The hospital frailty risk score in critically ill patients with sepsis is an independent risk factor for in-hospital mortality : results from MIMIC-IV database

The hospital frailty risk score in critically ill patients with sepsis is an independent risk factor for in-hospital mortality : results from MIMIC-IV database | 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 The hospital frailty risk score in critically ill patients with sepsis is an independent risk factor for in-hospital mortality : results from MIMIC-IV database Yali Xu, Xiya Wang, Andong Li, Shubin Guo, Xiaomei Zhu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7540379/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective: To investigate the relationship between frailty assessed by the HFRS and in-hospital mortality in ICU patients with sepsis. Method: A retrospective analysis of septic ICU patients from the MIMIC-IV database assessed frailty using the Hospital Frailty Risk Score (HFRS). Patients were categorized into non-frail (HFRS < 5, n = 3,744), pre-frail (5 ≤HFRS < 15, n = 2,539), and frail (HFRS ≥15, n = 2,147) groups. The primary outcome was in-hospital mortality. Logistic regression, with restricted cubic splines (RCS), analyzed the relationship between HFRS ,both as a categorical and continuous variable, and mortality. Inverse probability weighting (IPW) validated the results, and subgroup analyses explored frailty-mortality correlations in different patient groups. Results: A total of 8,430 patients were included, with 3,761 (44.6%) males and a mean age of 69.39 [58.37, 79.76] years. Among them, 2,704 (32.1%) died during hospitalization. The analysis showed that in-hospital mortality increased with higher frailty levels, regardless of whether HFRS was treated as a continuous or categorical variable. RCS revealed a nonlinear relationship between HFRS and mortality. After adjusting for confounders, both pre-frail and frail statuses were significantly associated with higher in-hospital mortality risk (OR [95% CI]: pre-frail vs. non-frail: 1.33 [1.15–1.53], p < 0.001; frail vs. non-frail: 1.38 [1.18–1.62], p < 0.001). These findings were confirmed by IPW. Subgroup analyses showed significant interactions between frailty and mortality in patients receiving vasopressors, continuous renal replacement therapy (CRRT), mechanical ventilation, and those with varying heart rates, respiratory rates, and creatinine levels. Conclusion: Elevated HFRS is an independent risk factor for in-hospital mortality in septic ICU patients. Figures Figure 1 Figure 2 1. Introduction Frailty is a complex, clinically identifiable syndrome characterized by the decline in the function of multiple physiological systems, accompanied by an increased susceptibility to stressors, which in turn leads to an elevated risk of various adverse outcomes[ 1 ]. Frailty becomes more prevalent with age and is a significant risk factor for the progression of older ICU patients to persistent critical illness and death. Approximately 40% of ICU patients with infections are frail[ 2 , 3 ]. As a common geriatric syndrome, frailty is often associated with immune dysfunction and low-grade chronic inflammation, which resemble the immune imbalance seen in sepsis patients. This similarity complicates the diagnosis and treatment of sepsis. Increasingly, research has identified frailty as one of the key factors influencing the prognosis of sepsis patients[ 4 – 7 ]. There are currently several frailty assessment tools available for clinical and research practice[ 8 ]. Among these, the Hospital Frailty Risk Score (HFRS), developed and validated by Gilbert et al., is designed to identify frail patients at low, moderate, and high risk. This score relies on the diagnostic codes from the International Classification of Diseases, 10th edition (ICD-10) to identify patients who may be at risk for adverse healthcare outcomes, offering stronger clinical applicability[ 9 ]. Sepsis is defined as a life-threatening organ dysfunction caused by a dysregulated host response to infection[ 10 ], and it is one of the most common causes of morbidity and mortality among critically ill patients[ 11 , 12 ]. The HFRS has demonstrated its ability to predict clinical outcomes in various populations, including those with heart failure[ 13 , 14 ], chronic obstructive pulmonary disease[ 15 ], malignancy[ 16 ], and pancreatitis[ 17 ]. However, there remains limited application research on the use of HFRS in sepsis patients in the ICU. The objective of this study is to explore the relationship between frailty, as assessed by the HFRS, and inpatient mortality in ICU patients with sepsis. 2. Methods 2.1 Data Extraction This study is an observational, retrospective research based on data from the MIMIC-IV database (Record ID: 66067288). The database is an open relational database managed by the Massachusetts Institute of Technology's Laboratory for Computational Physiology. It includes data from ICU patients at the Beth Israel Deaconess Medical Center in Boston, covering the period from 2008 to 2019. The dataset consists of 431,231 hospital admission records and 73,181 ICU admission records, with diagnoses coded according to the ICD-9 and ICD-10 classification systems to ensure accurate disease categorization. 2.2 Study Population For analysis, we included patients who were admitted to the ICU for the first time and diagnosed with sepsis. We excluded patients who were younger than 18 years or whose ICU stay was less than 24 hours. A total of 8,430 sepsis patients were included in the study. 2.3 Frailty Assessment Based on ICD-10 Codes Frailty levels in sepsis patients were assessed using the Hospital Frailty Risk Score (HFRS). The HFRS is a weighted scoring system based on ICD-10 codes for hospitalized patients. It calculates the initial hospital score for each patient based on one or more of the 109 ICD-10 diagnostic codes recorded at the time of their first admission. The HFRS value is the sum of the weights of these codes. The HFRS includes 109 ICD-10 codes, which not only encompass traditional chronic diseases but also codes for limitations in mobility, cognitive impairment, and emotional disorders. This expanded scope allows the identification of frailty-related factors that go beyond conventional comorbidities, making it a valuable tool for identifying high-risk frail patients. Frailty was categorized according to HFRS as follows: non-frail (HFRS < 5), pre-frail (HFRS between 5 and 14), and frail (HFRS ≥ 15). 2.4 Outcomes The primary outcome was inpatient mortality in sepsis patients, while the secondary outcomes were ICU stay duration and total hospital stay. 2.5 Statistical Analysis During the data preprocessing phase, variables with more than 30% missing data were excluded. Missing values for other variables were imputed using the PMM multiple imputation method from the MICE package. Baseline characteristics were summarized by frailty stage. Normality of continuous variables was tested using the Anderson-Darling test. For skewed distributions, median values and interquartile ranges were reported, and the Kruskal-Wallis H test was used to compare characteristics across different frailty stages. Categorical variables were described using frequencies (proportions), and the chi-square test was applied. A P-value of less than 0.05 was considered statistically significant. Univariate and multivariate logistic regression models were used to examine the relationship between frailty levels and mortality in sepsis patients. The "non-frail" group served as the reference, and the impacts of "pre-frail" and "frail" statuses on the outcomes were compared. Frailty scores were also treated as a continuous variable in the regression models to assess the relative change in adverse outcomes for every one-point increase in the score. Three regression models were constructed: Model 1 was a univariate analysis that included frailty status alone; Model 2 adjusted for age, sex, the scoring table, and comorbid conditions; and Model 3 further adjusted for other potential confounders using a bidirectional stepwise regression approach. To validate the robustness of the multivariate regression results, an additional analysis using the inverse probability weighting (IPW) method was performed. All regression results are reported using odds ratios (OR) with 95% confidence intervals. Furthermore, to further explore the relationship between frailty status and mortality in sepsis patients, we conducted a subgroup analysis to assess how different subgroup characteristics influenced the relationship between frailty and mortality. 3. Results 3.1 Baseline Characteristics A total of 8,430 sepsis patients were included in this study from the MIMIC-IV database. Baseline characteristics are shown in Table 1 . According to the HFRS score, 3,744 patients (44.4%) were classified as non-frail, 2,539 patients (30.1%) as pre-frail, and 2,147 patients (25.5%) as frail. The mean age was 69.39 [58.37, 79.76] years, with 3,761 males (44.6%). Patients with higher frailty levels tended to be older, with a higher proportion of females. In terms of vital signs, significant differences were observed in heart rate, systolic blood pressure, diastolic blood pressure, mean arterial pressure, temperature, oxygen saturation, and blood glucose levels across different frailty groups. Specifically, patients with higher frailty levels had lower heart rates, and higher blood pressure and blood glucose levels. Laboratory tests showed significant differences in white blood cells, neutrophils, platelets, hematocrit, hemoglobin, bicarbonate, creatinine, blood urea nitrogen, calcium, sodium, potassium, INR, APTT, Alt, Alp, and bilirubin across different frailty stages. As frailty increased, comorbidities such as hypertension, coronary artery disease, cerebrovascular diseases, congestive heart failure, chronic lung diseases, kidney disease, and diabetes became more prevalent. Additionally, pre-frail and frail patients received more mechanical ventilation and CRRT interventions compared to non-frail patients. Table 1 Baseline characteristics septic patients in MIMIC-IV database stratified according to frailty levels. Overall (n = 8430) Non-frailty (n = 3744) Pre-frailty (n = 2539) Frailty (n = 2147) p Age (year) 69.39 [58.37, 79.76] 68.82 [57.24, 80.77] 68.12 [57.57, 77.33] 71.69 [61.03, 80.77] < 0.001 Gender = Male (%) 3761 (44.6) 1713 (45.8) 1101 (43.4) 947 (44.1) 0.150 Vital sign Heart_rate(beats/min) 90.85 [78.77, 103.58] 91.92 [79.68, 104.38] 90.36 [78.60, 103.76] 89.48 [77.71, 101.25] < 0.001 Resp_rate (beats/min) 20.91 [18.04, 24.08] 20.91 [18.09, 23.95] 20.94 [18.04, 24.20] 20.85 [17.96, 24.10] 0.847 SBP(mmHg) 107.68 [101.08, 116.06] 107.36 [100.97, 115.37] 107.00 [100.68, 115.33] 109.18 [102.14, 117.52] < 0.001 DBP(mmHg) 59.19 [53.71, 65.22] 58.19 [52.72, 63.96] 59.89 [54.52, 65.94] 60.30 [54.50, 66.78] < 0.001 MBP(mmHg) 72.86 [68.04, 78.63] 71.35 [66.48, 76.83] 73.50 [69.15, 79.17] 74.45 [69.71, 80.62] < 0.001 Temperature (℃) 36.88 [36.60, 37.26] 36.88 [36.56, 37.29] 36.89 [36.65, 37.24] 36.87 [36.62, 37.24] 0.029 SpO 2 (%) 96.71 [95.27, 98.11] 96.84 [95.45, 98.20] 96.49 [95.06, 97.90] 96.74 [95.27, 98.15] < 0.001 Glu(mg/dl) 134.06 [109.33, 173.00] 132.40 [108.14, 168.39] 132.86 [109.00, 172.79] 139.00 [112.31, 179.95] < 0.001 Laboratory Results WBC (K/mcL) 13.50 [8.95, 19.10] 13.25 [8.70, 18.60] 13.90 [9.00, 19.80] 13.60 [9.15, 19.20] 0.001 Neutrophils (K/mcL) 10.32 [5.92, 15.82] 9.95 [5.55, 14.99] 10.96 [6.22, 16.80] 10.32 [6.30, 15.82] < 0.001 Lymphocyte (K/mcL) 0.81 [0.46, 1.33] 0.82 [0.46, 1.33] 0.81 [0.47, 1.33] 0.80 [0.45, 1.30] 0.988 Platelets (K/mcL) 183.00 [118.00, 266.50] 185.75 [120.50, 278.50] 178.50 [110.50, 257.50] 182.50 [122.25, 257.75] < 0.001 Hematocrit(%) 30.75 [26.65, 35.50] 31.05 [27.50, 35.40] 30.40 [26.25, 35.45] 30.40 [25.75, 35.83] < 0.001 Hemoglobin(g/dl) 9.90 [8.55, 11.50] 10.15 [8.90, 11.60] 9.75 [8.35, 11.30] 9.60 [8.15, 11.50] < 0.001 Aniongap(mmol/L) 15.50 [13.00, 18.50] 15.50 [13.00, 18.00] 15.50 [12.50, 18.50] 15.50 [13.00, 19.00] 0.073 Bicarbonate(mmol/L) 20.75 [18.00, 23.50] 21.00 [18.50, 24.00] 20.50 [17.50, 23.50] 20.50 [17.50, 23.50] < 0.001 Creatinine(mg/dl) 1.40 [0.90, 2.30] 1.35 [0.90, 2.25] 1.35 [0.90, 2.15] 1.60 [1.00, 2.65] < 0.001 BUN(mg/dl) 29.50 [18.00, 47.50] 28.00 [17.50, 46.50] 27.50 [16.50, 44.00] 35.00 [21.00, 55.00] < 0.001 Calcium(mg/dl) 8.05 [7.55, 8.55] 7.90 [7.45, 8.40] 8.10 [7.60, 8.60] 8.20 [7.70, 8.70] < 0.001 Sodium(mmol/L) 137.50 [134.50, 140.50] 138.00 [134.50, 140.50] 137.50 [134.50, 140.00] 138.00 [134.00, 141.50] < 0.001 Potassium (mmol/L) 4.20 [3.80, 4.70] 4.10 [3.75, 4.60] 4.20 [3.83, 4.70] 4.30 [3.85, 4.90] < 0.001 INR 1.45 [1.25, 1.85] 1.40 [1.20, 1.85] 1.45 [1.25, 1.90] 1.45 [1.20, 1.90] 0.003 PT (s) 15.70 [13.60, 20.30] 15.55 [13.60, 20.10] 15.90 [13.75, 20.45] 15.75 [13.45, 20.60] 0.149 APTT (s) 34.10 [29.00, 45.35] 34.90 [29.50, 45.90] 33.65 [28.70, 45.35] 33.10 [28.40, 43.88] < 