Abstract
Background
Acute kidney injury (AKI) is a frequent complication in patients with acute myocardial infarction (AMI) undergoing percutaneous coronary intervention (PCI), significantly increasing mortality. The Huaxi Fall Risk Assessment (H-FRA) scale, a multidimensional bedside tool, may indirectly reflect AKI risk through factors like impaired mobility and systemic vulnerability. This study investigated the association between H-FRA and AKI in AMI patients.
Methods
This multicenter retrospective cohort study utilized data from 2,883 AMI patients treated with PCI across seven Chinese tertiary hospitals (January 2017 - February 2019). Patients were stratified into low, intermediate, and high fall-risk groups based on H-FRA tertiles at admission. Baseline serum creatinine was the first value measured upon admission. Multivariable logistic regression adjusted for confounders (age, sex, comorbidities, lab values). Predictive performance was assessed using receiver operating characteristic (ROC) curve analysis, sensitivity and specificity.
Results
AKI incidence significantly increased across H-FRA groups (low-risk: 4.8%, intermediate-risk: 18.3%, high-risk: 41.3%, p < 0.001). After adjustment, high H-FRA risk was independently associated with AKI (adjusted odds ratio [OR] 2.12, 95% confidence interval [CI] 1.24–3.92, p < 0.001). Each 1-point H-FRA increase raised AKI risk by 30% (OR 1.30, 95% CI 1.14–1.48, p < 0.001). The H-FRA demonstrated moderate predictive performance (area under the curve [AUC] 0.731, 95% CI 0.714–0.748, p < 0.0001).
Conclusion
The H-FRA is independently associated with AKI in patients with AMI. The H-FRA may serve as a useful and simple bedside tool for identifying AMI patients at increased risk of AKI. However further prospective validation is required.
Keywords: huaxi fall risk assessment scale, acute kidney injury, acute myocardial infarction
Introduction
Acute myocardial infarction (AMI), a severe manifestation of acute coronary syndrome (ACS), remains a leading cause of global morbidity and mortality.1 Percutaneous coronary intervention (PCI) has become an effective treatment for AMI.2 Acute kidney injury (AKI) is a common complication in AMI patients, closely linked to mechanisms such as hemodynamic instability and contrast-induced nephrotoxicity.3 A meta-analysis revealed an AKI incidence of 15.8% in ACS patients, with AKI increasing mortality by two-fold to three-fold.4 Current treatment options for AKI in AMI patients are limited. Therefore, early identification of patients at high risk for AKI is crucial for implementing preventive strategies. In recent years, research has increasingly focused on the use of multidimensional risk assessment tools for AKI prediction. Both the Braden Scale (BS) and the Nutritional Risk Screening 2002 (NRS-2002) have been shown to indirectly predict AKI risk by evaluating patients’ physiological reserves and systemic vulnerability.5,6
Falls, a common complication among hospitalized patients, reflect impaired mobility and multisystem frailty. The risk factors for falls may share underlying pathophysiological links with AKI. Patients at high risk for falls often exhibit heightened systemic inflammatory responses, and inflammatory cytokines have been demonstrated to exacerbate renal tubular epithelial cell apoptosis.7–9 Mobility limitations increase the risk of venous congestion, subsequently reducing effective circulating blood volume.10–12 Furthermore, studies indicate that patients with renal dysfunction also face a relatively higher risk of falls.13,14 Consequently, we hypothesize that fall risk may independently predict AKI in AMI patients.
The Huaxi Fall Risk Assessment (H-FRA) scale is a standardized clinical tool for evaluating fall risk, encompassing multiple dimensions such as age, mobility, cognitive function, medication history, comorbidities, and fall history.15 While prior studies have linked general frailty or nutritional risk scores (such as BS or NRS-2002) to AKI, the H‑FRA scale assesses a broader range of clinically relevant dimensions. Previous research has associated high fall risk with mortality in cardiovascular disease.16 However, the relationship between fall risk and AKI in AMI patients remains unclear. This study aims to investigate the association between the H-FRA scale and AKI in AMI patients, assess its predictive value, and explore its clinical utility for early risk stratification.
