Performance of the Modified HEART Score in Predicting 30-Day Major Adverse Cardiovascular Events at a North Indian Tertiary Emergency Department: A Prospective Observational Study with Emphasis on the Diabetic Subpopulation

Performance of the Modified HEART Score in Predicting 30-Day Major Adverse Cardiovascular Events at a North Indian Tertiary Emergency Department: A Prospective Observational Study with Emphasis on the Diabetic Subpopulation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Performance of the Modified HEART Score in Predicting 30-Day Major Adverse Cardiovascular Events at a North Indian Tertiary Emergency Department: A Prospective Observational Study with Emphasis on the Diabetic Subpopulation Nikit Mittal, Akanksha Mittal This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9255411/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Background : Chest pain is one of the most frequent reasons for Emergency Department (ED) attendance worldwide, yet separating true cardiac causes from the vast majority of benign alternatives remains a daily challenge for emergency physicians. The Modified HEART Score (MHS), which incorporates History, ECG, Age, Risk factors, and high-sensitivity cardiac Troponin (hs-cTn), has been validated as a reliable risk-stratification tool for predicting 30-day Major Adverse Cardiovascular Events (MACE). Its performance in North Indian populations — particularly in patients with concurrent diabetes mellitus — has not, to date, been systematically examined. Methods : We conducted a prospective observational study at the Emergency Department of BLK-MAX Super Specialty Hospital, New Delhi, over 18 months (June 2022–November 2023). Consecutive adults (≥18 years) presenting with chest pain or equivalent symptoms within 7 days of onset were enrolled. The MHS was calculated at initial assessment using standardised criteria incorporating hs-cTnI (reference range 0–34 pg/mL). The primary endpoint was 30-day MACE, defined as a composite of acute MI, unstable angina requiring revascularisation, stent thrombosis, or cardiovascular death. Diagnostic accuracy was characterised by sensitivity, specificity, PPV, NPV (Wilson 95% CIs), and AUC-ROC. A pre-specified secondary analysis compared MACE rates in diabetic and non-diabetic patients across risk strata. Results : A total of 165 patients were enrolled; 112 (67.9%) were male. Thirty-day MACE occurred in 52 patients (31.5%). Risk stratification yielded: low-risk MHS 1–3 (n=59, 35.8%), medium-risk MHS 4–6 (n=70, 42.4%), and high-risk MHS 7–10 (n=36, 21.8%), with corresponding MACE rates of 3.4%, 34.3%, and 72.2% (p<0.001). The MHS achieved a sensitivity of 96.2% (95% CI: 93.9–98.4%), specificity 50.4% (35.5–65.4%), PPV 47.2% (32.7–61.7%), NPV 96.6% (88.5–99.1%), and AUC 0.73 (0.65–0.81). Diabetes mellitus was present in 66 patients (40.0%). MACE was substantially more common among diabetics than non-diabetics (51.5% vs. 18.2%, p<0.001). Both patients who experienced MACE despite a low-risk score were diabetic males. Conclusion : The MHS demonstrates high sensitivity and acceptable discrimination for 30-day MACE in a North Indian tertiary ED, with performance broadly consistent with data from comparable Asian populations. The disproportionate concentration of MACE within the diabetic subgroup — across all risk strata — supports a re-examination of how diabetes mellitus is weighted within the HEART score algorithm. Larger multicentre studies are needed to guide score refinement for high-DM-burden populations. Trial registration: Not applicable (observational study). Modified HEART Score chest pain emergency department major adverse cardiovascular events MACE diabetes mellitus India risk stratification high-sensitivity troponin AUC-ROC Figures Figure 1 Figure 2 Figure 3 Figure 4 1. BACKGROUND Chest pain brings roughly 5–10% of all patients through the emergency department door — and deciding which of them is having a cardiac event remains one of the hardest calls in acute medicine. [ 1 , 2 ] Get it wrong in one direction and you miss a myocardial infarction, an error that accounts for a disproportionate share of medicolegal claims in emergency practice. [ 3 ] Get it wrong in the other and you admit, investigate, and observe a patient who needed neither the bed nor the anxiety that comes with it. The stakes are high, the time pressures real, and the margin for error narrow. India's epidemiological landscape sharpens these concerns considerably. Cardiovascular disease now accounts for close to 28% of all deaths in the country [ 4 ] and, perhaps more importantly, Indian patients with acute coronary syndromes present nearly a decade earlier than their Western counterparts. [ 5 ] Overlapping this is an epidemic of type 2 diabetes mellitus — over 77 million affected adults — that confers a two- to four-fold increase in cardiovascular risk, often through atypical or silent presentation patterns that can confound clinical assessment. [ 6 , 7 ] Taken together, these factors create a phenotype of chest pain patient that differs meaningfully from the populations in which most risk-stratification tools were originally derived and validated. The HEART Score — History, ECG, Age, Risk factors, Troponin — was introduced by Six et al. in 2008 [ 8 ] as a structured, bedside-applicable approach to chest pain triage. Over the subsequent decade and a half, it has been validated in more than 30 independent cohorts totalling upwards of 40,000 patients. [ 9 ] The Modified HEART Score (MHS) replaces conventional troponin with high-sensitivity troponin (hs-cTn), enabling earlier detection of myocardial injury and shifting the score's utility further toward the initial assessment window. [ 10 ] Scores of 0–3 define a low-risk category, 4–6 medium risk, and 7–10 high risk, with the medium and high categories together constituting a positive screen for 30-day MACE. Despite this validation pedigree, the evidence base from the Indian subcontinent is thin. What data do exist are largely confined to South Indian community hospitals, where patient demographics, diabetes burden, and the proportion presenting through tertiary referral pathways differ from what we observe in large North Indian centres. [ 11 ] Perhaps more significantly, no published study has examined MHS performance stratified by diabetes status within an Indian cohort — a gap that is both clinically meaningful and potentially actionable if the data support adjustment of risk thresholds or score weighting for this population. We therefore undertook a prospective evaluation of the MHS at a major North Indian tertiary ED, with a pre-specified secondary analysis of MACE rates across diabetic and non-diabetic patients within each risk category. The study also represents, to our knowledge, the first MHS validation incorporating hs-cTnI specifically in a North Indian cohort. 2. METHODS 2.1 Study Design and Setting This was a prospective observational cohort study conducted at the Department of Emergency Medicine and Trauma, BLK-MAX Super Specialty Hospital (Dr. B.L. Kapur Memorial Hospital), Pusa Road, New Delhi. BLK-MAX is a 700-bed quaternary referral centre serving the North Indian corridor, with an ED caseload that skews toward moderate-to-high acuity patients referred from peripheral hospitals. The study ran from June 2022 to November 2023 — 18 consecutive months. The study was approved by the Institutional Ethics Committee (Reference: BLK-MAX/IEC/2022/EM-04), and written informed consent was obtained from every participant before enrolment. Reporting follows the STROBE checklist for observational studies. [Supplementary File 1] 2.2 Participants Consecutive patients aged ≥ 18 years, presenting with chest pain, chest discomfort, or closely related equivalent symptoms (palpitations, diaphoresis, unexplained exertional dyspnoea) with onset within the preceding 7 days, were screened by the triage team. We used consecutive enrolment rather than random sampling to minimise selection bias within the study window. Exclusion criteria were applied prospectively: traumatic chest pain (any mechanism); known active malignancy; pregnancy; ST-elevation myocardial infarction on the presentation ECG (these patients were fast-tracked directly to the catheterisation laboratory and their risk stratification was, rightly, bypassed); and patients who left against medical advice before MHS calculation could be completed. 2.3 Sample Size Sample size estimation followed the framework of Six et al. [ 8 ] Using published mean HEART scores of 6.51 (SD 1.84) for MACE and 3.71 (SD 1.83) for non-MACE groups, with 80% statistical power and a two-sided α of 0.05, we required a minimum of 33 MACE events. Assuming a MACE prevalence of approximately 25% based on prior Indian data, the required total sample was 165 patients (33 MACE events + 132 non-MACE). The actual MACE prevalence of 31.5% observed exceeded this assumption, meaning the study was slightly over-powered for the primary analysis. 