The Acute Illness, Not Cumulative Comorbidity, Dictates Short-Term Functional Recovery in An ACE Unit: A retrospective cohort study

The Acute Illness, Not Cumulative Comorbidity, Dictates Short-Term Functional Recovery in An ACE Unit: A retrospective cohort study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Acute Illness, Not Cumulative Comorbidity, Dictates Short-Term Functional Recovery in An ACE Unit: A retrospective cohort study Li Chen, Dongyan Pu, Guiqing Wang, Meiying Zhao, Wenqian Shi, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7476124/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Accurate pre-intervention assessment in an ACE unit is crucial, yet heavily reliant on comorbidity indices like the Charlson Comorbidity Index (CCI), which often fail to explain significant outcome heterogeneity. This study aimed to determine whether the primary admission diagnosis, representing the acute physiological insult, is a more powerful predictor of short-term functional recovery than the CCI. Methods The primary outcome was short-term functional recovery, measured as the change in the Barthel Index (BI) from admission to discharge (ΔBI). Hierarchical multiple linear regression was used to create three models: a base model (demographic/clinical covariates), a CCI model, and a primary diagnosis model. Model performance was compared using Adjusted R², Area Under the Curve (AUC) from ROC analysis, Net Reclassification Improvement (NRI), and information criteria (AIC/BIC). Results Compared to matched controls, ACE unit patients had significantly shorter lengths of stay (9.82 vs. 11.15 days, p = 0.004), lower hospitalization costs (p < 0.001), higher mean discharge BI (80.49 vs. 75.70, p = 0.033), and lower 15-day readmission rates (1.41% vs. 9.86%, p < 0.001). In the multivariable analysis of the ACE cohort, the primary admission diagnosis was a strong predictor of ΔBI (e.g., Neurological vs. Renal disease, B = 5.091, p = 0.001), while the CCI was not a significant predictor (p = 0.628). The Diagnosis Model demonstrated superior performance over the CCI Model, with a higher Adjusted R² (0.85 vs. 0.78), a significantly better AUC for predicting clinically significant recovery (0.928 vs. 0.888, p = 0.031), and a positive NRI (0.38, p = 0.004). Conclusions The short-term functional recovery of hospitalized older adults is dictated by the nature of the acute illness, not the accumulation of chronic diseases. This suggests the acute diagnosis reflects a unique pathophysiological stress and recovery potential not captured by comorbidity indices. Clinicians should prioritize the primary diagnosis for risk assessment and tailoring interventions in ACE units. Functional recovery Activities of daily living Aging Acute Care for Elders unit Disease Diagnosis Figures Figure 1 Figure 2 Figure 3 Introduction In hospitalized older adults due to acute health problems, the presence of multimorbidity is strongly associated with a higher risk of adverse outcomes, including prolonged hospitalizations,functional decline, activity limitations, prolonged recovery and in-hospital deaths [ 1 ]-[ 2 ][ 3 ]. To facilitate the rapid recovery of elderly patients and meet the complex demands, the Acute Care for Elders (ACE) program was proposed [ 4 ]. It integrated a specially prepared, age-friendly environment with patient-centered care protocols focused on preventing functional decline, promoting rehabilitation, and optimizing medication management. The goal is to improve clinical outcomes in acute disease or acute exacerbation of chronic disease, reducing incidence of functional decline for older adults during hospitalizations [ 5 ]. Systematic reviews have documented the effectiveness of ACE units in improving various outcomes, including reducing the incidence of falls, pressure injuries, and delirium [ 6 ]. In ACE units, a dedicated Multidisciplinary Team (MDT) applies the principles of Comprehensive Geriatric Assessment (CGA) to manage multimorbidity, a central characteristic of this patient population [ 7 ][ 8 ]. Despite this structured approach, significant heterogeneity is observed in the functional recovery of these patients [ 9 ]. A potential reason for this variability may lie in the limitations of the assessment tools commonly used. Within the CGA framework, the Charlson Comorbidity Index (CCI) is widely employed to quantify the cumulative burden of chronic diseases [ 10 ]. However, the CCI was primarily designed to predict long-term mortality, which limits its utility in forecasting short-term functional outcomes [ 11 ]. Specifically, it tends to overlook the diagnosis-specific nuances and the impact of the acute physiological insult that are critical for determining recovery potential [ 12 ]. The primary admission diagnosis, which dictates distinct clinical needs and recovery, represents a crucial factor that is often underemphasized in ACE initial assessments [ 13 ]. For instance, patients with heart failure may particularly benefit from tailored cardiopulmonary rehabilitation due to exercise limitations [ 14 ], while stroke patients often require intensive early neurological rehabilitation [ 15 ]. Therefore, this study hypothesizes that the primary admission diagnosis, representing the acute physiological stress, will be a superior predictor of short-term functional recovery compared to the CCI, which reflects the cumulative chronic burden. By directly comparing the predictive efficacy of these two factors, this research aims to provide a more precise and clinically meaningful indicator for risk assessment within the ACE unit. This study was designed to address a critical gap in the ACE prognosis evaluation. Accordingly, our primary objective was to determine whether the primary admission diagnosis is a more powerful and clinically relevant predictor of short-term functional recovery than the widely-used Charlson Comorbidity Index (CCI). A secondary aim was to characterize these diagnosis-specific recovery patterns to inform the development of tailored intervention strategies within the ACE unit model. Methods Study design and setting This study examined patients admitted to the Department of Acute Geriatrics and General Geriatrics at Zhengzhou Affiliated Central Hospital of Zhengzhou University from January 2023 to December 2024. First, to establish a valid baseline, the study compared older adults (≥ 65 years) admitted to a dedicated Acute Care for Elders (ACE) unit with a matched control group receiving standard care in the general geriatric wards. Second, according to the different primary diagnoses, the age, gender, baseline function, especially the CCI, of the ACE cohort were strictly matched to enhance comparability. The ACE unit implements core principles including routine Comprehensive Geriatric Assessment (CGA), individualized care plans delivered by a multidisciplinary team, early rehabilitation focus, patient-centered environmental modifications, and proactive discharge planning. We included patients 65 years of age or older who had been admitted within 2 weeks of symptom onset for an acute or acute exacerbation of a chronic illness. Patients who were totally dependent on personal care, had severe mobility impairment, severe dementia, and were admitted to the hospital with end-stage disease such as multiple organ failure and severe cardiopulmonary and renal insufficiency were excluded. Data collection Data were retrospectively extracted from electronic medical records and hospital administrative databases by two trained geriatric resource nurses using a standardized data collection form. Baseline data collected at admission included: primary diagnosis for admission, CCI, age, gender, previous hospitalization times, baseline albumin, baseline hemoglobin and baseline ADL. Outcome data included: ADL, LOS, total hospitalization costs, albumin and hemoglobin at discharge, in-hospital deaths, and 15-day all-cause readmission. Classification of primary diagnosis The primary diagnosis recorded at hospital admission was used for classification. Based on the underlying pathophysiology and affected organ system, we used the International Classification of Diseases (ICD-11), diagnoses were grouped into seven mutually exclusive major categories: Cardiovascular diseases (e.g., heart failure, coronary artery disease), Digestive diseases (e.g., gastrointestinal bleeding, cholecystitis), Endocrine diseases (e.g., diabetes mellitus and complications), Fever/Infection (primarily non-respiratory systemic infections), Neurological diseases (e.g., stroke, Parkinson's disease), Respiratory diseases (e.g., COPD, pneumonia), and Renal diseases (e.g., acute kidney injury, exacerbation of chronic kidney disease). The results of classification were discussed with experienced clinicians from various specialities and validated with input from academic clinicians not involved in the study. CCI The Charlson Comorbidity Index (CCI) was used to assess the baseline comorbidity burden for each patient [ 11 ]. This validated index assigns a specific weight (1, 2, 3, or 6) to 17 predefined comorbid conditions, based on their established association with one-year mortality. The total CCI score was calculated by summing the weights of all present comorbidities for a given patient. A higher total score indicates a greater burden of comorbid disease and corresponds to a higher predicted risk of mortality. The age-adjusted CCI was also calculated, where one point was added to the score for each decade of age from 50 years onward. ADL Functional recovery was assessed by improvement in activities of daily living (ADL) from admission to discharge. The ADL of patients in the ACE unit group before and after intervention were evaluated by the Barthel Index rating scale [ 16 ],[ 17 ]. Basic self-care of daily living includes the following ten items: eating, bathing, grooming, dressing, bowel control, bladder control, toilet use, transfers, and movements on the floor and stairs. The score of 100 was complete independence, 61–99, mild dependence; 41 to 60 points, moderate dependence; 40 points or less, severe dependence; 0 points, total dependence. Statistical analysis All statistical analyses were conducted using R statistical software (Version 4.2.0). A two-sided p-value of < 0.05 was considered statistically significant for all tests. The analytical approach was designed to first establish the comparability of the cohorts and the effectiveness of the ACE unit, and second, to formally compare the predictive value of the Charlson Comorbidity Index (CCI) against the primary admission diagnosis for short-term functional recovery. 1. Primary Outcome Definition. The primary outcome was short-term functional recovery, quantified as the change in the Barthel Index from admission to discharge (ΔMBI). For the purpose of assessing model discrimination, this continuous outcome was dichotomized to identify patients who achieved a "clinically significant functional recovery," defined as a ΔMBI of ≥ 10 points. This threshold was selected based on established minimal clinically important difference (MCID) literature for the Barthel Index [ 18 ][ 19 ][ 20 ]. 2. Baseline and Outcome Comparisons. Initial analyses were performed to compare the ACE and non-ACE cohorts. For continuous variables (e.g., age, length of stay, costs, Barthel Index scores), independent samples Student's t-tests were used. For categorical variables (e.g., gender, readmission rates, primary diagnosis distribution), Pearson’s chi-squared test or Fisher’s exact test was employed as appropriate. 3. Fitting the Hierarchical Multivariate Models. The central analysis involved the construction of three nested multiple linear regression models to predict the primary continuous outcome (ΔMBI) within the ACE cohort. The goal was to isolate the added predictive value of the CCI and the primary diagnosis. Model 1 (Base Model): This initial model included established demographic and clinical covariates known to influence functional recovery: age (continuous), gender (binary), prior hospitalization times (continuous), and Baseline ADL score (continuous). Model 2 (CCI Model): The age-adjusted CCI score (continuous) was added to the covariates from the Base Model. Model 3 (Diagnosis Model): The primary admission diagnosis (a seven-level categorical variable, with "Renal diseases" as the reference category) was added to the covariates from the Base Model. 