{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T19:50:16Z","timestamp":1785873016235,"version":"3.56.0"},"reference-count":57,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR Res Protoc"],"abstract":"Abstract<\/jats:title>\n \n Background<\/jats:title>\n Thirty-day unplanned readmission following coronary artery bypass grafting (CABG) affects 10%-20% of patients and is a key quality indicator, particularly in low- and middle-income countries (LMICs) where access to cardiac rehabilitation is limited. Existing risk models are static, lack real-time engagement, and no validated large language model (LLM)\u2013based clinical decision support (CDS) system exists for post-CABG readmission prevention.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n This protocol describes the development, validation, and evaluation of Smart CABGuard, a nurse-led, LLM-based CDS chatbot with continuous remote electrocardiographic (ECG) monitoring, to predict and prevent postoperative complications, and 30-day unplanned readmission after isolated CABG.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n This multiphase translational study is conducted at Amrita Institute of Medical Sciences, Kochi, India, following TRIPOD-LLM (Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis\u2013Large Language Model) and CONSORT (Consolidated Standards of Reporting Trials) AI reporting guidelines. Phase I uses an ambispective design (retrospective: January 2020 to December 2024; prospective needs survey: June 2025 to January 2026) to develop a logistic regression-based Complication Risk Index (CRI), assessed via receiver operating characteristic area under the curve, Brier score, and decision curve analysis. Phase II refines Smart CABGuard by integrating a frozen Mistral-7B LLM (low-rank adaptation fine-tuned), the CRI engine, explainable AI, and single-lead ECG telemetry (Amrita Spandanam device), with a usability threshold of \u226580%. Phase III is a prospective, parallel-group, open-label randomized controlled trial (RCT). Adults aged \u226518 years undergoing isolated CABG with smartphone access are randomized 1:1 via permuted block randomization, with allocation concealment. The intervention arm receives Smart CABGuard\u2013assisted care (daily chatbot check-ins, CRI-based risk stratification, ECG monitoring, and nurse-led triage) for approximately 24 days postdischarge plus standard care; the control arm receives standard care with structured telephone follow-up for outcome ascertainment only. Outcome assessors and statisticians are blinded; analyses follow the intention-to-treat principle. The primary outcome is 30-day all-cause unplanned readmission. Based on a baseline rate of 10.71%, a 50% relative reduction, \u03b1=.10, and 80% power, the target sample size is 800 patients (400 per arm, including 10% attrition).<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n Ethics approval was granted by the Institutional Ethics Committee of Amrita Institute of Medical Sciences on April 16, 2025. Funding was awarded by Sigma Theta Tau International Honor Society of Nursing, Small Grants Program (grant 21650) in June 2025. Phase I data extraction commenced in May 2025 and is projected to be completed by April 2026; the patient needs survey (June 2025 to January 2026) is ongoing. Phase II usability evaluation is projected from May to July 2026. Phase III recruitment is anticipated from August 2026 to September 2027. Results are expected to be published in early 2028.<\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n Smart CABGuard is the first RCT protocol of an LLM-based nurse-led CDS system for post-CABG readmission prevention in an LMIC setting, aiming to establish the usefulness, safety, and feasibility of AI-augmented postoperative surveillance.<\/jats:p>\n <\/jats:sec>","DOI":"10.2196\/103717","type":"journal-article","created":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T19:42:02Z","timestamp":1785872522000},"page":"e103717-e103717","source":"Crossref","is-referenced-by-count":0,"title":["Nurse-Led Large Language Model Chatbot for Predicting and Preventing Complications After Coronary Artery Bypass Grafting: Protocol for a Randomized Controlled Trial"],"prefix":"10.2196","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-7749-2260","authenticated-orcid":false,"given":"Salini","family":"K","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7877-731X","authenticated-orcid":false,"given":"Ajee","family":"K