Harnessing Machine Learning to Predict Time-Sensitive Conditions in Prehospital Care: The Impact of Response Times in Patients with Breathing Problems

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However, the ways in which response time interacts with patient characteristics (like age and sex) remain less clear. This study uses machine learning to examine how response time, age, and sex influence the likelihood of high-risk time-sensitive conditions (HRTS) in patients initially assessed for breathing difficulties by the Emergency Medical Call Centre (EMCC) Methods: A retrospective analysis of 132,395 Emergency Medical Services (EMS) missions in Stockholm, Sweden (2017–2022) was conducted. Development of a gradient boosting model, a type of machine learning method that can handle complex, nonlinear relationships, to predict HRTS in patients initially assessed for breathing difficulties by EMCC was conducted. The focus is on the effect of response time—the interval between the emergency call and the EMS team’s arrival—alongside patient age and sex. Model performance was evaluated using Youden’s Index and misclassification rates, and interpretation of the model was facilitated through partial dependence (PD) and individual conditional expectation (ICE) plots. Results: The gradient boosting model performed best among several tested methods, achieving a Youden’s Index of 0.2411. Age, response time, and sex were the most important predictors of HRTS. Shorter response times tended to be associated with higher chances of HRTS, likely reflecting proper prioritization. Nonlinear patterns emerged with longer response times, especially in patients over 60 years old, suggesting potential gaps in how patients are initially triaged. Male patients across all age groups showed a higher probability of HRTS compared to females. Conclusions: Incorporating predictive modelling into EMCC workflows—especially by considering age and sex—could improve the accuracy of initial prioritization and potentially enhance outcomes for patients with breathing problems. By better aligning EMS response times with actual patient risk, healthcare systems may optimize resource allocation, improve patient safety, and reduce the impact of prolonged response times. Emergency Medical Services Response Time Machine Learning Time-Sensitive Conditions Breathing Problems Clinical Decision Support Systems Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Prehospital emergency care encompasses both Emergency Medical Call Centres (EMCC) and Emergency Medical Services (EMS), forming an integrated system aimed at timely patient interventions. Response time—defined as the interval between an EMCC call and EMS arrival—is critical for conditions such as cardiac arrest, stroke, and trauma. While many studies highlight the importance of rapid response 1 – 10 , others find limited or no association with improved outcomes 11 – 17 . In Sweden, EMCC personnel categorize calls into three priority levels: Priority 1 (immediately life-threatening), Priority 2 (time-sensitive but non-life-threatening), and Priority 3 (non-time-sensitive) 18 19 . However, EMCC assessments face challenges, often resulting in varied accuracy 20 21 . On-scene, EMS personnel use triage tools like The Rapid Emergency Triage and Treatment System (RETTS). In Sweden, RETTS is widely used to categorize patients into five color-coded priority levels based on the risk of having a time-sensitive condition. In this study setting only four colors is used: Red (most urgent), Orange, Yellow, and Green (least urgent) 22 – 24 . For this study, Red and Orange levels were combined into a High Risk of Time-Sensitive Condition (HRTS) group due to the increased probability of having a time-sensitive condition 22 23 . Patients with breathing problems constitute a significant, complex group in prehospital care, facing heightened health risks. For instance, this patient group is often associated with acute respiratory distress and higher mortality rates than other groups, necessitating timely and effective prehospital interventions to mitigate adverse outcomes 25 . Several anatomical and physiological functions of the respiratory system decrease with aging 26 . This study focuses on this group due to their clinical importance. This study aims to address the gap in research, examine the association between EMS response times and the likelihood of encountering HRTS conditions in patients with breathing problems. It also explores how age and sex might influence these outcomes. We use a machine learning approach to capture nonlinear patterns—instances where the risk does not simply rise or fall steadily but may curve or plateau at different points—to better understand these complex relationships. 1.1. Objective To examine how response time affects the likelihood of HRTS in patients initially assessed with breathing problems and to understand how age and sex interact with response time in shaping these probabilities. 2. Materials and methods 2.1. Study design The study was conducted as a retrospective observational analysis of over one million prehospital missions in Stockholm, Sweden, between 2017 and 2022. The study adhered to STROBE guidelines 27 . Data were obtained from Region Stockholm’s VAL databases 28 , offering pseudonymized, individual-level data. Machine learning was employed to explore nonlinear relationships. Specifically, gradient boosting—a method that builds many small decision trees and combines them to improve predictions—was used to assess how response time, age, and sex impact the probability of HRTS 29 . Variables such as age, sex, and response time were analysed because of their impact on patient outcome 1 – 5 7 14 – 17 22 30 – 34 . 2.2. Participants The initial dataset (N = 1,297,858) was refined based on inclusion and exclusion criteria (Table 1), resulting in a final subset of 132,395 observations. No missing data were present after preprocessing. Information was retrieved from Region Stockholm about skewed timestamps in a few cases, which would give negative time values and extreme time values. That was handled by excluding the cases with negative response times and response times exceeding 10 hours. Table 1: Inclusion and exclusion criteria Initial dataset N = 1 297 858 observations Exclusion criteria Number of excluded observations Dispatch priority not 1, 2, or 3 (Transport assignments) n = 1 165 463 EMS unit type not Emergency Ambulance Year not between 2017 and 2022 Response time not between 0 and 10 hours* Intrahospital transports Reason of contact to EMCC not “Breathing problems” Observations included n = 132 395 observations * Observations with skewed timestamps caused by technical issues in the prehospital digital platform FRAPP were excluded. Specifically, data with negative time values or response times exceeding 10 hours were considered highly unlikely on the basis of the authors' domain expertise and were therefore omitted from the analysis. 2.3. Variables and Features A Directed Acyclic Graph (DAG) was constructed to guide which variables to include, ensuring alignment with our objective of linking response time to HRTS (Fig. 1 ) 35 . Key variables included age, response time, month, and hour of the day. Our outcome of interest was whether the patient had HRTS (equivalent to Red/Orange in RETTS). To enhance the depth of analysis, the models incorporated variables such as age, hourly average response time, month, and hour of the day. The outcome of interest was HRTS. Detailed program code for feature engineering and machine learning methods is available in Appendix B. 2.4. Statistical software The study employed SAS Enterprise Guide 8.2 for data standardization and manipulation, ensuring adherence to rigorous statistical procedures. SAS Model Studio 8.5 was used for detailed data analyses, offering control over model settings and hyperparameter tuning. Code for these analyses is documented in Appendix B, ensuring transparency and reproducibility. 2.5. Machine Learning Models The dataset was split into training (60%), validation (30%), and testing (10%) subsets. Models evaluated included random forest, gradient boosting, neural networks, and logistic regression 29 36 – 40 . The autotuning feature was used for optimal hyperparameter configuration. Gradient boosting outperformed random forest, neural networks, and logistic regression models, achieving the highest Youden’s Index 41 . Table 2 Gradient boosting settings Since the objective is to investigate the association between variables that are nonlinear, partial dependence (PD) and Individual Conditional Expectation (ICE) plots was used to interpret results. These visualization tools help illustrate how changes in a single variable (like response time) influence predicted risk, while holding other factors constant. Imagine a graph that shows how the predicted risk of HRTS changes as response time increases, for different ages or sexes. ICE plots show how each individual patient’s predicted risk responds to changes in one variable, revealing hidden patterns that might not be captured by average effects alone 42 . 2.6. Ethical Considerations This study complied with ethical guidelines and received approval from the Swedish Ethical Review Authority (Dnr 2022-03701-01). The study adhered to the principles of the Declaration of Helsinki and good clinical practice standards 43 44 . 