4. Determining Added Predictive Value. To determine whether the primary diagnosis offered superior predictive value compared to the CCI, the performance of Model 2 and Model 3 was formally compared against the Base Model and each other using a comprehensive set of metrics: Overall Model Fit and Explained Variance: The performance of each model was evaluated using the Adjusted R-squared (R²) to quantify the proportion of variance in ΔBI explained by the predictors, while penalizing for model complexity. The Root Mean Square Error (RMSE) was calculated as a measure of the average prediction error. A formal comparison of nested models was conducted using the Likelihood Ratio Chi-Squared (χ²) test, which assesses whether the addition of new predictors (CCI or Diagnosis) results in a statistically significant improvement in model fit over the Base Model. Discrimination: The ability of each model to discriminate between patients who did and did not achieve clinically significant functional recovery (ΔMBI ≥ 10) was assessed using Receiver Operating Characteristic (ROC) curve analysis. The Area Under the Curve (AUC) was calculated for each model. To test for a statistically significant difference in discriminative ability, the AUCs of Model 2 and Model 3 were formally compared using the DeLong's test. Reclassification: The continuous Net Reclassification Improvement (NRI) was calculated to determine the extent to which the Diagnosis Model led to a change in the correct direction of predicted risk compared to the CCI Model. The NRI measures the net percentage of patients correctly reclassified to higher or lower probabilities of recovery, providing a sensitive measure of improvement in model performance. Model Selection Criteria: To further compare the non-nested models while accounting for goodness-of-fit and model complexity, the Akaike's Information Criterion (AIC) and Bayesian Information Criterion (BIC) were calculated. Lower values for these criteria indicate a more optimal model, balancing explanatory power with parsimony. 5. Model Diagnostics. The final multiple linear regression model (Model 3) was checked for multicollinearity among predictor variables using the Variance Inflation Factor (VIF). A VIF value > 5 was considered indicative of significant multicollinearity. 6. Presentation and Validation of the Final Model. The clinical utility of the final, superior model (Model 4) was visually presented. Ethical Considerations This study was approved by the Ethics Committee of Zhengzhou Central Hospital Affiliated Zhengzhou University (Ethics number: 2018020781). Patient consent was waived due to the anonymized use of historical medical records, adhering to institutional and national guidelines. Results Patient Cohort and Baseline Characteristics A total of 221 patients admitted to the ACE unit met eligibility criteria. After excluding eight patients with severe dependency, mobility disorders, dementia, or end-stage disease, 213 ACE patients remained (Fig. 1 ). To minimize confounding variables, a control group was matched 1:1 from geriatric wards based on age, gender, baseline ADL and CCI. Finally, 213 pairs of balanced samples were obtained. Table 1 Baseline characteristics of study participants ACE (n=213,50) Non-ACE (n=213,50) P -value Age (years), mean (SD) 76.4 ± 7.7 77.5 ± 7.9 0.135 Female, n (%) 117 (48.5) 124 (41.5) 0.494 CCI, mean ± SD 4.75 ± 1.83 4.90 ± 1.93 0.457 Endocrine disease 6.00 ± 3.40 4.60 ± 1.81 0.254 Cardiovascular system diseases 4.76 ± 1.74 4.57 ± 2.14 0.581 Neurological diseases 4.51 ± 1.80 5.09 ± 2.06 0.177 Respiratory diseases 4.50 ± 1.89 5.30 ± 2.15 0.044 Renal impairment 5.25 ± 1.77 5.16 ± 1.58 0.884 Digestive system diseases 4.18 ± 1.08 4.77 ± 1.30 0.259 Fever/infection 4.78 ± 1.48 4.27 ± 1.68 0.354 Primary diagonosis for admission, n (%) Endocrine disease 15 (7.0) 15 (7.0) 1.000 Cardiovascular system diseases 45 (21.0) 49 (22.9) 0.658 Neurological diseases 35 (16.4) 47 (22.0) 0.141 Respiratory diseases 70 (32.7) 60 (28.0) 0.288 Renal impairment 20 (9.3) 19 (8.9) 0.881 Digestive system diseases 11 (5.1) 13 (6.1) 0.677 Fever/infection 18 (8.4) 11 (5.1) 0.180 Barthel index, mean (SD) 72.69 ± 22.07 74.77 ± 25.68 0.368 Complete, n 14 34 0.003 Mild, n 151 121 Moderate, n 19 33 Severe, n 29 25 Albumin (g/L), mean (SD) 42.67 ± 5.94 41.56 ± 7.13 0.125 Hemoglobin (g/L), mean (SD) 127.60 ± 19.57 127.74 ± 19.56 0.863 Prior hospitalization times, mean (SD) 3.38 ± 2.85 3.65 ± 2.61 0.305 Note. Data are presented as n (%) or mean (standard deviation). As detailed in Table 1, the baseline demographic and clinical characteristics of the ACE and non-ACE cohorts were well-matched. There were no statistically significant differences in mean age (76.4 vs. 77.5 years, p = 0.135), gender distribution, mean Charlson Comorbidity Index (CCI) score (4.75 vs. 4.90, p = 0.457), or baseline Barthel Index (72.69 vs. 74.77, p = 0.368). The level of functional status of patients was divided into complete independence, mild dependence, moderate dependence and severe dependence according to the Barthel index. Baseline albumin, hemoglobin, and prior hospitalization times were also comparable between the groups ( P > 0.05 for all). The distribution of primary admission diagnoses was also largely similar between the two groups, confirming the effectiveness of the matching procedure. Comparison of Clinical Outcomes between ACE and Non-ACE Units The pre-discharge outcomes analysis revealed significant benefits for patients managed in the ACE unit compared to the matched non-ACE group (Table 2). Patients in the ACE unit experienced a significantly shorter mean length of stay (9.82 vs. 11.15 days, p = 0.004) and incurred lower total hospitalization costs (8647.85 vs. 10119.87, p < 0.001). Furthermore, the ACE cohort demonstrated superior functional outcomes at discharge, with a higher mean Barthel Index (80.49 vs. 75.70, p = 0.033). Critically, the rate of 15-day all-cause readmission was substantially lower in the ACE group (1.41% vs. 9.86%, p < 0.001). Predictors of Short-Term Functional Recovery in the ACE Cohort The primary analysis focused exclusively on the 213 patients within the ACE unit to identify predictors of short-term functional recovery, defined as the change in Barthel Index from admission to discharge (ΔBI). The multiple linear regression analysis, incorporating all potential predictors, identified several factors significantly associated with functional recovery (Table 4 ). Baseline ADL was a strong negative predictor (B=-0.161, p < 0.001), indicating that patients with higher initial function had less room for improvement. Table 2 Pre-discharge comparisons results ACE (n = 213) Non-ACE (n = 213) P -value Albumin (g/L) 38.56 ± 7.73 39.60 ± 5.76 0.225 Hemoglobin (g/L) 120.12 ± 19.76 123.22 ± 19.39 0.227 Length of stay (d) 9.82 ± 4.15 11.15 ± 5.33 0.004 * Barthel index 80.49 ± 19.78 75.70 ± 25.93 0.033 * Complete, n 77 42 ＜0.001 * Mild, n 112 116 0.713 Moderate, n 11 31 0.001 * Severe, n 14 25 0.071 Costs 8647.85 ± 3892.20 10119.87 ± 4832.23 < 0.001 * Deaths, n (%) 2 (0.94) 4 (1.88) 0.411 Readmission in 15 days, n (%) 3 (1.41) 21 (9.86) < 0.001 * * P value statistically significant at the P ≤ 0.05. Most notably, the primary admission diagnosis emerged as a powerful predictor. Compared to patients with renal diseases (the reference category), patients with Neurological (B = 5.091, p = 0.001), Endocrine (B = 4.245, p = 0.006), Cardiovascular (B = 3.589, p = 0.026), and Respiratory (B = 3.443, p = 0.019) diagnoses all showed significantly greater functional recovery. In stark contrast, the Charlson Comorbidity Index (CCI) was not a significant predictor of short-term functional recovery (B=-0.099, p = 0.628). Comparative Performance of Predictive Models To formally test the hypothesis that primary diagnosis is a superior predictor to cumulative comorbidity, the performance of three hierarchical models was compared (Table 3 ). Model 1 (Base Model) had an adjusted R² of 0.75. The addition of CCI in Model 2 yielded only a marginal improvement (Adjusted R²=0.78). However, the inclusion of the primary diagnosis in Model 3 resulted in a substantial increase in explanatory power, with an Adjusted R² of 0.85. Table 3 Comparison of the three models Model comparison Base Model CCI Model Diagnosis Model p Value (CCI vs Diagnosis) Adjusted R² 0.75 0.78 0.85 NA RMSE 8.91 8.62 8.51 0.032 * Likelihood ratio χ2 17.28(4 df) 17.88 (5 df) 32.1 (8 df) 0.0038 * AIC 1452.3 1441.8 1438.5 0.036 * BIC 1466.1 1459.2 1462.7 0.029 * NRI NA 0.31(0.05, 0.58) 0.38 (0.11, 0.65) 0.004 * * P value statistically significant at the P ≤ 0.05. Adjusted R², adjusted coefficient of determination; RMSE, root mean square error; Likelihood ratio χ ², Likelihood Ratio Chi-Squared test, A higher χ 2 -df value indicates a stronger association; AIC, Akaike's information criterion; BIC, Bayesian information criterion; NRI, Net Reclassification Improvement. As shown in Table 4 , the superiority of the Diagnosis Model (Model 3) over the CCI Model (Model 2) was consistent across all evaluation metrics. Model 3 demonstrated a significantly lower Root Mean Square Error (RMSE: 8.51 vs. 8.62, p = 0.032) and a significantly higher Likelihood Ratio χ² value (32.1 vs. 17.88, p = 0.0038), indicating a better model fit. This was further supported by lower Akaike's Information Criterion (AIC: 1438.5 vs. 1441.8, p = 0.036) and Bayesian Information Criterion (BIC: 1462.7 vs. 1459.2, p = 0.029). Furthermore, the Net Reclassification Improvement (NRI) for Model 3 compared to Model 2 was 0.38, indicating that the Diagnosis Model correctly reclassified a significant proportion of patients (p = 0.004). The clinical utility of the superior Diagnosis Model was demonstrated through risk stratification. As illustrated in the violin plot (Fig. 2 ), Model 3 effectively stratified the ACE cohort into two distinct prognostic groups: "Functional Maintenance" and "Clinically Significant Recovery." The actual functional recovery observed in the group predicted to have significant recovery was substantially and statistically higher than that of the maintenance group (t-test: p < 0.001). Finally, Receiver Operating Characteristic (ROC) curve analysis for predicting clinically significant functional recovery (ΔBI ≥ 10 points) further confirmed these findings (Fig. 3 ). As shown in the ROC Curves figure, the Area Under the Curve (AUC) for Model 3 (Diagnosis: AUC = 0.928) was significantly higher than that for Model 2 (CCI: AUC = 0.888), with a DeLong's test p-value of 0.031. Table 4 Model 4: Multiple linear regression analysis performed with all variables Variable Unstandardized coefficients B Standardized coefficients B 95% CI P -value Predictor Age -0.102 -0.089 -0.209 to 0.005 0.062 CCI -0.099 -0.034 -0.501 to 0.303 0.628 Prior hospitalization times -0.137 -0.046 -0.465 to 0.191 0.413 Baseline ADL -0.161 -0.273 -0.232 to -0.090 <0.001 * Male vs. female -0.838 -0.042 -2.646 to 0.970 0.364 Primary diagnosis Neurological vs Renal 5.091 0.208 2.084 to 8.098 0.001 * Endocrine vs Renal 4.245 0.195 1.199 to 7.291 0.006 * Cardiovascular vs Renal 3.589 0.145 0.427 to 6.751 0.026 * Respiratory vs Renal 3.443 0.157 0.557 to 6.329 0.019 * Digestive vs Renal 2.460 0.078 -1.178 to 6.098 0.186 Infection vs Renal 1.403 0.053 -1.861 to 4.667 0.399 * P value statistically significant at the P ≤ 0.05. Discussion Main findings This study's principal finding is that in an ACE unit, the primary diagnosis for acute admission serves as a robust, independent predictor of functional recovery, outperforming the cumulative chronic disease burden as measured by the Charlson Comorbidity Index (CCI). This suggests that while a patient's history of multimorbidity is important, the specific pathophysiological impact of the acute illness itself is a more dominant factor in determining the short-term rehabilitation within an ACE unit. Consequently, these results highlight a critical need for the multidisciplinary team to integrate the primary diagnosis more explicitly into the Comprehensive Geriatric Assessment (CGA) for a more accurate risk stratification and care planning. In addition to this primary finding, our study also reaffirms the overall effectiveness of the ACE unit model. Consistent with established literature [ 21 ][ 22 ][ 23 ], our cohort demonstrated significant improvements in functional recovery, alongside reduced length of stay, lower hospitalization costs, and decreased 15-day readmission rates. This