0.001 Alt(U/L) 30.00 [17.00, 68.38] 31.00 [17.50, 70.00] 30.00 [17.00, 70.00] 29.00 [16.00, 63.00] 0.020 Alp (U/L) 97.00 [67.00, 153.00] 94.00 [65.00, 148.62] 99.50 [68.00, 161.75] 99.00 [71.00, 152.00] < 0.001 Ast(U/L) 47.00 [26.50, 105.00] 47.00 [26.50, 100.12] 47.50 [26.00, 113.50] 48.00 [27.00, 105.75] 0.257 Bilirubin(mg/dl) 0.70 [0.40, 1.50] 0.70 [0.40, 1.50] 0.70 [0.40, 1.70] 0.60 [0.40, 1.40] 0.001 Scores GCS 15.00 [13.00, 15.00] 15.00 [13.00, 15.00] 15.00 [14.00, 15.00] 15.00 [13.00, 15.00] < 0.001 Apsiii 57.50 [45.00, 75.00] 58.00 [45.00, 75.25] 55.00 [43.00, 72.00] 60.00 [47.00, 76.00] < 0.001 Lods 7.00 [4.00, 9.00] 6.00 [4.00, 9.00] 6.00 [4.00, 9.00] 7.00 [5.00, 9.00] < 0.001 Oasis 36.00 [30.00, 43.00] 37.00 [30.00, 43.00] 35.00 [29.00, 42.00] 37.00 [31.00, 44.00] < 0.001 Sapsii 45.00 [35.00, 56.00] 44.00 [35.00, 55.00] 44.00 [34.00, 54.00] 47.00 [38.00, 57.50] < 0.001 Charlson_comorbidity_index 6.00 [4.00, 8.00] 6.00 [4.00, 8.00] 6.00 [4.00, 8.00] 6.00 [4.00, 9.00] < 0.001 Sofa_score 4.00 [3.00, 5.00] 4.00 [2.00, 5.00] 4.00 [3.00, 5.00] 4.00 [3.00, 6.00] 0.001 Comorbidity Hypertesion = YES (%) 1430 (17.0) 65 (1.7) 779 (30.7) 586 (27.3) < 0.001 Coronary heart disease = YES (%) 2215 (26.3) 617 (16.5) 805 (31.7) 793 (36.9) < 0.001 Congestive_heart_failure = YES (%) 2998 (35.6) 1269 (33.9) 870 (34.3) 859 (40.0) < 0.001 cerebrovascular disease = YES (%) 1012 (12.0) 358 (9.6) 186 (7.3) 468 (21.8) < 0.001 Chronic_pulmonary_disease = YES (%) 2284 (27.1) 1097 (29.3) 623 (24.5) 564 (26.3) < 0.001 Renal_disease = YES (%) 2405 (28.5) 975 (26.0) 624 (24.6) 806 (37.5) < 0.001 Rheumatic_disease = YES (%) 355 (4.2) 166 (4.4) 114 (4.5) 75 (3.5) 0.158 Diabetes = YES 2886 (34.2) 1220 (32.6) 842 (33.2) 824 (38.4) < 0.001 Intervention CRRT = YES (%) 1151 (13.7) 356 (9.5) 414 (16.3) 381 (17.7) < 0.001 MV = YES (%) 4921 (58.4) 2135 (57.0) 1404 (55.3) 1382 (64.4) < 0.001 VAS = YES (%) 5382 (63.8) 2574 (68.8) 1547 (60.9) 1261 (58.7) < 0.001 Outcomes Death = YES (%) 2704 (32.1) 1115 (29.8) 844 (33.2) 745 (34.7) < 0.001 Hospital time(days) 11.00 [6.00, 22.00] 10.00 [5.00, 18.00] 11.00 [6.00, 20.00] 17.00 [9.00, 30.00] < 0.001 ICU time (days) 3.92 [2.06, 8.77] 3.71 [2.02, 8.05] 3.59 [1.88, 7.61] 5.19 [2.55, 11.34] < 0.001 Abbreviations: SBP, Systolic Blood Pressure; DBP, Diastolic Blood Pressure; MBP, Mean Blood Pressure;Glu, Glucose;WBC, White Blood Cell count; BUN, Blood Urea Nitrogen;INR, International Normalized Ratio;PT, Prothrombin Time;APTT: Activated Partial Thromboplastin Time;ALT, Alanine Aminotransferase;ALP, Alkaline Phosphatase;AST, Aspartate Aminotransferase;GCS, Glasgow Coma Scale;APACHE II, Acute Physiology and Chronic Health Evaluation II (often abbreviated as APSI in some contexts); LODS. Logistic Organ Dysfunction Score;OASIS, Oxford Acute Severity of Illness Score;SAPS II, Simplified Acute Physiology Score II;CRRT, Continuous Renal Replacement Therapy;MV, Mechanical Ventilation 3.2 Frailty and Outcome Events Outcome events for patients at different frailty levels are shown in Table 1 . The total number of in-hospital deaths was 2,704 (32.1%), with mortality significantly increasing with higher frailty levels (non-frail vs. pre-frail vs. frail: 29.8% vs. 33.2% vs. 34.7%, p < 0.001). ICU length of stay (non-frail vs. pre-frail vs. frail: 3.71 [2.02, 8.05] vs. 3.59 [1.88, 7.61] vs. 5.19 [2.55, 11.34], p < 0.001) and hospital length of stay (non-frail vs. pre-frail vs. frail: 10.00 [5.00, 18.00] vs. 11.00 [6.00, 20.00] vs. 17.00 [9.00, 30.00], p < 0.001) increased with higher frailty levels. Logistic regression analysis was performed to examine the impact of frailty on outcome events, as detailed in Table 2. Model 1 showed that both pre-frail and frail statuses were associated with increased in-hospital mortality (OR and 95% CI: pre-frail vs. non-frail: 1.17 [1.05–1.31], p = 0.003; frail vs. non-frail: 1.25 [1.12–1.40], p < 0.001). Furthermore, when HFRS was treated as a continuous variable, each one-point increase in the score was associated with an increased risk of in-hospital death (OR and 95% CI: 1.01 [1.01–1.02], p < 0.001). Model 2 adjusted for age, sex, the scoring table, and comorbidities, demonstrated that pre-frail and frail patients had higher in-hospital mortality compared to non-frail patients (OR and 95% CI: pre-frail vs. non-frail: 1.37 [1.20–1.56], p < 0.001; frail vs. non-frail: 1.15 [1.00–1.31], p = 0.048). In Model 2, when HFRS was treated as a continuous variable, the direction of the effect on mortality remained consistent with Model 1 (OR and 95% CI:). Model 3, which included additional variables using stepwise regression, showed similar results,both pre-frailty and frailty states continued to show a significant association with in-hospital mortality risk(OR and 95% CI: pre-frail vs. non-frail: 1.33 [1.15–1.53], p < 0.001; frail vs. non-frail: 1.38 [1.18–1.62], p < 0.001). In Model 3, each one-point increase in the HFRS was independently associated with a higher risk of in-hospital mortality (OR and 95% CI: 1.02 [1.01–1.03], p < 0.001). IPW was used for validation, and the results were consistent with those from Model 3, showing that pre-frail and frail patients had an increased in-hospital mortality risk compared to non-frail patients (OR and 95% CI: pre-frail vs. non-frail: 1.28 [1.09–1.49], p = 0.002; frail vs. non-frail: 1.39 [1.16–1.67], p < 0.001). Table2. The association between HFRS and in-hospital mortality. OR (95% CI) P Value Model 1 Non-frailty Reference Pre-frailty 1.17(1.05–1.31) 0.003 Frailty 1.25(1.12–1.40) <0.001 frailty-score 1.01(1.01–1.02) <0.001 Model 2 Non-frailty Reference Pre-frailty 1.37(1.20–1.56) <0.001 Frailty 1.15(1.00–1.31) 0.048 frailty-score 1.01(1.00–1.01) 0.045 Model 3 Non-frailty Reference Pre-frailty 1.33(1.15–1.53) <0.001 Frailty 1.38(1.18–1.62) <0.001 frailty-score 1.02(1.01–1.03) <0.001 IPW model Non-frailty Reference Pre-frailty 1.28(1.09–1.49) 0.002 Frailty 1.39(1.16–1.67) <0.001 Non-frailty as the reference group. P < 0.001 is displayed as "<0.001". Model 1: Unadjusted.Model 2: Adjusted for age, gender, OASIS, SAPS II, LODS, APACHE III, hypertension, chronic hepatitis B, congestive heart failure, cerebrovascular disease, chronic pulmonary disease, renal disease, diabetes, and rheumatic disease.Model 3: Based on Model 2, adjusted using stepwise regression for vasopressors, CRRT, mechanical ventilation (MV), length of hospital stay, ICU stay duration, heart rate, respiratory rate, mean blood pressure (MBP), temperature, SpO2, neutrophils, platelets, hemoglobin, anion gap, bicarbonate, calcium, creatinine, sodium, potassium, activated Partial Thromboplastin Time (APTT), alkaline phosphatase (ALP), and bilirubin.IPW model: Performed weighted regression adjustment and incorporated all the factors from Model 3. Restrictive cubic spline analysis showed a consistent nonlinear positive relationship between HFRS as a continuous variable and the in-hospital mortality risk in sepsis patients. As HFRS increased, the risk of in-hospital death also increased. 3.3 Subgroup Analysis To further validate the robustness and consistency of the results, a subgroup analysis was conducted to assess the relationship between HFRS and in-hospital mortality in sepsis patients. In the subgroup analysis, HFRS staging was positively associated with in-hospital death overall. Statistically significant interactions were found with the use of vasopressors, CRRT, mechanical ventilation (MV), heart rate, respiratory rate, and creatinine (P_interaction < 0.05), indicating that the strength of the association between HFRS and mortality varied across different subgroups. 4. Discussion We explored the relationship between frailty levels, as assessed by the HFRS, and the prognosis of ICU sepsis patients. Our findings suggest that frailty is associated with higher in-hospital mortality and longer hospital stays in sepsis patients. After adjusting for potential confounders, both pre-frail and frail statuses remained independent risk factors for in-hospital death. Restrictive cubic spline analysis (RCS) showed a nonlinear relationship between HFRS and in-hospital mortality risk, with mortality increasing as HFRS scores rose. Frailty is common among critically ill adults and is associated with increased mortality, healthcare utilization, and post-discharge disability,which may also improve risk stratification in ICU patients[ 18 – 20 ]. Sepsis, a systemic inflammatory response syndrome triggered by infection, leads to immune imbalance and widespread tissue damage, resulting in multi-organ dysfunction[ 10 ]. Sepsis-related mortality is not merely due to infection itself but is often the "final blow" for frail individuals[ 3 ]. Aging individuals experience a decline in physiological reserves for various reasons, and their immune and anti-inflammatory functions are compromised, which exacerbates the vulnerability of frail patients. This makes them more susceptible to the severe effects of inappropriate inflammatory responses, which become the “last straw” leading to death[ 21 ]. The HFRS provides additional information beyond age and standard risk factors, including whether a patient has mobility limitations, emotional abnormalities, or cognitive impairment. Previous studies have shown that frailty is significantly associated with a range of adverse outcomes, including disability, infection, and death[ 22 – 24 ]. Over half of sepsis patients are frail[ 25 , 26 ], and frail sepsis patients face double the in-hospital risk compared to non-frail patients[ 26 ], with increased risks of mortality and adverse events[ 12 , 27 ]. Frailty also affects the one-year prognosis of discharged patients[ 28 ], highlighting the need for early frailty screening and intervention. Frailty is a dynamic process, not irreversible[ 21 ], and can be mitigated through moderate exercise, nutritional support, and other interventions, potentially improving outcomes for sepsis patients[ 30 , 31 ]. Previous studies have indicated that frailty is an independent predictor of mortality in sepsis patients[ 29 ]. Using clinical frailty scales, frailty at the time of admission is associated with increased in-hospital mortality (adjusted OR and 95% CI: 2.00 [1.39–2.89], p < 0.001)[ 26 ]. Our study, using the HFRS to assess frailty in sepsis patients, yielded similar results. Even after adjusting for confounding variables and performing IPW analysis, the risk of in-hospital death continued to increase with frailty. The HFRS reflects frailty-related information not captured by age or the Charlson Comorbidity Index. In patients with congestive heart failure, Kwok et al. found that HFRS was a risk factor for increased in-hospital mortality[ 32 ]. Moreover, frailty based on HFRS is an independent risk factor for higher stroke risk[ 33 ], delirium in elderly hospitalized patients[ 34 ], and acute exacerbation in COPD patients[ 35 ], where it is related to longer hospital stays. HFRS, derived from a weighted score based on ICD-10 codes, can be seamlessly integrated into electronic health record systems and holds promise for further clinical application. Subgroup analysis also provided valuable insights. The relationship between frailty and in-hospital mortality in sepsis patients varied by baseline characteristics. Statistically significant interactions were found between frailty and the use of vasopressors, CRRT, mechanical ventilation (MV), heart rate, respiratory rate, and creatinine. Notably, frailty levels did not significantly correlate with in-hospital death in septic shock patients. For sepsis patients who did not receive CRRT and MV, the risk of in-hospital death increased with higher frailty levels. However, for those who received these interventions, the risk of in-hospital death was significantly higher for pre-frail patients, while frail patients showed no significant increase in mortality risk. Additionally, compared to frail patients, pre-frail patients with tachycardia had a higher risk of in-hospital death. This suggests that, for pre-frail patients, the decision to implement mechanical ventilation or CRRT should take their frailty status into account to assess the risk-benefit ratio. Our study has several strengths. First, to our knowledge, this is the first study to investigate the relationship between HFRS and in-hospital mortality in ICU sepsis patients. Second, the data come from the real-world MIMIC-IV database, with a large sample size and robust database, and we used IPW to minimize the effects of confounders. However, our study has some limitations. First, as a retrospective study, it is subject to inherent biases, and potential confounders may not be fully controlled, which could affect the regression analysis. Additionally, the MIMIC database contains some missing or incomplete diagnostic data, which may lead to an underestimation of the HFRS for some patients. Since the HFRS is based on ICD-10 codes, it may not capture all frailty-related factors, potentially introducing some bias in frailty assessment. Lastly, because this is an observational study, it is unable to establish causal relationships between frailty and mortality, and further prospective studies are needed for validation. 