Usefulness of MEESSI-AHF and EHMRG score in predicting short term mortality in Acute Heart failure in an Indian population

Usefulness of MEESSI-AHF and EHMRG score in predicting short term mortality in Acute Heart failure in an Indian population | 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 Usefulness of MEESSI-AHF and EHMRG score in predicting short term mortality in Acute Heart failure in an Indian population Nitin Bhat, Satyajeet Rakh, Pooja Hanji This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9202689/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Context: Acute heart failure (AHF) is a complex, potentially fatal condition characterized by rapid symptom onset due to inefficient heart function. Globally, AHF poses a significant burden, with incidence and outcomes varying by geography, demographics, and clinical factors. In India, cardiovascular diseases contribute heavily to morbidity and mortality, making accurate prediction of short-term mortality in AHF patients critical for improving patient management. There is a critical lack of prospective data validating these international scores within the Indian healthcare ecosystem. Aims: To compare the MEESSI-AHF and EHMRG scores in predicting short-term mortality in AHF patients in India. This study aims to determine which model provides better risk stratification, potentially guiding clinical decisions and enhancing patient management strategies. Settings and Design: This is a single-center, prospective, cross-sectional observational study conducted from January to July 2024 in a tertiary care center in Coastal Karnataka. Methods and Material: AHF patients diagnosed using the Framingham criteria and ESC algorithm were assessed upon admission, with MEESSI-AHF and EHMRG scores calculated. Mortality outcomes were tracked on day 7 and day 30 via phone calls or follow-up visits. Statistical analysis used: Data were analyzed using SPSS 29.0, with ROC curve analysis used to assess predictive scores for mortality, considering a p-value of 0.05 as significant. Results: MEESSI-AHF (AUC = 0.876) and EHMRG (AUC = 0.821) showed strong predictive power. Significant predictors included NYHA Class 4, ACS episodes, NT-proBNP, and serum creatinine. Conclusions: Both MEESSI-AHF and EHMRG scores are effective tools for stratifying AHF patients and guiding management in the Indian population. Acute heart failure MEESSI-AHF EHMRG Figures Figure 1 Figure 2 Key Messages MEESSI-AHF and EHMRG scores are effective tools for stratifying AHF patients and guiding management in the Indian population. Introduction Acute heart failure (AHF) is a complex and a potentially fatal illness marked by symptoms that appear quickly as a result of the heart's ineffective pumping. The global burden of AHF is substantial, and its incidence and outcomes can vary significantly based on geographical, demographic, and clinical factors. In India, where cardiovascular diseases are a major contributor to morbidity and death, accurate prediction of short-term mortality in AHF patients is crucial for improving patient management and outcomes. Estimates place the annual incidence of heart failure in India at 0.5–1.8 million, and the prevalence at 1.3–22.7 million ( 1 ) .Early risk assessment and effective prognostication are pivotal in managing AHF. Several scoring systems designed to estimate the chance of death in these patients, aiding clinicians in stratifying risk and tailoring treatment strategies such as OHFRS ( 2 ), EAHFE-3D ( 3 ), ADHERE ( 4 ). Among these, the MEESSI ( 5 ) and EHMRG ( 6 ) scores have emerged as notable tools. The MEESSI score incorporates various clinical parameters and biomarkers, while the EHMRG score is designed specifically for emergency settings, taking into account a combination of clinical and demographic factors. Despite their utility in risk stratification, there is a need for a detailed analysis of these scores to determine their relative effectiveness and applicability in different populations. While previous studies have validated these scoring systems in various settings, their performance in the Indian population, characterized by unique clinical and socio-economic factors, remains underexplored. The purpose of this study was to fill that void through an analysis of the MEESSI and EHMRG scores in predicting AHF patients' short-term mortality within the Indian context. By evaluating these scoring systems' predictive accuracy, we seek to identify risk stratification utility of these scores for Indian patients, thereby informing clinical decision-making and potentially guiding improvements in patient management strategies. Improving patient outcomes and optimizing healthcare resources in a high-burden setting was our ultimate goal in conducting this research, which we hope will provide useful insights that can improve the effectiveness of AHF management in India. Subjects and Methods The study was designed as a: single-center, prospective, cross-sectional observational study. A total of 128 patients presenting with AHF, diagnosed as per the Framingham criteria and ESC diagnostic algorithm were recruited from January 2024 to July 2024. This study was done after explaining the study details in a language understandable to the patient. The patient was provided with information sheet and a proper informed consent was taken. MEESSI-AHF and EHMRG scores were calculated at admission. 7th day and 30th day mortality was followed up by either phone call or follow up visit to the hospital. Inclusion criteria: Age > 18 years. Diagnosed as AHF based upon Framingham HF diagnostic criteria and ESC diagnostic algorithm. Exclusion criteria: Patients with ST-Elevation MI Patients undergoing dialysis Statistical analysis: The collected data were entered in the Microsoft Excel 2016 and analysed with IBM SPSS Statistics for Windows, Version 29.0. (Armonk, NY: IBM Corp). To describe the data Descriptive statistics frequency analysis, percentage analysis was used for categorical Variables and the mean and S.D were used for continuous variables. To find the significant difference between the bivariate samples in Independent groups the Independent sample t-test was used. To find the significance in categorical data Chi-Square test was used similarly if the expected cell frequency is less than 5 in 2×2 tables then the Fisher’s Exact was used. To find the scores to predict the Mortality the Receiver Operating Characteristics curve (ROC) was used with Sensitivity, Specificity and cut off. In all the above statistical tools the probability value .05 is considered as significant level. Results The data revealed that the majority of patients were aged between 61–70 years (28.1%), with the 51–60 years age group closely following at 26.6%. The gender distribution was predominantly male (64.1%) compared to female (35.9%). At 30 days, the mortality rate was 10.9%, while 89.1% of the patients remained alive. Statistical analyses showed no significant association between age and 30-day mortality (p = 0.630), nor was there any significant link between gender and mortality (p = 0.080). Other clinical factors like systolic blood pressure (p = 0.062), heart rate (p = 0.069), respiratory rate (p = 0.427), oxygen saturation (SpO2) (p = 0.182), serum potassium levels (p = 0.167), and the duration of hospital stay (p = 0.995) also showed no significant differences between those who survived and those who died at 30 days. Positive troponin values (p = 0.142) and hypertrophy on ECG (p = 0.756) did not show significant associations either. However, there were notable findings: NT-proBNP (p = 0.0005) and serum creatinine levels (p = 0.002) showed highly significant associations with 30-day mortality, indicating that elevated levels of these biomarkers are strongly linked to worse outcomes. Barthel's Index (p = 0.053) also showed a significant association, suggesting that lower functional status correlates with higher mortality risk. Furthermore, a highly significant association was observed between NYHA Class 4 at admission (p = 0.009) and 30-day mortality, reflecting that patients with severe heart failure symptoms had a greater likelihood of dying within 30 days. Episodes associated with acute coronary syndrome (ACS) (p = 0.007) and transport by EMS (p = 0.020) were also significantly associated with higher mortality. In contrast, low output symptoms (p = 0.223) were not significantly associated with 30-day mortality. These findings underscore the importance of NT-proBNP, serum creatinine, NYHA class, ACS, and EMS transport as critical factors in predicting mortality outcomes, while other clinical variables showed no significant predictive value. EHMRG Score for 7-Day Mortality: ROC Curve : AUC = 0.821, p = 0.0005 Highly significant, indicating good predictive ability for 7-day mortality. Sensitivity (72.7%) and specificity (74.4%) suggest a reliable test for short-term mortality risk. MEESSI-AHF and EHMRG Scores for 30-Day Mortality: MEESSI-AHF Score : AUC = 0.876, p = 0.0005 Highly significant, with sensitivity (78.6%) and specificity (75.4%), indicating strong predictive performance. EHMRG Score : AUC = 0.884, p = 0.0005 Highly significant, with sensitivity (78.6%) and specificity (76.3%), showing slightly better predictive performance compared to MEESSI-AHF for 30-day mortality. Discussion AHF has proven to be among the most commonly seen, yet tricky to recognize, stratify and manage illnesses observed in common practice and provides an interesting challenge to physicians around the world be it tertiary care centers or primary health care With the advent of introduction of risk stratification scores for commonly seen illnesses having shown benefit in management and prognosis, it is of great importance to have found scores which do the same for a disease like AHF. MEESSI-AHF ( 5 ) and EHMRG ( 6 ) are two such scores which have shown promising results around the world in countries like Canada, Spain, Italy and Switzerland ( 7 ) ( 8 ) ( 9 ) ( 10 ) ( 11 ) ( 12 ). In our study conducted these are the following results. The age distribution shows that a majority of patients in our study are between 51–80 years old, indicating maximum proportion in the 61–70 years age group (28.1%). This indicates a predominance of older patients, which