confirmation provides a robust context for our primary finding, suggesting that while the ACE model is broadly effective, its prognostic accuracy can be further enhanced by prioritizing the primary diagnosis over traditional comorbidity indices. Why Does the Acute Primary Diagnosis Outweigh Chronic Comorbidity in Predicting Functional Outcome? A central contribution of our research is the elucidation of the complex interplay between acute illness and chronic comorbidity in determining short-term outcomes. CCI was not a significant predictor of functional recovery in multiple regression models, however, this is not to suggest that multimorbidity is unimportant; on the contrary, it may reveal a deeper problem. While CCI reflects a patient's chronic, cumulative health deficit, the 'primary admission diagnosis' represents the nature and intensity of the current acute physiological stress [ 24 ]. This finding highlights that while the ACE unit is beneficial overall, the magnitude of functional recovery is not uniform across all conditions treated within this specialized environment. And the impact of the acute event itself (e.g., inflammatory, metabolic derangering, and hemodynamic instability) and the specific rehabilitation pathways associated with it may outweigh the chronic comorbid background in the short term for functional recovery in older patients. This finding challenges the conventional reliance on comorbidity indices alone for short-term prognostication and calls for a recalibration of how we weigh acute versus chronic factors in geriatric acute care. Why Do Different Diagnoses Lead to Differences in the Potential for Functional Recovery? The differential recovery degrees observed across diagnostic groups provide compelling evidence for this thesis. Patients with neurological diseases had the most significant ADL recovery effect during hospitalization, followed by endocrine diseases, cardiovascular diseases and respiratory diseases. The recovery potential of digestive diseases, fever/infection, and renal diseases is relatively low. Patients with neurological conditions such as stroke demonstrated notable functional improvements, may attributable to the ACE unit's focus on early and intensive rehabilitation.The structured, goal-oriented therapeutic model intrinsic to the ACE unit directly addresses the specific motor, sensory, and cognitive deficits post-stroke. This approach is designed to maximize neuroplasticity—the brain's ability to reorganize itself—by promoting cortical reorganization through targeted, repetitive exercises like daily gait training and fine motor practice [ 25 ]. Such intensive intervention facilitates a more rapid and substantial recovery by fostering the development of new neural pathways. In contrast, the substantial recovery seen in patients with acute exacerbations of endocrine, cardiovascular, and respiratory diseases likely reflects a different mechanism. For these patients, functional decline is often a direct consequence of a reversible physiological derangement like hyperglycemia, fluid overload and bronchospasm. The comprehensive geriatric assessment (CGA) and patient-centered care within the ACE unit are effective at stabilizing these conditions through meticulous medication management, nutritional support, and tailored therapy. Once the acute physiological stressor is controlled, patients can rapidly revert to their baseline functional status, resulting in a significant measured recovery during the relatively short hospitalization. The substantial gains in patients with endocrine diseases (e.g., diabetic complications) might relate to rapid symptomatic improvement achievable through metabolic stabilization, optimized medication, nutritional support, and early mobilization facilitated by the ACE unit [ 26 ]. For patients with acute cardiovascular events, such as a heart failure exacerbation, functional decline is often due to dyspnea, fatigue, and edema-induced immobility rather than a primary musculoskeletal deficit[ 27 ]. While exertional limitations and complex fluid management present significant challenges, the ACE unit's expertise in aggressive yet careful diuresis, afterload reduction, and medication optimization can rapidly restore hemodynamic stability. Once euvolemic and stable, patients can engage in graded mobilization. Similarly, for patients with acute respiratory illnesses (e.g., COPD exacerbation, pneumonia), functional impairment is a direct consequence of hypoxemia, systemic inflammation, and increased work of breathing [ 28 ]. After nutritional support and combining targeted pharmacotherapy in ACE unit, muscle strength and activity endurance may be rapidly improved, thus effectively stabilizing the patient's physiological state and improving self-care ability. Conversely, the patient groups with the lowest recovery potential—those with fever/infection, renal diseases, and digestive diseases—share a underlying pathophysiology: a profound systemic inflammatory and catabolic state [ 29 ]. This aligns with known associations between chronic kidney disease, systemic inflammation, uremic toxin accumulation, sarcopenia, and poorer functional outcomes [ 30 ][ 31 ]; indeed, evidence showed that lower eGFR has been linked to worse outcomes in patients [ 32 ]. This systemic insult leads to global deconditioning, profound weakness, anorexia, and fatigue that are not localized to a single organ system and are not rapidly reversible. The recovery from such a catabolic state is a slow, anabolic process requiring weeks or months of nutritional rebuilding and physical therapy, a timeline that extends far beyond a typical acute hospitalization. Therefore, while the ACE unit can effectively manage the acute illness, the profound systemic deconditioning prevents significant short-term functional gains as measured by ΔBI at discharge. The patient is stabilized but leaves the hospital in a weakened, frail state, necessitating a protracted post-discharge recovery period. Therefore, even if CCI is similar across primary diagnoses, the recovery potential is quite different. The Clinical Assessment within the ACE unit Should Shift From Standardization to Precision The findings of this study have significant, actionable implications for the evolution of care within the ACE unit, mandating a shift from broad, standardized assessments toward a more precise assessment. First, they suggest a need to refine our approach to risk stratification. Rather than relying solely on the cumulative comorbidity score, clinical assessments should incorporate the primary diagnosis as one of the key predictors of short-term functional outcome. Second, our results highlight an opportunity for more precise, tailored interventions. These diagnosis-specific patterns suggest that while the core ACE units are broadly effective, optimizing outcomes might require tailoring intervention intensity and focus based on the primary diagnosis. For conditions with high recovery potential driven by the reversal of acute physiological derangements—such as endocrine, cardiovascular, and respiratory diseases—protocols should prioritize intensive metabolic management, careful balancing of cardiopulmonary demands with rehabilitation intensity, and targeted post-inflammatory pulmonary rehabilitation [ 33 ]. Patients with conditions portending a slower recovery, such as severe infections or renal disease, may require not only treatment of hyperkalemia and anti-infection in the acute phase but also specific regimens targeting factors such as nutrition and inflammation in the chronic phase [ 34 ]. Finally, a clear understanding of an unit's typical patient diagnoses is essential for strategic resource planning. A high prevalence of respiratory disease in our ACE unit can also indicate the importance of understanding the predominant case-mix, for informing team composition and resource allocation to best meet patient needs. In the future, it is recommended to carry out larger prospective studies, use more objective functional evaluation indicators, and expand the sample size to further verify the difference in the effectiveness of ACE unit in the recovery of functional status of patients with primary diagnosis diseases and comorbidities, and put forward nursing optimization suggestions for patients with complex diseases, so as to promote the improvement of precision nursing system. Strengths and Limitations The primary strength of this study lies in its rigorous design, which directly compared the predictive utility of two competing prognostic factors within a well-characterized cohort. By using a hierarchical modeling approach and multiple robust statistical metrics (Adjusted R², AUC, NRI), we provide a comprehensive and statistically sound basis for our conclusions. Furthermore, focusing on data from a period of stable ACE unit operation likely provides a realistic assessment of its ongoing clinical and economic impact. Nevertheless, several limitations must be acknowledged. First, the exclusion of fully dependent patients, while appropriate as they often require palliative rather than rehabilitative care, may limit the generalizability of our findings. Second, its single-center design, though ensuring a high degree of care homogeneity, restricts external validity. Replicating these findings in multi-center studies is therefore a critical next step. These limitations highlight the need for further research to confirm the diagnosis-specific recovery patterns observed here across diverse populations and healthcare settings. Conclusion For older adults in an ACE unit, the primary diagnosis—not the burden of multimorbidity—is the decisive factor in predicting short-term functional recovery. The specific nature of the acute illness, therefore, offers superior prognostic insight compared to generic comorbidity scores. Based on this evidence, the primary diagnosis should be systematically incorporated as a key component of the Comprehensive Geriatric Assessment (CGA) to better stratify patient risk and tailor therapeutic interventions within the ACE unit. Declarations Conflicts of interest None. Funding source This work was supported by the Key Research Program of Henan Province for Medical Science and Technology (Grant No. 2018020781). Author Contribution Special thanks to the individuals who contributed to this research: Guiqing Wang and Meiying Zhao for providing essential data; Wenqian Shi and Nannan Yang for their thorough proofreading; Xiwen Ma and Yaqin Zhang for supplying ACE-related materials and supporting the research implementation. Dongyan Pu and Li Chen checked and analyzed the data, prepared figures 1-3 and wrote the manuscript. We extend our deepest gratitude to Dr. Mingzi Li and Director Chunhong Li for their exceptional guidance and expertise, which were instrumental in shaping this study. All authors reviewed the manuscript. Acknowledgments Special thanks to the individuals who contributed to this research: Guiqing Wang and Meiying Zhao for providing essential data; Wenqian Shi and Nannan Yang for their thorough proofreading; Xiwen Ma and Yaqin Zhang for supplying ACE-related materials and supporting the research implementation. Dongyan Pu and Li Chen checked and analyzed the data and wrote the manuscript. We extend our deepest gratitude to Dr. Mingzi Li and Director Chunhong Li for their exceptional guidance and expertise, which were instrumental in shaping this study. Finally, we acknowledge the dedication of all participants and collaborators whose efforts made this work possible. Data Availability To protect the privacy of the participants, this data will not be made public. 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Massini G, Caldiroli L, Molinari P, Carminati FMI, Castellano G, Vettoretti S. Nutritional Strategies to Prevent Muscle Loss and Sarcopenia in Chronic Kidney Disease: What Do We Currently Know? Nutrients . 2023;15(14):3107. Published 2023 Jul 11. 