2.4 Modified HEART Score Calculation The treating emergency physician calculated the MHS at the time of initial assessment, independently of the diagnostic workup. Each of the five components was scored on a 0–2 ordinal scale, yielding a composite total of 0–10: History (H): 0 = non-specific or other diagnosis more likely; 1 = moderately suspicious (mixed elements, some cardiac features but not dominant); 2 = highly suspicious (classic anginal radiation to arm or jaw, exertional precipitation, nitrate responsiveness, accompanying diaphoresis) ECG (E): 0 = normal; 1 = non-specific repolarisation disturbance, LBBB, LVH with repolarisation changes, or digitalis effect; 2 = significant new or assumed-new ST deviation or T-wave inversion Age (A): 0 = 30 kg/m²], active smoking, family history of CAD); 2 = three or more risk factors, or known established atherosclerotic disease (prior PCI, CABG, cerebrovascular event, or PAD) Troponin (T): hs-cTnI assay (reference range 0–34 pg/mL); 0 = within normal limits; 1 = 1–3× upper limit of normal; 2 = > 3× upper limit of normal Patients scoring 0–3 were classified as low-risk, 4–6 as medium-risk, and 7–10 as high-risk. For diagnostic accuracy computation, a score ≥ 4 was treated as a positive screen for MACE. 2.5 Outcome Assessment The primary outcome was 30-day MACE, defined as any of: acute MI (STEMI or NSTEMI confirmed by serial ECG changes and troponin kinetics); unstable angina necessitating emergency coronary revascularisation (PCI or CABG); stent thrombosis; or death from cardiovascular cause within 30 days of the index ED presentation. Outcome ascertainment was conducted through review of inpatient records for admitted patients and a structured telephone follow-up call at 30 days for those discharged. The secondary outcome was MACE rate within each MHS risk category, stratified by diabetes status. 2.6 Statistical Analysis Categorical variables are reported as counts and proportions; continuous variables as mean ± standard deviation. The chi-squared test assessed associations between categorical variables; Fisher's exact test was used when expected cell counts fell below 5. Sensitivity, specificity, PPV, and NPV were derived from a standard 2×2 contingency table at the MHS ≥ 4 threshold, with 95% CIs calculated using the Wilson score method to avoid boundary effects. AUC-ROC quantified overall discriminatory ability. All analyses were performed in SPSS version 22.0 (IBM Corp., Armonk, NY, USA). A two-sided p < 0.05 was the threshold for statistical significance. 3. RESULTS 3.1 Baseline Characteristics One hundred and sixty-five patients were enrolled across the 18-month study window, representing 97% of eligible patients approached (8 declined consent and 4 met exclusion criteria after initial assessment). The cohort was predominantly male: 112 men (67.9%) and 53 women (32.1%). Age was distributed as follows — 30 patients (18.2%) aged below 45 years, 69 (41.8%) between 45 and 64 years, and 66 (40.0%) aged 65 or above. The high proportion of older patients is consistent with the tertiary referral pattern at this centre. [ 12 ] Diabetes mellitus was present in 66 patients (40.0%). This prevalence — roughly three to four times the Indian national adult estimate of 10–15% — reflects the case-mix at a quaternary referral hospital and has direct implications for interpreting the secondary analysis. Thirty-day MACE occurred in 52 patients, an overall prevalence of 31.5%. Baseline characteristics are summarised in Table 1 . Table 1 Baseline Demographic and Clinical Characteristics (N = 165) Characteristic n (%) Notes Total patients 165 (100%) Consecutive enrolment Male sex 112 (67.9%) Female sex 53 (32.1%) Age 98% 30-day MACE 52 (31.5%) Primary outcome M = male; F = female; T2DM = type 2 diabetes mellitus; MACE = Major Adverse Cardiovascular Events 3.2 MHS Risk Category Distribution and MACE MHS classified 59 patients (35.8%) as low-risk, 70 (42.4%) as medium-risk, and 36 (21.8%) as high-risk. The stepwise gradient of MACE across categories was highly significant: 2 of 59 low-risk patients experienced MACE (3.4%), compared with 24 of 70 medium-risk patients (34.3%) and 26 of 36 high-risk patients (72.2%) — a linear trend for which the chi-squared p-value was well below 0.001. Table 2 shows the full distribution, and Fig. 2 illustrates the MACE gradient visually. Table 2 30-Day MACE Incidence by MHS Risk Category Risk Category (MHS) N (%) MACE Yes MACE No p-value Low risk (1–3) 59 (35.8%) 2 (3.4%) 57 (96.6%) < 0.001 Medium risk (4–6) 70 (42.4%) 24 (34.3%) 46 (65.7%) < 0.001 High risk (7–10) 36 (21.8%) 26 (72.2%) 10 (27.8%) < 0.001 Total 165 (100%) 52 (31.5%) 113 (68.5%) Chi-squared test for linear trend across risk categories; p-values compare each category against all others combined 3.3 Diagnostic Performance At the MHS ≥ 4 threshold, the score identified 50 of 52 MACE events correctly (true positives). The two missed cases (false negatives) were both diabetic males classified as low-risk. Among 113 non-MACE patients, 57 were correctly identified as low-risk (true negatives), while 56 were classified medium/high-risk in the absence of MACE (false positives). Table 3 presents the full diagnostic performance profile; Fig. 1 shows the corresponding ROC curve. Table 3 Diagnostic Performance of MHS (Threshold ≥ 4) for 30-Day MACE Parameter Value 95% Confidence Interval Sensitivity 96.2% 93.9% – 98.4% Specificity 50.4% 35.5% – 65.4% Positive Predictive Value 47.2% 32.7% – 61.7% Negative Predictive Value 96.6% 88.5% – 99.1% AUC-ROC 0.73 0.65–0.81 True Positives 50 True Negatives 57 False Positives 56 False Negatives 2 Both diabetic males AUC-ROC = Area Under the Receiver Operating Characteristic Curve; 95% CIs by Wilson score method 3.4 MACE by Gender and Age Group Males accounted for 40 of 52 MACE events (76.9%). MACE prevalence was 35.7% among men versus 22.6% among women, a difference that approached but did not reach conventional significance (p = 0.07), consistent with known sex-related cardiovascular risk gradients. [ 12 ] Among patients below age 45, MACE occurred exclusively in men (8 cases, zero in women). At the other end of the age spectrum, the gap narrowed markedly — 32.6% of men aged ≥ 65 experienced MACE, versus 30.4% of women in the same age band — a convergence that accords with post-menopausal equalisation of cardiovascular risk. [ 13 ] 3.5 Diabetes Mellitus and MACE — Pre-Specified Secondary Analysis Sixty-six of 165 patients (40.0%) had diabetes mellitus. The difference in MACE rates between diabetics and non-diabetics was striking: 34 of 66 diabetic patients experienced MACE (51.5%) compared with 18 of 99 non-diabetic patients (18.2%), yielding a chi-squared p-value of < 0.001 and an odds ratio of approximately 4.7 (Table 4 , Fig. 3 ). Stated differently, diabetic patients in this cohort were 4.7 times more likely to experience a 30-day MACE event than their non-diabetic counterparts — a relationship that held up across all three MHS risk strata (Table 5 , Fig. 4 ). Of the 52 total MACE events, 34 occurred in diabetic patients, representing 65.4% of all MACE. Given that diabetics comprised only 40% of the cohort, this represents a substantial over-representation. Conversely, among the 113 patients who did not experience MACE, only 32 (28.3%) were diabetic. Within-stratum analysis showed a consistent stepwise MACE gradient for diabetic patients that tracked — and in the low- and medium-risk bands, exceeded — the overall cohort rates: 20.0% of low-risk diabetics experienced MACE (versus 3.4% in the overall low-risk group), 43.8% of medium-risk diabetics (versus 34.3% overall), and 75.0% of high-risk diabetics (versus 72.2% overall). The two false-negative MACE cases were both diabetic males with MHS scores of 3 — patients who would have been designated safe for early discharge under current guidelines. This pairing of findings deserves particular attention in any discussion of score recalibration. Table 4 30-Day MACE in Diabetic vs. Non-Diabetic Patients 30-day MACE Diabetic (n = 66) Non-Diabetic (n = 99) p-value 34 (51.5%) 18 (18.2%) < 0.001 No MACE 32 (48.5%) 81 (81.8%) Chi-squared test; OR ≈ 4.7 (diabetics vs. non-diabetics for 30-day MACE) Table 5 MACE Rates in Diabetic Patients by MHS Risk Stratum (n = 66 diabetics) MHS Category Diabetic n MACE Yes (%) MACE No (%) p-value† Low risk (1–3) 10 (15.2%) 2 (20.0%) 8 (80.0%) 0.002 Medium risk (4–6) 32 (48.5%) 14 (43.8%) 18 (56.3%) 0.007 High risk (7–10) 24 (36.4%) 18 (75.0%) 6 (25.0%) < 0.001 Total 66 (100%) 34 (51.5%) 32 (48.5%) † Chi-squared test comparing MACE vs. no-MACE within each stratum; MACE column percentages refer to proportion within that diabetic risk stratum. Total DM MACE (n = 34) derived from stratified data; percentages within the MACE column reflect proportions within each DM risk stratum. 4. DISCUSSION This study, to our knowledge the first prospective MHS validation incorporating hs-cTnI in a North Indian tertiary ED, yields five principal observations. The MHS produces a clinically meaningful stepwise gradient of MACE risk. Its sensitivity, at 96.2%, is high and comparable to pooled estimates from meta-analytic data. The NPV of 96.6% matches the pooled NPV from Western cohorts, validating the score as a robust rule-out instrument in this population. The overall AUC of 0.73 places it within the range reported across Asian validation cohorts. And — the finding of most practical import — diabetes mellitus exerts a markedly disproportionate influence on MACE risk that the current Risk component weighting does not fully capture. 4.1 Diagnostic Accuracy in Context The sensitivity of 96.2% aligns well with the pooled sensitivity of 96.7% (95% CI 94.0–98.2%) reported in the meta-analysis by Van Den Berg and Body. [ 14 ] This is reassuring: the score is picking up the overwhelming majority of true cardiac events in this population. The NPV of 96.6% closely matches the pooled NPV of 96.7% from Western cohorts—a consistency across ethnically and epidemiologically diverse populations that further validates the MHS as a robust rule-out instrument [ 14 ] . The two false-negative events (both diabetic males, MHS = 3) represent a low-risk category MACE rate of 3.4%, consistent with accepted miss rates across validated HEART score cohorts. This finding likely reflects the lower DM prevalence and lower pre-test MACE probability in that community setting. The contrast between studies reveals a recurring pattern: in settings with higher DM burden, the score's positive predictive performance is compressed, pointing to DM prevalence as a key moderator of how the HEART score behaves operationally. That 20% of low-risk diabetics still experienced MACE, and that both false-negative low-risk MACE events were diabetic males, provides further empirical support for a diabetes-specific scoring adjustment. One actionable proposal — not tested in this study but supported by the data — would be to treat any diabetic patient with MHS ≥ 3 as non-low-risk for triage purposes, rather than the standard ≥ 4 cut-off. The 2023 ESC Guidelines for Cardiovascular Disease in Diabetes designate T2DM as a very-high-risk condition independent of co-existing factors [ 19 ] and recommend earlier and more aggressive investigation in diabetics presenting with chest pain. A modified threshold acknowledging this risk would be consistent with those guidelines and with our empirical findings. 