
==== Front
Heliyon
Heliyon
Heliyon
2405-8440
Elsevier

S2405-8440(24)11827-0
10.1016/j.heliyon.2024.e35796
e35796
Research Article
Association between red blood cell distribution width-platelet ratio (RPR) and mortality in patients with heart failure from the MIMIC IV database: A retrospective cohort study
Tang Shanshan 1
Zhang Zhiqiang 1
Wang Yulong 1
Li Yongle liyongle@aliyun.com
∗
Department of Cardiology, Tianjin Medical University General Hospital, Tianjin Medical University, Tianjin, China
∗ Corresponding author. Department of Cardiology, Tianjin Medical University General Hospital, 154, Anshan Road, Heping District, Tianjin, 300052, China. liyongle@aliyun.com
1 Authors contributed equally to this work.

03 8 2024
30 8 2024
03 8 2024
10 16 e3579618 4 2024
7 6 2024
2 8 2024
© 2024 Published by Elsevier Ltd.
2024

https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
The association between the red blood cell distribution width-platelet ratio (RPR) and mortality in heart failure patients remains unclear. We aimed to investigate the potential non-linear relationship between RPR and 1-year mortality risk. A retrospective cohort study was conducted involving 6982 participants from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. Multivariable Cox regression and restricted cubic spline analyses were performed to evaluate the association between RPR and 1-year mortality, adjusting for potential confounders. We observed 1091 patients died in hospital and 2535 patients died during 1 year follow-up period. The prevalence or incidence of mortality did not show statistically significant differences among RPR groups in the overall study population. However, a positive association between RPR and the risk of mortality was noted after adjusting for multiple variables (HR = 1.38, 95 % CI = 1.06–1.81, P = 0.018). Analysis using restricted cubic splines indicated a U-shaped relationship between RPR levels and the risk of mortality (P nonlinearity <0.05), with the point of lowest risk at 0.104. Compared to this level, lower RPR (<0.104) was associated with increased mortality (HR = 0.046, 95 % CI: 0.004–0.546), as was higher RPR (>0.104) (HR = 2.656, 95 % CI: 1.692–4.170).This U-shaped association was consistent across subgroup analyses (all interaction P values > 0.05). RPR exhibits a U-shaped association with 1-year mortality in heart failure patients, suggesting both low and high RPR levels are linked to increased risk. RPR may serve as a relevant biomarker for risk stratification in this population. We incorporated RPR into the SOFA (AUC 0.731) and SAPS II (AUC 0.746) models, which significantly improved their predictive ability for in-hospital mortality. For 1-year mortality prediction, RPR + SAPS II (AUC 0.683) showed significantly improved accuracy, while RPR + SOFA (AUC 0.626) did not improve significantly.

Keywords

Heart failure
Red blood cell distribution width/platelet ratio
MIMIC-IV
Mortality
Prognostic predictor
==== Body
pmc1 Introduction

Red blood cell distribution width-platelet ratio (RPR) is a vital hematologic biomarker that offers valuable insights into human physiology and overall health [[1], [2], [3]]. Its measurement serves as a sensitive indicator of the intricate homeostatic mechanisms within the body, providing real-time information on internal changes. As an integral component of routine health assessments, RPR levels play a significant role in understanding various metabolic processes and organ functions. These values are influenced by a complex interplay of genetic factors, lifestyle choices, and environmental influences. Abnormal RPR levels outside the established reference range can indicate underlying health issues, prompting further clinical investigation and intervention [[4], [5], [6]].

The clinical relevance of RPR extends to its utility in the diagnosis and management of a wide range of acute and chronic conditions. Its dynamic nature makes it a valuable tool for monitoring disease progression, assessing treatment effectiveness, and customizing personalized healthcare strategies. Ongoing research efforts aim to elucidate the physiological factors that influence RPR levels, thereby enhancing our understanding of its significance in health and disease. This knowledge contributes to the development of innovative diagnostic and therapeutic approaches, as well as the identification of individuals at risk for specific health conditions [7,8].

In summary, RPR is a versatile and essential hematologic parameter that provides a dynamic assessment of human health. Regular measurement and interpretation of RPR are crucial for timely diagnosis, effective management, and personalized care in diverse clinical settings.