10.3390/nu15143107 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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class=\"CitationRef\"\u003e2\u003c/span\u003e][\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. To facilitate the rapid recovery of elderly patients and meet the complex demands, the Acute Care for Elders (ACE) program was proposed [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. It integrated a specially prepared, age-friendly environment with patient-centered care protocols focused on preventing functional decline, promoting rehabilitation, and optimizing medication management. The goal is to improve clinical outcomes in acute disease or acute exacerbation of chronic disease, reducing incidence of functional decline for older adults during hospitalizations [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Systematic reviews have documented the effectiveness of ACE units in improving various outcomes, including reducing the incidence of falls, pressure injuries, and delirium [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn ACE units, a dedicated Multidisciplinary Team (MDT) applies the principles of Comprehensive Geriatric Assessment (CGA) to manage multimorbidity, a central characteristic of this patient population [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e][\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Despite this structured approach, significant heterogeneity is observed in the functional recovery of these patients [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. A potential reason for this variability may lie in the limitations of the assessment tools commonly used. Within the CGA framework, the Charlson Comorbidity Index (CCI) is widely employed to quantify the cumulative burden of chronic diseases [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. However, the CCI was primarily designed to predict long-term mortality, which limits its utility in forecasting short-term functional outcomes [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Specifically, it tends to overlook the diagnosis-specific nuances and the impact of the acute physiological insult that are critical for determining recovery potential [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe primary admission diagnosis, which dictates distinct clinical needs and recovery, represents a crucial factor that is often underemphasized in ACE initial assessments [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. For instance, patients with heart failure may particularly benefit from tailored cardiopulmonary rehabilitation due to exercise limitations [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], while stroke patients often require intensive early neurological rehabilitation [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Therefore, this study hypothesizes that the primary admission diagnosis, representing the acute physiological stress, will be a superior predictor of short-term functional recovery compared to the CCI, which reflects the cumulative chronic burden. By directly comparing the predictive efficacy of these two factors, this research aims to provide a more precise and clinically meaningful indicator for risk assessment within the ACE unit.\u003c/p\u003e\u003cp\u003eThis study was designed to address a critical gap in the ACE prognosis evaluation. Accordingly, our primary objective was to determine whether the primary admission diagnosis is a more powerful and clinically relevant predictor of short-term functional recovery than the widely-used Charlson Comorbidity Index (CCI). A secondary aim was to characterize these diagnosis-specific recovery patterns to inform the development of tailored intervention strategies within the ACE unit model.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n\u003ch2\u003eStudy design and setting\u003c/h2\u003e\n\u003cp\u003eThis study examined patients admitted to the Department of Acute Geriatrics and General Geriatrics at Zhengzhou Affiliated Central Hospital of Zhengzhou University from January 2023 to December 2024. First, to establish a valid baseline, the study compared older adults (\u0026ge;\u0026thinsp;65 years) admitted to a dedicated Acute Care for Elders (ACE) unit with a matched control group receiving standard care in the general geriatric wards. Second, according to the different primary diagnoses, the age, gender, baseline function, especially the CCI, of the ACE cohort were strictly matched to enhance comparability. The ACE unit implements core principles including routine Comprehensive Geriatric Assessment (CGA), individualized care plans delivered by a multidisciplinary team, early rehabilitation focus, patient-centered environmental modifications, and proactive discharge planning. We included patients 65 years of age or older who had been admitted within 2 weeks of symptom onset for an acute or acute exacerbation of a chronic illness. Patients who were totally dependent on personal care, had severe mobility impairment, severe dementia, and were admitted to the hospital with end-stage disease such as multiple organ failure and severe cardiopulmonary and renal insufficiency were excluded.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eData collection\u003c/h3\u003e\n\u003cp\u003eData were retrospectively extracted from electronic medical records and hospital administrative databases by two trained geriatric resource nurses using a standardized data collection form. Baseline data collected at admission included: primary diagnosis for admission, CCI, age, gender, previous hospitalization times, baseline albumin, baseline hemoglobin and baseline ADL. Outcome data included: ADL, LOS, total hospitalization costs, albumin and hemoglobin at discharge, in-hospital deaths, and 15-day all-cause readmission.\u003c/p\u003e\n\u003ch3\u003eClassification of primary diagnosis\u003c/h3\u003e\n\u003cp\u003eThe primary diagnosis recorded at hospital admission was used for classification. Based on the underlying pathophysiology and affected organ system, we used the International Classification of Diseases (ICD-11), diagnoses were grouped into seven mutually exclusive major categories: Cardiovascular diseases (e.g., heart failure, coronary artery disease), Digestive diseases (e.g., gastrointestinal bleeding, cholecystitis), Endocrine diseases (e.g., diabetes mellitus and complications), Fever/Infection (primarily non-respiratory systemic infections), Neurological diseases (e.g., stroke, Parkinson's disease), Respiratory diseases (e.g., COPD, pneumonia), and Renal diseases (e.g., acute kidney injury, exacerbation of chronic kidney disease). The results of classification were discussed with experienced clinicians from various specialities and validated with input from academic clinicians not involved in the study.\u003c/p\u003e\n\u003ch3\u003eCCI\u003c/h3\u003e\n\u003cp\u003eThe Charlson Comorbidity Index (CCI) was used to assess the baseline comorbidity burden for each patient [\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e]. This validated index assigns a specific weight (1, 2, 3, or 6) to 17 predefined comorbid conditions, based on their established association with one-year mortality. The total CCI score was calculated by summing the weights of all present comorbidities for a given patient. A higher total score indicates a greater burden of comorbid disease and corresponds to a higher predicted risk of mortality. The age-adjusted CCI was also calculated, where one point was added to the score for each decade of age from 50 years onward.\u003c/p\u003e\n\u003ch3\u003eADL\u003c/h3\u003e\n\u003cp\u003eFunctional recovery was assessed by improvement in activities of daily living (ADL) from admission to discharge. The ADL of patients in the ACE unit group before and after intervention were evaluated by the Barthel Index rating scale [\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e],[\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e]. Basic self-care of daily living includes the following ten items: eating, bathing, grooming, dressing, bowel control, bladder control, toilet use, transfers, and movements on the floor and stairs. The score of 100 was complete independence, 61\u0026ndash;99, mild dependence; 41 to 60 points, moderate dependence; 40 points or less, severe dependence; 0 points, total dependence.\u003c/p\u003e\n\u003ch2\u003eStatistical analysis\u003c/h2\u003e\n\u003cp\u003eAll statistical analyses were conducted using R statistical software (Version 4.2.0). A two-sided p-value of \u0026lt;\u0026thinsp;0.05 was considered statistically significant for all tests. The analytical approach was designed to first establish the comparability of the cohorts and the effectiveness of the ACE unit, and second, to formally compare the predictive value of the Charlson Comorbidity Index (CCI) against the primary admission diagnosis for short-term functional recovery.\u003c/p\u003e\n\u003cp id=\"Sec8\" class=\"Section2\"\u003e1. Primary Outcome Definition. The primary outcome was short-term functional recovery, quantified as the change in the Barthel Index from admission to discharge (\u0026Delta;MBI). For the purpose of assessing model discrimination, this continuous outcome was dichotomized to identify patients who achieved a \"clinically significant functional recovery,\" defined as a \u0026Delta;MBI of \u0026ge;\u0026thinsp;10 points. This threshold was selected based on established minimal clinically important difference (MCID) literature for the Barthel Index [\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e][\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e][\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e\n\u003cp class=\"Section2\"\u003e2. Baseline and Outcome Comparisons. Initial analyses were performed to compare the ACE and non-ACE cohorts. For continuous variables (e.g., age, length of stay, costs, Barthel Index scores), independent samples Student's t-tests were used. For categorical variables (e.g., gender, readmission rates, primary diagnosis distribution), Pearson\u0026rsquo;s chi-squared test or Fisher\u0026rsquo;s exact test was employed as appropriate.\u003c/p\u003e\n\u003cp class=\"Section2\"\u003e3. Fitting the Hierarchical Multivariate Models. The central analysis involved the construction of three nested multiple linear regression models to predict the primary continuous outcome (\u0026Delta;MBI) within the ACE cohort. The goal was to isolate the added predictive value of the CCI and the primary diagnosis.\u003c/p\u003e\n\u003col style=\"list-style-type: upper-alpha;\"\u003e\n\u003cli\u003e\n\u003cp\u003eModel 1 (Base Model): This initial model included established demographic and clinical covariates known to influence functional recovery: age (continuous), gender (binary), prior hospitalization times (continuous), and Baseline ADL score (continuous).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eModel 2 (CCI Model): The age-adjusted CCI score (continuous) was added to the covariates from the Base Model.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eModel 3 (Diagnosis Model): The primary admission diagnosis (a seven-level categorical variable, with \"Renal diseases\" as the reference category) was added to the covariates from the Base Model.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp style=\"display: inline !important;\"\u003e4. Determining Added Predictive Value. To determine whether the primary diagnosis offered superior predictive value compared to the CCI, the performance of Model 2 and Model 3 was formally compared against the Base Model and each other using a comprehensive set of metrics:\u003c/p\u003e\n\u003col style=\"list-style-type: lower-alpha;\"\u003e\n\u003cli\u003eOverall Model Fit and Explained Variance: The performance of each model was evaluated using the Adjusted R-squared (R\u0026sup2;) to quantify the proportion of variance in \u0026Delta;BI explained by the predictors, while penalizing for model complexity. The Root Mean Square Error (RMSE) was calculated as a measure of the average prediction error. A formal comparison of nested models was conducted using the Likelihood Ratio Chi-Squared (\u0026chi;\u0026sup2;) test, which assesses whether the addition of new predictors (CCI or Diagnosis) results in a statistically significant improvement in model fit over the Base Model.\u003c/li\u003e\n\u003cli\u003eDiscrimination: The ability of each model to discriminate between patients who did and did not achieve clinically significant functional recovery (\u0026Delta;MBI\u0026thinsp;\u0026ge;\u0026thinsp;10) was assessed using Receiver Operating Characteristic (ROC) curve analysis. The Area Under the Curve (AUC) was calculated for each model. To test for a statistically significant difference in discriminative ability, the AUCs of Model 2 and Model 3 were formally compared using the DeLong's test.