4.3 Gender and Age Observations The male predominance among MACE cases (76.9%) and the absence of any MACE in women below age 45 replicate well-established patterns of sex-related cardiovascular risk, with premenopausal oestrogen conferring meaningful cardioprotection. [ 13 ] Bank et al. [ 20 ] reported significantly higher 6-week MACE in men than women (20.8% vs. 10%) while the HEART score showed equivalent discriminatory ability between sexes — a dissociation suggesting that threshold adjustment by sex, rather than score reformulation, may be the more appropriate response. The convergence of male and female MACE rates in our ≥ 65-year group (32.6% vs. 30.4%) is consistent with post-menopausal risk equalisation and argues for applying similar thresholds across sexes at older ages. 4.4 Clinical Implications for Indian Emergency Medicine The clinical implications of this study for Indian emergency medicine are clear. The MHS works in this population, delivering the high sensitivity and NPV that make it suitable as a rule-out tool. At the same time, several modifications to standard application seem warranted in tertiary Indian EDs: An MHS ≥ 4 should trigger admission, serial troponin sampling, and cardiology review as a minimum, regardless of the initial troponin result. In diabetic patients classified as low-risk (MHS 0–3), a 20% observed MACE rate makes early discharge without a second troponin and cardiology review inadvisable. A pragmatic proposal would be to apply an MHS ≥ 3 threshold as the admission/observation criterion in all known diabetics. The corrected NPV of 96.6% confirms that a negative low-risk MHS is a reliable rule-out in the general chest pain population at this centre. This high NPV does not apply to the diabetic low-risk subgroup, where empirical MACE rates of 20% were observed — the DM-specific caveat in the preceding point remains paramount. 4.5 Limitations Several limitations should be acknowledged. This was a single-centre study at a quaternary referral hospital; the tertiary referral case-mix enriches the cohort with high-acuity presentations and explains the MACE prevalence of 31.5% — roughly double community estimates. Community-level NPV is likely to exceed the 96.6% observed here, given lower pre-test probability. The sample size of 165 was adequate for the primary analysis but renders the subgroup of low-risk MACE patients (n = 2) too small for firm conclusions — a point that should temper the specificity of any threshold-adjustment recommendation. Loss-to-follow-up for telephone-based 30-day assessments was not systematically captured, introducing potential outcome ascertainment bias. Interrater reliability in HEART score assignment — particularly for the subjective History and ECG components — was not formally evaluated. The effect of pre-hospital medication use on component scoring could not be quantified. Finally, no multivariate analysis was conducted, meaning the independent contribution of diabetes to MACE risk, after adjustment for age and other covariates, remains uncharacterised. We additionally note that the NPV reported in the initial submission (73.1%) contained an transcription error in the SPSS denominator; this has been verified and corrected to 96.6% (95% CI: 88.5–99.1%), derived from TN = 57 and FN = 2. The AUC of 0.73 was derived from the binary ≥ 4 classification threshold using the trapezoidal method; the continuous MHS score AUC is 0.83 (95% CI: 0.77–0.89), providing additional discriminatory information. 5. CONCLUSION The Modified HEART Score performs with high sensitivity (96.2%) and an NPV of 96.6% in a North Indian tertiary ED, demonstrating a clear stepwise MACE gradient across risk categories that validates its utility as a structured triage framework in this setting. Its performance is broadly consistent with data from comparable Asian populations, and the high NPV closely matches pooled Western cohort benchmarks. The striking concentration of MACE events within the diabetic subgroup — 65.4% of all events in a subpopulation accounting for 40% of the cohort, with elevated MACE rates observed even in low-risk diabetic patients — is the most clinically impactful finding of this study. That both missed low-risk MACE cases were diabetic males strengthens the empirical case for recalibrating the HEART score's approach to diabetes, whether through independent score weighting, a modified low-risk threshold in known diabetics, or explicit guideline-level guidance on enhanced observation requirements for this subgroup. The MHS is paper-based, requires no additional investigations beyond those already part of routine ED chest pain assessment, and is readily teachable — properties that make it well-suited to wide adoption across Indian emergency departments at varying levels of resource availability. Multicentre prospective validation in diverse Indian cohorts, with subgroup analyses by DM status and pre-hospital medication use, is the logical and urgent next step. Declarations Ethics approval and consent to participate: The study was approved by the Institutional Ethics Committee, BLK-MAX Super Specialty Hospital, New Delhi (Reference: BLK-MAX/IEC/2022/EM-04). Written informed consent was obtained from all participants. The study was conducted in accordance with the Declaration of Helsinki (2013 revision). Consent for publication: Not applicable. Availability of data and materials: The dataset supporting the conclusions of this article is available from the corresponding author on reasonable request. Competing interests: The authors declare no competing interests. Funding: No external funding was received for this study. Authors' contributions: NM (MBBS, MEM): Conceptualisation, study design, data collection, statistical analysis, manuscript preparation, and final approval. AM (MBBS, DNB): Study oversight, guidance on methodology, critical revision of the manuscript for important intellectual content, and final approval. Both authors have read and approved the final manuscript. Use of artificial intelligence tools: The authors used AI-based writing assistance software (Claude, Anthropic) to support manuscript language editing and formatting during preparation of this article. All study design, data collection, data analysis, clinical interpretation, and scientific conclusions are entirely the authors' own. The AI tool was not involved in any aspect of the research itself and has not been listed as an author, in accordance with the guidelines of the International Committee of Medical Journal Editors (ICMJE), the Committee on Publication Ethics (COPE), and the World Association of Medical Editors (WAME). The authors take full responsibility for the integrity and accuracy of all content. Acknowledgements: The authors gratefully acknowledge the nursing staff and emergency physicians of the BLK-MAX ED for their assistance with patient identification and enrolment, and the medical records department for facilitating outcome verification. 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The HEART Pathway randomized trial: identifying emergency department patients with acute chest pain for early discharge. Circ Cardiovasc Qual Outcomes. 2015;8(2):195–203. Sinclair AJ, Robert IE, Croxson SC. Mortality in older people with diabetes mellitus. Diabet Med. 1997;14(8):639–47. Halder D, Mathew R, Jamshed N, et al. Utility of HEART pathway in identifying low-risk chest pain in emergency department. J Emerg Med. 2021;60(4):421–427. doi:10.1016/j.jemermed.2020.12.004. Additional Declarations No competing interests reported. Supplementary Files MHSSubmission2AdditionalFile1STROBEChecklist.docx MHSSUBMISSION2FLOWDIAGRAM.png Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 28 Apr, 2026 Reviewers invited by journal 22 Apr, 2026 Editor assigned by journal 01 Apr, 2026 Submission checks completed at journal 01 Apr, 2026 First submitted to journal 28 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9255411","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":630747447,"identity":"1616a6bb-613d-4fe3-9fca-6b6686c40a2d","order_by":0,"name":"Nikit Mittal","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA40lEQVRIiWNgGAWjYDACZjYIzSb//uEDIM3DR7QWPoYcZgOQFjbC1kCVyDHksEkg8XEDc3a25A8/Ku7lsTGcPVb5NcdOho2B+eGjG3i0WDazHZPsOVNczMbYl3Zbdlsy0GFsxsY5eLQYHGZvY2ZsS0hsY2Ywuy25jRmohYdNmoCW5s9gLWwMZsWS2+qJ0cJ2QBqshYfHjPHjtsNEaUkD+iWhmE2CLVmacdtxHjZmQn45f8wYGGIJefIzmA9+/Lmt2p6fvfnhY3xaYCABRDDzgEkilMO1MP4gUvUoGAWjYBSMLAAAD4dAG+NSd2oAAAAASUVORK5CYII=","orcid":"","institution":"BLK-MAX Super Specialty Hospital","correspondingAuthor":true,"prefix":"","firstName":"Nikit","middleName":"","lastName":"Mittal","suffix":""},{"id":630747448,"identity":"23100f1a-3933-44bb-9cfa-28bd221f4caf","order_by":1,"name":"Akanksha Mittal","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Akanksha","middleName":"","lastName":"Mittal","suffix":""}],"badges":[],"createdAt":"2026-03-28 22:23:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9255411/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9255411/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108492565,"identity":"1b49c6f6-cb70-4e11-b147-a1631bb83c13","added_by":"auto","created_at":"2026-05-05 09:58:04","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":120087,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eReceiver Operating Characteristic (ROC) curve for the Modified HEART Score in predicting 30-day MACE. AUC = 0.73 (95% CI: 0.65–0.81). The operating point (sensitivity 96.2%, specificity 50.4%) is marked.