Regarding the current state of evidence on the relationship between RPR and the mortality of heart failure patients, there is a lack of sufficient data. There is no established evidence specific to the study population in question. Therefore, it is imperative to conduct a retrospective cohort study to investigate the potential association between RPR and the mortality of heart failure patients. The secondary objectives of this study include exploring the potential non-linear dose-response relationship between RPR and the 1-year mortality in heart failure patients, as well as identifying threshold levels of RPR that may impact mortality in patients with heart failure. The study involved a retrospective cohort analysis of 6982 participants from the MIMIC IV database.

2 Materials and methods

2.1 Study design and setting

A retrospective cohort study using patient data from patients hospitalized for heart failure was conducted. Data were gathered from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database (version:2.2), included critical care information of 76,540 patients who had been admitted to intensive care units and treated in hospitals at the Beth Israel Deaconess Medical Center (Boston, MA, USA) from 2008 to 2019 [9]. The database is accessible to anybody who has passed the Collaborative Institutional Training Initiative exam (Certification number 52219361 for Tang and 60071489 for Zhang). After obtaining permission, we downloaded the database to the local database and used Structured Query Language (SQL) with PostgreSQL (version 13.0) and Navicat software (version 16.0) to identify the cohort and extract the relevant clinical information. Specifically, for clinical parameters with multiple outcomes during a patient's hospitalization, only the initial outcome was included. Moreover, each variable extraction underwent double-checking by two individuals to ensure data accuracy and reliability.Considering that this was a retrospective study and all patients in this study were extracted from a public database, informed consent was waived. This cohort study adhered to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guidelines.

2.2 Patients

Patients first admitted to the intensive care unit were identified in the MIMIC-IV database from 2008 to 2019. The inclusion criteria were as follows: adult patients with heart failure admitted to the ICU (age ≥18 years); heart failure defined as International Classification of Disease (ICD), Ninth or Tenth Revision (Supplementary Table 1). Of the 8094 patients extracted from the MIMIC-IV database, 823 were excluded because of hospitalized in the ICU less than 24 h. In addition, 289 patients were excluded because of missing key data, such as red blood cell distribution width (RDW) and platelet. Finally, 6982 patients with heart failure were included in this study (Fig. 1). Survival information was extracted from a table named ‘patients’ from the MIMIC-IV database. Data regarding the length of hospital stay were extracted from the table named ‘admissions’ of the MIMIC-IV database.Fig. 1 The flowchart of patients' selection.

Abbreviations: ICU, intensive care unit; MIMIC-IV, Medical Information Mart for Intensive Care IV; RDW, red blood cell distribution width.

Fig. 1

2.3 Covariates

Our variable selection was based on the recommendations from Refs. [6,10,11] and clinical practice experience, aiming to comprehensively reflect patients' demographic characteristics, clinical manifestations, comorbidities, laboratory test results, and treatment conditions, laying a solid data foundation for subsequent analyses. RPR, the main factor we intended to study, was calculated as follows: RPR=RDW (%)/platelet count (K/uL). The baseline RDW and platelet count obtained was the first time value measured within 24 h of ICU admission in the MIMIC-IV database. The baseline RDW and platelet count used to calculate RPR were the first recorded values measured simultaneously within 24 h of ICU admission in the MIMIC-IV database, cross-verified by two independent reviewers.

We included the following variables of enrolled participants in the database based on published literature and clinical experience: 1) demographic characteristics: age, sex, ethnicity, marital status, insurance, body mass index (BMI) [the calculation formula for BMI is: weight(kg)/height(m)2] 2) Physical examination: heart rate, systolic blood pressure (SBP), diastolic blood pressure (DBP). 3) Co-morbidities: myocardial infarct, atrial fibrillation, hypertension, cerebrovascular disease, chronic pulmonary disease, chronic kidney disease (CKD), diabetes, cancer. 4)Laboratory tests measured the first time within 24 h of ICU admission: hemoglobin, RDW, white blood cell (WBC), platelet, prothrombin time (PT), creatinine, potassium, sodium, chloride, PH. 5) Medications: angiotensin converting enzyme inhibitor/angiotension receptor antagonists(ACEI/ARB), beta-blockers, aspirin, warfarin, vasoactive drugs(including dobutamine, dopamine, vasopressin, phenylephrine, norepinephrine, nitrate, nesiritide, epinephrine).6)Treatments: coronary artery bypass graft (CABG), PCI(percutaneous coronary intervention), mechanical ventilation, hemodialysis).