\u003c/li\u003e\n\u003cli\u003eReclassification: The continuous Net Reclassification Improvement (NRI) was calculated to determine the extent to which the Diagnosis Model led to a change in the correct direction of predicted risk compared to the CCI Model. The NRI measures the net percentage of patients correctly reclassified to higher or lower probabilities of recovery, providing a sensitive measure of improvement in model performance.\u003c/li\u003e\n\u003cli\u003eModel Selection Criteria: To further compare the non-nested models while accounting for goodness-of-fit and model complexity, the Akaike's Information Criterion (AIC) and Bayesian Information Criterion (BIC) were calculated. Lower values for these criteria indicate a more optimal model, balancing explanatory power with parsimony.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e5. Model Diagnostics. The final multiple linear regression model (Model 3) was checked for multicollinearity among predictor variables using the Variance Inflation Factor (VIF). A VIF value\u0026thinsp;\u0026gt;\u0026thinsp;5 was considered indicative of significant multicollinearity.\u003c/p\u003e\n\u003cp class=\"Section2\"\u003e6. Presentation and Validation of the Final Model. The clinical utility of the final, superior model (Model 4) was visually presented.\u003c/p\u003e\n\u003ch3\u003eEthical Considerations\u003c/h3\u003e\n\u003cp\u003eThis study was approved by the Ethics Committee of Zhengzhou Central Hospital Affiliated Zhengzhou University (Ethics number: 2018020781). Patient consent was waived due to the anonymized use of historical medical records, adhering to institutional and national guidelines.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n\u003ch2\u003ePatient Cohort and Baseline Characteristics\u003c/h2\u003e\n\u003cp\u003eA total of 221 patients admitted to the ACE unit met eligibility criteria. After excluding eight patients with severe dependency, mobility disorders, dementia, or end-stage disease, 213 ACE patients remained (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). To minimize confounding variables, a control group was matched 1:1 from geriatric wards based on age, gender, baseline ADL and CCI. Finally, 213 pairs of balanced samples were obtained.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e Baseline characteristics of study participants\u003c/p\u003e\n\u003ctable style=\"width: 702px;\" border=\"1\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 333.563px;\"\u003e\n\u003cp\u003e \u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 128.437px;\"\u003e\n\u003cp\u003eACE (n=213,50)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 150px;\"\u003e\n\u003cp\u003eNon-ACE (n=213,50)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\"\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 333.563px;\"\u003e\n\u003cp\u003eAge (years), mean (SD)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 128.437px;\"\u003e\n\u003cp\u003e76.4 \u0026plusmn; 7.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 150px;\"\u003e\n\u003cp\u003e77.5 \u0026plusmn; 7.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\"\u003e\n\u003cp\u003e0.135\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 333.563px;\"\u003e\n\u003cp\u003eFemale, n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 128.437px;\"\u003e\n\u003cp\u003e117 (48.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 150px;\"\u003e\n\u003cp\u003e124 (41.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\"\u003e\n\u003cp\u003e0.494\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 333.563px;\"\u003e\n\u003cp\u003eCCI, mean \u0026plusmn; SD\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 128.437px;\"\u003e\n\u003cp\u003e4.75 \u0026plusmn; 1.83\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 150px;\"\u003e\n\u003cp\u003e4.90 \u0026plusmn; 1.93\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\"\u003e\n\u003cp\u003e0.457\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 333.563px;\"\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp; \u0026nbsp;Endocrine disease\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 128.437px;\"\u003e\n\u003cp\u003e6.00 \u0026plusmn; 3.40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 150px;\"\u003e\n\u003cp\u003e4.60 \u0026plusmn; 1.81\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\"\u003e\n\u003cp\u003e0.254\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 333.563px;\"\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp;\u0026nbsp; Cardiovascular system diseases\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 128.437px;\"\u003e\n\u003cp\u003e4.76 \u0026plusmn; 1.74\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 150px;\"\u003e\n\u003cp\u003e4.57 \u0026plusmn; 2.14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\"\u003e\n\u003cp\u003e0.581\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 333.563px;\"\u003e\n\u003cp\u003eNeurological diseases\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 128.437px;\"\u003e\n\u003cp\u003e4.51 \u0026plusmn; 1.80\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 150px;\"\u003e\n\u003cp\u003e5.09 \u0026plusmn; 2.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\"\u003e\n\u003cp\u003e0.177\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 333.563px;\"\u003e\n\u003cp\u003eRespiratory diseases\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 128.437px;\"\u003e\n\u003cp\u003e4.50 \u0026plusmn; 1.89\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 150px;\"\u003e\n\u003cp\u003e5.30 \u0026plusmn; 2.15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\"\u003e\n\u003cp\u003e0.044\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 333.563px;\"\u003e\n\u003cp\u003eRenal impairment\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 128.437px;\"\u003e\n\u003cp\u003e5.25 \u0026plusmn; 1.77\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 150px;\"\u003e\n\u003cp\u003e5.16 \u0026plusmn; 1.58\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\"\u003e\n\u003cp\u003e0.884\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 333.563px;\"\u003e\n\u003cp\u003eDigestive system diseases\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 128.437px;\"\u003e\n\u003cp\u003e4.18 \u0026plusmn; 1.08\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 150px;\"\u003e\n\u003cp\u003e4.77 \u0026plusmn; 1.30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\"\u003e\n\u003cp\u003e0.259\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 333.563px;\"\u003e\n\u003cp\u003eFever/infection\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 128.437px;\"\u003e\n\u003cp\u003e4.78 \u0026plusmn; 1.48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 150px;\"\u003e\n\u003cp\u003e4.27 \u0026plusmn; 1.68\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\"\u003e\n\u003cp\u003e0.354\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 333.563px;\"\u003e\n\u003cp\u003ePrimary diagonosis for admission, n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 128.437px;\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 150px;\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 333.563px;\"\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp;\u0026nbsp; Endocrine disease\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 128.437px;\"\u003e\n\u003cp\u003e15 (7.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 150px;\"\u003e\n\u003cp\u003e15 (7.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\"\u003e\n\u003cp\u003e1.000\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 333.563px;\"\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp;\u0026nbsp; Cardiovascular system diseases\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 128.437px;\"\u003e\n\u003cp\u003e45 (21.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 150px;\"\u003e\n\u003cp\u003e49 (22.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\"\u003e\n\u003cp\u003e0.658\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 333.563px;\"\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp;\u0026nbsp; Neurological diseases\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 128.437px;\"\u003e\n\u003cp\u003e35 (16.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 150px;\"\u003e\n\u003cp\u003e47 (22.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\"\u003e\n\u003cp\u003e0.141\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 333.563px;\"\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp;\u0026nbsp; Respiratory diseases\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 128.437px;\"\u003e\n\u003cp\u003e70 (32.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 150px;\"\u003e\n\u003cp\u003e60 (28.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\"\u003e\n\u003cp\u003e0.288\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 333.563px;\"\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp;\u0026nbsp; Renal impairment\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 128.437px;\"\u003e\n\u003cp\u003e20 (9.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 150px;\"\u003e\n\u003cp\u003e19 (8.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\"\u003e\n\u003cp\u003e0.881\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 333.563px;\"\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp;\u0026nbsp; Digestive system diseases\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 128.437px;\"\u003e\n\u003cp\u003e11 (5.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 150px;\"\u003e\n\u003cp\u003e13 (6.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\"\u003e\n\u003cp\u003e0.677\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 333.563px;\"\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp;\u0026nbsp; Fever/infection\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 128.437px;\"\u003e\n\u003cp\u003e18 (8.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 150px;\"\u003e\n\u003cp\u003e11 (5.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\"\u003e\n\u003cp\u003e0.180\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 333.563px;\"\u003e\n\u003cp\u003eBarthel index, mean (SD)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 128.437px;\"\u003e\n\u003cp\u003e72.69 \u0026plusmn; 22.07\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 150px;\"\u003e\n\u003cp\u003e74.77 \u0026plusmn; 25.68\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\"\u003e\n\u003cp\u003e0.368\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 333.563px;\"\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp;\u0026nbsp; Complete, n\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 128.437px;\"\u003e\n\u003cp\u003e14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 150px;\"\u003e\n\u003cp\u003e34\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\"\u003e\n\u003cp\u003e0.003\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 333.563px;\"\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp;\u0026nbsp; Mild, n\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 128.437px;\"\u003e\n\u003cp\u003e151\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 150px;\"\u003e\n\u003cp\u003e121\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 333.563px;\"\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp;\u0026nbsp; Moderate, n\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 128.437px;\"\u003e\n\u003cp\u003e19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 150px;\"\u003e\n\u003cp\u003e33\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 333.563px;\"\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp;\u0026nbsp; Severe, n\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 128.437px;\"\u003e\n\u003cp\u003e29\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 150px;\"\u003e\n\u003cp\u003e25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 333.563px;\"\u003e\n\u003cp\u003eAlbumin (g/L), mean (SD)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 128.437px;\"\u003e\n\u003cp\u003e42.67 \u0026plusmn; 5.94\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 150px;\"\u003e\n\u003cp\u003e41.56 \u0026plusmn; 7.13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\"\u003e\n\u003cp\u003e0.125\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 333.563px;\"\u003e\n\u003cp\u003eHemoglobin (g/L), mean (SD)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 128.437px;\"\u003e\n\u003cp\u003e127.60 \u0026plusmn; 19.57\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 150px;\"\u003e\n\u003cp\u003e127.74 \u0026plusmn; 19.56\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\"\u003e\n\u003cp\u003e0.863\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 333.563px;\"\u003e\n\u003cp\u003ePrior hospitalization times, mean (SD)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 128.437px;\"\u003e\n\u003cp\u003e3.38 \u0026plusmn; 2.85\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 150px;\"\u003e\n\u003cp\u003e3.65 \u0026plusmn; 2.61\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\"\u003e\n\u003cp\u003e0.305\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote. Data are presented as n (%) or mean (standard deviation).