L","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3978-2874","authenticated-orcid":false,"given":"Praveen","family":"Kerala Varma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1188-1838","authenticated-orcid":false,"given":"Premjith","family":"B","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5924-3304","authenticated-orcid":false,"given":"Anila","family":"K P","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-6187-0699","authenticated-orcid":false,"given":"Vandana","family":"Agnihotri","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7818-516X","authenticated-orcid":false,"given":"Priyalatha","family":"Muthu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5630-5776","authenticated-orcid":false,"given":"Renjitha","family":"Bhaskaran","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-2607-5164","authenticated-orcid":false,"given":"Namrata","family":"Nair","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2026,8,4]]},"reference":[{"issue":"25","key":"R1","doi-asserted-by":"publisher","first-page":"2982","DOI":"10.1016\/j.jacc.2020.11.010","article-title":"Global burden of cardiovascular diseases and risk factors, 1990-2019: update from the GBD 2019 study","volume":"76","author":"Roth","journal-title":"J Am Coll Cardiol"},{"key":"R2","unstructured":"Cardiovascular diseases (CVDs). World Health Organization. URL: https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/cardiovascular-diseases-(cvds) [Accessed 02-06-2026]"},{"issue":"3","key":"R3","doi-asserted-by":"publisher","first-page":"1960","DOI":"10.21037\/jtd.2018.02.43","article-title":"Fifty years of coronary artery bypass grafting","volume":"10","author":"Melly","journal-title":"J Thorac Dis"},{"key":"R4","unstructured":"Bachar BJ Manna B . Coronary artery bypass graft. In: StatPearls. StatPearls Publishing; 2026. URL: http:\/\/www.ncbi.nlm.nih.gov\/books\/NBK507836\/ [Accessed 02-06-2026]"},{"issue":"10","key":"R5","doi-asserted-by":"publisher","first-page":"961","DOI":"10.1056\/NEJMoa0804626","article-title":"Percutaneous coronary intervention versus coronary-artery bypass grafting for severe coronary artery disease","volume":"360","author":"Serruys","journal-title":"N Engl J Med"},{"key":"R6","doi-asserted-by":"publisher","DOI":"10.1016\/j.lansea.2023.100156","article-title":"The burgeoning cardiovascular disease epidemic in Indians - perspectives on contextual factors and potential solutions","volume":"12","author":"Kalra","journal-title":"Lancet Reg Health Southeast Asia"},{"issue":"5","key":"R7","doi-asserted-by":"publisher","first-page":"394","DOI":"10.1016\/j.ihj.2020.08.002","article-title":"Profile of coronary artery disease in Indian rural youth (< 35 yrs)","volume":"72","author":"Patil","journal-title":"Indian Heart J"},{"issue":"01","key":"R8","doi-asserted-by":"publisher","DOI":"10.4172\/2155-9600.1000331","article-title":"Risk factors associated with the increasing cardiovascular diseases prevalence in India: a review","volume":"05","author":"Bani T Aeri","journal-title":"J Nutr Food Sci"},{"issue":"3","key":"R9","doi-asserted-by":"publisher","first-page":"906","DOI":"10.1016\/j.athoracsur.2020.05.159","article-title":"Very early discharge after coronary artery bypass grafting does not affect readmission or survival","volume":"111","author":"Afflu","journal-title":"Ann Thorac Surg"},{"key":"R10","doi-asserted-by":"publisher","DOI":"10.3389\/fcvm.2023.1224302","article-title":"The effect of hospital-to-home transitional care using a digital messaging application on the health outcomes of patients undergoing CABG and their family caregivers: a randomized controlled trial study protocol","volume":"10","author":"Maleki","journal-title":"Front Cardiovasc Med"},{"issue":"1","key":"R11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.29252\/IJMR-060101","article-title":"Complications of coronary artery bypass grafting","volume":"6","author":"Hussain","journal-title":"Int J Med Rev"},{"issue":"2","key":"R12","doi-asserted-by":"publisher","first-page":"197","DOI":"10.3724\/SP.J.1263.2011.12221","article-title":"Depression, anxiety, and cardiac morbidity outcomes after coronary artery bypass surgery: a contemporary and practical review","volume":"9","author":"Tully","journal-title":"J Geriatr Cardiol"},{"issue":"2","key":"R13","doi-asserted-by":"publisher","first-page":"484","DOI":"10.1016\/j.athoracsur.2019.11.042","article-title":"Thirty-day nonindex readmissions and clinical outcomes after cardiac