A Diagnosis-Independent Frailty Index at Admission Improves Risk Stratification in Hospitalized Internal Medicine Patients​

A Diagnosis-Independent Frailty Index at Admission Improves Risk Stratification in Hospitalized Internal Medicine Patients​ | 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 A Diagnosis-Independent Frailty Index at Admission Improves Risk Stratification in Hospitalized Internal Medicine Patients​ Marco Montagna, Sarah Damanti, Giulia Lanzetta, Rebecca De Lorenzo, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9449571/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Background Patients admitted to Internal Medicine wards are increasingly characterized by multimorbidity, functional decline, and complex care needs. Prognostic tools based primarily on diagnoses may fail to fully capture this complexity. We evaluated whether a multidimensional Frailty Index (FI), constructed independently of past medical history and admission diagnosis, could predict adverse in-hospital outcomes. Methods In this prospective, single-center study, 395 adults admitted to the Internal Medicine ward of a tertiary university hospital between February 1, 2024, and January 31, 2025, were enrolled. The composite adverse outcome included in-hospital death, prolonged length of stay, or discharge to a non-home destination. The predictive performance of an admission-based, diagnosis-independent FI was assessed using a machine learning logistic regression model and compared with the Charlson Comorbidity Index (CCI). Results A total of 411 hospitalization episodes were analyzed (median age 76 years; 46% female), with high levels of multimorbidity and polypharmacy; 45% met the composite outcome. The admission-based FI, with or without inclusion of medical history, was strongly associated with the outcome (median 0.34 vs. 0.24; p < 0.001), showed good discriminative ability (AUC = 0.70), and outperformed the CCI (AUC = 0.62). Functional impairment was the main contributor to frailty, whereas comorbidities were more closely associated with revolving admissions. Notably, over half of robust patients experiencing adverse outcomes had oncologic disease. Conclusions An admission-based, diagnosis-independent FI effectively predicts adverse in-hospital outcomes and may enhance early risk stratification in Internal Medicine wards. Frailty index Charlson comorbidity index Risk score prediction Multimorbidity Internal medicine Hospitalization outcomes Figures Figure 1 Figure 2 Figure 3 Figure 4 INTRODUCTION The ongoing demographic and epidemiological transition is profoundly reshaping the clinical profile of patients admitted to Internal Medicine wards. These settings are increasingly characterized by older adults with multimorbidity, polypharmacy, functional impairment, and complex care needs, resulting in heightened vulnerability and heterogeneous clinical trajectories [1–7]. Large observational studies and national registries consistently document a progressive increase in patient age, clinical complexity, and length of hospital stay over time, highlighting a growing mismatch between patient needs and traditional disease-oriented models of hospital care [4–8]. Despite this evolution, prognostic assessment at hospital admission remains largely driven by diagnoses and comorbidity burden. Diagnosis-based tools are well established and widely used in Internal Medicine, where past medical history is usually available and accurately documented, unlike in emergency settings. However, while comorbidities capture disease burden, they may insufficiently reflect the patient’s functional reserve, physiological resilience, and ability to cope with acute stressors—dimensions that are increasingly recognized as key determinants of short-term hospital outcomes [9,10]. Frailty has emerged as a complementary construct that addresses this gap by conceptualizing vulnerability as the cumulative effect of deficits across multiple domains of health, including functional, clinical, and biological dimensions. Frailty has been consistently associated with adverse long-term outcomes such as disability, institutionalization, and mortality, and growing evidence supports its relevance also for short-term outcomes in hospitalized patients, including prolonged length of stay, discharge to non-home destinations, and in-hospital mortality [8,11–15]. Most frailty instruments currently applied in hospital settings, however, incorporate comorbidities or diagnostic information, either explicitly or implicitly, thus overlapping conceptually and operationally with comorbidity indices. This overlap poses a critical methodological and clinical challenge. When frailty measures include comorbidities, it becomes difficult to disentangle whether their predictive value reflects global vulnerability or simply mirrors disease burden. This is particularly relevant in Internal Medicine, where the Charlson Comorbidity Index (CCI) represents a well-validated and widely accepted benchmark for prognostic stratification [16,17]. Direct comparison between frailty- and comorbidity-based approaches is therefore often confounded by shared components, limiting interpretability and clinical translation. For this reason, in the present study we deliberately constructed a Frailty Index that excludes comorbidities and the acute admitting diagnosis. Importantly, this choice was not driven by limitations in data availability—since comprehensive medical history is routinely accessible in Internal Medicine wards—but by a precise conceptual aim: to isolate the prognostic contribution of frailty as a multidimensional expression of vulnerability, independent of disease burden, and to allow a fair and methodologically sound comparison with a purely comorbidity-based index such as the CCI. By deriving a multidimensional Frailty Index exclusively from information available at admission and independent of comorbidities, we aimed to test whether frailty captures a distinct and clinically meaningful dimension of risk in acutely hospitalized Internal Medicine patients. Furthermore, this approach allows exploration of the relative roles of frailty and comorbidity in shaping different adverse trajectories, including in-hospital outcomes and repeated admissions, which are often conflated but may be driven by different underlying mechanisms [18-20]. We therefore hypothesized that an admission-based, diagnosis-independent Frailty Index would predict short-term adverse hospital outcomes with accuracy comparable to or greater than that of the Charlson Comorbidity Index, despite excluding comorbidities. The aims of this study were: (i) to develop and validate a multidimensional Frailty Index based solely on non-diagnostic information available at admission; (ii) to directly compare its prognostic performance with the Charlson Comorbidity Index; and (iii) to investigate the differential contributions of frailty and comorbidity to adverse in-hospital outcomes and revolving admission patterns in an Internal Medicine population. PATIENTS AND METHODS Study design, participants, and sampling method: Five hundred six patients were admitted to the Department of Internal Medicine (Unit of General Medicine with a focus on Metabolic and Aging Medicine) of the IRCCS San Raffaele Hospital, a tertiary care center in Milan, Italy, and discharged between February 1, 2024, and January 31, 2025. Among them 395 provided written informed consent and were enrolled in the MED-Cli study, single-center, prospective, observational study. The study protocol was reviewed and approved by the institutional ethics committee of IRCCS San Raffaele Hospital in June 2021, study number MED-Cli , and is registered at clinicaltrials.gov (Identifier : NCT05780099). Figure 1 . Schematic representation of the MED-Cli project’s real-world data pipeline . Workflow of real-world data (RWD) acquisition and analysis within the MED-Cli study. RWD generated during routine care in internal medicine wards include clinical assessments, prescriptions, laboratory results, and administrative records. These data are collected via a dedicated electronic case report form hosted on the Cohort Genomic Platform, exported in structured formats, and analysed using machine learning models and interactive dashboards for clinical decision support and research. Data generation, collection, and management: A dedicated electronic case report form (eCRF) was developed on the Cohort Genomic Platform (CGP) to standardize and structure data collection ( Figure 1 ). Data are collected through direct clinical assessment, patient interviews, review of medical records, and automated integration with the hospital’s electronic health record system. Variables include demographics, anthropometrics, past medical history, clinical state at admission, imaging and microbiological data, laboratory values, treatment interventions, complications, and outcomes. Variables that had been included in the calculation of FIs are listed in Table 1 . Multimorbidity was described according to the definition of WHO and the Academy of Medical Sciences [21]. Polypharmacy was described as the chronic use of 5 or more medications at the same time [22]. Abnormal Body Mass Index (BMI) was defined as 27 kg/m² in those aged ≥65 years, and 25 kg/m² in those aged <65 years. BMI cut-offs were age-adjusted according to published evidence indicating that optimal weight ranges differ in older adults, with lower thresholds for underweight and higher thresholds for overweight in those aged ≥65 years [23]. The CCI was calculated for each patient by summing weighted scores assigned to predefined chronic conditions, with higher totals indicating greater comorbidity burden and mortality risk. To enable a direct comparison of discriminative performance with the FI, the CCI was normalized to a 0–1 scale. While the FI captures multidimensional deficits, including functional and social domains, the CCI focuses exclusively on the presence and severity of chronic diseases. Comparing these indices allowed us to assess whether a multidimensional frailty-based approach performs as well as a disease-based measure in predicting unfavorable outcomes. Primary composite outcome: The hospital stay outcome was classified as unfavorable according to a composite endpoint comprising: (1) in-hospital mortality after a length of stay exceeding the median of the study population (14 days); (2) early mortality (<8 days) in patients who had been hospitalized at least once in the previous 6 months; (3) failure to be discharged home (i.e., discharge to palliative care or to a lower-intensity care facility); and (4) discharge alive after a length of stay in the ward exceeding the mean of the study population (18 days). Construction of Frailty Indices: Frailty was assessed using a deficit-accumulation Frailty Index (FI) following the Rockwood/Theou “10-step” framework [24]. Each variable was recoded on a 0–1 scale, and the FI was calculated as the proportion of deficits present among available items. The admission-based FI (primary index) was derived exclusively from information available at hospital admission (29 variables), covering function/ADLs (Barthel items—feeding, bathing, grooming, dressing, bowel, bladder, toilet use, transfers, mobility, stairs—and performance), vital/baseline parameters (BMI with age-informed thresholds, systolic/diastolic blood pressure, heart rate, temperature, SpO₂), and routine laboratory tests (hemoglobin, white blood cells, platelets, C-reactive protein, creatinine, sodium, potassium, AST/ALT, LDH), together with care context (dependency, home assistance) and polypharmacy (≥5 chronic medications). The FI including medical history (41 variables) was constructed by adding prior comorbidities (e.g., hypertension, diabetes, COPD, CKD, cardiovascular/cerebrovascular disease, cancer), while excluding the cause of admission to avoid risk inflation. Items were selected to ensure multisystem coverage, non-extreme prevalence, and monotonicity with age, and were recoded using clinically motivated thresholds. Items with excessive missingness or extreme prevalence (80%) were excluded. The FI was computed only when ≥80% of items were available; otherwise, the episode was not assigned an FI value (124, 30%). Table 1 presents the variables included in the indices along with the corresponding prevalence of deficits. Correlation between the selected variables was assessed with Spearman’s test. Variable % with deficit Variable % with deficit Past Medical History* Laboratory values Hypertension 50.9 Haemoglobin 68.1 Myocardial Infarction 10.9 White Blood Cells 53.0 Congestive Heart Failure 15.4 Platelets 37.3 Cerebrovascular event 10.5 C-reactive Protein 83.3 Peripheral Vascular Disease 9.8 Creatinine 34.2 Diabetes 27.2 Sodium 28.4 Dementia 7.7 Potassium 21.5 Chronic Kidney Disease 20.3 AST 17.4 Liver Disease 8.4 ALT 11.7 COPD 16.1 LDH 24.9 Cancer 38.8 Polypharmacy 75.1 Barthel score items Feeding 18.5 Vitals and baseline info Bathing 28.2 Body Mass Index 55.5 Hygiene 24.0 Systolic Blood Pressure 39.2 Dressing 28.0 Diastolic Blood Pressure 27.8 Bowel 16.4 Heart Rate 24.5 Bladder 22.6 Dependency 28.2 Toilet 28.0 Home Assistance 23.3 Transfers 30.0 Body Temperature 16.5 Walking 30.4 SpO2 20.0 Stairs 35.0 Karnofsky Scale 88.6 Table 1 – List of variables included in the calculation of the frailty indices and their corresponding deficit prevalence . The table reports the variables used to construct the two frailty indices, with and without information on patients’ past medical history. For each variable, the percentage prevalence of the deficit is shown. COPD: Chronic Obstructive Pulmonary Disease; SBP: systolic blood pressure; DBP: diastolic blood pressure; SpO2: peripheral blood oxygen saturation; WBC: white blood cells; AST: aspartate amino-transferase; ALT: alanine amino-transferase; LDH: lactic dehydrogenase. *not used in the calculation of the FI admission-based. Statistical analysis: Descriptive statistics were calculated for all variables: categorical data as frequencies and percentages; continuous data as medians and interquartile ranges (IQR). Group comparisons were performed using χ² tests (categorical) and Kruskal–Wallis tests (continuous). Missing data were not imputed. All statistical analyses and graphical outputs were produced using Python 3.12 in Visual Studio Code (Microsoft Corporation) and SPSS, with two-sided p-values <0.05 considered significant. Logistic regression machine learning algorithms were trained using stratified shuffle split methodology with a test size of 30%, applying class weights, with 1000 maximum iterations and liblinear solver. Moreover logistic regression models adjusted for age and sex were performed to assess the association of frailty and comorbidity with the composite outcome. RESULTS Population Overview at Study Entry : Of the 506 patients discharged from the Department of Internal Medicine of the IRCCS San Raffaele Hospital between February 1st, 2024, and January 31st, 2025, a total of 395 (82%) provided informed consent and were enrolled in the study. Sixteen patients experienced two admissions during the study period, resulting in 411 hospitalization episodes included in the analyses. The cohort was characterized by advanced age and a high clinical complexity: the median age was 76 years (IQR 65–84), and 46% of patients were female. Patients presented a substantial burden of multimorbidity and polypharmacy, with a median of seven chronic conditions (IQR 5–10) and seven chronic therapies (IQR 5–10) per patient (Table 2). Based on age-adjusted BMI thresholds, more than half of the cohort (231 patients, 56%) had values outside the normal range, being classified as underweight or overweight/obese. Most hospitalization episodes originated from the Emergency Department (86%), while a smaller proportion were elective admissions (8%) or transfers from lower- (5%) or higher-intensity care wards (1%). The median length of hospital stay, excluding time spent in the Emergency Department, was 14 days (IQR 9–23), reflecting the clinical complexity and care needs of this Internal Medicine population. Characteristic Overall (N=411) No outcome (N=225) Outcome (N=186) p CCI 6.00 (4.00–8.00) 6.00 (4.00–8.00) 7.00 (4.00–9.00) < 0.001 CCI Normalized 0.16 (0.11–0.22) 0.16 (0.11–0.22) 0.19 (0.11–0.24) < 0.001 FI admission-based 0.28 (0.19–0.45) 0.24 (0.17–0.34) 0.34 (0.24–0.57) < 0.001 FI including medical history 0.27 (0.20–0.40) 0.25 (0.18–0.34) 0.33 (0.23-0.47) < 0.001 Sociodemographic Age 76.00 (65.00–84.00) 73.00 (63.00–82.00) 78.00 (67.00–85.00) < 0.01 Female gender 46% 41% 59% 0.11 Years of Education 10.00 (5.75–13.00) 10.00 (6.50–13.00) 10.00 (5.75–13.00) 0.89 Smoking: Never 39% 84% 16% 0.57 Smoking: Current 16% 89% 11% 0.57 Smoking: Ex 44% 81% 19% 0.57 Smoking: Passive 1% 80% 20% 0.57 Pack Years smoked 0.00 (0.00–31.50) 0.00 (0.00–36.00) 0.00 (0.00–30.00) 0.92 Chronic Diseases Count 7.00 (5.00–10.00) 7.00 (5.00–9.25) 7.00 (5.00–10.00) 0.27 Days in the Emergency Room 4.00 (3.00–6.00) 4.00 (3.00–5.00) 5.00 (3.00–6.00) < 0.05 Admissions in the last 12 months 1.00 (0.00–1.00) 1.00 (0.00–1.00) 1.00 (0.00–2.00) < 0.05 Admissions in the last 6 months 1.00 (0.00–1.00) 1.00 (0.00–1.00) 1.00 (0.00–1.00) < 0.05 Admissions in the last month 0.00 (0.00–0.00) 0.00 (0.00–0.00) 0.00 (0.00–0.25) 0.70 Admission Reasons Count 1.00 (1.00–2.00) 1.00 (1.00–2.00) 1.00 (1.00–2.00) < 0.01 Therapy Count 7.00 (5.00–10.00) 7.00 (4.00–10.00) 8.00 (5.00–11.00) 0.08 Anthropometric BMI 24.34 (21.48–27.68) 24.55 (21.72–28.18) 24.08 (21.12–27.14) 0.11 SBP 126.54 (23.56) 125.53 (23.88) 127.37 (23.31) 0.43 DBP 70.00 (63.50–80.00) 73.00 (64.75–80.00) 70.00 (62.50–80.50) 0.80 Heart Rate 85.00 (76.00–100.00) 85.00 (77.00–100.00) 85.00 (75.00–100.00) 0.45 Body Temp 36.40 (36.00–37.00) 