3. Results Among the models evaluated, the Gradient Boosting model demonstrated the highest performance, achieving a Youden’s Index of 0.2411 41 , and a misclassification rate of 0.3694. While not perfect, this score compares favorably to the accuracy of standard EMCC prioritization. The classification rate is reasonable considering the relatively few variables on which the prediction is made. The Kolmogorov-Smirnov (KS) cutoff point was established at a threshold value of 0.45, where 1-specificity reached 0.3286, and sensitivity reached 0.5697 on the test data (Fig. 2 ). The model’s most important predictors were age, followed by response time, and average response time current hour. (Fig. 3 ). The ICE plot illustrates the factors influencing the probability of a patient presenting with a time-sensitive condition (Fig. 5 ) 45 . Shorter response times were generally associated with higher probabilities of identifying patients with HRTS. This makes sense: patients with obvious signs of severe trouble should, in theory, receive faster EMS response. However, as response times became longer, we observed complex, nonlinear patterns. For patients over 60, the likelihood of HRTS started to rise again after about two hours, suggesting that some patients who were not given highest priority initially may actually have been quite ill or deteriorated in their illness. For younger male patients, the probability of HRTS continued to increase almost linearly with very long response times, again hinting that some serious cases might be missed by initial assessments. When analyzing the interaction of multiple variables using ICE plots, the results reveal an increased patient probability of HRTS as response times lengthen under certain conditions 46 . Specifically, for patients aged over 60, the probability of a time-sensitive condition rises as response times approach approximately two hours, followed by a subsequent nonlinear decrease. In contrast, for male patients under 60 years of age, the probability of a time-sensitive condition demonstrates a nearly linear increase as response times exceed two hours. A deeper analyze of this finding is shown by scatterplots (Fig. 6 ). 4. Discussion 4.1. Key results This study establishes a causal effect between prehospital response time and HRTS. The novel approach measures outcomes on scene in the prehospital setting. Since no hospital interventions or treatments were made to the patient, and HRTS is measured by the first assessment by EMS (it can be updated during EMS care process), measurement bias was minimized. Machine learning models are suitable to describe the association between the variables in this study since the variables of interest have nonlinear effects on each other (Fig. 1 ), and the objective is to investigate a possible association between response time and HRTS. The machine learning model used in this study predicted HRTS with a sensitivity of 0.5697 and specificity of 0.3286 on the test dataset. This performance outperforms the accuracy of EMCC priorities in a similar Swedish setting in a study conducted by Torlén Wennlund et al. (2022). Their study showed the predictive values for Red/Orange RETTS (comparable with HRTS) with a sensitivity of 0.296 and specificity of 0.848 when an emergency call was assessed by an Emergency Medical Dispatcher and a Registered Nurse 23 . Short response times were associated with a higher probability of HRTS, reflecting the prioritization of high-risk cases as expected, since the idea of triage is that patients with HRTS should have shorter response times than patients who are not HRTS 22 24 . However, nonlinear patterns emerged as response times increased, especially for older patients. This may be explained by the low accuracy of EMCC assessments, where patients might not be prioritized according to their actual condition 20 23 . When response time increases over about two hours, the probability of HRTS increases (Fig. 6 ). Age was the most important variable affecting the probability of HRTS. A study by Lindskou et al. (2019) shows that respiratory emergencies are more common in individuals over 60 years of age than in individuals under 60 years of age 25 . Figure 6 shows increased probability of HRTS if the age is over 60 years. The result aligns with Sharma & Goodwin’s (2006) study that shows several anatomical and physiological functions of the respiratory system decrease with aging 26 . Men have an increased probability of HRTS compared to women in both age groups (Fig. 6 ). The reason is not identified in this study and is suitable for future research. 4.2. Implications for EMS Management These findings suggest incorporating age and sex into EMCC assessments to improve accuracy in evaluating emergency calls, since the machine learning model seems to outperform EMCC prioritization. Resource deployment should focus on reducing response times to mitigate the higher probabilities of HRTS associated with longer response times. If an increased probability of HRTS could be avoided with shorter response times under two hours, it is reasonable to assume it would be beneficial to the patient and to the healthcare system, which would otherwise have to deal with a patient more likely to have HRTS. 4.3. Limitations and future research While machine learning provides powerful insights, these models can feel like “black boxes” to practitioners. We used PD and ICE plots to improve interpretability, but future studies might focus on even more transparent models or incorporate other factors like socioeconomic status or geographic differences. Additionally, these findings are focused on one patient group—those with breathing problems—and one region. Future research should explore whether similar patterns exist for conditions like cardiac arrest or stroke and in other healthcare systems. 4.4. Generalizability Our large sample size suggests the results are applicable to other EMS systems with similar healthcare structures and resources. However, local differences—such as variations in healthcare policy or infrastructure—should be considered before generalizing these findings widely. 5. Conclusion This study demonstrates that EMS response time, age, and sex collectively shape the likelihood of HRTS for patients initially presented with breathing problems at EMCC. In other words, it’s not just how quickly help arrives, but who the patient is and how they were initially assessed. By using machine learning models that capture nonlinear relationships, we can gain richer insights into these dynamics. Integrating these predictive insights into EMCC workflows could enable more accurate triage, better resource allocation, and improved patient safety. Ultimately, this approach may reduce the harmful impact of prolonged response times on patients who need urgent care the most. Declarations Clinical trial number: not applicable Ethics approval and consent to participate This study was conducted in full compliance with applicable laws and institutional guidelines, with ethical approval granted by the Swedish Ethical Review Authority on 13 September 2022 (Dnr 2022-03701-01). The Swedish Ethical Review Authority explicitly exempted and waived the requirement for individual consent, in accordance with the Swedish Law (SFS 2003:460) on the Ethical Review of Research Involving Humans, which allows for research without consent under specified conditions like register studies. Consent for publication Not applicable Availability of data and materials Due to the sensitive nature of the used data in this study, where data is confidential due to the Swedish Public Access to Information and Secrecy Act. The variables used in the study is reported in supplementary material. It is possible to use the reported variables to request the data from Region Stockholm and their Centre of Health data. Competing interests ☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. ☐ The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Authors' contributions Peter Hill: Conceptualization, Methodology, Formal analysis, Investigation, Data Curation, Writing - Original Draft, Visualization. Daniel Jonsson: Conceptualization, Methodology, Supervision. Jakob Lederman: Conceptualization, Methodology,Supervision. Peter Bolin: Conceptualization, Methodology. Veronica Vicente: Conceptualization, Methodology, Supervision, Project administration. Acknowledgements Special thanks to Mathias Lanner at SAS Institute for support with SAS software. References Yuting P, Xiangping C, Guifang Y. Association between prehospital time and in-hospital outcomes in out-of-hospital cardiac arrests according to resuscitation outcomes consortium epidemiologic registry. Heart lung. 2024;64:168. 10.1016/j.hrtlng.2024.01.004 . Vaajanen VKE, Vuorinen PET, Setälä PA, et al. Impact of the first response unit on prehospital on-scene time among paramedic-suspected stroke patients: a retrospective before-after cohort study in Finland. Scand J Trauma Resusc Emerg Med. 2023;31(1):28. 10.1186/s13049-023-01089-7 . Goh JL, Pek PP, Fook-Chong SMC, et al. Impact of time-to-compression on out-of-hospital cardiac arrest survival outcomes: A national registry study. Resuscitation. 2023;190:109917. 