Another factor to consider is the first 24-h Sequential Organ Failure Assessment (SOFA) score, the Simplified Acute Physiology Score II(SAPSII) score, as well as Charlson comorbidity index. SOFA score was designed to objectively assess the organ function from six aspects: respiration, coagulation, liver, cardiovascular, central nervous system, and renal. Each item was scored 0–4 points, and the higher the score, the more severe the organ dysfunction was reflected [12]. SAPSII score was assessed based on age, 12 physiological variables (heart rate, SBP, body temperature, PaO2/FiO2 ratio, urinary output, serum urea or serum urea nitrogen level, WBC count, serum potassium level, serum sodium level, serum bicarbonate level, bilirubin level, and Glasgow Coma Scale score), type of admission, and 3 chronic diseases (acquired immuno-deficiency syndrome, haematological malignancy, and metastatic cancer).The higher score indicated the more severe condition [13]. Charlson Comorbidity Index (CCI) consisted of 19 items corresponding to different medical comorbid conditions is a tool used to predict mortality by classifying or weighting comorbid conditions. It assigns a score to various pre-existing medical conditions, with higher scores indicating a greater comorbidity burden [14].

2.4 Outcome

The outcome of this study was 1-year mortality from the moment of admission to the ICU.

2.5 Statistical analysis

Histogram distribution, Q-Q plot, and Kolmogorov-Smirnov test were utilized to assess the normality of the variables. Normally distributed continuous variables were reported as mean ± standard deviation (SD), while skewed continuous variables were represented as median (interquartile range [IQR]). Categorical variables were expressed as frequencies and percentages (%). Statistical analysis for continuous variables between groups was conducted using either the independent samples Student's t-test or Mann-Whitney U test, depending on the distribution normality. Categorical data were compared using the chi-square test as deemed appropriate. We used dummy variables to indicate missing covariate values.

We adopted multi-variable Cox regression analyses and smooth curve fitting to assess the independent association between RPR and in-hospital mortality. We further applied a two-piecewise linear regression model using a smoothing curve to examine the nonlinear association between platelet count and1-year mortality. A likelihood ratio test was conducted to compare the one-line linear regression model with the two-piecewise linear model. Meanwhile, Cox proportional hazards models with robust estimators were employed to assess the relationship between RPR and 1-year mortality in the study. The selection of confounders was based on clinical relevance and existing scientific literature. Four models were constructed: Model 1 adjust for: none; Model 2 which included adjustments for covariates included in demographics; Model 3, which further adjusted for covariates included in physical examination, scores and laboratory test; Model 4, which additionally adjusted for covariates included in medications and treatments. To evaluate the role of RPR in predicting short-term and long-term prognosis, we constructed binary logistic regression models and Cox regression models to analyze the association between RPR and in-hospital mortality risk and 1-year mortality risk, respectively. We also conducted comparative analyses with red blood cell distribution width (RDW) and platelet count (PLT) alone.

We utilized a restricted cubic spline model to construct smooth curves in order to investigate potential non-linear dose-response relationships between RPR and mortality. According to Chen 's recommendations [15], we set 3 knots during the fitting process, located at the 25th, 50th, and 75th percentiles of RPR. Non-linearity was assessed by introducing a quadratic term into the regression models. In cases where a non-linear correlation was identified, a two-piecewise regression model was employed to determine the threshold effect of RPR on mortality. We performed Kaplan-Meier survival curve analysis and log-rank tests to compare the survival curves between high RPR group and low RPR group, divided by the cutoff value of RPR, to visually present the association between RPR levels and patient survival. Additionally, we constructed ROC curves for the RPR + SOFA and RPR + SAPS II models and compared them with the ROC curves of the SOFA score and SAPS II score alone to evaluate whether RPR could enhance the predictive performance of these scoring systems.

Subgroup analysis was conducted to explore the association between RPR and mortality based on subgroup variables. In the data analysis process, we noticed missing values for some variables. To address the missing data and reduce potential bias, we employed multiple imputation methods. Specifically, we used the "mice" package in R software and performed multiple imputation using chained equations, generating 5 imputed datasets. During the imputation process, we constructed imputation models based on other complete variables and used methods such as logistic regression and predictive mean matching to estimate missing values for categorical and continuous variables, respectively (Table S2).

All statistical analyses were carried out using R Statistical Software (Version 4.2.2, http://www.R-project.org, The R Foundation) and Free Statistics analysis platform (Version 1.9, Beijing, China, http://www.clinicalscientists.cn/freestatistics). A two-tailed test was performed and p < 0.05 was considered statistically significant.