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9255411/v1/cfd33bf151f245a2c786cb26.png"},{"id":108407527,"identity":"11f7ee6a-7728-4aec-b4f3-023676b61e64","added_by":"auto","created_at":"2026-05-04 09:52:32","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":80524,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e30-Day MACE rates across Modified HEART Score risk categories. The stepwise escalation from 3.4% to 72.2% was statistically significant (p \u0026lt; 0.001 for trend). *** p \u0026lt; 0.001.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9255411/v1/e1d7916fc76f2257b5459fb9.png"},{"id":108407530,"identity":"f4d6a54d-07af-4db5-861a-e7af06c7e751","added_by":"auto","created_at":"2026-05-04 09:52:32","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":81482,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e30-Day MACE rates in diabetic versus non-diabetic patients. MACE occurred in 51.5% of diabetics versus 18.2% of non-diabetics (p \u0026lt; 0.001).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9255411/v1/a9c6bbfeda03b195978f79cc.png"},{"id":108407529,"identity":"89b91224-0cf3-4083-ac06-94ca4705467b","added_by":"auto","created_at":"2026-05-04 09:52:32","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":77222,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eMACE rates within diabetic patients (red) versus the overall cohort (blue) across MHS risk strata, demonstrating elevated MACE risk in diabetics at every risk level, most pronounced in the low-risk category (20.0% vs. 3.4%).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9255411/v1/557c6d25971c13cdbc51495e.png"},{"id":108494749,"identity":"ff700952-faef-4b31-bf21-3cb655ec63f2","added_by":"auto","created_at":"2026-05-05 10:07:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":583594,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9255411/v1/455c1ee9-348a-400b-9552-7c86be0bbcd7.pdf"},{"id":108407525,"identity":"5a68f9bc-45e7-42ca-9746-dd58ffbd5f4e","added_by":"auto","created_at":"2026-05-04 09:52:32","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":15083,"visible":true,"origin":"","legend":"","description":"","filename":"MHSSubmission2AdditionalFile1STROBEChecklist.docx","url":"https://assets-eu.researchsquare.com/files/rs-9255411/v1/af4e4b30374e8a0a2060c935.docx"},{"id":108493338,"identity":"cbe19fee-48fa-4a67-9fe2-e2222b50272a","added_by":"auto","created_at":"2026-05-05 09:59:59","extension":"png","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":534040,"visible":true,"origin":"","legend":"","description":"","filename":"MHSSUBMISSION2FLOWDIAGRAM.png","url":"https://assets-eu.researchsquare.com/files/rs-9255411/v1/09ed8858fba7d0ee205658bd.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"Performance of the Modified HEART Score in Predicting 30-Day Major Adverse Cardiovascular Events at a North Indian Tertiary Emergency Department: A Prospective Observational Study with Emphasis on the Diabetic Subpopulation","fulltext":[{"header":"1. BACKGROUND","content":"\u003cp\u003eChest pain brings roughly 5\u0026ndash;10% of all patients through the emergency department door \u0026mdash; and deciding which of them is having a cardiac event remains one of the hardest calls in acute medicine. \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e Get it wrong in one direction and you miss a myocardial infarction, an error that accounts for a disproportionate share of medicolegal claims in emergency practice.\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e Get it wrong in the other and you admit, investigate, and observe a patient who needed neither the bed nor the anxiety that comes with it. The stakes are high, the time pressures real, and the margin for error narrow.\u003c/p\u003e\n\u003cp\u003eIndia\u0026apos;s epidemiological landscape sharpens these concerns considerably. Cardiovascular disease now accounts for close to 28% of all deaths in the country \u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e and, perhaps more importantly, Indian patients with acute coronary syndromes present nearly a decade earlier than their Western counterparts.\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e Overlapping this is an epidemic of type 2 diabetes mellitus \u0026mdash; over 77\u0026nbsp;million affected adults \u0026mdash; that confers a two- to four-fold increase in cardiovascular risk, often through atypical or silent presentation patterns that can confound clinical assessment.\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e Taken together, these factors create a phenotype of chest pain patient that differs meaningfully from the populations in which most risk-stratification tools were originally derived and validated.\u003c/p\u003e\n\u003cp\u003eThe HEART Score \u0026mdash; History, ECG, Age, Risk factors, Troponin \u0026mdash; was introduced by Six et al. in 2008 \u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e as a structured, bedside-applicable approach to chest pain triage. Over the subsequent decade and a half, it has been validated in more than 30 independent cohorts totalling upwards of 40,000 patients.\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e The Modified HEART Score (MHS) replaces conventional troponin with high-sensitivity troponin (hs-cTn), enabling earlier detection of myocardial injury and shifting the score\u0026apos;s utility further toward the initial assessment window.\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e Scores of 0\u0026ndash;3 define a low-risk category, 4\u0026ndash;6 medium risk, and 7\u0026ndash;10 high risk, with the medium and high categories together constituting a positive screen for 30-day MACE.\u003c/p\u003e\n\u003cp\u003eDespite this validation pedigree, the evidence base from the Indian subcontinent is thin. What data do exist are largely confined to South Indian community hospitals, where patient demographics, diabetes burden, and the proportion presenting through tertiary referral pathways differ from what we observe in large North Indian centres. \u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e Perhaps more significantly, no published study has examined MHS performance stratified by diabetes status within an Indian cohort \u0026mdash; a gap that is both clinically meaningful and potentially actionable if the data support adjustment of risk thresholds or score weighting for this population.\u003c/p\u003e\n\u003cp\u003eWe therefore undertook a prospective evaluation of the MHS at a major North Indian tertiary ED, with a pre-specified secondary analysis of MACE rates across diabetic and non-diabetic patients within each risk category. The study also represents, to our knowledge, the first MHS validation incorporating hs-cTnI specifically in a North Indian cohort.\u003c/p\u003e"},{"header":"2. METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1 Study Design and Setting\u003c/h2\u003e\n \u003cp\u003eThis was a prospective observational cohort study conducted at the Department of Emergency Medicine and Trauma, BLK-MAX Super Specialty Hospital (Dr. B.L. Kapur Memorial Hospital), Pusa Road, New Delhi. BLK-MAX is a 700-bed quaternary referral centre serving the North Indian corridor, with an ED caseload that skews toward moderate-to-high acuity patients referred from peripheral hospitals. The study ran from June 2022 to November 2023 \u0026mdash; 18 consecutive months. The study was approved by the Institutional Ethics Committee (Reference: BLK-MAX/IEC/2022/EM-04), and written informed consent was obtained from every participant before enrolment. Reporting follows the STROBE checklist for observational studies.\u003csup\u003e[Supplementary File 1]\u003c/sup\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2 Participants\u003c/h2\u003e\n \u003cp\u003eConsecutive patients aged\u0026thinsp;\u0026ge;\u0026thinsp;18 years, presenting with chest pain, chest discomfort, or closely related equivalent symptoms (palpitations, diaphoresis, unexplained exertional dyspnoea) with onset within the preceding 7 days, were screened by the triage team. We used consecutive enrolment rather than random sampling to minimise selection bias within the study window.\u003c/p\u003e\n \u003cp\u003eExclusion criteria were applied prospectively: traumatic chest pain (any mechanism); known active malignancy; pregnancy; ST-elevation myocardial infarction on the presentation ECG (these patients were fast-tracked directly to the catheterisation laboratory and their risk stratification was, rightly, bypassed); and patients who left against medical advice before MHS calculation could be completed.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3 Sample Size\u003c/h2\u003e\n \u003cp\u003eSample size estimation followed the framework of Six et al. \u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e Using published mean HEART scores of 6.51 (SD 1.84) for MACE and 3.71 (SD 1.83) for non-MACE groups, with 80% statistical power and a two-sided \u0026alpha; of 0.05, we required a minimum of 33 MACE events. Assuming a MACE prevalence of approximately 25% based on prior Indian data, the required total sample was 165 patients (33 MACE events\u0026thinsp;+\u0026thinsp;132 non-MACE). The actual MACE prevalence of 31.5% observed exceeded this assumption, meaning the study was slightly over-powered for the primary analysis.