36.40 (36.00–37.00) 36.50 (36.00–37.00) 0.80 SpO2 96.00 (94.00–98.00) 97.00 (94.70–98.00) 96.00 (94.00–98.00) 0.10 GCS Score 15.00 (15.00–15.00) 15.00 (15.00–15.00) 15.00 (15.00–15.00) 0.10 Functional Barthel Score (Before Admission) 100.00 (65.00–100.00) 100.00 (95.00–100.00) 90.00 (50.00–100.00) < 0.001 Barthel Score (Admission) 55.00 (25.00–100.00) 90.00 (50.00–100.00) 45.00 (10.00–60.00) < 0.001 ECOG 2.00 (1.00–3.00) 1.00 (0.00–3.00) 3.00 (1.00–3.00) < 0.001 Karnofsky Scale 70.00 (50.00–90.00) 80.00 (50.00–90.00) 60.00 (50.00–80.00) < 0.001 Braden Score 18.00 (14.00–22.00) 20.00 (17.00–22.25) 16.00 (13.00–18.00) < 0.001 CIRS 6.00 (0.00–9.00) 4.00 (0.00–9.00) 6.50 (0.00–10.00) < 0.05 Laboratory Hemoglobin 11.45 (2.51) 11.26 (2.53) 11.61 (2.48) 0.17 WBC 9.85 (7.17–13.22) 9.55 (7.12–12.82) 10.40 (7.20–14.90) 0.19 Platelets 213.00 (149.75–309.25) 213.00 (160.50–303.25) 210.50 (144.25–316.75) 0.82 AST 28.00 (20.25–47.00) 28.00 (20.50–45.00) 29.00 (20.50–50.00) 0.76 ALT 22.00 (14.00–40.00) 22.00 (15.00–40.00) 22.00 (13.00–39.50) 0.84 LDH 279.50 (227.00–370.25) 268.00 (224.25–358.00) 290.00 (236.25–422.50) 0.07 Creatinine 1.06 (0.78–1.64) 1.01 (0.78–1.62) 1.15 (0.78–1.67) 0.48 eGFR (CKD-EPI) 63.3 (39.69-92.71) 63.70 (38.22–92.80) 59.44 (45.16–89.32) 0.83 Glucose 116 (100.00-147.00) 116.00 (100.0–147.00) 121.00 (97.00–152.00) 0.75 CRP 48.80 (13.10–134.00) 40.40 (9.30–121.20) 60.60 (15.28–146.57) < 0.05 Sodium 137.75 (134.50–140.53) 138.00 (135.20–140.50) 137.25 (132.90–140.60) 0.06 Potassium 4.30 (3.92–4.75) 4.25 (3.91–4.67) 4.36 (3.96–4.79) 0.18 Table 2 – Baseline characteristics according to composite outcome occurrence. Sociodemographic, anthropometric, functional, and laboratory variables for the overall study population and by composite outcome status, highlighting significant differences. Data are shown as mean (SD) or median (Q1-Q3) as appropriate. Frailty: From the multidimensional set of clinical, functional, laboratory, and sociodemographic variables collected at hospital admission (106 variables), a deficit-accumulation Frailty Index (FI) was constructed, deliberately excluding the cause of hospitalization. Two FI variants were developed and compared: one incorporating information on past medical history (“FI including medical history”, 41 variables) and one relying exclusively on data available at admission (“FI admission-based”, 29 variables). Correlation analysis showed minimal redundancy among the included variables, with no strong pairwise correlations detected (Pearson’s r > 0.95), supporting the independence of individual deficits and the stability of both the statistical and machine learning models (Figure 2A). In line with this, the overall distributions of the two indices largely overlapped (Figure 2B), indicating that the inclusion of medical history had a limited effect on the global quantification of frailty. In the overall cohort, median FI values were 0.27 (IQR 0.20–0.40) for the FI including medical history and 0.28 (IQR 0.19–0.45) for the FI admission-based. When patients were classified according to established FI categories—fit (0–0.09), pre-frail (0.10–0.19), frail (0.20–0.54), and end-stage frail (0.55–1.00) [25]—both indices yielded broadly comparable frailty profiles. Using the FI including medical history, 1.7% of patients were classified as fit, 19.9% as pre-frail, 64.5% as frail, and 13.9% as end-stage frail. With the FI admission-based, the corresponding proportions were 3.1%, 23.0%, 54.7%, and 19.2%, respectively. Overall, the inclusion of past medical history slightly reduced the proportion of patients classified at the extremes of the frailty spectrum, with a relative shift toward intermediate frailty categories. Consistently, visual inspection of the density plots (Figure 2B) showed that the admission-based FI displayed a broader distribution, with a more evident secondary peak at higher frailty values. While this pattern suggests a wider spread of frailty when assessed using admission-only data, potential differences in discriminative performance were formally evaluated in subsequent predictive analyses. Figure 2 . Spearman correlation matrix of numerical variables and density plots of the Frailty Index . (A) Demographic, clinical, functional, and laboratory variables used for frailty index FI construction were tested for correlation using the non-parametric Spearman coefficient, appropriate for non-normally distributed variables. Significance levels are indicated in each cell (*p < 0.05). Variables differing between the “FI including medical history” and “FI admission-based” are highlighted. (B) Density plots showing the distribution overlap between “FI including medical history” (blue) and “FI admission-based” (red). Abbreviations: BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; SpO₂, peripheral oxygen saturation; WBC: white blood cells; AST: aspartate amino-transferase; ALT: alanine amino-transferase; LDH: lactic dehydrogenase. Hospitalization Outcomes: Among the 411 hospitalization episodes analysed, 186 (45%) met the criteria for the unfavourable composite outcome, defined as the occurrence of at least one of the following events during the hospital stay: in-hospital death, prolonged length of stay (exceeding the cohort mean), or discharge to a non-home destination (Figure 3). In the majority of cases, the unfavourable outcome was driven by prolonged hospitalization, which accounted for 59% of events. Discharge to a destination other than home contributed to 31% of cases, while in-hospital death accounted for the remaining 10%. Figure 3 . Proportion and composition of the composite negative outcome . Proportion of hospital episodes meeting the unfavourable composite outcome (Yes) or not (No) and relative contribution of each component. Table 2 compares baseline characteristics between hospitalization episodes meeting or not meeting the unfavourable composite outcome. No statistically significant differences were observed between groups with respect to sex, body mass index, number of chronic conditions, or vital parameters at emergency department admission. By contrast, patients experiencing the unfavourable outcome were significantly older. Among laboratory parameters, only C-reactive protein levels were significantly higher in patients meeting the composite outcome. These patients also had a longer stay in the emergency department and a higher number of hospital admissions in the preceding 6 and 12 months. Notably, all functional and care-dependency measures—including Barthel Score [26] before admission, Barthel Score at admission, ECOG performance status [27], Karnofsky Scale [28], Braden Score [29], and Cumulative Illness Rating Scale (CIRS) [30]—were significantly worse in patients who met the unfavourable outcome. Both Frailty Index (FI) variants showed a strong and graded association with the composite outcome. Median FI values among patients experiencing the outcome were 0.34 (IQR 0.24–0.57) for the FI admission-based and 0.33 (IQR 0.23–0.47) for the FI including medical history, compared with 0.24 (IQR 0.17–0.34) and 0.25 (IQR 0.18–0.34), respectively, among those without the outcome (p < 0.001 for both comparisons). The magnitude of association and overall discriminative performance were comparable between the two FI versions, indicating that exclusion of past medical history did not materially reduce prognostic accuracy. Importantly, in this cohort both FI measures showed a stronger association with the unfavourable composite outcome than any individual variable included in their construction. Although several single parameters were independently associated with the outcome, their predictive performance was consistently inferior to that of the composite FI, highlighting the added value of a multidimensional frailty assessment over isolated clinical or functional measures. Frailty and Comorbidity in Relation to the Hospitalization Outcome . The CCI is a widely validated instrument for estimating short- and long-term mortality risk in hospitalized patients, particularly among older adults, and was therefore used as a benchmark to contextualize the prognostic performance of the admission-based Frailty Index (FI) in our cohort. As shown in Table 2, the median normalized CCI among patients meeting the unfavourable composite outcome was 0.19 (IQR 0.11–0.24), only marginally higher than in those not meeting the outcome (0.16, IQR 0.11–0.22). In contrast, differences in FI admission-based values between outcome groups were substantially larger. Patients experiencing the composite outcome had a median FI of 0.34 (IQR 0.24–0.57), compared with 0.24 (IQR 0.17–0.34) among those without the outcome. Although both indices were significantly associated with the outcome (p < 0.001 for both), the separation between groups was more pronounced for the FI. Consistently, density plots (Figure 4A) demonstrated less overlap between outcome groups for the FI admission-based than for the CCI, indicating superior discrimination. Moreover, CCI values were largely clustered in the lower portion of the scale (<0.5), whereas FI values spanned the full 0–1 range, reflecting greater resolution and granularity across levels of patient vulnerability. In logistic regression models predicting the composite outcome, the FI admission-based achieved higher discriminative performance than the CCI (AUC-ROC 0.70 vs. 0.62; Figure 4B). This superiority was maintained in age- and sex-adjusted analyses, with the FI admission-based showing a stronger association with the outcome (OR 37.13, 95% CI 8.31–166.04, p < 0.001) compared with the CCI (OR 25.70, 95% CI 1.66–397.79, p = 0.02). Notably, a subset of patients who met the unfavourable outcome had FI admission-based values below the cohort median. Further stratified analysis revealed that these cases were predominantly driven by oncologic disease: 51% had localized or metastatic cancer, compared with 24% among patients meeting the outcome with FI values above the median (p < 0.01). Figure 3 . Discriminative performance of the Charlson Comorbidity Index and Frailty Index. (A) Density plots of Charlson Comorbidity Index (CCI) and Frailty Index (FI) values according to composite negative outcome status (blue = meeting outcome; grey = not meeting outcome). (B) Receiver operating characteristic (ROC) curves and corresponding area under the curve (AUC) for two machine learning models using either CCI or FI as the sole predictive feature. The Youden’s index for both models is reported in the legend and the J point is marked on the corresponding ROC curves. Revolving patients. In our cohort, revolving patients—defined as those experiencing multiple hospital admissions within a one-year period—were more strongly characterized by markers of multimorbidity and care complexity than by composite indices of comorbidity (CCI) or frailty (FI) (Table 3). In particular, a higher number of chronic therapies (p < 0.001) and greater disease burden as assessed by the Cumulative Illness Rating Scale (CIRS; p < 0.001) were significantly associated with revolving admissions. Additional distinguishing features included longer stays in the emergency department (p < 0.01) and lower Barthel scores prior to admission (p < 0.01). By contrast, neither the CCI nor the admission-based FI significantly discriminated between revolving and non-revolving patients, despite incorporating variables theoretically related to multimorbidity and vulnerability. These findings indicate that, in this population, repeated hospitalizations were more closely linked to overall care complexity and chronic disease burden across organ systems—captured by the CIRS and treatment intensity—rather than to global frailty or comorbidity scores. Characteristic Not revolving (N=143) Revolving (N=268) p CCI 6.00 (4.00–9.00) 6.00 (4.00–8.00) 0.92 CCI Normalized 0.16 (0.11–0.24) 0.16 (0.11–0.22) 0.92 FI admission based 0.26 (0.21–0.35) 0.28 (0.18–0.52) 0.29 Sociodemographic Age 77.00 (66.00–85.00) 75.00 (63.00–83.00) 0.23 Female gender 44% 56% < 0.05 Years of Education 8.00 (5.00–13.00) 11.00 (7.75–13.00) 0.19 Pack Years smoked 0.50 (0.00–34.50) 0.00 (0.00–30.00) 0.17 Chronic Diseases Count 7.00 (5.00–10.00) 7.00 (4.00–9.00) 0.34 daysInER 4.00 (3.00–5.00) 4.00 (3.00–6.00) < 0.01 Admission Reasons Count 1.00 (1.00–2.00) 1.00 (1.00–2.00) < 0.05 Therapy Count 6.00 (4.00–9.00) 8.00 (5.00–11.00) < 0.001 Anthropometric BMI 24.26 (21.34–28.42) 24.44 (21.54–27.55) 0.85 SBP 129.85 (23.87) 124.51 (23.17) < 0.05 DBP 74.50 (65.00–85.00) 70.00 (60.00–80.00) 0.14 Heart Rate 85.00 (77.00–100.00) 86.00 (76.00–100.00) 0.70 Body Temp 36.50 (36.00–36.90) 36.40 (36.00–37.00) 0.71 SpO2 97.00 (95.00–98.00) 96.00 (94.00–98.00) 0.18 GCS Score 15.00 (15.00–15.00) 15.00 (15.00–15.00) 0.20 Functional Barthel Score (Before Admission) 100.00 (90.00–100.00) 100.00 (55.00–100.00) < 0.01 Barthel Score (Admission) 65.00 (40.00–100.00) 55.00 (20.00–100.00) 0.09 ECOG 2.00 (0.00–3.00) 2.00 (1.00–3.00) 0.12 Karnofsky Scale 80.00 (50.00–90.00) 70.00 (50.00–90.00) 0.17 Braden Score 18.00 (15.00–22.00) 17.00 (14.00–21.00) < 0.05 CIRS 3.00 (0.00–9.00) 6.00 (1.00–10.00) < 0.001 Laboratory Hemoglobin 11.66 (2.53) 11.31 (2.49) 0.18 WBC 10.80 (7.90–15.95) 9.30 (6.70–12.57) < 0.01 Platelets 213.00 (153.00–306.25) 213.00 (149.25–317.75) 0.99 AST 27.50 (20.00–51.00) 28.50 (21.00–44.50) 0.83 ALT 21.00 (14.75–40.50) 22.00 (14.00–39.00) 0.78 LDH 276.00 (232.00–370.00) 280.00 (220.50–370.00) 0.81 Creatinine 1.10 (0.80–1.80) 1.02 (0.77–1.49) 0.17 CRP 56.10 (12.30–138.80) 46.90 (13.90–130.45) 0.71 Sodium 137.50 (133.67–140.20) 138.05 (134.80–140.62) 0.11 Table 3 – Characteristics of the study population by revolving status. Variables are reported as mean (SD) or median (Q1–Q3), as appropriate; p-values refer to comparisons between revolving and non-revolving patients. DISCUSSION In this prospective study conducted in an Internal Medicine setting, we demonstrated that frailty, assessed at hospital admission through a multidimensional Frailty Index deliberately constructed independently of comorbidities and admission diagnosis, is a strong predictor of short-term adverse in-hospital outcomes. By directly comparing this diagnosis-independent frailty measure with the CCI, our findings provide novel insight into the distinct and complementary prognostic roles of global vulnerability and disease burden in acutely hospitalized medical patients. Frailty is increasingly recognized as a biological syndrome reflecting reduced physiological reserve and impaired resilience to stressors [25] . A growing body of literature has shown that frailty is closely linked to adverse outcomes across multiple clinical settings, including cardiovascular, metabolic, and hypertensive populations [31]. In the multicentre REPOSI registry [32], a 34-item deficit-accumulation Frailty Index constructed from admission data (including functional, cognitive, laboratory and disease variables) independently predicted in-hospital (OR≈1.6 per 0.1 FI) and 12-month mortality (HR≈1.4), underscoring the prognostic value of frailty among hospitalized older adults. In contrast, the present study implemented a diagnosis- and comorbidity-independent FI at admission and directly benchmarked it against the CCI , thereby separating global vulnerability from disease burden in the acute setting. While REPOSI demonstrates that a broad, multimorbidity-inclusive FI is feasible and prognostically informative across internal medicine/geriatric wards, the current approach shows that an admission-time, comorbidity-free FI retains independent and superior discrimination for short-term in-hospital outcomes, indicating that frailty and comorbidity capture partly distinct risk dimensions and can be usefully combined for clinical decision-making across settings Our findings are consistent with CO-CARED study [33], which showed that social frailty and care dependency constitute vulnerability dimensions partly independent of comorbidity and relevant to hospital outcomes and discharge barriers. In CO-CARED, Internal Medicine inpatients were profiled along complementary axes: clinical instability (e.g., early warning scores), care dependency (nursing intensity/functional needs), and a composite caring complexity index, with social frailty explicitly captured as discharge difficulty and need for social/organizational support. Length of stay and in-hospital outcomes increased stepwise across care-dependency and social-frailty strata, whereas patterns were not fully explained by comorbidity alone—indicating that organizational constraints, functional dependence, and social needs map a separate risk plane from disease burden. In this framework, our admission-time, comorbidity-free FI similarly isolates patient-level vulnerability (function, reserves, dependency) and shows prognostic value for short-term inpatient outcomes, complementing comorbidity rather than replacing it. Indeed in our study multiple readmissions within one year were better explained by chronic multi-system needs and organizational factors. Together, these data support a multidimensional risk model in which comorbidity, frailty, and social/care complexity each contribute distinct information for planning monitoring intensity, rehabilitation, and discharge pathways. Recent work across cardio-metabolic and geriatric settings indicates that frailty reflects systemic vulnerability beyond comorbidity. In CARYATID, frailty by the comorbidity-independent Fried phenotype clustered with organ-damage markers (albuminuria, reduced eGFR) and worse cognition in frail hypertensive older adults with prediabetes and CKD [34] . Among patients meeting Fried frailty criteria, P-wave dispersion correlated with MMSE independent of comorbidity, indicating an electrophysiologic signal of frailty-linked cognitive vulnerability [35] . Metabolic factors also contribute to this cross-system vulnerability: in the Monteforte study of pre-frail hypertensive older adults, hyperglycemia (>140 mg/dL) independently predicted the 6-month transition from pre-frailty to frailty, pointing to a modifiable pathway of progression [36]. Evidence from nursing-home residents further underscores the physical–cognitive axis, with Fried frailty closely related to cognition via gait speed [37]. In this context, the present study focuses on acute in-hospital outcomes using a multidimensional, admission-time, comorbidity-free FI and presents a direct comparison with the CCI; taken together, these strands suggest that frailty and comorbidity represent related yet distinct dimensions of risk across settings and time horizons. From a clinical perspective, our study supports the integration of frailty assessment into early decision-making processes in Internal Medicine wards. Importantly, the exclusion of comorbidities from the FIwas not motivated by data unavailability—as comprehensive medical history is usually accessible in Internal Medicine—but by the need to isolate frailty as a distinct prognostic construct. This approach allows clinicians to complement, rather than replace, traditional comorbidity-based risk stratification. The superior discriminative performance of the diagnosis-independent FI compared with the CCI suggests that early identification of frailty may help guide individualized management strategies, including intensity of monitoring, rehabilitation planning, and discharge coordination. Moreover, recognizing that frailty and comorbidity drive different adverse trajectories highlights the importance of a multidimensional, tailored approach to risk assessment in complex medical patients. The strengths of this study include its prospective design, real-world Internal Medicine population, and the explicit methodological separation of frailty and comorbidity. Limitations include the single-center setting and the absence of long-term follow-up outcomes beyond hospitalization, which warrants further investigation in multicenter cohorts (primarily affects external validity; no systematic bias on effect estimates expected). As limit of our study we have also to mention that frailty was assessed at hospital admission in an acute care context, whereas standard assessments are often performed in outpatient/community settings. Consequently, some measurements may reflect both baseline vulnerability and acute illness, introducing potential setting bias and limiting generalizability to community populations (likely away from the null; small–moderate). This risk was partly mitigated by leveraging pre-admission functional status (baseline ADLs/performance) alongside diagnosis-independent items (likely toward the null; small). Future studies should compare outpatient and admission FIs, assess longitudinal stability, and establish setting-specific thresholds for clinical decision-making. Further sources of bias might be admission (selection) bias that may enrich the cohort with patients at simultaneously higher frailty and event risk, potentially inflating frailty–outcome associations (away from the null; moderate). Finally competing risk of early discharge may reduce observed event rates, dampening associations (toward the null; small). In conclusion, our findings demonstrated that frailty, assessed through a multidimensional, diagnosis- and comorbidity-independent FI at hospital admission, was a powerful predictor of short-term adverse outcomes in Internal Medicine patients. By disentangling frailty from disease burden and directly comparing it with a traditional comorbidity index, this study provided new evidence supporting frailty as a distinct and clinically meaningful dimension of vulnerability. Integrating frailty assessment alongside comorbidity and care complexity measures may represent a pragmatic step toward more personalized and effective care for the modern Internal Medicine patient. Adopting frailty-informed approaches in the acute care setting, where early identification of patients at higher risk may guide individualized management and resource allocation. Implementation of such tools could support more appropriate, patient-centred decision-making, potentially improving both clinical outcomes and healthcare efficiency. LIST OF ABBREVIATIONS EBM Evidence Based Medicine eCRF Electronic Case Report Form FI Frailty Index ML Machine Learning RCT Randomized Controlled Trial RWD Real World Data RWE Real Wolrd Evidence DECLARATIONS AKNOWLEDGEMENTS The results presented in this manuscript are the output of experimental work for the award of the PhD qualification of MM. AUTHOR CONTRIBUTIONS STATEMENT MM, SD: Conceptualization, Formal Analysis, Data curation, Methodology, Writing - Original Draft; RDL: Conceptualization, Methodology, Writing - Original Draft; CB, CP, CS, ER, GP: Investigation, Data curation; AD: Writing – Review & Editing; MT: Conceptualization; PRQ: Conceptualization, Supervision, Writing – Review & Editing. DATA AVAILABILITY STATEMENT The data and code that support the findings of this study are available on the San Raffaele Open Research Data Repository at the following DOI: [ created at the paper publication ]. ETHICS APPROVAL This study was approved by the Ethic Review Board of IRCCS San Raffaele Hospital with the code name “MED-Cli”. CONFLICT OF INTEREST The authors declare no relevant conflict of interest to the aims of this study. DECLARATION OF GENERATIVE AI AND AI-ASSISTED TECHNOLOGIES IN THE WRITING PROCESS During the preparation of this work the authors used ChatGPT and Claude in order to improve readability and language. After using this tool, the authors reviewed and edited the content as needed and take full responsibility for the content of the publication. BIBLIOGRAPHY Nicolaus S, Crelier B, Donzé JD, Aubert CE. Definition of patient complexity in adults: A narrative review. Journal of Multimorbidity and Comorbidity 2022;12. https://doi.org/10.1177/26335565221081288. Grant RW, Ashburner JM, Hong CC, Chang Y, Barry MJ, Atlas SJ. Defining patient complexity from the primary care physician’s perspective: A cohort study. 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Psychiatry Res 1992;41:237–48. https://doi.org/10.1016/0165-1781(92)90005-N. Morley JE, Vellas B, van Kan GA et al Frailty consensus: a call to action. J Am Med Dir Assoc. 2013 Jun;14(6):392-7. doi: 10.1016/j.jamda.2013.03.022. Cesari M, Franchi C, Cortesi L, Nobili A, Ardoini I, Mannucci PM, REPOSI collaborators. Implementation of the Frailty Index in hospitalized older patients: Results from the REPOSI register. Eur J Intern Med. 2018 Oct:56:11-18. doi: 10.1016/j.ejim.2018.06.001. Ceriani E, Milani O, Donadoni M, Benetti A, Berra SA, Canetta C, Colombo F, Dentali F, Magnani L, Mazzone A, Montano N, Muiesan ML, Podda GM, Querini PR, Squizzato A, Casazza G, Cogliati C; SIMI-FADOI Lombardy Network. COmplexity of CARE and Discharge barriers: the 'modern internal medicine patient'. Results from the CO-CARED Study. Intern Emerg Med. 2025 Mar;20(2):471-479. doi: 10.1007/s11739-024-03823-0 Santulli G, Visco V, Ciccarelli M et al. 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Eur J Intern Med. 2023 Sep:115:152-153. doi: 10.1016/j.ejim.2023.05.027 Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 28 Apr, 2026 Reviewers invited by journal 27 Apr, 2026 Editor assigned by journal 25 Apr, 2026 First submitted to journal 23 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9449571","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":630361320,"identity":"a314cd05-80fb-463a-8e98-b6137d2ac592","order_by":0,"name":"Marco Montagna","email":"","orcid":"","institution":"San Raffaele Institute: IRCCS Ospedale San Raffaele","correspondingAuthor":false,"prefix":"","firstName":"Marco","middleName":"","lastName":"Montagna","suffix":""},{"id":630361321,"identity":"9cd88b1f-c4b6-4020-a5dc-7837b271928c","order_by":1,"name":"Sarah 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Raffaele","correspondingAuthor":false,"prefix":"","firstName":"Patrizia","middleName":"","lastName":"Rovere-Querini","suffix":""}],"badges":[],"createdAt":"2026-04-17 13:11:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9449571/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9449571/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108630026,"identity":"717dbdb9-acfe-4104-8dc6-c2826d8dfcf6","added_by":"auto","created_at":"2026-05-06 16:32:26","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":76566,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSchematic representation of the MED-Cli project’s real-world data pipeline\u003c/strong\u003e. Workflow of real-world data\u003cstrong\u003e \u003c/strong\u003e(RWD) acquisition and analysis within the MED-Cli study. RWD generated during routine care in internal medicine wards include clinical assessments, prescriptions, laboratory results, and administrative records. These data are collected via a dedicated electronic case report form hosted on the Cohort Genomic Platform, exported in structured formats, and analysed using machine learning models and interactive dashboards for clinical decision support and research.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9449571/v1/5d4a63127ed6bf70de3fc931.png"},{"id":108805570,"identity":"89cc3104-5735-40fb-94a6-e7935598100c","added_by":"auto","created_at":"2026-05-08 15:26:18","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":299517,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSpearman correlation matrix of numerical variables and density plots of the Frailty Index\u003c/strong\u003e. (A) Demographic, clinical, functional, and laboratory variables used for frailty index FI construction were tested for correlation using the non-parametric Spearman coefficient, appropriate for non-normally distributed variables. Significance levels are indicated in each cell (*p \u0026lt; 0.05). Variables differing between the “FI including medical history” and “FI admission-based” are highlighted. (B) Density plots showing the distribution overlap between “FI including medical history” (blue) and “FI admission-based” (red). Abbreviations: BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; SpO₂, peripheral oxygen saturation; WBC: white blood cells; AST: aspartate amino-transferase; ALT: alanine amino-transferase; LDH: lactic dehydrogenase.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9449571/v1/73b31d6ad36f764222183ea6.png"},{"id":108630028,"identity":"38bcc499-f652-446b-ab62-50a1df53d628","added_by":"auto","created_at":"2026-05-06 16:32:26","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":102181,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProportion and composition of the composite negative outcome\u003c/strong\u003e. Proportion of hospital episodes meeting the unfavourable composite outcome (Yes) or not (No) and relative contribution of each component.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9449571/v1/51ebf33f954a4e59d0baf2b8.png"},{"id":108805161,"identity":"567bbeee-9568-40e4-b7ee-882e72ea1fa3","added_by":"auto","created_at":"2026-05-08 15:25:02","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":143435,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDiscriminative performance of the Charlson Comorbidity Index and Frailty Index. \u003c/strong\u003e(A) Density plots of Charlson Comorbidity Index (CCI) and Frailty Index (FI) values according to composite negative outcome status (blue = meeting outcome; grey = not meeting outcome).\u003cbr\u003e\n(B) Receiver operating characteristic (ROC) curves and corresponding area under the curve (AUC) for two machine learning models using either CCI or FI as the sole predictive feature. The Youden’s index for both models is reported in the legend and the J point is marked on the corresponding ROC curves.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9449571/v1/1a28bd59426cd5bdca6bc593.png"},{"id":108809647,"identity":"93f4b4ad-c7c3-40ab-ac95-8dbdbed47a38","added_by":"auto","created_at":"2026-05-08 15:54:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1073113,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9449571/v1/cdcbd94b-32b6-46fc-ab7c-55f8828f43a0.pdf"}],"financialInterests":"","formattedTitle":"A Diagnosis-Independent Frailty Index at Admission Improves Risk Stratification in Hospitalized Internal Medicine Patients​","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eThe ongoing demographic and epidemiological transition is profoundly reshaping the clinical profile of patients admitted to Internal Medicine wards. These settings are increasingly characterized by older adults with multimorbidity, polypharmacy, functional impairment, and complex care needs, resulting in heightened vulnerability and heterogeneous clinical trajectories [1\u0026ndash;7]. Large observational studies and national registries consistently document a progressive increase in patient age, clinical complexity, and length of hospital stay over time, highlighting a growing mismatch between patient needs and traditional disease-oriented models of hospital care [4\u0026ndash;8].\u003c/p\u003e\n\u003cp\u003eDespite this evolution, prognostic assessment at hospital admission remains largely driven by diagnoses and comorbidity burden. Diagnosis-based tools are well established and widely used in Internal Medicine, where past medical history is usually available and accurately documented, unlike in emergency settings. However, while comorbidities capture disease burden, they may insufficiently reflect the patient\u0026rsquo;s functional reserve, physiological resilience, and ability to cope with acute stressors\u0026mdash;dimensions that are increasingly recognized as key determinants of short-term hospital outcomes [9,10].\u003c/p\u003e\n\u003cp\u003eFrailty has emerged as a complementary construct that addresses this gap by conceptualizing vulnerability as the cumulative effect of deficits across multiple domains of health, including functional, clinical, and biological dimensions. Frailty has been consistently associated with adverse long-term outcomes such as disability, institutionalization, and mortality, and growing evidence supports its relevance also for short-term outcomes in hospitalized patients, including prolonged length of stay, discharge to non-home destinations, and in-hospital mortality [8,11\u0026ndash;15]. Most frailty instruments currently applied in hospital settings, however, incorporate comorbidities or diagnostic information, either explicitly or implicitly, thus overlapping conceptually and operationally with comorbidity indices.\u003c/p\u003e\n\u003cp\u003eThis overlap poses a critical methodological and clinical challenge. When frailty measures include comorbidities, it becomes difficult to disentangle whether their predictive value reflects global vulnerability or simply mirrors disease burden. This is particularly relevant in Internal Medicine, where the Charlson Comorbidity Index (CCI) represents a well-validated and widely accepted benchmark for prognostic stratification [16,17]. Direct comparison between frailty- and comorbidity-based approaches is therefore often confounded by shared components, limiting interpretability and clinical translation.\u003c/p\u003e\n\u003cp\u003eFor this reason, in the present study we deliberately constructed a Frailty Index that excludes comorbidities and the acute admitting diagnosis. Importantly, this choice was not driven by limitations in data availability\u0026mdash;since comprehensive medical history is routinely accessible in Internal Medicine wards\u0026mdash;but by a precise conceptual aim: to isolate the prognostic contribution of frailty as a multidimensional expression of vulnerability, independent of disease burden, and to allow a fair and methodologically sound comparison with a purely comorbidity-based index such as the CCI.\u003c/p\u003e\n\u003cp\u003eBy deriving a multidimensional Frailty Index exclusively from information available at admission and independent of comorbidities, we aimed to test whether frailty captures a distinct and clinically meaningful dimension of risk in acutely hospitalized Internal Medicine patients. Furthermore, this approach allows exploration of the relative roles of frailty and comorbidity in shaping different adverse trajectories, including in-hospital outcomes and repeated admissions, which are often conflated but may be driven by different underlying mechanisms [18-20].\u003c/p\u003e\n\u003cp\u003eWe therefore hypothesized that an admission-based, diagnosis-independent Frailty Index would predict short-term adverse hospital outcomes with accuracy comparable to or greater than that of the Charlson Comorbidity Index, despite excluding comorbidities. The aims of this study were: (i) to develop and validate a multidimensional Frailty Index based solely on non-diagnostic information available at admission; (ii) to directly compare its prognostic performance with the Charlson Comorbidity Index; and (iii) to investigate the differential contributions of frailty and comorbidity to adverse in-hospital outcomes and revolving admission patterns in an Internal Medicine population.\u003c/p\u003e"},{"header":"PATIENTS AND METHODS","content":"\u003cp\u003e\u003cstrong\u003eStudy design, participants, and sampling method:\u003c/strong\u003e Five hundred six patients were admitted to the Department of Internal Medicine (Unit of General Medicine with a focus on Metabolic and Aging Medicine) of the IRCCS San Raffaele Hospital, a tertiary care center in Milan, Italy, and discharged between February 1, 2024, and January 31, 2025. Among them 395 provided written informed consent and were enrolled in the MED-Cli study, single-center, prospective, observational study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe study protocol was reviewed and approved by the institutional ethics committee of IRCCS San Raffaele Hospital in June 2021, study number MED-Cli\u003cem\u003e,\u0026nbsp;\u003c/em\u003eand is registered at clinicaltrials.gov (Identifier\u003cem\u003e:\u0026nbsp;\u003c/em\u003eNCT05780099).