3 Results

We enrolled 6982 patients with a mean age of 72 ± 13.3 years. Among them, 69.5 % were of white, and 58.0 % were male. The overall prevalence of 1 year mortality was 36.31 %. Table 1 presents the general characteristics of the participants based on the presence of 1 year mortality. Participants with mortality were observed to be older, with a higher proportion of males, lower BMI, lower eGFR, higher in SAPSII, SOFA and CCI, and a higher likelihood of having atrial fibrillation, chronic pulmonary disease, chronic kidney disease, diabetes, and cancer (all P < 0.05). However, there were no significant differences among the two groups in terms of ethnicity, SBP, DBP, myocardial infarction, vasoactive agents, and PCI.Table 1 Baseline characteristics of the study participants.

Table 1Variables	Total (n = 6982)	Survival (n = 4447)	Death (n = 2535)	P-value	
Demographics	
Age（years）	72.01 ± 13.32	69.76 ± 13.51	75.96 ± 11.99	<0.001	
Gender (%)				0.022	
 Female	2932 (41.99)	1822 (40.97)	1110 (43.79)		
 Male	4050 (58.01)	2625 (59.03)	1425 (56.21)		
Ethnicity (%)				0.053	
 White	4855 (69.54)	3111 (69.96)	1744 (68.8)		
 African American	658 (9.42)	416 (9.35)	242 (9.55)		
 Others	600 (8.59)	399 (8.97)	201 (7.93)		
 Unknown	869 (12.45)	521 (11.72)	348 (13.73)		
Marital status (%)				<0.001	
 Single	1504 (21.54)	1021 (22.96)	483 (19.05)		
 Married	3231 (46.28)	2114 (47.54)	1117 (44.06)		
 Divorced	531 (7.61)	347 (7.8)	184 (7.26)		
 Widowed	1195 (17.12)	678 (15.25)	517 (20.39)		
 Unknown	521 (7.46)	287 (6.45)	234 (9.23)		
Insurance (%)				<0.001	
 Medicare	4032 (57.75)	2380 (53.52)	1652 (65.17)		
 Medicaid	308 (4.41)	225 (5.06)	83 (3.27)		
 Others	2642 (37.84)	1842 (41.42)	800 (31.56)		
BMI (kg/m2)	30.02 ± 8.13	30.80 ± 8.19	28.63 ± 7.82	<0.001	
Physical examination	
Heart rate (bpm)	87.63 ± 19.73	86.41 ± 19.04	89.79 ± 20.70	<0.001	
Systolic blood pressure (mmHg)	119.56 ± 23.84	119.85 ± 23.36	119.07 ± 24.66	0.187	
Diastolic blood pressure (mmHg)	64.75 ± 17.63	64.52 ± 16.76	65.16 ± 19.06	0.14	
Scores	
SAPSII (score)	41.63 ± 13.73	38.56 ± 12.45	47.03 ± 14.20	<0.001	
SOFA (score)	6.68 ± 3.79	6.05 ± 3.47	7.79 ± 4.06	<0.001	
CCI (score)	7.40 ± 2.49	6.84 ± 2.36	8.40 ± 2.39	<0.001	
Co-morbidities (%)	
Myocardial infarction	2483 (35.56)	1544 (34.72)	939 (37.04)	0.051	
Atrial fibrillation	3594 (51.48)	2178 (48.98)	1416 (55.86)	<0.001	
Hypertension	2015 (28.86)	1427 (32.09)	588 (23.2)	<0.001	
Cerebrovascular disease	958 (13.7)	542 (12.2)	416 (16.4)	<0.001	
Chronic pulmonary disease	2597 (37.20)	1604 (36.07)	993 (39.17)	<0.001	
Chronic kidney disease	2628 (37.64)	1429 (32.13)	1199 (47.3)	<0.001	
Diabetes	2830 (40.53)	1739 (39.11)	1091 (43.04)	<0.001	
Cancer	620 (8.88)	246 (5.53)	374 (14.75)	<0.001	
Laboratory test	
RPR (ratio)	0.08 (0.06, 0.11)	0.08 (0.06, 0.11)	0.08 (0.06, 0.12)	<0.001	
Hemoglobin (g/dL)	10.24 ± 2.24	10.30 ± 2.31	10.13 ± 2.13	<0.001	
RDW (%)	15.52 ± 2.36	15.05 ± 2.08	16.36 ± 2.59	<0.001	
WBC (K/μL)	12.88 ± 8.74	12.66 ± 8.33	13.26 ± 9.41	<0.001	
Platelet (K/μL)	186.0 (136.0, 251.0)	183.0 (136.5, 245.0)	192.0 (134.0, 261.5)	0.011	
PT (s)	15.10 (13.20, 18.34)	15.00 (13.10, 17.50)	15.50 (13.30, 20.67)	<0.001	
Creatinine(mg/dL)	1.20 (0.90, 1.90)	1.10 (0.80, 1.60)	1.50 (1.00, 2.50)	<0.001	
eGFR (ratio)	55.76 (32.46, 83.68)	64.25 (40.80, 88.41)	41.52 (22.84, 65.86)	<0.001	
Potassium (mmol/L)	4.29 ± 0.72	4.24 ± 0.67	4.38 ± 0.80	<0.001	
Sodium (mmol/L)	138.45 ± 5.08	138.61 ± 4.54	138.18 ± 5.91	<0.001	
Chloride (mmol/L)	103.03 ± 6.92	103.82 ± 6.50	101.64 ± 7.40	<0.001	
PH	7.37 ± 0.09	7.38 ± 0.08	7.36 ± 0.10	<0.001	
Medications (%)	
ACEI/ARB	3094 (44.31)	2339 (52.6)	755 (29.78)	<0.001	
Beta-blockers	4209 (60.28)	2926 (65.8)	1283 (50.61)	<0.001	
Aspirin	3835 (54.93)	2692 (60.54)	1143 (45.09)	<0.001	
Warfarin	1294 (18.53)	959 (21.57)	335 (13.21)	<0.001	
Vasoactive agents	5071 (72.63)	3228 (72.59)	1843 (72.7)	0.918	
Treatments (%)	
CABG	1112 (15.93)	980 (22.04)	132 (5.21)	<0.001	
PCI (%)	408 (5.84)	264 (5.94)	144 (5.68)	0.661	
Mechanical ventilation (%)	6603 (94.57)	4183 (94.06)	2420 (95.46)	0.013	
Hemodialysis (%)	927 (13.28)	389 (8.75)	538 (21.22)	<0.001	
Heart failure type (%)	
AHF	2730 (39.10)	1699 (38.21)	1031 (40.67)	0.042	
CHF	3328 (47.67)	2027 (45.58)	1301 (51.32)	<0.001	
Length of hospitalization (days)	5.46 ± 6.34	4.76 ± 5.51	6.69 ± 7.43	<0.001	
Abbreviations: BMI, body mass index; AHF, acute heart failure; CHF, Chronic heart failure; SAPSII, Simplified Acute Physiology Score II; SOFA, Sequential Organ Failure Assessment; CCI, Charlson Comorbidity Index; RPR, cell distribution width to platelet ratio; RDW, red blood cell distribution width; WBC, white blood cell; PT, prothrombin time; eGFR, estimate glomerular filtration rate; ACEI/ARB, angiotensin converting enzyme inhibitors/angiotension receptor antagonists; CABG, coronary artery bypass graft; PCI, percutaneous coronary intervention.