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e2.4 Modified HEART Score Calculation\u003c/h2\u003e\n \u003cp\u003eThe treating emergency physician calculated the MHS at the time of initial assessment, independently of the diagnostic workup. Each of the five components was scored on a 0\u0026ndash;2 ordinal scale, yielding a composite total of 0\u0026ndash;10:\u003c/p\u003e\n \u003cul\u003e\n \u003cli\u003e\n \u003cp\u003eHistory (H): 0\u0026thinsp;=\u0026thinsp;non-specific or other diagnosis more likely; 1\u0026thinsp;=\u0026thinsp;moderately suspicious (mixed elements, some cardiac features but not dominant); 2\u0026thinsp;=\u0026thinsp;highly suspicious (classic anginal radiation to arm or jaw, exertional precipitation, nitrate responsiveness, accompanying diaphoresis)\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eECG (E): 0\u0026thinsp;=\u0026thinsp;normal; 1\u0026thinsp;=\u0026thinsp;non-specific repolarisation disturbance, LBBB, LVH with repolarisation changes, or digitalis effect; 2\u0026thinsp;=\u0026thinsp;significant new or assumed-new ST deviation or T-wave inversion\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eAge (A): 0\u0026thinsp;=\u0026thinsp;\u0026lt;\u0026thinsp;45 years; 1\u0026thinsp;=\u0026thinsp;45\u0026ndash;64 years; 2\u0026thinsp;=\u0026thinsp;\u0026ge;\u0026thinsp;65 years\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eRisk factors (R): 0\u0026thinsp;=\u0026thinsp;no known risk factors; 1\u0026thinsp;=\u0026thinsp;one to two factors present (diabetes mellitus, hypertension, dyslipidaemia, obesity [BMI\u0026thinsp;\u0026gt;\u0026thinsp;30 kg/m\u0026sup2;], active smoking, family history of CAD); 2\u0026thinsp;=\u0026thinsp;three or more risk factors, or known established atherosclerotic disease (prior PCI, CABG, cerebrovascular event, or PAD)\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eTroponin (T): hs-cTnI assay (reference range 0\u0026ndash;34 pg/mL); 0\u0026thinsp;=\u0026thinsp;within normal limits; 1\u0026thinsp;=\u0026thinsp;1\u0026ndash;3\u0026times; upper limit of normal; 2\u0026thinsp;=\u0026thinsp;\u0026gt;\u0026thinsp;3\u0026times; upper limit of normal\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ul\u003e\n \u003cp\u003ePatients scoring 0\u0026ndash;3 were classified as low-risk, 4\u0026ndash;6 as medium-risk, and 7\u0026ndash;10 as high-risk. For diagnostic accuracy computation, a score\u0026thinsp;\u0026ge;\u0026thinsp;4 was treated as a positive screen for MACE.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e2.5 Outcome Assessment\u003c/h2\u003e\n \u003cp\u003eThe primary outcome was 30-day MACE, defined as any of: acute MI (STEMI or NSTEMI confirmed by serial ECG changes and troponin kinetics); unstable angina necessitating emergency coronary revascularisation (PCI or CABG); stent thrombosis; or death from cardiovascular cause within 30 days of the index ED presentation. Outcome ascertainment was conducted through review of inpatient records for admitted patients and a structured telephone follow-up call at 30 days for those discharged. The secondary outcome was MACE rate within each MHS risk category, stratified by diabetes status.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e2.6 Statistical Analysis\u003c/h2\u003e\n \u003cp\u003eCategorical variables are reported as counts and proportions; continuous variables as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation. The chi-squared test assessed associations between categorical variables; Fisher\u0026apos;s exact test was used when expected cell counts fell below 5. Sensitivity, specificity, PPV, and NPV were derived from a standard 2\u0026times;2 contingency table at the MHS\u0026thinsp;\u0026ge;\u0026thinsp;4 threshold, with 95% CIs calculated using the Wilson score method to avoid boundary effects. AUC-ROC quantified overall discriminatory ability. All analyses were performed in SPSS version 22.0 (IBM Corp., Armonk, NY, USA). A two-sided p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was the threshold for statistical significance.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. RESULTS","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Baseline Characteristics\u003c/h2\u003e\n \u003cp\u003eOne hundred and sixty-five patients were enrolled across the 18-month study window, representing 97% of eligible patients approached (8 declined consent and 4 met exclusion criteria after initial assessment). The cohort was predominantly male: 112 men (67.9%) and 53 women (32.1%). Age was distributed as follows \u0026mdash; 30 patients (18.2%) aged below 45 years, 69 (41.8%) between 45 and 64 years, and 66 (40.0%) aged 65 or above. The high proportion of older patients is consistent with the tertiary referral pattern at this centre. \u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003eDiabetes mellitus was present in 66 patients (40.0%). This prevalence \u0026mdash; roughly three to four times the Indian national adult estimate of 10\u0026ndash;15% \u0026mdash; reflects the case-mix at a quaternary referral hospital and has direct implications for interpreting the secondary analysis. Thirty-day MACE occurred in 52 patients, an overall prevalence of 31.5%. Baseline characteristics are summarised in Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eBaseline Demographic and Clinical Characteristics (N\u0026thinsp;=\u0026thinsp;165)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003en (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eNotes\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\" colname=\"c1\"\u003e\n \u003cp\u003eTotal patients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e165 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eConsecutive enrolment\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eMale sex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e112 (67.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eFemale sex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e53 (32.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eAge\u0026thinsp;\u0026lt;\u0026thinsp;45 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e30 (18.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e23M / 7F\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eAge 45\u0026ndash;64 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e69 (41.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e45M / 24F\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eAge\u0026thinsp;\u0026ge;\u0026thinsp;65 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e66 (40.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e44M / 22F\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eDiabetes mellitus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e66 (40.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eAny type; T2DM\u0026thinsp;\u0026gt;\u0026thinsp;98%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e30-day MACE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e52 (31.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003ePrimary outcome\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eM\u0026thinsp;=\u0026thinsp;male; F\u0026thinsp;=\u0026thinsp;female; T2DM\u0026thinsp;=\u0026thinsp;type 2 diabetes mellitus; MACE\u0026thinsp;=\u0026thinsp;Major Adverse Cardiovascular Events\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 MHS Risk Category Distribution and MACE\u003c/h2\u003e\n \u003cp\u003eMHS classified 59 patients (35.8%) as low-risk, 70 (42.4%) as medium-risk, and 36 (21.8%) as high-risk. The stepwise gradient of MACE across categories was highly significant: 2 of 59 low-risk patients experienced MACE (3.4%), compared with 24 of 70 medium-risk patients (34.3%) and 26 of 36 high-risk patients (72.2%) \u0026mdash; a linear trend for which the chi-squared p-value was well below 0.001. Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the full distribution, and Fig. \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates the MACE gradient visually.\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e30-Day MACE Incidence by MHS Risk Category\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eRisk Category (MHS)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eN (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eMACE Yes\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eMACE No\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003ep-value\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\" colname=\"c1\"\u003e\n \u003cp\u003eLow risk (1\u0026ndash;3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e59 (35.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e2 (3.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e57 (96.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eMedium risk (4\u0026ndash;6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e70 (42.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e24 (34.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e46 (65.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eHigh risk (7\u0026ndash;10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e36 (21.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e26 (72.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e10 (27.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e165 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e52 (31.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e113 (68.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eChi-squared test for linear trend across risk categories; p-values compare each category against all others combined\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 Diagnostic Performance\u003c/h2\u003e\n \u003cp\u003eAt the MHS\u0026thinsp;\u0026ge;\u0026thinsp;4 threshold, the score identified 50 of 52 MACE events correctly (true positives). The two missed cases (false negatives) were both diabetic males classified as low-risk. Among 113 non-MACE patients, 57 were correctly identified as low-risk (true negatives), while 56 were classified medium/high-risk in the absence of MACE (false positives). Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the full diagnostic performance profile; Fig. \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the corresponding ROC curve.\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDiagnostic Performance of MHS (Threshold\u0026thinsp;\u0026ge;\u0026thinsp;4) for 30-Day MACE\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eParameter\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eValue\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e95% Confidence Interval\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\" colname=\"c1\"\u003e\n \u003cp\u003eSensitivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e96.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e93.9% \u0026ndash; 98.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eSpecificity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e50.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e35.5% \u0026ndash; 65.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ePositive Predictive Value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e47.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e32.7% \u0026ndash; 61.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eNegative Predictive Value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e96.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e88.5% \u0026ndash; 99.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eAUC-ROC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.65\u0026ndash;0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eTrue Positives\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eTrue Negatives\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eFalse Positives\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eFalse Negatives\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eBoth diabetic males\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eAUC-ROC\u0026thinsp;=\u0026thinsp;Area Under the Receiver Operating Characteristic Curve; 95% CIs by Wilson score method\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4 MACE by Gender and Age Group\u003c/h2\u003e\n \u003cp\u003eMales accounted for 40 of 52 MACE events (76.9%). MACE prevalence was 35.7% among men versus 22.6% among women, a difference that approached but did not reach conventional significance (p\u0026thinsp;=\u0026thinsp;0.07), consistent with known sex-related cardiovascular risk gradients. \u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e Among patients below age 45, MACE occurred exclusively in men (8 cases, zero in women). At the other end of the age spectrum, the gap narrowed markedly \u0026mdash; 32.6% of men aged\u0026thinsp;\u0026ge;\u0026thinsp;65 experienced MACE, versus 30.4% of women in the same age band \u0026mdash; a convergence that accords with post-menopausal equalisation of cardiovascular risk.\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e3.5 Diabetes Mellitus and MACE \u0026mdash; Pre-Specified Secondary Analysis\u003c/h2\u003e\n \u003cp\u003eSixty-six of 165 patients (40.0%) had diabetes mellitus. The difference in MACE rates between diabetics and non-diabetics was striking: 34 of 66 diabetic patients experienced MACE (51.5%) compared with 18 of 99 non-diabetic patients (18.2%), yielding a chi-squared p-value of \u0026lt;\u0026thinsp;0.001 and an odds ratio of approximately 4.7 (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Stated differently, diabetic patients in this cohort were 4.7 times more likely to experience a 30-day MACE event than their non-diabetic counterparts \u0026mdash; a relationship that held up across all three MHS risk strata (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eOf the 52 total MACE events, 34 occurred in diabetic patients, representing 65.4% of all MACE. Given that diabetics comprised only 40% of the cohort, this represents a substantial over-representation. Conversely, among the 113 patients who did not experience MACE, only 32 (28.3%) were diabetic.\u003c/p\u003e\n \u003cp\u003eWithin-stratum analysis showed a consistent stepwise MACE gradient for diabetic patients that tracked \u0026mdash; and in the low- and medium-risk bands, exceeded \u0026mdash; the overall cohort rates: 20.0% of low-risk diabetics experienced MACE (versus 3.4% in the overall low-risk group), 43.8% of medium-risk diabetics (versus 34.3% overall), and 75.0% of high-risk diabetics (versus 72.2% overall). The two false-negative MACE cases were both diabetic males with MHS scores of 3 \u0026mdash; patients who would have been designated safe for early discharge under current guidelines. This pairing of findings deserves particular attention in any discussion of score recalibration.\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e30-Day MACE in Diabetic vs. Non-Diabetic Patients\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003e30-day MACE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eDiabetic (n\u0026thinsp;=\u0026thinsp;66)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eNon-Diabetic (n\u0026thinsp;=\u0026thinsp;99)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e34 (51.5%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e18 (18.2%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\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\" colname=\"c1\"\u003e\n \u003cp\u003eNo MACE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e32 (48.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e81 (81.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eChi-squared test; OR\u0026thinsp;\u0026asymp;\u0026thinsp;4.7 (diabetics vs. non-diabetics for 30-day MACE)\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMACE Rates in Diabetic Patients by MHS Risk Stratum (n\u0026thinsp;=\u0026thinsp;66 diabetics)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eMHS Category\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eDiabetic n\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eMACE Yes (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eMACE No (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003ep-value\u0026dagger;\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\" colname=\"c1\"\u003e\n \u003cp\u003eLow risk (1\u0026ndash;3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e10 (15.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e2 (20.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e8 (80.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eMedium risk (4\u0026ndash;6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e32 (48.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e14 (43.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e18 (56.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eHigh risk (7\u0026ndash;10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e24 (36.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e18 (75.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e6 (25.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e66 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e34 (51.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e32 (48.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003e\u0026dagger; Chi-squared test comparing MACE vs. no-MACE within each stratum; MACE column percentages refer to proportion within that diabetic risk stratum. Total DM MACE (n\u0026thinsp;=\u0026thinsp;34) derived from stratified data; percentages within the MACE column reflect proportions within each DM risk stratum.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. DISCUSSION","content":"\u003cp\u003eThis study, to our knowledge the first prospective MHS validation incorporating hs-cTnI in a North Indian tertiary ED, yields five principal observations. The MHS produces a clinically meaningful stepwise gradient of MACE risk. Its sensitivity, at 96.2%, is high and comparable to pooled estimates from meta-analytic data. The NPV of 96.6% matches the pooled NPV from Western cohorts, validating the score as a robust rule-out instrument in this population. The overall AUC of 0.73 places it within the range reported across Asian validation cohorts. And \u0026mdash; the finding of most practical import \u0026mdash; diabetes mellitus exerts a markedly disproportionate influence on MACE risk that the current Risk component weighting does not fully capture.\u003c/p\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003e4.1 Diagnostic Accuracy in Context\u003c/h2\u003e\n \u003cp\u003eThe sensitivity of 96.2% aligns well with the pooled sensitivity of 96.7% (95% CI 94.0\u0026ndash;98.2%) reported in the meta-analysis by Van Den Berg and Body. \u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e This is reassuring: the score is picking up the overwhelming majority of true cardiac events in this population. The NPV of 96.6% closely matches the pooled NPV of 96.7% from Western cohorts\u0026mdash;a consistency across ethnically and epidemiologically diverse populations that further validates the MHS as a robust rule-out instrument\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. The two false-negative events (both diabetic males, MHS\u0026thinsp;=\u0026thinsp;3) represent a low-risk category MACE rate of 3.4%, consistent with accepted miss rates across validated HEART score cohorts.\u003c/p\u003e\n \u003cp\u003eThis finding likely reflects the lower DM prevalence and lower pre-test MACE probability in that community setting. The contrast between studies reveals a recurring pattern: in settings with higher DM burden, the score\u0026apos;s positive predictive performance is compressed, pointing to DM prevalence as a key moderator of how the HEART score behaves operationally. That 20% of low-risk diabetics still experienced MACE, and that both false-negative low-risk MACE events were diabetic males, provides further empirical support for a diabetes-specific scoring adjustment.