bears similarities to the Lee et al. study in Canada ( 10 ) which showed mean age to be 75.4–75.7 years while the research done by Miro et al. ( 7 ) found the mean age to be 79.7 years, Falsetti et al. ( 12 ) in Italy found to have 84.6 years, Rosello et al ( 8 ) found the mean age to be 80. Which indicates AHF is a disease which predominantly affects the older population which may be due to the higher probability of common etiologies of heart failure affecting older population like IHD, Hypertension, as explained by Chaturvedi et al in 2016. ( 13 ). There is a notable gender imbalance in our study with 64.1% males and 35.9% females, suggesting that males are more prevalent in this patient cohort. While a recently conducted study by Harikrishnan et al across twenty-one Indian states plus four union territories found to have 31% women which is similar to our study ( 14 ) .This could indicate most HF event instances involve men, as shown in the National heart failure registry is frequently noted in Indian patients ( 15 ). While patients from High-Income countries show a relatively high proportion of women ( 16 ) ( 17 ). One explanation to this might be explained by the greater prevalence of IHD in men than in women which is one of the commonest causes of AHF ( 18 ). Another explanation to be grievously considered is the lack of available tertiary care to female population in India of which few instances have been reported ( 19 ). Mortality rates at 7 days and 30 days are relatively low, with 8.6% and 10.9% mortality respectively. This provides a snapshot of early and short-term outcomes in the studied population. Among studies done which were similar to ours mortality rate at 7 days in the study done by Lee et al was found to be 2% ( 6 ), Sepehrvand et al 5.2% ( 11 ). In studies similar to ours for mortality rate at 30 days Sepehrvand et al found it to be 12.7% ( 11 ), Miro et al found their mortality at 30 days to be 10.1% ( 7 ). Which suggests that, relative to other nations, individuals with AHF who visit Indian ED’s have a greater risk of 7-day mortality. According to our research, there is no evidence of a meaningful relationship between gender and short term mortality, showing that the 30-day mortality risk in this sample is not significantly influenced by gender, which is consistent with research done by Parissis et al. ( 20 ) that analyzed the in-hospital risk of death of patients with AHF and discovered that it was similar in both genders (10.5 percent in males and 11.1 percent in females, p = 0.475). There was no discernible correlation found between short term mortality and positive troponin values, indicating that troponin levels may not be a reliable indicator of short-term mortality in this population, which contrasts with what Lee et al ( 6 ), Wussler et al ( 9 ) and Sepehrvand et al ( 11 ) found in their studies. Although it is evident that a raised troponin level does not necessarily indicate AHF, a wealth of data indicates that patients who appear with both an elevated troponin level and AHF have higher rates of morbidity and mortality. It has been discovered that cardiac troponin-T, a historically significant biomarker investigated in a variety of cardiac diseases, is associated with a higher probability of adverse events and death in healthcare facilities. as demonstrated by Peacock et al ( 21 ), Del Carlo et al ( 22 ) and Perna ER et al ( 23 ). A significant association was found in our study which indicates that patients in NYHA Class 4 have a higher short term mortality risk, emphasizing the severity of symptoms at admission as a critical factor which has undergone substantial research by Ahmed et al. who established that “NYHA class 4 patients had about 5 times higher risk of hospitalization due to worsening heart failure versus those with NYHA class I” across USA and Canada (81) and Asano et al. in Japan who concluded that “compared to patients with NYHA class 2 or 3 symptoms at admission, those with NYHA class 4 symptoms had a noticeably increased risk of all-cause or cardiac death while in the hospital” ( 25 ). Low output symptoms were found to have no significant association which was in contrast to similar research by Miro et al. ( 7 ), Wussler et al. ( 9 ) along with Rosello et al ( 8 ). A significant association was found in episodes associated with ACS which indicates elevated risk with respect to increased short term mortality according to research conducted by Wussler et al. ( 9 ), Miro et al. ( 7 ). This can be understood by looking at a study done by Kaul et al. that looked at the HF incidence and mortality following ACS and concluded that “HF developed either during the ACS hospitalization or after discharge confers a substantial risk of death during the first year” ( 26 ) as well as a study conducted by AlFaleh et al. in Saudi Arabia which studied ACS-AHF, found that “ACS-AHF patients had higher hospital mortality as well as higher hospital adverse cardiovascular outcomes”( 27 ), while In a research spanning all of Finland, Tawaraski et al. discovered that ACS-AHF patients had significantly greater chance of in-hospital death (12 percent vs. 5 percent; P = .002) and 30-day mortality (13 percent vs. 8 percent; P = .027) (85). Hypertrophy at ECG when analysed in our study revealed no significant association. Transport by EMS? considered in this study as transported by ambulance services seems to have a high statistical significance as it might correlate with the increased severity of illness and need for urgent care hence explaining its significant association with mortality. Our study determined that the Barthel index has close to statistical significance, suggesting that a higher Barthel Index (which assesses functional independence) might be related to short term mortality, though it does not quite reach conventional significance levels, which is out of accord to the index study by Miro et al where Barthel’s index was found to have highest significance among the variables considered for determination of score ( 5 ), but when it was excluded by Wussler et al as a part of their comprehensive statistical analysis, it was found to have a high C-statistic of 0.800 which indicated the exclusion of BI does not affect the usefulness of the score significantly ( 9 ). A study done by Rosello et al who studied the influence of the BI on the probabilities of death within thirty days in patients who are experiencing AHF and who are brought to the ED concluded that “Barthel’s index score at the ED visit is a strong independent predictor for all-cause 30-day mortality in ED acute heart failure patients” ( 28 ). In spite of the fact that it is a “straightforward and practical measure for evaluating impairment and tracking changes in functional status over time”, some variability is to be expected in spite of good inter-observer reproducibility which can explain the results of our study and the variability in other similar studies conducted throughout the world. Our study found no significant association between the Heart rate, Systolic blood pressure, Respiratory rate, Oxygen saturation at presentation and short term mortality. Our investigation yielded a highly significant statistical analysis showing a high level of association between the biomarker NT-proBNP and short term mortality, consistent with its function as a biomarker for the diagnosis, assessment and prognostication of AHF. The mean Nt-proBNP in individuals who passed away in 30 days was found to be 21316 while in patients who survived it was found to be 6597 with a very substantial p value of 0.0005. While the research by Wussler et al reveals mean Nt-proBNP in patients who died within 30 days to be 10420 while in patients who survived 4864 with a p value of < 0.001 (66), While the mean Nt-proBNP in research carried out by Rosello et al. ( 8 ) and Miro et al. ( 7 ) was discovered to be 3263 and 7738 respectively. This proves to reinforce the excellent role played by Nt-proBNP in overall management of heart failure as evidenced by the systematic review of 79 studies conducted by Santaguida et al. who sought to investigate the advantages of NT-proBNP and BNP as predictive indicators in AHF patients and concluded that “higher levels of admission BNP or NT-proBNP were associated with greater risk of mortality, morbidity, or a combination of mortality and morbidity. A decrease in BNP levels post-admission was also predictive of decreased rates of mortality and morbidity” ( 29 ). Our study analysing serum creatinine as a biomarker in determining imminent danger of mortality in AHF patients, revealed highly significant results with p value of 0.002 suggesting serum creatinine is an important predictor of mortality, reflecting renal function and its impact on heart failure outcomes. Serum creatinine is one of the most important biomarkers of renal dysfunction which often accompanies AHF which was validated by Schefold et al. who found that “Patients with baseline eGFR < 30 mL/min/1.73 m2 had an exceptionally high risk of death” ( 30 ). It appears that serum potassium levels do not significantly affect short term mortality due to AHF, as our investigation did not find any significant association between these two variables which is in accord to Tromp et al. who evaluated AHF patients' serum potassium levels and their results and concluded that “potassium levels at admission or its change during hospitalization are not associated with mortality” ( 31 ). You can see how different areas under the curve are compared in the table (c-statistic) for various studies assessing predictive performance. Our study demonstrates the highest c-statistic at 0.876, indicating the best model performance among those listed. The derivation cohort had a c-statistic of 0.836 while the validation cohort had a 0.828, according to the index study by Miro et al., suggesting robust but slightly lower predictive accuracy compared to our findings ( 5 ). In comparison, Miro et al. (in a separate validation study) achieved a c-statistic of 0.810 ( 7 ), while Rosello et al. ( 8 ) and Wussler et al. ( 9 ) reported c- statistics of 0.82 and 0.80, respectively. This comparison highlights that while our model shows superior predictive capability, other studies also present valuable insights with slightly