Prognostic Impact of Laboratory Frailty Index in Elderly Patients with Acute Myocardial Infarction: A Retrospective Cohort Study

Prognostic Impact of Laboratory Frailty Index in Elderly Patients with Acute Myocardial Infarction: A Retrospective Cohort Study | 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 Prognostic Impact of Laboratory Frailty Index in Elderly Patients with Acute Myocardial Infarction: A Retrospective Cohort Study Qiqi Jiang, Jinyang Li, Huanrui Zhang, Xiaopo Gao, Liye Shi, Ling Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8738011/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Background: Acute myocardial infarction (AMI) is a leading cause of death in the elderly. Frailty syndrome is associated with adverse outcomes. The Laboratory Frailty Index (FI-LAB) provides an objective assessment of frailty, but its prognostic value in older AMI patients remains inadequately investigated. Methods: This retrospective cohort study utilized the MIMIC-IV database and enrolled 3,257 AMI patients aged ≥65 years. The FI-LAB was calculated from 27 laboratory parameters within 24 hours of admission, and patients were stratified into quartiles (Q1-Q4). The primary outcome was in-hospital mortality. Secondary outcomes included 30-day, 90-day, and 360-day all-cause mortality. Multivariable Cox regression, Kaplan-Meier analysis, and ROC curves were used to evaluate the predictive performance of FI-LAB. Results: The median patient age ranged from 76.14 to 77.54 years, with the proportion of females decreasing from 47.66% in Q1 to 64.18% in Q4. In-hospital mortality increased significantly across FI-LAB quartiles (Q1: 8.09% vs. Q4: 28.06%, p<0.001). The 360-day mortality rose from 24.46% in Q1 to 52.74% in Q4. In multivariate Cox analysis, the highest FI-LAB quartile (Q4) was independently related to an elevated risk of in-hospital mortality risk (HR=1.86, 95% CI: 1.35-2.57, p<0.001). The area under the ROC curve (AUC) of FI-LAB alone for predicting in-hospital mortality was 0.646. When combined with conventional severity scores (e.g., OASIS), the predictive performance improved significantly (ΔAUC +0.022). Subgroup analysis demonstrated a more prominent connection among FI-LAB and in-hospital mortality for people lacking of renal illness. (OR=2.98, 95% CI: 2.27-3.90). Conclusion: The FI-LAB serves as an independent predictor of short- and long-term mortality in elderly AMI patients. Its integration with established severity scores enhances risk stratification, offering such an appropriate objective measure for frailty screening in acute care settings. Acute Myocardial Infarction Frailty Laboratory Frailty Index Critical Care Scores Figures Figure 1 Figure 2 Figure 3 Figure 4 1 Introduction Myocardial infarction (MI), a primary manifestation of acute coronary syndrome (ACS), is a significant worldwide health challenge. In 2021, ischemic heart disease caused 9.1 million deaths, accounting for 16% of all-cause mortality, with acute MI responsible for 42% of these fatalities [ 1 ]. Older adults, particularly those aged ≥ 75 years, represent a highly vulnerable subgroup, comprising 35–40% of all hospitalized ACS cases [ 2 ]. This elevated risk stems from age-related physiological changes and a greater incidence of comorbidities like hypertension and diabetes. Furthermore, atypical clinical presentations in the elderly often delay diagnosis and intervention, contributing to poorer outcomes [ 3 ]. Frailty, a clinical illness marked by reduced physiological reserve and heightened susceptibility to stresses, demonstrates a pronounced age-dependent prevalence. It affects approximately 10–15% of community-dwelling adults aged ≥ 65 years, rising to over 25% in those beyond 85 years [ 4 , 5 ]. Among hospitalized elderly patients with AMI, its prevalence is substantially higher. The development of frailty is multifactorial, involving chronic inflammation, endocrine dysregulation, sarcopenia, and cognitive decline [ 6 , 7 ], with additional contributions from socio-demographic factors, psychological conditions, nutritional deficits, and comorbidities [ 8 ]. While several frailty assessment tools exist—such as the Fried Frailty Phenotype, Clinical Frailty Scale, electronic Frailty Index, and Frailty Trait Scale [ 9 – 13 ]—many rely on physical performance or subjective evaluation, limiting their applicability in acute care. In contrast, the Laboratory Frailty Index (FI-LAB) offers an objective, quantitative alternative derived from routine laboratory tests and vital signs [ 14 ]. By quantifying the proportion of abnormal results among predefined parameters, the FI-LAB minimizes subjective bias and correlates strongly with adverse outcomes, including mortality [ 14 – 16 ]. Its objectivity and scalability make it particularly suitable for critical care environments. Although established severity scores (e.g., Acute Physiology Score III (APS III), Sequential Organ Failure Assessment (SOFA), Simplified Acute Physiology Score II (SAPS II), and Oxford Acute Severity of Illness Score (OASIS)) and triage tools (e.g., National Early Warning Score (NEWS) and the Japan Triage and Acuity Scale (JTAS)) are widely used for outcome prediction in critically ill patients [ 17 – 23 ], they do not specifically address frailty. Whether integrating FI-LAB can improve prognostic accuracy beyond these conventional systems in elderly AMI patients remains unclear. Although prior studies have associated FI-LAB with increased mortality in general AMI populations, its prognostic relevance in older adults—who disproportionately experience AMI and exhibit distinct physiological vulnerabilities—remains inadequately investigated [ 24 ]. This study therefore aims to rigorously assess the predictive significance of FI-LAB for older AMI patients, with the goal of establishing an evidence base for enhanced risk stratification in this growing demographic. 2 Materials and methods 2.1 Data source and patients This study analyzed data from the MIMIC-IV database, which contains de-identified records of over 73,000 ICU admissions (2008–2019). We identified adult patients with AMI using ICD codes and excluded those aged < 65 years, with ICU stays < 24 hours, or with insufficient laboratory data (< 27 parameters within 24 hours). The final cohort comprised 3,257 elderly AMI patients, categorized into four quartiles according to FI-LAB scores: Q1 (FI-LAB < 0.44, n = 556), Q2 (0.44 ≤ FI-LAB 0.60, n = 1005). Data extraction was performed by an authorized investigator (Approval ID: 66236335). 2.2 FI-LAB Calculation and Data Management The FI-LAB was constructed from 27 laboratory parameters (Supplementary Table S1 ). Each parameter was assigned a binary score based on its established normal range: values outside the range were coded as a deficit. The FI-LAB score for each patient was computed as the total number of deficits divided by the total parameters assessed. Covariates were systematically extracted from electronic medical records and grouped into five domains: (1) demographics (age, sex); (2) laboratory variables (the 27 FI-LAB indicators, Supplementary Table S1 ); (3) critical illness severity scores (APS III, SOFA, SAPS II, OASIS); (4) comorbidities, identified via ICD-9/10 codes, including congestive heart failure, diabetes, renal disease, and others; and (5) in-hospital medications (e.g., angiotensin-converting enzyme inhibitors/angiotensin receptor blockers, aspirin, statins, dopamine), recorded as binary variables (use vs. non-use). Variables with > 20% missing data (e.g., height, fibrinogen) were excluded. Remaining missing values (≤ 20%) were imputed using Multiple Imputation by Chained Equations (MICE) implemented in R (mice package v3.15.0). 2.3 Outcomes The primary outcome was in-hospital mortality. Secondary outcomes included all-cause mortality at 30-, 90-, and 360-days post-admission. 2.4 Statistical Analysis Continuous and categorical variables were summarized as medians (IQRs) and frequencies, respectively. Group comparisons used non-parametric tests (Mann-Whitney U) or χ² tests. Univariable and multivariable Cox regression analyses assessed associations between FI-LAB and mortality. Survival curves were compared utilizing Kaplan-Meier analysis with log-rank testing. The incremental prognostic value of FI-LAB beyond conventional severity scores was evaluated. Restricted cubic splines examined potential non-linear relationships. Prespecified subgroup analyses assessed interaction effects. Multivariable models were checked for multicollinearity (VIF > 5 threshold), and sensitivity analyses were conducted to test robustness. All analyses used SPSS v.27 with two-tailed p < 0.05 indicating significance. 3 Results 3.1 Baseline Characteristics This study enrolled 3,257 eligible patients stratified by Laboratory Frailty Index (FI-LAB) quartiles. Higher frailty was associated with advanced age (median 76.14 to 77.54 years, p = 0.017) and progressively elevated severity scores (APSIII, SOFA, SAPSII, OASIS; all p < 0.001). Sex distribution shifted from female predominance in Q1 (47.66%) to male predominance in Q4 (64.18%, p < 0.001). Key comorbidities—congestive heart failure, diabetes, renal disease, and dementia—showed significantly increased prevalence with frailty grade (all p < 0.01). Vasoactive drug use rose markedly (norepinephrine: 17.27% to 43.28%; vasopressin: 5.04% to 19.40%), while cardioprotective medications declined (ACEI: 38.13% to 24.88%; statins: 76.26% to 63.88%; all p < 0.001), which may be attributable to contraindications (e.g., hypotension, liver dysfunction) or reduced tolerance in severe illness. In contrast, cerebrovascular, rheumatic, and chronic lung diseases showed no significant association with FI-LAB. These findings demonstrate that FI-LAB effectively captures a clinical phenotype characterized by escalating illness severity, specific comorbidity patterns, and distinct therapeutic profiles. ( Table 1 ) Table 1 Baseline characteristics Variables Total (n = 3257) Q1 (n = 556) Q2 (n = 753) Q3 (n = 943) Q4 (n = 1005) P Age, M (Q1,Q3) 76.79 (70.99, 83.55) 76.14 (69.86, 83.33) 75.94 (70.85, 83.13) 76.86 (71.31, 83.66) 77.54 (71.38, 83.97) 0.017 Gender, n(%) < .001 F 1266 (38.87) 265 (47.66) 310 (41.17) 331 (35.10) 360 (35.82) M 1991 (61.13) 291 (52.34) 443 (58.83) 612 (64.90) 645 (64.18) Disease severity scoring system (score) APSⅢ 45.00 (34.00, 58.00) 36.00 (27.00,46.00) 41.00 (31.00,53.00) 46.00 (35.50,58.00) 54.00 (42.00,69.00) < .001 SOFA 5.00 (3.00, 7.00) 3.00 (1.00,5.00) 4.00 (2.00,7.00) 5.00 (3.00,7.00) 6.00 (4.00,9.00) < .001 SAPSⅡ 40.00 (33.00, 50.00) 35.00 (29.00,41.00) 38.00 (32.00,46.00) 41.00 (35.00,50.00) 46.00 (38.00,55.00) < .001 OASIS 33.00 (27.00, 39.00) 30.00 (24.00,35.00) 32.00 (26.00,38.00) 33.00 (27.00,39.00) 35.00 (30.00,42.00) < .001 FI-LAB 0.52 (0.44, 0.60) 0.36 (0.32,0.40) 0.48 (0.44,0.48) 0.52 (0.52,0.56) 0.64 (0.60,0.68) < .001 Outcome, n(%) In-hospital death (n, %) < .001 NO 2648 (81.30) 511 (91.91) 652 (86.59) 762 (80.81) 723 (71.94) YES 609 (18.70) 45 (8.09) 101 (13.41) 181 (19.19) 282 (28.06) 30-day death, n(%) < .001 NO 2496 (76.63) 486 (87.41) 618 (82.07) 722 (76.56) 670 (66.67) YES 761 (23.37) 70 (12.59) 135 (17.93) 221 (23.44) 335 (33.33) 90-day death, n(%) < .001 NO 2264 (69.51) 455 (81.83) 569 (75.56) 661 (70.10) 579 (57.61) YES 993 (30.49) 101 (18.17) 184 (24.44) 282 (29.90) 426 (42.39) 360-day death, n(%) < .001 NO 1962 (60.24) 420 (75.54) 492 (65.34) 575 (60.98) 475 (47.26) YES 1295 (39.76) 136 (24.46) 261 (34.66) 368 (39.02) 530 (52.74) Comorbidities, n(%) Congestive Heart Failure, n(%) < .001 NO 1309 (40.19) 284 (51.08) 317 (42.10) 356 (37.75) 352 (35.02) YES 1948 (59.81) 272 (48.92) 436 (57.90) 587 (62.25) 653 (64.98) Peripheral Vascular Disease, n(%) 0.04 NO 2734 (83.94) 482 (86.69) 646 (85.79) 776 (82.29) 830 (82.59) YES 523 (16.06) 74 (13.31) 107 (14.21) 167 (17.71) 175 (17.41) Cerebrovascular Disease, n(%) 0.231 NO 2719 (83.48) 449 (80.76) 639 (84.86) 793 (84.09) 838 (83.38) YES 538 (16.52) 107 (19.24) 114 (15.14) 150 (15.91) 167 (16.62) Diabetes, n(%) < .001 NO 1790 (54.96) 347 (62.41) 428 (56.84) 506 (53.66) 509 (50.65) YES 1467 (45.04) 209 (37.59) 325 (43.16) 437 (46.34) 496 (49.35) Malignant Cancer, n(%) 0.002 NO 2915 (89.50) 515 (92.63) 684 (90.84) 843 (89.40) 873 (86.87) YES 342 (10.50) 41 (7.37) 69 (9.16) 100 (10.60) 132 (13.13) Renal Disease, n(%) < .001 NO 1983 (60.88) 415 (74.64) 476 (63.21) 540 (57.26) 552 (54.93) YES 1274 (39.12) 141 (25.36) 277 (36.79) 403 (42.74) 453 (45.07) Rheumatic Disease, n(%) 0.818 NO 3122 (95.86) 532 (95.68) 724 (96.15) 907 (96.18) 959 (95.42) YES 135 (4.14) 24 (4.32) 29 (3.85) 36 (3.82) 46 (4.58) Chronic Pulmonary Disease, n(%) 0.877 NO 2411 (74.03) 412 (74.10) 562 (74.63) 689 (73.06) 748 (74.43) YES 846 (25.97) 144 (25.90) 191 (25.37) 254 (26.94) 257 (25.57) Dementia, n(%) 0.003 NO 3006 (92.29) 521 (93.71) 707 (93.89) 876 (92.90) 902 (89.75) YES 251 (7.71) 35 (6.29) 46 (6.11) 67 (7.10) 103 (10.25) Peptic Ulcer Disease, n(%) 0.099 NO 3152 (96.78) 544 (97.84) 729 (96.81) 917 (97.24) 962 (95.72) YES 105 (3.22) 12 (2.16) 24 (3.19) 26 (2.76) 43 (4.28) Initial therapies, n(%) Norepinephrine, n(%) 0.018 NO 2235 (68.62) 460 (82.73) 558 (74.10) 647 (68.61) 570 (56.72) YES 1022 (31.38) 96 (17.27) 195 (25.90) 296 (31.39) 435 (43.28) Vasopressin, n(%) < .001 NO 2884 (88.55) 528 (94.96) 702 (93.23) 844 (89.50) 810 (80.60) YES 373 (11.45) 28 (5.04) 51 (6.77) 99 (10.50) 195 (19.40) Dopamine, n(%) < .001 NO 3052 (93.71) 537 (96.58) 717 (95.22) 878 (93.11) 920 (91.54) YES 205 (6.29) 19 (3.42) 36 (4.78) 65 (6.89) 85 (8.46) Dobutamine, n(%) < .001 NO 3098 (95.12) 546 (98.20) 720 (95.62) 899 (95.33) 933 (92.84) YES 159 (4.88) 10 (1.80) 33 (4.38) 44 (4.67) 72 (7.16) ACEI/ARB, n(%) < .001 NO 2303 (70.71) 344 (61.87) 507 (67.33) 697 (73.91) 755 (75.12) YES 954 (29.29) 212 (38.13) 246 (32.67) 246 (26.09) 250 (24.88) Aspiri, n(%) < .001 NO 947 (29.08) 134 (24.10) 198 (26.29) 263 (27.89) 352 (35.02) YES 2310 (70.92) 422 (75.90) 555 (73.71) 680 (72.11) 653 (64.98) Statins, n(%) < .001 NO 947 (29.08) 132 (23.74) 198 (26.29) 254 (26.94) 363 (36.12) YES 2310 (70.92) 424 (76.26) 555 (73.71) 689 (73.06) 642 (63.88) Notes: Data are presented as n (%) for categorical variables and Median (Q1, Q3) for continuous variables. P-values are derived from the Kruskal-Wallis test for continuous variables and the Chi-square test for categorical variables. FI-LAB: Laboratory Frailty Index; APS III: Acute Physiology Score III; SOFA: Sequential Organ Failure Assessment; SAPS II: Simplified Acute Physiology Score II; OASIS: Oxford Acute Severity of Illness Score; ACEI/ARB: Angiotensin-converting enzyme inhibitors/Angiotensin receptor blockers.. 