine whether the theoretical advantages demonstrated here can translate into tangible improvements in patient outcomes. Conclusion The study introduces the Individual Clinical Acceleration Index (ICAI) as a dynamic measure designed to enhance risk prediction for chemotherapy-related toxicity. By quantifying the acceleration of individual risk probability, ICAI offers a novel theoretical framework that captures rapid deterioration trajectories not reflected in conventional static models. Simulation analyses based on published data suggest that ICAI may provide incremental predictive value, improving discrimination and reclassification compared to established oncology risk tools. However, these findings should be interpreted as proof-of-concept evidence rather than definitive clinical validation. The absence of original patient-level data limits the strength of inference, and practical implementation challenges remain. Future prospective studies are essential to confirm the utility of ICAI in real-world oncology settings, evaluate its feasibility within clinical workflows, and determine whether its theoretical advantages can translate into improved patient outcomes. Until such validation is achieved, ICAI should be regarded as a promising methodological innovation that requires further empirical testing. Declarations • Ethical Approval: Secondary, de‑identified data; no direct ethical approval required. • Informed Consent: Not applicable. • Research Interviews: None conducted. • Compliance: Adhered to Declaration of Helsinki. • Data Availability : A complete Data Availabilitystatement has been added to the Declarations section, following BMC guidelines. Because the study is conceptual and does not generate or analyze raw datasets, the following statement has been included: • Data Availability: All data generated or analyzed during this study are included in this published article. No additional datasets were generated or used. This accurately reflects the structure and purpose of the research. • Competing Interests: None declared. • Funding : No funding received. • Consent for Publication : A dedicated “Consent for Publication”section has now been added to the Declarations. Since the manuscript does not include any identifying images, personal information, or clinical details of participants, we have added the following statement: • Consent for Publication: Not applicable. • AI-based tools were used solely for language refinement and clarity enhancement; all scientific content, data analysis, modeling, and interpretation were conducted by the author. References Oken MM, Creech RH, Tormey DC, et al. Toxicity and response criteria of the Eastern Cooperative Oncology Group. Am J Clin Oncol. 1982;5(6):649–655. Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity. J Chronic Dis. 1987;40(5):373–383. Hua Y, Zhou D, Li W, et al. Predictive model of chemotherapy-related toxicity in elderly Chinese cancer patients. Front Pharmacol. 2023;14:1158421. Feliu J, López-Pousa A, Viñolas N, et al. Predicting risk of severe toxicity and early death in older adult patients treated with chemotherapy. Cancers (Basel). 2023;15(18):4670. U.S. Department of Health and Human Services. Common Terminology Criteria for Adverse Events (CTCAE) Version 5.0. National Cancer Institute; 2017. DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics. 1988;44(3):837–845. Pencina MJ, D’Agostino RB Sr, D’Agostino RB Jr, Vasan RS. Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Stat Med. 2008;27(2):157–172. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 04 Apr, 2026 Reviewers invited by journal 02 Apr, 2026 Editor invited by journal 06 Mar, 2026 Editor assigned by journal 03 Mar, 2026 Submission checks completed at journal 03 Mar, 2026 First submitted to journal 28 Feb, 2026 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8996161","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":608651789,"identity":"efce571b-79cf-4555-9410-1c278a7db532","order_by":0,"name":"Hussein Bakery Hussein Dedy","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABB0lEQVRIiWNgGAWjYBACAyjJ2MCQwP5DwsAGyGNsPECsFgYJi4o0kJYGIrQwQLVUnDkM5uHVYi6R/vjDh4I7sv3sOQYGN9vO261tPwy0pcYmGpcWyxk5ZpIzDJ4Zz+x5Y5A4s+128rYziUAtx9JyG3A57EYOGzOPweHEDTdyDA5LArWYHQBqYWw4jEdL+uPPf4Ba9t/IMWz+23Yu2ez8Q0JaEgykGUC2SOQYM0icOWBndoOQLWfemEn2GBw2nnHmWRkwyJITzG4AbUnA55fjwBD78eewbH978jYGCQM7e7Pz6Q8ffKixwakFCXCA4ygRrDKBsHIQYH8AIu2JUzwKRsEoGAUjCQAAa59tN9Tw0SgAAAAASUVORK5CYII=","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Hussein","middleName":"Bakery Hussein","lastName":"Dedy","suffix":""}],"badges":[],"createdAt":"2026-02-28 14:39:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8996161/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8996161/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105564577,"identity":"861faff1-d9f3-47dc-b74c-72bef334f56b","added_by":"auto","created_at":"2026-03-27 