lower but still significant predictive performance. The table compares the area under the curve for various studies evaluating predictive models. Our study reports a highly significant c-statistic of 0.821, indicating solid predictive performance. A c-statistic of 0.805 was recorded for the derivation cohort and 0.826 for the validation cohort, in the index research conducted by Lee et al., suggesting that their model’s performance is comparable to ours and slightly better in validation ( 6 ). Lee et al. (in a different validatory study) obtained a c-statistic of 0.81 ( 10 ), reflecting somewhat lower performance than in their index study validation cohort and our study. In contrast, Sepehrvand et al. ( 11 ) and Falsetti et al. ( 12 ) performed worse in terms of predictive accuracy when compared to the other investigations, with c-statistics of 0.73 and 0.754, respectively. This comparison underscores that while our study demonstrates strong predictive capabilities, other studies also provide valuable insights but with varying levels of accuracy. Conclusion The analysis concludes that both the MEESSI-AHF and EHMRG scores are valuable tools for risk stratification of acute heart failure (AHF) patients in an Indian population. However, in a sub-group analysis, many parameters, including age, gender, troponin levels, low output symptoms, ECG findings, and vital signs, did not show statistical significance in predicting short-term mortality, as their p-values were greater than 0.05. On the other hand, factors such as NYHA Class 4 at admission, ACS episodes, EMS transportation, NT-proBNP, serum creatinine levels, and Barthel's Index were identified as significant predictors. Among these, serum creatinine and NT-proBNP stood out as the most critical indicators of short-term mortality. Despite both scores displaying excellent predictive capabilities, the EHMRG score was slightly superior, likely due to its reliance on fewer, easily accessible parameters in emergency settings. The study advocates for the use of both scores either independently or in combination for accurate risk stratification and management of AHF patients in India, while suggesting that further research in diverse populations could enhance their applicability in routine clinical practice. Limitations The MEESSI-AHF and EHMRG scores were both derived and validated across multiple centers across different diasporas while ours is a single center study catering to a limited population. Another important consideration is the sample size of our study which was much smaller in comparison to the studies undertaken in derivation and validation of both MEESSI-AHF and EHMRG scores. Abbreviations Abbreviation Description AHF Acute heart failure ED Emergency department ICU Intensive care unit OP Out-patient ACC American College of Cardiology AHA American Heart Association HFSA Heart Failure Society of America ESC European Society of Cardiology JVP Jugular venous p HF Heart failure HFrEF Heart failure with reduced ejection fraction CVS Cardio-vascular system MI Myocardial infarction AS Aortic stenosis AR Aortic regurgitation MR Mitral regurgitation HFpEF Heart failure with preserved ejection fraction NO Nitric oxide SNS Sympathetic nervous system RAAS Renin–Angiotensin–Aldosterone System CO Cardiac output ANP Atrial Natriuretic Peptide BNP Brain Natriuretic Peptide PGE1 Prostaglandin E1 PGI2 Prostaglandin I2 or Prostacyclin TNF Tumour Necrosis Factor TGF-β Transforming Growth Factor - β IL-6 Interleukin-6 IL-1 Interleukin-1 LV Left Ventricle SGLT-2 Sodium-Glucose Transport Protein 2 PCT Proximal Convoluted Tubule cGMP Cyclic Guanosine Monophosphate CRT Cardiac Re-synchronization Therapy BVP Bi-ventricular Pacing LBBB Left Bundle Branch Block MV Mitral valve GDMT Guideline-directed medical therapy CRS Cardio-Renal Syndrome CVP Central venous pressure LPS Lipopolysaccharide COPD Chronic obstructive pulmonary disease GFR Glomerular filtration rate PND Paroxysmal Nocturnal Dyspnea NYHA New York Heart Association Functional Classification HR Heart rate (beats/minute) BP Blood pressure (mmHg) SBP Systolic blood pressure (mmHg) Spo2 Oxygen saturation (%) NT-proBNP N-terminal prohormone of brain natriuretic peptide LVH Left ventricular hypertrophy AF Atrial Fibrillation ECG Electrocardiogram TTE Trans-Thoracic Echocardiography POCUS Point-of-care Ultrasound MRI Magnetic Resonance Imaging CT Computed Tomography CAG Coronary angiogram BI Barthel Index RR Respiratory rate (cycles/minute) ACS Acute Coronary Syndrome EMS Emergency Medical Services MEESSI-AHF Multiple Estimation of risk based on the Emergency department Spanish Score in patients with AHF EHMRG Emergency Heart Failure Mortality Risk Grade AUC Area under the curve ROC Receiver operating characteristic curve IHD Ischemic Heart Disease DCM Dilated Cardiomyopathy ACS-AHF Acute coronary syndrome with acute heart failure Declarations Ethical approval : Kasturba medical college and Kasturba hospital institutional ethics committee Institutional ethics committee approval was obtained prior to the study. 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Incidence of heart failure and mortality after acute coronary syndromes. Am Heart J. 2013;165(3):379–85. AlFaleh H, Elasfar AA, Ullah A, AlHabib KF, Hersi A, Mimish L, Almasood A, Al Ghamdi S, Ghabashi A, Malik A, Hussein GA. Acute heart failure with and without acute coronary syndrome: clinical correlates and prognostic impact (From the HEARTS registry). BMC Cardiovasc Disord. 2016;16:1–2. Rossello X, Miró Ò, Llorens P, Jacob J, Herrero-Puente P, Gil V, Rizzi MA, Pérez-Durá MJ, Espiga FR, Romero R, Sevillano JA. Effect of Barthel index on the risk of thirty-day mortality in patients with acute heart failure attending the emergency department: a cohort study of nine thousand ninety-eight patients from the epidemiology of acute heart failure in emergency departments registry. Ann Emerg Med. 2019;73(6):589–98. Santaguida PL, Don-Wauchope AC, Oremus M, McKelvie R, Ali U, Hill SA, Balion C, Booth RA, Brown JA, Bustamam A, Sohel N. BNP and NT-proBNP as prognostic markers in persons with acute decompensated heart failure: a systematic review. Heart Fail Rev. 2014;19:453–70. Schefold JC, Lainscak M, Hodoscek LM, Blöchlinger S, Doehner W, von Haehling S. Single baseline serum creatinine measurements predict mortality in critically ill patients hospitalized for acute heart failure. ESC heart Fail. 2015;2(4):122–8. Tromp J, Ter Maaten JM, Damman K, O'Connor CM, Metra M, Dittrich HC, Ponikowski P, Teerlink JR, Cotter G, Davison B, Cleland JG. Serum potassium levels and outcome in acute heart failure (data from the PROTECT and COACH trials). Am J Cardiol. 2017;119(2):290–6. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9202689","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":628465189,"identity":"3b38b878-0d2a-437e-9f4a-a39017757eed","order_by":0,"name":"Nitin Bhat","email":"","orcid":"","institution":"Manipal Academy of Higher Education","correspondingAuthor":false,"prefix":"","firstName":"Nitin","middleName":"","lastName":"Bhat","suffix":""},{"id":628465193,"identity":"c6a711ed-859b-4d81-a47c-e07b55403c45","order_by":1,"name":"Satyajeet Rakh","email":"","orcid":"","institution":"Manipal Academy of Higher Education","correspondingAuthor":false,"prefix":"","firstName":"Satyajeet","middleName":"","lastName":"Rakh","suffix":""},{"id":628465197,"identity":"dfbd6b40-18b6-4d0b-9661-fda233fde0ed","order_by":2,"name":"Pooja Hanji","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCUlEQVRIiWNgGAWjYJACAwaGA0DqYOMDIMnDwMDcwMDARpyWZgOIFkbCWhggWhjYJCAcAlrM2ZsPFPxguCNvzni4repGzR0Zc/aDDQwfyg7j1GLZcyzBsIfhmeHOhoNtt3OOPeOx7ElsYJxxDrcWgxs5BgY8DIcZNxwAaWE7zGNwILGBmbcNvxbDPwyH7UFainP+AbWcf9jA/JeAFmOgLYkgLcy5bUAtN4C2MOLRAvKLsYzB4WSglmbp3L7DPJYzHjYc7DmXjlMLMMSOGb6pOGy74cbxh59zvh22N+dPPvjgR5k1bocBo8AARDJIHICLMBzAqR6igPkBmMXfgNAyCkbBKBgFowAZAACO2WPByNILIgAAAABJRU5ErkJggg==","orcid":"","institution":"Manipal Academy of Higher Education","correspondingAuthor":true,"prefix":"","firstName":"Pooja","middleName":"","lastName":"Hanji","suffix":""}],"badges":[],"createdAt":"2026-03-23 15:55:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9202689/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9202689/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108388385,"identity":"95f3be37-dd50-4c70-9f8d-b2e3ef8758a8","added_by":"auto","created_at":"2026-05-04 06:42:11","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":90036,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eA comparison of performance of MEESSI-AHF score in assessing 30th day mortality\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9202689/v1/cac0f226824d05ab570361a0.png"},{"id":108388388,"identity":"ade674b2-1dd5-4ef1-b171-cbed2dd4eca1","added_by":"auto","created_at":"2026-05-04 06:42:18","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":89263,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eA comparison of performance of EHMRG score in assessing 7th day mortality\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9202689/v1/9bcb093bd9696ee6f5100e09.png"},{"id":108388523,"identity":"421f3604-f875-42ee-9235-18a970da0a33","added_by":"auto","created_at":"2026-05-04 06:42:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":445757,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9202689/v1/dffb5077-702e-470d-a470-07f5ce6dc77c.pdf"},{"id":108388386,"identity":"1f394533-29c7-4117-853f-0d58c321318c","added_by":"auto","created_at":"2026-05-04 06:42:11","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2709045,"visible":true,"origin":"","legend":"","description":"","filename":"tablesandcharts3d1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9202689/v1/f494e8aa8251c4aef00641df.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Usefulness of MEESSI-AHF and EHMRG score in predicting short term mortality in Acute Heart failure in an Indian population","fulltext":[{"header":"Key Messages","content":"\u003cp\u003eMEESSI-AHF and EHMRG scores are effective tools for stratifying AHF patients and guiding management in the Indian population.