3.2 Relationship between FI-LAB and clinical outcomes Analysis of the FI-LAB quartiles in relation to patient prognosis revealed that mortality rates, both short-term and long-term, varied significantly across different frailty levels (all p < 0.001). The primary outcome, in-hospital mortality, increased in a stepwise fashion from 8.09% in group Q1 to 28.06% in group Q4 (p < 0.001), demonstrating the strong predictive value of the FI-LAB for the risk of in-hospital mortality. Secondary outcome analysis further confirmed that the risk of death continued to increase with longer follow-up, with a 360-day mortality rate of 52.74% in the Q4 group, which was 2.16 times that of 24.46% in the Q1 group. ( Table 2 ) Table 2 Mortality rate of AMI patients according to FI-LAB quartiles FI-LAB In-hospital P-value 30-day P-value 90-day P-value 360-day P-value Mortality, n (%) Mortality, n (%) Mortality, n (%) Mortality, n (%) Q1 (n = 556) 45 (8.09) < .001 70 (12.59) < .001 101 (18.17) < .001 136 (24.46) < .001 Q2 (n = 753) 101 (13.41) 135 (17.93) 184 (24.44) 261 (34.66) Q3 (n = 943) 181 (19.19) 221 (23.44) 282 (29.90) 368 (39.02) Q4 (n = 1005) 282 (28.06) 335 (33.33) 426 (42.39) 530 (52.74) Note: FI-LAB, Frailty index based on laboratory test; AMI, Acute myocardial infarction Univariate Cox regression analysis confirmed a significant association between elevated FI-LAB quartiles and risk of in-hospital mortality (p < 0.001 for trend test). Compared to group Q1, group Q3 had a 92% increased risk of death (HR = 1.92, 95% CI: 1.38–2.66, p < 0.001), and group Q4 had a 147% surge in risk (HR = 2.47, 95% CI: 1.81–3.39, p < 0.001). Group Q2 did not reach statistical significance, although the risk increased by 39% (HR = 1.39, 95% CI: 0.98–1.97, p = 0.067). In addition, multivariate COX analysis of secondary outcomes showed that the risk of death was significantly higher in groups Q2, Q3, and Q4 than in group Q1 (p < 0.05) ( Table 3 ) . Cox regression was performed using a stepwise correction strategy: model 1 was uncorrected; model 2 added age and sex; model 3 further added 10 comorbidities; and model 4 additionally corrected for 7 classes of therapeutic drugs ( Table 3). After gradual correction for confounders, elevated quartiles of the laboratory-based index of frailty (FI-LAB) remained an independent predictor of risk of death. The risk of death was consistently significantly higher in the highest quartile of the FI-LAB group than in the reference group and demonstrated a significant risk relationship (increasing risk from Q1 to Q4). After correction for therapeutic factors (vasoactive agents, cardiovascular medications), the effect value of FI-LAB attenuated but remained statistically significant, suggesting that its predictive value extends beyond the effect of therapeutic interventions.FI-LAB predicted the risk of mortality with stability: even after adjusting for demographic characteristics, comorbidities, and treatment measures (Model 4), the risk of death at 360 days in the Q4 group amounted to 1.86 times the risk of death in the Q1 group (95% CI: 1.53–2.26, p < 0.001). ( Table 3 ) Furthermore, Kaplan-Meier survival analysis at 30, 90 and 360 days ( Fig. 1 ) demonstrated a significantly higher mortality risk in the Q2, Q3, and Q4 groups compared to the Q1 group (log-rank p < 0.001). Mortality risk increased progressively across quartiles, with the highest risk observed in Q4. 3.3 Predictive value and incremental effect of FI-LAB In this study, the FI-LAB exhibited a moderate but statistically significant capacity to predict in-hospital mortality among elderly patients with AMI. In the ROC analysis, the area under the curve (AUC) of FI-LAB versus outcome, which reflects the ability of the model to discriminate between patients who died and those who survived, had a value of 0.646, which is higher than the random probability (0.5) but lower than the strong predictive performance (≥ 0.8), suggesting an average discriminatory ability. The AUC value of 0.646 signifies that, according to FI-LAB scores, there is a 64.6% likelihood that a randomly chosen patient who succumbed will receive a higher risk score compared to a randomly selected patient who survived. Although statistically better than chance, this AUC suggests limited clinical usefulness if used in isolation. It may serve as a risk stratification aid but may not be reliable enough alone for decision-making without combining with other predictors. ( Fig. 2 ) Considering the moderate predictive capability of FI-LAB when used independently, we posited that its combination with established critical illness severity scoring systems could improve risk stratification. To evaluate this hypothesis, FI-LAB was integrated with APS III, SOFA, SAPS II, and OASIS scores. This integrative approach consistently resulted in statistically significant enhancements in prognostic predictive accuracy. The integration of FI-LAB with established critical care scores (APS III, SOFA, SAPS II, and OASIS) yielded significant improvements in prognostic predictive performance. Specifically, the model combining FI-LAB and APS III demonstrated the highest discriminative ability for patient mortality. However, the incremental improvement in AUROC for APS III following FI-LAB integration was minimal. In contrast, the greatest incremental improvement was observed for the OASIS score, with its AUC increasing significantly from 0.711 to 0.733 following FI-LAB incorporation. 3.4 Nonlinear relationship between FI-LAB and patient prognosis RCS modeling was utilized to analyze the correlation among the FI-LAB and mortality in the hospital among AMI patients. An overall statistically significant correlation was detected (Overall P < 0.001). The test for nonlinearity (P = 0.289) indicated no significant departure from linearity, supporting a predominantly linear relationship between FI-LAB and mortality across its observed range ( Fig. 3 ) . Further analysis of secondary outcomes also showed that there was no nonlinear relationship between FI-LAB and patient prognosis, which was approximately linear within the range. (Figure S1 S2 S3) 3.5 Subgroup analysis To assess the robustness of FI-LAB's predictive performance across diverse patient cohorts, we conducted prespecified subgroup analyses. In this subgroup analysis, FI-LAB was significantly associated with in-hospital mortality in the overall cohort (OR 2.48, 95% CI 2.03–3.04; P < 0.001) and across most comorbidity subgroups. Notably, the association was stronger in patients without renal disease (OR 2.98, 95% CI 2.27–3.90) than in those with renal impairment (OR 1.79, 95% CI 1.32–2.43), with a significant interaction (P = 0.014). A significant interaction was also observed for cerebrovascular disease (P = 0.014), though effect sizes were similar between subgroups. No significant interaction was found for other comorbidities, including congestive heart failure (P = 0.089) and peripheral vascular disease (P = 0.925). These results support FI-LAB as a consistent predictor of in-hospital mortality, with possible effect modification by renal function. ( Fig. 4 ) 4 Discussion This study of 3,257 elderly patients with AMI establishes the FI-LAB as a robust and independent predictor of both short- and long-term all-cause mortality. A clear graded association was observed between FI-LAB quartiles and mortality risk. Patients in the highest frailty quartile (Q4) demonstrated a 147% increased risk of in-hospital mortality (unadjusted HR = 2.47; 95% CI: 1.81–3.39; P < 0.001) compared to the lowest quartile (Q1). After multivariable adjustment for age, comorbidities, illness severity, and treatment factors, this risk remained significantly elevated at 86% (adjusted HR = 1.86; 95% CI: 1.35–2.57; P < 0.001). The prognostic stratification capability of FI-LAB was further evidenced by the marked increase in mortality rates across quartiles, with in-hospital mortality rising from 8.09% in Q1 to 28.06% in Q4, and 360-day mortality from 24.46% to 52.74%. At 360 days, the adjusted HR for Q4 versus Q1 was 1.86 (95% CI: 1.53–2.26, p < 0.001), confirming its independent prognostic value beyond conventional risk factors. The lack of statistical significance in the Q2 group in all models (in-hospital mortality HR = 1.24, p = 0.235) suggests that the prognostic impact of mild frailty may be variable and that further research with larger sample sizes is needed. Consistent with the results reported by previous research [ 24 ], the present study confirms that the FI-LAB substantially improves the prediction of clinical outcomes in patients experiencing AMI. The study by Nagae et al. [ 21 ], involving 872 emergency department patients, similarly reported an association between elevated FI-LAB scores and in-hospital mortality. However, our research specifically focuses on the elderly demographic, which bears the highest burden of AMI and possesses distinct physiological vulnerabilities, thereby providing a more representative and clinically relevant assessment of FI-LAB's utility in the typical AMI population. The restricted cubic spline analysis confirmed a linear relationship between continuous FI-LAB values and mortality risk (P for nonlinearity = 0.289), indicating that even minor increases in frailty burden correspond to progressively higher mortality. A linear association between FI-LAB and all-cause mortality was consistently identified in patients with severe heart failure, corroborating our results [ 25 ]. A key finding is that FI-LAB provided significant incremental prognostic value when combined with established critical illness scores. Integrated models demonstrated improved predictive accuracy for SOFA (ΔAUC + 0.017), OASIS (ΔAUC + 0.022), and SAPS II (ΔAUC + 0.007), though not for APS III (ΔAUC + 0.001). This pattern suggests that FI-LAB contributes complementary information, particularly to scores focused on acute organ dysfunction, rather than acting as a redundant metric. The greatest synergy with SOFA and OASIS implies that frailty exacerbates mortality risk through impaired multi-organ reserve. In contrast, FI-LAB had minimal effect on APSIII, a comprehensive system that has incorporated laboratory abnormalities, suggesting possible overlap in their predictive domains. An important interaction was observed with renal disease (P for interaction = 0.014). FI-LAB was more strongly associated with in-hospital mortality in patients without renal disease (OR = 2.98; 95% CI: 2.27–3.90) than in those with renal disease (OR = 1.79; 95% CI: 1.32–2.43). This attenuated effect may be attributed to the overlap between FI-LAB components (e.g., eGFR, creatinine) and the pathophysiology of renal impairment. In patients with pre-existing renal disease, these biomarkers may already be chronically abnormal, reducing FI-LAB's dynamic range and predictive sensitivity for death. Conversely, in their absence, FI-LAB may more sensitively reflect systemic physiological decline. The biological plausibility of FI-LAB is supported by its reflection of multisystem physiological dysregulation. The index encapsulates pathways such as chronic inflammation (e.g., elevated CRP), renal dysfunction, and metabolic disturbances, which collectively promote atherosclerosis, plaque instability, and impaired stress response—core elements of the frailty phenotype that heighten vulnerability to adverse outcomes after AMI [ 26 – 29 ]. Several limitations warrant consideration. First, the retrospective design using the MIMIC-IV database, despite rigorous adjustments and multiple imputation for missing data, remains susceptible to unmeasured confounding (e.g., socioeconomic status, functional mobility). Second, the FI-LAB originated from data collected in the initial 24 hours of being admitted to the ICU and may not reflect the fluctuations of frailty throughout the hospitalization period. Third, generalizability may be limited by the single-center, U.S.