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 1\u003c/strong\u003e. \u003cstrong\u003eSchematic representation of the MED-Cli project\u0026rsquo;s real-world data pipeline\u003c/strong\u003e. Workflow of real-world data\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e(RWD) acquisition and analysis within the MED-Cli study. RWD generated during routine care in internal medicine wards include clinical assessments, prescriptions, laboratory results, and administrative records. These data are collected via a dedicated electronic case report form hosted on the Cohort Genomic Platform, exported in structured formats, and analysed using machine learning models and interactive dashboards for clinical decision support and research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData generation, collection, and management:\u0026nbsp;\u003c/strong\u003eA dedicated electronic case report form (eCRF) was developed on the Cohort Genomic Platform (CGP) to standardize and structure data collection (\u003cstrong\u003eFigure 1\u003c/strong\u003e). Data are collected through direct clinical assessment, patient interviews, review of medical records, and automated integration with the hospital\u0026rsquo;s electronic health record system. Variables include demographics, anthropometrics, past medical history, clinical state at admission, imaging and microbiological data, laboratory values, treatment interventions, complications, and outcomes. Variables that had been included in the calculation of FIs are listed in \u003cstrong\u003eTable 1\u003c/strong\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMultimorbidity was described according to the definition of WHO and the Academy of Medical Sciences [21]. Polypharmacy was described as the chronic use of 5 or more medications at the same time [22]. Abnormal Body Mass Index (BMI) was defined as \u0026lt;22 or \u0026gt;27 kg/m\u0026sup2; in those aged \u0026ge;65 years, and \u0026lt;18.5 or \u0026gt;25 kg/m\u0026sup2; in those aged \u0026lt;65 years. BMI cut-offs were age-adjusted according to published evidence indicating that optimal weight ranges differ in older adults, with lower thresholds for underweight and higher thresholds for overweight in those aged \u0026ge;65 years [23]. The CCI was calculated for each patient by summing weighted scores assigned to predefined chronic conditions, with higher totals indicating greater comorbidity burden and mortality risk. To enable a direct comparison of discriminative performance with the FI, the CCI was normalized to a 0\u0026ndash;1 scale. While the FI captures multidimensional deficits, including functional and social domains, the CCI focuses exclusively on the presence and severity of chronic diseases. Comparing these indices allowed us to assess whether a multidimensional frailty-based approach performs as well as a disease-based measure in predicting unfavorable outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrimary composite outcome:\u0026nbsp;\u003c/strong\u003eThe hospital stay outcome was classified as unfavorable according to a composite endpoint comprising: (1) in-hospital mortality after a length of stay exceeding the median of the study population (14 days); (2) early mortality (\u0026lt;8 days) in patients who had been hospitalized at least once in the previous 6 months; (3) failure to be discharged home (i.e., discharge to palliative care or to a lower-intensity care facility); and (4) discharge alive after a length of stay in the ward exceeding the mean of the study population (18 days).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConstruction of Frailty Indices:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFrailty was assessed using a deficit-accumulation Frailty Index (FI) following the Rockwood/Theou \u0026ldquo;10-step\u0026rdquo; framework [24]. Each variable was recoded on a 0\u0026ndash;1 scale, and the FI was calculated as the proportion of deficits present among available items. The admission-based FI (primary index) was derived exclusively from information available at hospital admission (29 variables), covering function/ADLs (Barthel items\u0026mdash;feeding, bathing, grooming, dressing, bowel, bladder, toilet use, transfers, mobility, stairs\u0026mdash;and performance), vital/baseline parameters (BMI with age-informed thresholds, systolic/diastolic blood pressure, heart rate, temperature, SpO₂), and routine laboratory tests (hemoglobin, white blood cells, platelets, C-reactive protein, creatinine, sodium, potassium, AST/ALT, LDH), together with care context (dependency, home assistance) and polypharmacy (\u0026ge;5 chronic medications). The FI including medical history (41 variables) was constructed by adding prior comorbidities (e.g., hypertension, diabetes, COPD, CKD, cardiovascular/cerebrovascular disease, cancer), while excluding the cause of admission to avoid risk inflation. Items were selected to ensure multisystem coverage, non-extreme prevalence, and monotonicity with age, and were recoded using clinically motivated thresholds. Items with excessive missingness or extreme prevalence (\u0026lt;1% or \u0026gt;80%) were excluded. The FI was computed only when \u0026ge;80% of items were available; otherwise, the episode was not assigned an FI value (124, 30%).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e presents the variables included in the indices along with the corresponding prevalence of deficits. \u0026nbsp;Correlation between the selected variables was assessed with Spearman\u0026rsquo;s test.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"643\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 26.85%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 14.7%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e% with deficit\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 3.75%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 20.25%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" valign=\"bottom\" style=\"width: 9.108%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e% with deficit\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 26.85%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 14.7%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 3.75%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" valign=\"bottom\" style=\"width: 21.6%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 12.45%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 26.85%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePast Medical History*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 14.7%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 3.75%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" valign=\"bottom\" style=\"width: 21.6%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLaboratory values\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 12.45%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 26.85%;\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 14.7%;\"\u003e\n \u003cp\u003e50.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 3.75%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" valign=\"bottom\" style=\"width: 21.6%;\"\u003e\n \u003cp\u003eHaemoglobin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 12.45%;\"\u003e\n \u003cp\u003e68.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 26.85%;\"\u003e\n \u003cp\u003eMyocardial Infarction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 14.7%;\"\u003e\n \u003cp\u003e10.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 3.75%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" valign=\"bottom\" style=\"width: 21.6%;\"\u003e\n \u003cp\u003eWhite Blood Cells\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 12.45%;\"\u003e\n \u003cp\u003e53.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 26.85%;\"\u003e\n \u003cp\u003eCongestive Heart Failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 14.7%;\"\u003e\n \u003cp\u003e15.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 3.75%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" valign=\"bottom\" style=\"width: 21.6%;\"\u003e\n \u003cp\u003ePlatelets\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 12.45%;\"\u003e\n \u003cp\u003e37.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 26.85%;\"\u003e\n \u003cp\u003eCerebrovascular event\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 14.7%;\"\u003e\n \u003cp\u003e10.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 3.75%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" valign=\"bottom\" style=\"width: 21.6%;\"\u003e\n \u003cp\u003eC-reactive Protein\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 12.45%;\"\u003e\n \u003cp\u003e83.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 26.85%;\"\u003e\n \u003cp\u003ePeripheral Vascular Disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 14.7%;\"\u003e\n \u003cp\u003e9.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 3.75%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" valign=\"bottom\" style=\"width: 21.6%;\"\u003e\n \u003cp\u003eCreatinine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 12.45%;\"\u003e\n \u003cp\u003e34.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 26.85%;\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 14.7%;\"\u003e\n \u003cp\u003e27.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 3.75%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" valign=\"bottom\" style=\"width: 21.6%;\"\u003e\n \u003cp\u003eSodium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 12.45%;\"\u003e\n \u003cp\u003e28.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 26.85%;\"\u003e\n \u003cp\u003eDementia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 14.7%;\"\u003e\n \u003cp\u003e7.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 3.75%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" valign=\"bottom\" style=\"width: 21.6%;\"\u003e\n \u003cp\u003ePotassium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 12.45%;\"\u003e\n \u003cp\u003e21.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 26.85%;\"\u003e\n \u003cp\u003eChronic Kidney Disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 14.7%;\"\u003e\n \u003cp\u003e20.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 3.75%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" valign=\"bottom\" style=\"width: 21.6%;\"\u003e\n \u003cp\u003eAST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 12.45%;\"\u003e\n \u003cp\u003e17.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 26.85%;\"\u003e\n \u003cp\u003eLiver Disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 14.7%;\"\u003e\n \u003cp\u003e8.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 3.75%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" valign=\"bottom\" style=\"width: 21.6%;\"\u003e\n \u003cp\u003eALT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 12.45%;\"\u003e\n \u003cp\u003e11.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 26.85%;\"\u003e\n \u003cp\u003eCOPD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 14.7%;\"\u003e\n \u003cp\u003e16.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 3.75%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" valign=\"bottom\" style=\"width: 21.6%;\"\u003e\n \u003cp\u003eLDH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 12.45%;\"\u003e\n \u003cp\u003e24.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 26.85%;\"\u003e\n \u003cp\u003eCancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 14.7%;\"\u003e\n \u003cp\u003e38.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 3.75%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" valign=\"bottom\" style=\"width: 21.6%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 12.45%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 26.85%;\"\u003e\n \u003cp\u003ePolypharmacy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 14.7%;\"\u003e\n \u003cp\u003e75.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 3.75%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" valign=\"bottom\" style=\"width: 21.6%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBarthel score items\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 12.45%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 26.85%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 14.7%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 3.75%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" valign=\"bottom\" style=\"width: 21.6%;\"\u003e\n \u003cp\u003eFeeding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 12.45%;\"\u003e\n \u003cp\u003e18.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 26.85%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVitals and baseline info\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 14.7%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 3.75%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" valign=\"bottom\" style=\"width: 21.6%;\"\u003e\n \u003cp\u003eBathing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 12.45%;\"\u003e\n \u003cp\u003e28.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 26.85%;\"\u003e\n \u003cp\u003eBody Mass Index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 14.7%;\"\u003e\n \u003cp\u003e55.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 3.75%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" valign=\"bottom\" style=\"width: 21.6%;\"\u003e\n \u003cp\u003eHygiene\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 12.45%;\"\u003e\n \u003cp\u003e24.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 26.85%;\"\u003e\n \u003cp\u003eSystolic Blood Pressure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 14.7%;\"\u003e\n \u003cp\u003e39.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 3.75%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" valign=\"bottom\" style=\"width: 21.6%;\"\u003e\n \u003cp\u003eDressing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 12.45%;\"\u003e\n \u003cp\u003e28.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 26.85%;\"\u003e\n \u003cp\u003eDiastolic Blood Pressure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 14.7%;\"\u003e\n \u003cp\u003e27.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 3.75%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" valign=\"bottom\" style=\"width: 21.6%;\"\u003e\n \u003cp\u003eBowel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 12.45%;\"\u003e\n \u003cp\u003e16.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 26.85%;\"\u003e\n \u003cp\u003eHeart Rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 14.7%;\"\u003e\n \u003cp\u003e24.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 3.75%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" valign=\"bottom\" style=\"width: 21.6%;\"\u003e\n \u003cp\u003eBladder\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 12.45%;\"\u003e\n \u003cp\u003e22.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 26.85%;\"\u003e\n \u003cp\u003eDependency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 14.7%;\"\u003e\n \u003cp\u003e28.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 3.75%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" valign=\"bottom\" style=\"width: 21.6%;\"\u003e\n \u003cp\u003eToilet\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 12.45%;\"\u003e\n \u003cp\u003e28.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 26.85%;\"\u003e\n \u003cp\u003eHome Assistance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 14.7%;\"\u003e\n \u003cp\u003e23.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 3.75%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" valign=\"bottom\" style=\"width: 21.6%;\"\u003e\n \u003cp\u003eTransfers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 12.45%;\"\u003e\n \u003cp\u003e30.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 26.85%;\"\u003e\n \u003cp\u003eBody Temperature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 14.7%;\"\u003e\n \u003cp\u003e16.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 3.75%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" valign=\"bottom\" style=\"width: 21.6%;\"\u003e\n \u003cp\u003eWalking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 12.45%;\"\u003e\n \u003cp\u003e30.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 26.85%;\"\u003e\n \u003cp\u003eSpO2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 14.7%;\"\u003e\n \u003cp\u003e20.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 3.75%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" valign=\"bottom\" style=\"width: 21.6%;\"\u003e\n \u003cp\u003eStairs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 12.45%;\"\u003e\n \u003cp\u003e35.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 26.85%;\"\u003e\n \u003cp\u003eKarnofsky Scale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 14.7%;\"\u003e\n \u003cp\u003e88.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 3.75%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" valign=\"bottom\" style=\"width: 21.6%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 12.45%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1 \u0026ndash; List of variables included in the calculation of the frailty indices and their corresponding deficit prevalence\u003c/strong\u003e. The table reports the variables used to construct the two frailty indices, with and without information on patients\u0026rsquo; past medical history. For each variable, the percentage prevalence of the deficit is shown. COPD: Chronic Obstructive Pulmonary Disease; SBP: systolic blood pressure; DBP: diastolic blood pressure; SpO2: peripheral blood oxygen saturation; WBC: white blood cells; AST: aspartate amino-transferase; ALT: alanine amino-transferase; LDH: lactic dehydrogenase. *not used in the calculation of the FI admission-based.