Platelet Count as an Independent Risk Factor for Acute Kidney Injury Induced by Rhabdomyolysis

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Platelet Count as an Independent Risk Factor for Acute Kidney Injury Induced by Rhabdomyolysis

Rhabdomyolysis is a severe clinical syndrome characterized by the breakdown of damaged striated muscle tissue, leading to the release of intracellular components into the bloodstream. Acute kidney injury (AKI) is the most frequent and life-threatening complication of rhabdomyolysis, contributing to increased mortality rates. Patients with rhabdomyolysis-associated AKI exhibit significantly higher mortality compared to those without AKI, and AKI itself has been identified as an independent predictor of mortality in this population. Early identification of patients at high risk for AKI is critical for initiating timely interventions to improve outcomes. This study investigated the role of platelet count as an independent risk factor for AKI in rhabdomyolysis, while exploring potential mechanistic links between platelet dynamics and kidney injury.

Study Design and Methodology

A retrospective analysis was conducted on 162 patients diagnosed with rhabdomyolysis at a university-affiliated teaching hospital between January 2017 and August 2020. The diagnosis of rhabdomyolysis required serum creatine kinase (CK) levels exceeding 1,000 U/L within the first 72 hours of admission. Exclusion criteria included concurrent acute myocardial infarction, pre-existing end-stage renal disease, or prior renal replacement therapy. Ethical approval was obtained, and informed consent was waived due to the retrospective nature of the study.

Demographic, clinical, and laboratory data were extracted from electronic medical records. Key variables included age, gender, etiology of rhabdomyolysis, comorbidities, and Acute Physiology and Chronic Health Evaluation II (APACHE-II) scores. Laboratory parameters assessed upon admission included CK, CK-myocardial band (CK-MB), myoglobin, liver enzymes, creatinine, blood urea nitrogen (BUN), coagulation markers (prothrombin time [PT], activated partial thromboplastin time [APTT], fibrinogen, D-dimer), and platelet indices (platelet count, mean platelet volume [MPV], platelet distribution width [PDW], plateletcrit [PCT]). For patients with multiple platelet measurements within 24 hours, the first recorded value was used.

AKI was diagnosed using the 2012 Kidney Disease Improving Global Outcomes (KDIGO) criteria, relying on serum creatinine levels due to incomplete urine output data. Baseline creatinine was defined as the value at admission or the most recent measurement within three months prior.

Key Findings

Patient Characteristics and Etiologies

The cohort had a median age of 52.5 years (range: 18–96), with 72.2% being male. The most common etiologies of rhabdomyolysis were infections (32.7%), exercise (14.8%), trauma or muscle hypoxia (14.2%), and drugs/toxins (12.3%). AKI developed in 70 patients (43.2%), who were older (median age 56.5 vs. 42.5 years, P = 0.058) and had higher APACHE-II scores (median 19 vs. 17, P < 0.001) compared to non-AKI patients.

Laboratory Parameters

Significant differences in laboratory markers were observed between AKI and non-AKI groups:

  • Myoglobin: Median levels were 3,243.46 ng/mL in AKI vs. 973.75 ng/mL in non-AKI (P < 0.001).
  • Creatinine and BUN: AKI patients had markedly elevated creatinine (226.50 vs. 63.00 mmol/L) and BUN (18.72 vs. 5.20 mmol/L) (P < 0.001).
  • Coagulation Markers: Prolonged PT (15.95 vs. 13.05 s) and APTT (44.00 vs. 36.00 s), along with higher D-dimer levels (5.55 vs. 1.26 mg/L), were noted in AKI patients (P < 0.001).
  • Platelet Indices: AKI patients exhibited lower platelet counts (89.00 vs. 194.50 × 10⁹/L) and PCT (0.11% vs. 0.21%), but higher MPV (11.30 vs. 10.40 fL) and PDW (16.70 vs. 15.90 fL) (P < 0.001).

Risk Factor Analysis

Univariate logistic regression identified multiple variables associated with AKI, including etiology, CK, myoglobin, creatinine, BUN, PT, APTT, D-dimer, platelet indices, and APACHE-II scores (P < 0.1). In multivariate analysis, three independent risk factors emerged:

  1. Myoglobin (OR = 1.001, 95% CI = 1.000–1.001, P = 0.043).
  2. Creatinine (OR = 1.136, 95% CI = 1.033–1.250, P = 0.009).
  3. Platelet Count (OR = 0.972, 95% CI = 0.946–0.999, P = 0.042).

Predictive Performance

Receiver operating characteristic (ROC) analysis demonstrated strong predictive value for platelet count (AUC = 0.857, 95% CI = 0.798–0.916) and myoglobin (AUC = 0.741, 95% CI = 0.662–0.819). The optimal cutoff for platelet count was ≤126 × 10⁹/L (sensitivity 68.6%, specificity 91.3%), while myoglobin ≥2,181 ng/mL provided sensitivity of 61.4% and specificity of 80.4%.

Mechanistic Insights

The inverse relationship between platelet count and AKI risk may reflect platelet activation and consumption during systemic inflammation. Proinflammatory cytokines (e.g., IL-1β, IL-6, TNF-α) and Toll-like receptor 4/nuclear factor-kappa B (TLR4/NF-κB) signaling are upregulated in rhabdomyolysis, promoting leukocyte-platelet interactions and platelet activation. Activated platelets contribute to renal injury through multiple pathways:

  1. Complement Activation: Platelets trigger complement cascades, exacerbating endothelial inflammation.
  2. Macrophage Extracellular Traps (METs): Heme released from myoglobin activates platelets, which interact with monocytes to form METs in renal tubules, directly damaging tubular cells.
  3. Microthrombi: Platelet aggregation may obstruct renal microvasculature, reducing perfusion.

The observed thrombocytopenia in AKI patients likely results from platelet consumption during these processes. Elevated MPV and PDW further support platelet activation, as larger, more heterogeneous platelets are markers of increased turnover.

Clinical Implications and Limitations

This study highlights platelet count as a readily available biomarker for AKI risk stratification in rhabdomyolysis. A threshold of ≤126 × 10⁹/L identifies high-risk patients with high specificity, enabling early interventions such as aggressive fluid resuscitation or close monitoring. However, limitations include the single-center design, small sample size, and lack of causality assessment. Confounding factors like fluid therapy prior to admission were not analyzed, and platelet function tests were not performed to confirm activation states.

Future studies should validate these findings in larger, prospective cohorts and explore whether therapies targeting platelet activation (e.g., antiplatelet agents) mitigate AKI risk. Investigating longitudinal platelet dynamics and their association with renal recovery could further clarify the role of platelets in rhabdomyolysis-induced AKI.

Conclusion

Platelet count is an independent predictor of AKI in rhabdomyolysis, with lower counts signaling higher risk. The combination of platelet count and myoglobin provides robust prognostic information, guiding clinical decision-making. The mechanistic interplay between platelet activation, inflammation, and renal injury underscores the potential for novel therapeutic strategies targeting these pathways to improve outcomes in this high-risk population.

doi.org/10.1097/CM9.0000000000001651

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