-based ICU population, necessitating external validation in prospective, multicenter cohorts. Fourth, the exploratory subgroup analysis suggesting effect modification by renal status requires further mechanistic investigation. Finally, while FI-LAB added incremental value, its standalone discriminative ability was modest (AUC = 0.646), underscoring that it should complement, not replace, comprehensive clinical assessment. 5 Conclusion This study validates the FI-LAB as a strong, independent predictor of mortality in elderly AMI patients. This objective tool enables feasible frailty screening in acute care, overcoming limitations of traditional assessments. Multicenter studies are needed to confirm its broad applicability. Declarations Acknowledgments The authors thank all the patients whose data were included in this database, as well as the clinicians and researchers involved in its curation. Author contribution L. Chen: conceptualization, writing—review and editing, and supervision; Q.Jiang: methodology and writing— original draft; X. Gao: data curation and validation; J. Li : writing—review and editing; H. Zhang: data curation and software; L. Shi: writing—review and editing, and supervision. All authors had full access to all data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. All authors approved the final version of the manuscript. Funding The author declares that no financial support was received during the research and/or publication of this article. Ethics Statement This study used data from the MIMIC-IV database. The database received approval from the MIT and BIDMC Institutional Review Boards, which waived the requirement for individual patient consent due to the use of de-identified data. All data were handled in accordance with ethical standards. Conflicts of Interest The authors declare no conflicts of interest. Consent for publication Not applicable. Use of Large Language Models, AI and Machine Learning Tools None declared. References Damluji AA, Forman DE, Wang TY, Chikwe J, Kunadian V, Rich MW, et al. Management of Acute Coronary Syndrome in the Older Adult Population: A Scientific Statement From the American Heart Association. Circulation. 2023;147(3):e32–62. http://doi.org/10.1161/cir.0000000000001112 . Tumminello G, D'Errico A, Maruccio A, Gentile D, Barbieri L, Carugo S. Age-Related Mortality in STEMI Patients: Insight from One Year of HUB Centre Experience during the Pandemic. J Cardiovasc Dev Dis. 2022;9(12). http://doi.org/10.3390/jcdd9120432 . Engberding N, Wenger NK. Acute Coronary Syndromes in the Elderly. F1000Res. 2017;6. 1791.http://doi.org/10.12688/f1000research.11064.1 . Clegg A, Young J, Iliffe S, Rikkert MO, Rockwood K. Frailty in elderly people. Lancet. 2013;381(9868):752–62. http://doi.org/10.1016/s0140-6736(12)62167-9 . Xue QL. The frailty syndrome: definition and natural history. Clin Geriatr Med. 2011;27(1):1–15. http://doi.org/10.1016/j.cger.2010.08.009 . Fabbri E, An Y, Zoli M, Simonsick EM, Guralnik JM, Bandinelli S, et al. Aging and the burden of multimorbidity: associations with inflammatory and anabolic hormonal biomarkers. J Gerontol Biol Sci Med Sci. 2015;70(1):63–70. http://doi.org/10.1093/gerona/glu127 . Franceschi C, Garagnani P, Parini P, Giuliani C, Santoro A. Inflammaging: a new immune-metabolic viewpoint for age-related diseases. Nat Rev Endocrinol. 2018;14(10):576–90. http://doi.org/10.1038/s41574-018-0059-4 . Dent E, Kowal P, Hoogendijk EO. Frailty measurement in research and clinical practice: A review. Eur J Intern Med. 2016;31:3–10. http://doi.org/10.1016/j.ejim.2016.03.007 . Chen S, Chen T, Kishimoto H, Susaki Y, Kumagai S. Development of a Fried Frailty Phenotype Questionnaire for Use in Screening Community-Dwelling Older Adults. J Am Med Dir Assoc. 2020;21(2):272–6. .e1.http://doi.org/10.1016/j.jamda.2019.07.015 . Church S, Rogers E, Rockwood K, Theou O. A scoping review of the Clinical Frailty Scale. BMC Geriatr. 2020;20(1):393. http://doi.org/10.1186/s12877-020-01801-7 . Clegg A, Bates C, Young J, Ryan R, Nichols L, Ann Teale E, et al. Development and validation of an electronic frailty index using routine primary care electronic health record data. Age Ageing. 2016;45(3):353–60. http://doi.org/10.1093/ageing/afw039 . Gleason LJ, Benton EA, Alvarez-Nebreda ML, Weaver MJ, Harris MB, Javedan H. FRAIL Questionnaire Screening Tool and Short-Term Outcomes in Geriatric Fracture Patients. J Am Med Dir Assoc. 2017;18(12):1082–6. .http://doi.org/10.1016/j.jamda.2017.07.005 . García-García FJ, Carcaillon L, Fernandez-Tresguerres J, Alfaro A, Larrion JL, Castillo C, et al. A new operational definition of frailty: the Frailty Trait Scale. J Am Med Dir Assoc. 2014;15(5):371. 13.http://doi.org/10.1016/j.jamda.2014.01.004 . .e7-.e . Sapp DG, Cormier BM, Rockwood K, Howlett SE, Heinze SS. The frailty index based on laboratory test data as a tool to investigate the impact of frailty on health outcomes: a systematic review and meta-analysis. Age Ageing. 2023;52(1). http://doi.org/10.1093/ageing/afac309 . Ysea-Hill O, Gomez CJ, Mansour N, Wahab K, Hoang M, Labrada M, et al. The association of a frailty index from laboratory tests and vital signs with clinical outcomes in hospitalized older adults. J Am Geriatr Soc. 2022;70(11):3163–75. .http://doi.org/10.1111/jgs.17977 . Rockwood K, McMillan M, Mitnitski A, Howlett SE. A Frailty Index Based on Common Laboratory Tests in Comparison With a Clinical Frailty Index for Older Adults in Long-Term Care Facilities. J Am Med Dir Assoc. 2015;16(10):842–7. .http://doi.org/10.1016/j.jamda.2015.03.027 . Le Gall JR, Lemeshow S, Saulnier F. A new Simplified Acute Physiology Score (SAPS II) based on a European/North American multicenter study. JAMA. 1993;270(24):2957–63. .http://doi.org/10.1001/jama.270.24.2957 . Vincent JL, de Mendonça A, Cantraine F, Moreno R, Takala J, Suter PM, et al. Use of the SOFA score to assess the incidence of organ dysfunction/failure in intensive care units: results of a multicenter, prospective study. Working group on sepsis-related problems of the European Society of Intensive Care Medicine. Crit Care Med. 1998;26(11):1793–800. http://doi.org/10.1097/00003246-199811000-00016 . LeGall JR, Loirat P, Alpérovitch A. APACHE II–a severity of disease classification system. Crit Care Med. 1986;14(8):754–5. .http://doi.org/10.1097/00003246-198608000-00027 . Johnson AE, Kramer AA, Clifford GD. A new severity of illness scale using a subset of Acute Physiology And Chronic Health Evaluation data elements shows comparable predictive accuracy. Crit Care Med. 2013;41(7):1711–8. .http://doi.org/10.1097/CCM.0b013e31828a24fe . Nagae M, Umegaki H, Nakashima H, Nishiuchi T. FI-lab in the emergency department and adverse outcomes among acutely hospitalized older adults. Arch Gerontol Geriatr. 2025;129:105649. http://doi.org/10.1016/j.archger.2024.105649 . Funakoshi H, Shiga T, Homma Y, Nakashima Y, Takahashi J, Kamura H, et al. Validation of the modified Japanese Triage and Acuity Scale-based triage system emphasizing the physiologic variables or mechanism of injuries. Int J Emerg Med. 2016;9(1):1. http://doi.org/10.1186/s12245-015-0097-9 . Williams B. The National Early Warning Score: from concept to NHS implementation. Clin Med (Lond). 2022;22(6):499–505. http://doi.org/10.7861/clinmed.2022-news-concept . Bai W, Hao B, Xu L, Qin J, Xu W, Qin L. Frailty index based on laboratory tests improves prediction of short-and long-term mortality in patients with critical acute myocardial infarction. Front Med (Lausanne). 2022;9:1070951. http://doi.org/10.3389/fmed.2022.1070951 . Wang S, Wang L, Wang Y, Zong S, Fan H, Jiang Y, et al. Association between frailty index based on laboratory tests and all-cause mortality in critically ill patients with heart failure. ESC Heart Fail. 2024;11(6):3662–73. .http://doi.org/10.1002/ehf2.14948 . Held C, White HD, Stewart RAH, Budaj A, Cannon CP, Hochman JS, et al. Inflammatory Biomarkers Interleukin-6 and C-Reactive Protein and Outcomes in Stable Coronary Heart Disease: Experiences From the STABILITY (Stabilization of Atherosclerotic Plaque by Initiation of Darapladib Therapy) Trial. J Am Heart Assoc. 2017;6(10). http://doi.org/10.1161/jaha.116.005077 . Kosmas CE, Silverio D, Tsomidou C, Salcedo MD, Montan PD, Guzman E. The Impact of Insulin Resistance and Chronic Kidney Disease on Inflammation and Cardiovascular Disease. Clin Med Insights Endocrinol Diabetes. 2018;11:1179551418792257. http://doi.org/10.1177/1179551418792257 . Anghel L, Tudurachi BS, Tudurachi A, Zăvoi A, Clement A, Roungos A, et al. Patient-Related Factors Predicting Stent Thrombosis in Percutaneous Coronary Interventions. J Clin Med. 2023;12(23). http://doi.org/10.3390/jcm12237367 . de Almeida A, de Almeida Rezende MS, Dantas SH, de Lima Silva S, de Oliveira J Lourdes Assunção Araújo, de Azevedo F et al. Unveiling the Role of Inflammation and Oxidative Stress on Age-Related Cardiovascular Diseases. Oxid Med Cell Longev. 2020;2020:1954398 .http://doi.org/10.1155/2020/1954398 Table 3 Table 3 is available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files flowchart.tiff Table3.docx Supplementaryinformation.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 02 Mar, 2026 Reviews received at journal 25 Feb, 2026 Reviewers agreed at journal 25 Feb, 2026 Reviewers invited by journal 24 Feb, 2026 Editor invited by journal 02 Feb, 2026 Editor assigned by journal 02 Feb, 2026 Submission checks completed at journal 02 Feb, 2026 First submitted to journal 30 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8738011","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":596119399,"identity":"c534f0f3-9db7-419a-baa1-5ae12ce82f1d","order_by":0,"name":"Qiqi Jiang","email":"","orcid":"","institution":"The First Affiliated Hospital of China Medical University, China Medical University","correspondingAuthor":false,"prefix":"","firstName":"Qiqi","middleName":"","lastName":"Jiang","suffix":""},{"id":596119400,"identity":"80422f06-77ae-4ea7-ad0d-bac66bf1dfcd","order_by":1,"name":"Jinyang Li","email":"","orcid":"","institution":"The First Affiliated Hospital of China Medical University, China Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jinyang","middleName":"","lastName":"Li","suffix":""},{"id":596119402,"identity":"7ff4b235-fe01-4451-8b75-3d1b20edbd8d","order_by":2,"name":"Huanrui Zhang","email":"","orcid":"","institution":"The First Affiliated Hospital of China Medical University, China Medical University","correspondingAuthor":false,"prefix":"","firstName":"Huanrui","middleName":"","lastName":"Zhang","suffix":""},{"id":596119403,"identity":"e9c8f8e1-cdcc-4b82-9ca2-170bc3625647","order_by":3,"name":"Xiaopo Gao","email":"","orcid":"","institution":"Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Xiaopo","middleName":"","lastName":"Gao","suffix":""},{"id":596119404,"identity":"378ab973-c449-42f1-a0af-fc4e0e025c68","order_by":4,"name":"Liye Shi","email":"","orcid":"","institution":"The First Affiliated Hospital of China Medical University, China Medical University","correspondingAuthor":false,"prefix":"","firstName":"Liye","middleName":"","lastName":"Shi","suffix":""},{"id":596119406,"identity":"301e426d-b8ee-4b96-9218-53cb3a79fbda","order_by":5,"name":"Ling Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxUlEQVRIiWNgGAWjYBACPgYeIGlQIwfhshGhhQ2speCYMalaPjAnNhCvhf3swccFBmzp/dPOGDB8KDvMwD+7gYAWnrxk4xkGMrkzbucYMM44d5hB4s4BAlokeMykeQzYcjdI5xgw87YdZjCQSCCoxfw3jwFzugFIy18itZgxA7UkgLUwEqWFJ8cY6LBjhjNupxUc7DmXziNxg4AWfvYzhp95/tTI889O3vjgR5m1HP8MAlpQwAEg5iFB/SgYBaNgFIwCXAAAbbE1/D1G+eIAAAAASUVORK5CYII=","orcid":"","institution":"The First Affiliated Hospital of China Medical University, China Medical University","correspondingAuthor":true,"prefix":"","firstName":"Ling","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2026-01-30 06:53:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8738011/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8738011/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104399163,"identity":"2a40c93e-9c2c-43a9-ac55-b5333d416ece","added_by":"auto","created_at":"2026-03-11 12:04:56","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":689945,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier survival curves for 30-day (a), 90-day (b), and 360-day (c) all-cause mortality in elderly acute myocardial infarction patients, stratified by Laboratory Frailty Index (FI-LAB) quartiles. The number of patients at risk during each follow-up period is shown below the corresponding survival curves. Q1 (FI-LAB \u0026lt;0.44, blue line), Q2 (0.44≤ FI-LAB \u0026lt;0.52, green line), Q3 (0.52≤ FI-LAB ≤0.60, orange line), Q4 (FI-LAB \u0026gt;0.60, red line). Log-rank P \u0026lt;0.001 for all comparisons.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8738011/v1/062a4ec3394c7940bf1fa14c.png"},{"id":103582998,"identity":"e81584c8-bffc-4e1b-8538-2f36c1217ef6","added_by":"auto","created_at":"2026-02-27 10:37:14","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2496654,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristic (ROC) curves comparing the predictive performance of the Laboratory Frailty Index (FI-LAB) alone and in combination with conventional critical illness severity scores for in-hospital mortality in elderly patients with acute myocardial infarction.