Tables S3 and S4 present the associations of RPR, RDW, and PLT levels with in-hospital mortality risk and 1-year mortality risk, respectively After adjusting for various confounding factors (Model 4), RPR was significantly associated with in-hospital mortality risk (OR 1.89, 95 % CI 1.06–3.39). In contrast, the predictive ability of RDW and PLT levels alone was weaker, with the OR value of RDW (1.08, 95 % CI 1.05–1.11) being significantly lower than that of RPR, and the OR value of PLT (1.00, 95 % CI 1.00–1.00) being close to 1. RPR was an independent predictor of 1-year mortality risk, with a fully adjusted HR of 1.38 (95 % CI 1.06–1.81). In comparison, the predictive ability of RDW and PLT was weaker. Univariate Cox and multivariable Cox regression analysis of risk factors for 1-year mortality in patients with heart failure is reported in Table 2. We found age, physical examination, scores, history of chronic kidney disease, history of cerebrovascular disease, eGFR, PT, ACEI/ARB, beta-blockers, warfarin, aspirin, CABG and hemodialysis were independent risk factors for in-hospital mortality in this cohort (all P < 0.05). In the multivariable Cox proportional hazard model, after adjusting for potential confounders as shown in Table 2, RPR was analyzed as a continuous variable (per 1 unit) and found to be inversely associated with the risk of mortality (hazard ratio [HR] = 1.38, 95 % CI = 1.06–1.81, P = 0.018) (Table 3).Table 2 Univariate Cox and Multivariable Cox analysis evaluating the association between RPR and 1 year morality.