5. Conclusion This study examined the relationship between frailty levels, as assessed by the HFRS, and in-hospital mortality in ICU sepsis patients. The results show that frailty levels are associated with higher in-hospital mortality in sepsis patients. Both pre-frail and frail statuses are independent risk factors for in-hospital mortality in sepsis patients, and HFRS scores show a nonlinear positive correlation with in-hospital mortality risk. Abbreviations SBP, Systolic Blood Pressure; DBP, Diastolic Blood Pressure; MBP, Mean Blood Pressure;Glu, Glucose;WBC, White Blood Cell count; BUN, Blood Urea Nitrogen;INR, International Normalized Ratio;PT, Prothrombin Time;APTT: Activated Partial Thromboplastin Time;ALT, Alanine Aminotransferase;ALP, Alkaline Phosphatase;AST, Aspartate Aminotransferase;GCS, Glasgow Coma Scale;APACHE II, Acute Physiology and Chronic Health Evaluation II (often abbreviated as APSI in some contexts); LODS. Logistic Organ Dysfunction Score;OASIS, Oxford Acute Severity of Illness Score;SAPS II, Simplified Acute Physiology Score II;CRRT, Continuous Renal Replacement Therapy;MV, Mechanical Ventilation. Declarations Ethics approval and consent to participate The dataset for this study was obtained from the MIMIC-IV database. We have completed the CITI Program courses on "Human Research and Data" and "Specimen-Only Research" to apply for access to the database (Record ID: 66067288). All personal information of patients included in this database is anonymized, exempting it from ethical review and informed consent requirements. All methods were performed in accordance with the relevant guidelines and regulations. Clinical Trial:Clinical trial number: not applicable. Consent for publication: Not Applicable. Availability of data and materials The datasets used and analysed during the current study available from the corresponding author on reasonable request.The data screening codes used in our analyses, provided by the authors of the MIMIC-IV database, are available on GitHub at (https://github.com/MIT-LCP/ mimic-code). Competing Interests: No conflict of interest is declared by all the authors. Funding: Not Applicable. Authors' contributions YLX Contributed to data curation, conceptualization, data analysis, and manuscript writing. XYW and ADL Contributed to methodology and data curation. XMZ reviewed the initial draft. SBG supervised and reviewed the manuscript. All authors approved the fi6nal manuscript and are responsible for its content. Acknowledgements The authors would like to thank all those who contributed to the completion of this work. References Hoogendijk EO, Afilalo J, Ensrud KE, Kowal P, Onder G, Fried LP. Frailty: implications for clinical practice and public health. Lancet. 2019;394(10206):1365–75. 10.1016/S0140-6736(19)31786-6 . Darvall JN, Bellomo R, Bailey M, Young PJ, Rockwood K, Pilcher D. Impact of frailty on persistent critical illness: a population-based cohort study. Intensive Care Med. 2022;48(3):343–51. 10.1007/s00134-022-06617-0 . Torvik MA, Nymo SH, Nymo SH, Bjørnsen LP, Kvarenes HW, Ofstad EH. Patient characteristics in sepsis-related deaths: prevalence of advanced frailty, comorbidity, and age in a Norwegian hospital trust. Infection. 2023;51(4):1103–15. 10.1007/s15010-023-02013-y . Kim DH, Rockwood K. Frailty in Older Adults. N Engl J Med. 2024;391(6):538–48. 10.1056/NEJMra2301292 . Clegg A, Young J, Iliffe S, Rikkert MO, Rockwood K. Frailty in elderly people. Lancet. 2013;381(9868):752–62. 10.1016/S0140-6736(12)62167-9 . Zhang L, Zeng X, He F, Huang X. Inflammatory biomarkers of frailty: A review. Exp Gerontol. 2023;179:112253. 10.1016/j.exger.2023.112253 . Martín S, Pérez A, Aldecoa C. Sepsis and Immunosenescence in the Elderly Patient: A Review. Front Med (Lausanne). 2017;4:20. 10.3389/fmed.2017.00020 . Afilalo J, Alexander KP, Mack MJ, et al. Frailty assessment in the cardiovascular care of older adults. J Am Coll Cardiol. 2014;63(8):747–62. 10.1016/j.jacc.2013.09.070 . Gilbert T, Neuburger J, Kraindler J, et al. Development and validation of a Hospital Frailty Risk Score focusing on older people in acute care settings using electronic hospital records: an observational study. Lancet. 2018;391(10132):1775–82. 10.1016/S0140-6736(18)30668-8 . Singer M, Deutschman CS, Seymour CW, et al. The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). JAMA. 2016;315(8):801–10. 10.1001/jama.2016.0287 . Cecconi M, Evans L, Levy M, Rhodes A. Sepsis and septic shock. Lancet. 2018;392(10141):75–87. 10.1016/S0140-6736(18)30696-2 . Rudd KE, Johnson SC, Agesa KM, et al. Global, regional, and national sepsis incidence and mortality, 1990–2017: analysis for the Global Burden of Disease Study. Lancet. 2020;395(10219):200–11. 10.1016/S0140-6736(19)32989-7 . Su D, Wang F, Yang Y, et al. The association between frailty and in-hospital mortality in critically ill patients with congestive heart failure: results from MIMIC-IV database. Front Cardiovasc Med. 2024;11:1361542. 10.3389/fcvm.2024.1361542 . Abassi NK, Nouhravesh N, Elmegaard M, et al. Temporal Trends in Mortality and Hospitalization Risk in Patients With Heart Failure According to the Hospital Frailty Risk Score. J Am Heart Assoc. 2025;14(3):e037973. 10.1161/JAHA.124.037973 . Ushida K, Shimizu A, Hori S, Yamamoto Y, Momosaki R. Hospital Frailty Risk Score Predicts Outcomes in Chronic Obstructive Pulmonary Disease Exacerbations. Arch Gerontol Geriatr. 2022;100:104658. 10.1016/j.archger.2022.104658 . Kandula RA, Linquest LA, Kandregula S, Latour M, Ahmed OG, Yim MT. Utility of hospital frailty risk score in predicting postoperative outcomes of sinonasal malignancies. Int Forum Allergy Rhinol. 2024;14(6):1097–100. 10.1002/alr.23307 . Kumar V, Barkoudah E, Jin DX, Banks P, McNabb-Baltar J. Hospital Frailty Risk Score (HFRS) Predicts Adverse Outcomes Among Hospitalized Patients with Chronic Pancreatitis. Dig Dis Sci. 2023;68(7):2890–8. 10.1007/s10620-023-07946-w . De Biasio JC, Mittel AM, Mueller AL, Ferrante LE, Kim DH, Shaefi S. Frailty in Critical Care Medicine: A Review. Anesth Analg. 2020;130(6):1462–73. 10.1213/ANE.0000000000004665 . Brummel NE, Bell SP, Girard TD, et al. Frailty and Subsequent Disability and Mortality among Patients with Critical Illness. Am J Respir Crit Care Med. 2017;196(1):64–72. 10.1164/rccm.201605-0939OC . Fernando SM, McIsaac DI, Perry JJ, et al. Frailty and Associated Outcomes and Resource Utilization Among Older ICU Patients With Suspected Infection. Crit Care Med. 2019;47(8):e669–76. 10.1097/CCM.0000000000003831 . Dent E, Martin FC, Bergman H, Woo J, Romero-Ortuno R, Walston JD. Management of frailty: opportunities, challenges, and future directions. Lancet. 2019;394(10206):1376–86. 10.1016/S0140-6736(19)31785-4 . Fan J, Yu C, Guo Y, et al. Frailty index and all-cause and cause-specific mortality in Chinese adults: a prospective cohort study. Lancet Public Health. 2020;5(12):e650–60. 10.1016/S2468-2667(20)30113-4 . Xu M, Gong Y, Yin X. Association of Frailty With Risk of Incident Hospital-Treated Infections in Middle-Aged and Older Adults: A Large-Scale Prospective Cohort Study. J Gerontol Biol Sci Med Sci. 2024;79(8):glae146. 10.1093/gerona/glae146 . Muscedere J, Waters B, Varambally A, et al. The impact of frailty on intensive care unit outcomes: a systematic review and meta-analysis. Intensive Care Med. 2017;43(8):1105–22. 10.1007/s00134-017-4867-0 . Li Q, Shang N, Gao Q, Guo S, Yang T. Prevalence of sarcopenia and its association with frailty and malnutrition among older patients with sepsis-a cross-sectional study in the emergency department. BMC Geriatr. 2025;25(1):377. 10.1186/s12877-025-06060-y . Lee HY, Lee J, Jung YS, et al. Preexisting Clinical Frailty Is Associated With Worse Clinical Outcomes in Patients With Sepsis. Crit Care Med. 2022;50(5):780–90. 10.1097/CCM.0000000000005360 . Patrizio E, Zambon A, Mazzola P, et al. Assessing the mortality risk in older patients hospitalized with a diagnosis of sepsis: the role of frailty and acute organ dysfunction. Aging Clin Exp Res. 2022;34(10):2335–43. 10.1007/s40520-022-02182-0 . Dong J, Chen R, Song X, Guo Z, Sun W. Quality of life and mortality in older adults with sepsis after one-year follow up: A prospective cohort study demonstrating the significant impact of frailty. Heart Lung. 2023;60:74–80. 10.1016/j.hrtlng.2023.03.002 . Ding H, Li X, Zhang X, Li J, Li Q. The association of a frailty index derived from laboratory tests and vital signs with clinical outcomes in critical care patients with septic shock: a retrospective study based on the MIMIC-IV database. BMC Infect Dis. 2024;24(1):573. 10.1186/s12879-024-09430-w . de Souto Barreto P, Rolland Y, Maltais M, Vellas B, MAPT Study Group. Associations of Multidomain Lifestyle Intervention with Frailty: Secondary Analysis of a Randomized Controlled Trial. Am J Med. 2018;131(11):1382. .e7-1382.e13. Apóstolo J, Cooke R, Bobrowicz-Campos E, et al. Effectiveness of interventions to prevent pre-frailty and frailty progression in older adults: a systematic review. JBI Database Syst Rev Implement Rep. 2018;16(1):140–232. 10.11124/JBISRIR-2017-003382 . Kwok CS, Zieroth S, Van Spall HGC, et al. The Hospital Frailty Risk Score and its association with in-hospital mortality, cost, length of stay and discharge location in patients with heart failure short running title: Frailty and outcomes in heart failure. Int J Cardiol. 2020;300:184–90. 10.1016/j.ijcard.2019.09.064 . Renedo D, Acosta JN, Koo AB, et al. Higher Hospital Frailty Risk Score Is Associated With Increased Risk of Stroke: Observational and Genetic Analyses. Stroke. 2023;54(6):1538–47. 10.1161/STROKEAHA.122.041891 . Lim Z, Ling N, Ho VWT, et al. Delirium is significantly associated with hospital frailty risk score derived from administrative data. Int J Geriatr Psychiatry. 2023;38(1):e5872. 10.1002/gps.5872 . Ushida K, Shimizu A, Hori S, Yamamoto Y, Momosaki R. Hospital Frailty Risk Score Predicts Outcomes in Chronic Obstructive Pulmonary Disease Exacerbations. Arch Gerontol Geriatr. 2022;100:104658. 10.1016/j.archger.2022.104658 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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-7540379","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":530645176,"identity":"f9badc6e-5db4-4d6e-85a9-7291f37d14e2","order_by":0,"name":"Yali Xu","email":"","orcid":"","institution":"Beijing Chaoyang Hospital, Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yali","middleName":"","lastName":"Xu","suffix":""},{"id":530645177,"identity":"c578effe-b1fe-4618-aa51-77c68f05665e","order_by":1,"name":"Xiya Wang","email":"","orcid":"","institution":"Beijing Chaoyang Hospital, Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiya","middleName":"","lastName":"Wang","suffix":""},{"id":530645178,"identity":"a29cf559-a000-4cdd-a20f-fc0f060ecae2","order_by":2,"name":"Andong Li","email":"","orcid":"","institution":"Beijing Chaoyang Hospital, Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Andong","middleName":"","lastName":"Li","suffix":""},{"id":530645179,"identity":"9ebc05a6-8919-4b55-b572-536d1a74ed20","order_by":3,"name":"Shubin Guo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA60lEQVRIiWNgGAWjYJACZgaGAzwMzIyNj39USMjJE6+FvfmwMcMZC2PDBiK1MDDwHEuTZmyrSASx8QL5iOTDnwvb7siYS+QYSBfOk0hgbGB++OgGHi2GN9LSpGe2PeOxnJFjYDxzm0QeOwObsXEOPi0zcsyYedsO8xjcyDFI4N0mUczYwMMmTUCL8WeYlgO8cyQSGw4Q0CIP8gJYy5ljic28DURoMeB5libNc+4Zj8Hx5sOMM45JGBs2E/CLfDswxHjK7tgbHGZs//Ghpk5Onr354WO8thzAEGLGoxxsSwMBBaNgFIyCUTAKGADMyk07YZc/sQAAAABJRU5ErkJggg==","orcid":"","institution":"Beijing Chaoyang Hospital, Capital Medical University","correspondingAuthor":true,"prefix":"","firstName":"Shubin","middleName":"","lastName":"Guo","suffix":""},{"id":530645180,"identity":"3eb03b80-b8fa-4cf0-961e-b41542454314","order_by":4,"name":"Xiaomei Zhu","email":"","orcid":"","institution":"Beijing Chaoyang Hospital, Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiaomei","middleName":"","lastName":"Zhu","suffix":""}],"badges":[],"createdAt":"2025-09-05 03:38:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7540379/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7540379/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":93775240,"identity":"eb6ddcb1-a395-4356-b454-bd5540be91e9","added_by":"auto","created_at":"2025-10-17 12:29:55","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":85440,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.docx","url":"https://assets-eu.researchsquare.com/files/rs-7540379/v1/53fe594b12c91160519a4ad5.docx"},{"id":93775237,"identity":"7ac5530c-3375-422d-83a9-9fc7495ab62b","added_by":"auto","created_at":"2025-10-17 12:29:55","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":28619,"visible":true,"origin":"","legend":"","description":"","filename":"table1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7540379/v1/e1de3f33fb9c6af0d0638206.docx"},{"id":93778173,"identity":"01574f96-1fdd-4e5a-8912-b4477172b399","added_by":"auto","created_at":"2025-10-17 