\u003c/p\u003e"},{"header":"Introduction","content":" \u003cp\u003eAcute heart failure (AHF) is a complex and a potentially fatal illness marked by symptoms that appear quickly as a result of the heart's ineffective pumping. The global burden of AHF is substantial, and its incidence and outcomes can vary significantly based on geographical, demographic, and clinical factors. In India, where cardiovascular diseases are a major contributor to morbidity and death, accurate prediction of short-term mortality in AHF patients is crucial for improving patient management and outcomes. Estimates place the annual incidence of heart failure in India at 0.5\u0026ndash;1.8\u0026nbsp;million, and the prevalence at 1.3\u0026ndash;22.7\u0026nbsp;million (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) .Early risk assessment and effective prognostication are pivotal in managing AHF. Several scoring systems designed to estimate the chance of death in these patients, aiding clinicians in stratifying risk and tailoring treatment strategies such as OHFRS (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e), EAHFE-3D (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e), ADHERE (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Among these, the MEESSI (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) and EHMRG (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) scores have emerged as notable tools. The MEESSI score incorporates various clinical parameters and biomarkers, while the EHMRG score is designed specifically for emergency settings, taking into account a combination of clinical and demographic factors. Despite their utility in risk stratification, there is a need for a detailed analysis of these scores to determine their relative effectiveness and applicability in different populations. While previous studies have validated these scoring systems in various settings, their performance in the Indian population, characterized by unique clinical and socio-economic factors, remains underexplored. The purpose of this study was to fill that void through an analysis of the MEESSI and EHMRG scores in predicting AHF patients' short-term mortality within the Indian context. By evaluating these scoring systems' predictive accuracy, we seek to identify risk stratification utility of these scores for Indian patients, thereby informing clinical decision-making and potentially guiding improvements in patient management strategies. Improving patient outcomes and optimizing healthcare resources in a high-burden setting was our ultimate goal in conducting this research, which we hope will provide useful insights that can improve the effectiveness of AHF management in India.\u003c/p\u003e "},{"header":"Subjects and Methods","content":"\u003cp\u003eThe study was designed as a: single-center, prospective, cross-sectional observational study. A total of 128 patients presenting with AHF, diagnosed as per the Framingham criteria and ESC diagnostic algorithm were recruited from January 2024 to July 2024. This study was done after explaining the study details in a language understandable to the patient. The patient was provided with information sheet and a proper informed consent was taken. MEESSI-AHF and EHMRG scores were calculated at admission. 7th day and 30th day mortality was followed up by either phone call or follow up visit to the hospital.\u003c/p\u003e \u003cp\u003eInclusion criteria:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eAge\u0026thinsp;\u0026gt;\u0026thinsp;18 years.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eDiagnosed as AHF based upon Framingham HF diagnostic criteria and ESC diagnostic algorithm.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eExclusion criteria:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003ePatients with ST-Elevation MI\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ePatients undergoing dialysis\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis:\u003c/h2\u003e \u003cp\u003eThe collected data were entered in the Microsoft Excel 2016 and analysed with IBM SPSS\u003c/p\u003e \u003cp\u003eStatistics for Windows, Version 29.0. (Armonk, NY: IBM Corp). To describe the data\u003c/p\u003e \u003cp\u003eDescriptive statistics frequency analysis, percentage analysis was used for categorical Variables and the mean and S.D were used for continuous variables. To find the significant difference between the bivariate samples in Independent groups the Independent sample t-test was used. To find the significance in categorical data Chi-Square test was used similarly if the expected cell frequency is less than 5 in 2\u0026times;2 tables then the Fisher\u0026rsquo;s Exact was used. To find the scores to predict the Mortality the Receiver Operating Characteristics curve (ROC) was used with Sensitivity, Specificity and cut off. In all the above statistical tools the probability value .05 is considered as significant level.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThe data revealed that the majority of patients were aged between 61\u0026ndash;70 years (28.1%), with the 51\u0026ndash;60 years age group closely following at 26.6%. The gender distribution was predominantly male (64.1%) compared to female (35.9%). At 30 days, the mortality rate was 10.9%, while 89.1% of the patients remained alive. Statistical analyses showed no significant association between age and 30-day mortality (p\u0026thinsp;=\u0026thinsp;0.630), nor was there any significant link between gender and mortality (p\u0026thinsp;=\u0026thinsp;0.080). Other clinical factors like systolic blood pressure (p\u0026thinsp;=\u0026thinsp;0.062), heart rate (p\u0026thinsp;=\u0026thinsp;0.069), respiratory rate (p\u0026thinsp;=\u0026thinsp;0.427), oxygen saturation (SpO2) (p\u0026thinsp;=\u0026thinsp;0.182), serum potassium levels (p\u0026thinsp;=\u0026thinsp;0.167), and the duration of hospital stay (p\u0026thinsp;=\u0026thinsp;0.995) also showed no significant differences between those who survived and those who died at 30 days. Positive troponin values (p\u0026thinsp;=\u0026thinsp;0.142) and hypertrophy on ECG (p\u0026thinsp;=\u0026thinsp;0.756) did not show significant associations either.\u003c/p\u003e \u003cp\u003eHowever, there were notable findings: NT-proBNP (p\u0026thinsp;=\u0026thinsp;0.0005) and serum creatinine levels (p\u0026thinsp;=\u0026thinsp;0.002) showed highly significant associations with 30-day mortality, indicating that elevated levels of these biomarkers are strongly linked to worse outcomes. Barthel's Index (p\u0026thinsp;=\u0026thinsp;0.053) also showed a significant association, suggesting that lower functional status correlates with higher mortality risk. Furthermore, a highly significant association was observed between NYHA Class 4 at admission (p\u0026thinsp;=\u0026thinsp;0.009) and 30-day mortality, reflecting that patients with severe heart failure symptoms had a greater likelihood of dying within 30 days. Episodes associated with acute coronary syndrome (ACS) (p\u0026thinsp;=\u0026thinsp;0.007) and transport by EMS (p\u0026thinsp;=\u0026thinsp;0.020) were also significantly associated with higher mortality. In contrast, low output symptoms (p\u0026thinsp;=\u0026thinsp;0.223) were not significantly associated with 30-day mortality. These findings underscore the importance of NT-proBNP, serum creatinine, NYHA class, ACS, and EMS transport as critical factors in predicting mortality outcomes, while other clinical variables showed no significant predictive value.\u003c/p\u003e\n\u003ch3\u003eEHMRG Score for 7-Day Mortality:\u003c/h3\u003e\n\u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eROC Curve\u003c/b\u003e: AUC\u0026thinsp;=\u0026thinsp;0.821, p\u0026thinsp;=\u0026thinsp;0.0005\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eHighly significant, indicating good predictive ability for 7-day mortality. Sensitivity (72.7%) and specificity (74.4%) suggest a reliable test for short-term mortality risk.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eMEESSI-AHF and EHMRG Scores for 30-Day Mortality:\u003c/h2\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eMEESSI-AHF Score\u003c/b\u003e: AUC\u0026thinsp;=\u0026thinsp;0.876, p\u0026thinsp;=\u0026thinsp;0.0005\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eHighly significant, with sensitivity (78.6%) and specificity (75.4%), indicating strong predictive performance.\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eEHMRG Score\u003c/b\u003e: AUC\u0026thinsp;=\u0026thinsp;0.884, p\u0026thinsp;=\u0026thinsp;0.0005\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eHighly significant, with sensitivity (78.6%) and specificity (76.3%), showing slightly better predictive performance compared to MEESSI-AHF for 30-day mortality.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e "},{"header":"Discussion","content":"\u003cp\u003eAHF has proven to be among the most commonly seen, yet tricky to recognize, stratify and manage illnesses observed in common practice and provides an interesting challenge to physicians around the world be it tertiary care centers or primary health care With the advent of introduction of risk stratification scores for commonly seen illnesses having shown benefit in management and prognosis, it is of great importance to have found scores which do the same for a disease like AHF. MEESSI-AHF (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) and EHMRG (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) are two such scores which have shown promising results around the world in countries like Canada, Spain, Italy and Switzerland (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e) (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e) (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e) (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e) (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e) (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). In our study conducted these are the following results. The age distribution shows that a majority of patients in our study are between 51\u0026ndash;80 years old, indicating maximum proportion in the 61\u0026ndash;70 years age group (28.1%). This indicates a predominance of older patients, which bears similarities to the Lee et al. study in Canada (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e) which showed mean age to be 75.4\u0026ndash;75.7 years while the research done by Miro et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e) found the mean age to be 79.7 years, Falsetti et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e) in Italy found to have 84.6 years, Rosello et al (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e) found the mean age to be 80. Which indicates AHF is a disease which predominantly affects the older population which may be due to the higher probability of common etiologies of heart failure affecting older population like IHD, Hypertension, as explained by Chaturvedi et al in 2016. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). There is a notable gender imbalance in our study with 64.1% males and 35.9% females, suggesting that males are more prevalent in this patient cohort. While a recently conducted study by Harikrishnan et al across twenty-one Indian states plus four union territories found to have 31% women which is similar to our study (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e) .This could indicate most HF event instances involve men, as shown in the National heart failure registry is frequently noted in Indian patients (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). While patients from High-Income countries show a relatively high proportion of women (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e) (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). One explanation to this might be explained by the greater prevalence of IHD in men than in women which is one of the commonest causes of AHF (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Another explanation to be grievously considered is the lack of available tertiary care to female population in India of which few instances have been reported (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Mortality rates at 7 days and 30 days are relatively low, with 8.6% and 10.9% mortality respectively. This provides a snapshot of early and short-term outcomes in the studied population. Among studies done which were similar to ours mortality rate at 7 days in the study done by Lee et al was found to be 2% (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e), Sepehrvand et al 5.2% (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). In studies similar to ours for mortality rate at 30 days Sepehrvand et al found it to be 12.7% (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e), Miro et al found their mortality at 30 days to be 10.1% (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Which suggests that, relative to other nations, individuals with AHF who visit Indian ED\u0026rsquo;s have a greater risk of 7-day mortality. According to our research, there is no evidence of a meaningful relationship between gender and short term mortality, showing that the 30-day mortality risk in this sample is not significantly influenced by gender, which is consistent with research done by Parissis et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e) that analyzed the in-hospital risk of death of patients with AHF and discovered that it was similar in both genders (10.5 percent in males and 11.1 percent in females, p\u0026thinsp;=\u0026thinsp;0.475). There was no discernible correlation found between short term mortality and positive troponin values, indicating that troponin levels may not be a reliable indicator of short-term mortality in this population, which contrasts with what Lee et al (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e), Wussler et al (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e) and Sepehrvand et al (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e) found in their studies. Although it is evident that a raised troponin level does not necessarily indicate AHF, a wealth of data indicates that patients who appear with both an elevated troponin level and AHF have higher rates of morbidity and mortality. It has been discovered that cardiac troponin-T, a historically significant biomarker investigated in a variety of cardiac diseases, is associated with a higher probability of adverse events and death in healthcare facilities. as demonstrated by Peacock et al (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e), Del Carlo et al (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e) and Perna ER et al (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). A significant association was found in our study which indicates that patients in NYHA Class 4 have a higher short term mortality risk, emphasizing the severity of symptoms at admission as a critical factor which has undergone substantial research by Ahmed et al. who established that \u0026ldquo;NYHA class 4 patients had about 5 times higher risk of hospitalization due to worsening heart failure versus those with NYHA class I\u0026rdquo; across USA and Canada (81) and Asano et al. in Japan who concluded that \u0026ldquo;compared to patients with NYHA class 2 or 3 symptoms at admission, those with NYHA class 4 symptoms had a noticeably increased risk of all-cause or cardiac death while in the hospital\u0026rdquo; (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Low output symptoms were found to have no significant association which was in contrast to similar research by Miro et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e), Wussler et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e) along with Rosello et al (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). A significant association was found in episodes associated with ACS which indicates elevated risk with respect to increased short term mortality according to research conducted by Wussler et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e), Miro et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). This can be understood by looking at a study done by Kaul et al. that looked at the HF incidence and mortality following ACS and concluded that \u0026ldquo;HF developed either during the ACS hospitalization or after discharge confers a substantial risk of death during the first year\u0026rdquo; (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e) as well as a study conducted by AlFaleh et al. in Saudi Arabia which studied ACS-AHF, found that \u0026ldquo;ACS-AHF patients had higher hospital mortality as well as higher hospital adverse cardiovascular outcomes\u0026rdquo;(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e), while In a research spanning all of Finland, Tawaraski et al. discovered that ACS-AHF patients had significantly greater chance of in-hospital death (12 percent vs. 5 percent; P =\u0026thinsp;.002) and 30-day mortality (13 percent vs. 8 percent; P =\u0026thinsp;.027) (85). Hypertrophy at ECG when analysed in our study revealed no significant association. Transport by EMS? considered in this study as transported by ambulance services seems to have a high statistical significance as it might correlate with the increased severity of illness and need for urgent care hence explaining its significant association with mortality. Our study determined that the Barthel index has close to statistical significance, suggesting that a higher Barthel Index (which assesses functional independence) might be related to short term mortality, though it does not quite reach conventional significance levels, which is out of accord to the index study by Miro et al where Barthel\u0026rsquo;s index was found to have highest significance among the variables considered for determination of score (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e), but when it was excluded by Wussler et al as a part of their comprehensive statistical analysis, it was found to have a high C-statistic of 0.800 which indicated the exclusion of BI does not affect the usefulness of the score significantly (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). A study done by Rosello et al who studied the influence of the BI on the probabilities of death within thirty days in patients who are experiencing AHF and who are brought to the ED concluded that \u0026ldquo;Barthel\u0026rsquo;s index score at the ED visit is a strong independent predictor for all-cause 30-day mortality in ED acute heart failure patients\u0026rdquo; (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). In spite of the fact that it is a \u0026ldquo;straightforward and practical measure for evaluating impairment and tracking changes in functional status over time\u0026rdquo;, some variability is to be expected in spite of good inter-observer reproducibility which can explain the results of our study and the variability in other similar studies conducted throughout the world. Our study found no significant association between the Heart rate, Systolic blood pressure, Respiratory rate, Oxygen saturation at presentation and short term mortality. Our investigation yielded a highly significant statistical analysis showing a high level of association between the biomarker NT-proBNP and short term mortality, consistent with its function as a biomarker for the diagnosis, assessment and prognostication of AHF. The mean Nt-proBNP in individuals who passed away in 30 days was found to be 21316 while in patients who survived it was found to be 6597 with a very substantial p value of 0.0005. While the research by Wussler et al reveals mean Nt-proBNP in patients who died within 30 days to be 10420 while in patients who survived 4864 with a p value of \u0026lt;\u0026thinsp;0.001 (66), While the mean Nt-proBNP in research carried out by Rosello et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e) and Miro et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e) was discovered to be 3263 and 7738 respectively. This proves to reinforce the excellent role played by Nt-proBNP in overall management of heart failure as evidenced by the systematic review of 79 studies conducted by Santaguida et al. who sought to investigate the advantages of NT-proBNP and BNP as predictive indicators in AHF patients and concluded that \u0026ldquo;higher levels of admission BNP or NT-proBNP were associated with greater risk of mortality, morbidity, or a combination of mortality and morbidity. A decrease in BNP levels post-admission was also predictive of decreased rates of mortality and morbidity\u0026rdquo; (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Our study analysing serum creatinine as a biomarker in determining