Table 2Variables	Univariate Cox analysis	Multivariable Cox analysis	
HR (95%CI)	P-value	HR (95%CI)	P-value	
Demographics	
Age（years）	1.03 (1.03–1.04)	<0.001	1.02 (1.02–1.03)	<0.001	
Gender (Male vs Female)	0.91 (0.84–0.99)	0.022	0.95 (0.87–1.04)	0.276	
Race	
 White	Ref.		Ref.		
 African American	1.02 (0.89–1.16)	0.805	0.95 (0.83–1.1)	0.485	
 Others	0.92 (0.79–1.06)	0.243	0.92 (0.79–1.07)	0.272	
 Unknown	1.21 (1.08–1.36)	0.001	1.12 (0.97–1.3)	0.117	
Marital status	
 Single	Ref.		Ref.		
 Married	1.1 (0.99–1.22)	0.091	0.95 (0.85–1.06)	0.39	
 Divorced	1.08 (0.92–1.29)	0.349	1.09 (0.91–1.29)	0.346	
 Widowed	1.46 (1.29–1.65)	<0.001	0.98 (0.85–1.12)	0.746	
 Unknown	1.66 (1.42–1.94)	<0.001	1.25 (1.03–1.51)	0.023	
Insurance	
 Medicare	Ref.		Ref.		
 Medicaid	0.59 (0.47–0.74)	<0.001	1.08 (0.85–1.36)	0.532	
 Others	0.69 (0.63–0.75)	<0.001	0.98 (0.9–1.07)	0.7	
BMI	0.97 (0.96–0.97)	<0.001	0.98 (0.97–0.98)	<0.001	
Physical examination	
Heart rate (bpm)	1.01 (1.01–1.01)	<0.001	1 (1∼1.01)	<0.001	
Systolic blood pressure (mmHg)	1 (1∼1)	0.09	1 (1∼1)	0.004	
Diastolic blood pressure (mmHg)	1 (1∼1)	0.093	1 (1∼1.01)	0.006	
Scores	
CCI	1.04 (1.03–1.04)	<0.001	1.01 (1.01–1.01)	<0.001	
SAPSII	1.11 (1.1–1.12)	<0.001	1.05 (1.03–1.06)	<0.001	
SOFA	1.21 (1.19–1.23)	<0.001	1.13 (1.1–1.16)	<0.001	
Co-morbidities (%)	
Myocardial infarction	1.1 (1.01–1.19)	0.026	1.08 (0.99–1.19)	0.091	
Atrial fibrillation	1.25 (1.16–1.35)	<0.001	1.04 (0.96–1.14)	0.328	
Hypertension	0.69 (0.63–0.76)	<0.001	0.97 (0.87–1.08)	0.598	
Diabetes	1.13 (1.04–1.22)	0.003	1 (0.92–1.1)	0.92	
Chronic kidney disease	1.64 (1.51–1.77)	<0.001	0.79 (0.7–0.89)	<0.001	
Chronic pulmonary disease	1.1 (1.01–1.19)	0.025	0.95 (0.87–1.04)	0.263	
Cerebrovascular_disease1	1.35 (1.22–1.5)	<0.001	1.21 (1.08–1.36)	0.001	
Cancer	2.16 (1.93–2.41)	<0.001	1.09 (0.95–1.26)	0.199	
Laboratory test	
eGFR	0.98 (0.98–0.99)	<0.001	1 (0.99–1)	<0.001	
PT (s)	1.02 (1.01–1.02)	<0.001	1.01 (1.01–1.01)	<0.001	
WBC (K/μL)	1.01 (1∼1.01)	<0.001	1 (1∼1)	0.619	
RPR	2.76 (2.27–3.36)	<0.001	1.38 (1.06–1.81)	0.018	
Medications (%)	
ACEI/ARB	0.44 (0.4–0.48)	<0.001	0.6 (0.55–0.66)	<0.001	
Beta-blockers	0.59 (0.54–0.63)	<0.001	0.85 (0.78–0.92)	<0.001	
Warfarin	0.59 (0.53–0.67)	<0.001	0.7 (0.62–0.79)	<0.001	
Aspirin	0.6 (0.55–0.65)	<0.001	0.8 (0.73–0.87)	<0.001	
Vasoactive agents	1.05 (0.97–1.15)	0.238	1 (0.9–1.1)	0.939	
Treatments (%)	
PCI	0.95 (0.81–1.13)	0.585	1.11 (0.93–1.33)	0.241	
CABG	0.24 (0.2–0.29)	<0.001	0.31 (0.26–0.37)	<0.001	
Hemodialysis	2.23 (2.03–2.45)	<0.001	1.33 (1.18–1.49)	<0.001	
Mechanical ventilation	1.33 (1.11–1.61)	0.003	1.2 (0.99–1.45)	0.064	
Note: Multivariable Cox analysis adjust for demographics, physical examination, Scores, laboratory test, medications and treatments.