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis:\u0026nbsp;\u003c/strong\u003eDescriptive statistics were calculated for all variables: categorical data as frequencies and percentages; continuous data as medians and interquartile ranges (IQR). Group comparisons were performed using \u0026chi;\u0026sup2; tests (categorical) and Kruskal\u0026ndash;Wallis tests (continuous). Missing data were not imputed. All statistical analyses and graphical outputs were produced using Python 3.12 in Visual Studio Code (Microsoft Corporation) and SPSS, with two-sided p-values \u0026lt;0.05 considered significant. Logistic regression machine learning algorithms were trained using stratified shuffle split methodology with a test size of 30%, applying class weights, with 1000 maximum iterations and liblinear solver. Moreover logistic regression models adjusted for age and sex were performed to assess the association of frailty and comorbidity with the composite outcome.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003e\u003cstrong\u003ePopulation Overview at Study Entry\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOf the 506 patients discharged from the Department of Internal Medicine of the IRCCS San Raffaele Hospital between February 1st, 2024, and January 31st, 2025, a total of 395 (82%) provided informed consent and were enrolled in the study. Sixteen patients experienced two admissions during the study period, resulting in 411 hospitalization episodes included in the analyses.\u003c/p\u003e\n\u003cp\u003eThe cohort was characterized by advanced age and a high clinical complexity: the median age was 76 years (IQR 65\u0026ndash;84), and 46% of patients were female. Patients presented a substantial burden of multimorbidity and polypharmacy, with a median of seven chronic conditions (IQR 5\u0026ndash;10) and seven chronic therapies (IQR 5\u0026ndash;10) per patient (Table 2).\u003c/p\u003e\n\u003cp\u003eBased on age-adjusted BMI thresholds, more than half of the cohort (231 patients, 56%) had values outside the normal range, being classified as underweight or overweight/obese. Most hospitalization episodes originated from the Emergency Department (86%), while a smaller proportion were elective admissions (8%) or transfers from lower- (5%) or higher-intensity care wards (1%).\u003c/p\u003e\n\u003cp\u003eThe median length of hospital stay, excluding time spent in the Emergency Department, was 14 days (IQR 9\u0026ndash;23), reflecting the clinical complexity and care needs of this Internal Medicine population.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"699\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 28.3262%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 21.6023%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall (N=411)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 20.7439%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo outcome (N=225)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 21.1731%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOutcome (N=186)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 8.15451%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 28.3262%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 21.6023%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 20.7439%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 21.1731%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 8.15451%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 28.3262%;\"\u003e\n \u003cp\u003eCCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 21.6023%;\"\u003e\n \u003cp\u003e6.00 (4.00\u0026ndash;8.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 20.7439%;\"\u003e\n \u003cp\u003e6.00 (4.00\u0026ndash;8.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 21.1731%;\"\u003e\n \u003cp\u003e7.00 (4.00\u0026ndash;9.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 8.15451%;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 28.3262%;\"\u003e\n \u003cp\u003eCCI Normalized\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 21.6023%;\"\u003e\n \u003cp\u003e0.16 (0.11\u0026ndash;0.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 20.7439%;\"\u003e\n \u003cp\u003e0.16 (0.11\u0026ndash;0.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 21.1731%;\"\u003e\n \u003cp\u003e0.19 (0.11\u0026ndash;0.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 8.15451%;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 28.3262%;\"\u003e\n \u003cp\u003eFI admission-based\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 21.6023%;\"\u003e\n \u003cp\u003e0.28 (0.19\u0026ndash;0.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 20.7439%;\"\u003e\n \u003cp\u003e0.24 (0.17\u0026ndash;0.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 21.1731%;\"\u003e\n \u003cp\u003e0.34 (0.24\u0026ndash;0.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 8.15451%;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 28.3262%;\"\u003e\n \u003cp\u003eFI including medical history\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 21.6023%;\"\u003e\n \u003cp\u003e0.27 (0.20\u0026ndash;0.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 20.7439%;\"\u003e\n \u003cp\u003e0.25 (0.18\u0026ndash;0.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 21.1731%;\"\u003e\n \u003cp\u003e0.33 (0.23-0.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 8.15451%;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 28.3262%;\"\u003e\n \u003cp\u003eSociodemographic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 21.6023%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 20.7439%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 21.1731%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 8.15451%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 28.3262%;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 21.6023%;\"\u003e\n \u003cp\u003e76.00 (65.00\u0026ndash;84.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 20.7439%;\"\u003e\n \u003cp\u003e73.00 (63.00\u0026ndash;82.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 21.1731%;\"\u003e\n \u003cp\u003e78.00 (67.00\u0026ndash;85.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 8.15451%;\"\u003e\n \u003cp\u003e\u0026lt; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 28.3262%;\"\u003e\n \u003cp\u003eFemale gender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 21.6023%;\"\u003e\n \u003cp\u003e46%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 20.7439%;\"\u003e\n \u003cp\u003e41%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 21.1731%;\"\u003e\n \u003cp\u003e59%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 8.15451%;\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 28.3262%;\"\u003e\n \u003cp\u003eYears of Education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 21.6023%;\"\u003e\n \u003cp\u003e10.00 (5.75\u0026ndash;13.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 20.7439%;\"\u003e\n \u003cp\u003e10.00 (6.50\u0026ndash;13.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 21.1731%;\"\u003e\n \u003cp\u003e10.00 (5.75\u0026ndash;13.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 8.15451%;\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 28.3262%;\"\u003e\n \u003cp\u003eSmoking: Never\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 21.6023%;\"\u003e\n \u003cp\u003e39%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 20.7439%;\"\u003e\n \u003cp\u003e84%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 21.1731%;\"\u003e\n \u003cp\u003e16%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 8.15451%;\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 28.3262%;\"\u003e\n \u003cp\u003eSmoking: Current\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 21.6023%;\"\u003e\n \u003cp\u003e16%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 20.7439%;\"\u003e\n \u003cp\u003e89%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 21.1731%;\"\u003e\n \u003cp\u003e11%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 8.15451%;\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 28.3262%;\"\u003e\n \u003cp\u003eSmoking: Ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 21.6023%;\"\u003e\n \u003cp\u003e44%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 20.7439%;\"\u003e\n \u003cp\u003e81%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 21.1731%;\"\u003e\n \u003cp\u003e19%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 8.15451%;\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 28.3262%;\"\u003e\n \u003cp\u003eSmoking: Passive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 21.6023%;\"\u003e\n \u003cp\u003e1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 20.7439%;\"\u003e\n \u003cp\u003e80%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 21.1731%;\"\u003e\n \u003cp\u003e20%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 8.15451%;\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 28.3262%;\"\u003e\n \u003cp\u003ePack Years smoked\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 21.6023%;\"\u003e\n \u003cp\u003e0.00 (0.00\u0026ndash;31.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 20.7439%;\"\u003e\n \u003cp\u003e0.00 (0.00\u0026ndash;36.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 21.1731%;\"\u003e\n \u003cp\u003e0.00 (0.00\u0026ndash;30.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 8.15451%;\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 28.3262%;\"\u003e\n \u003cp\u003eChronic Diseases Count\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 21.6023%;\"\u003e\n \u003cp\u003e7.00 (5.00\u0026ndash;10.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 20.7439%;\"\u003e\n \u003cp\u003e7.00 (5.00\u0026ndash;9.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 21.1731%;\"\u003e\n \u003cp\u003e7.00 (5.00\u0026ndash;10.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 8.15451%;\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 28.3262%;\"\u003e\n \u003cp\u003eDays in the Emergency Room\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 21.6023%;\"\u003e\n \u003cp\u003e4.00 (3.00\u0026ndash;6.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 20.7439%;\"\u003e\n \u003cp\u003e4.00 (3.00\u0026ndash;5.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 21.1731%;\"\u003e\n \u003cp\u003e5.00 (3.00\u0026ndash;6.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 8.15451%;\"\u003e\n \u003cp\u003e\u0026lt; 0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 28.3262%;\"\u003e\n \u003cp\u003eAdmissions in the last 12 months\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 21.6023%;\"\u003e\n \u003cp\u003e1.00 (0.00\u0026ndash;1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 20.7439%;\"\u003e\n \u003cp\u003e1.00 (0.00\u0026ndash;1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 21.1731%;\"\u003e\n \u003cp\u003e1.00 (0.00\u0026ndash;2.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 8.15451%;\"\u003e\n \u003cp\u003e\u0026lt; 0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 28.3262%;\"\u003e\n \u003cp\u003eAdmissions in the last 6 months\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 21.6023%;\"\u003e\n \u003cp\u003e1.00 (0.00\u0026ndash;1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 20.7439%;\"\u003e\n \u003cp\u003e1.00 (0.00\u0026ndash;1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 21.1731%;\"\u003e\n \u003cp\u003e1.00 (0.00\u0026ndash;1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 8.15451%;\"\u003e\n \u003cp\u003e\u0026lt; 0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 28.3262%;\"\u003e\n \u003cp\u003eAdmissions in the last month\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 21.6023%;\"\u003e\n \u003cp\u003e0.00 (0.00\u0026ndash;0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 20.7439%;\"\u003e\n \u003cp\u003e0.00 (0.00\u0026ndash;0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 21.1731%;\"\u003e\n \u003cp\u003e0.00 (0.00\u0026ndash;0.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 8.15451%;\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 28.3262%;\"\u003e\n \u003cp\u003eAdmission Reasons Count\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 21.6023%;\"\u003e\n \u003cp\u003e1.00 (1.00\u0026ndash;2.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 20.7439%;\"\u003e\n \u003cp\u003e1.00 (1.00\u0026ndash;2.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 21.1731%;\"\u003e\n \u003cp\u003e1.00 (1.00\u0026ndash;2.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 8.15451%;\"\u003e\n \u003cp\u003e\u0026lt; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 28.3262%;\"\u003e\n \u003cp\u003eTherapy Count\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 21.6023%;\"\u003e\n \u003cp\u003e7.00 (5.00\u0026ndash;10.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 20.7439%;\"\u003e\n \u003cp\u003e7.00 (4.00\u0026ndash;10.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 21.1731%;\"\u003e\n \u003cp\u003e8.00 (5.00\u0026ndash;11.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 8.15451%;\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 28.3262%;\"\u003e\n \u003cp\u003eAnthropometric\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 21.6023%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 20.7439%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 21.1731%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 8.15451%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 28.3262%;\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 21.6023%;\"\u003e\n \u003cp\u003e24.34 (21.48\u0026ndash;27.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 20.7439%;\"\u003e\n \u003cp\u003e24.55 (21.72\u0026ndash;28.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 21.1731%;\"\u003e\n \u003cp\u003e24.08 (21.12\u0026ndash;27.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 8.15451%;\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 28.3262%;\"\u003e\n \u003cp\u003eSBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 21.6023%;\"\u003e\n \u003cp\u003e126.54 (23.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 20.7439%;\"\u003e\n \u003cp\u003e125.53 (23.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 21.1731%;\"\u003e\n \u003cp\u003e127.37 (23.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 8.15451%;\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 28.3262%;\"\u003e\n \u003cp\u003eDBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 21.6023%;\"\u003e\n \u003cp\u003e70.00 (63.50\u0026ndash;80.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 20.7439%;\"\u003e\n \u003cp\u003e73.00 (64.75\u0026ndash;80.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 21.1731%;\"\u003e\n \u003cp\u003e70.00 (62.50\u0026ndash;80.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 8.15451%;\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 28.3262%;\"\u003e\n \u003cp\u003eHeart Rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 21.6023%;\"\u003e\n \u003cp\u003e85.00 (76.00\u0026ndash;100.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 20.7439%;\"\u003e\n \u003cp\u003e85.00 (77.00\u0026ndash;100.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 21.1731%;\"\u003e\n \u003cp\u003e85.00 (75.00\u0026ndash;100.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 8.15451%;\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 28.3262%;\"\u003e\n \u003cp\u003eBody Temp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 21.6023%;\"\u003e\n \u003cp\u003e36.40 (36.00\u0026ndash;37.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 20.7439%;\"\u003e\n \u003cp\u003e36.40 (36.00\u0026ndash;37.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 21.1731%;\"\u003e\n \u003cp\u003e36.50 (36.00\u0026ndash;37.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 8.15451%;\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 28.3262%;\"\u003e\n \u003cp\u003eSpO2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 21.6023%;\"\u003e\n \u003cp\u003e96.00 (94.00\u0026ndash;98.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 20.7439%;\"\u003e\n \u003cp\u003e97.00 (94.70\u0026ndash;98.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 21.1731%;\"\u003e\n \u003cp\u003e96.00 (94.00\u0026ndash;98.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 8.15451%;\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 28.3262%;\"\u003e\n \u003cp\u003eGCS Score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 21.6023%;\"\u003e\n \u003cp\u003e15.00 (15.00\u0026ndash;15.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 20.7439%;\"\u003e\n \u003cp\u003e15.00 (15.00\u0026ndash;15.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 21.1731%;\"\u003e\n \u003cp\u003e15.00 (15.00\u0026ndash;15.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 8.15451%;\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 28.3262%;\"\u003e\n \u003cp\u003eFunctional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 21.6023%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 20.7439%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 21.1731%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 8.15451%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 28.3262%;\"\u003e\n \u003cp\u003eBarthel Score (Before Admission)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 21.6023%;\"\u003e\n \u003cp\u003e100.00 (65.00\u0026ndash;100.