\u003c/p\u003e\n \u003cp\u003eOne actionable proposal \u0026mdash; not tested in this study but supported by the data \u0026mdash; would be to treat any diabetic patient with MHS\u0026thinsp;\u0026ge;\u0026thinsp;3 as non-low-risk for triage purposes, rather than the standard\u0026thinsp;\u0026ge;\u0026thinsp;4 cut-off. The 2023 ESC Guidelines for Cardiovascular Disease in Diabetes designate T2DM as a very-high-risk condition independent of co-existing factors\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003eand recommend earlier and more aggressive investigation in diabetics presenting with chest pain. A modified threshold acknowledging this risk would be consistent with those guidelines and with our empirical findings.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003e4.3 Gender and Age Observations\u003c/h2\u003e\n \u003cp\u003eThe male predominance among MACE cases (76.9%) and the absence of any MACE in women below age 45 replicate well-established patterns of sex-related cardiovascular risk, with premenopausal oestrogen conferring meaningful cardioprotection. \u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e Bank et al.\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e reported significantly higher 6-week MACE in men than women (20.8% vs. 10%) while the HEART score showed equivalent discriminatory ability between sexes \u0026mdash; a dissociation suggesting that threshold adjustment by sex, rather than score reformulation, may be the more appropriate response. The convergence of male and female MACE rates in our\u0026thinsp;\u0026ge;\u0026thinsp;65-year group (32.6% vs. 30.4%) is consistent with post-menopausal risk equalisation and argues for applying similar thresholds across sexes at older ages.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n \u003ch2\u003e4.4 Clinical Implications for Indian Emergency Medicine\u003c/h2\u003e\n \u003cp\u003eThe clinical implications of this study for Indian emergency medicine are clear. The MHS works in this population, delivering the high sensitivity and NPV that make it suitable as a rule-out tool. At the same time, several modifications to standard application seem warranted in tertiary Indian EDs:\u003c/p\u003e\n \u003cul\u003e\n \u003cli\u003e\n \u003cp\u003eAn MHS\u0026thinsp;\u0026ge;\u0026thinsp;4 should trigger admission, serial troponin sampling, and cardiology review as a minimum, regardless of the initial troponin result.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eIn diabetic patients classified as low-risk (MHS 0\u0026ndash;3), a 20% observed MACE rate makes early discharge without a second troponin and cardiology review inadvisable. A pragmatic proposal would be to apply an MHS\u0026thinsp;\u0026ge;\u0026thinsp;3 threshold as the admission/observation criterion in all known diabetics.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eThe corrected NPV of 96.6% confirms that a negative low-risk MHS is a reliable rule-out in the general chest pain population at this centre. This high NPV does not apply to the diabetic low-risk subgroup, where empirical MACE rates of 20% were observed \u0026mdash; the DM-specific caveat in the preceding point remains paramount.\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ul\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n \u003ch2\u003e4.5 Limitations\u003c/h2\u003e\n \u003cp\u003eSeveral limitations should be acknowledged. This was a single-centre study at a quaternary referral hospital; the tertiary referral case-mix enriches the cohort with high-acuity presentations and explains the MACE prevalence of 31.5% \u0026mdash; roughly double community estimates. Community-level NPV is likely to exceed the 96.6% observed here, given lower pre-test probability. The sample size of 165 was adequate for the primary analysis but renders the subgroup of low-risk MACE patients (n\u0026thinsp;=\u0026thinsp;2) too small for firm conclusions \u0026mdash; a point that should temper the specificity of any threshold-adjustment recommendation. Loss-to-follow-up for telephone-based 30-day assessments was not systematically captured, introducing potential outcome ascertainment bias. Interrater reliability in HEART score assignment \u0026mdash; particularly for the subjective History and ECG components \u0026mdash; was not formally evaluated. The effect of pre-hospital medication use on component scoring could not be quantified. Finally, no multivariate analysis was conducted, meaning the independent contribution of diabetes to MACE risk, after adjustment for age and other covariates, remains uncharacterised. We additionally note that the NPV reported in the initial submission (73.1%) contained an transcription error in the SPSS denominator; this has been verified and corrected to 96.6% (95% CI: 88.5\u0026ndash;99.1%), derived from TN\u0026thinsp;=\u0026thinsp;57 and FN\u0026thinsp;=\u0026thinsp;2. The AUC of 0.73 was derived from the binary\u0026thinsp;\u0026ge;\u0026thinsp;4 classification threshold using the trapezoidal method; the continuous MHS score AUC is 0.83 (95% CI: 0.77\u0026ndash;0.89), providing additional discriminatory information.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"5. CONCLUSION","content":"\u003cp\u003eThe Modified HEART Score performs with high sensitivity (96.2%) and an NPV of 96.6% in a North Indian tertiary ED, demonstrating a clear stepwise MACE gradient across risk categories that validates its utility as a structured triage framework in this setting. Its performance is broadly consistent with data from comparable Asian populations, and the high NPV closely matches pooled Western cohort benchmarks.\u003c/p\u003e\n\u003cp\u003eThe striking concentration of MACE events within the diabetic subgroup \u0026mdash; 65.4% of all events in a subpopulation accounting for 40% of the cohort, with elevated MACE rates observed even in low-risk diabetic patients \u0026mdash; is the most clinically impactful finding of this study. That both missed low-risk MACE cases were diabetic males strengthens the empirical case for recalibrating the HEART score\u0026apos;s approach to diabetes, whether through independent score weighting, a modified low-risk threshold in known diabetics, or explicit guideline-level guidance on enhanced observation requirements for this subgroup.\u003c/p\u003e\n\u003cp\u003eThe MHS is paper-based, requires no additional investigations beyond those already part of routine ED chest pain assessment, and is readily teachable \u0026mdash; properties that make it well-suited to wide adoption across Indian emergency departments at varying levels of resource availability. Multicentre prospective validation in diverse Indian cohorts, with subgroup analyses by DM status and pre-hospital medication use, is the logical and urgent next step.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate: The study was approved by the Institutional Ethics Committee, BLK-MAX Super Specialty Hospital, New Delhi (Reference: BLK-MAX/IEC/2022/EM-04). Written informed consent was obtained from all participants. The study was conducted in accordance with the Declaration of Helsinki (2013 revision).\u003c/p\u003e\n\u003cp\u003eConsent for publication: Not applicable.\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials: The dataset supporting the conclusions of this article is available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003eCompeting interests: The authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003eFunding: No external funding was received for this study.\u003c/p\u003e\n\u003cp\u003eAuthors\u0026apos; contributions: NM (MBBS, MEM): Conceptualisation, study design, data collection, statistical analysis, manuscript preparation, and final approval. AM (MBBS, DNB): Study oversight, guidance on methodology, critical revision of the manuscript for important intellectual content, and final approval. Both authors have read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003eUse of artificial intelligence tools: The authors used AI-based writing assistance software (Claude, Anthropic) to support manuscript language editing and formatting during preparation of this article. All study design, data collection, data analysis, clinical interpretation, and scientific conclusions are entirely the authors\u0026apos; own. The AI tool was not involved in any aspect of the research itself and has not been listed as an author, in accordance with the guidelines of the International Committee of Medical Journal Editors (ICMJE), the Committee on Publication Ethics (COPE), and the World Association of Medical Editors (WAME). The authors take full responsibility for the integrity and accuracy of all content.\u003c/p\u003e\n\u003cp\u003eAcknowledgements: The authors gratefully acknowledge the nursing staff and emergency physicians of the BLK-MAX ED for their assistance with patient identification and enrolment, and the medical records department for facilitating outcome verification.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eFern\u0026aacute;ndez JB, Ezquerra EA, Genover XB, et al. Chest pain units: organisation and protocol for the diagnosis of acute coronary syndromes. Rev Esp Cardiol. 2002;55:143\u0026ndash;54.\u003c/li\u003e\n\u003cli\u003ePitts SR, Niska RW, Xu J, Burt CW. National Hospital Ambulatory Medical Care Survey: 2006 emergency department summary. Natl Health Stat Report. 2008;7:1\u0026ndash;38.\u003c/li\u003e\n\u003cli\u003eCurfman G. Acute chest pain in the emergency department. JAMA Intern Med. 2018;178(2):220. doi:10.1001/jamainternmed.2017.7519.\u003c/li\u003e\n\u003cli\u003ePrabhakaran D, Jeemon P, Roy A. Cardiovascular diseases in India: current epidemiology and future directions. Circulation. 2016;133(16):1605\u0026ndash;20.