Material and Methods
Study Design and Population
This multicenter retrospective cohort study utilized data from the Retrospective Evaluation of Acute Chest Pain (REACP) database to assess the predictive value of the H-FRA for AKI in AMI patients following PCI. The study enrolled patients presenting with acute chest pain to the chest pain centers of seven tertiary hospitals in China between January 2017 and February 2019. Inclusion criteria: 1. AMI diagnosis meeting ACC/AHA guidelines;1 2. Undergoing PCI; 3. > 18 years of age; 4. Availability of complete H-FRA and renal function data. Exclusion criteria: 1. Receiving thrombolytic therapy; 2. Discharge within 24 hours of admission; 3. Missing important data; 4. Loss to follow-up. Based on H-FRA scores at admission, patients were categorized into three groups (T1, T2, T3) using tertiles.
Data Collection and Definitions
Experienced clinicians extracted data from the REACP database, including demographic characteristics, medical history, laboratory parameters, coronary angiography findings, and outcomes. The H-FRA was assessed by trained nurses and comprises 10 dimensions: age, cognitive ability, mobility, toileting self-care ability, history of falls within the previous year, current use of specific medications (eg., sedatives, diuretics, antihypertensives, hypoglycemics, hypnotics), visual impairment, low compliance or communication barriers, agitation, and other high-risk factors.15 Scores were assigned as follows: age 18–64 years = 0 points, 65–75 years = 1 point, 76–80 years = 2 points, > 80 years = 3 points. All other items were scored 0 or 1 point (Table S1). The total score ranges from 0 to 15, with higher scores indicating a greater risk of falls. AKI was defined according to Kidney Disease: Improving Global Outcomes (KDIGO) criteria as an absolute increase in serum creatinine (SCr) of ≥ 0.3 mg/dL (26.5µmol/L) within 48 hours or ≥ 1.5-fold increase from baseline SCr within 7 days.17 AKI stage 1 criteria are SCr ≥ 0.3 mg/dL within 48 hours or 1.5-fold-1.9-fold increase from baseline SCr within 7 days. AKI stage 2 criterion is 2.0-fold-2.9-fold increase from baseline SCr. AKI stage 3 criteria are SCr ≥ 4.0 mg/dL (353.6µmol/L) or ≥ 3.0-fold increase from baseline SCr. Baseline SCr was the first value measured upon admission. AKI was assessed during the entire period of hospitalization after PCI.
Outcomes and Follow-up
The primary endpoint of the study was AKI. Follow-up was conducted by trained clinicians using structured telephone questionnaires. The follow-up time ranged from admission to death, end of follow-up or loss of follow-up. The median follow-up time for the study population was 17.3 months.
Statistical Analysis
Categorical variables are presented as frequencies and percentages and were compared using the Chi-square test. Continuous variables with a normal distribution are described as mean ± standard deviation and were compared using analysis of variance (ANOVA). Continuous variables with a non-normal distribution are described as median and interquartile range (IQR) and were compared using the Kruskal–Wallis H-test. The association between the H-FRA and AKI was analyzed using multivariable logistic regression, adjusting for potential confounding factors. All variables were included in a univariate model; the significant variables were re-entered into a multivariable model. Odds ratios (ORs) with corresponding 95% confidence intervals (CIs) were calculated. Subgroup analyses and sensitivity analysis were performed to evaluate the robustness of the association between H-FRA and AKI. Missing values were imputed using multiple imputation. All analyses were conducted on the imputed dataset.
Predictive performance of the H-FRA was assessed using receiver operating characteristic (ROC) curve analysis. Sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) were calculated. All analyses were performed using SPSS version 22.0 and R version 4.3.2. A two-sided p-value < 0.05 was considered statistically significant.