10.1016/j.resuscitation.2023.109917 . Huang LH, Ho Y-N, Tsai M-T, et al. Response Time Threshold for Predicting Outcomes of Patients with Out-of-Hospital Cardiac Arrest. Emerg Med Int. 2021;2021:5564885. 10.1155/2021/5564885 . Moderating effects of out-. of-hospital cardiac arrest characteristics on the association between EMS response time and survival. Resuscitation. 2021;169:31. 10.1016/j.resuscitation.2021.10.014 . Nasser AAH, Nederpelt C, El Hechi M, et al. Every minute counts: The impact of pre-hospital response time and scene time on mortality of penetrating trauma patients. Am J Surg. 2020;220(1):240–44. 10.1016/j.amjsurg.2019.11.018 . Shortening Ambulance Response Time. Increases Survival in Out-of-Hospital Cardiac Arrest. J Am Heart Association. 2020;9(21):1. 10.1161/JAHA.120.017048 . Association Between Emergency Medical Service Response Time and Motor Vehicle Crash Mortality in the United States. JAMA Surg. 2019;154(4):286. 10.1001/jamasurg.2018.5097 . Association between ambulance response time. neurologic outcome in patients with cardiac arrest. Am J Emerg Med. 2019;37(11):1999. 10.1016/j.ajem.2019.02.021 . The Effect of Ambulance Response Time on Survival Following Out-of-Hospital Cardiac Arrest. An Analysis from the German Resuscitation Registry. Deutsches Ärzteblatt international. 2018;115(33/34):541. 10.3238/arztebl.2018.0541 . Blackwell TH, Kaufman JS. Response time effectiveness: comparison of response time and survival in an urban emergency medical services system. Acad Emerg Med. 2002;9(4):288. Blackwell TH, Kline JA, Willis JJ, et al. Lack of association between prehospital response times and patient outcomes. Prehospital Emerg care. 2009;13(4):444–50. 10.1080/10903120902935363 . Blanchard IE, Doig C, Hagel BE, et al. Emergency medical services response time and mortality in an urban setting. Prehospital Emerg care. 2012;16(1):142. 10.3109/10903127.2011.614046 . Pons PT, Haukoos JS, Bludworth W, et al. Paramedic response time: does it affect patient survival? Acad Emerg Med. 2005;12(7):594–600. 10.1197/j.aem.2005.02.013 . Pons PT, Markovchick VJ. Eight minutes or less: does the ambulance response time guideline impact trauma patient outcome? J Emerg Med. 2002;23(1):43–8. 10.1016/s0736-4679(02)00460-2 . Weiss S, Fullerton L, Oglesbee S, et al. Does ambulance response time influence patient condition among patients with specific medical and trauma emergencies? South Med J. 2013;106(3):230–35. 10.1097/SMJ.0b013e3182882c70 . Wilde ET. Do emergency medical system response times matter for health outcomes? Health Econ. 2013;22(7):790–806. 10.1002/hec.2851 . Ambulance care in. Europe - Organization and practices of ambulance services in 14 European countries. NIVEL; 2015. SOSFS. 2009:10 Socialstyrelsens föreskrifter om ambulanssjukvård m.m. [SOSFS 2009:10 The National Board of Health and Welfare's regulations on ambulance medical care, etc.]. In: Socialstyrelsen, ed.: Socialstyrelsen, 2020. Bohm K, Kurland L. The accuracy of medical dispatch - a systematic review. Scand J Trauma Resusc Emerg Med. 2018;26(1):94. 10.1186/s13049-018-0528-8 . Holmström IK, Kaminsky E, Lindberg Y, et al. The perspectives of Swedish registered nurses about managing difficult calls to emergency medical dispatch centres: a qualitative descriptive study. BMC Nurs. 2021;20(1):150. 10.1186/s12912-021-00657-5 . Magnusson C, Herlitz J, Axelsson C. Pre-hospital triage performance and emergency medical services nurse's field assessment in an unselected patient population attended to by the emergency medical services: a prospective observational study. Scand J Trauma Resusc Emerg Med. 2020;28(1):81. 10.1186/s13049-020-00766-1 . Torlén Wennlund K, Kurland L, Olanders K, et al. A registry-based observational study comparing emergency calls assessed by emergency medical dispatchers with and without support by registered nurses. Scand J Trauma Resusc Emerg Med. 2022;30(1):1. 10.1186/s13049-021-00987-y . Widgren BR, Jourak M. Medical Emergency Triage and Treatment System (METTS): a new protocol in primary triage and secondary priority decision in emergency medicine. J Emerg Med. 2011;40(6):623–28. 10.1016/j.jemermed.2008.04.003 . Lindskou TA, Pilgaard L, Søvsø MB, et al. Symptom, diagnosis and mortality among respiratory emergency medical service patients. PLoS ONE. 2019;14(2):e0213145. 10.1371/journal.pone.0213145 . Sharma G, Goodwin J. Effect of aging on respiratory system physiology and immunology. Clin Interv Aging. 2006;1(3):253–60. 10.2147/ciia.2006.1.3.253 . Cuschieri S. The STROBE guidelines. Saudi J Anaesth. 2019;13(Suppl 1):S31–4. 10.4103/sja.SJA_543_18 . [published Online First: 2019/04/02]. VAL-handboken-. Datalager för uppföljning av vårdhändelser i Region Stockholm [VAL-handbook-Datalayers and follow up in Region Stockholm]. In: Stockholm R, ed., 2023. Leist AK, Klee M, Kim JH et al. Mapping of machine learning approaches for description, prediction, and causal inference in the social and health sciences. 2022;8(42):eabk1942. doi: 10.1126/sciadv.abk1942 Emergency Medical Services Time Intervals for Acute Chest Pain in the United States. 2015–2016. Prehospital Emerg care. 2020;24(4):557. 10.1080/10903127.2019.1676346 . The effect of. rural hospital closures on emergency medical service response and transport times. Health Serv research: HSR. 2020;55(2):288. 10.1111/1475-6773.13254 . Modeling the Optimal Transportation for Acute Stroke Treatment. The Impact of the Drip-and-Drive Paradigm. Stroke. 2020;51(1):275. 10.1161/STROKEAHA.119.027493 . Kim J, Song KJ, Shin SD, et al. Does Prehospital Time Influence Clinical Outcomes in Severe Trauma Patients? A Cross Sectional Study. Prehospital Emerg care. 2017;21(4):466. 10.1080/10903127.2017.1294223 . Swan D, Baumstark L. Does Every Minute Really Count? Road Time as an Indicator for the Economic Value of Emergency Medical Services. Value health. 2022;25(3):400–08. 10.1016/j.jval.2021.09.009 . Textor J, van der Zander B, Gilthorpe MS, et al. Robust causal inference using directed acyclic graphs: the R package ‘dagitty’. Int J Epidemiol. 2017;45(6):1887–94. https://doi.org/10.1093/ije/dyw341 . Breiman LJM. Random forests. 2001;45:5–32. Griesbach C, Säfken B, Waldmann E. Gradient boosting for linear mixed models. Int J biostatistics. 2021;17(2):317–29. 10.1515/ijb-2020-0136 . Premsagar P, Aldous C, Esterhuizen TM, et al. Comparing conventional statistical models and machine learning in a small cohort of South African cardiac patients. Inf Med Unlocked. 2022;34:101103. https://doi.org/10.1016/j.imu.2022.101103 . Uddin S, Khan A, Hossain ME, et al. Comparing different supervised machine learning algorithms for disease prediction. BMC Med Inf Decis Mak. 2019;19(1):281. 10.1186/s12911-019-1004-8 . Zeleke AJ, Palumbo P, Tubertini P, et al. Comparison of nine machine learning regression models in predicting hospital length of stay for patients admitted to a general medicine department. Inf Med Unlocked. 2024;47:101499. https://doi.org/10.1016/j.imu.2024.101499 . Youden WJ. Index for rating diagnostic tests. Cancer. 1950;3(1):32–5. 10.1002/1097-0142(1950)3:13.0.co;2-3 . Wright R. Interpreting Black-Box Machine Learning Models Using Partial Dependence and Individual Conditional Expectation Plots, 2018. WMA DECLARATION OF HELSINKI – ETHICAL PRINCIPLES FOR MEDICAL RESEARCH INVOLVING HUMAN SUBJECTS. In. Association WM, editor, 1964. Guideline for good clinical practice E6(R2.) https://www.ema.europa.eu : European Medicines Agency; 2016 [cited 2024 2024-03-10]. Available from: https://www.ema.europa.eu/en/documents/scientific-guideline/ich-guideline-good-clinical-practice-e6r2-step-5_en.pdf2024. Friedman J. Greedy Function Approximation: A Gradient Boosting Machine. Annals Stat. 2000;29. 10.1214/aos/1013203451 . Goldstein A, Kapelner A, Bleich J, et al. Peeking Inside the Black Box: Visualizing Statistical Learning With Plots of Individual Conditional Expectation. J Comput Graphical Stat. 2013;24. 10.1080/10618600.2014.907095 . Additional Declarations No competing interests reported. Supplementary Files Appendix.docx Cite Share Download PDF Status: Published Journal Publication published 03 Jun, 2025 Read the published version in BMC Medical Informatics and Decision Making → Version 1 posted Editorial decision: Revision requested 25 Dec, 2024 Editor assigned by journal 21 Dec, 2024 Submission checks completed at journal 21 Dec, 2024 First submitted to journal 20 Dec, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5684029","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":394307909,"identity":"4a45a7ff-bc54-40d5-baa8-1488d1c5f0a6","order_by":0,"name":"Peter Hill","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuElEQVRIiWNgGAWjYPCCAzx87MyHGRKIU80M0cLGzJZMmhYGNmYeY+I0mLP3H3xcwXBHBqjls8EDhjo5glosew4zG55heAZ0GO/mhASGw4StMriRzCbZwHAYrOVAAsOBxAaCWu4/Zv8J0cLzGKilrp6wlhvMbIxQLcxAhzEnEOGXZGPJBgOQFjZjgwSDw4YEbTFnP/jwY0PFYXt+9ubHkj8q6uQJ2mKARCIzCGoZBaNgFIyCUYAPAAC9YjGtRFZ1WAAAAABJRU5ErkJggg==","orcid":"","institution":"The Health and Medical Care Administration","correspondingAuthor":true,"prefix":"","firstName":"Peter","middleName":"","lastName":"Hill","suffix":""},{"id":394307910,"identity":"8f370797-22fc-4dba-9279-d65b6952999e","order_by":1,"name":"Daniel Jonsson","email":"","orcid":"","institution":"KTH