imminent danger of mortality in AHF patients, revealed highly significant results with p value of 0.002 suggesting serum creatinine is an important predictor of mortality, reflecting renal function and its impact on heart failure outcomes. Serum creatinine is one of the most important biomarkers of renal dysfunction which often accompanies AHF which was validated by Schefold et al. who found that \u0026ldquo;Patients with baseline eGFR\u0026thinsp;\u0026lt;\u0026thinsp;30 mL/min/1.73 m2 had an exceptionally high risk of death\u0026rdquo; (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). It appears that serum potassium levels do not significantly affect short term mortality due to AHF, as our investigation did not find any significant association between these two variables which is in accord to Tromp et al. who evaluated AHF patients\u0026apos; serum potassium levels and their results and concluded that \u0026ldquo;potassium levels at admission or its change during hospitalization are not associated with mortality\u0026rdquo; (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eYou can see how different areas under the curve are compared in the table (c-statistic) for various studies assessing predictive performance. Our study demonstrates the highest c-statistic at 0.876, indicating the best model performance among those listed. The derivation cohort had a c-statistic of 0.836 while the validation cohort had a 0.828, according to the index study by Miro et al., suggesting robust but slightly lower predictive accuracy compared to our findings (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). In comparison, Miro et al. (in a separate validation study) achieved a c-statistic of 0.810 (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e), while Rosello et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e) and Wussler et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e) reported c- statistics of 0.82 and 0.80, respectively. This comparison highlights that while our model shows superior predictive capability, other studies also present valuable insights with slightly lower but still significant predictive performance.\u003c/p\u003e\n\u003cp\u003eThe table compares the area under the curve for various studies evaluating predictive models. Our study reports a highly significant c-statistic of 0.821, indicating solid predictive performance. A c-statistic of 0.805 was recorded for the derivation cohort and 0.826 for the validation cohort, in the index research conducted by Lee et al., suggesting that their model\u0026rsquo;s performance is comparable to ours and slightly better in validation (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Lee et al. (in a different validatory study) obtained a c-statistic of 0.81 (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e), reflecting somewhat lower performance than in their index study validation cohort and our study. In contrast, Sepehrvand et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e) and Falsetti et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e) performed worse in terms of predictive accuracy when compared to the other investigations, with c-statistics of 0.73 and 0.754, respectively. This comparison underscores that while our study demonstrates strong predictive capabilities, other studies also provide valuable insights but with varying levels of accuracy.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe analysis concludes that both the MEESSI-AHF and EHMRG scores are valuable tools for risk stratification of acute heart failure (AHF) patients in an Indian population. However, in a sub-group analysis, many parameters, including age, gender, troponin levels, low output symptoms, ECG findings, and vital signs, did not show statistical significance in predicting short-term mortality, as their p-values were greater than 0.05. On the other hand, factors such as NYHA Class 4 at admission, ACS episodes, EMS transportation, NT-proBNP, serum creatinine levels, and Barthel\u0026apos;s Index were identified as significant predictors. Among these, serum creatinine and NT-proBNP stood out as the most critical indicators of short-term mortality. Despite both scores displaying excellent predictive capabilities, the EHMRG score was slightly superior, likely due to its reliance on fewer, easily accessible parameters in emergency settings. The study advocates for the use of both scores either independently or in combination for accurate risk stratification and management of AHF patients in India, while suggesting that further research in diverse populations could enhance their applicability in routine clinical practice.\u003c/p\u003e"},{"header":"Limitations","content":"\u003cp\u003eThe MEESSI-AHF and EHMRG scores were both derived and validated across multiple centers across different diasporas while ours is a single center study catering to a limited population.\u003c/p\u003e\n\u003cp\u003eAnother important consideration is the sample size of our study which was much smaller in comparison to the studies undertaken in derivation and validation of both MEESSI-AHF and EHMRG scores.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAbbreviation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eDescription\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAHF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAcute\u0026nbsp;heart failure\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eED\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eEmergency department\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eICU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eIntensive\u0026nbsp;care unit\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eOut-patient\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eACC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAmerican\u0026nbsp;College\u0026nbsp;of\u0026nbsp;Cardiology\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAHA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAmerican\u0026nbsp;Heart Association\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHFSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eHeart\u0026nbsp;Failure\u0026nbsp;Society\u0026nbsp;of America\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eESC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eEuropean\u0026nbsp;Society\u0026nbsp;of\u0026nbsp;Cardiology\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eJVP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eJugular\u0026nbsp;venous p\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eHeart\u0026nbsp;failure\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHFrEF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eHeart\u0026nbsp;failure\u0026nbsp;with\u0026nbsp;reduced\u0026nbsp;ejection fraction\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCVS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCardio-vascular system\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMyocardial infarction\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAortic\u0026nbsp;stenosis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAortic regurgitation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMitral\u0026nbsp;regurgitation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHFpEF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eHeart\u0026nbsp;failure\u0026nbsp;with\u0026nbsp;preserved\u0026nbsp;ejection fraction\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNitric oxide\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSNS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSympathetic\u0026nbsp;nervous system\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eRAAS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRenin\u0026ndash;Angiotensin\u0026ndash;Aldosterone\u0026nbsp;System\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ctable style=\"width: 100%;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003ctable\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCardiac output\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eANP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAtrial\u0026nbsp;Natriuretic Peptide\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBNP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eBrain\u0026nbsp;Natriuretic Peptide\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePGE1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eProstaglandin E1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePGI2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eProstaglandin\u0026nbsp;I2\u0026nbsp;or\u0026nbsp;Prostacyclin\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTNF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTumour\u0026nbsp;Necrosis Factor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTGF-\u0026beta;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTransforming\u0026nbsp;Growth\u0026nbsp;Factor\u0026nbsp;- \u0026beta;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eIL-6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eInterleukin-6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eIL-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eInterleukin-1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eLeft\u0026nbsp;Ventricle\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSGLT-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSodium-Glucose\u0026nbsp;Transport\u0026nbsp;Protein 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePCT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eProximal\u0026nbsp;Convoluted Tubule\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ecGMP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCyclic\u0026nbsp;Guanosine Monophosphate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCRT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCardiac\u0026nbsp;Re-synchronization\u0026nbsp;Therapy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBVP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eBi-ventricular Pacing\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLBBB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eLeft\u0026nbsp;Bundle\u0026nbsp;Branch