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAs detailed in Table\u0026nbsp;1, the baseline demographic and clinical characteristics of the ACE and non-ACE cohorts were well-matched. There were no statistically significant differences in mean age (76.4 vs. 77.5 years, p\u0026thinsp;=\u0026thinsp;0.135), gender distribution, mean Charlson Comorbidity Index (CCI) score (4.75 vs. 4.90, p\u0026thinsp;=\u0026thinsp;0.457), or baseline Barthel Index (72.69 vs. 74.77, p\u0026thinsp;=\u0026thinsp;0.368). The level of functional status of patients was divided into complete independence, mild dependence, moderate dependence and severe dependence according to the Barthel index. Baseline albumin, hemoglobin, and prior hospitalization times were also comparable between the groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05 for all). The distribution of primary admission diagnoses was also largely similar between the two groups, confirming the effectiveness of the matching procedure.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n\u003ch2\u003eComparison of Clinical Outcomes between ACE and Non-ACE Units\u003c/h2\u003e\n\u003cp\u003eThe pre-discharge outcomes analysis revealed significant benefits for patients managed in the ACE unit compared to the matched non-ACE group (Table\u0026nbsp;2). Patients in the ACE unit experienced a significantly shorter mean length of stay (9.82 vs. 11.15 days, p\u0026thinsp;=\u0026thinsp;0.004) and incurred lower total hospitalization costs (8647.85 vs. 10119.87, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Furthermore, the ACE cohort demonstrated superior functional outcomes at discharge, with a higher mean Barthel Index (80.49 vs. 75.70, p\u0026thinsp;=\u0026thinsp;0.033). Critically, the rate of 15-day all-cause readmission was substantially lower in the ACE group (1.41% vs. 9.86%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n\u003ch2\u003ePredictors of Short-Term Functional Recovery in the ACE Cohort\u003c/h2\u003e\n\u003cp\u003eThe primary analysis focused exclusively on the 213 patients within the ACE unit to identify predictors of short-term functional recovery, defined as the change in Barthel Index from admission to discharge (\u0026Delta;BI). The multiple linear regression analysis, incorporating all potential predictors, identified several factors significantly associated with functional recovery (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). Baseline ADL was a strong negative predictor (B=-0.161, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating that patients with higher initial function had less room for improvement.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e Pre-discharge comparisons results\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\n\u003ctable style=\"width: 728px;\" border=\"1\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 250.519px;\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 158.481px;\"\u003e\n\u003cp\u003eACE (n = 213)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 183px;\"\u003e\n\u003cp\u003eNon-ACE (n = 213)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 111px;\"\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 250.519px;\"\u003e\n\u003cp\u003eAlbumin (g/L)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 158.481px;\"\u003e\n\u003cp\u003e38.56 \u0026plusmn; 7.73\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 183px;\"\u003e\n\u003cp\u003e39.60 \u0026plusmn; 5.76\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 111px;\"\u003e\n\u003cp\u003e0.225\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 250.519px;\"\u003e\n\u003cp\u003eHemoglobin (g/L)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 158.481px;\"\u003e\n\u003cp\u003e120.12 \u0026plusmn; 19.76\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 183px;\"\u003e\n\u003cp\u003e123.22 \u0026plusmn; 19.39\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 111px;\"\u003e\n\u003cp\u003e0.227\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 250.519px;\"\u003e\n\u003cp\u003eLength of stay (d)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 158.481px;\"\u003e\n\u003cp\u003e9.82 \u0026plusmn; 4.15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 183px;\"\u003e\n\u003cp\u003e11.15 \u0026plusmn; 5.33\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 111px;\"\u003e\n\u003cp\u003e0.004\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 250.519px;\"\u003e\n\u003cp\u003eBarthel index\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 158.481px;\"\u003e\n\u003cp\u003e80.49 \u0026plusmn; 19.78\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 183px;\"\u003e\n\u003cp\u003e75.70 \u0026plusmn; 25.93\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 111px;\"\u003e\n\u003cp\u003e0.033\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 250.519px;\"\u003e\n\u003cp\u003eComplete, n\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 158.481px;\"\u003e\n\u003cp\u003e77\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 183px;\"\u003e\n\u003cp\u003e42\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 111px;\"\u003e\n\u003cp\u003e＜0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 250.519px;\"\u003e\n\u003cp\u003eMild, n\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 158.481px;\"\u003e\n\u003cp\u003e112\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 183px;\"\u003e\n\u003cp\u003e116\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 111px;\"\u003e\n\u003cp\u003e0.713\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 250.519px;\"\u003e\n\u003cp\u003eModerate, n\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 158.481px;\"\u003e\n\u003cp\u003e11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 183px;\"\u003e\n\u003cp\u003e31\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 111px;\"\u003e\n\u003cp\u003e0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 250.519px;\"\u003e\n\u003cp\u003eSevere, n\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 158.481px;\"\u003e\n\u003cp\u003e14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 183px;\"\u003e\n\u003cp\u003e25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 111px;\"\u003e\n\u003cp\u003e0.071\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 250.519px;\"\u003e\n\u003cp\u003eCosts\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 158.481px;\"\u003e\n\u003cp\u003e8647.85 \u0026plusmn; 3892.20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 183px;\"\u003e\n\u003cp\u003e10119.87 \u0026plusmn; 4832.23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 111px;\"\u003e\n\u003cp\u003e\u0026lt; 0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 250.519px;\"\u003e\n\u003cp\u003eDeaths, n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 158.481px;\"\u003e\n\u003cp\u003e2 (0.94)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 183px;\"\u003e\n\u003cp\u003e4 (1.88)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 111px;\"\u003e\n\u003cp\u003e0.411\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 250.519px;\"\u003e\n\u003cp\u003eReadmission in 15 days, n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 158.481px;\"\u003e\n\u003cp\u003e3 (1.41)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 183px;\"\u003e\n\u003cp\u003e21 (9.86)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 111px;\"\u003e\n\u003cp\u003e\u0026lt; 0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003csup\u003e*\u003c/sup\u003e\u003cem\u003e P \u003c/em\u003evalue statistically significant at the\u003cem\u003e P \u003c/em\u003e\u0026le; 0.05.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"char\"\u003eMost notably, the primary admission diagnosis emerged as a powerful predictor. Compared to patients with renal diseases (the reference category), patients with Neurological (B\u0026thinsp;=\u0026thinsp;5.091, p\u0026thinsp;=\u0026thinsp;0.001), Endocrine (B\u0026thinsp;=\u0026thinsp;4.245, p\u0026thinsp;=\u0026thinsp;0.006), Cardiovascular (B\u0026thinsp;=\u0026thinsp;3.589, p\u0026thinsp;=\u0026thinsp;0.026), and Respiratory (B\u0026thinsp;=\u0026thinsp;3.443, p\u0026thinsp;=\u0026thinsp;0.019) diagnoses all showed significantly greater functional recovery. In stark contrast, the Charlson Comorbidity Index (CCI) was not a significant predictor of short-term functional recovery (B=-0.099, p\u0026thinsp;=\u0026thinsp;0.628).\u003c/div\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n\u003ch2\u003eComparative Performance of Predictive Models\u003c/h2\u003e\n\u003cp\u003eTo formally test the hypothesis that primary diagnosis is a superior predictor to cumulative comorbidity, the performance of three hierarchical models was compared (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Model 1 (Base Model) had an adjusted R\u0026sup2; of 0.75. The addition of CCI in Model 2 yielded only a marginal improvement (Adjusted R\u0026sup2;=0.78). However, the inclusion of the primary diagnosis in Model 3 resulted in a substantial increase in explanatory power, with an Adjusted R\u0026sup2; of 0.85.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eComparison of the three models\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eModel comparison\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eBase Model\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCCI Model\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eDiagnosis Model\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ep Value (CCI vs Diagnosis)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAdjusted R\u0026sup2;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.75\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.78\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.85\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNA\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRMSE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.91\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.62\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.51\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.032\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLikelihood ratio \u0026chi;2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17.28(4 df)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17.88 (5 df)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e32.1 (8 df)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0038\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAIC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1452.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1441.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1438.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.036\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBIC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1466.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1459.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1462.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.029\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNRI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.31(0.05, 0.58)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.38 (0.11, 0.65)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.004\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"5\"\u003e\u003csup\u003e*\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e value statistically significant at the \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.05. Adjusted R\u0026sup2;, adjusted coefficient of determination; RMSE, root mean square error; Likelihood ratio \u003cstrong\u003e\u0026chi;\u003c/strong\u003e\u0026sup2;, Likelihood Ratio Chi-Squared test, A higher \u003cstrong\u003e\u0026chi;\u003c/strong\u003e \u003csup\u003e2\u003c/sup\u003e-df value indicates a stronger association; AIC, Akaike's information criterion; BIC, Bayesian information criterion; NRI, Net Reclassification Improvement.