\u003c/li\u003e\n\u003cli\u003ePais P, Pogue J, Gerstein H, et al. Risk factors for acute myocardial infarction in Indians: a case-control study. Lancet. 1996;348(9024):358\u0026ndash;63.\u003c/li\u003e\n\u003cli\u003eEinarson TR, Acs A, Ludwig C, Panton UH. Prevalence of cardiovascular disease in type 2 diabetes: a systematic review of scientific evidence from across the world in 2007\u0026ndash;2017. Cardiovasc Diabetol. 2018;17(1):83.\u003c/li\u003e\n\u003cli\u003eInternational Diabetes Federation. IDF Diabetes Atlas, 10th edition. Brussels: IDF; 2021.\u003c/li\u003e\n\u003cli\u003eSix AJ, Backus BE, Kelder JC. Chest pain in the emergency room: value of the HEART score. Neth Heart J. 2008;16(6):191\u0026ndash;6.\u003c/li\u003e\n\u003cli\u003eFernando SM, Tran A, Cheng W, et al. Prognostic accuracy of the HEART score for prediction of major adverse cardiac events: a systematic review and meta-analysis. Acad Emerg Med. 2019;26(2):140\u0026ndash;51.\u003c/li\u003e\n\u003cli\u003eStopyra JP, Harper WS, Higgins TJ, et al. Prehospital Modified HEART Score predictive of 30-day adverse cardiac events. Prehosp Disaster Med. 2018;33(1):58\u0026ndash;62.\u003c/li\u003e\n\u003cli\u003eMa CP, Wang X, Wang QS, et al. A modified HEART risk score in chest pain patients with suspected non-ST-segment elevation acute coronary syndrome. J Geriatr Cardiol. 2016;13(1):64\u0026ndash;9.\u003c/li\u003e\n\u003cli\u003eAlbrektsen G, Heuch I, L\u0026oslash;chen ML, et al. Lifelong gender gap in risk of incident myocardial infarction: the Troms\u0026oslash; Study. JAMA Intern Med. 2016;176(11):1673\u0026ndash;9.\u003c/li\u003e\n\u003cli\u003eTandon VR, Mahajan A, Sharma S, Sharma A. Prevalence of cardiovascular risk factors in postmenopausal women: a rural study. J Midlife Health. 2010;1(1):26\u0026ndash;9.\u003c/li\u003e\n\u003cli\u003eVan Den Berg P, Body R. The HEART score for early rule out of acute coronary syndromes: a systematic review and meta-analysis. Eur Heart J Acute Cardiovasc Care. 2018;7(2):111\u0026ndash;9.\u003c/li\u003e\n\u003cli\u003ede Hoog VC, Lim SH, Bank IE, et al. HEART score performance in Asian and Caucasian patients presenting to the emergency department with suspected acute coronary syndrome. Eur Heart J Acute Cardiovasc Care. 2018;7(7):591\u0026ndash;601.\u003c/li\u003e\n\u003cli\u003eReddy RH, Srinivasarangan M, Teja BSS, et al. Prognostic accuracy of the HEART score in predicting major adverse cardiac events: a prospective observational study. Cureus. 2025;17(6):e85966. doi:10.7759/cureus.85966.\u003c/li\u003e\n\u003cli\u003eWentworth JM, Fourlanos S, Colman PG. Body mass index correlates with ischaemic heart disease and albuminuria in long-standing type 2 diabetes. Diabetes Res Clin Pract. 2012;97(1):57\u0026ndash;62.\u003c/li\u003e\n\u003cli\u003eAnwar I, Sony D. HEART score: prospective evaluation of its accuracy and applicability. Indian J Crit Care Med. 2024;28(8):748\u0026ndash;752. doi:10.5005/jp-journals-10071-24773.\u003c/li\u003e\n\u003cli\u003eMarx N, Federici M, Sch\u0026uuml;tt K, et al. 2023 ESC Guidelines for the management of cardiovascular disease in patients with diabetes. Eur Heart J. 2023;44(39):4043\u0026ndash;140.\u003c/li\u003e\n\u003cli\u003eBank IEM, de Hoog VC, de Kleijn DPV, et al. Sex-based differences in the performance of the HEART score in patients presenting to the emergency department with acute chest pain. J Am Heart Assoc. 2017;6(6):e005373.\u003c/li\u003e\n\u003cli\u003ePoldervaart JM, Langedijk M, Backus BE, et al. Comparison of the GRACE, HEART and TIMI scores to predict MACE in chest pain patients at the emergency department. Int J Cardiol. 2017;227:656\u0026ndash;61.\u003c/li\u003e\n\u003cli\u003eBackus BE, Six AJ, Kelder JC, et al. A prospective validation of the HEART score for chest pain patients at the emergency department. Int J Cardiol. 2013;168(3):2153\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003eMahler SA, Riley RF, Hiestand BC, et al. The HEART Pathway randomized trial: identifying emergency department patients with acute chest pain for early discharge. Circ Cardiovasc Qual Outcomes. 2015;8(2):195\u0026ndash;203.\u003c/li\u003e\n\u003cli\u003eSinclair AJ, Robert IE, Croxson SC. Mortality in older people with diabetes mellitus. Diabet Med. 1997;14(8):639\u0026ndash;47.\u003c/li\u003e\n\u003cli\u003eHalder D, Mathew R, Jamshed N, et al. Utility of HEART pathway in identifying low-risk chest pain in emergency department. J Emerg Med. 2021;60(4):421\u0026ndash;427. doi:10.1016/j.jemermed.2020.12.004.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-emergency-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijem","sideBox":"Learn more about [International Journal of Emergency Medicine](https://intjem.biomedcentral.com/)","snPcode":"12245","submissionUrl":"https://submission.nature.com/new-submission/12245/3","title":"International Journal of Emergency Medicine","twitterHandle":"@IntJEmergMed","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Modified HEART Score, chest pain, emergency department, major adverse cardiovascular events, MACE, diabetes mellitus, India, risk stratification, high-sensitivity troponin, AUC-ROC","lastPublishedDoi":"10.21203/rs.3.rs-9255411/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9255411/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: Chest pain is one of the most frequent reasons for Emergency Department (ED) attendance worldwide, yet separating true cardiac causes from the vast majority of benign alternatives remains a daily challenge for emergency physicians. The Modified HEART Score (MHS), which incorporates History, ECG, Age, Risk factors, and high-sensitivity cardiac Troponin (hs-cTn), has been validated as a reliable risk-stratification tool for predicting 30-day Major Adverse Cardiovascular Events (MACE). Its performance in North Indian populations — particularly in patients with concurrent diabetes mellitus — has not, to date, been systematically examined.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: We conducted a prospective observational study at the Emergency Department of BLK-MAX Super Specialty Hospital, New Delhi, over 18 months (June 2022–November 2023). Consecutive adults (≥18 years) presenting with chest pain or equivalent symptoms within 7 days of onset were enrolled. The MHS was calculated at initial assessment using standardised criteria incorporating hs-cTnI (reference range 0–34 pg/mL). The primary endpoint was 30-day MACE, defined as a composite of acute MI, unstable angina requiring revascularisation, stent thrombosis, or cardiovascular death. Diagnostic accuracy was characterised by sensitivity, specificity, PPV, NPV (Wilson 95% CIs), and AUC-ROC. A pre-specified secondary analysis compared MACE rates in diabetic and non-diabetic patients across risk strata.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: A total of 165 patients were enrolled; 112 (67.9%) were male. Thirty-day MACE occurred in 52 patients (31.5%). Risk stratification yielded: low-risk MHS 1–3 (n=59, 35.8%), medium-risk MHS 4–6 (n=70, 42.4%), and high-risk MHS 7–10 (n=36, 21.8%), with corresponding MACE rates of 3.4%, 34.3%, and 72.2% (p\u0026lt;0.001). The MHS achieved a sensitivity of 96.2% (95% CI: 93.9–98.4%), specificity 50.4% (35.5–65.4%), PPV 47.2% (32.7–61.7%), NPV 96.6% (88.5–99.1%), and AUC 0.73 (0.65–0.81). Diabetes mellitus was present in 66 patients (40.0%). MACE was substantially more common among diabetics than non-diabetics (51.5% vs. 18.2%, p\u0026lt;0.001). Both patients who experienced MACE despite a low-risk score were diabetic males.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: The MHS demonstrates high sensitivity and acceptable discrimination for 30-day MACE in a North Indian tertiary ED, with performance broadly consistent with data from comparable Asian populations. The disproportionate concentration of MACE within the diabetic subgroup — across all risk strata — supports a re-examination of how diabetes mellitus is weighted within the HEART score algorithm. Larger multicentre studies are needed to guide score refinement for high-DM-burden populations.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTrial registration: Not applicable (observational study).\u003c/em\u003e\u003c/p\u003e","manuscriptTitle":"Performance of the Modified HEART Score in Predicting 30-Day Major Adverse Cardiovascular Events at a North Indian Tertiary Emergency Department: A Prospective Observational Study with Emphasis on the Diabetic Subpopulation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-04 09:52:27","doi":"10.21203/rs.3.rs-9255411/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"338466169108274272054653462419636648765","date":"2026-04-28T04:39:44+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-22T14:18:28+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-01T09:01:19+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-01T09:00:22+00:00","index":"","fulltext":""},{"type":"submitted","content":"International Journal of Emergency Medicine","date":"2026-03-28T22:12:31+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-emergency-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijem","sideBox":"Learn more about [International Journal of Emergency Medicine](https://intjem.biomedcentral.com/)","snPcode":"12245","submissionUrl":"https://submission.nature.com/new-submission/12245/3","title":"International Journal of Emergency Medicine","twitterHandle":"@IntJEmergMed","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9eea60e3-e55f-4620-88ef-e68ac7c8d345","owner":[],"postedDate":"May 4th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-04T09:52:28+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-04 09:52:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9255411","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9255411","identity":"rs-9255411","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}