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-8738011/v1/ca690f128efd371b61505dff.png"},{"id":103583003,"identity":"7b1fcdda-346b-4d23-bc61-6d61ca832af2","added_by":"auto","created_at":"2026-02-27 10:37:15","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":10248749,"visible":true,"origin":"","legend":"\u003cp\u003eRestricted cubic spline plot showing the association between the Laboratory Frailty Index (FI-LAB) as a continuous variable and the odds of in-hospital mortality in elderly patients with acute myocardial infarction.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-8738011/v1/9e54a222395fe36232189d94.png"},{"id":104398323,"identity":"91beb05e-2310-495c-941d-5a33337ebe16","added_by":"auto","created_at":"2026-03-11 12:01:43","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1056048,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of subgroup analyses for the association between the Laboratory Frailty Index (FI-LAB) and in-hospital mortality. Odds ratios (ORs) with 95% confidence intervals are shown for each subgroup. FI-LAB was significantly associated with increased in-hospital mortality in the overall cohort and across most subgroups\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-8738011/v1/dbcf116ae21cfe7dae5f7ecf.png"},{"id":104407630,"identity":"0a40aaa8-162f-4032-bcbe-6917eb5806cf","added_by":"auto","created_at":"2026-03-11 12:39:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":14349364,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8738011/v1/ac34d97d-2780-485e-ac3e-ffba2531fb42.pdf"},{"id":103583002,"identity":"19be4fac-4902-4367-9597-f135ecd227d9","added_by":"auto","created_at":"2026-02-27 10:37:14","extension":"tiff","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":66638,"visible":true,"origin":"","legend":"","description":"","filename":"flowchart.tiff","url":"https://assets-eu.researchsquare.com/files/rs-8738011/v1/4162d2153328b16b2c216c91.tiff"},{"id":103582999,"identity":"0b1cb835-7bec-4bd1-ae5b-85ec4fbdef0a","added_by":"auto","created_at":"2026-02-27 10:37:14","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":22475,"visible":true,"origin":"","legend":"","description":"","filename":"Table3.docx","url":"https://assets-eu.researchsquare.com/files/rs-8738011/v1/2778ffcf6b90b7beced4494b.docx"},{"id":103583004,"identity":"43f67873-e775-4645-ae67-0e53ce68b821","added_by":"auto","created_at":"2026-02-27 10:37:15","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":255871,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryinformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-8738011/v1/cb52df1b663ae8e2cc8306ad.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prognostic Impact of Laboratory Frailty Index in Elderly Patients with Acute Myocardial Infarction: A Retrospective Cohort Study","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eMyocardial infarction (MI), a primary manifestation of acute coronary syndrome (ACS), is a significant worldwide health challenge. In 2021, ischemic heart disease caused 9.1\u0026nbsp;million deaths, accounting for 16% of all-cause mortality, with acute MI responsible for 42% of these fatalities [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Older adults, particularly those aged\u0026thinsp;\u0026ge;\u0026thinsp;75 years, represent a highly vulnerable subgroup, comprising 35\u0026ndash;40% of all hospitalized ACS cases [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. This elevated risk stems from age-related physiological changes and a greater incidence of comorbidities like hypertension and diabetes. Furthermore, atypical clinical presentations in the elderly often delay diagnosis and intervention, contributing to poorer outcomes [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFrailty, a clinical illness marked by reduced physiological reserve and heightened susceptibility to stresses, demonstrates a pronounced age-dependent prevalence. It affects approximately 10\u0026ndash;15% of community-dwelling adults aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years, rising to over 25% in those beyond 85 years [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Among hospitalized elderly patients with AMI, its prevalence is substantially higher. The development of frailty is multifactorial, involving chronic inflammation, endocrine dysregulation, sarcopenia, and cognitive decline [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], with additional contributions from socio-demographic factors, psychological conditions, nutritional deficits, and comorbidities [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWhile several frailty assessment tools exist\u0026mdash;such as the Fried Frailty Phenotype, Clinical Frailty Scale, electronic Frailty Index, and Frailty Trait Scale [\u003cspan additionalcitationids=\"CR10 CR11 CR12\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u0026mdash;many rely on physical performance or subjective evaluation, limiting their applicability in acute care. In contrast, the Laboratory Frailty Index (FI-LAB) offers an objective, quantitative alternative derived from routine laboratory tests and vital signs [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. By quantifying the proportion of abnormal results among predefined parameters, the FI-LAB minimizes subjective bias and correlates strongly with adverse outcomes, including mortality [\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Its objectivity and scalability make it particularly suitable for critical care environments.\u003c/p\u003e \u003cp\u003eAlthough established severity scores (e.g., Acute Physiology Score III (APS III), Sequential Organ Failure Assessment (SOFA), Simplified Acute Physiology Score II (SAPS II), and Oxford Acute Severity of Illness Score (OASIS)) and triage tools (e.g., National Early Warning Score (NEWS) and the Japan Triage and Acuity Scale (JTAS)) are widely used for outcome prediction in critically ill patients [\u003cspan additionalcitationids=\"CR18 CR19 CR20 CR21 CR22\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], they do not specifically address frailty. Whether integrating FI-LAB can improve prognostic accuracy beyond these conventional systems in elderly AMI patients remains unclear. Although prior studies have associated FI-LAB with increased mortality in general AMI populations, its prognostic relevance in older adults\u0026mdash;who disproportionately experience AMI and exhibit distinct physiological vulnerabilities\u0026mdash;remains inadequately investigated [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. This study therefore aims to rigorously assess the predictive significance of FI-LAB for older AMI patients, with the goal of establishing an evidence base for enhanced risk stratification in this growing demographic.\u003c/p\u003e"},{"header":"2 Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1 Data source and patients\u003c/h2\u003e\n \u003cp\u003eThis study analyzed data from the MIMIC-IV database, which contains de-identified records of over 73,000 ICU admissions (2008\u0026ndash;2019). We identified adult patients with AMI using ICD codes and excluded those aged\u0026thinsp;\u0026lt;\u0026thinsp;65 years, with ICU stays\u0026thinsp;\u0026lt;\u0026thinsp;24 hours, or with insufficient laboratory data (\u0026lt;\u0026thinsp;27 parameters within 24 hours). The final cohort comprised 3,257 elderly AMI patients, categorized into four quartiles according to FI-LAB scores: Q1 (FI-LAB\u0026thinsp;\u0026lt;\u0026thinsp;0.44, n\u0026thinsp;=\u0026thinsp;556), Q2 (0.44\u0026thinsp;\u0026le;\u0026thinsp;FI-LAB\u0026thinsp;\u0026lt;\u0026thinsp;0.52, n\u0026thinsp;=\u0026thinsp;753), Q3 (0.52\u0026thinsp;\u0026le;\u0026thinsp;FI-LAB\u0026thinsp;\u0026le;\u0026thinsp;0.60, n\u0026thinsp;=\u0026thinsp;943), and Q4 (FI-LAB\u0026thinsp;\u0026gt;\u0026thinsp;0.60, n\u0026thinsp;=\u0026thinsp;1005). Data extraction was performed by an authorized investigator (Approval ID: 66236335).\u003c/p\u003e\n \u003ch2\u003e2.2 FI-LAB Calculation and Data Management\u003c/h2\u003eThe FI-LAB was constructed from 27 laboratory parameters (Supplementary Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e). Each parameter was assigned a binary score based on its established normal range: values outside the range were coded as a deficit. The FI-LAB score for each patient was computed as the total number of deficits divided by the total parameters assessed.\u003cbr\u003e\n \u003cp\u003eCovariates were systematically extracted from electronic medical records and grouped into five domains: (1) demographics (age, sex); (2) laboratory variables (the 27 FI-LAB indicators, Supplementary Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e); (3) critical illness severity scores (APS III, SOFA, SAPS II, OASIS); (4) comorbidities, identified via ICD-9/10 codes, including congestive heart failure, diabetes, renal disease, and others; and (5) in-hospital medications (e.g., angiotensin-converting enzyme inhibitors/angiotensin receptor blockers, aspirin, statins, dopamine), recorded as binary variables (use vs. non-use). Variables with \u0026gt;\u0026thinsp;20% missing data (e.g., height, fibrinogen) were excluded. Remaining missing values (\u0026le;\u0026thinsp;20%) were imputed using Multiple Imputation by Chained Equations (MICE) implemented in R (mice package v3.15.0).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3 Outcomes\u003c/h2\u003e\n \u003cp\u003eThe primary outcome was in-hospital mortality. Secondary outcomes included all-cause mortality at 30-, 90-, and 360-days post-admission.\u003c/p\u003e2.4 Statistical Analysis\u003cp\u003eContinuous and categorical variables were summarized as medians (IQRs) and frequencies, respectively. Group comparisons used non-parametric tests (Mann-Whitney U) or \u0026chi;\u0026sup2; tests. Univariable and multivariable Cox regression analyses assessed associations between FI-LAB and mortality. Survival curves were compared utilizing Kaplan-Meier analysis with log-rank testing. The incremental prognostic value of FI-LAB beyond conventional severity scores was evaluated. Restricted cubic splines examined potential non-linear relationships. Prespecified subgroup analyses assessed interaction effects. Multivariable models were checked for multicollinearity (VIF\u0026thinsp;\u0026gt;\u0026thinsp;5 threshold), and sensitivity analyses were conducted to test robustness. All analyses used SPSS v.27 with two-tailed p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicating significance.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec8\"\u003e\n \u003ch2\u003e3.1 Baseline Characteristics\u003c/h2\u003e\n \u003cp\u003eThis study enrolled 3,257 eligible patients stratified by Laboratory Frailty Index (FI-LAB) quartiles. Higher frailty was associated with advanced age (median 76.14 to 77.54 years, p = 0.017) and progressively elevated severity scores (APSIII, SOFA, SAPSII, OASIS; all p \u0026lt; 0.001). Sex distribution shifted from female predominance in Q1 (47.66%) to male predominance in Q4 (64.18%, p \u0026lt; 0.001). Key comorbidities—congestive heart failure, diabetes, renal disease, and dementia—showed significantly increased prevalence with frailty grade (all p \u0026lt; 0.01). Vasoactive drug use rose markedly (norepinephrine: 17.27% to 43.28%; vasopressin: 5.04% to 19.40%), while cardioprotective medications declined (ACEI: 38.13% to 24.88%; statins: 76.26% to 63.88%; all p \u0026lt; 0.001), which may be attributable to contraindications (e.g., hypotension, liver dysfunction) or reduced tolerance in severe illness. In contrast, cerebrovascular, rheumatic, and chronic lung diseases showed no significant association with FI-LAB. These findings demonstrate that FI-LAB effectively captures a clinical phenotype characterized by escalating illness severity, specific comorbidity patterns, and distinct therapeutic profiles. \u003cstrong\u003e(\u003c/strong\u003eTable\u0026nbsp;1\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 1\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eBaseline characteristics\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal (n = 3257)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQ1 (n = 556)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQ2 (n = 753)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQ3 (n = 943)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQ4 (n = 1005)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge, M (Q1,Q3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e76.79 (70.99, 83.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e76.14 (69.86, 83.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e75.94 (70.85, 83.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e76.86 (71.31, 83.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e77.54 (71.38, 83.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGender, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt; .001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1266 (38.