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 20.7439%;\"\u003e\n \u003cp\u003e100.00 (95.00\u0026ndash;100.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 21.1731%;\"\u003e\n \u003cp\u003e90.00 (50.00\u0026ndash;100.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 8.15451%;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 28.3262%;\"\u003e\n \u003cp\u003eBarthel Score (Admission)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 21.6023%;\"\u003e\n \u003cp\u003e55.00 (25.00\u0026ndash;100.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 20.7439%;\"\u003e\n \u003cp\u003e90.00 (50.00\u0026ndash;100.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 21.1731%;\"\u003e\n \u003cp\u003e45.00 (10.00\u0026ndash;60.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 8.15451%;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 28.3262%;\"\u003e\n \u003cp\u003eECOG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 21.6023%;\"\u003e\n \u003cp\u003e2.00 (1.00\u0026ndash;3.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 20.7439%;\"\u003e\n \u003cp\u003e1.00 (0.00\u0026ndash;3.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 21.1731%;\"\u003e\n \u003cp\u003e3.00 (1.00\u0026ndash;3.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 8.15451%;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 28.3262%;\"\u003e\n \u003cp\u003eKarnofsky Scale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 21.6023%;\"\u003e\n \u003cp\u003e70.00 (50.00\u0026ndash;90.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 20.7439%;\"\u003e\n \u003cp\u003e80.00 (50.00\u0026ndash;90.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 21.1731%;\"\u003e\n \u003cp\u003e60.00 (50.00\u0026ndash;80.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 8.15451%;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 28.3262%;\"\u003e\n \u003cp\u003eBraden Score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 21.6023%;\"\u003e\n \u003cp\u003e18.00 (14.00\u0026ndash;22.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 20.7439%;\"\u003e\n \u003cp\u003e20.00 (17.00\u0026ndash;22.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 21.1731%;\"\u003e\n \u003cp\u003e16.00 (13.00\u0026ndash;18.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 8.15451%;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 28.3262%;\"\u003e\n \u003cp\u003eCIRS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 21.6023%;\"\u003e\n \u003cp\u003e6.00 (0.00\u0026ndash;9.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 20.7439%;\"\u003e\n \u003cp\u003e4.00 (0.00\u0026ndash;9.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 21.1731%;\"\u003e\n \u003cp\u003e6.50 (0.00\u0026ndash;10.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 8.15451%;\"\u003e\n \u003cp\u003e\u0026lt; 0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 28.3262%;\"\u003e\n \u003cp\u003eLaboratory\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 21.6023%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 20.7439%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 21.1731%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 8.15451%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 28.3262%;\"\u003e\n \u003cp\u003eHemoglobin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 21.6023%;\"\u003e\n \u003cp\u003e11.45 (2.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 20.7439%;\"\u003e\n \u003cp\u003e11.26 (2.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 21.1731%;\"\u003e\n \u003cp\u003e11.61 (2.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 8.15451%;\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 28.3262%;\"\u003e\n \u003cp\u003eWBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 21.6023%;\"\u003e\n \u003cp\u003e9.85 (7.17\u0026ndash;13.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 20.7439%;\"\u003e\n \u003cp\u003e9.55 (7.12\u0026ndash;12.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 21.1731%;\"\u003e\n \u003cp\u003e10.40 (7.20\u0026ndash;14.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 8.15451%;\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 28.3262%;\"\u003e\n \u003cp\u003ePlatelets\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 21.6023%;\"\u003e\n \u003cp\u003e213.00 (149.75\u0026ndash;309.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 20.7439%;\"\u003e\n \u003cp\u003e213.00 (160.50\u0026ndash;303.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 21.1731%;\"\u003e\n \u003cp\u003e210.50 (144.25\u0026ndash;316.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 8.15451%;\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 28.3262%;\"\u003e\n \u003cp\u003eAST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 21.6023%;\"\u003e\n \u003cp\u003e28.00 (20.25\u0026ndash;47.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 20.7439%;\"\u003e\n \u003cp\u003e28.00 (20.50\u0026ndash;45.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 21.1731%;\"\u003e\n \u003cp\u003e29.00 (20.50\u0026ndash;50.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 8.15451%;\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 28.3262%;\"\u003e\n \u003cp\u003eALT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 21.6023%;\"\u003e\n \u003cp\u003e22.00 (14.00\u0026ndash;40.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 20.7439%;\"\u003e\n \u003cp\u003e22.00 (15.00\u0026ndash;40.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 21.1731%;\"\u003e\n \u003cp\u003e22.00 (13.00\u0026ndash;39.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 8.15451%;\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 28.3262%;\"\u003e\n \u003cp\u003eLDH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 21.6023%;\"\u003e\n \u003cp\u003e279.50 (227.00\u0026ndash;370.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 20.7439%;\"\u003e\n \u003cp\u003e268.00 (224.25\u0026ndash;358.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 21.1731%;\"\u003e\n \u003cp\u003e290.00 (236.25\u0026ndash;422.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 8.15451%;\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 28.3262%;\"\u003e\n \u003cp\u003eCreatinine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 21.6023%;\"\u003e\n \u003cp\u003e1.06 (0.78\u0026ndash;1.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 20.7439%;\"\u003e\n \u003cp\u003e1.01 (0.78\u0026ndash;1.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 21.1731%;\"\u003e\n \u003cp\u003e1.15 (0.78\u0026ndash;1.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 8.15451%;\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 28.3262%;\"\u003e\n \u003cp\u003eeGFR (CKD-EPI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 21.6023%;\"\u003e\n \u003cp\u003e63.3 (39.69-92.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 20.7439%;\"\u003e\n \u003cp\u003e63.70 (38.22\u0026ndash;92.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 21.1731%;\"\u003e\n \u003cp\u003e59.44 (45.16\u0026ndash;89.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 8.15451%;\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 28.3262%;\"\u003e\n \u003cp\u003eGlucose\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 21.6023%;\"\u003e\n \u003cp\u003e116 (100.00-147.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 20.7439%;\"\u003e\n \u003cp\u003e116.00 (100.0\u0026ndash;147.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 21.1731%;\"\u003e\n \u003cp\u003e121.00 (97.00\u0026ndash;152.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 8.15451%;\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 28.3262%;\"\u003e\n \u003cp\u003eCRP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 21.6023%;\"\u003e\n \u003cp\u003e48.80 (13.10\u0026ndash;134.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 20.7439%;\"\u003e\n \u003cp\u003e40.40 (9.30\u0026ndash;121.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 21.1731%;\"\u003e\n \u003cp\u003e60.60 (15.28\u0026ndash;146.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 8.15451%;\"\u003e\n \u003cp\u003e\u0026lt; 0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 28.3262%;\"\u003e\n \u003cp\u003eSodium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 21.6023%;\"\u003e\n \u003cp\u003e137.75 (134.50\u0026ndash;140.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 20.7439%;\"\u003e\n \u003cp\u003e138.00 (135.20\u0026ndash;140.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 21.1731%;\"\u003e\n \u003cp\u003e137.25 (132.90\u0026ndash;140.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 8.15451%;\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 28.3262%;\"\u003e\n \u003cp\u003ePotassium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 21.6023%;\"\u003e\n \u003cp\u003e4.30 (3.92\u0026ndash;4.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 20.7439%;\"\u003e\n \u003cp\u003e4.25 (3.91\u0026ndash;4.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 21.1731%;\"\u003e\n \u003cp\u003e4.36 (3.96\u0026ndash;4.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 8.15451%;\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 2 \u0026ndash; \u003cstrong\u003eBaseline characteristics according to composite outcome occurrence.\u0026nbsp;\u003c/strong\u003eSociodemographic, anthropometric, functional, and laboratory variables for the overall study population and by composite outcome status, highlighting significant differences. Data are shown as mean (SD) or median (Q1-Q3) as appropriate.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFrailty:\u003c/strong\u003e From the multidimensional set of clinical, functional, laboratory, and sociodemographic variables collected at hospital admission (106 variables), a deficit-accumulation Frailty Index (FI) was constructed, deliberately excluding the cause of hospitalization. Two FI variants were developed and compared: one incorporating information on past medical history (\u0026ldquo;FI including medical history\u0026rdquo;, 41 variables) and one relying exclusively on data available at admission (\u0026ldquo;FI admission-based\u0026rdquo;, 29 variables).\u003c/p\u003e\n\u003cp\u003eCorrelation analysis showed minimal redundancy among the included variables, with no strong pairwise correlations detected (Pearson\u0026rsquo;s r \u0026gt; 0.95), supporting the independence of individual deficits and the stability of both the statistical and machine learning models (Figure 2A). In line with this, the overall distributions of the two indices largely overlapped (Figure 2B), indicating that the inclusion of medical history had a limited effect on the global quantification of frailty.\u003c/p\u003e\n\u003cp\u003eIn the overall cohort, median FI values were 0.27 (IQR 0.20\u0026ndash;0.40) for the FI including medical history and 0.28 (IQR 0.19\u0026ndash;0.45) for the FI admission-based. When patients were classified according to established FI categories\u0026mdash;fit (0\u0026ndash;0.09), pre-frail (0.10\u0026ndash;0.19), frail (0.20\u0026ndash;0.54), and end-stage frail (0.55\u0026ndash;1.00) [25]\u0026mdash;both indices yielded broadly comparable frailty profiles. Using the FI including medical history, 1.7% of patients were classified as fit, 19.9% as pre-frail, 64.5% as frail, and 13.9% as end-stage frail. With the FI admission-based, the corresponding proportions were 3.1%, 23.0%, 54.7%, and 19.2%, respectively.\u003c/p\u003e\n\u003cp\u003eOverall, the inclusion of past medical history slightly reduced the proportion of patients classified at the extremes of the frailty spectrum, with a relative shift toward intermediate frailty categories. Consistently, visual inspection of the density plots (Figure 2B) showed that the admission-based FI displayed a broader distribution, with a more evident secondary peak at higher frailty values. While this pattern suggests a wider spread of frailty when assessed using admission-only data, potential differences in discriminative performance were formally evaluated in subsequent predictive analyses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 2\u003c/strong\u003e. \u003cstrong\u003eSpearman correlation matrix of numerical variables and density plots of the Frailty Index\u003c/strong\u003e. (A) Demographic, clinical, functional, and laboratory variables used for frailty index FI construction were tested for correlation using the non-parametric Spearman coefficient, appropriate for non-normally distributed variables. Significance levels are indicated in each cell (*p \u0026lt; 0.05). Variables differing between the \u0026ldquo;FI including medical history\u0026rdquo; and \u0026ldquo;FI admission-based\u0026rdquo; are highlighted. (B) Density plots showing the distribution overlap between \u0026ldquo;FI including medical history\u0026rdquo; (blue) and \u0026ldquo;FI admission-based\u0026rdquo; (red). Abbreviations: BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; SpO₂, peripheral oxygen saturation; WBC: white blood cells; AST: aspartate amino-transferase; ALT: alanine amino-transferase; LDH: lactic dehydrogenase.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHospitalization Outcomes:\u0026nbsp;\u003c/strong\u003eAmong the 411 hospitalization episodes analysed, 186 (45%) met the criteria for the unfavourable composite outcome, defined as the occurrence of at least one of the following events during the hospital stay: in-hospital death, prolonged length of stay (exceeding the cohort mean), or discharge to a non-home destination (Figure 3).\u003c/p\u003e\n\u003cp\u003eIn the majority of cases, the unfavourable outcome was driven by prolonged hospitalization, which accounted for 59% of events. Discharge to a destination other than home contributed to 31% of cases, while in-hospital death accounted for the remaining 10%.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 3\u003c/strong\u003e. \u003cstrong\u003eProportion and composition of the composite negative outcome\u003c/strong\u003e. Proportion of hospital episodes meeting the unfavourable composite outcome (Yes) or not (No) and relative contribution of each component.\u003c/p\u003e\n\u003cp\u003eTable 2 compares baseline characteristics between hospitalization episodes meeting or not meeting the unfavourable composite outcome. No statistically significant differences were observed between groups with respect to sex, body mass index, number of chronic conditions, or vital parameters at emergency department admission. By contrast, patients experiencing the unfavourable outcome were significantly older.\u003c/p\u003e\n\u003cp\u003eAmong laboratory parameters, only C-reactive protein levels were significantly higher in patients meeting the composite outcome. These patients also had a longer stay in the emergency department and a higher number of hospital admissions in the preceding 6 and 12 months. Notably, all functional and care-dependency measures\u0026mdash;including Barthel Score [26] before admission, Barthel Score at admission, ECOG performance status [27], Karnofsky Scale [28], Braden Score [29], and Cumulative Illness Rating Scale (CIRS) [30]\u0026mdash;were significantly worse in patients who met the unfavourable outcome.\u003c/p\u003e\n\u003cp\u003eBoth Frailty Index (FI) variants showed a strong and graded association with the composite outcome. Median FI values among patients experiencing the outcome were 0.34 (IQR 0.24\u0026ndash;0.57) for the FI admission-based and 0.33 (IQR 0.23\u0026ndash;0.47) for the FI including medical history, compared with 0.24 (IQR 0.17\u0026ndash;0.34) and 0.25 (IQR 0.18\u0026ndash;0.34), respectively, among those without the outcome (p \u0026lt; 0.001 for both comparisons). The magnitude of association and overall discriminative performance were comparable between the two FI versions, indicating that exclusion of past medical history did not materially reduce prognostic accuracy.\u003c/p\u003e\n\u003cp\u003eImportantly, in this cohort both FI measures showed a stronger association with the unfavourable composite outcome than any individual variable included in their construction. Although several single parameters were independently associated with the outcome, their predictive performance was consistently inferior to that of the composite FI, highlighting the added value of a multidimensional frailty assessment over isolated clinical or functional measures.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFrailty and Comorbidity in Relation to the Hospitalization Outcome\u003c/strong\u003e. The CCI is a widely validated instrument for estimating short- and long-term mortality risk in hospitalized patients, particularly among older adults, and was therefore used as a benchmark to contextualize the prognostic performance of the admission-based Frailty Index (FI) in our cohort. As shown in Table 2, the median normalized CCI among patients meeting the unfavourable composite outcome was 0.19 (IQR 0.11\u0026ndash;0.24), only marginally higher than in those not meeting the outcome (0.16, IQR 0.11\u0026ndash;0.22).\u003c/p\u003e\n\u003cp\u003eIn contrast, differences in FI admission-based values between outcome groups were substantially larger. Patients experiencing the composite outcome had a median FI of 0.34 (IQR 0.24\u0026ndash;0.57), compared with 0.24 (IQR 0.17\u0026ndash;0.34) among those without the outcome. Although both indices were significantly associated with the outcome (p \u0026lt; 0.001 for both), the separation between groups was more pronounced for the FI.\u003c/p\u003e\n\u003cp\u003eConsistently, density plots (Figure 4A) demonstrated less overlap between outcome groups for the FI admission-based than for the CCI, indicating superior discrimination. Moreover, CCI values were largely clustered in the lower portion of the scale (\u0026lt;0.5), whereas FI values spanned the full 0\u0026ndash;1 range, reflecting greater resolution and granularity across levels of patient vulnerability.