surgery","volume":"110","author":"Hirji","journal-title":"Ann Thorac Surg"},{"issue":"1","key":"R14","doi-asserted-by":"publisher","DOI":"10.1186\/s13019-021-01556-1","article-title":"Patient and hospital factors associated with 30-day readmissions after coronary artery bypass graft (CABG) surgery: a systematic review and meta-analysis","volume":"16","author":"Shawon","journal-title":"J Cardiothorac Surg"},{"issue":"4","key":"R15","doi-asserted-by":"publisher","first-page":"292","DOI":"10.1016\/j.jtumed.2020.05.004","article-title":"Thirty-day readmission rates and associated risk factors after coronary artery bypass grafting","volume":"15","author":"Alghafees","journal-title":"J Taibah Univ Med Sci"},{"issue":"5","key":"R16","doi-asserted-by":"publisher","first-page":"569","DOI":"10.1016\/j.jcin.2011.01.010","article-title":"30-day readmissions after coronary artery bypass graft surgery in New York State","volume":"4","author":"Hannan","journal-title":"JACC Cardiovasc Interv"},{"key":"R17","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijcha.2024.101552","article-title":"Incidence, timing and variation in unplanned readmissions within 30-days following isolated coronary artery bypass grafting","volume":"56","author":"Patel","journal-title":"Int J Cardiol Heart Vasc"},{"issue":"4","key":"R18","doi-asserted-by":"publisher","first-page":"518","DOI":"10.1016\/j.carrev.2019.08.005","article-title":"Unplanned 30-day readmission after coronary artery bypass in patients with acute myocardial infarction","volume":"21","author":"Case","journal-title":"Cardiovasc Revasc Med"},{"issue":"4","key":"R19","doi-asserted-by":"publisher","DOI":"10.5812\/jjcdc.104180","article-title":"Post-operative symptoms and complications in patients having undergone coronary artery bypass graft in Hamadan: a descriptive cross-sectional study","volume":"9","author":"Oshvandi","journal-title":"Jundishapur J Chronic Dis Care"},{"issue":"1","key":"R20","doi-asserted-by":"publisher","DOI":"10.2174\/0118744346368558250320043722","article-title":"Impact of a modified discharge program on health-related quality of life following coronary artery bypass grafting: a randomized controlled trial","volume":"19","author":"Abu Shaikha","journal-title":"Open Nurse J"},{"key":"R21","doi-asserted-by":"publisher","DOI":"10.3389\/fmed.2025.1650700","article-title":"Machine learning prediction of post-CABG atrial fibrillation using clinical and pharmacogenomic biomarkers","volume":"12","author":"Hua","journal-title":"Front Med"},{"issue":"3","key":"R22","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1016\/j.jhin.2019.01.009","article-title":"Predictive models of surgical site infections after coronary surgery: insights from a validation study on 7090 consecutive patients","volume":"102","author":"Gatti","journal-title":"J Hosp Infect"},{"key":"R23","doi-asserted-by":"publisher","DOI":"10.3389\/fcvm.2025.1616003","article-title":"Risk prediction models for prolonged mechanical ventilation following coronary artery bypass grafting surgery: a systematic review and meta-analysis","volume":"12","author":"Jia","journal-title":"Front Cardiovasc Med"},{"issue":"4","key":"R24","doi-asserted-by":"publisher","first-page":"2519","DOI":"10.21037\/jtd-2025-264","article-title":"Machine learning-based model for the prediction of acute kidney injury following coronary artery bypass graft surgery in elderly Chinese patients","volume":"17","author":"Li","journal-title":"J Thorac Dis"},{"issue":"3","key":"R25","doi-asserted-by":"publisher","first-page":"711","DOI":"10.1016\/j.athoracsur.2021.08.040","article-title":"Machine learning to predict outcomes and cost by phase of care after coronary artery bypass grafting","volume":"114","author":"Zea-Vera","journal-title":"Ann Thorac Surg"},{"issue":"3","key":"R26","doi-asserted-by":"publisher","first-page":"336","DOI":"10.1016\/j.atssr.2024.02.005","article-title":"Intraoperative features improve model risk predictions after coronary artery bypass grafting","volume":"2","author":"Potosnak","journal-title":"Ann Thorac Surg Short Rep"},{"issue":"2","key":"R27","doi-asserted-by":"publisher","first-page":"1097","DOI":"10.3390\/biomedinformatics4020062","article-title":"Recent advances in large language models for healthcare","volume":"4","author":"Nassiri","journal-title":"BioMedInformatics"},{"issue":"4","key":"R28","doi-asserted-by":"publisher","DOI":"10.7759\/cureus.82397","article-title":"Understanding large language models