Royal Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Daniel","middleName":"","lastName":"Jonsson","suffix":""},{"id":394307911,"identity":"782201a2-6835-4308-9d37-6724c6779bf6","order_by":2,"name":"Jakob Lederman","email":"","orcid":"","institution":"The Health and Medical Care Administration","correspondingAuthor":false,"prefix":"","firstName":"Jakob","middleName":"","lastName":"Lederman","suffix":""},{"id":394307912,"identity":"68a0d861-bf22-43c6-8864-ae5bb782bdb8","order_by":3,"name":"Peter Bolin","email":"","orcid":"","institution":"The Health and Medical Care Administration","correspondingAuthor":false,"prefix":"","firstName":"Peter","middleName":"","lastName":"Bolin","suffix":""},{"id":394307913,"identity":"d25dfe69-2855-4c12-a1f4-7de86c6a3b80","order_by":4,"name":"Veronica Vicente","email":"","orcid":"","institution":"Academic EMS in Stockholm","correspondingAuthor":false,"prefix":"","firstName":"Veronica","middleName":"","lastName":"Vicente","suffix":""}],"badges":[],"createdAt":"2024-12-20 12:38:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5684029/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5684029/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12911-025-03046-z","type":"published","date":"2025-06-03T15:57:50+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":75045533,"identity":"98da66fb-2c25-4649-8a31-085c061eade8","added_by":"auto","created_at":"2025-01-29 20:31:39","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":50861,"visible":true,"origin":"","legend":"\u003cp\u003eDepicts a directed acyclic graph illustrating the relationships between exposures and the outcome, indicating the absence of any open biasing path\u003csup\u003e35\u003c/sup\u003e. The features highlighted in green represent variables that were included in the study.\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5684029/v1/b8b426a3dee52fd24c3aafd9.jpg"},{"id":75045777,"identity":"ebe908b0-f522-4b20-addc-827fe9fbe2ef","added_by":"auto","created_at":"2025-01-29 20:39:39","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":55066,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve for the used model. The KS cutoff reference line is drawn at the value of 1-specificity where the greatest difference between sensitivity and 1-specificity is observed for the Validate partition. The KS cutoff line is drawn at the cutoff value 0.45, where the 1-specificity value is 0.3286 and the sensitivity value is 0.5697 in the test data.\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5684029/v1/9fb17cde13e788208ac592ab.jpg"},{"id":75045535,"identity":"296fcea7-a986-46cb-acda-39141486462c","added_by":"auto","created_at":"2025-01-29 20:31:39","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":54706,"visible":true,"origin":"","legend":"\u003cp\u003eThe plot shows the 6 most important features, as determined by the relative importance calculated using the Gradient Boosting model. The most important input for this model is Age, indicating that age highly influences the probability of HRTS. The input Response time has a relative importance of 0.64, for example, which means it is 0.64 times as important as Age.\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5684029/v1/174781af66fb6cfa52f709cf.jpg"},{"id":75045778,"identity":"ea1560d4-d01b-4bc3-99c0-7d1efb794c09","added_by":"auto","created_at":"2025-01-29 20:39:39","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":104418,"visible":true,"origin":"","legend":"\u003cp\u003eThe plots show the partial dependency (PD) and the relationship between Response time (in seconds), age, and sex and the predicted target HRTS for each individual observation. For each observation, the ICE plot displays values of interest on the x-axis and the corresponding prediction for the target variable on the y-axis, holding the other inputs constant at their values for each observation \u003csup\u003e46\u003c/sup\u003e. The group is coded with different identification number for the specific group.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5684029/v1/00bfa2d5d268cddbc4f54397.jpg"},{"id":75045540,"identity":"a0d1bb24-325d-49f8-b00a-c267b0bcf07c","added_by":"auto","created_at":"2025-01-29 20:31:39","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":207351,"visible":true,"origin":"","legend":"\u003cp\u003eScatterplot showing the distribution of probability of HRTS compared to response time in different age groups and sexes. In some circumstances, the probability, after the initial expected decrease, starts to increase with longer response times in a nonlinear way.\u003c/p\u003e","description":"","filename":"Picture5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5684029/v1/4e166c9784f10f95bef9c195.jpg"},{"id":84242643,"identity":"844a2e77-bd87-4217-a48e-756094a16f9e","added_by":"auto","created_at":"2025-06-09 16:10:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1372090,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5684029/v1/9ae4f129-ee06-4dad-9120-49e571079a4d.pdf"},{"id":75045780,"identity":"ee1f89f2-fec4-4a3f-a43d-76fff6ff86ed","added_by":"auto","created_at":"2025-01-29 20:39:39","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":423822,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-5684029/v1/937b8f1ef3e9611accb7a93a.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Harnessing Machine Learning to Predict Time-Sensitive Conditions in Prehospital Care: The Impact of Response Times in Patients with Breathing Problems","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003ePrehospital emergency care encompasses both Emergency Medical Call Centres (EMCC) and Emergency Medical Services (EMS), forming an integrated system aimed at timely patient interventions. Response time\u0026mdash;defined as the interval between an EMCC call and EMS arrival\u0026mdash;is critical for conditions such as cardiac arrest, stroke, and trauma. While many studies highlight the importance of rapid response\u003csup\u003e\u003cspan additionalcitationids=\"CR2 CR3 CR4 CR5 CR6 CR7 CR8 CR9\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e, others find limited or no association with improved outcomes \u003csup\u003e\u003cspan additionalcitationids=\"CR12 CR13 CR14 CR15 CR16\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn Sweden, EMCC personnel categorize calls into three priority levels: Priority 1 (immediately life-threatening), Priority 2 (time-sensitive but non-life-threatening), and Priority 3 (non-time-sensitive)\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. However, EMCC assessments face challenges, often resulting in varied accuracy\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOn-scene, EMS personnel use triage tools like The Rapid Emergency Triage and Treatment System (RETTS). In Sweden, RETTS is widely used to categorize patients into five color-coded priority levels based on the risk of having a time-sensitive condition. In this study setting only four colors is used: Red (most urgent), Orange, Yellow, and Green (least urgent)\u003csup\u003e\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. For this study, Red and Orange levels were combined into a High Risk of Time-Sensitive Condition (HRTS) group due to the increased probability of having a time-sensitive condition\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003ePatients with breathing problems constitute a significant, complex group in prehospital care, facing heightened health risks. For instance, this patient group is often associated with acute respiratory distress and higher mortality rates than other groups, necessitating timely and effective prehospital interventions to mitigate adverse outcomes\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Several anatomical and physiological functions of the respiratory system decrease with aging\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. This study focuses on this group due to their clinical importance.\u003c/p\u003e \u003cp\u003eThis study aims to address the gap in research, examine the association between EMS response times and the likelihood of encountering HRTS conditions in patients with breathing problems. It also explores how age and sex might influence these outcomes. We use a machine learning approach to capture nonlinear patterns\u0026mdash;instances where the risk does not simply rise or fall steadily but may curve or plateau at different points\u0026mdash;to better understand these complex relationships.\u003c/p\u003e \u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003e1.1. Objective\u003c/h2\u003e \u003cp\u003eTo examine how response time affects the likelihood of HRTS in patients initially assessed with breathing problems and to understand how age and sex interact with response time in shaping these probabilities.