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e265 (47.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e310 (41.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e331 (35.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e360 (35.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1991 (61.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e291 (52.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e443 (58.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e612 (64.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e645 (64.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDisease severity scoring system (score)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAPSⅢ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45.00 (34.00, 58.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36.00 (27.00,46.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41.00 (31.00,53.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46.00 (35.50,58.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e54.00 (42.00,69.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt; .001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSOFA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.00 (3.00, 7.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.00 (1.00,5.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.00 (2.00,7.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.00 (3.00,7.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.00 (4.00,9.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt; .001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSAPSⅡ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40.00 (33.00, 50.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35.00 (29.00,41.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38.00 (32.00,46.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41.00 (35.00,50.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46.00 (38.00,55.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt; .001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOASIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33.00 (27.00, 39.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30.00 (24.00,35.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32.00 (26.00,38.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33.00 (27.00,39.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35.00 (30.00,42.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt; .001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFI-LAB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.52 (0.44, 0.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.36 (0.32,0.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.48 (0.44,0.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.52 (0.52,0.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.64 (0.60,0.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt; .001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOutcome, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIn-hospital death (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt; .001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2648 (81.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e511 (91.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e652 (86.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e762 (80.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e723 (71.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e609 (18.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45 (8.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e101 (13.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e181 (19.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e282 (28.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30-day death, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt; .001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2496 (76.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e486 (87.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e618 (82.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e722 (76.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e670 (66.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e761 (23.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e70 (12.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e135 (17.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e221 (23.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e335 (33.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90-day death, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt; .001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2264 (69.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e455 (81.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e569 (75.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e661 (70.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e579 (57.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e993 (30.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e101 (18.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e184 (24.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e282 (29.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e426 (42.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e360-day death, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt; .001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1962 (60.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e420 (75.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e492 (65.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e575 (60.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e475 (47.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1295 (39.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e136 (24.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e261 (34.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e368 (39.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e530 (52.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eComorbidities, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCongestive Heart Failure, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt; .001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1309 (40.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e284 (51.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e317 (42.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e356 (37.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e352 (35.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1948 (59.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e272 (48.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e436 (57.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e587 (62.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e653 (64.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePeripheral Vascular Disease, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2734 (83.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e482 (86.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e646 (85.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e776 (82.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e830 (82.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e523 (16.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e74 (13.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e107 (14.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e167 (17.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e175 (17.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCerebrovascular Disease, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.231\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2719 (83.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e449 (80.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e639 (84.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e793 (84.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e838 (83.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e538 (16.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e107 (19.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e114 (15.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e150 (15.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e167 (16.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiabetes, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt; .001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1790 (54.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e347 (62.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e428 (56.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e506 (53.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e509 (50.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1467 (45.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e209 (37.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e325 (43.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e437 (46.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e496 (49.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMalignant Cancer, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2915 (89.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e515 (92.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e684 (90.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e843 (89.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e873 (86.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e342 (10.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41 (7.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e69 (9.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e100 (10.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e132 (13.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRenal Disease, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt; .001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1983 (60.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e415 (74.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e476 (63.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e540 (57.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e552 (54.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1274 (39.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e141 (25.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e277 (36.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e403 (42.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e453 (45.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRheumatic Disease, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.818\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3122 (95.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e532 (95.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e724 (96.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e907 (96.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e959 (95.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e135 (4.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24 (4.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29 (3.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36 (3.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46 (4.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChronic Pulmonary Disease, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.877\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2411 (74.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e412 (74.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e562 (74.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e689 (73.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e748 (74.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e846 (25.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e144 (25.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e191 (25.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e254 (26.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e257 (25.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDementia, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3006 (92.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e521 (93.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e707 (93.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e876 (92.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e902 (89.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e251 (7.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35 (6.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46 (6.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e67 (7.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e103 (10.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePeptic Ulcer Disease, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.099\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3152 (96.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e544 (97.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e729 (96.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e917 (97.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e962 (95.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e105 (3.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12 (2.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24 (3.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26 (2.