Abbreviations: BMI, body mass index; SAPSII, Simplified Acute Physiology Score II; SOFA, Sequential Organ Failure Assessment; CCI, Charlson Comorbidity Index; RPR, cell distribution width to platelet ratio; WBC, white blood cell; PT, prothrombin time; eGFR, estimate glomerular filtration rate; ACEI/ARB, angiotensin converting enzyme inhibitors/angiotension receptor antagonists; CABG, coronary artery bypass graft; PCI, percutaneous coronary intervention; Ref., reference.

Table 3 The relationship between the RPR ratio and 1 year mortality in a Cox regression model.

Table 3Models	HR (95 % CI)	P -value	
Model 1	2.76 (2.27, 3.36)	<0.001	
Model 2	2.9 (2.36, 3.56)	<0.001	
Model 3	1.76 (1.35–2.31)	<0.001	
Model 4	1.38 (1.06–1.81)	0.018	
Note: Model 1 adjust for: none; Model 2 adjust for: covariates included in demographics; Model 3 adjust for: model 2 +covariates included in physical examination, Scores and laboratory test; Model 4 adjust for: model 3 + covariates included in medications and treatments.

Abbreviations: HR, hazard ratio; CI, confidence interval.

In order to investigate the presence of a dose-response relationship between RPR levels and the incidence of mortality, we conducted a smoothing function analysis. We observed that the relationship between RPR and mortality was non-linear. Following adjustment for potential confounders, we identified a nonlinear association between RPR and mortality (P for nonlinearity = 0.001, as shown in Fig. 2).Fig. 2 Dose-response relationship between the RPR and all-cause mortality in patients with heart failure.

Note：Adjusted for demographics, physical examination, Scores, laboratory test, medications and treatments. Solid and dashed lines indicate the predicted value and 95 % CI.

Fig. 2

Data were fit to a piecewise multivariate Cox regression model and found two different slopes. The risk of mortality was found to be positively correlated with RPR levels up to a peak at 0.104 (0.063–0.145) (P = 0.003). On the left side of the inflection point, the HR was 0.046(95%CI: 0.004–0.546, p = 0.0148). On the right side of the inflection point, the HR was 2.656 (95%CI: 1.692–4.17, p < 0.001). It suggests that the risk of 1-year mortality started to decrease by 95.4 % per RPR change until RPR of 0.104. Then the risk of 1-year mortality started to increase by 1.656 per RPR change (P-value for non-linear test was <0.001) (Table 4). The Log-rank test indicated a significant difference between the two survival curves (p < 0.001) (Fig. 3). Patients in the high RPR group exhibited significantly lower 1-year survival rates compared to those in the low RPR group. This finding visually demonstrates that heart failure patients with elevated RPR levels have a poorer prognosis. Incorporating RPR into the SOFA score (AUC: 0.626) and SAPS II score (AUC: 0.683) models significantly improved their predictive ability for 1-year mortality risk prediction (Fig. 4).Table 4 The non-linearity relationship between RPR and 1 year mortality.

Table 4Threshold of RPR	HR	95 % CI	P-value	
<0.104	0.046	0.004–0.546	0.0148	
≥0.104	2.656	1.692–4.17	<0.001	
Non-linear test			<0.001	
Note: Adjusted for demographics, physical examination, Scores, laboratory test, medications and treatments.

Abbreviations: HR, hazard ratio; CI, confidence interval.

Fig. 3 K–M curve for 1-year mortality

Note: Group 1: RPR <0.104, Group 2: RPR ≥0.104.