\u003c/p\u003e\n\u003cp\u003eIn logistic regression models predicting the composite outcome, the FI admission-based achieved higher discriminative performance than the CCI (AUC-ROC 0.70 vs. 0.62; Figure 4B). This superiority was maintained in age- and sex-adjusted analyses, with the FI admission-based showing a stronger association with the outcome (OR 37.13, 95% CI 8.31\u0026ndash;166.04, p \u0026lt; 0.001) compared with the CCI (OR 25.70, 95% CI 1.66\u0026ndash;397.79, p = 0.02).\u003c/p\u003e\n\u003cp\u003eNotably, a subset of patients who met the unfavourable outcome had FI admission-based values below the cohort median. Further stratified analysis revealed that these cases were predominantly driven by oncologic disease: 51% had localized or metastatic cancer, compared with 24% among patients meeting the outcome with FI values above the median (p \u0026lt; 0.01).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 3\u003c/strong\u003e. \u003cstrong\u003eDiscriminative performance of the Charlson Comorbidity Index and Frailty Index.\u0026nbsp;\u003c/strong\u003e(A) Density plots of Charlson Comorbidity Index (CCI) and Frailty Index (FI) values according to composite negative outcome status (blue = meeting outcome; grey = not meeting outcome). (B) Receiver operating characteristic (ROC) curves and corresponding area under the curve (AUC) for two machine learning models using either CCI or FI as the sole predictive feature. The Youden\u0026rsquo;s index for both models is reported in the legend and the J point is marked on the corresponding ROC curves.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRevolving patients.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn our cohort, revolving patients\u0026mdash;defined as those experiencing multiple hospital admissions within a one-year period\u0026mdash;were more strongly characterized by markers of multimorbidity and care complexity than by composite indices of comorbidity (CCI) or frailty (FI) (Table 3). In particular, a higher number of chronic therapies (p \u0026lt; 0.001) and greater disease burden as assessed by the Cumulative Illness Rating Scale (CIRS; p \u0026lt; 0.001) were significantly associated with revolving admissions. Additional distinguishing features included longer stays in the emergency department (p \u0026lt; 0.01) and lower Barthel scores prior to admission (p \u0026lt; 0.01).\u003c/p\u003e\n\u003cp\u003eBy contrast, neither the CCI nor the admission-based FI significantly discriminated between revolving and non-revolving patients, despite incorporating variables theoretically related to multimorbidity and vulnerability. These findings indicate that, in this population, repeated hospitalizations were more closely linked to overall care complexity and chronic disease burden across organ systems\u0026mdash;captured by the CIRS and treatment intensity\u0026mdash;rather than to global frailty or comorbidity scores.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"582\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNot revolving (N=143)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRevolving (N=268)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 198px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 161px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 157px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003eCCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003e6.00 (4.00\u0026ndash;9.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003e6.00 (4.00\u0026ndash;8.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003eCCI Normalized\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003e0.16 (0.11\u0026ndash;0.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003e0.16 (0.11\u0026ndash;0.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003eFI admission based\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003e0.26 (0.21\u0026ndash;0.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003e0.28 (0.18\u0026ndash;0.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eSociodemographic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 161px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 157px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003e77.00 (66.00\u0026ndash;85.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003e75.00 (63.00\u0026ndash;83.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003eFemale gender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003e44%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003e56%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt; 0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003eYears of Education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003e8.00 (5.00\u0026ndash;13.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003e11.00 (7.75\u0026ndash;13.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003ePack Years smoked\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003e0.50 (0.00\u0026ndash;34.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003e0.00 (0.00\u0026ndash;30.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003eChronic Diseases Count\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003e7.00 (5.00\u0026ndash;10.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003e7.00 (4.00\u0026ndash;9.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003edaysInER\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003e4.00 (3.00\u0026ndash;5.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003e4.00 (3.00\u0026ndash;6.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003eAdmission Reasons Count\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003e1.00 (1.00\u0026ndash;2.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003e1.00 (1.00\u0026ndash;2.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt; 0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003eTherapy Count\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003e6.00 (4.00\u0026ndash;9.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003e8.00 (5.00\u0026ndash;11.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eAnthropometric\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 161px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 157px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003e24.26 (21.34\u0026ndash;28.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003e24.44 (21.54\u0026ndash;27.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003eSBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003e129.85 (23.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003e124.51 (23.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt; 0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003eDBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003e74.50 (65.00\u0026ndash;85.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003e70.00 (60.00\u0026ndash;80.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003eHeart Rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003e85.00 (77.00\u0026ndash;100.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003e86.00 (76.00\u0026ndash;100.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003eBody Temp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003e36.50 (36.00\u0026ndash;36.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003e36.40 (36.00\u0026ndash;37.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003eSpO2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003e97.00 (95.00\u0026ndash;98.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003e96.00 (94.00\u0026ndash;98.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003eGCS Score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003e15.00 (15.00\u0026ndash;15.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003e15.00 (15.00\u0026ndash;15.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eFunctional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 161px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 157px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003eBarthel Score (Before Admission)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003e100.00 (90.00\u0026ndash;100.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003e100.00 (55.00\u0026ndash;100.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003eBarthel Score (Admission)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003e65.00 (40.00\u0026ndash;100.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003e55.00 (20.00\u0026ndash;100.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003eECOG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003e2.00 (0.00\u0026ndash;3.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003e2.00 (1.00\u0026ndash;3.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003eKarnofsky Scale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003e80.00 (50.00\u0026ndash;90.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003e70.00 (50.00\u0026ndash;90.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003eBraden Score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003e18.00 (15.00\u0026ndash;22.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003e17.00 (14.00\u0026ndash;21.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt; 0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003eCIRS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003e3.00 (0.00\u0026ndash;9.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003e6.00 (1.00\u0026ndash;10.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 198px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 161px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 157px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eLaboratory\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 161px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 157px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003eHemoglobin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003e11.66 (2.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003e11.31 (2.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003eWBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003e10.80 (7.90\u0026ndash;15.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003e9.30 (6.70\u0026ndash;12.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003ePlatelets\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003e213.00 (153.00\u0026ndash;306.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003e213.00 (149.25\u0026ndash;317.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003eAST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003e27.50 (20.00\u0026ndash;51.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003e28.50 (21.00\u0026ndash;44.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003eALT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003e21.00 (14.75\u0026ndash;40.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003e22.00 (14.00\u0026ndash;39.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003eLDH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003e276.00 (232.00\u0026ndash;370.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003e280.00 (220.50\u0026ndash;370.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003eCreatinine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003e1.10 (0.80\u0026ndash;1.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003e1.02 (0.77\u0026ndash;1.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003eCRP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003e56.10 (12.30\u0026ndash;138.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003e46.90 (13.90\u0026ndash;130.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003eSodium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003e137.50 (133.67\u0026ndash;140.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003e138.05 (134.80\u0026ndash;140.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 3 \u0026ndash; \u003cstrong\u003eCharacteristics of the study population by revolving status.\u003c/strong\u003e Variables are reported as mean (SD) or median (Q1\u0026ndash;Q3), as appropriate; p-values refer to comparisons between revolving and non-revolving patients.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eIn this prospective study conducted in an Internal Medicine setting, we demonstrated that frailty, assessed at hospital admission through a multidimensional Frailty Index deliberately constructed independently of comorbidities and admission diagnosis, is a strong predictor of short-term adverse in-hospital outcomes. By directly comparing this diagnosis-independent frailty measure with the CCI, our findings provide novel insight into the distinct and complementary prognostic roles of global vulnerability and disease burden in acutely hospitalized medical patients.\u003c/p\u003e\n\u003cp\u003eFrailty is increasingly recognized as a biological syndrome reflecting reduced physiological reserve and impaired resilience to stressors [25] . A growing body of literature has shown that frailty is closely linked to adverse outcomes across multiple clinical settings, including cardiovascular, metabolic, and hypertensive populations [31].\u003c/p\u003e\n\u003cp\u003eIn the multicentre REPOSI registry [32], a 34-item deficit-accumulation Frailty Index constructed from admission data (including functional, cognitive, laboratory and disease variables) independently predicted in-hospital (OR\u0026asymp;1.6 per 0.1 FI) and 12-month mortality (HR\u0026asymp;1.4), underscoring the prognostic value of frailty among hospitalized older adults. In contrast, the present study implemented a diagnosis- and comorbidity-independent FI at admission and directly benchmarked it against the CCI , thereby separating global vulnerability from disease burden in the acute setting. While REPOSI demonstrates that a broad, multimorbidity-inclusive FI is feasible and prognostically informative across internal medicine/geriatric wards, the current approach shows that an admission-time, comorbidity-free FI retains independent and superior discrimination for short-term in-hospital outcomes, indicating that frailty and comorbidity capture partly distinct risk dimensions and can be usefully combined for clinical decision-making across settings\u003c/p\u003e\n\u003cp\u003eOur findings are consistent with CO-CARED study [33], which showed that social frailty and care dependency constitute vulnerability dimensions partly independent of comorbidity and relevant to hospital outcomes and discharge barriers. In CO-CARED, Internal Medicine inpatients were profiled along complementary axes: clinical instability (e.g., early warning scores), care dependency (nursing intensity/functional needs), and a composite caring complexity index, with social frailty explicitly captured as discharge difficulty and need for social/organizational support. Length of stay and in-hospital outcomes increased stepwise across care-dependency and social-frailty strata, whereas patterns were not fully explained by comorbidity alone\u0026mdash;indicating that organizational constraints, functional dependence, and social needs map a separate risk plane from disease burden. In this framework, our admission-time, comorbidity-free FI similarly isolates patient-level vulnerability (function, reserves, dependency) and shows prognostic value for short-term inpatient outcomes, complementing comorbidity rather than replacing it. Indeed in our study multiple readmissions within one year were better explained by chronic multi-system needs and organizational factors. Together, these data support a multidimensional risk model in which comorbidity, frailty, and social/care complexity each contribute distinct information for planning monitoring intensity, rehabilitation, and discharge pathways.\u003c/p\u003e\n\u003cp\u003eRecent work across cardio-metabolic and geriatric settings indicates that frailty reflects systemic vulnerability beyond comorbidity. In CARYATID, frailty by the comorbidity-independent Fried phenotype clustered with organ-damage markers (albuminuria, reduced eGFR) and worse cognition in frail hypertensive older adults with prediabetes and CKD [34] . Among patients meeting Fried frailty criteria, P-wave dispersion correlated with MMSE independent of comorbidity, indicating an electrophysiologic signal of frailty-linked cognitive vulnerability [35] . Metabolic factors also contribute to this cross-system vulnerability: in the Monteforte study of pre-frail hypertensive older adults, hyperglycemia (\u0026gt;140 mg/dL) independently predicted the 6-month transition from pre-frailty to frailty, pointing to a modifiable pathway of progression [36]. Evidence from nursing-home residents further underscores the physical\u0026ndash;cognitive axis, with Fried frailty closely related to cognition via gait speed [37]. In this context, the present study focuses on acute in-hospital outcomes using a multidimensional, admission-time, comorbidity-free FI and presents a direct comparison with the CCI; taken together, these strands suggest that frailty and comorbidity represent related yet distinct dimensions of risk across settings and time horizons.\u003c/p\u003e\n\u003cp\u003eFrom a clinical perspective, our study supports the integration of frailty assessment into early decision-making processes in Internal Medicine wards. Importantly, the exclusion of comorbidities from the FIwas not motivated by data unavailability\u0026mdash;as comprehensive medical history is usually accessible in Internal Medicine\u0026mdash;but by the need to isolate frailty as a distinct prognostic construct. This approach allows clinicians to complement, rather than replace, traditional comorbidity-based risk stratification.