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43 (4.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInitial therapies, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNorepinephrine, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2235 (68.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e460 (82.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e558 (74.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e647 (68.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e570 (56.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1022 (31.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e96 (17.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e195 (25.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e296 (31.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e435 (43.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVasopressin, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt; .001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2884 (88.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e528 (94.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e702 (93.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e844 (89.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e810 (80.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e373 (11.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28 (5.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e51 (6.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e99 (10.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e195 (19.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDopamine, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt; .001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3052 (93.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e537 (96.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e717 (95.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e878 (93.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e920 (91.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e205 (6.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19 (3.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36 (4.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e65 (6.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e85 (8.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDobutamine, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt; .001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3098 (95.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e546 (98.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e720 (95.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e899 (95.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e933 (92.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e159 (4.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10 (1.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33 (4.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44 (4.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e72 (7.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eACEI/ARB, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt; .001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2303 (70.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e344 (61.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e507 (67.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e697 (73.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e755 (75.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e954 (29.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e212 (38.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e246 (32.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e246 (26.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e250 (24.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAspiri, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt; .001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e947 (29.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e134 (24.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e198 (26.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e263 (27.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e352 (35.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2310 (70.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e422 (75.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e555 (73.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e680 (72.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e653 (64.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStatins, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt; .001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e947 (29.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e132 (23.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e198 (26.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e254 (26.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e363 (36.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2310 (70.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e424 (76.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e555 (73.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e689 (73.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e642 (63.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003eNotes: Data are presented as n (%) for categorical variables and Median (Q1, Q3) for continuous variables. P-values are derived from the Kruskal-Wallis test for continuous variables and the Chi-square test for categorical variables. FI-LAB: Laboratory Frailty Index; APS III: Acute Physiology Score III; SOFA: Sequential Organ Failure Assessment; SAPS II: Simplified Acute Physiology Score II; OASIS: Oxford Acute Severity of Illness Score; ACEI/ARB: Angiotensin-converting enzyme inhibitors/Angiotensin receptor blockers..\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\"\u003e\n \u003ch2\u003e3.2 Relationship between FI-LAB and clinical outcomes\u003c/h2\u003e\n \u003cp\u003eAnalysis of the FI-LAB quartiles in relation to patient prognosis revealed that mortality rates, both short-term and long-term, varied significantly across different frailty levels (all p \u0026lt; 0.001). The primary outcome, in-hospital mortality, increased in a stepwise fashion from 8.09% in group Q1 to 28.06% in group Q4 (p \u0026lt; 0.001), demonstrating the strong predictive value of the FI-LAB for the risk of in-hospital mortality. Secondary outcome analysis further confirmed that the risk of death continued to increase with longer follow-up, with a 360-day mortality rate of 52.74% in the Q4 group, which was 2.16 times that of 24.46% in the Q1 group. \u003cstrong\u003e(\u003c/strong\u003eTable\u0026nbsp;2\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 2\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eMortality rate of AMI patients according to FI-LAB quartiles\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eFI-LAB\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIn-hospital\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e30-day\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e90-day\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e360-day\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMortality, n (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMortality, n (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMortality, n (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMortality, n (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ1 (n = 556)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45 (8.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; .001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e70 (12.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; .001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e101 (18.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; .001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e136 (24.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; .001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ2 (n = 753)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e101 (13.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e135 (17.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e184 (24.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e261 (34.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ3 (n = 943)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e181 (19.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e221 (23.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e282 (29.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e368 (39.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ4 (n = 1005)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e282 (28.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e335 (33.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e426 (42.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e530 (52.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\"\u003eNote: FI-LAB, Frailty index based on laboratory test; AMI, Acute myocardial infarction\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eUnivariate Cox regression analysis confirmed a significant association between elevated FI-LAB quartiles and risk of in-hospital mortality (p \u0026lt; 0.001 for trend test). Compared to group Q1, group Q3 had a 92% increased risk of death (HR = 1.92, 95% CI: 1.38–2.66, p \u0026lt; 0.001), and group Q4 had a 147% surge in risk (HR = 2.47, 95% CI: 1.81–3.39, p \u0026lt; 0.001). Group Q2 did not reach statistical significance, although the risk increased by 39% (HR = 1.39, 95% CI: 0.98–1.97, p = 0.067). In addition, multivariate COX analysis of secondary outcomes showed that the risk of death was significantly higher in groups Q2, Q3, and Q4 than in group Q1 (p \u0026lt; 0.05) \u003cstrong\u003e(\u003c/strong\u003eTable\u0026nbsp;3\u003cstrong\u003e)\u003c/strong\u003e. Cox regression was performed using a stepwise correction strategy: model 1 was uncorrected; model 2 added age and sex; model 3 further added 10 comorbidities; and model 4 additionally corrected for 7 classes of therapeutic drugs \u003cstrong\u003e(\u003c/strong\u003eTable 3). After gradual correction for confounders, elevated quartiles of the laboratory-based index of frailty (FI-LAB) remained an independent predictor of risk of death. The risk of death was consistently significantly higher in the highest quartile of the FI-LAB group than in the reference group and demonstrated a significant risk relationship (increasing risk from Q1 to Q4). After correction for therapeutic factors (vasoactive agents, cardiovascular medications), the effect value of FI-LAB attenuated but remained statistically significant, suggesting that its predictive value extends beyond the effect of therapeutic interventions.FI-LAB predicted the risk of mortality with stability: even after adjusting for demographic characteristics, comorbidities, and treatment measures (Model 4), the risk of death at 360 days in the Q4 group amounted to 1.86 times the risk of death in the Q1 group (95% CI: 1.53–2.26, p \u0026lt; 0.001). \u003cstrong\u003e(\u003c/strong\u003eTable\u0026nbsp;3\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003cdiv\u003eFurthermore, Kaplan-Meier survival analysis at 30, 90 and 360 days \u003cstrong\u003e(\u003c/strong\u003eFig.\u0026nbsp;1\u003cstrong\u003e)\u003c/strong\u003e demonstrated a significantly higher mortality risk in the Q2, Q3, and Q4 groups compared to the Q1 group (log-rank p \u0026lt; 0.001). Mortality risk increased progressively across quartiles, with the highest risk observed in Q4.\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\"\u003e\n \u003ch2\u003e3.3 Predictive value and incremental effect of FI-LAB\u003c/h2\u003e\n \u003cp\u003eIn this study, the FI-LAB exhibited a moderate but statistically significant capacity to predict in-hospital mortality among elderly patients with AMI. In the ROC analysis, the area under the curve (AUC) of FI-LAB versus outcome, which reflects the ability of the model to discriminate between patients who died and those who survived, had a value of 0.646, which is higher than the random probability (0.5) but lower than the strong predictive performance (≥ 0.8), suggesting an average discriminatory ability. The AUC value of 0.646 signifies that, according to FI-LAB scores, there is a 64.6% likelihood that a randomly chosen patient who succumbed will receive a higher risk score compared to a randomly selected patient who survived. Although statistically better than chance, this AUC suggests limited clinical usefulness if used in isolation. It may serve as a risk stratification aid but may not be reliable enough alone for decision-making without combining with other predictors. \u003cstrong\u003e(\u003c/strong\u003eFig.