\u003c/p\u003e \u003c/div\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1. Study design\u003c/h2\u003e\n \u003cp\u003eThe study was conducted as a retrospective observational analysis of over one million prehospital missions in Stockholm, Sweden, between 2017 and 2022. The study adhered to STROBE guidelines\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Data were obtained from Region Stockholm\u0026rsquo;s VAL databases\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e, offering pseudonymized, individual-level data. Machine learning was employed to explore nonlinear relationships. Specifically, gradient boosting\u0026mdash;a method that builds many small decision trees and combines them to improve predictions\u0026mdash;was used to assess how response time, age, and sex impact the probability of HRTS\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Variables such as age, sex, and response time were analysed because of their impact on patient outcome\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e \u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e \u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e \u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e \u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2. Participants\u003c/h2\u003e\n \u003cp\u003eThe initial dataset (N\u0026thinsp;=\u0026thinsp;1,297,858) was refined based on inclusion and exclusion criteria (Table 1), resulting in a final subset of 132,395 observations. No missing data were present after preprocessing. Information was retrieved from Region Stockholm about skewed timestamps in a few cases, which would give negative time values and extreme time values. That was handled by excluding the cases with negative response times and response times exceeding 10 hours.\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eTable 1: Inclusion and exclusion criteria\u003c/em\u003e\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Taba\" border=\"1\"\u003e\n \u003ccolgroup cols=\"2\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eInitial dataset N\u0026thinsp;=\u0026thinsp;1 297 858 observations\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eExclusion criteria\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNumber of excluded observations\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDispatch priority not 1, 2, or 3 (Transport assignments)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u0026thinsp;=\u0026thinsp;1 165 463\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEMS unit type not Emergency Ambulance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYear not between 2017 and 2022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eResponse time not between 0 and 10 hours*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntrahospital transports\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReason of contact to EMCC not \u0026ldquo;Breathing problems\u0026rdquo;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eObservations included n\u0026thinsp;=\u0026thinsp;132 395 observations\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e* Observations with skewed timestamps caused by technical issues in the prehospital digital platform FRAPP were excluded. Specifically, data with negative time values or response times exceeding 10 hours were considered highly unlikely on the basis of the authors\u0026apos; domain expertise and were therefore omitted from the analysis.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3. Variables and Features\u003c/h2\u003e\n \u003cp\u003eA Directed Acyclic Graph (DAG) was constructed to guide which variables to include, ensuring alignment with our objective of linking response time to HRTS (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e) \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Key variables included age, response time, month, and hour of the day. Our outcome of interest was whether the patient had HRTS (equivalent to Red/Orange in RETTS).\u003c/p\u003e\n \u003cp\u003eTo enhance the depth of analysis, the models incorporated variables such as age, hourly average response time, month, and hour of the day. The outcome of interest was HRTS. Detailed program code for feature engineering and machine learning methods is available in Appendix B.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e2.4. Statistical software\u003c/h2\u003e\n \u003cp\u003eThe study employed SAS Enterprise Guide 8.2 for data standardization and manipulation, ensuring adherence to rigorous statistical procedures. SAS Model Studio 8.5 was used for detailed data analyses, offering control over model settings and hyperparameter tuning. Code for these analyses is documented in Appendix B, ensuring transparency and reproducibility.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e2.5. Machine Learning Models\u003c/h2\u003e\n \u003cp\u003eThe dataset was split into training (60%), validation (30%), and testing (10%) subsets. Models evaluated included random forest, gradient boosting, neural networks, and logistic regression \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e \u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. The autotuning feature was used for optimal hyperparameter configuration. Gradient boosting outperformed random forest, neural networks, and logistic regression models, achieving the highest Youden\u0026rsquo;s Index\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n \u003cp\u003eTable 2 Gradient boosting settings\u003c/p\u003e\n \u003cp\u003e\u003cimg 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\"\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eSince the objective is to investigate the association between variables that are nonlinear, partial dependence (PD) and Individual Conditional Expectation (ICE) plots was used to interpret results. These visualization tools help illustrate how changes in a single variable (like response time) influence predicted risk, while holding other factors constant. Imagine a graph that shows how the predicted risk of HRTS changes as response time increases, for different ages or sexes. ICE plots show how each individual patient\u0026rsquo;s predicted risk responds to changes in one variable, revealing hidden patterns that might not be captured by average effects alone\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e2.6. Ethical Considerations\u003c/h2\u003e\n \u003cp\u003eThis study complied with ethical guidelines and received approval from the Swedish Ethical Review Authority (Dnr 2022-03701-01). The study adhered to the principles of the Declaration of Helsinki and good clinical practice standards\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e43\u003c/span\u003e \u003cspan class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eAmong the models evaluated, the Gradient Boosting model demonstrated the highest performance, achieving a Youden\u0026rsquo;s Index of 0.2411\u003csup\u003e41\u003c/sup\u003e, and a misclassification rate of 0.3694. While not perfect, this score compares favorably to the accuracy of standard EMCC prioritization. The classification rate is reasonable considering the relatively few variables on which the prediction is made. The Kolmogorov-Smirnov (KS) cutoff point was established at a threshold value of 0.45, where 1-specificity reached 0.3286, and sensitivity reached 0.5697 on the test data (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe model\u0026rsquo;s most important predictors were age, followed by response time, and average response time current hour. (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe ICE plot illustrates the factors influencing the probability of a patient presenting with a time-sensitive condition (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e) \u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. Shorter response times were generally associated with higher probabilities of identifying patients with HRTS. This makes sense: patients with obvious signs of severe trouble should, in theory, receive faster EMS response. However, as response times became longer, we observed complex, nonlinear patterns. For patients over 60, the likelihood of HRTS started to rise again after about two hours, suggesting that some patients who were not given highest priority initially may actually have been quite ill or deteriorated in their illness. For younger male patients, the probability of HRTS continued to increase almost linearly with very long response times, again hinting that some serious cases might be missed by initial assessments.