Results
Baseline Characteristics
A total of 2,883 AMI patients treated with PCI were included in this study. The mean age was 65 years, and 2,201 (76.3%) were male. The median follow-up time was 17.3 months. Patients were stratified into low, intermediate, and high fall-risk groups based on H-FRA scores (Table 1). Patients in the high-risk group were significantly older, had a higher prevalence of comorbidities (including hypertension, diabetes mellitus, or chronic obstructive pulmonary disease), a greater proportion with Killip class ≥ 2, higher heart rate, higher levels of blood glucose, D-dimer, NT-proBNP, serum creatinine, GRACE score, and Gensini score, and lower diastolic blood pressure, left ventricular ejection fraction, and eGFR compared to lower-risk groups (p < 0.05 for all).
Table 1.
Baseline Characteristics According to Different Risk Levels of H-FRA
| Variables | Low Fall Risk (n=1146) | Medium Fall Risk (n=1216) | High Fall Risk (n=521) | p-value |
|---|---|---|---|---|
| Age, years | 54 ± 9 | 69 ± 9 | 81 ± 7 | <0.001 |
| Males, n (%) | 1016 (88.7) | 883 (72.6) | 302 (76.3) | <0.001 |
| Smoking, n (%) | 783 (68.3) | 630 (51.8) | 190 (36.5) | <0.001 |
| Drinking, n (%) | 451 (39.5) | 344 (28.3) | 93 (17.9) | <0.001 |
| Hypertension, n (%) | 509 (44.4) | 687 (56.5) | 324 (62.2) | <0.001 |
| Diabetes, n (%) | 237 (20.7) | 354 (29.1) | 166 (31.9) | <0.001 |
| Hyperlipidemia, n (%) | 176 (15.4) | 129 (10.6) | 55 (10.6) | <0.001 |
| COPD, n (%) | 12 (1.0) | 36 (3.0) | 43 (8.1) | <0.001 |
| SBP, mmHg | 127 ± 23 | 127 ± 25 | 128 ± 25 | 0.69 |
| DBP, mmHg | 82 ± 16 | 76 ± 16 | 74 ± 16 | <0.001 |
| Heart rate, beats/min | 80 ± 17 | 82 ± 19 | 83 ± 20 | 0.012 |
| BMI, Kg/m2 | 25.06 ± 3.28 | 23.90 ± 3.39 | 22.74 ± 3.55 | <0.001 |
| Killip≥2 | 359 (31.3) | 572 (47) | 334 (64.1) | <0.001 |
| LVEF, (%) | 55 ± 11 | 52 ± 12 | 51 ± 12 | <0.001 |
| Laboratory findings | ||||
| RBC, 1012/L | 4.72 ± 0.59 | 4.30 ± 0.72 | 4.01 ± 0.70 | <0.001 |
| WBC, 109/L | 10.07 (7.85–12.23) | 9.25 (7.26–11.79) | 8.72 (6.88–11.77) | <0.05 |
| PLT, 109/L | 195.41 ± 68.42 | 183.77 ± 79.65 | 180.80 ± 75.21 | <0.001 |
| SCr, µmol/L | 75 (65–88) | 81 (67–99) | 91 (74–125) | <0.001 |
| BUN, mmol/L | 2.60 ± 0.77 | 4.15 ± 0.20 | 5.64 ± 0.25 | <0.001 |
| Blood glucose, mmol/L | 8.51 ± 4.28 | 9.24 ± 4.35 | 9.42 ± 4.44 | <0.001 |
| LDL, mmol/L | 3.06 ± 1.12 | 2.72 ± 1.01 | 2.52 ± 1.02 | <0.001 |
| HDL, mmol/L | 1.09 ± 0.30 | 1.15 ± 0.35 | 1.20 ± 0.38 | <0.001 |
| TC, mmol/L | 4.80 ± 1.28 | 4.36 ± 1.15 | 4.19 ± 1.18 | <0.001 |
| TG, mmol/L | 1.70 (1.07–2.72) | 1.27 (0.92–1.84) | 1.15 (0.82–1.61) | <0.001 |
| eGFR, mL/min/1.73m2 | 92.83 ± 19.75 | 75.55 ± 23.90 | 57.52 ± 23.62 | <0.001 |
| CK, µ/mL | 341 (125–1088) | 326 (127–1152) | 244 (103–820) | <0.001 |
| D-dimer, mg/L | 0.25 (0.16–0.44) | 0.50 (0.27–1.13) | 0.82 (0.45–1.81) | <0.001 |
| cTnT, pg/mL | 563.7 (137.5–2104.0) | 877.5 (229.3–3412.5) | 837.3 (184.5–2640.3) | <0.05 |
| Myo, mg/mL | 108.1 (31.74–438.5) | 134.8 (53.4–503.0) | 146.7 (62.3–561.4) | <0.05 |