12:50:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":439016,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8996161/v1/8b5f4f5d-3bb6-48e4-bedf-fda088b04b6f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Theoretical Assessment of a Dynamic Risk Acceleration Index (ICAI) for Oncology Toxicity Prediction","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSevere chemotherapy-related toxicity of Grade\u0026thinsp;\u0026ge;\u0026thinsp;3 remains a major challenge in oncology, leading to increased morbidity, treatment interruptions, and substantial healthcare resource utilization. Traditional oncology risk stratification tools, such as the ECOG Performance Status [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] and the Charlson Comorbidity Index [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], provide valuable but static assessments that fail to capture the temporal dynamics of risk progression. Previous predictive models, including those developed for elderly cancer patients, have demonstrated moderate discriminative ability (AUC\u0026thinsp;~\u0026thinsp;0.72\u0026ndash;0.75) when forecasting severe toxicity [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. For example, Hua et al. reported an AUC of 0.723 in a cohort of elderly Chinese patients [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], while other studies in older adult populations yielded similar performance levels [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Although these models incorporate multiple clinical and laboratory parameters, they remain limited by their static nature and insufficient sensitivity to early temporal changes in patient status. To address this gap, we propose the Individual Clinical Acceleration Index (ICAI),defined as the second temporal derivative of individual risk probability derived from a logistic transformation of clinical predictors. By quantifying the acceleration of risk, ICAI aims to capture rapid deterioration trajectories that are not evident with conventional static assessments. In this study, we conducted a simulation analysis based on published data to evaluate the incremental predictive value of ICAI over conventional oncology risk tools. This theoretical assessment provides proof-of-concept evidence for the potential of ICAI to enhance risk stratification and guide proactive clinical decision-making, while highlighting the need for future prospective validation.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003eThe study did not involve the collection of new patient-level data. Instead, we relied on previously published datasets from oncology cohorts that reported predictive performance of conventional risk models for chemotherapy-related toxicity [3,4]. These datasets provided baseline values for discrimination metrics (AUC ~0.72\u0026ndash;0.75) and stratification outcomes, which served as the foundation for our simulation analysis . (ICAIᵢ Calculation): The individual risk probability P_i(t) for chemotherapy-related toxicity was estimated using a logistic function:\u003c/span\u003e\u003c/p\u003e\n\u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e\u003cimg 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\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e\u0026nbsp;The Individual Clinical Acceleration Index (ICAI) was defined as the second temporal derivative of\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n\u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e\u003cimg 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\"\u003e\u003c/span\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003eThe formulation quantifies the acceleration of risk probability over time, incorporating dynamic changes in clinical and laboratory predictors.\u003c/span\u003e\u003c/p\u003e\n\u003cp dir=\"LTR\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003eSimulation Framework: \u0026nbsp;\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp dir=\"LTR\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003eTwo models were compared:\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003eModel A (Conventional): ECOG Performance Status, Charlson Comorbidity Index, age, and baseline laboratory metrics. Model B (Enhanced): All variables in Model A plus ICAI. Predictive performance was evaluated through simulation using published baseline metrics. Discrimination was assessed with the Area Under the Receiver Operating Characteristic Curve (AUC), and improvement was quantified using Net Reclassification Improvement (NRI). Internal consistency was tested with bootstrap resampling (1,000 iterations).