12:45:55","extension":"json","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":7155,"visible":true,"origin":"","legend":"","description":"","filename":"d5d0840cfd5e45ca9dde0324f7e82150.json","url":"https://assets-eu.researchsquare.com/files/rs-7540379/v1/8441993140fdf601de71705e.json"},{"id":93775241,"identity":"8b3f6227-9467-4223-9b9b-327bd7b89b52","added_by":"auto","created_at":"2025-10-17 12:29:55","extension":"xml","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":112480,"visible":true,"origin":"","legend":"","description":"","filename":"d5d0840cfd5e45ca9dde0324f7e821501enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7540379/v1/b8259d5f2820cfd88c9001c5.xml"},{"id":93776419,"identity":"7939a7fc-36bf-42a3-a8a3-c120711fde13","added_by":"auto","created_at":"2025-10-17 12:37:55","extension":"pdf","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":41083,"visible":true,"origin":"","legend":"","description":"","filename":"figure1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7540379/v1/8c4d00a2c5cc5051b916e098.pdf"},{"id":93775243,"identity":"fd489484-cf66-4091-8bc7-38f8a39b48dd","added_by":"auto","created_at":"2025-10-17 12:29:55","extension":"pdf","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":11251,"visible":true,"origin":"","legend":"","description":"","filename":"figure2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7540379/v1/bde4979a5b16e286efa49106.pdf"},{"id":93775245,"identity":"02a93353-8504-46d1-adb5-42dc709d1310","added_by":"auto","created_at":"2025-10-17 12:29:55","extension":"xml","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":112069,"visible":true,"origin":"","legend":"","description":"","filename":"d5d0840cfd5e45ca9dde0324f7e821501structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7540379/v1/b80aea9db9aa7a1a0c07d378.xml"},{"id":93775246,"identity":"205ebe11-0f30-42a9-be59-526e1d64e3e3","added_by":"auto","created_at":"2025-10-17 12:29:55","extension":"html","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":119098,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7540379/v1/b8fdbbea17ed02fd3be532d6.html"},{"id":93775239,"identity":"6e871d65-559f-4a8c-b412-219a0095b0fa","added_by":"auto","created_at":"2025-10-17 12:29:55","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":99084,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version.\u003c/p\u003e","description":"","filename":"figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7540379/v1/b144347d57339ebd5c2cc30e.png"},{"id":93775244,"identity":"a41ba8f4-65ef-44a8-8c8f-3588742d6597","added_by":"auto","created_at":"2025-10-17 12:29:55","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":227115,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version.\u003c/p\u003e","description":"","filename":"figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7540379/v1/a698e6f9d289d9fdae7f1c8f.png"},{"id":95221601,"identity":"7c50caa7-1134-45c8-9409-b43ecb6d5fd9","added_by":"auto","created_at":"2025-11-05 16:19:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1169136,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7540379/v1/815639be-5ecd-417b-a935-d8b66e7d8dbc.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eThe hospital frailty risk score in critically ill patients with sepsis is an independent risk factor for in-hospital mortality : results from MIMIC-IV database\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eFrailty is a complex, clinically identifiable syndrome characterized by the decline in the function of multiple physiological systems, accompanied by an increased susceptibility to stressors, which in turn leads to an elevated risk of various adverse outcomes[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Frailty becomes more prevalent with age and is a significant risk factor for the progression of older ICU patients to persistent critical illness and death. Approximately 40% of ICU patients with infections are frail[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. As a common geriatric syndrome, frailty is often associated with immune dysfunction and low-grade chronic inflammation, which resemble the immune imbalance seen in sepsis patients. This similarity complicates the diagnosis and treatment of sepsis. Increasingly, research has identified frailty as one of the key factors influencing the prognosis of sepsis patients[\u003cspan additionalcitationids=\"CR5 CR6\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThere are currently several frailty assessment tools available for clinical and research practice[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Among these, the Hospital Frailty Risk Score (HFRS), developed and validated by Gilbert et al., is designed to identify frail patients at low, moderate, and high risk. This score relies on the diagnostic codes from the International Classification of Diseases, 10th edition (ICD-10) to identify patients who may be at risk for adverse healthcare outcomes, offering stronger clinical applicability[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Sepsis is defined as a life-threatening organ dysfunction caused by a dysregulated host response to infection[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], and it is one of the most common causes of morbidity and mortality among critically ill patients[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The HFRS has demonstrated its ability to predict clinical outcomes in various populations, including those with heart failure[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], chronic obstructive pulmonary disease[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], malignancy[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], and pancreatitis[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. However, there remains limited application research on the use of HFRS in sepsis patients in the ICU. The objective of this study is to explore the relationship between frailty, as assessed by the HFRS, and inpatient mortality in ICU patients with sepsis.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Data Extraction\u003c/h2\u003e\u003cp\u003eThis study is an observational, retrospective research based on data from the MIMIC-IV database (Record ID: 66067288). The database is an open relational database managed by the Massachusetts Institute of Technology's Laboratory for Computational Physiology. It includes data from ICU patients at the Beth Israel Deaconess Medical Center in Boston, covering the period from 2008 to 2019. The dataset consists of 431,231 hospital admission records and 73,181 ICU admission records, with diagnoses coded according to the ICD-9 and ICD-10 classification systems to ensure accurate disease categorization.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Study Population\u003c/h2\u003e\u003cp\u003eFor analysis, we included patients who were admitted to the ICU for the first time and diagnosed with sepsis. We excluded patients who were younger than 18 years or whose ICU stay was less than 24 hours. A total of 8,430 sepsis patients were included in the study.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Frailty Assessment Based on ICD-10 Codes\u003c/h2\u003e\u003cp\u003eFrailty levels in sepsis patients were assessed using the Hospital Frailty Risk Score (HFRS). The HFRS is a weighted scoring system based on ICD-10 codes for hospitalized patients. It calculates the initial hospital score for each patient based on one or more of the 109 ICD-10 diagnostic codes recorded at the time of their first admission. The HFRS value is the sum of the weights of these codes. The HFRS includes 109 ICD-10 codes, which not only encompass traditional chronic diseases but also codes for limitations in mobility, cognitive impairment, and emotional disorders. This expanded scope allows the identification of frailty-related factors that go beyond conventional comorbidities, making it a valuable tool for identifying high-risk frail patients. Frailty was categorized according to HFRS as follows: non-frail (HFRS\u0026thinsp;\u0026lt;\u0026thinsp;5), pre-frail (HFRS between 5 and 14), and frail (HFRS\u0026thinsp;\u0026ge;\u0026thinsp;15).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Outcomes\u003c/h2\u003e\u003cp\u003eThe primary outcome was inpatient mortality in sepsis patients, while the secondary outcomes were ICU stay duration and total hospital stay.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Statistical Analysis\u003c/h2\u003e\u003cp\u003eDuring the data preprocessing phase, variables with more than 30% missing data were excluded. Missing values for other variables were imputed using the PMM multiple imputation method from the MICE package. Baseline characteristics were summarized by frailty stage. Normality of continuous variables was tested using the Anderson-Darling test. For skewed distributions, median values and interquartile ranges were reported, and the Kruskal-Wallis H test was used to compare characteristics across different frailty stages. Categorical variables were described using frequencies (proportions), and the chi-square test was applied. A P-value of less than 0.05 was considered statistically significant.\u003c/p\u003e\u003cp\u003eUnivariate and multivariate logistic regression models were used to examine the relationship between frailty levels and mortality in sepsis patients. The \"non-frail\" group served as the reference, and the impacts of \"pre-frail\" and \"frail\" statuses on the outcomes were compared. Frailty scores were also treated as a continuous variable in the regression models to assess the relative change in adverse outcomes for every one-point increase in the score. Three regression models were constructed: Model 1 was a univariate analysis that included frailty status alone; Model 2 adjusted for age, sex, the scoring table, and comorbid conditions; and Model 3 further adjusted for other potential confounders using a bidirectional stepwise regression approach. To validate the robustness of the multivariate regression results, an additional analysis using the inverse probability weighting (IPW) method was performed. All regression results are reported using odds ratios (OR) with 95% confidence intervals. Furthermore, to further explore the relationship between frailty status and mortality in sepsis patients, we conducted a subgroup analysis to assess how different subgroup characteristics influenced the relationship between frailty and mortality.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Baseline Characteristics\u003c/h2\u003e\u003cp\u003eA total of 8,430 sepsis patients were included in this study from the MIMIC-IV database. Baseline characteristics are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. According to the HFRS score, 3,744 patients (44.4%) were classified as non-frail, 2,539 patients (30.1%) as pre-frail, and 2,147 patients (25.5%) as frail. The mean age was 69.39 [58.37, 79.76] years, with 3,761 males (44.6%). Patients with higher frailty levels tended to be older, with a higher proportion of females. In terms of vital signs, significant differences were observed in heart rate, systolic blood pressure, diastolic blood pressure, mean arterial pressure, temperature, oxygen saturation, and blood glucose levels across different frailty groups. Specifically, patients with higher frailty levels had lower heart rates, and higher blood pressure and blood glucose levels. Laboratory tests showed significant differences in white blood cells, neutrophils, platelets, hematocrit, hemoglobin, bicarbonate, creatinine, blood urea nitrogen, calcium, sodium, potassium, INR, APTT, Alt, Alp, and bilirubin across different frailty stages. As frailty increased, comorbidities such as hypertension, coronary artery disease, cerebrovascular diseases, congestive heart failure, chronic lung diseases, kidney disease, and diabetes became more prevalent. Additionally, pre-frail and frail patients received more mechanical ventilation and CRRT interventions compared to non-frail patients.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline characteristics septic patients in MIMIC-IV database stratified according to frailty levels.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOverall (n\u0026thinsp;=\u0026thinsp;8430)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNon-frailty (n\u0026thinsp;=\u0026thinsp;3744)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePre-frailty (n\u0026thinsp;=\u0026thinsp;2539)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eFrailty (n\u0026thinsp;=\u0026thinsp;2147)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (year)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e69.39 [58.37, 79.76]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e68.82 [57.24, 80.77]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e68.12 [57.57, 77.33]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e71.69 [61.03, 80.77]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u0026thinsp;=\u0026thinsp;Male (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3761 (44.