| CKMB, mg/L | 17.43 (4.04–80.84) | 20.23 (4.86–94.18) | 14.04 (3.89–71.80) | 0.008 |
| NT-proBNP, ng/L | 403 (125–1080) | 1283 (312–3477) | 2882 (877–6327) | <0.001 |
| Risk scores | ||||
| Grace score | 124.28 ± 30.39 | 153.68 ± 35.93 | 182.75 ± 38.24 | <0.001 |
| Gensini score | 52 (28–86) | 62 (32–99) | 76 (35–120) | <0.001 |
Abbreviations: COPD, chronic obstructive pulmonary disease; SBP, systolic blood pressure; DBP, diastolic blood pressure; BMI, body mass index; WBC, white blood cell count; RBC, red blood cell count; PLT, platelet count; SCr, serum creatinine; BUN, blood urea nitrogen; HDL, high-density lipoprotein; LDL, low-density lipoprotein; TC, total cholesterol; TG, triglycerides; eGFR, estimated glomerular filtration rate; CK, creatine kinase; cTnT, cardiac troponin T; Myo, Myoglobin; CK-MB, Creatine Kinase MB Isoenzyme; NT-proBNP, N-terminal pro-brain natriuretic peptide; LVEF, left ventricular ejection fraction; Grace score, global registry of acute coronary event.
Association Between H-FRA and AKI
The incidence of AKI increased significantly with rising fall risk (low-risk group: 4.8% vs. intermediate-risk group: 18.3% vs. high-risk group: 41.3%, p < 0.001) (Figure 1). Multivariable logistic regression analysis (Table 2), after adjusting for age, sex, comorbidities, and laboratory parameters, revealed that patients in the high-risk group had a 2.12-fold increased risk of developing AKI compared to the reference group (OR 2.12, 95% CI 1.24–3.92, p < 0.001). For each 1-point increase in the H-FRA score, the risk of AKI increased by 30% (OR 1.30, 95% CI 1.14–1.48, p < 0.001). Subgroup analyses confirmed that this association was consistent across different age groups, sexes, and clinical subgroups (Table 3). The association between higher H-FRA scores and increased risk of AKI remained robust in the sensitivity analysis (Table S2).
Figure 1.
Incidence ratio of AKI in different risks of H-FRA groups.
Table 2.
Logistic Regression Models for the Relationship Between H-FRA and the Risk of AKI in Patients with AMI
| Variables | Model 1 | Model 2 | Model 3 | |||
|---|---|---|---|---|---|---|
| OR (95% CI) | p-value | OR (95% CI) | p-value | OR (95% CI) | p-value | |
| H-FRA (category) | <0.001 | <0.001 | <0.05 | |||
| T1 | 1 | 1 | 1 | |||
| T2 | 2.44 (1.60–3.72) | <0.001 | 2.11 (1.37–3.23) | <0.05 | 1.61 (1.03–2.51) | <0.05 |
| T3 | 3.73 (2.17–6.42) | <0.001 | 3.16 (1.82–5.48) | <0.001 | 2.21 (1.24–3.92 | <0.001 |
| H-FRA (per 1 unite increase) | 1.43 (1.27–1.61) | <0.001 | 1.41 (1.25–1.59) | <0.001 | 1.30 (1.14–1.48) | <0.001 |
Notes: Model 1 adjusted for age, sex, systolic blood pressure, diastolic blood pressure, smoking, drinking, body mass index.Model 2 adjusted for age, sex, systolic blood pressure, diastolic blood pressure, smoking, drinking, body mass index, hypertension, diabetes, hyperlipidemia.Model 3 adjusted for age, sex, systolic blood pressure, diastolic blood pressure, smoking, drinking, body mass index, hypertension, diabetes, hyperlipidemia, white blood cell count, blood glucose, cardiac troponin T, global registry of acute coronary even.