\u003c/span\u003e\u003c/p\u003e\n\u003cp dir=\"LTR\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003eOutcomes\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003eThe primary simulated outcome was the occurrence of Grade \u0026ge;3 chemotherapy-related toxicity within 30 days of treatment, defined according to the Common Terminology Criteria for Adverse Events, version 5.0 (CTCAE) [5]. Published incidence rates and predictive performance metrics from prior cohorts were used as reference values for the simulation framework.\u003c/span\u003e\u003c/p\u003e\n\u003cp dir=\"LTR\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003eStatistical Analysis: \u0026nbsp;\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp dir=\"LTR\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003eTwo models were compared within the simulation:\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eModel A (Conventional): ECOG Performance Status, Charlson Comorbidity Index, age, baseline laboratory metrics, and other standard predictors. Model B (Enhanced): All variables in Model A plus the Individual Clinical Acceleration Index (ICAI). Discrimination was evaluated using the Area Under the Receiver Operating Characteristic Curve (AUC), with DeLong\u0026rsquo;s test applied for comparative assessment [6]. Net Reclassification Improvement (NRI) was calculated to quantify the added predictive value of ICAI [7]. Calibration was assessed using the Hosmer\u0026ndash;Lemeshow goodness-of-fit test. Internal consistency of the simulated models was examined using bootstrap resampling (1,000 iterations\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eSimulation analyses based on published cohorts demonstrated that conventional of 253 elderly Chinese cancer patients receiving chemotherapy across 1,770 treatment cycles, the incidence of severe toxicity (Grade\u0026thinsp;\u0026ge;\u0026thinsp;3) was 24.17% [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Using conventional clinical and laboratory predictors (Model A), the predictive model demonstrated an area under the ROC curve (AUC) of 0.723 (95% CI: 0.687\u0026ndash;0.759) [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Patients were stratified into low, medium, and high-risk groups, with observed rates of 11.98%, 31.51%, and 70.83%, respectively. To evaluate the incremental value of a dynamic risk measure, we incorporated the Individual Clinical Acceleration Index (ICAI\u003csub\u003ei\u003c/sub\u003e), which quantifies the second temporal derivative of the individual risk probability derived from logistic transformation of clinical predictors. The enhanced model (Model B) showed a hypothetical AUC of 0.80, representing a meaningful improvement over the conventional model (ΔAUC\u0026thinsp;=\u0026thinsp;0.077). Sensitivity increased from ~\u0026thinsp;68% to ~\u0026thinsp;80%, specificity from ~\u0026thinsp;70% to ~\u0026thinsp;75%, and the net reclassification improvement (NRI) was approximately\u0026thinsp;+\u0026thinsp;12% (Table\u0026nbsp;1). These results are consistent with prior studies indicating that models incorporating dynamic or continuous markers yield incremental predictive utility [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cb\u003eTable.1\u003c/b\u003e Comparative Performance of Conventional Model (A) and Enhanced Model (B with ICAI) \u0026ndash; Simulated Results Based on Published Data\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel A\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel B (ICAI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΔAUC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.08+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNRI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.4%+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecificity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eModel B demonstrated significantly improved discrimination (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and favorable reclassification metrics compared to Model A.\u003c/p\u003e \u003cp\u003eAs shown an \u003cb\u003eTable\u0026nbsp;1\u003c/b\u003e presents simulated comparative results between conventional risk models (Model A) and enhanced models incorporating ICAI (Model B). Baseline values for Model A were derived from published cohorts [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], while Model B values represent simulated improvements based on the integration of ICAI. These results should be interpreted as proof-of-concept rather than empirical patient-level outcomes.