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1713 (45.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1101 (43.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e947 (44.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.150\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVital sign\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeart_rate(beats/min)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e90.85 [78.77, 103.58]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e91.92 [79.68, 104.38]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e90.36 [78.60, 103.76]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e89.48 [77.71, 101.25]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eResp_rate (beats/min)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20.91 [18.04, 24.08]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20.91 [18.09, 23.95]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20.94 [18.04, 24.20]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e20.85 [17.96, 24.10]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.847\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSBP(mmHg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e107.68 [101.08, 116.06]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e107.36 [100.97, 115.37]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e107.00 [100.68, 115.33]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e109.18 [102.14, 117.52]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDBP(mmHg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e59.19 [53.71, 65.22]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e58.19 [52.72, 63.96]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e59.89 [54.52, 65.94]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e60.30 [54.50, 66.78]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMBP(mmHg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e72.86 [68.04, 78.63]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e71.35 [66.48, 76.83]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e73.50 [69.15, 79.17]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e74.45 [69.71, 80.62]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTemperature (℃)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e36.88 [36.60, 37.26]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e36.88 [36.56, 37.29]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e36.89 [36.65, 37.24]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e36.87 [36.62, 37.24]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.029\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpO\u003csub\u003e2\u003c/sub\u003e (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e96.71 [95.27, 98.11]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e96.84 [95.45, 98.20]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e96.49 [95.06, 97.90]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e96.74 [95.27, 98.15]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGlu(mg/dl)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e134.06 [109.33, 173.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e132.40 [108.14, 168.39]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e132.86 [109.00, 172.79]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e139.00 [112.31, 179.95]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLaboratory Results\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWBC (K/mcL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13.50 [8.95, 19.10]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13.25 [8.70, 18.60]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13.90 [9.00, 19.80]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e13.60 [9.15, 19.20]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeutrophils (K/mcL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.32 [5.92, 15.82]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.95 [5.55, 14.99]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.96 [6.22, 16.80]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10.32 [6.30, 15.82]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLymphocyte (K/mcL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.81 [0.46, 1.33]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.82 [0.46, 1.33]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.81 [0.47, 1.33]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.80 [0.45, 1.30]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.988\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlatelets (K/mcL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e183.00 [118.00, 266.50]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e185.75 [120.50, 278.50]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e178.50 [110.50, 257.50]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e182.50 [122.25, 257.75]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHematocrit(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e30.75 [26.65, 35.50]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e31.05 [27.50, 35.40]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30.40 [26.25, 35.45]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e30.40 [25.75, 35.83]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHemoglobin(g/dl)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.90 [8.55, 11.50]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.15 [8.90, 11.60]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.75 [8.35, 11.30]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9.60 [8.15, 11.50]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAniongap(mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15.50 [13.00, 18.50]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15.50 [13.00, 18.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15.50 [12.50, 18.50]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e15.50 [13.00, 19.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.073\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBicarbonate(mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20.75 [18.00, 23.50]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21.00 [18.50, 24.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20.50 [17.50, 23.50]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e20.50 [17.50, 23.50]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCreatinine(mg/dl)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.40 [0.90, 2.30]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.35 [0.90, 2.25]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.35 [0.90, 2.15]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.60 [1.00, 2.65]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBUN(mg/dl)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e29.50 [18.00, 47.50]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28.00 [17.50, 46.50]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e27.50 [16.50, 44.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e35.00 [21.00, 55.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCalcium(mg/dl)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.05 [7.55, 8.55]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.90 [7.45, 8.40]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.10 [7.60, 8.60]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8.20 [7.70, 8.70]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSodium(mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e137.50 [134.50, 140.50]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e138.00 [134.50, 140.50]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e137.50 [134.50, 140.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e138.00 [134.00, 141.50]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePotassium (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.20 [3.80, 4.70]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.10 [3.75, 4.60]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.20 [3.83, 4.70]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.30 [3.85, 4.90]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eINR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.45 [1.25, 1.85]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.40 [1.20, 1.85]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.45 [1.25, 1.90]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.45 [1.20, 1.90]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePT (s)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15.70 [13.60, 20.30]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15.55 [13.60, 20.10]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15.90 [13.75, 20.45]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e15.75 [13.45, 20.60]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.149\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAPTT (s)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e34.10 [29.00, 45.35]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e34.90 [29.50, 45.90]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e33.65 [28.70, 45.35]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e33.10 [28.40, 43.88]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlt(U/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e30.00 [17.00, 68.38]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e31.00 [17.50, 70.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30.00 [17.00, 70.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e29.00 [16.00, 63.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.020\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlp (U/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e97.00 [67.00, 153.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e94.00 [65.00, 148.62]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e99.50 [68.00, 161.75]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e99.00 [71.00, 152.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAst(U/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e47.00 [26.50, 105.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e47.00 [26.50, 100.12]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e47.50 [26.00, 113.50]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e48.00 [27.00, 105.75]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.257\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBilirubin(mg/dl)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.70 [0.40, 1.50]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.70 [0.40, 1.50]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.70 [0.40, 1.70]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.60 [0.40, 1.40]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eScores\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGCS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15.00 [13.00, 15.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15.00 [13.00, 15.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15.00 [14.00, 15.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e15.00 [13.00, 15.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eApsiii\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e57.50 [45.00, 75.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e58.00 [45.00, 75.25]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e55.00 [43.00, 72.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e60.00 [47.00, 76.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLods\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.00 [4.00, 9.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.00 [4.00, 9.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.00 [4.00, 9.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.00 [5.00, 9.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOasis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e36.00 [30.00, 43.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e37.00 [30.00, 43.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e35.00 [29.00, 42.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e37.00 [31.00, 44.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSapsii\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e45.00 [35.00, 56.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e44.00 [35.00, 55.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e44.00 [34.00, 54.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e47.00 [38.00, 57.50]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharlson_comorbidity_index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.00 [4.00, 8.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.00 [4.00, 8.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.00 [4.00, 8.