Table 3.
Subgroup Analysis of the Association Between H-FRA and the Risk of AKI in Patients with AMI
| Variable | Medium Risk Vs. Low Risk | High Risk vs. Low Risk | p for Interaction | ||
|---|---|---|---|---|---|
| OR (95% CI) | p-value | OR (95% CI) | p-value | ||
| Age, years | |||||
| ≤60 | 2.41 (1.33–4.37) | 0.004 | 4.03 (0.96–16.96) | 0.058 | 0.42 |
| >60 | 3.18 (1.99–5.06) | <0.001 | 6.80 (4.27–10.82) | <0.001 | |
| Sex | |||||
| Male | 4.13 (2.89–5.92) | <0.001 | 9.80 (6.75–14.24) | <0.001 | 0.13 |
| Female | 2.16 (1.12–4.14) | 0.021 | 4.63 (2.45–8.75) | <0.001 | |
| Smoking | |||||
| No | 2.99 (1.82–4.93) | <0.001 | 7.20 (4.42–11.75) | <0.001 | 0.86 |
| Yes | 4.23 (2.82–6.35) | <0.001 | 9.01 (5.82–13.94) | <0.001 | |
| Drinking | |||||
| No | 3.24 (2.21–4.76) | <0.001 | 7.25 (4.95–10.60) | <0.001 | 0.50 |
| Yes | 4.37 (2.52–7.55) | <0.001 | 9.75 (5.37–17.71) | <0.001 | |
| Hypertension | |||||
| No | 7.33 (3.74–14.39) | <0.001 | 19.38 (9.80–38.29) | <0.001 | <0.001 |
| Yes | 2.45 (1.71–3.50) | <0.001 | 5.02 (3.51–7.20) | <0.001 | |
| Diabetes | |||||
| No | 4.45 (2.89–6.86) | <0.001 | 11.68 (7.58–18.02) | <0.001 | 0.001 |
| Yes | 2.27 (1.44–3.57) | <0.001 | 4.03 (2.53–6.42) | <0.001 | |
| Hyperlipidemia | |||||
| No | 3.67 (2.63–5.13) | <0.001 | 8.43 (6.02–11.80) | <0.001 | 0.82 |
| Yes | 4.21 (1.69–10.49) | 0.002 | 10.30 (3.98–26.61) | <0.001 | |
| Types of AMI | |||||
| STEMI | 3.86 (2.59–5.77) | <0.001 | 9.50 (6.33–14.26) | <0.001 | 0.17 |
| NSTEMI | 3.17 (1.91–5.24) | <0.001 | 6.34 (3.81–10.55) | <0.001 | |
| WBC, 109/L | |||||
| ≤9.15 | 2.49 (1.56–3.96) | <0.001 | 5.73 (3.60–9.11) | <0.001 | 0.048 |
| >9.15 | 5.02 (3.28–7.72) | <0.001 | 12.12 (7.86–18.70) | <0.001 | |
| PLT, 109/L | |||||
| ≤179 | 3.69 (2.42–5.63) | <0.001 | 8.52 (5.55–13.06) | <0.001 | 0.92 |
| >179 | 3.15 (2.18–5.66) | <0.001 | 8.57 (5.33–13.80) | <0.001 | |
| Grace score | |||||
| ≤140 | 3.50 (1.89–6.47) | <0.001 | 14.62 (6.88–31.06) | <0.001 | 0.002 |
| >140 | 2.13 (1.47–3.10) | <0.001 | 3.76 (2.59–5.46) | <0.001 | |
| Gensini score | |||||
| ≤26 | 2.81 (1.26–6.27) | 0.012 | 7.01 (2.96–16.62) | <0.001 | 0.86 |
| >26 | 3.74 (2.58–5.44) | <0.001 | 8.37 (5.73–12.21) | <0.001 | |
| EF, % | |||||
| ≤55 | 5.00 (3.02–8.29) | <0.001 | 9.97 (5.97–16.67) | <0.001 | 0.71 |
| >55 | 5.01 (2.50–10.02) | <0.001 | 11.13 (5.52–22.45) | <0.001 | |
Abbreviations:AMI, acute myocardial infarction; STEMI, ST-elevation myocardial infarction; NSTEMI, non-ST elevation myocardial infarction; WBC, white blood cell count; PLT, platelet count; LVEF, left ventricular ejection fraction; Grace score, global registry of acute coronary event.