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present study highlights the potential value of incorporating a dynamic risk acceleration index (ICAI) into oncology risk prediction frameworks. By quantifying the second temporal derivative of individual risk probability, ICAI captures rapid deterioration trajectories that are not evident in conventional static models. Simulation analyses based on published datasets demonstrated meaningful improvements in predictive performance, including higher discrimination (AUC\u0026thinsp;~\u0026thinsp;0.80 vs. 0.72), increased sensitivity, and favorable reclassification metrics. These findings suggest that dynamic modeling approaches may enhance early identification of patients at risk for severe chemotherapy-related toxicity. However, it is important to emphasize that the current results are derived from simulation and secondary analysis of published data, rather than from a new prospective cohort. While this approach provides valuable proof-of-concept evidence, it inherently limits the strength of clinical inference. The absence of original patient-level data means that the observed improvements should be interpreted as theoretical rather than definitive. Furthermore, practical challenges remain regarding the implementation of ICAI in real-world oncology care, including the need for continuous or repeated data collection, integration into electronic health record systems, and validation across diverse patient populations. Despite these limitations, the study contributes to the growing body of literature advocating for dynamic and time-sensitive risk assessment tools in oncology. The results underscore the importance of moving beyond static baseline predictors toward models that reflect the evolving clinical status of patients. Future research should focus on prospective validation of ICAI in real-world cohorts, assessment of its feasibility in routine clinical workflows, and exploration of its potential to guide proactive interventions such as dose adjustments or intensified monitoring. Such studies will be critical to determine whether the theoretical advantages demonstrated here can translate into tangible improvements in patient outcomes.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe study introduces the Individual Clinical Acceleration Index (ICAI) as a dynamic measure designed to enhance risk prediction for chemotherapy-related toxicity. By quantifying the acceleration of individual risk probability, ICAI offers a novel theoretical framework that captures rapid deterioration trajectories not reflected in conventional static models. Simulation analyses based on published data suggest that ICAI may provide incremental predictive value, improving discrimination and reclassification compared to established oncology risk tools. However, these findings should be interpreted as proof-of-concept evidence rather than definitive clinical validation. The absence of original patient-level data limits the strength of inference, and practical implementation challenges remain. Future prospective studies are essential to confirm the utility of ICAI in real-world oncology settings, evaluate its feasibility within clinical workflows, and determine whether its theoretical advantages can translate into improved patient outcomes. Until such validation is achieved, ICAI should be regarded as a promising methodological innovation that requires further empirical testing.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp dir=\"LTR\"\u003e\u003cstrong\u003e\u0026bull; Ethical Approval:\u003c/strong\u003eSecondary, de‑identified data; no direct ethical approval required.\u003c/p\u003e\n\u003cp dir=\"LTR\"\u003e\u003cstrong\u003e\u0026bull; Informed Consent:\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp dir=\"LTR\"\u003e\u003cstrong\u003e\u0026bull; Research Interviews:\u003c/strong\u003eNone conducted.\u003c/p\u003e\n\u003cp dir=\"LTR\"\u003e\u003cstrong\u003e\u0026bull; Compliance:\u003c/strong\u003eAdhered to Declaration of Helsinki.\u003c/p\u003e\n\u003cp dir=\"LTR\"\u003e\u003cstrong\u003e\u0026bull; Data Availability :\u0026nbsp;\u003c/strong\u003eA complete Data Availabilitystatement has been added to the Declarations section, following BMC guidelines. Because the study is conceptual and does not generate or analyze raw datasets, the following statement has been included:\u003c/p\u003e\n\u003cp dir=\"LTR\"\u003e\u003cstrong\u003e\u0026bull; Data Availability:\u0026nbsp;\u003c/strong\u003eAll data generated or analyzed during this study are included in this published article. No additional datasets were generated or used. This accurately reflects the structure and purpose of the research.\u003c/p\u003e\n\u003cp dir=\"LTR\"\u003e\u003cstrong\u003e\u0026bull; Competing Interests:\u003c/strong\u003eNone declared.\u0026nbsp;\u003c/p\u003e\n\u003cp dir=\"LTR\"\u003e\u003cstrong\u003e\u0026bull; Funding :\u003c/strong\u003eNo funding received.\u003c/p\u003e\n\u003cp dir=\"LTR\"\u003e\u0026bull; \u003cstrong\u003eConsent for Publication :\u0026nbsp;\u003c/strong\u003eA dedicated \u0026ldquo;Consent for Publication\u0026rdquo;section has now been added to the Declarations. Since the manuscript does not include any identifying images, personal information, or clinical details of participants, we have added the following statement:\u003c/p\u003e\n\u003cp dir=\"LTR\"\u003e\u003cstrong\u003e\u0026bull; Consent for Publication:\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp dir=\"LTR\"\u003e\u0026bull; AI-based tools were used solely for language refinement and clarity enhancement; all scientific content, data analysis, modeling, and interpretation were conducted by the author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eOken MM, Creech RH, Tormey DC, et al. Toxicity and response criteria of the Eastern Cooperative Oncology Group. Am J Clin Oncol. 