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWhen analyzing the interaction of multiple variables using ICE plots, the results reveal an increased patient probability of HRTS as response times lengthen under certain conditions\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. Specifically, for patients aged over 60, the probability of a time-sensitive condition rises as response times approach approximately two hours, followed by a subsequent nonlinear decrease. In contrast, for male patients under 60 years of age, the probability of a time-sensitive condition demonstrates a nearly linear increase as response times exceed two hours. A deeper analyze of this finding is shown by scatterplots (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Key results\u003c/h2\u003e \u003cp\u003eThis study establishes a causal effect between prehospital response time and HRTS. The novel approach measures outcomes on scene in the prehospital setting. Since no hospital interventions or treatments were made to the patient, and HRTS is measured by the first assessment by EMS (it can be updated during EMS care process), measurement bias was minimized. Machine learning models are suitable to describe the association between the variables in this study since the variables of interest have nonlinear effects on each other (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), and the objective is to investigate a possible association between response time and HRTS. The machine learning model used in this study predicted HRTS with a sensitivity of 0.5697 and specificity of 0.3286 on the test dataset. This performance outperforms the accuracy of EMCC priorities in a similar Swedish setting in a study conducted by Torl\u0026eacute;n Wennlund et al. (2022). Their study showed the predictive values for Red/Orange RETTS (comparable with HRTS) with a sensitivity of 0.296 and specificity of 0.848 when an emergency call was assessed by an Emergency Medical Dispatcher and a Registered Nurse\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eShort response times were associated with a higher probability of HRTS, reflecting the prioritization of high-risk cases as expected, since the idea of triage is that patients with HRTS should have shorter response times than patients who are not HRTS\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHowever, nonlinear patterns emerged as response times increased, especially for older patients. This may be explained by the low accuracy of EMCC assessments, where patients might not be prioritized according to their actual condition \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. When response time increases over about two hours, the probability of HRTS increases (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAge was the most important variable affecting the probability of HRTS. A study by Lindskou et al. (2019) shows that respiratory emergencies are more common in individuals over 60 years of age than in individuals under 60 years of age\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows increased probability of HRTS if the age is over 60 years. The result aligns with Sharma \u0026amp; Goodwin\u0026rsquo;s (2006) study that shows several anatomical and physiological functions of the respiratory system decrease with aging \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eMen have an increased probability of HRTS compared to women in both age groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The reason is not identified in this study and is suitable for future research.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Implications for EMS Management\u003c/h2\u003e \u003cp\u003eThese findings suggest incorporating age and sex into EMCC assessments to improve accuracy in evaluating emergency calls, since the machine learning model seems to outperform EMCC prioritization. Resource deployment should focus on reducing response times to mitigate the higher probabilities of HRTS associated with longer response times. If an increased probability of HRTS could be avoided with shorter response times under two hours, it is reasonable to assume it would be beneficial to the patient and to the healthcare system, which would otherwise have to deal with a patient more likely to have HRTS.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.3. Limitations and future research\u003c/h2\u003e \u003cp\u003eWhile machine learning provides powerful insights, these models can feel like \u0026ldquo;black boxes\u0026rdquo; to practitioners. We used PD and ICE plots to improve interpretability, but future studies might focus on even more transparent models or incorporate other factors like socioeconomic status or geographic differences. Additionally, these findings are focused on one patient group\u0026mdash;those with breathing problems\u0026mdash;and one region. Future research should explore whether similar patterns exist for conditions like cardiac arrest or stroke and in other healthcare systems.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.4. Generalizability\u003c/h2\u003e \u003cp\u003eOur large sample size suggests the results are applicable to other EMS systems with similar healthcare structures and resources. However, local differences\u0026mdash;such as variations in healthcare policy or infrastructure\u0026mdash;should be considered before generalizing these findings widely.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study demonstrates that EMS response time, age, and sex collectively shape the likelihood of HRTS for patients initially presented with breathing problems at EMCC. In other words, it\u0026rsquo;s not just how quickly help arrives, but who the patient is and how they were initially assessed. By using machine learning models that capture nonlinear relationships, we can gain richer insights into these dynamics. Integrating these predictive insights into EMCC workflows could enable more accurate triage, better resource allocation, and improved patient safety. Ultimately, this approach may reduce the harmful impact of prolonged response times on patients who need urgent care the most.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eClinical trial number:\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003enot applicable\u003c/p\u003e\n\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\n\u003cp\u003eThis study was conducted in full compliance with applicable laws and institutional guidelines, with ethical approval granted by the Swedish Ethical Review Authority on 13 September 2022 (Dnr 2022-03701-01). The Swedish Ethical Review Authority explicitly exempted and waived the requirement for individual consent, in accordance with the Swedish Law (SFS 2003:460) on the Ethical Review of Research Involving Humans, which allows for research without consent under specified conditions like register studies.\u003c/p\u003e\n\u003ch2\u003eConsent for publication\u003c/h2\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e\n\u003cp\u003eDue to the sensitive nature of the used data in this study, where data is confidential due to the Swedish Public Access to Information and Secrecy Act. The variables used in the study is reported in supplementary material. It is possible to use the reported variables to request the data from Region Stockholm and their Centre of Health data.\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003e☒\u0026nbsp;The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003cbr\u003e\u0026nbsp;\u0026nbsp;\u003cbr\u003e\u0026nbsp;☐\u0026nbsp;The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003ch2\u003eAuthors' contributions\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003ePeter Hill:\u0026nbsp;\u003c/strong\u003eConceptualization, Methodology, Formal analysis, Investigation, Data Curation, Writing - Original Draft, Visualization. \u003cstrong\u003eDaniel Jonsson:\u003c/strong\u003e Conceptualization, Methodology, Supervision. \u003cstrong\u003eJakob Lederman:\u0026nbsp;\u003c/strong\u003eConceptualization, Methodology,Supervision.\u003cstrong\u003e\u0026nbsp;Peter Bolin:\u0026nbsp;\u003c/strong\u003eConceptualization, Methodology. \u003cstrong\u003eVeronica Vicente:\u0026nbsp;\u003c/strong\u003eConceptualization, Methodology, Supervision, Project administration.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eSpecial thanks to Mathias Lanner at SAS Institute for support with SAS software.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eYuting P, Xiangping C, Guifang Y. Association between prehospital time and in-hospital outcomes in out-of-hospital cardiac arrests according to resuscitation outcomes consortium epidemiologic registry. Heart lung. 2024;64:168. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.hrtlng.2024.01.004\u003c/span\u003e\u003cspan address=\"10.1016/j.hrtlng.2024.01.004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVaajanen VKE, Vuorinen PET, Set\u0026auml;l\u0026auml; PA, et al. Impact of the first response unit on prehospital on-scene time among paramedic-suspected stroke patients: a retrospective before-after cohort study in Finland. Scand J Trauma Resusc Emerg Med. 2023;31(1):28. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s13049-023-01089-7\u003c/span\u003e\u003cspan address=\"10.1186/s13049-023-01089-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGoh JL, Pek PP, Fook-Chong SMC, et al. Impact of time-to-compression on out-of-hospital cardiac arrest survival outcomes: A national registry study. Resuscitation. 2023;190:109917. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.resuscitation.2023.109917\u003c/span\u003e\u003cspan address=\"10.1016/j.resuscitation.2023.109917\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang LH, Ho Y-N, Tsai M-T, et al. Response Time Threshold for Predicting Outcomes of Patients with Out-of-Hospital Cardiac Arrest. Emerg Med Int. 2021;2021:5564885. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1155/2021/5564885\u003c/span\u003e\u003cspan address=\"10.1155/2021/5564885\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eModerating effects of out-. of-hospital cardiac arrest characteristics on the association between EMS response time and survival. Resuscitation. 2021;169:31. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.resuscitation.2021.10.014\u003c/span\u003e\u003cspan address=\"10.1016/j.resuscitation.2021.10.014\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNasser AAH, Nederpelt C, El Hechi M, et al. Every minute counts: The impact of pre-hospital response time and scene time on mortality of penetrating trauma patients. Am J Surg. 2020;220(1):240\u0026ndash;44. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.amjsurg.2019.11.018\u003c/span\u003e\u003cspan address=\"10.1016/j.amjsurg.2019.11.018\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShortening Ambulance Response Time. Increases Survival in Out-of-Hospital Cardiac Arrest. J Am Heart Association. 2020;9(21):1. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1161/JAHA.120.017048\u003c/span\u003e\u003cspan address=\"10.1161/JAHA.120.017048\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAssociation Between Emergency Medical Service Response Time and Motor Vehicle Crash Mortality in the United States. JAMA Surg. 2019;154(4):286. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1001/jamasurg.2018.5097\u003c/span\u003e\u003cspan address=\"10.1001/jamasurg.2018.5097\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAssociation between ambulance response time. neurologic outcome in patients with cardiac arrest. Am J Emerg Med. 2019;37(11):1999. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ajem.2019.02.021\u003c/span\u003e\u003cspan address=\"10.1016/j.ajem.2019.02.021\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThe Effect of Ambulance Response Time on Survival Following Out-of-Hospital Cardiac Arrest. An Analysis from the German Resuscitation Registry. Deutsches \u0026Auml;rzteblatt international. 2018;115(33/34):541. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3238/arztebl.2018.0541\u003c/span\u003e\u003cspan address=\"10.3238/arztebl.2018.0541\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBlackwell TH, Kaufman JS. Response time effectiveness: comparison of response time and survival in an urban emergency medical services system. Acad Emerg Med. 2002;9(4):288.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBlackwell TH, Kline JA, Willis JJ, et al. Lack of association between prehospital response times and patient outcomes. Prehospital Emerg care. 2009;13(4):444\u0026ndash;50. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/10903120902935363\u003c/span\u003e\u003cspan address=\"10.1080/10903120902935363\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBlanchard IE, Doig C, Hagel BE, et al. Emergency medical services response time and mortality in an urban setting. Prehospital Emerg care. 2012;16(1):142. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3109/10903127.2011.614046\u003c/span\u003e\u003cspan address=\"10.3109/10903127.2011.614046\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePons PT, Haukoos JS, Bludworth W, et al. Paramedic response time: does it affect patient survival? Acad Emerg Med. 2005;12(7):594\u0026ndash;600. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1197/j.aem.2005.02.013\u003c/span\u003e\u003cspan address=\"10.1197/j.aem.2005.02.013\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePons PT, Markovchick VJ. Eight minutes or less: does the ambulance response time guideline impact trauma patient outcome? J Emerg Med. 2002;23(1):43\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/s0736-4679(02)00460-2\u003c/span\u003e\u003cspan address=\"10.1016/s0736-4679(02)00460-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWeiss S, Fullerton L, Oglesbee S, et al. Does ambulance response time influence patient condition among patients with specific medical and trauma emergencies? South Med J. 2013;106(3):230\u0026ndash;35. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/SMJ.0b013e3182882c70\u003c/span\u003e\u003cspan address=\"10.1097/SMJ.0b013e3182882c70\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWilde ET. Do emergency medical system response times matter for health outcomes? Health Econ. 2013;22(7):790\u0026ndash;806. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/hec.2851\u003c/span\u003e\u003cspan address=\"10.1002/hec.2851\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAmbulance care in. Europe - Organization and practices of ambulance services in 14 European countries. NIVEL; 2015.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSOSFS. 2009:10 Socialstyrelsens f\u0026ouml;reskrifter om ambulanssjukv\u0026aring;rd m.m. [SOSFS 2009:10 The National Board of Health and Welfare's regulations on ambulance medical care, etc.]. In: Socialstyrelsen, ed.: Socialstyrelsen, 2020.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBohm K, Kurland L. The accuracy of medical dispatch - a systematic review. Scand J Trauma Resusc Emerg Med. 2018;26(1):94. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s13049-018-0528-8\u003c/span\u003e\u003cspan address=\"10.1186/s13049-018-0528-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHolmstr\u0026ouml;m IK, Kaminsky E, Lindberg Y, et al. The perspectives of Swedish registered nurses about managing difficult calls to emergency medical dispatch centres: a qualitative descriptive study. BMC Nurs. 2021;20(1):150. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12912-021-00657-5\u003c/span\u003e\u003cspan address=\"10.1186/s12912-021-00657-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMagnusson C, Herlitz J, Axelsson C. Pre-hospital triage performance and emergency medical services nurse's field assessment in an unselected patient population attended to by the emergency medical services: a prospective observational study. Scand J Trauma Resusc Emerg Med. 2020;28(1):81. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s13049-020-00766-1\u003c/span\u003e\u003cspan address=\"10.1186/s13049-020-00766-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTorl\u0026eacute;n Wennlund K, Kurland L, Olanders K, et al. A registry-based observational study comparing emergency calls assessed by emergency medical dispatchers with and without support by registered nurses. Scand J Trauma Resusc Emerg Med. 2022;30(1):1. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s13049-021-00987-y\u003c/span\u003e\u003cspan address=\"10.1186/s13049-021-00987-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWidgren BR, Jourak M. Medical Emergency Triage and Treatment System (METTS): a new protocol in primary triage and secondary priority decision in emergency medicine. J Emerg Med. 2011;40(6):623\u0026ndash;28. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jemermed.2008.04.003\u003c/span\u003e\u003cspan address=\"10.1016/j.jemermed.2008.04.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLindskou TA, Pilgaard L, S\u0026oslash;vs\u0026oslash; MB, et al. Symptom, diagnosis and mortality among respiratory emergency medical service patients. PLoS ONE. 2019;14(2):e0213145. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pone.0213145\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0213145\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSharma G, Goodwin J. Effect of aging on respiratory system physiology and immunology. Clin Interv Aging. 2006;1(3):253\u0026ndash;60. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2147/ciia.2006.1.3.253\u003c/span\u003e\u003cspan address=\"10.2147/ciia.2006.1.3.253\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCuschieri S. The STROBE guidelines. Saudi J Anaesth. 2019;13(Suppl 1):S31\u0026ndash;4. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.4103/sja.SJA_543_18\u003c/span\u003e\u003cspan address=\"10.4103/sja.SJA_543_18\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. [published Online First: 2019/04/02].\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVAL-handboken-. Datalager f\u0026ouml;r uppf\u0026ouml;ljning av v\u0026aring;rdh\u0026auml;ndelser i Region Stockholm [VAL-handbook-Datalayers and follow up in Region Stockholm]. In: Stockholm R, ed., 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLeist AK, Klee M, Kim JH et al. Mapping of machine learning approaches for description, prediction, and causal inference in the social and health sciences. 2022;8(42):eabk1942. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1126/sciadv.abk1942\u003c/span\u003e\u003cspan address=\"10.1126/sciadv.abk1942\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEmergency Medical Services Time Intervals for Acute Chest Pain in the United States. 2015\u0026ndash;2016. Prehospital Emerg care. 2020;24(4):557. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/10903127.2019.1676346\u003c/span\u003e\u003cspan address=\"10.1080/10903127.2019.1676346\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThe effect of. rural hospital closures on emergency medical service response and transport times. Health Serv research: HSR. 2020;55(2):288. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/1475-6773.13254\u003c/span\u003e\u003cspan address=\"10.1111/1475-6773.13254\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eModeling the Optimal Transportation for Acute Stroke Treatment. The Impact of the Drip-and-Drive Paradigm. Stroke. 2020;51(1):275. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1161/STROKEAHA.119.027493\u003c/span\u003e\u003cspan address=\"10.1161/STROKEAHA.119.027493\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim J, Song KJ, Shin SD, et al. Does Prehospital Time Influence Clinical Outcomes in Severe Trauma Patients? A Cross Sectional Study. Prehospital Emerg care. 2017;21(4):466. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/10903127.2017.1294223\u003c/span\u003e\u003cspan address=\"10.1080/10903127.2017.1294223\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSwan D, Baumstark L. Does Every Minute Really Count? Road Time as an Indicator for the Economic Value of Emergency Medical Services. Value health. 2022;25(3):400\u0026ndash;08. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jval.2021.09.009\u003c/span\u003e\u003cspan address=\"10.1016/j.jval.2021.09.009\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTextor J, van der Zander B, Gilthorpe MS, et al. Robust causal inference using directed acyclic graphs: the R package \u0026lsquo;dagitty\u0026rsquo;. Int J Epidemiol. 2017;45(6):1887\u0026ndash;94. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/ije/dyw341\u003c/span\u003e\u003cspan address=\"10.1093/ije/dyw341\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBreiman LJM. Random forests. 2001;45:5\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGriesbach C, S\u0026auml;fken B, Waldmann E. Gradient boosting for linear mixed models. Int J biostatistics. 2021;17(2):317\u0026ndash;29. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1515/ijb-2020-0136\u003c/span\u003e\u003cspan address=\"10.1515/ijb-2020-0136\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePremsagar P, Aldous C, Esterhuizen TM, et al. Comparing conventional statistical models and machine learning in a small cohort of South African cardiac patients. Inf Med Unlocked. 2022;34:101103. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.imu.2022.101103\u003c/span\u003e\u003cspan address=\"10.1016/j.imu.2022.101103\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUddin S, Khan A, Hossain ME, et al. Comparing different supervised machine learning algorithms for disease prediction. BMC Med Inf Decis Mak. 2019;19(1):281. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12911-019-1004-8\u003c/span\u003e\u003cspan address=\"10.1186/s12911-019-1004-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZeleke AJ, Palumbo P, Tubertini P, et al. Comparison of nine machine learning regression models in predicting hospital length of stay for patients admitted to a general medicine department. Inf Med Unlocked. 2024;47:101499. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.imu.2024.101499\u003c/span\u003e\u003cspan address=\"10.1016/j.imu.2024.101499\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYouden WJ. Index for rating diagnostic tests. Cancer. 1950;3(1):32\u0026ndash;5. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/1097-0142(1950)3:1\u0026lt;32::aid-cncr2820030106\u0026gt;3.0.co;2-3\u003c/span\u003e\u003cspan address=\"10.1002/1097-0142(1950)3:1%3C32::aid-cncr2820030106%3E3.0.co;2-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWright R. Interpreting Black-Box Machine Learning Models Using Partial Dependence and Individual Conditional Expectation Plots, 2018.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWMA DECLARATION OF HELSINKI \u0026ndash; ETHICAL PRINCIPLES FOR MEDICAL RESEARCH INVOLVING HUMAN SUBJECTS. In. Association WM, editor, 1964.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuideline for good clinical practice E6(R2.) \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ema.europa.eu\u003c/span\u003e\u003cspan address=\"https://www.ema.europa.eu\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e: European Medicines Agency; 2016 [cited 2024 2024-03-10]. Available from: https://www.ema.europa.eu/en/documents/scientific-guideline/ich-guideline-good-clinical-practice-e6r2-step-5_en.pdf2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFriedman J. Greedy Function Approximation: A Gradient Boosting Machine. Annals Stat. 2000;29. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1214/aos/1013203451\u003c/span\u003e\u003cspan address=\"10.1214/aos/1013203451\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGoldstein A, Kapelner A, Bleich J, et al. Peeking Inside the Black Box: Visualizing Statistical Learning With Plots of Individual Conditional Expectation. J Comput Graphical Stat. 2013;24. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/10618600.2014.907095\u003c/span\u003e\u003cspan address=\"10.1080/10618600.2014.907095\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":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-medical-informatics-and-decision-making","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"midm","sideBox":"Learn more about [BMC Medical Informatics and Decision Making](http://bmcmedinformdecismak.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/midm/default.aspx","title":"BMC Medical Informatics and Decision Making","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Emergency Medical Services, Response Time, Machine Learning, Time-Sensitive Conditions, Breathing Problems, Clinical Decision Support Systems","lastPublishedDoi":"10.21203/rs.3.rs-5684029/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5684029/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e \u003cp\u003eShorter response times in prehospital care are often linked to better outcomes, especially for patients presenting with breathing problems\u0026mdash;a group that frequently faces life-threatening conditions if not treated promptly. However, the ways in which response time interacts with patient characteristics (like age and sex) remain less clear. This study uses machine learning to examine how response time, age, and sex influence the likelihood of high-risk time-sensitive conditions (HRTS) in patients initially assessed for breathing difficulties by the Emergency Medical Call Centre (EMCC)\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e \u003cp\u003eA retrospective analysis of 132,395 Emergency Medical Services (EMS) missions in Stockholm, Sweden (2017\u0026ndash;2022) was conducted. Development of a gradient boosting model, a type of machine learning method that can handle complex, nonlinear relationships, to predict HRTS in patients initially assessed for breathing difficulties by EMCC was conducted. The focus is on the effect of response time\u0026mdash;the interval between the emergency call and the EMS team\u0026rsquo;s arrival\u0026mdash;alongside patient age and sex. Model performance was evaluated using Youden\u0026rsquo;s Index and misclassification rates, and interpretation of the model was facilitated through partial dependence (PD) and individual conditional expectation (ICE) plots.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e \u003cp\u003eThe gradient boosting model performed best among several tested methods, achieving a Youden\u0026rsquo;s Index of 0.2411. Age, response time, and sex were the most important predictors of HRTS. Shorter response times tended to be associated with higher chances of HRTS, likely reflecting proper prioritization. Nonlinear patterns emerged with longer response times, especially in patients over 60 years old, suggesting potential gaps in how patients are initially triaged. Male patients across all age groups showed a higher probability of HRTS compared to females.\u003c/p\u003e\u003ch2\u003eConclusions:\u003c/h2\u003e \u003cp\u003eIncorporating predictive modelling into EMCC workflows\u0026mdash;especially by considering age and sex\u0026mdash;could improve the accuracy of initial prioritization and potentially enhance outcomes for patients with breathing problems. By better aligning EMS response times with actual patient risk, healthcare systems may optimize resource allocation, improve patient safety, and reduce the impact of prolonged response times.\u003c/p\u003e","manuscriptTitle":"Harnessing Machine Learning to Predict Time-Sensitive Conditions in Prehospital Care: The Impact of Response Times in Patients with Breathing Problems","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-29 20:31:34","doi":"10.21203/rs.3.rs-5684029/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-12-25T10:54:57+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-12-21T10:55:42+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-12-21T10:55:15+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Informatics and Decision Making","date":"2024-12-20T12:35:39+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-informatics-and-decision-making","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"midm","sideBox":"Learn more about [BMC Medical Informatics and Decision Making](http://bmcmedinformdecismak.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/midm/default.aspx","title":"BMC Medical Informatics and Decision Making","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"13e69af7-237f-4982-aa4a-09c8285abfaf","owner":[],"postedDate":"January 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-06-09T16:04:26+00:00","versionOfRecord":{"articleIdentity":"rs-5684029","link":"https://doi.org/10.1186/s12911-025-03046-z","journal":{"identity":"bmc-medical-informatics-and-decision-making","isVorOnly":false,"title":"BMC Medical Informatics and Decision Making"},"publishedOn":"2025-06-03 15:57:50","publishedOnDateReadable":"June 3rd, 2025"},"versionCreatedAt":"2025-01-29 20:31:34","video":"","vorDoi":"10.1186/s12911-025-03046-z","vorDoiUrl":"https://doi.org/10.1186/s12911-025-03046-z","workflowStages":[]},"version":"v1","identity":"rs-5684029","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5684029","identity":"rs-5684029","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}