\u003c/p\u003e\n\u003cp\u003eThe superior discriminative performance of the diagnosis-independent FI compared with the CCI suggests that early identification of frailty may help guide individualized management strategies, including intensity of monitoring, rehabilitation planning, and discharge coordination. Moreover, recognizing that frailty and comorbidity drive different adverse trajectories highlights the importance of a multidimensional, tailored approach to risk assessment in complex medical patients.\u003c/p\u003e\n\u003cp\u003eThe strengths of this study include its prospective design, real-world Internal Medicine population, and the explicit methodological separation of frailty and comorbidity. Limitations include the single-center setting and the absence of long-term follow-up outcomes beyond hospitalization, which warrants further investigation in multicenter cohorts (primarily affects external validity; no systematic bias on effect estimates expected). As limit of our study we have also to mention that frailty was assessed at hospital admission in an acute care context, whereas standard assessments are often performed in outpatient/community settings. Consequently, some measurements may reflect both baseline vulnerability and acute illness, introducing potential setting bias and limiting generalizability to community populations (likely away from the null; small\u0026ndash;moderate). This risk was partly mitigated by leveraging pre-admission functional status (baseline ADLs/performance) alongside diagnosis-independent items (likely toward the null; small). Future studies should compare outpatient and admission FIs, assess longitudinal stability, and establish setting-specific thresholds for clinical decision-making. Further sources of bias might be admission (selection) bias that may enrich the cohort with patients at simultaneously higher frailty and event risk, potentially inflating frailty\u0026ndash;outcome associations (away from the null; moderate). Finally competing risk of early discharge may reduce observed event rates, dampening associations (toward the null; small).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn conclusion, our findings demonstrated that frailty, assessed through a multidimensional, diagnosis- and comorbidity-independent FI at hospital admission, was a powerful predictor of short-term adverse outcomes in Internal Medicine patients. By disentangling frailty from disease burden and directly comparing it with a traditional comorbidity index, this study provided new evidence supporting frailty as a distinct and clinically meaningful dimension of vulnerability. Integrating frailty assessment alongside comorbidity and care complexity measures may represent a pragmatic step toward more personalized and effective care for the modern Internal Medicine patient. Adopting frailty-informed approaches in the acute care setting, where early identification of patients at higher risk may guide individualized management and resource allocation. Implementation of such tools could support more appropriate, patient-centred decision-making, potentially improving both clinical outcomes and healthcare efficiency.\u003c/p\u003e"},{"header":"LIST OF ABBREVIATIONS","content":"\u003cp\u003eEBM Evidence Based Medicine\u003c/p\u003e\n\u003cp\u003eeCRF Electronic Case Report Form \u003c/p\u003e\n\u003cp\u003eFI Frailty Index\u003c/p\u003e\n\u003cp\u003eML Machine Learning\u003c/p\u003e\n\u003cp\u003eRCT Randomized Controlled Trial\u003c/p\u003e\n\u003cp\u003eRWD Real World Data\u003c/p\u003e\n\u003cp\u003eRWE Real Wolrd Evidence\u003c/p\u003e"},{"header":"DECLARATIONS","content":"\u003cp\u003eAKNOWLEDGEMENTS\u003c/p\u003e\n\u003cp\u003eThe results presented in this manuscript are the output of experimental work for the award of the PhD qualification of MM.\u003c/p\u003e\n\u003cp\u003eAUTHOR CONTRIBUTIONS STATEMENT\u003c/p\u003e\n\u003cp\u003eMM, SD: Conceptualization, Formal Analysis, Data curation, Methodology, Writing - Original Draft; RDL: Conceptualization, Methodology, Writing - Original Draft; CB, CP, CS, ER, GP: Investigation, Data curation; AD: Writing \u0026ndash; Review \u0026amp; Editing; MT: Conceptualization; PRQ: Conceptualization, Supervision, Writing \u0026ndash; Review \u0026amp; Editing.\u003c/p\u003e\n\u003cp\u003eDATA AVAILABILITY STATEMENT\u003c/p\u003e\n\u003cp\u003eThe data and code that support the findings of this study are available on the San Raffaele Open Research Data Repository at the following DOI: [\u003cem\u003ecreated at the paper publication\u003c/em\u003e].\u003c/p\u003e\n\u003cp\u003eETHICS APPROVAL\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethic Review Board of IRCCS San Raffaele Hospital with the code name \u0026ldquo;MED-Cli\u0026rdquo;.\u003c/p\u003e\n\u003cp\u003eCONFLICT OF INTEREST\u003c/p\u003e\n\u003cp\u003eThe authors declare no relevant conflict of interest to the aims of this study.\u003c/p\u003e\n\u003cp\u003eDECLARATION OF GENERATIVE AI AND AI-ASSISTED TECHNOLOGIES IN THE WRITING PROCESS\u003c/p\u003e\n\u003cp\u003eDuring the preparation of this work the authors used ChatGPT and Claude in order to improve readability and language. After using this tool, the authors reviewed and edited the content as needed and take full responsibility for the content of the publication.\u003c/p\u003e"},{"header":"BIBLIOGRAPHY","content":"\u003col\u003e\n\u003cli\u003eNicolaus S, Crelier B, Donz\u0026eacute; JD, Aubert CE. Definition of patient complexity in adults: A narrative review. Journal of Multimorbidity and Comorbidity 2022;12. https://doi.org/10.1177/26335565221081288.\u003c/li\u003e\n\u003cli\u003eGrant RW, Ashburner JM, Hong CC, Chang Y, Barry MJ, Atlas SJ. Defining patient complexity from the primary care physician\u0026rsquo;s perspective: A cohort study. Ann Intern Med 2011;155. https://doi.org/10.7326/0003-4819-155-12-201112200-00001.\u003c/li\u003e\n\u003cli\u003eSalisbury C. Multimorbidity: Redesigning health care for people who use it. The Lancet 2012;380. https://doi.org/10.1016/S0140-6736(12)60482-6.\u003c/li\u003e\n\u003cli\u003eNaik H, Murray TM, Khan M, Daly-Grafstein D, Liu G, Kassen BO, et al. Population-Based Trends in Complexity of Hospital Inpatients. JAMA Intern Med 2024;184. https://doi.org/10.1001/jamainternmed.2023.7410.\u003c/li\u003e\n\u003cli\u003eColacci M, Loffler A, Roberts SB, Straus S, Verma AA, Razak F. Patient Complexity, Social Factors, and Hospitalization Outcomes at Academic and Community Hospitals. JAMA Netw Open 2025;8. https://doi.org/10.1001/JAMANETWORKOPEN.2024.54745.\u003c/li\u003e\n\u003cli\u003eFaitna P, Bottle A, Klaber B, Aylin PP. Has multimorbidity and frailty in adult hospital admissions changed over the last 15 years? A retrospective study of 107 million admissions in England. BMC Med 2024;22:1\u0026ndash;15. https://doi.org/10.1186/S12916-024-03572-Z/TABLES/4.\u003c/li\u003e\n\u003cli\u003eSnedden SJ, Thein P, Chee WJ, Ong J, Junckerstorff R. Are our patients becoming more complex? Trends in comorbidity and functional dependence in General Medicine 2011\u0026ndash;2019. 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COmplexity of CARE and Discharge barriers: the \u0026ldquo;modern internal medicine patient\u0026rdquo;. Results from the CO-CARED Study. Intern Emerg Med 2025;20:471\u0026ndash;9. https://doi.org/10.1007/S11739-024-03823-0.\u003c/li\u003e\n\u003cli\u003eLv J, Li R, Yuan L, Yang X ling, Wang Y, Ye ZW, et al. Research on the frailty status and adverse outcomes of elderly patients with multimorbidity. BMC Geriatr 2022;22. https://doi.org/10.1186/S12877-022-03194-1.\u003c/li\u003e\n\u003cli\u003eCarrasco-Ribelles LA, Cabrera-Bea M, Dan\u0026eacute;s-Castell M, Zabaleta-Del-Olmo E, Roso-Llorac A, Viol\u0026aacute;n C. Contribution of Frailty to Multimorbidity Patterns and Trajectories: Longitudinal Dynamic Cohort Study of Aging People. JMIR Public Health Surveill 2023;9. https://doi.org/10.2196/45848.\u003c/li\u003e\n\u003cli\u003eKim DH, Rockwood K. Frailty in Older Adults. New England Journal of Medicine 2024;391:538\u0026ndash;48. https://doi.org/10.1056/NEJMRA2301292/SUPPL_FILE/NEJMRA2301292_DISCLOSURES.PDF.\u003c/li\u003e\n\u003cli\u003eDamanti S, De Lorenzo R, Citterio L, Zagato L, Brioni E, Magnaghi C, et al. Frailty index, frailty phenotype and 6-year mortality trends in the FRASNET cohort. Front Med (Lausanne) 2024;11:1465066. https://doi.org/10.3389/FMED.2024.1465066/BIBTEX.\u003c/li\u003e\n\u003cli\u003eCharlson ME, Carrozzino D, Guidi J, Patierno C. Charlson Comorbidity Index: A Critical Review of Clinimetric Properties. Psychother Psychosom 2022;91. https://doi.org/10.1159/000521288.\u003c/li\u003e\n\u003cli\u003eFrenkel WJ, Jongerius EJ, Mandjes-Van Uitert MJ, Van Munster BC, De Rooij SE. Validation of the Charlson Comorbidity Index in acutely hospitalized elderly adults: A prospective cohort study. J Am Geriatr Soc 2014;62. https://doi.org/10.1111/jgs.12635.\u003c/li\u003e\n\u003cli\u003eSetiati S, Ardian LJ, Fitriana I, Azwar MK. Improvement of scoring system used before discharge to predict 30-day all-cause unplanned readmission in geriatric population: a prospective cohort study. BMC Geriatr 2024;24. https://doi.org/10.1186/S12877-024-04875-9.\u003c/li\u003e\n\u003cli\u003eBortolani A, Fantin F, Giani A, Zivelonghi A, Pernice B, Bortolazzi E, et al. Predictors of hospital readmission rate in geriatric patients. Aging Clin Exp Res 2024;36. https://doi.org/10.1007/S40520-023-02664-9.\u003c/li\u003e\n\u003cli\u003eCilla F, Sabione I, D\u0026rsquo;Amelio P. Risk Factors for Early Hospital Readmission in Geriatric Patients: A Systematic Review. Int J Environ Res Public Health 2023;20. https://doi.org/10.3390/IJERPH20031674.\u003c/li\u003e\n\u003cli\u003eThe Academy of Medical Sciences. Multimorbidity: a priority for global health research 2018.\u003c/li\u003e\n\u003cli\u003eVarghese D, Ishida C, Patel P, Koya HH. Polypharmacy. Home-Based Medical Care for Older Adults: A Clinical Case Book 2024:105\u0026ndash;10. https://doi.org/10.1007/978-3-030-23483-6_16.\u003c/li\u003e\n\u003cli\u003eWinter JE, MacInnis RJ, Nowson CA. The influence of age on the BMI and all-cause mortality association: A meta-analysis. Journal of Nutrition, Health and Aging 2017;21. https://doi.org/10.1007/s12603-016-0837-4.\u003c/li\u003e\n\u003cli\u003eTheou O, Haviva C, Wallace L, Searle SD, Rockwood K. How to construct a frailty index from an existing dataset in 10 steps. Age Ageing. 2023 Dec 1;52(12):afad221. doi: 10.1093/ageing/afad221.\u003c/li\u003e\n\u003cli\u003eKim DH, Rockwood K. Frailty in Older Adults. N Engl J Med. 2024 Aug 8;391(6):538-548. doi: 10.1056/NEJMra2301292.\u003c/li\u003e\n\u003cli\u003eMahoney FI, Barthel DW, Functional evaluation: the Barthel Index. Md State Med J. 1965 Feb;14:61-5.\u003c/li\u003e\n\u003cli\u003eOken MM, Creech RH, Tormey DC et al. Toxicity and response criteria of the Eastern Cooperative Oncology Group. Am J Clin Oncol. 1982 Dec;5(6):649-55.\u003c/li\u003e\n\u003cli\u003eCrooks V, Waller S, Smith T, Hahn TJ. The use of the Karnofsky Performance Scale in determining outcomes and risk in geriatric outpatients. J Gerontol. 1991 Jul;46(4):M139-44. doi: 10.1093/geronj/46.4.m139.\u003c/li\u003e\n\u003cli\u003eBergstrom N, Braden BJ, Laguzza A, Holman V. The Braden Scale for Predicting Pressure Sore Risk. Nurs Res. 1987 Jul-Aug;36(4):205-10.\u003c/li\u003e\n\u003cli\u003eMiller MD, Paradis CF, Houck PR, Mazumdar S, Stack JA, Rifai AH, et al. Rating chronic medical illness burden in geropsychiatric practice and research: application of the Cumulative Illness Rating Scale. Psychiatry Res 1992;41:237\u0026ndash;48. https://doi.org/10.1016/0165-1781(92)90005-N.\u003c/li\u003e\n\u003cli\u003eMorley JE, Vellas B, van Kan GA et al Frailty consensus: a call to action. J Am Med Dir Assoc. 2013 Jun;14(6):392-7. doi: 10.1016/j.jamda.2013.03.022.\u003c/li\u003e\n\u003cli\u003eCesari M, Franchi C, Cortesi L, Nobili A, Ardoini I, Mannucci PM, REPOSI collaborators. Implementation of the Frailty Index in hospitalized older patients: Results from the REPOSI register. Eur J Intern Med. 2018 Oct:56:11-18. doi: 10.1016/j.ejim.2018.06.001. \u003c/li\u003e\n\u003cli\u003eCeriani E, Milani O, Donadoni M, Benetti A, Berra SA, Canetta C, Colombo F, Dentali F, Magnani L, Mazzone A, Montano N, Muiesan ML, Podda GM, Querini PR, Squizzato A, Casazza G, Cogliati C; SIMI-FADOI Lombardy Network. COmplexity of CARE and Discharge barriers: the \u0026apos;modern internal medicine patient\u0026apos;. Results from the CO-CARED Study. Intern Emerg Med. 2025 Mar;20(2):471-479. doi: 10.1007/s11739-024-03823-0\u003c/li\u003e\n\u003cli\u003eSantulli G, Visco V, Ciccarelli\u003csup\u003e \u003c/sup\u003e M et al. Frail hypertensive older adults with prediabetes and chronic kidney disease: insights on organ damage and cognitive performance - preliminary results from the CARYATID study. Cardiovasc Diabetol. 2024 Apr 10;23(1):125. doi: 10.1186/s12933-024-02218-x.\u003c/li\u003e\n\u003cli\u003eMone P, Pansini A, Calabr\u0026ograve; F. Global cognitive function correlates with P-wave dispersion in frail hypertensive older adults. J Clin Hypertens (Greenwich). 2022 May;24(5):638-643. doi: 10.1111/jch.14439.\u003c/li\u003e\n\u003cli\u003eMone P, De Gennaro S, Frullone S et al. Hyperglycemia drives the transition from pre-frailty to frailty: The Monteforte study. Eur J Intern Med. 2023 May:111:135-137. doi: 10.1016/j.ejim.2023.01.006\u003c/li\u003e\n\u003cli\u003eRizzo M, Pasini A, Colucci M. Frailty in nursing home residents. Eur J Intern Med. 2023 Sep:115:152-153. doi: 10.1016/j.ejim.2023.05.027\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"internal-and-emergency-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"iaem","sideBox":"Learn more about [Internal and Emergency Medicine](http://link.springer.com/journal/11739)","snPcode":"11739","submissionUrl":"https://www.editorialmanager.com/iaem/default.aspx","title":"Internal and Emergency Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Frailty index, Charlson comorbidity index, Risk score prediction, Multimorbidity, Internal medicine, Hospitalization outcomes","lastPublishedDoi":"10.21203/rs.3.rs-9449571/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9449571/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e \u003cp\u003ePatients admitted to Internal Medicine wards are increasingly characterized by multimorbidity, functional decline, and complex care needs. Prognostic tools based primarily on diagnoses may fail to fully capture this complexity. We evaluated whether a multidimensional Frailty Index (FI), constructed independently of past medical history and admission diagnosis, could predict adverse in-hospital outcomes.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e \u003cp\u003eIn this prospective, single-center study, 395 adults admitted to the Internal Medicine ward of a tertiary university hospital between February 1, 2024, and January 31, 2025, were enrolled. The composite adverse outcome included in-hospital death, prolonged length of stay, or discharge to a non-home destination. The predictive performance of an admission-based, diagnosis-independent FI was assessed using a machine learning logistic regression model and compared with the Charlson Comorbidity Index (CCI).\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eA total of 411 hospitalization episodes were analyzed (median age 76 years; 46% female), with high levels of multimorbidity and polypharmacy; 45% met the composite outcome. The admission-based FI, with or without inclusion of medical history, was strongly associated with the outcome (median 0.34 vs. 0.24; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), showed good discriminative ability (AUC\u0026thinsp;=\u0026thinsp;0.70), and outperformed the CCI (AUC\u0026thinsp;=\u0026thinsp;0.62). Functional impairment was the main contributor to frailty, whereas comorbidities were more closely associated with revolving admissions. Notably, over half of robust patients experiencing adverse outcomes had oncologic disease.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusions\u003c/b\u003e\u003c/p\u003e \u003cp\u003eAn admission-based, diagnosis-independent FI effectively predicts adverse in-hospital outcomes and may enhance early risk stratification in Internal Medicine wards.\u003c/p\u003e","manuscriptTitle":"A Diagnosis-Independent Frailty Index at Admission Improves Risk Stratification in Hospitalized Internal Medicine Patients​","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-06 16:32:22","doi":"10.21203/rs.3.rs-9449571/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2026-04-28T15:59:38+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-27T13:12:27+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-25T06:38:14+00:00","index":"","fulltext":""},{"type":"submitted","content":"Internal and Emergency Medicine","date":"2026-04-23T08:45:16+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"internal-and-emergency-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"iaem","sideBox":"Learn more about [Internal and Emergency Medicine](http://link.springer.com/journal/11739)","snPcode":"11739","submissionUrl":"https://www.editorialmanager.com/iaem/default.aspx","title":"Internal and Emergency Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"238cad33-335e-4954-a0ee-eca41f93a470","owner":[],"postedDate":"May 6th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-06T16:32:22+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-06 16:32:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9449571","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9449571","identity":"rs-9449571","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}