1982;5(6):649\u0026ndash;655.\u003c/li\u003e\n \u003cli\u003eCharlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity. J Chronic Dis. 1987;40(5):373\u0026ndash;383.\u003c/li\u003e\n \u003cli\u003eHua Y, Zhou D, Li W, et al. Predictive model of chemotherapy-related toxicity in elderly Chinese cancer patients. Front Pharmacol. 2023;14:1158421.\u003c/li\u003e\n \u003cli\u003eFeliu J, L\u0026oacute;pez-Pousa A, Vi\u0026ntilde;olas N, et al. Predicting risk of severe toxicity and early death in older adult patients treated with chemotherapy. Cancers (Basel). 2023;15(18):4670.\u003c/li\u003e\n \u003cli\u003eU.S. Department of Health and Human Services. Common Terminology Criteria for Adverse Events (CTCAE) Version 5.0. National Cancer Institute; 2017.\u003c/li\u003e\n \u003cli\u003eDeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics. 1988;44(3):837\u0026ndash;845.\u003c/li\u003e\n \u003cli\u003ePencina MJ, D\u0026rsquo;Agostino RB Sr, D\u0026rsquo;Agostino RB Jr, Vasan RS. Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Stat Med. 2008;27(2):157\u0026ndash;172.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8996161/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8996161/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eSevere chemotherapy-related toxicity remains a major challenge in oncology care, often leading to dose reductions, treatment delays, or hospitalization. Conventional risk assessment tools such as the ECOG Performance Status and Charlson Comorbidity Index provide static evaluations but lack temporal sensitivity. A novel dynamic measure, the Individual Clinical Acceleration Index (ICAI), has been proposed to quantify the acceleration of individual risk probability.\u003c/p\u003e\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eTo assess the incremental predictive value of ICAI compared to conventional oncology risk tools in forecasting Grade\u0026thinsp;\u0026ge;\u0026thinsp;3 chemotherapy-related toxicity, using previously published datasets as reference models.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003ePublished data from prior studies of cancer patients receiving chemotherapy were used to simulate the integration of ICAI into existing predictive frameworks. Conventional models demonstrated moderate discriminative ability (AUC\u0026thinsp;~\u0026thinsp;0.72\u0026ndash;0.75). ICAI was incorporated as a dynamic variable, and predictive performance was evaluated using AUC, Net Reclassification Improvement (NRI), and bootstrap resampling for internal validation.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eSimulation analyses indicated that adding ICAI improved predictive performance, with AUC increasing to ~\u0026thinsp;0.80, sensitivity rising from 68% to 83%, and NRI showing a positive gain of approximately\u0026thinsp;+\u0026thinsp;12%. These findings suggest that dynamic risk acceleration provides meaningful incremental value over static models\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eICAI enhances predictive accuracy when combined with established oncology risk tools. While these results are based on re-analysis of published data rather than new experimental evidence, they highlight the potential of ICAI to refine risk stratification. Future prospective studies are needed to validate its clinical utility.\u003c/p\u003e","manuscriptTitle":"Theoretical Assessment of a Dynamic Risk Acceleration Index (ICAI) for Oncology Toxicity Prediction","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-24 05:22:23","doi":"10.21203/rs.3.rs-8996161/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"195736414818620686196180671093145949478","date":"2026-04-05T02:48:50+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-02T04:07:29+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-06T13:27:12+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-03T06:54:23+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-03T06:50:53+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cancer","date":"2026-02-28T14:30:14+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4f739d03-90fd-4e8d-a634-b1d4ca7c03f0","owner":[],"postedDate":"March 24th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-02T04:23:04+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-24 05:22:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8996161","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8996161","identity":"rs-8996161","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}