Block\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMitral\u0026nbsp;valve\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGDMT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGuideline-directed\u0026nbsp;medical therapy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCRS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCardio-Renal Syndrome\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCVP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCentral\u0026nbsp;venous pressure\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLPS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eLipopolysaccharide\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCOPD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eChronic\u0026nbsp;obstructive\u0026nbsp;pulmonary disease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGFR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGlomerular\u0026nbsp;filtration\u0026nbsp;rate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ctable style=\"width: 100%;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003ctable\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eParoxysmal\u0026nbsp;Nocturnal Dyspnea\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNYHA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNew\u0026nbsp;York\u0026nbsp;Heart\u0026nbsp;Association\u0026nbsp;Functional Classification\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eHeart\u0026nbsp;rate (beats/minute)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eBlood\u0026nbsp;pressure (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSystolic\u0026nbsp;blood\u0026nbsp;pressure\u0026nbsp;(mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSpo2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eOxygen\u0026nbsp;saturation (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNT-proBNP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eN-terminal\u0026nbsp;prohormone\u0026nbsp;of\u0026nbsp;brain\u0026nbsp;natriuretic peptide\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLVH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eLeft\u0026nbsp;ventricular hypertrophy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAtrial Fibrillation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eECG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eElectrocardiogram\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTTE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTrans-Thoracic Echocardiography\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePOCUS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePoint-of-care Ultrasound\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMRI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMagnetic\u0026nbsp;Resonance Imaging\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eComputed Tomography\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCAG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCoronary angiogram\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eBarthel Index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eRR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRespiratory\u0026nbsp;rate (cycles/minute)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eACS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAcute\u0026nbsp;Coronary Syndrome\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEMS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eEmergency\u0026nbsp;Medical Services\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMEESSI-AHF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMultiple Estimation of risk based on the Emergency\u003c/p\u003e\n \u003cp\u003edepartment\u0026nbsp;Spanish\u0026nbsp;Score\u0026nbsp;in\u0026nbsp;patients\u0026nbsp;with AHF\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEHMRG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eEmergency\u0026nbsp;Heart\u0026nbsp;Failure\u0026nbsp;Mortality\u0026nbsp;Risk Grade\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eArea\u0026nbsp;under\u0026nbsp;the curve\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ctable\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eROC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eReceiver\u0026nbsp;operating\u0026nbsp;characteristic curve\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eIHD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eIschemic\u0026nbsp;Heart\u0026nbsp;Disease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDCM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eDilated Cardiomyopathy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eACS-AHF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAcute coronary syndrome with acute heart failure\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cu\u003eEthical approval\u003c/u\u003e: Kasturba medical college and Kasturba hospital institutional ethics committee\u003c/p\u003e\n\u003cp\u003eInstitutional ethics committee approval was obtained prior to the study. Ref no: 473/2022\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eConsent to participants\u003c/u\u003e: Written and informed consent taken from participants\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eConsent for publication\u003c/u\u003e: All authors consent for publication.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eAvailability of data and materials\u003c/u\u003e: The datasets used and analyzed during the current study available from corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eCompeting interest\u003c/u\u003e: Nil\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eFunding:\u003c/u\u003e Nil\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eAuthors\u0026apos; contributions:\u003c/u\u003e All authors are contributed for this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eAcknowledgements:\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to express our sincere gratitude to Dr Douglas Lee and Dr Oscar Miro for permitting me to use their scores for the assessment of heart failure risk.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003ePillai HS, Ganapathi S. Heart failure in South Asia. Curr Cardiol Rev., Huffman MD, Prabhakaran D. Heart failure: Epidemiology and prevention in India. Natl Med J India. 2010;23:283-8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStiell IG, Clement CM, Brison RJ, Rowe BH, Borgundvaag B, Aaron SD, Lang E, Calder LA, Perry JJ, Forster AJ, Wells GA. A risk scoring system to identify emergency department patients with heart failure at high risk for serious adverse events. Acad Emerg Med. 2013;20(1):17\u0026ndash;26.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGarcia-Gutierrez S, Quintana JM, Ant\u0026oacute;n-Ladislao A, Gallardo MS, Pulido E, Rilo I, Zubillaga E, Morillas M, Onaindia JJ, Murga N, Palenzuela R. Creation and validation of the acute heart failure risk score: AHFRS. Intern Emerg Med. 2017;12:1197\u0026ndash;206.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFonarow GC, The Acute Decompensated Heart Failure National Registry (ADHERE). opportunities to improve care of patients hospitalized with acute decompensated heart failure. Rev Cardiovasc Med. 2003;4:S21\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMir\u0026oacute; \u0026Ograve;, Rossello X, Gil V, Mart\u0026iacute;n-S\u0026aacute;nchez FJ, Llorens P, Herrero-Puente P, Jacob J, Bueno H, Pocock SJ, ICA-SEMES Research Group*. Predicting 30-day mortality for patients with acute heart failure in the emergency department: a cohort study. Ann Intern Med. 2017;167(10):698\u0026ndash;705.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee DS, Stitt A, Austin PC, Stukel TA, Schull MJ, Chong A, Newton GE, Lee JS, Tu JV. Prediction of heart failure mortality in emergent care: a cohort study. Ann Intern Med. 2012;156(11):767\u0026ndash;75.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMir\u0026oacute; \u0026Ograve;, Rossell\u0026oacute; X, Gil V, Mart\u0026iacute;n-S\u0026aacute;nchez FJ, Llorens P, Herrero P, Jacob J, L\u0026oacute;pez-Grima ML, Gil C, Imbern\u0026oacute;n FJ, Garrido JM. The usefulness of the MEESSI score for risk stratification of patients with acute heart failure at the emergency department. Revista Espa\u0026ntilde;ola de Cardiolog\u0026iacute;a (English Edition). 2019;72(3):198\u0026ndash;207.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRossello X, Bueno H, Gil V, Jacob J, Javier Mart\u0026iacute;n-S\u0026aacute;nchez F, Llorens P, Herrero Puente P, Alqu\u0026eacute;zar-Arb\u0026eacute; A, Raposeiras-Roub\u0026iacute;n S, L\u0026oacute;pez-D\u0026iacute;ez MP, Pocock S. MEESSI-AHF risk score performance to predict multiple post-index event and post-discharge short-term outcomes. Eur Heart J Acute Cardiovasc Care. 2021;10(2):142\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWussler D, Kozhuharov N, Sabti Z, Walter J, Strebel I, Scholl L, Mir\u0026oacute; O, Rossello X, Mart\u0026iacute;n-S\u0026aacute;nchez FJ, Pocock SJ, Nowak A. External validation of the MEESSI acute heart failure risk score: a cohort study. Ann Intern Med. 2019;170(4):248\u0026ndash;56.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee DS, Lee JS, Schull MJ, Borgundvaag B, Edmonds ML, Ivankovic M, McLeod SL, Dreyer JF, Sabbah S, Levy PD, O\u0026rsquo;Neill T. Prospective validation of the emergency heart failure mortality risk grade for acute heart failure: the ACUTE study. Circulation. 2019;139(9):1146\u0026ndash;56.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSepehrvand N, Youngson E, Bakal JA, McAlister FA, Rowe BH, Ezekowitz JA. External validation and refinement of emergency heart failure mortality risk grade risk model in patients with heart failure in the emergency department. CJC open. 2019;1(3):123\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFalsetti L, Zaccone V, Guerrieri E, Perrotta G, Diblasi I, Giuliani L, Palma LE, Viticchi G, Fioranelli A, Moroncini G, Pansoni A. Implementation of EHMRG Risk Model in an Italian Population of Elderly Patients with Acute Heart Failure. J Clin Med. 2022;11(11):2982.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChaturvedi V, Parakh N, Seth S, Bhargava B, Ramakrishnan S, Roy A, Saxena A, Gupta N, Misra P, Rai SK, Anand K. Heart failure in India: the INDUS (India Ukieri study) study. J Prim Care Specialties. 