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.00 [4.00, 9.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSofa_score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.00 [3.00, 5.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.00 [2.00, 5.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.00 [3.00, 5.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.00 [3.00, 6.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eComorbidity\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertesion\u0026thinsp;=\u0026thinsp;YES (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1430 (17.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e65 (1.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e779 (30.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e586 (27.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCoronary heart disease\u0026thinsp;=\u0026thinsp;YES (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2215 (26.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e617 (16.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e805 (31.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e793 (36.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCongestive_heart_failure\u0026thinsp;=\u0026thinsp;YES (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2998 (35.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1269 (33.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e870 (34.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e859 (40.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ecerebrovascular disease\u0026thinsp;=\u0026thinsp;YES (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1012 (12.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e358 (9.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e186 (7.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e468 (21.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChronic_pulmonary_disease\u0026thinsp;=\u0026thinsp;YES (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2284 (27.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1097 (29.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e623 (24.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e564 (26.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRenal_disease\u0026thinsp;=\u0026thinsp;YES (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2405 (28.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e975 (26.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e624 (24.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e806 (37.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRheumatic_disease\u0026thinsp;=\u0026thinsp;YES (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e355 (4.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e166 (4.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e114 (4.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e75 (3.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.158\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes\u0026thinsp;=\u0026thinsp;YES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2886 (34.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1220 (32.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e842 (33.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e824 (38.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eIntervention\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCRRT\u0026thinsp;=\u0026thinsp;YES (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1151 (13.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e356 (9.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e414 (16.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e381 (17.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMV\u0026thinsp;=\u0026thinsp;YES (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4921 (58.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2135 (57.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1404 (55.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1382 (64.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVAS\u0026thinsp;=\u0026thinsp;YES (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5382 (63.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2574 (68.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1547 (60.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1261 (58.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eOutcomes\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDeath\u0026thinsp;=\u0026thinsp;YES (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2704 (32.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1115 (29.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e844 (33.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e745 (34.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHospital time(days)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11.00 [6.00, 22.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.00 [5.00, 18.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11.00 [6.00, 20.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e17.00 [9.00, 30.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eICU time (days)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.92 [2.06, 8.77]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.71 [2.02, 8.05]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.59 [1.88, 7.61]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.19 [2.55, 11.34]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eAbbreviations: SBP, Systolic Blood Pressure; DBP, Diastolic Blood Pressure; MBP, Mean Blood Pressure;Glu, Glucose;WBC, White Blood Cell count; BUN, Blood Urea Nitrogen;INR, International Normalized Ratio;PT, Prothrombin Time;APTT: Activated Partial Thromboplastin Time;ALT, Alanine Aminotransferase;ALP, Alkaline Phosphatase;AST, Aspartate Aminotransferase;GCS, Glasgow Coma Scale;APACHE II, Acute Physiology and Chronic Health Evaluation II (often abbreviated as APSI in some contexts); LODS. Logistic Organ Dysfunction Score;OASIS, Oxford Acute Severity of Illness Score;SAPS II, Simplified Acute Physiology Score II;CRRT, Continuous Renal Replacement Therapy;MV, Mechanical Ventilation\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Frailty and Outcome Events\u003c/h2\u003e\u003cp\u003eOutcome events for patients at different frailty levels are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The total number of in-hospital deaths was 2,704 (32.1%), with mortality significantly increasing with higher frailty levels (non-frail vs. pre-frail vs. frail: 29.8% vs. 33.2% vs. 34.7%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). ICU length of stay (non-frail vs. pre-frail vs. frail: 3.71 [2.02, 8.05] vs. 3.59 [1.88, 7.61] vs. 5.19 [2.55, 11.34], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and hospital length of stay (non-frail vs. pre-frail vs. frail: 10.00 [5.00, 18.00] vs. 11.00 [6.00, 20.00] vs. 17.00 [9.00, 30.00], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) increased with higher frailty levels.\u003c/p\u003e\u003cp\u003eLogistic regression analysis was performed to examine the impact of frailty on outcome events, as detailed in Table\u0026nbsp;2. Model 1 showed that both pre-frail and frail statuses were associated with increased in-hospital mortality (OR and 95% CI: pre-frail vs. non-frail: 1.17 [1.05\u0026ndash;1.31], p\u0026thinsp;=\u0026thinsp;0.003; frail vs. non-frail: 1.25 [1.12\u0026ndash;1.40], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Furthermore, when HFRS was treated as a continuous variable, each one-point increase in the score was associated with an increased risk of in-hospital death (OR and 95% CI: 1.01 [1.01\u0026ndash;1.02], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Model 2 adjusted for age, sex, the scoring table, and comorbidities, demonstrated that pre-frail and frail patients had higher in-hospital mortality compared to non-frail patients (OR and 95% CI: pre-frail vs. non-frail: 1.37 [1.20\u0026ndash;1.56], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; frail vs. non-frail: 1.15 [1.00\u0026ndash;1.31], p\u0026thinsp;=\u0026thinsp;0.048). In Model 2, when HFRS was treated as a continuous variable, the direction of the effect on mortality remained consistent with Model 1 (OR and 95% CI:). Model 3, which included additional variables using stepwise regression, showed similar results,both pre-frailty and frailty states continued to show a significant association with in-hospital mortality risk(OR and 95% CI: pre-frail vs. non-frail: 1.33 [1.15\u0026ndash;1.53], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; frail vs. non-frail: 1.38 [1.18\u0026ndash;1.62], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In Model 3, each one-point increase in the HFRS was independently associated with a higher risk of in-hospital mortality (OR and 95% CI: 1.02 [1.01\u0026ndash;1.03], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). IPW was used for validation, and the results were consistent with those from Model 3, showing that pre-frail and frail patients had an increased in-hospital mortality risk compared to non-frail patients (OR and 95% CI: pre-frail vs. non-frail: 1.28 [1.09\u0026ndash;1.49], p\u0026thinsp;=\u0026thinsp;0.002; frail vs. non-frail: 1.39 [1.16\u0026ndash;1.67], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003eTable2. The association between HFRS and in-hospital mortality.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 502px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eNon-frailty\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003ePre-frailty\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003e1.17(1.05\u0026ndash;1.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eFrailty\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003e1.25(1.12\u0026ndash;1.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003efrailty-score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003e1.01(1.01\u0026ndash;1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 502px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eNon-frailty\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003ePre-frailty\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003e1.37(1.20\u0026ndash;1.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eFrailty\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003e1.15(1.00\u0026ndash;1.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003efrailty-score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003e1.01(1.00\u0026ndash;1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 502px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eNon-frailty\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003ePre-frailty\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003e1.33(1.15\u0026ndash;1.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eFrailty\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003e1.38(1.18\u0026ndash;1.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003efrailty-score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003e1.02(1.01\u0026ndash;1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 502px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIPW model\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eNon-frailty\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003ePre-frailty\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003e1.28(1.09\u0026ndash;1.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eFrailty\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003e1.39(1.16\u0026ndash;1.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 502px;\"\u003e\n \u003cp\u003eNon-frailty as the reference group. P \u0026lt; 0.001 is displayed as \u0026quot;\u0026lt;0.001\u0026quot;.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eModel 1: Unadjusted.Model 2: Adjusted for age, gender, OASIS, SAPS II, LODS, APACHE III, hypertension, chronic hepatitis B, congestive heart failure, cerebrovascular disease, chronic pulmonary disease, renal disease, diabetes, and rheumatic disease.Model 3: Based on Model 2, adjusted using stepwise regression for vasopressors, CRRT, mechanical ventilation (MV), length of hospital stay, ICU stay duration, heart rate, respiratory rate, mean blood pressure (MBP), temperature, SpO2, neutrophils, platelets, hemoglobin, anion gap, bicarbonate, calcium, creatinine, sodium, potassium, activated Partial Thromboplastin Time (APTT), alkaline phosphatase (ALP), and bilirubin.IPW model: Performed weighted regression adjustment and incorporated all the factors from Model 3.\u003c/p\u003e\u003cp\u003eRestrictive cubic spline analysis showed a consistent nonlinear positive relationship between HFRS as a continuous variable and the in-hospital mortality risk in sepsis patients. As HFRS increased, the risk of in-hospital death also increased.