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, the superiority of the Diagnosis Model (Model 3) over the CCI Model (Model 2) was consistent across all evaluation metrics. Model 3 demonstrated a significantly lower Root Mean Square Error (RMSE: 8.51 vs. 8.62, p\u0026thinsp;=\u0026thinsp;0.032) and a significantly higher Likelihood Ratio \u0026chi;\u0026sup2; value (32.1 vs. 17.88, p\u0026thinsp;=\u0026thinsp;0.0038), indicating a better model fit. This was further supported by lower Akaike's Information Criterion (AIC: 1438.5 vs. 1441.8, p\u0026thinsp;=\u0026thinsp;0.036) and Bayesian Information Criterion (BIC: 1462.7 vs. 1459.2, p\u0026thinsp;=\u0026thinsp;0.029). Furthermore, the Net Reclassification Improvement (NRI) for Model 3 compared to Model 2 was 0.38, indicating that the Diagnosis Model correctly reclassified a significant proportion of patients (p\u0026thinsp;=\u0026thinsp;0.004).\u003c/p\u003e\n\u003cp\u003eThe clinical utility of the superior Diagnosis Model was demonstrated through risk stratification. As illustrated in the violin plot (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e), Model 3 effectively stratified the ACE cohort into two distinct prognostic groups: \"Functional Maintenance\" and \"Clinically Significant Recovery.\" The actual functional recovery observed in the group predicted to have significant recovery was substantially and statistically higher than that of the maintenance group (t-test: p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFinally, Receiver Operating Characteristic (ROC) curve analysis for predicting clinically significant functional recovery (\u0026Delta;BI\u0026thinsp;\u0026ge;\u0026thinsp;10 points) further confirmed these findings (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). As shown in the ROC Curves figure, the Area Under the Curve (AUC) for Model 3 (Diagnosis: AUC\u0026thinsp;=\u0026thinsp;0.928) was significantly higher than that for Model 2 (CCI: AUC\u0026thinsp;=\u0026thinsp;0.888), with a DeLong's test p-value of 0.031.\u003c/p\u003e\n\u003cdiv class=\"colspec\" align=\"char\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eModel 4: Multiple linear regression analysis performed with all variables\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eVariable\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eUnstandardized coefficients B\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eStandardized coefficients B\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e95% \u003cem\u003eCI\u003c/em\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePredictor\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAge\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.102\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.089\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.209 to 0.005\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.062\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCCI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.099\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.034\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.501 to 0.303\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.628\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePrior hospitalization times\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.137\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.046\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.465 to 0.191\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.413\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBaseline ADL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.161\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.273\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.232 to -0.090\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMale vs. female\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.838\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.042\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-2.646 to 0.970\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.364\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePrimary diagnosis\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNeurological vs Renal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5.091\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.208\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.084 to 8.098\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEndocrine vs Renal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4.245\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.195\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.199 to 7.291\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.006\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCardiovascular vs Renal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.589\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.145\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.427 to 6.751\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.026\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRespiratory vs Renal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.443\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.157\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.557 to 6.329\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.019\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDigestive vs Renal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.460\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.078\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1.178 to 6.098\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.186\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eInfection vs Renal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.403\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.053\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1.861 to 4.667\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.399\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"5\"\u003e\u003csup\u003e*\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e value statistically significant at the \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.05.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eMain findings\u003c/h2\u003e\u003cp\u003eThis study's principal finding is that in an ACE unit, the primary diagnosis for acute admission serves as a robust, independent predictor of functional recovery, outperforming the cumulative chronic disease burden as measured by the Charlson Comorbidity Index (CCI). This suggests that while a patient's history of multimorbidity is important, the specific pathophysiological impact of the acute illness itself is a more dominant factor in determining the short-term rehabilitation within an ACE unit. Consequently, these results highlight a critical need for the multidisciplinary team to integrate the primary diagnosis more explicitly into the Comprehensive Geriatric Assessment (CGA) for a more accurate risk stratification and care planning.\u003c/p\u003e\u003cp\u003eIn addition to this primary finding, our study also reaffirms the overall effectiveness of the ACE unit model. Consistent with established literature [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e][\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e][\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], our cohort demonstrated significant improvements in functional recovery, alongside reduced length of stay, lower hospitalization costs, and decreased 15-day readmission rates. This confirmation provides a robust context for our primary finding, suggesting that while the ACE model is broadly effective, its prognostic accuracy can be further enhanced by prioritizing the primary diagnosis over traditional comorbidity indices.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eWhy Does the Acute Primary Diagnosis Outweigh Chronic Comorbidity in Predicting Functional Outcome?\u003c/h2\u003e\u003cp\u003eA central contribution of our research is the elucidation of the complex interplay between acute illness and chronic comorbidity in determining short-term outcomes. CCI was not a significant predictor of functional recovery in multiple regression models, however, this is not to suggest that multimorbidity is unimportant; on the contrary, it may reveal a deeper problem. While CCI reflects a patient's chronic, cumulative health deficit, the 'primary admission diagnosis' represents the nature and intensity of the current acute physiological stress [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. This finding highlights that while the ACE unit is beneficial overall, the magnitude of functional recovery is not uniform across all conditions treated within this specialized environment. And the impact of the acute event itself (e.g., inflammatory, metabolic derangering, and hemodynamic instability) and the specific rehabilitation pathways associated with it may outweigh the chronic comorbid background in the short term for functional recovery in older patients. This finding challenges the conventional reliance on comorbidity indices alone for short-term prognostication and calls for a recalibration of how we weigh acute versus chronic factors in geriatric acute care.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eWhy Do Different Diagnoses Lead to Differences in the Potential for Functional Recovery?\u003c/h2\u003e\u003cp\u003eThe differential recovery degrees observed across diagnostic groups provide compelling evidence for this thesis. Patients with neurological diseases had the most significant ADL recovery effect during hospitalization, followed by endocrine diseases, cardiovascular diseases and respiratory diseases. The recovery potential of digestive diseases, fever/infection, and renal diseases is relatively low. Patients with neurological conditions such as stroke demonstrated notable functional improvements, may attributable to the ACE unit's focus on early and intensive rehabilitation.The structured, goal-oriented therapeutic model intrinsic to the ACE unit directly addresses the specific motor, sensory, and cognitive deficits post-stroke. This approach is designed to maximize neuroplasticity\u0026mdash;the brain's ability to reorganize itself\u0026mdash;by promoting cortical reorganization through targeted, repetitive exercises like daily gait training and fine motor practice [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Such intensive intervention facilitates a more rapid and substantial recovery by fostering the development of new neural pathways.\u003c/p\u003e\u003cp\u003eIn contrast, the substantial recovery seen in patients with acute exacerbations of endocrine, cardiovascular, and respiratory diseases likely reflects a different mechanism. For these patients, functional decline is often a direct consequence of a reversible physiological derangement like hyperglycemia, fluid overload and bronchospasm. The comprehensive geriatric assessment (CGA) and patient-centered care within the ACE unit are effective at stabilizing these conditions through meticulous medication management, nutritional support, and tailored therapy. Once the acute physiological stressor is controlled, patients can rapidly revert to their baseline functional status, resulting in a significant measured recovery during the relatively short hospitalization. The substantial gains in patients with endocrine diseases (e.g., diabetic complications) might relate to rapid symptomatic improvement achievable through metabolic stabilization, optimized medication, nutritional support, and early mobilization facilitated by the ACE unit [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. For patients with acute cardiovascular events, such as a heart failure exacerbation, functional decline is often due to dyspnea, fatigue, and edema-induced immobility rather than a primary musculoskeletal deficit[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. While exertional limitations and complex fluid management present significant challenges, the ACE unit's expertise in aggressive yet careful diuresis, afterload reduction, and medication optimization can rapidly restore hemodynamic stability. Once euvolemic and stable, patients can engage in graded mobilization. Similarly, for patients with acute respiratory illnesses (e.g., COPD exacerbation, pneumonia), functional impairment is a direct consequence of hypoxemia, systemic inflammation, and increased work of breathing [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. After nutritional support and combining targeted pharmacotherapy in ACE unit, muscle strength and activity endurance may be rapidly improved, thus effectively stabilizing the patient's physiological state and improving self-care ability.