Predictive Performance of H-FRA
ROC curve analysis demonstrated that the H-FRA had moderate predictive performance for AKI (area under the curve [AUC] 0.731, 95% CI 0.714–0.748, p<0.0001) (Figure 2). At an H-FRA score cut-off of 4 points, the sensitivity for predicting AKI was 71.9%, the specificity was 64.1%, the PPV was 25.9%, and the NPV was 92.9% (Table 4).
Figure 2.
ROC curve analysis according to H-FRA for patients with AKI.
Table 4.
The Predictive Value of H-FRA for AKI with AMI
| H-FRA | Sensitivity, % | Specificity, % | Accuracy, % | PPV, % | NPV, % |
|---|---|---|---|---|---|
| ≥1 (n = 2883) | 100 | 0 | 14.85 | 14.85 | - |
| ≥2 (n = 2549) | 97.40 | 12.76 | 25.34 | 16.31 | 96.66 |
| ≥3 (n = 1737) | 87.34 | 43.49 | 50.00 | 21.23 | 95.17 |
| ≥4 (n = 1167) | 71.90 | 64.11 | 65.26 | 25.89 | 92.90 |
| ≥5 (n = 521) | 41.77 | 85.56 | 79.06 | 33.54 | 89.39 |
| ≥6 (n = 218) | 21.77 | 94.61 | 83.80 | 41.35 | 87.40 |
| ≥7 (n = 79) | 9.37 | 98.23 | 85.04 | 48.05 | 86.14 |
Abbreviations:PPV, positive predictive value; NPV, negative predictive value.
Discussion
This multicenter retrospective cohort study demonstrates that the H-FRA is independently associated with AKI risk in AMI patients. The incidence of AKI progressively increased with higher H-FRA scores. After adjusting for sex, age, and other confounding factors, the H-FRA remained an independent predictor of AKI in this population. This finding was consistent across various clinical subgroups, suggesting that the H-FRA, beyond its role in fall risk assessment, can also be used to screen for high AKI risk in AMI patients. Our previous research showed that lower scores on comprehensive assessment tools were significantly associated with increased AKI risk in AMI patients.5 The current study further underscores the importance of functional status evaluation in predicting renal injury.
Previous research indicates that frailty and systemic inflammation may exacerbate renal injury through mechanisms such as hemodynamic instability and oxidative stress.18–21 Our results revealed that AMI patients with higher H-FRA scores presented with higher Killip class and NT-proBNP levels, lower left ventricular ejection fraction, and lower diastolic blood pressure, suggesting that cardiac dysfunction leads to hemodynamic instability and potentially reduced renal perfusion. Furthermore, mobility limitations can exacerbate venous congestion, decrease effective circulating blood volume, and activate the renin-angiotensin system, thereby inducing renal ischemia.10,22 This study provides further insight into the distinct link between impaired mobility and the pathophysiology of AKI. Systemic inflammation can directly damage renal tubular epithelial cells through mediators such as TNF-α and IL-6, and impaired mobility has been identified as a key trigger for inflammatory responses.3,23 These pathways represent potential mechanisms underlying the H-FRA’s ability to predict AKI in AMI patients.