\u0026nbsp;2\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eConsidering the moderate predictive capability of FI-LAB when used independently, we posited that its combination with established critical illness severity scoring systems could improve risk stratification. To evaluate this hypothesis, FI-LAB was integrated with APS III, SOFA, SAPS II, and OASIS scores. This integrative approach consistently resulted in statistically significant enhancements in prognostic predictive accuracy. The integration of FI-LAB with established critical care scores (APS III, SOFA, SAPS II, and OASIS) yielded significant improvements in prognostic predictive performance. Specifically, the model combining FI-LAB and APS III demonstrated the highest discriminative ability for patient mortality. However, the incremental improvement in AUROC for APS III following FI-LAB integration was minimal. In contrast, the greatest incremental improvement was observed for the OASIS score, with its AUC increasing significantly from 0.711 to 0.733 following FI-LAB incorporation.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\"\u003e\n \u003ch2\u003e3.4 Nonlinear relationship between FI-LAB and patient prognosis\u003c/h2\u003e\n \u003cp\u003eRCS modeling was utilized to analyze the correlation among the FI-LAB and mortality in the hospital among AMI patients. An overall statistically significant correlation was detected (Overall P \u0026lt; 0.001). The test for nonlinearity (P = 0.289) indicated no significant departure from linearity, supporting a predominantly linear relationship between FI-LAB and mortality across its observed range \u003cstrong\u003e(\u003c/strong\u003eFig.\u0026nbsp;3\u003cstrong\u003e)\u003c/strong\u003e. Further analysis of secondary outcomes also showed that there was no nonlinear relationship between FI-LAB and patient prognosis, which was approximately linear within the range. \u003cstrong\u003e(Figure S1 S2 S3)\u003c/strong\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\"\u003e\n \u003ch2\u003e3.5 Subgroup analysis\u003c/h2\u003e\n \u003cp\u003eTo assess the robustness of FI-LAB's predictive performance across diverse patient cohorts, we conducted prespecified subgroup analyses. In this subgroup analysis, FI-LAB was significantly associated with in-hospital mortality in the overall cohort (OR 2.48, 95% CI 2.03–3.04; P \u0026lt; 0.001) and across most comorbidity subgroups. Notably, the association was stronger in patients without renal disease (OR 2.98, 95% CI 2.27–3.90) than in those with renal impairment (OR 1.79, 95% CI 1.32–2.43), with a significant interaction (P = 0.014). A significant interaction was also observed for cerebrovascular disease (P = 0.014), though effect sizes were similar between subgroups. No significant interaction was found for other comorbidities, including congestive heart failure (P = 0.089) and peripheral vascular disease (P = 0.925). These results support FI-LAB as a consistent predictor of in-hospital mortality, with possible effect modification by renal function. \u003cstrong\u003e(\u003c/strong\u003eFig.\u0026nbsp;4\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eThis study of 3,257 elderly patients with AMI establishes the FI-LAB as a robust and independent predictor of both short- and long-term all-cause mortality. A clear graded association was observed between FI-LAB quartiles and mortality risk. Patients in the highest frailty quartile (Q4) demonstrated a 147% increased risk of in-hospital mortality (unadjusted HR\u0026thinsp;=\u0026thinsp;2.47; 95% CI: 1.81\u0026ndash;3.39; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) compared to the lowest quartile (Q1). After multivariable adjustment for age, comorbidities, illness severity, and treatment factors, this risk remained significantly elevated at 86% (adjusted HR\u0026thinsp;=\u0026thinsp;1.86; 95% CI: 1.35\u0026ndash;2.57; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The prognostic stratification capability of FI-LAB was further evidenced by the marked increase in mortality rates across quartiles, with in-hospital mortality rising from 8.09% in Q1 to 28.06% in Q4, and 360-day mortality from 24.46% to 52.74%. At 360 days, the adjusted HR for Q4 versus Q1 was 1.86 (95% CI: 1.53\u0026ndash;2.26, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), confirming its independent prognostic value beyond conventional risk factors. The lack of statistical significance in the Q2 group in all models (in-hospital mortality HR\u0026thinsp;=\u0026thinsp;1.24, p\u0026thinsp;=\u0026thinsp;0.235) suggests that the prognostic impact of mild frailty may be variable and that further research with larger sample sizes is needed.\u003c/p\u003e \u003cp\u003eConsistent with the results reported by previous research [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], the present study confirms that the FI-LAB substantially improves the prediction of clinical outcomes in patients experiencing AMI. The study by Nagae et al. [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], involving 872 emergency department patients, similarly reported an association between elevated FI-LAB scores and in-hospital mortality. However, our research specifically focuses on the elderly demographic, which bears the highest burden of AMI and possesses distinct physiological vulnerabilities, thereby providing a more representative and clinically relevant assessment of FI-LAB's utility in the typical AMI population. The restricted cubic spline analysis confirmed a linear relationship between continuous FI-LAB values and mortality risk (P for nonlinearity\u0026thinsp;=\u0026thinsp;0.289), indicating that even minor increases in frailty burden correspond to progressively higher mortality. A linear association between FI-LAB and all-cause mortality was consistently identified in patients with severe heart failure, corroborating our results [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA key finding is that FI-LAB provided significant incremental prognostic value when combined with established critical illness scores. Integrated models demonstrated improved predictive accuracy for SOFA (ΔAUC\u0026thinsp;+\u0026thinsp;0.017), OASIS (ΔAUC\u0026thinsp;+\u0026thinsp;0.022), and SAPS II (ΔAUC\u0026thinsp;+\u0026thinsp;0.007), though not for APS III (ΔAUC\u0026thinsp;+\u0026thinsp;0.001). This pattern suggests that FI-LAB contributes complementary information, particularly to scores focused on acute organ dysfunction, rather than acting as a redundant metric. The greatest synergy with SOFA and OASIS implies that frailty exacerbates mortality risk through impaired multi-organ reserve. In contrast, FI-LAB had minimal effect on APSIII, a comprehensive system that has incorporated laboratory abnormalities, suggesting possible overlap in their predictive domains.\u003c/p\u003e \u003cp\u003eAn important interaction was observed with renal disease (P for interaction\u0026thinsp;=\u0026thinsp;0.014). FI-LAB was more strongly associated with in-hospital mortality in patients without renal disease (OR\u0026thinsp;=\u0026thinsp;2.98; 95% CI: 2.27\u0026ndash;3.90) than in those with renal disease (OR\u0026thinsp;=\u0026thinsp;1.79; 95% CI: 1.32\u0026ndash;2.43). This attenuated effect may be attributed to the overlap between FI-LAB components (e.g., eGFR, creatinine) and the pathophysiology of renal impairment. In patients with pre-existing renal disease, these biomarkers may already be chronically abnormal, reducing FI-LAB's dynamic range and predictive sensitivity for death. Conversely, in their absence, FI-LAB may more sensitively reflect systemic physiological decline.\u003c/p\u003e \u003cp\u003eThe biological plausibility of FI-LAB is supported by its reflection of multisystem physiological dysregulation. The index encapsulates pathways such as chronic inflammation (e.g., elevated CRP), renal dysfunction, and metabolic disturbances, which collectively promote atherosclerosis, plaque instability, and impaired stress response\u0026mdash;core elements of the frailty phenotype that heighten vulnerability to adverse outcomes after AMI [\u003cspan additionalcitationids=\"CR27 CR28\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSeveral limitations warrant consideration. First, the retrospective design using the MIMIC-IV database, despite rigorous adjustments and multiple imputation for missing data, remains susceptible to unmeasured confounding (e.g., socioeconomic status, functional mobility). Second, the FI-LAB originated from data collected in the initial 24 hours of being admitted to the ICU and may not reflect the fluctuations of frailty throughout the hospitalization period. Third, generalizability may be limited by the single-center, U.S.-based ICU population, necessitating external validation in prospective, multicenter cohorts. Fourth, the exploratory subgroup analysis suggesting effect modification by renal status requires further mechanistic investigation. Finally, while FI-LAB added incremental value, its standalone discriminative ability was modest (AUC\u0026thinsp;=\u0026thinsp;0.646), underscoring that it should complement, not replace, comprehensive clinical assessment.\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eThis study validates the FI-LAB as a strong, independent predictor of mortality in elderly AMI patients. This objective tool enables feasible frailty screening in acute care, overcoming limitations of traditional assessments. Multicenter studies are needed to confirm its broad applicability.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank all the patients whose data were included in this database, as well as the clinicians and researchers involved in its curation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contribution\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eL. Chen: conceptualization, writing\u0026mdash;review and editing, and supervision; Q.Jiang: methodology and writing\u0026mdash; original draft; X. Gao: data curation and validation; J. Li : writing\u0026mdash;review and editing; H. Zhang: data curation and software; L. Shi: writing\u0026mdash;review and editing, and supervision. All authors had full access to all data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. All authors approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author declares that no financial support was received during the research and/or publication of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study used data from the MIMIC-IV database. The database received approval from the MIT and BIDMC Institutional Review Boards, which waived the requirement for individual patient consent due to the use of de-identified data. All data were handled in accordance with ethical standards.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUse of Large Language Models, AI and Machine Learning Tools\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone declared.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eDamluji AA, Forman DE, Wang TY, Chikwe J, Kunadian V, Rich MW, et al. Management of Acute Coronary Syndrome in the Older Adult Population: A Scientific Statement From the American Heart Association. Circulation. 2023;147(3):e32\u0026ndash;62. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi.org/10.1161/cir.0000000000001112\u003c/span\u003e\u003cspan address=\"10.1161/cir.0000000000001112\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTumminello G, D'Errico A, Maruccio A, Gentile D, Barbieri L, Carugo S. Age-Related Mortality in STEMI Patients: Insight from One Year of HUB Centre Experience during the Pandemic. J Cardiovasc Dev Dis. 2022;9(12). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi.org/10.3390/jcdd9120432\u003c/span\u003e\u003cspan address=\"10.3390/jcdd9120432\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEngberding N, Wenger NK. Acute Coronary Syndromes in the Elderly. F1000Res. 2017;6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e1791.http://doi.org/10.12688/f1000research.11064.1\u003c/span\u003e\u003cspan address=\"1791.10.12688/f1000research.11064.1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eClegg A, Young J, Iliffe S, Rikkert MO, Rockwood K. Frailty in elderly people. Lancet. 2013;381(9868):752\u0026ndash;62. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi.org/10.1016/s0140-6736(12)62167-9\u003c/span\u003e\u003cspan address=\"10.1016/s0140-6736(12)62167-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXue QL. The frailty syndrome: definition and natural history. Clin Geriatr Med. 2011;27(1):1\u0026ndash;15. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi.org/10.1016/j.cger.2010.08.009\u003c/span\u003e\u003cspan address=\"10.1016/j.cger.2010.08.009\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFabbri E, An Y, Zoli M, Simonsick EM, Guralnik JM, Bandinelli S, et al. Aging and the burden of multimorbidity: associations with inflammatory and anabolic hormonal biomarkers. J Gerontol Biol Sci Med Sci. 2015;70(1):63\u0026ndash;70. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi.org/10.1093/gerona/glu127\u003c/span\u003e\u003cspan address=\"10.1093/gerona/glu127\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFranceschi C, Garagnani P, Parini P, Giuliani C, Santoro A. Inflammaging: a new immune-metabolic viewpoint for age-related diseases. Nat Rev Endocrinol. 2018;14(10):576\u0026ndash;90. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi.org/10.1038/s41574-018-0059-4\u003c/span\u003e\u003cspan address=\"10.1038/s41574-018-0059-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDent E, Kowal P, Hoogendijk EO. Frailty measurement in research and clinical practice: A review. Eur J Intern Med. 2016;31:3\u0026ndash;10. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi.org/10.1016/j.ejim.2016.03.007\u003c/span\u003e\u003cspan address=\"10.1016/j.ejim.2016.03.007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen S, Chen T, Kishimoto H, Susaki Y, Kumagai S. Development of a Fried Frailty Phenotype Questionnaire for Use in Screening Community-Dwelling Older Adults. J Am Med Dir Assoc. 2020;21(2):272\u0026ndash;6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e.e1.http://doi.org/10.1016/j.jamda.2019.07.015\u003c/span\u003e\u003cspan address=\".e1.10.1016/j.jamda.2019.07.015\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChurch S, Rogers E, Rockwood K, Theou O. A scoping review of the Clinical Frailty Scale. BMC Geriatr. 2020;20(1):393. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi.org/10.1186/s12877-020-01801-7\u003c/span\u003e\u003cspan address=\"10.1186/s12877-020-01801-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eClegg A, Bates C, Young J, Ryan R, Nichols L, Ann Teale E, et al. Development and validation of an electronic frailty index using routine primary care electronic health record data. Age Ageing. 2016;45(3):353\u0026ndash;60. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi.org/10.1093/ageing/afw039\u003c/span\u003e\u003cspan address=\"10.1093/ageing/afw039\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGleason LJ, Benton EA, Alvarez-Nebreda ML, Weaver MJ, Harris MB, Javedan H. FRAIL Questionnaire Screening Tool and Short-Term Outcomes in Geriatric Fracture Patients. J Am Med Dir Assoc. 2017;18(12):1082\u0026ndash;6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e.http://doi.org/10.1016/j.jamda.2017.07.005\u003c/span\u003e\u003cspan address=\".10.1016/j.jamda.2017.07.005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGarc\u0026iacute;a-Garc\u0026iacute;a FJ, Carcaillon L, Fernandez-Tresguerres J, Alfaro A, Larrion JL, Castillo C, et al. A new operational definition of frailty: the Frailty Trait Scale. J Am Med Dir Assoc. 2014;15(5):371. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e13.http://doi.org/10.1016/j.jamda.2014.01.004\u003c/span\u003e\u003cspan address=\"13.10.1016/j.jamda.2014.01.004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e.e7-.e\u003c/span\u003e\u003cspan address=\"http://.e7-.e\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSapp DG, Cormier BM, Rockwood K, Howlett SE, Heinze SS. The frailty index based on laboratory test data as a tool to investigate the impact of frailty on health outcomes: a systematic review and meta-analysis. Age Ageing. 2023;52(1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi.org/10.1093/ageing/afac309\u003c/span\u003e\u003cspan address=\"10.1093/ageing/afac309\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYsea-Hill O, Gomez CJ, Mansour N, Wahab K, Hoang M, Labrada M, et al. The association of a frailty index from laboratory tests and vital signs with clinical outcomes in hospitalized older adults. J Am Geriatr Soc. 2022;70(11):3163\u0026ndash;75. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e.http://doi.org/10.1111/jgs.17977\u003c/span\u003e\u003cspan address=\".10.1111/jgs.17977\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRockwood K, McMillan M, Mitnitski A, Howlett SE. A Frailty Index Based on Common Laboratory Tests in Comparison With a Clinical Frailty Index for Older Adults in Long-Term Care Facilities. J Am Med Dir Assoc. 2015;16(10):842\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e.http://doi.org/10.1016/j.jamda.2015.03.027\u003c/span\u003e\u003cspan address=\".10.1016/j.jamda.2015.03.027\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLe Gall JR, Lemeshow S, Saulnier F. A new Simplified Acute Physiology Score (SAPS II) based on a European/North American multicenter study. JAMA. 1993;270(24):2957\u0026ndash;63. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e.http://doi.org/10.1001/jama.270.24.2957\u003c/span\u003e\u003cspan address=\".10.1001/jama.270.24.2957\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVincent JL, de Mendon\u0026ccedil;a A, Cantraine F, Moreno R, Takala J, Suter PM, et al. Use of the SOFA score to assess the incidence of organ dysfunction/failure in intensive care units: results of a multicenter, prospective study. Working group on sepsis-related problems of the European Society of Intensive Care Medicine. Crit Care Med. 1998;26(11):1793\u0026ndash;800. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi.org/10.1097/00003246-199811000-00016\u003c/span\u003e\u003cspan address=\"10.1097/00003246-199811000-00016\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLeGall JR, Loirat P, Alp\u0026eacute;rovitch A. APACHE II\u0026ndash;a severity of disease classification system. Crit Care Med. 1986;14(8):754\u0026ndash;5. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e.http://doi.org/10.1097/00003246-198608000-00027\u003c/span\u003e\u003cspan address=\".10.1097/00003246-198608000-00027\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJohnson AE, Kramer AA, Clifford GD. A new severity of illness scale using a subset of Acute Physiology And Chronic Health Evaluation data elements shows comparable predictive accuracy. Crit Care Med. 2013;41(7):1711\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e.http://doi.org/10.1097/CCM.0b013e31828a24fe\u003c/span\u003e\u003cspan address=\".10.1097/CCM.0b013e31828a24fe\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNagae M, Umegaki H, Nakashima H, Nishiuchi T. FI-lab in the emergency department and adverse outcomes among acutely hospitalized older adults. Arch Gerontol Geriatr. 2025;129:105649. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi.org/10.1016/j.archger.2024.105649\u003c/span\u003e\u003cspan address=\"10.1016/j.archger.2024.105649\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFunakoshi H, Shiga T, Homma Y, Nakashima Y, Takahashi J, Kamura H, et al. Validation of the modified Japanese Triage and Acuity Scale-based triage system emphasizing the physiologic variables or mechanism of injuries. Int J Emerg Med. 2016;9(1):1. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi.org/10.1186/s12245-015-0097-9\u003c/span\u003e\u003cspan address=\"10.1186/s12245-015-0097-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWilliams B. The National Early Warning Score: from concept to NHS implementation. Clin Med (Lond). 2022;22(6):499\u0026ndash;505. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi.org/10.7861/clinmed.2022-news-concept\u003c/span\u003e\u003cspan address=\"10.7861/clinmed.2022-news-concept\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBai W, Hao B, Xu L, Qin J, Xu W, Qin L. Frailty index based on laboratory tests improves prediction of short-and long-term mortality in patients with critical acute myocardial infarction. Front Med (Lausanne). 2022;9:1070951. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi.org/10.3389/fmed.2022.1070951\u003c/span\u003e\u003cspan address=\"10.3389/fmed.2022.1070951\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang S, Wang L, Wang Y, Zong S, Fan H, Jiang Y, et al. Association between frailty index based on laboratory tests and all-cause mortality in critically ill patients with heart failure. ESC Heart Fail. 2024;11(6):3662\u0026ndash;73. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e.http://doi.org/10.1002/ehf2.14948\u003c/span\u003e\u003cspan address=\".10.1002/ehf2.14948\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHeld C, White HD, Stewart RAH, Budaj A, Cannon CP, Hochman JS, et al. Inflammatory Biomarkers Interleukin-6 and C-Reactive Protein and Outcomes in Stable Coronary Heart Disease: Experiences From the STABILITY (Stabilization of Atherosclerotic Plaque by Initiation of Darapladib Therapy) Trial. J Am Heart Assoc. 2017;6(10). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi.org/10.1161/jaha.116.005077\u003c/span\u003e\u003cspan address=\"10.1161/jaha.116.005077\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKosmas CE, Silverio D, Tsomidou C, Salcedo MD, Montan PD, Guzman E. The Impact of Insulin Resistance and Chronic Kidney Disease on Inflammation and Cardiovascular Disease. Clin Med Insights Endocrinol Diabetes. 2018;11:1179551418792257. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi.org/10.1177/1179551418792257\u003c/span\u003e\u003cspan address=\"10.1177/1179551418792257\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAnghel L, Tudurachi BS, Tudurachi A, Zăvoi A, Clement A, Roungos A, et al. Patient-Related Factors Predicting Stent Thrombosis in Percutaneous Coronary Interventions. J Clin Med. 2023;12(23). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi.org/10.3390/jcm12237367\u003c/span\u003e\u003cspan address=\"10.3390/jcm12237367\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ede Almeida A, de Almeida Rezende MS, Dantas SH, de Lima Silva S, de Oliveira J Lourdes Assun\u0026ccedil;\u0026atilde;o Ara\u0026uacute;jo, de Azevedo F et al. Unveiling the Role of Inflammation and Oxidative Stress on Age-Related Cardiovascular Diseases. Oxid Med Cell Longev. 2020;2020:1954398\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e.http://doi.org/10.1155/2020/1954398\u003c/span\u003e\u003cspan address=\".10.1155/2020/1954398\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Table 3","content":"\u003cp\u003eTable 3 is available in the Supplementary Files section.\u003c/p\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 Myocardial Infarction, Frailty, Laboratory Frailty Index, Critical Care Scores","lastPublishedDoi":"10.21203/rs.3.rs-8738011/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8738011/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eAcute myocardial infarction (AMI) is a leading cause of death in the elderly. Frailty syndrome is associated with adverse outcomes. The Laboratory Frailty Index (FI-LAB) provides an objective assessment of frailty, but its prognostic value in older AMI patients remains inadequately investigated.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003eThis retrospective cohort study utilized the MIMIC-IV database and enrolled 3,257 AMI patients aged ≥65 years. The FI-LAB was calculated from 27 laboratory parameters within 24 hours of admission, and patients were stratified into quartiles (Q1-Q4). The primary outcome was in-hospital mortality. Secondary outcomes included 30-day, 90-day, and 360-day all-cause mortality. Multivariable Cox regression, Kaplan-Meier analysis, and ROC curves were used to evaluate the predictive performance of FI-LAB.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eThe median patient age ranged from 76.14 to 77.54 years, with the proportion of females decreasing from 47.66% in Q1 to 64.18% in Q4. In-hospital mortality increased significantly across FI-LAB quartiles (Q1: 8.09% vs. Q4: 28.06%, p\u0026lt;0.001). The 360-day mortality rose from 24.46% in Q1 to 52.74% in Q4. In multivariate Cox analysis, the highest FI-LAB quartile (Q4) was independently related to an elevated risk of in-hospital mortality risk (HR=1.86, 95% CI: 1.35-2.57, p\u0026lt;0.001). The area under the ROC curve (AUC) of FI-LAB alone for predicting in-hospital mortality was 0.646. When combined with conventional severity scores (e.g., OASIS), the predictive performance improved significantly (ΔAUC +0.022). Subgroup analysis demonstrated a more prominent connection among FI-LAB and in-hospital mortality for people lacking of renal illness. (OR=2.98, 95% CI: 2.27-3.90).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eThe FI-LAB serves as an independent predictor of short- and long-term mortality in elderly AMI patients. Its integration with established severity scores enhances risk stratification, offering such an appropriate objective measure for frailty screening in acute care settings.\u003c/p\u003e","manuscriptTitle":"Prognostic Impact of Laboratory Frailty Index in Elderly Patients with Acute Myocardial Infarction: A Retrospective Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-27 10:37:09","doi":"10.21203/rs.3.rs-8738011/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"63810274327031989862912473415534057052","date":"2026-03-02T06:48:15+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-25T18:08:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"211021872463467191673353563908552189971","date":"2026-02-25T16:55:55+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-24T07:04:10+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-02T10:49:05+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-02T09:51:32+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-02T09:47:15+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cardiovascular Disorders","date":"2026-01-30T06:25:55+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":"ff908993-9ee5-4415-8557-d405fd50c477","owner":[],"postedDate":"February 27th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-02-27T10:37:09+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-27 10:37:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8738011","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8738011","identity":"rs-8738011","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}