in healthcare: a guide to clinical implementation and interpreting publications","volume":"17","author":"Maslinski","journal-title":"Cureus"},{"issue":"6","key":"R29","doi-asserted-by":"publisher","DOI":"10.3390\/bioengineering12060631","article-title":"Large language models in healthcare and medical applications: a review","volume":"12","author":"Maity","journal-title":"Bioengineering (Basel)"},{"issue":"3","key":"R30","doi-asserted-by":"publisher","DOI":"10.1007\/s42979-021-00592-x","article-title":"Machine learning: algorithms, real-world applications and research directions","volume":"2","author":"Sarker","journal-title":"SN Comput Sci"},{"key":"R31","doi-asserted-by":"publisher","DOI":"10.1007\/s10389-025-02523-z","article-title":"GenAI in nursing and clinical practice: a rapid review of applications and challenges","author":"Tischendorf","journal-title":"J Public Health (Berl)"},{"issue":"1","key":"R32","doi-asserted-by":"publisher","DOI":"10.2196\/79039","article-title":"Large language model\u2013based virtual patient systems for history-taking in medical education: comprehensive systematic review","volume":"14","author":"Li","journal-title":"JMIR Med Inform"},{"key":"R33","doi-asserted-by":"publisher","DOI":"10.1200\/CCI-25-00244","article-title":"Simulation-based evaluation of a large language model-enabled clinical decision support platform in oncology","volume":"10","author":"Lajmi","journal-title":"JCO Clin Cancer Inform"},{"key":"R34","unstructured":"Nabia S . Assessing the effectiveness of large language model (LLM)-enabled nurse treatment planning in 2 Indian districts: a pilot study. ClinicalTrials.gov. 2026. URL: https:\/\/clinicaltrials.gov\/study\/NCT07432893 [Accessed 02-06-2026]"},{"issue":"6","key":"R35","doi-asserted-by":"publisher","first-page":"2173","DOI":"10.1016\/j.athoracsur.2021.11.011","article-title":"Leveraging machine learning to predict 30-day hospital readmission after cardiac surgery","volume":"114","author":"Sherman","journal-title":"Ann Thorac Surg"},{"issue":"2","key":"R36","doi-asserted-by":"publisher","first-page":"33","DOI":"10.37737\/ace.22005","article-title":"Introduction to matching in case-control and cohort studies","volume":"4","author":"Iwagami","journal-title":"Ann Clin Epidemiol"},{"key":"R37","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.m441","article-title":"Calculating the sample size required for developing a clinical prediction model","volume":"368","author":"Riley","journal-title":"BMJ"},{"issue":"1","key":"R38","doi-asserted-by":"publisher","first-page":"W1","DOI":"10.7326\/M14-0698","article-title":"Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): explanation and elaboration","volume":"162","author":"Moons","journal-title":"Ann Intern Med"},{"issue":"12","key":"R39","doi-asserted-by":"publisher","first-page":"1472","DOI":"10.4244\/EIJV9I12A245","article-title":"Tools and techniques--statistics: how many variables are allowed in the logistic and Cox regression models?","volume":"9","author":"van Domburg","journal-title":"EuroIntervention"},{"issue":"8","key":"R40","doi-asserted-by":"publisher","first-page":"2455","DOI":"10.1177\/0962280218784726","article-title":"Sample size for binary logistic prediction models: beyond events per variable criteria","volume":"28","author":"van Smeden","journal-title":"Stat Methods Med Res"},{"issue":"3","key":"R41","doi-asserted-by":"publisher","first-page":"395","DOI":"10.1016\/j.pec.2014.05.027","article-title":"Development of the Patient Education Materials Assessment Tool (PEMAT): a new measure of understandability and actionability for print and audiovisual patient information","volume":"96","author":"Shoemaker","journal-title":"Patient Educ Couns"},{"issue":"1","key":"R42","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1007\/s00779-021-01582-9","article-title":"The Chatbot Usability Scale: the design and pilot of a Usability Scale for interaction with AI-based conversational agents","volume":"26","author":"Borsci","journal-title":"Pers Ubiquit Comput"},{"issue":"3","key":"R43","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1016\/0149-7189(79)90094-6","article-title":"Assessment of client\/patient satisfaction: development of a general scale","volume":"2","author":"Larsen","journal-title":"Eval Program Plann"},{"key":"R44","unstructured":"How many participants for quantitative usability studies: a summary of sample-size recommendations. Nielsen Norman Group; URL: https:\/\/www.nngroup.com\/articles\/summary-quant-sample-sizes\/ [Accessed 25-06-2026]"},{"key":"R45","unstructured":"Garbutt J . Acceptability and effectiveness of an mHealth intervention to provide cognitive behavioral therapy (CBT) for adolescents with depression\/anxiety. ClinicalTrials.gov. 2022. URL: https:\/\/clinicaltrials.gov\/study\/NCT04603053 [Accessed 26-06-2026]"},{"key":"R46","doi-asserted-by":"publisher","DOI":"10.2196\/43135","article-title":"Evaluating user experience with a chatbot designed as a public health response to the COVID-19 pandemic in Brazil: mixed methods study","volume":"10","author":"Chagas","journal-title":"JMIR Hum Factors"},{"key":"R47","doi-asserted-by":"publisher","DOI":"10.1016\/j.xjon.2023.09.016","article-title":"Results of a postoperative telemedicine trial after cardiac surgery and incorporation into practice","volume":"16","author":"Downing","journal-title":"JTCVS Open"},{"issue":"2","key":"R48","doi-asserted-by":"publisher","first-page":"354","DOI":"10.1093\/eurjcn\/zvaf190","article-title":"A pilot randomized controlled trial to test the feasibility of a mobile health (mHealth) self-help intervention for adults after cardiac surgery","volume":"25","author":"Wynne","journal-title":"Eur J Cardiovasc Nurs"},{"key":"R49","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2024.3409759","article-title":"IoMT and AI enabled time critical system for tele-cardiac rehabilitation","volume":"12","author":"Shaji","journal-title":"IEEE Access"},{"key":"R50","doi-asserted-by":"crossref","unstructured":"Dilraj N Rakesh K Krishnan R Ramesh M . A low cost remote cardiac monitoring framework for rural regions. Presented at: 5th EAI International Conference on Wireless Mobile Communication and Healthcare; Oct 14-16, 2015:231-236; London, Great Britain. 2015.[doi: 10.4108\/eai.14-10-2015.2261648]","DOI":"10.4108\/eai.14-10-2015.2261648"},{"issue":"2","key":"R51","doi-asserted-by":"publisher","first-page":"2449","DOI":"10.1109\/JIOT.2018.2870068","article-title":"IoT-based smart edge for global health: remote monitoring with severity detection and alerts transmission","volume":"6","author":"Pathinarupothi","journal-title":"IEEE Internet Things J"},{"issue":"4","key":"R52","doi-asserted-by":"publisher","first-page":"423","DOI":"10.1016\/j.jclinepi.2011.11.004","article-title":"The development of a postoperative morbidity score to assess total morbidity burden after cardiac surgery","volume":"65","author":"Sanders","journal-title":"J Clin Epidemiol"},{"key":"R53","doi-asserted-by":"publisher","DOI":"10.1016\/j.hrtlng.2025.04.023","article-title":"Non-invasive monitoring strategies for atrial fibrillation detection in adult cardiac surgery patients after hospital discharge: A scoping review","volume":"73","author":"Jaradat","journal-title":"Heart Lung"},{"issue":"7","key":"R54","doi-asserted-by":"publisher","first-page":"106841","DOI":"10.4330\/wjc.v17.i7.106841","article-title":"Advancing cardiac arrhythmia management: the integration of wearable technology and remote monitoring","volume":"17","author":"Bokhari","journal-title":"World J Cardiol"},{"issue":"14","key":"R55","doi-asserted-by":"publisher","DOI":"10.3390\/jcm14144887","article-title":"Feasibility and safety of early cardiac rehabilitation using remote electrocardiogram monitoring in patients with cardiac surgery: a pilot study","volume":"14","author":"Kim","journal-title":"J Clin Med"},{"key":"R56","doi-asserted-by":"publisher","DOI":"10.1016\/j.xjon.2023.07.003","article-title":"Remote monitoring following adult cardiac surgery: a paradigm shift?","volume":"15","author":"Lobdell","journal-title":"JTCVS Open"},{"issue":"5","key":"R57","doi-asserted-by":"publisher","DOI":"10.7759\/cureus.84761","article-title":"The role of machine learning in predicting hospital readmissions among general internal medicine patients: a systematic review","volume":"17","author":"Sharda","journal-title":"Cureus"}],"container-title":["JMIR Research Protocols"],"original-title":[],"language":"en","deposited":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T19:42:05Z","timestamp":1785872525000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.researchprotocols.org\/2026\/1\/e103717"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,4]]},"references-count":57,"URL":"https:\/\/doi.org\/10.2196\/103717","relation":{},"ISSN":["1929-0748"],"issn-type":[{"value":"1929-0748","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,4]]},"article-number":"v15i25e103717"}}