This study found that the H-FRA demonstrated a relatively low PPV for AKI in AMI patients following PCI. This may be explained by the fact that the H-FRA reflects a multidimensional profile of geriatric syndromes and frailty, which are also established risk factors for AKI. Consequently, a high H-FRA score identifies a cohort with poor physiological reserve and high vulnerability, among whom AKI represents only one of many potential adverse outcomes. This likely leads to a considerable number of false-positive results, thereby lowering the PPV. Hence, we propose that the H-FRA should be regarded as a supplementary tool for AKI risk assessment rather than a replacement for conventional AKI prediction models.
The H-FRA is a simple bedside fall risk assessment tool that can be completed within 24 hours of admission. Moreover, this score integrates multidimensional indicators reflecting multisystem vulnerability, including age, mobility limitations, cognitive impairment and visual impairment. Critically, early intervention targeting these factors has been linked to frailty reversal.24 Furthermore, this systematic evaluation may provide a more comprehensive reflection of risks related to renal hypoperfusion and tubular injury.
In conclusion, the H-FRA serves as a simple tool for AKI risk stratification in AMI patients, facilitating early identification of high-risk individuals and the implementation of renal protective strategies.25 For patients identified as high-risk by the H-FRA, clinicians should consider individualized approaches, such as setting a minimum contrast threshold based on renal function, implementing appropriate hydration protocols, and avoiding nephrotoxic drugs.26,27 Furthermore, integrating multidisciplinary interventions into peri-procedural care may directly support AKI prevention. Early bedside rehabilitation can enhance exercise tolerance and ameliorate venous congestion.28 Protocol-driven nutritional support may attenuate sarcopenia and systemic inflammation, which are recognized contributors to AKI risk.29,30 In the current context of constrained healthcare resources, the H-FRA, being part of routine nursing assessment, can optimize resource allocation without incurring additional costs.
This study has several limitations. First, unmeasured variables such as contrast volume, hydration techniques, and medication use may influence AKI risk. Second, using the first serum creatinine measurement upon admission as the baseline may underestimate the true incidence of AKI. Third, the predictive value of dynamic changes in the H-FRA score for AKI was not analyzed. Fourth, the causal relationship between H-FRA and AKI cannot be inferred due to the retrospective design, necessitating validation in prospective studies. Future research should explore the preventive effects of serial H-FRA assessments and targeted interventions on AKI occurrence.
Conclusions
The results of this multi-center retrospective cohort showed that a higher risk of AKI after AMI is independently associated with higher H-FRA scores. The H-FRA may aid in identifying patients who requires enhanced renal protective care; however further prospective validation and direct comparisons with established AKI risk models is required before clinical translation.
Acknowledgments
We are grateful for the support of the cooperating institutions participating in this research. We also sincerely thank the researchers for their contributions to this research.
Funding Statement
This work was supported by the National Science and Technology Major Project (2024ZD0523904), and Sichuan Science and Technology Program (2025YFHZ0150, 2024YFFK0061), and Health Care Project for Cadres in Sichuan Province (2024-108, 2024-101).
Data Sharing Statement
All data reported in this paper will be shared by the lead contact upon request. The data are available upon request from the corresponding author (lidongze@wchscu.edu.cn).
Ethics Approval and Informed Consent
The study was conducted in accordance with the Declaration of Helsinki. This study was approved by the Ethics Committee of West China Hospital of Sichuan University (Ethic Approval Number: 2019-565). As the study was retrospective, the requirement for informed consent was waived by the committee. All data were anonymized to protect patient confidentiality.
Author Contributions
All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.
Clinical Trial Registration
ChiCTR1900024657 (http://www.chictr.org.cn/).
Disclosure
The authors declare no conflicts of interest in this work.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
All data reported in this paper will be shared by the lead contact upon request. The data are available upon request from the corresponding author (lidongze@wchscu.edu.cn).