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Subgroup Analysis\u003c/h2\u003e\u003cp\u003eTo further validate the robustness and consistency of the results, a subgroup analysis was conducted to assess the relationship between HFRS and in-hospital mortality in sepsis patients. In the subgroup analysis, HFRS staging was positively associated with in-hospital death overall. Statistically significant interactions were found with the use of vasopressors, CRRT, mechanical ventilation (MV), heart rate, respiratory rate, and creatinine (P_interaction\u0026thinsp;\u0026lt;\u0026thinsp;0.05), indicating that the strength of the association between HFRS and mortality varied across different subgroups.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eWe explored the relationship between frailty levels, as assessed by the HFRS, and the prognosis of ICU sepsis patients. Our findings suggest that frailty is associated with higher in-hospital mortality and longer hospital stays in sepsis patients. After adjusting for potential confounders, both pre-frail and frail statuses remained independent risk factors for in-hospital death. Restrictive cubic spline analysis (RCS) showed a nonlinear relationship between HFRS and in-hospital mortality risk, with mortality increasing as HFRS scores rose.\u003c/p\u003e\u003cp\u003eFrailty is common among critically ill adults and is associated with increased mortality, healthcare utilization, and post-discharge disability,which may also improve risk stratification in ICU patients[\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Sepsis, a systemic inflammatory response syndrome triggered by infection, leads to immune imbalance and widespread tissue damage, resulting in multi-organ dysfunction[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Sepsis-related mortality is not merely due to infection itself but is often the \"final blow\" for frail individuals[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Aging individuals experience a decline in physiological reserves for various reasons, and their immune and anti-inflammatory functions are compromised, which exacerbates the vulnerability of frail patients. This makes them more susceptible to the severe effects of inappropriate inflammatory responses, which become the \u0026ldquo;last straw\u0026rdquo; leading to death[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe HFRS provides additional information beyond age and standard risk factors, including whether a patient has mobility limitations, emotional abnormalities, or cognitive impairment. Previous studies have shown that frailty is significantly associated with a range of adverse outcomes, including disability, infection, and death[\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Over half of sepsis patients are frail[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], and frail sepsis patients face double the in-hospital risk compared to non-frail patients[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], with increased risks of mortality and adverse events[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Frailty also affects the one-year prognosis of discharged patients[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], highlighting the need for early frailty screening and intervention. Frailty is a dynamic process, not irreversible[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], and can be mitigated through moderate exercise, nutritional support, and other interventions, potentially improving outcomes for sepsis patients[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e\u003cp\u003ePrevious studies have indicated that frailty is an independent predictor of mortality in sepsis patients[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Using clinical frailty scales, frailty at the time of admission is associated with increased in-hospital mortality (adjusted OR and 95% CI: 2.00 [1.39\u0026ndash;2.89], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Our study, using the HFRS to assess frailty in sepsis patients, yielded similar results. Even after adjusting for confounding variables and performing IPW analysis, the risk of in-hospital death continued to increase with frailty.\u003c/p\u003e\u003cp\u003eThe HFRS reflects frailty-related information not captured by age or the Charlson Comorbidity Index. In patients with congestive heart failure, Kwok et al. found that HFRS was a risk factor for increased in-hospital mortality[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Moreover, frailty based on HFRS is an independent risk factor for higher stroke risk[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], delirium in elderly hospitalized patients[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], and acute exacerbation in COPD patients[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], where it is related to longer hospital stays. HFRS, derived from a weighted score based on ICD-10 codes, can be seamlessly integrated into electronic health record systems and holds promise for further clinical application.\u003c/p\u003e\u003cp\u003eSubgroup analysis also provided valuable insights. The relationship between frailty and in-hospital mortality in sepsis patients varied by baseline characteristics. Statistically significant interactions were found between frailty and the use of vasopressors, CRRT, mechanical ventilation (MV), heart rate, respiratory rate, and creatinine. Notably, frailty levels did not significantly correlate with in-hospital death in septic shock patients. For sepsis patients who did not receive CRRT and MV, the risk of in-hospital death increased with higher frailty levels. However, for those who received these interventions, the risk of in-hospital death was significantly higher for pre-frail patients, while frail patients showed no significant increase in mortality risk. Additionally, compared to frail patients, pre-frail patients with tachycardia had a higher risk of in-hospital death. This suggests that, for pre-frail patients, the decision to implement mechanical ventilation or CRRT should take their frailty status into account to assess the risk-benefit ratio.\u003c/p\u003e\u003cp\u003eOur study has several strengths. First, to our knowledge, this is the first study to investigate the relationship between HFRS and in-hospital mortality in ICU sepsis patients. Second, the data come from the real-world MIMIC-IV database, with a large sample size and robust database, and we used IPW to minimize the effects of confounders.\u003c/p\u003e\u003cp\u003eHowever, our study has some limitations. First, as a retrospective study, it is subject to inherent biases, and potential confounders may not be fully controlled, which could affect the regression analysis. Additionally, the MIMIC database contains some missing or incomplete diagnostic data, which may lead to an underestimation of the HFRS for some patients. Since the HFRS is based on ICD-10 codes, it may not capture all frailty-related factors, potentially introducing some bias in frailty assessment. Lastly, because this is an observational study, it is unable to establish causal relationships between frailty and mortality, and further prospective studies are needed for validation.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study examined the relationship between frailty levels, as assessed by the HFRS, and in-hospital mortality in ICU sepsis patients. The results show that frailty levels are associated with higher in-hospital mortality in sepsis patients. Both pre-frail and frail statuses are independent risk factors for in-hospital mortality in sepsis patients, and HFRS scores show a nonlinear positive correlation with in-hospital mortality risk.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eSBP, Systolic Blood Pressure; DBP, Diastolic Blood Pressure; MBP, Mean Blood Pressure;Glu, Glucose;WBC, White Blood Cell count; BUN, Blood Urea Nitrogen;INR, International Normalized Ratio;PT, Prothrombin Time;APTT: Activated Partial Thromboplastin Time;ALT, Alanine Aminotransferase;ALP, Alkaline Phosphatase;AST, Aspartate Aminotransferase;GCS, Glasgow Coma Scale;APACHE II, Acute Physiology and Chronic Health Evaluation II (often abbreviated as APSI in some contexts); LODS. Logistic Organ Dysfunction Score;OASIS, Oxford Acute Severity of Illness Score;SAPS II, Simplified Acute Physiology Score II;CRRT, Continuous Renal Replacement Therapy;MV, Mechanical Ventilation.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eThe dataset for this study was obtained from the MIMIC-IV database. We have completed the CITI Program courses on \"Human Research and Data\" and \"Specimen-Only Research\" to apply for access to the database (Record ID: 66067288). All personal information of patients included in this database is anonymized, exempting it from ethical review and informed consent requirements. All methods were performed in accordance with the relevant guidelines and regulations.\u003c/p\u003e\n\u003cp\u003eClinical Trial:Clinical trial number: not applicable.\u003c/p\u003e\n\u003cp\u003eConsent for publication: Not Applicable.\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials\u003c/p\u003e\n\u003cp\u003eThe datasets used and analysed during the current study available from the corresponding author on reasonable request.The data screening codes used in our analyses, provided by the authors of the MIMIC-IV database, are available on GitHub at (https://github.com/MIT-LCP/ mimic-code).\u003c/p\u003e\n\u003cp\u003eCompeting Interests: No conflict of interest is declared by all the authors.\u003c/p\u003e\n\u003cp\u003eFunding: Not Applicable.\u003c/p\u003e\n\u003cp\u003eAuthors' contributions\u003c/p\u003e\n\u003cp\u003eYLX Contributed to data curation, conceptualization, data analysis, and manuscript writing. XYW and ADL Contributed to methodology and data curation. XMZ \u0026nbsp;reviewed the initial draft. SBG supervised and reviewed the manuscript. All authors approved the fi6nal manuscript and are responsible for its content.\u003c/p\u003e\n\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank all those who contributed to the completion of this work.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHoogendijk EO, Afilalo J, Ensrud KE, Kowal P, Onder G, Fried LP. Frailty: implications for clinical practice and public health. Lancet. 2019;394(10206):1365\u0026ndash;75. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S0140-6736(19)31786-6\u003c/span\u003e\u003cspan address=\"10.1016/S0140-6736(19)31786-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDarvall JN, Bellomo R, Bailey M, Young PJ, Rockwood K, Pilcher D. Impact of frailty on persistent critical illness: a population-based cohort study. Intensive Care Med. 2022;48(3):343\u0026ndash;51. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00134-022-06617-0\u003c/span\u003e\u003cspan address=\"10.1007/s00134-022-06617-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTorvik MA, Nymo SH, Nymo SH, Bj\u0026oslash;rnsen LP, Kvarenes HW, Ofstad EH. Patient characteristics in sepsis-related deaths: prevalence of advanced frailty, comorbidity, and age in a Norwegian hospital trust. Infection. 2023;51(4):1103\u0026ndash;15. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s15010-023-02013-y\u003c/span\u003e\u003cspan address=\"10.1007/s15010-023-02013-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKim DH, Rockwood K. Frailty in Older Adults. N Engl J Med. 2024;391(6):538\u0026ndash;48. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1056/NEJMra2301292\u003c/span\u003e\u003cspan address=\"10.1056/NEJMra2301292\" 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\"\u003e10.1016/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\u003eZhang L, Zeng X, He F, Huang X. Inflammatory biomarkers of frailty: A review. Exp Gerontol. 2023;179:112253. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.exger.2023.112253\u003c/span\u003e\u003cspan address=\"10.1016/j.exger.2023.112253\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMart\u0026iacute;n S, P\u0026eacute;rez A, Aldecoa C. Sepsis and Immunosenescence in the Elderly Patient: A Review. Front Med (Lausanne). 2017;4:20. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fmed.2017.00020\u003c/span\u003e\u003cspan address=\"10.3389/fmed.2017.00020\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAfilalo J, Alexander KP, Mack MJ, et al. Frailty assessment in the cardiovascular care of older adults. J Am Coll Cardiol. 2014;63(8):747\u0026ndash;62. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jacc.2013.09.070\u003c/span\u003e\u003cspan address=\"10.1016/j.jacc.2013.09.070\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGilbert T, Neuburger J, Kraindler J, et al. Development and validation of a Hospital Frailty Risk Score focusing on older people in acute care settings using electronic hospital records: an observational study. Lancet. 2018;391(10132):1775\u0026ndash;82. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S0140-6736(18)30668-8\u003c/span\u003e\u003cspan address=\"10.1016/S0140-6736(18)30668-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSinger M, Deutschman CS, Seymour CW, et al. The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). JAMA. 2016;315(8):801\u0026ndash;10. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1001/jama.2016.0287\u003c/span\u003e\u003cspan address=\"10.1001/jama.2016.0287\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCecconi M, Evans L, Levy M, Rhodes A. Sepsis and septic shock. Lancet. 2018;392(10141):75\u0026ndash;87. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S0140-6736(18)30696-2\u003c/span\u003e\u003cspan address=\"10.1016/S0140-6736(18)30696-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRudd KE, Johnson SC, Agesa KM, et al. Global, regional, and national sepsis incidence and mortality, 1990\u0026ndash;2017: analysis for the Global Burden of Disease Study. Lancet. 2020;395(10219):200\u0026ndash;11. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S0140-6736(19)32989-7\u003c/span\u003e\u003cspan address=\"10.1016/S0140-6736(19)32989-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSu D, Wang F, Yang Y, et al. The association between frailty and in-hospital mortality in critically ill patients with congestive heart failure: results from MIMIC-IV database. Front Cardiovasc Med. 2024;11:1361542. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fcvm.2024.1361542\u003c/span\u003e\u003cspan address=\"10.3389/fcvm.2024.1361542\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAbassi NK, Nouhravesh N, Elmegaard M, et al. Temporal Trends in Mortality and Hospitalization Risk in Patients With Heart Failure According to the Hospital Frailty Risk Score. J Am Heart Assoc. 2025;14(3):e037973. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1161/JAHA.124.037973\u003c/span\u003e\u003cspan address=\"10.1161/JAHA.124.037973\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eUshida K, Shimizu A, Hori S, Yamamoto Y, Momosaki R. Hospital Frailty Risk Score Predicts Outcomes in Chronic Obstructive Pulmonary Disease Exacerbations. Arch Gerontol Geriatr. 2022;100:104658. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.archger.2022.104658\u003c/span\u003e\u003cspan address=\"10.1016/j.archger.2022.104658\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKandula RA, Linquest LA, Kandregula S, Latour M, Ahmed OG, Yim MT. Utility of hospital frailty risk score in predicting postoperative outcomes of sinonasal malignancies. Int Forum Allergy Rhinol. 2024;14(6):1097\u0026ndash;100. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/alr.23307\u003c/span\u003e\u003cspan address=\"10.1002/alr.23307\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKumar V, Barkoudah E, Jin DX, Banks P, McNabb-Baltar J. Hospital Frailty Risk Score (HFRS) Predicts Adverse Outcomes Among Hospitalized Patients with Chronic Pancreatitis. Dig Dis Sci. 2023;68(7):2890\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s10620-023-07946-w\u003c/span\u003e\u003cspan address=\"10.1007/s10620-023-07946-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDe Biasio JC, Mittel AM, Mueller AL, Ferrante LE, Kim DH, Shaefi S. Frailty in Critical Care Medicine: A Review. Anesth Analg. 2020;130(6):1462\u0026ndash;73. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1213/ANE.0000000000004665\u003c/span\u003e\u003cspan address=\"10.1213/ANE.0000000000004665\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBrummel NE, Bell SP, Girard TD, et al. Frailty and Subsequent Disability and Mortality among Patients with Critical Illness. Am J Respir Crit Care Med. 2017;196(1):64\u0026ndash;72. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1164/rccm.201605-0939OC\u003c/span\u003e\u003cspan address=\"10.1164/rccm.201605-0939OC\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFernando SM, McIsaac DI, Perry JJ, et al. Frailty and Associated Outcomes and Resource Utilization Among Older ICU Patients With Suspected Infection. Crit Care Med. 2019;47(8):e669\u0026ndash;76. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/CCM.0000000000003831\u003c/span\u003e\u003cspan address=\"10.1097/CCM.0000000000003831\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDent E, Martin FC, Bergman H, Woo J, Romero-Ortuno R, Walston JD. Management of frailty: opportunities, challenges, and future directions. Lancet. 2019;394(10206):1376\u0026ndash;86. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S0140-6736(19)31785-4\u003c/span\u003e\u003cspan address=\"10.1016/S0140-6736(19)31785-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFan J, Yu C, Guo Y, et al. Frailty index and all-cause and cause-specific mortality in Chinese adults: a prospective cohort study. Lancet Public Health. 2020;5(12):e650\u0026ndash;60. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S2468-2667(20)30113-4\u003c/span\u003e\u003cspan address=\"10.1016/S2468-2667(20)30113-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXu M, Gong Y, Yin X. Association of Frailty With Risk of Incident Hospital-Treated Infections in Middle-Aged and Older Adults: A Large-Scale Prospective Cohort Study. J Gerontol Biol Sci Med Sci. 2024;79(8):glae146. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/gerona/glae146\u003c/span\u003e\u003cspan address=\"10.1093/gerona/glae146\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMuscedere J, Waters B, Varambally A, et al. The impact of frailty on intensive care unit outcomes: a systematic review and meta-analysis. Intensive Care Med. 2017;43(8):1105\u0026ndash;22. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00134-017-4867-0\u003c/span\u003e\u003cspan address=\"10.1007/s00134-017-4867-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi Q, Shang N, Gao Q, Guo S, Yang T. Prevalence of sarcopenia and its association with frailty and malnutrition among older patients with sepsis-a cross-sectional study in the emergency department. BMC Geriatr. 2025;25(1):377. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12877-025-06060-y\u003c/span\u003e\u003cspan address=\"10.1186/s12877-025-06060-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLee HY, Lee J, Jung YS, et al. Preexisting Clinical Frailty Is Associated With Worse Clinical Outcomes in Patients With Sepsis. Crit Care Med. 2022;50(5):780\u0026ndash;90. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/CCM.0000000000005360\u003c/span\u003e\u003cspan address=\"10.1097/CCM.0000000000005360\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePatrizio E, Zambon A, Mazzola P, et al. Assessing the mortality risk in older patients hospitalized with a diagnosis of sepsis: the role of frailty and acute organ dysfunction. Aging Clin Exp Res. 2022;34(10):2335\u0026ndash;43. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s40520-022-02182-0\u003c/span\u003e\u003cspan address=\"10.1007/s40520-022-02182-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDong J, Chen R, Song X, Guo Z, Sun W. Quality of life and mortality in older adults with sepsis after one-year follow up: A prospective cohort study demonstrating the significant impact of frailty. Heart Lung. 2023;60:74\u0026ndash;80. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.hrtlng.2023.03.002\u003c/span\u003e\u003cspan address=\"10.1016/j.hrtlng.2023.03.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDing H, Li X, Zhang X, Li J, Li Q. The association of a frailty index derived from laboratory tests and vital signs with clinical outcomes in critical care patients with septic shock: a retrospective study based on the MIMIC-IV database. BMC Infect Dis. 2024;24(1):573. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12879-024-09430-w\u003c/span\u003e\u003cspan address=\"10.1186/s12879-024-09430-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ede Souto Barreto P, Rolland Y, Maltais M, Vellas B, MAPT Study Group. Associations of Multidomain Lifestyle Intervention with Frailty: Secondary Analysis of a Randomized Controlled Trial. Am J Med. 2018;131(11):1382. .e7-1382.e13.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAp\u0026oacute;stolo J, Cooke R, Bobrowicz-Campos E, et al. Effectiveness of interventions to prevent pre-frailty and frailty progression in older adults: a systematic review. JBI Database Syst Rev Implement Rep. 2018;16(1):140\u0026ndash;232. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.11124/JBISRIR-2017-003382\u003c/span\u003e\u003cspan address=\"10.11124/JBISRIR-2017-003382\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKwok CS, Zieroth S, Van Spall HGC, et al. The Hospital Frailty Risk Score and its association with in-hospital mortality, cost, length of stay and discharge location in patients with heart failure short running title: Frailty and outcomes in heart failure. Int J Cardiol. 2020;300:184\u0026ndash;90. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ijcard.2019.09.064\u003c/span\u003e\u003cspan address=\"10.1016/j.ijcard.2019.09.064\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRenedo D, Acosta JN, Koo AB, et al. Higher Hospital Frailty Risk Score Is Associated With Increased Risk of Stroke: Observational and Genetic Analyses. Stroke. 2023;54(6):1538\u0026ndash;47. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1161/STROKEAHA.122.041891\u003c/span\u003e\u003cspan address=\"10.1161/STROKEAHA.122.041891\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLim Z, Ling N, Ho VWT, et al. Delirium is significantly associated with hospital frailty risk score derived from administrative data. Int J Geriatr Psychiatry. 2023;38(1):e5872. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/gps.5872\u003c/span\u003e\u003cspan address=\"10.1002/gps.5872\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eUshida K, Shimizu A, Hori S, Yamamoto Y, Momosaki R. Hospital Frailty Risk Score Predicts Outcomes in Chronic Obstructive Pulmonary Disease Exacerbations. Arch Gerontol Geriatr. 2022;100:104658. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.archger.2022.104658\u003c/span\u003e\u003cspan address=\"10.1016/j.archger.2022.104658\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7540379/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7540379/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective:\u003c/strong\u003e To investigate the relationship between frailty assessed by the HFRS and in-hospital mortality in ICU patients with sepsis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethod:\u003c/strong\u003e A retrospective analysis of septic ICU patients from the MIMIC-IV database assessed frailty using the Hospital Frailty Risk Score (HFRS). Patients were categorized into non-frail (HFRS \u0026lt; 5, n = 3,744), pre-frail (5 ≤HFRS \u0026lt; 15, n = 2,539), and frail (HFRS ≥15, n = 2,147) groups. The primary outcome was in-hospital mortality. Logistic regression, with restricted cubic splines (RCS), analyzed the relationship between HFRS ,both as a categorical and continuous variable, and mortality. Inverse probability weighting (IPW) validated the results, and subgroup analyses explored frailty-mortality correlations in different patient groups.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e A total of 8,430 patients were included, with 3,761 (44.6%) males and a mean age of 69.39 [58.37, 79.76] years. Among them, 2,704 (32.1%) died during hospitalization. The analysis showed that in-hospital mortality increased with higher frailty levels, regardless of whether HFRS was treated as a continuous or categorical variable. RCS revealed a nonlinear relationship between HFRS and mortality. After adjusting for confounders, both pre-frail and frail statuses were significantly associated with higher in-hospital mortality risk (OR [95% CI]: pre-frail vs. non-frail: 1.33 [1.15–1.53], p \u0026lt; 0.001; frail vs. non-frail: 1.38 [1.18–1.62], p \u0026lt; 0.001). These findings were confirmed by IPW. Subgroup analyses showed significant interactions between frailty and mortality in patients receiving vasopressors, continuous renal replacement therapy (CRRT), mechanical ventilation, and those with varying heart rates, respiratory rates, and creatinine levels.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e Elevated HFRS is an independent risk factor for in-hospital mortality in septic ICU patients.\u003c/p\u003e","manuscriptTitle":"The hospital frailty risk score in critically ill patients with sepsis is an independent risk factor for in-hospital mortality : results from MIMIC-IV database","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-17 12:29:50","doi":"10.21203/rs.3.rs-7540379/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"82df8b92-9b37-439d-bedc-999dfb304823","owner":[],"postedDate":"October 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-03T13:39:12+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-17 12:29:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7540379","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7540379","identity":"rs-7540379","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}