\u003c/p\u003e\u003cp\u003eConversely, the patient groups with the lowest recovery potential\u0026mdash;those with fever/infection, renal diseases, and digestive diseases\u0026mdash;share a underlying pathophysiology: a profound systemic inflammatory and catabolic state [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. This aligns with known associations between chronic kidney disease, systemic inflammation, uremic toxin accumulation, sarcopenia, and poorer functional outcomes [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e][\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]; indeed, evidence showed that lower eGFR has been linked to worse outcomes in patients [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. This systemic insult leads to global deconditioning, profound weakness, anorexia, and fatigue that are not localized to a single organ system and are not rapidly reversible. The recovery from such a catabolic state is a slow, anabolic process requiring weeks or months of nutritional rebuilding and physical therapy, a timeline that extends far beyond a typical acute hospitalization. Therefore, while the ACE unit can effectively manage the acute illness, the profound systemic deconditioning prevents significant short-term functional gains as measured by ΔBI at discharge. The patient is stabilized but leaves the hospital in a weakened, frail state, necessitating a protracted post-discharge recovery period. Therefore, even if CCI is similar across primary diagnoses, the recovery potential is quite different.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eThe Clinical Assessment within the ACE unit Should Shift From Standardization to Precision\u003c/h2\u003e\u003cp\u003e The findings of this study have significant, actionable implications for the evolution of care within the ACE unit, mandating a shift from broad, standardized assessments toward a more precise assessment.\u003c/p\u003e\u003cp\u003eFirst, they suggest a need to refine our approach to risk stratification. Rather than relying solely on the cumulative comorbidity score, clinical assessments should incorporate the primary diagnosis as one of the key predictors of short-term functional outcome. Second, our results highlight an opportunity for more precise, tailored interventions. These diagnosis-specific patterns suggest that while the core ACE units are broadly effective, optimizing outcomes might require tailoring intervention intensity and focus based on the primary diagnosis. For conditions with high recovery potential driven by the reversal of acute physiological derangements\u0026mdash;such as endocrine, cardiovascular, and respiratory diseases\u0026mdash;protocols should prioritize intensive metabolic management, careful balancing of cardiopulmonary demands with rehabilitation intensity, and targeted post-inflammatory pulmonary rehabilitation [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Patients with conditions portending a slower recovery, such as severe infections or renal disease, may require not only treatment of hyperkalemia and anti-infection in the acute phase but also specific regimens targeting factors such as nutrition and inflammation in the chronic phase [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Finally, a clear understanding of an unit's typical patient diagnoses is essential for strategic resource planning. A high prevalence of respiratory disease in our ACE unit can also indicate the importance of understanding the predominant case-mix, for informing team composition and resource allocation to best meet patient needs.\u003c/p\u003e\u003cp\u003eIn the future, it is recommended to carry out larger prospective studies, use more objective functional evaluation indicators, and expand the sample size to further verify the difference in the effectiveness of ACE unit in the recovery of functional status of patients with primary diagnosis diseases and comorbidities, and put forward nursing optimization suggestions for patients with complex diseases, so as to promote the improvement of precision nursing system.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003eStrengths and Limitations\u003c/h2\u003e\u003cp\u003eThe primary strength of this study lies in its rigorous design, which directly compared the predictive utility of two competing prognostic factors within a well-characterized cohort. By using a hierarchical modeling approach and multiple robust statistical metrics (Adjusted R\u0026sup2;, AUC, NRI), we provide a comprehensive and statistically sound basis for our conclusions. Furthermore, focusing on data from a period of stable ACE unit operation likely provides a realistic assessment of its ongoing clinical and economic impact.\u003c/p\u003e\u003cp\u003eNevertheless, several limitations must be acknowledged. First, the exclusion of fully dependent patients, while appropriate as they often require palliative rather than rehabilitative care, may limit the generalizability of our findings. Second, its single-center design, though ensuring a high degree of care homogeneity, restricts external validity. Replicating these findings in multi-center studies is therefore a critical next step. These limitations highlight the need for further research to confirm the diagnosis-specific recovery patterns observed here across diverse populations and healthcare settings.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eFor older adults in an ACE unit, the primary diagnosis\u0026mdash;not the burden of multimorbidity\u0026mdash;is the decisive factor in predicting short-term functional recovery. The specific nature of the acute illness, therefore, offers superior prognostic insight compared to generic comorbidity scores. Based on this evidence, the primary diagnosis should be systematically incorporated as a key component of the Comprehensive Geriatric Assessment (CGA) to better stratify patient risk and tailor therapeutic interventions within the ACE unit.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eConflicts of interest\u003c/h2\u003e\u003cp\u003eNone.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding source\u003c/h2\u003e\u003cp\u003eThis work was supported by the Key Research Program of Henan Province for Medical Science and Technology (Grant No. 2018020781).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eSpecial thanks to the individuals who contributed to this research: Guiqing Wang and Meiying Zhao for providing essential data; Wenqian Shi and Nannan Yang for their thorough proofreading; Xiwen Ma and Yaqin Zhang for supplying ACE-related materials and supporting the research implementation. Dongyan Pu and Li Chen checked and analyzed the data, prepared figures 1-3 and wrote the manuscript. We extend our deepest gratitude to Dr. Mingzi Li and Director Chunhong Li for their exceptional guidance and expertise, which were instrumental in shaping this study. All authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e\u003cp\u003eSpecial thanks to the individuals who contributed to this research: Guiqing Wang and Meiying Zhao for providing essential data; Wenqian Shi and Nannan Yang for their thorough proofreading; Xiwen Ma and Yaqin Zhang for supplying ACE-related materials and supporting the research implementation. Dongyan Pu and Li Chen checked and analyzed the data and wrote the manuscript. We extend our deepest gratitude to Dr. Mingzi Li and Director Chunhong Li for their exceptional guidance and expertise, which were instrumental in shaping this study. Finally, we acknowledge the dedication of all participants and collaborators whose efforts made this work possible.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eTo protect the privacy of the participants, this data will not be made public. You can contact the author to obtain the data for analysis.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAshton CM, Del Junco DJ, Souchek J, Wray NP, Mansyur CL. The association between the quality of inpatient care and early readmission: a meta-analysis of the evidence. Med Care Oct. 1997;35(10):1044\u0026ndash;59. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/00005650-199710000-00006\u003c/span\u003e\u003cspan address=\"10.1097/00005650-199710000-00006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBenetos A, Petrovic M, Strandberg T. 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Published 2023 Jul 11. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/nu15143107\u003c/span\u003e\u003cspan address=\"10.3390/nu15143107\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Functional recovery, Activities of daily living, Aging, Acute Care for Elders unit, Disease Diagnosis","lastPublishedDoi":"10.21203/rs.3.rs-7476124/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7476124/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eAccurate pre-intervention assessment in an ACE unit is crucial, yet heavily reliant on comorbidity indices like the Charlson Comorbidity Index (CCI), which often fail to explain significant outcome heterogeneity. This study aimed to determine whether the primary admission diagnosis, representing the acute physiological insult, is a more powerful predictor of short-term functional recovery than the CCI.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThe primary outcome was short-term functional recovery, measured as the change in the Barthel Index (BI) from admission to discharge (ΔBI). Hierarchical multiple linear regression was used to create three models: a base model (demographic/clinical covariates), a CCI model, and a primary diagnosis model. Model performance was compared using Adjusted R\u0026sup2;, Area Under the Curve (AUC) from ROC analysis, Net Reclassification Improvement (NRI), and information criteria (AIC/BIC).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eCompared to matched controls, ACE unit patients had significantly shorter lengths of stay (9.82 vs. 11.15 days, p\u0026thinsp;=\u0026thinsp;0.004), lower hospitalization costs (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), higher mean discharge BI (80.49 vs. 75.70, p\u0026thinsp;=\u0026thinsp;0.033), and lower 15-day readmission rates (1.41% vs. 9.86%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In the multivariable analysis of the ACE cohort, the primary admission diagnosis was a strong predictor of ΔBI (e.g., Neurological vs. Renal disease, B\u0026thinsp;=\u0026thinsp;5.091, p\u0026thinsp;=\u0026thinsp;0.001), while the CCI was not a significant predictor (p\u0026thinsp;=\u0026thinsp;0.628). The Diagnosis Model demonstrated superior performance over the CCI Model, with a higher Adjusted R\u0026sup2; (0.85 vs. 0.78), a significantly better AUC for predicting clinically significant recovery (0.928 vs. 0.888, p\u0026thinsp;=\u0026thinsp;0.031), and a positive NRI (0.38, p\u0026thinsp;=\u0026thinsp;0.004).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eThe short-term functional recovery of hospitalized older adults is dictated by the nature of the acute illness, not the accumulation of chronic diseases. This suggests the acute diagnosis reflects a unique pathophysiological stress and recovery potential not captured by comorbidity indices. Clinicians should prioritize the primary diagnosis for risk assessment and tailoring interventions in ACE units.\u003c/p\u003e","manuscriptTitle":"The Acute Illness, Not Cumulative Comorbidity, Dictates Short-Term Functional Recovery in An ACE Unit: A retrospective cohort study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-17 15:51:18","doi":"10.21203/rs.3.rs-7476124/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8069e81e-827b-4ceb-bd63-c2c403f1d684","owner":[],"postedDate":"October 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-03T18:23:33+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-17 15:51:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7476124","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7476124","identity":"rs-7476124","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}