2016;2(1):28\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHarikrishnan S, Sanjay G, Anees T, Viswanathan S, Vijayaraghavan G, Bahuleyan CG, Sreedharan M, Biju R, Nair T, Suresh K, Rao AC, Dalus D, Huffman MD, Jeemon P, for the Trivandrum Heart Failure Registry. Clinical presentation, management, in-hospital and 90-day outcomes of heart failure patients in Trivandrum, Kerala, India: the Trivandrum Heart Failure Registry. Eur J Heart Fail. 2015;17:794\u0026ndash;800.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDokainish H, Teo K, Zhu J, Roy A, AlHabib KF, ElSayed A, et al. Global mortality variations in patients with heart failure: results from the International Congestive Heart Failure (INTER-CHF) prospective cohort study. Lancet Glob Health. 2017;5:e665\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAdams KF, Fonarow GC, Emerman CL, LeJemtel TH, Costanzo MR, Abraham WT, Berkowitz RL, Galvao M, Horton DP, ADHERE Scientific Advisory Committee andInvestigators. Characteristics and outcomes of patients hospitalized for heart failure in the United States: rationale, design, and preliminary observations from the first 100,000 cases in the Acute Decompensated Heart Failure National Registry (ADHERE). Am Heart J. 2005;149:209\u0026ndash;16.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCrespo-Leiro MG, Anker SD, Maggioni AP, Coats AJ, Filippatos G, Ruschitzka F, Ferrari R, Piepoli MF, Delgado Jimenez JF, Metra M, Fonseca C, Hradec J, Amir O, Logeart D, Dahlstr\u0026ouml;m U, Merkely B, Drozdz J, Goncalvesova E, Hassanein M, Chioncel O, Lainscak M, Seferovic PM, Tousoulis D, Kavoliuniene A, Fruhwald F, Fazlibegovic E, Temizhan A, Gatzov P, Erglis A, Laroche C. Mebazaa A, on behalf of the Heart Failure Association (HFA) of the European Society of Cardiology (ESC). European Society of Cardiology Heart Failure Long-Term Registry (ESC-HF-LT): 1-year follow-up outcomes and differences across regions. Eur J Heart Fail. 2016;18:613\u0026ndash;25.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKumbhalkar SD, Bisne VV. Clinical and angiographic profile of young patients with ischemic heart disease: A central India study. J Clin Prev Cardiol. 2019;8(1):6\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKapoor M, Agrawal D, Ravi S, Roy A, Subramanian SV, Guleria R. Missing female patients: an observational analysis of sex ratio among outpatients in a referral tertiary care public hospital in India. BMJ Open. 2019;9:e026850.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eParissis JT, Mantziari L, Kaldoglou N, Ikonomidis I, Nikolaou M, Mebazaa A, Altenberger J, Delgado J, Vilas-Boas F, Paraskevaidis I, Anastasiou-Nana M. Gender-related differences in patients with acute heart failure: management and predictors of in-hospital mortality. Int J Cardiol. 2013;168(1):185\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeacock IVWF, De Marco T, Fonarow GC, Diercks D, Wynne J, Apple FS, Wu AH. Cardiac troponin and outcome in acute heart failure. N Engl J Med. 2008;358(20):2117\u0026ndash;26.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDel Carlo CH, Pereira-Barretto AC, Cassaro-Strunz CM, et al. Cardiac troponin T for risk stratification in decompensated chronic heart failure. Arq Bras Cardiol. 2009;92:372\u0026ndash;80. 389\u0026ndash;397.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePerna ER, Macin SM, Cimbaro Canella JP, et al. Minor myocardial damage detected by troponin T is a powerful predictor of long-term prognosis in patients with acute decompensated heart failure. Int J Cardiol. 2005;99:253\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAhmed A, Aronow WS, Fleg JL. Higher New York Heart Association classes and increased mortality and hospitalization in patients with heart failure and preserved left ventricular function. Am Heart J. 2006;151(2):444\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAsano R, Kajimoto K, Oka T, Sugiura R, Okada H, Kamishima K, Hirata T, Sato N. investigators of the Acute Decompensated Heart Failure Syndromes (ATTEND) registry. Association of New York Heart Association functional class IV symptoms at admission and clinical features with outcomes in patients hospitalized for acute heart failure syndromes. Int J Cardiol. 2017;230:585\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKaul P, Ezekowitz JA, Armstrong PW, Leung BK, Savu A, Welsh RC, Quan H, Knudtson ML, McAlister FA. Incidence of heart failure and mortality after acute coronary syndromes. Am Heart J. 2013;165(3):379\u0026ndash;85.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlFaleh H, Elasfar AA, Ullah A, AlHabib KF, Hersi A, Mimish L, Almasood A, Al Ghamdi S, Ghabashi A, Malik A, Hussein GA. Acute heart failure with and without acute coronary syndrome: clinical correlates and prognostic impact (From the HEARTS registry). BMC Cardiovasc Disord. 2016;16:1\u0026ndash;2.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRossello X, Mir\u0026oacute; \u0026Ograve;, Llorens P, Jacob J, Herrero-Puente P, Gil V, Rizzi MA, P\u0026eacute;rez-Dur\u0026aacute; MJ, Espiga FR, Romero R, Sevillano JA. Effect of Barthel index on the risk of thirty-day mortality in patients with acute heart failure attending the emergency department: a cohort study of nine thousand ninety-eight patients from the epidemiology of acute heart failure in emergency departments registry. Ann Emerg Med. 2019;73(6):589\u0026ndash;98.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSantaguida PL, Don-Wauchope AC, Oremus M, McKelvie R, Ali U, Hill SA, Balion C, Booth RA, Brown JA, Bustamam A, Sohel N. BNP and NT-proBNP as prognostic markers in persons with acute decompensated heart failure: a systematic review. Heart Fail Rev. 2014;19:453\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchefold JC, Lainscak M, Hodoscek LM, Bl\u0026ouml;chlinger S, Doehner W, von Haehling S. Single baseline serum creatinine measurements predict mortality in critically ill patients hospitalized for acute heart failure. ESC heart Fail. 2015;2(4):122\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTromp J, Ter Maaten JM, Damman K, O'Connor CM, Metra M, Dittrich HC, Ponikowski P, Teerlink JR, Cotter G, Davison B, Cleland JG. Serum potassium levels and outcome in acute heart failure (data from the PROTECT and COACH trials). Am J Cardiol. 2017;119(2):290\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Acute heart failure, MEESSI-AHF, EHMRG","lastPublishedDoi":"10.21203/rs.3.rs-9202689/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9202689/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eContext:\u003c/strong\u003e\u003cbr\u003e\nAcute heart failure (AHF) is a complex, potentially fatal condition characterized by rapid symptom onset due to inefficient heart function. Globally, AHF poses a significant burden, with incidence and outcomes varying by geography, demographics, and clinical factors. In India, cardiovascular diseases contribute heavily to morbidity and mortality, making accurate prediction of short-term mortality in AHF patients critical for improving patient management. There is a critical lack of prospective data validating these international scores within the Indian healthcare ecosystem.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAims:\u003c/strong\u003e\u003cbr\u003e\nTo compare the MEESSI-AHF and EHMRG scores in predicting short-term mortality in AHF patients in India. This study aims to determine which model provides better risk stratification, potentially guiding clinical decisions and enhancing patient management strategies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSettings and Design:\u003c/strong\u003e\u003cbr\u003e\nThis is a single-center, prospective, cross-sectional observational study conducted from January to July 2024 in a tertiary care center in Coastal Karnataka.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods and Material:\u003c/strong\u003e\u003cbr\u003e\nAHF patients diagnosed using the Framingham criteria and ESC algorithm were assessed upon admission, with MEESSI-AHF and EHMRG scores calculated. Mortality outcomes were tracked on day 7 and day 30 via phone calls or follow-up visits.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis used:\u003c/strong\u003e\u003cbr\u003e\nData were analyzed using SPSS 29.0, with ROC curve analysis used to assess predictive scores for mortality, considering a p-value of 0.05 as significant.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e\u003cbr\u003e\nMEESSI-AHF (AUC = 0.876) and EHMRG (AUC = 0.821) showed strong predictive power. Significant predictors included NYHA Class 4, ACS episodes, NT-proBNP, and serum creatinine.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e\u003cbr\u003e\nBoth MEESSI-AHF and EHMRG scores are effective tools for stratifying AHF patients and guiding management in the Indian population.\u003c/p\u003e","manuscriptTitle":"Usefulness of MEESSI-AHF and EHMRG score in predicting short term mortality in Acute Heart failure in an Indian population","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-04 06:40:23","doi":"10.21203/rs.3.rs-9202689/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-04-23T15:04:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"207180316137890409702364100543881967553","date":"2026-04-23T14:35:55+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-23T06:46:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"156097871714051886421645199223007169915","date":"2026-04-23T05:37:04+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-21T10:06:17+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-20T07:56:42+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-02T08:33:08+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-31T07:30:32+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cardiovascular Disorders","date":"2026-03-31T05:08:05+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cc565540-6865-4b63-aa91-785cb8a4fd49","owner":[],"postedDate":"May 4th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-04T06:40:24+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-04 06:40:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9202689","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9202689","identity":"rs-9202689","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}