Fig. 3

Fig. 4 Incremental effect of the RPR for predicting 1-year mortality

Abbreviations: RPR, Red cell distribution width to platelet ratio; SOFA, sequential organ failure assessment; SAPSII, simplified acute physiology score II; AUC, The area under the curve.

Fig. 4

In several subgroups, stratified analysis was performed to assess potential effect modifications on the relationship between RPR and 1-year mortality. No significant interactions were found in any subgroups after stratifying by age, sex, BMI, eGFR, myocardial infarction, hypertension, cerebrovascular disease, chronic pulmonary disease, chronic kidney disease, diabetes, cancer and SOFA. The subgroup analysis did not reveal any evidence of effect modification or interaction based on common risk factors for mortality, as indicated by the P values for interaction, all of which were greater than 0.05 (Figure S1).

4 Discussion

In this extensive retrospective cohort study of MIMIC IV patients, we have demonstrated that red blood cell distribution width-platelet ratio (RPR) is independently associated with a 38 % increase in the risk of 1-year mortality for the first time. Notably, the analysis using restricted cubic splines indicated a U-shaped relationship between the levels of RPR and the risk of 1-year mortality (P nonlinearity <0.05), with the lowest risk observed at 0.104. We found that incorporating RPR into the SOFA and SAPS II models significantly improved their accuracy in predicting 1-year mortality risks, with the RPR + SAPS II model showing the most significant improvement. Subsequent exploratory subgroup analysis revealed no significant interactions. These findings hold significant clinical implications.

Our study investigated the nonlinear relationships of RPR with in hospital and 1-year mortality among patients with heart failure in the MIMIC IV database. Our findings align with those of other observational studies. Previous research has delved into the association between RPR and heart failure. For instance, L et al. identified a significant link between RPR and 28-day readmission in the heart failure population (OR = 1.082, 95 % CI = 1.012–1.158, P = 0.0212) [8]. Additionally, a cross-sectional study based on the National Health and Nutrition Examination Survey 1999–2020 highlighted a significant association between RPR and cardiovascular disease, the ORs with 95 % CIs across the second to fourth quartiles were 1.04 (0.92–1.17), 1.22 (1.05–1.42) and 1.64 (1.43–1.87) for the RPR compared with the lowest quartile (p for trend <0.0001), respectively [3]. Recent studies have begun to shed light on the connection between RPR and mortality. a recent prospective longitudinal cohort study involving patients with acute myocardial infarction demonstrated a noteworthy connection between RPR and 180-day in-hospital mortality (HR: 2.677, 95 % CI: 1.159–6.188, P = 0.021) [7]. Moreover, pooled analyses of prospective cohort studies investigating the relationship between RPR and mortality have affirmed these findings. Furthermore, we found that RPR can provide valuable supplementary information to the SOFA and SAPS II scoring systems, enhancing their predictive capability. Our study builds upon and reinforces these existing findings within the MIMIC IV patient cohort, revealing that a significant association between RPR and the risk of in-hospital and 1-year mortality.

The magnitudes of the significant associations between RPR and incident mortality were modest yet consistent across different event subtypes, including chronic pulmonary disease and cancer, as well as across various subgroups based on age, sex, BMI, eGFR, SOFA and cardiovascular disease risk factors such as myocardial infarction, hypertension, chronic kidney disease, and diabetes (with the exception of cerebrovascular disease: adjusted HR = 0.43, 95 % CI = 0.08–2.31). The observed significant association with mortality aligns with the current body of literature. Our research results showed that RPR not only predicted the long-term prognosis of heart failure patients but also was significantly associated with in-hospital mortality risk after adjusting for confounding factors, suggesting that RPR may also have some value in assessing early prognosis. Compared to RDW and PLT, RPR, as an indicator that comprehensively combines anemia, platelet count, and other pathological factors, exhibited a significant advantage in predicting prognosis, which may be due to its ability to more comprehensively reflect the patient's inflammatory state and disease severity. Therefore, RPR can not only be used as an auxiliary prognostic assessment indicator in clinical practice but also provide clues for further mechanistic research.

The possible mechanism between RPR and increased risk of death in heart failure patients is as follows: 1) Inflammation: Heart failure is often accompanied by a systemic inflammatory response, and an increase in RDW has been considered a marker of inflammation. Inflammation can lead to increased heterogeneity in the production and maturation of red blood cells, thereby increasing RDW [16]. Inflammation can also reduce platelet by shortening the lifespan of platelets and/or affecting their production, thus raising RPR [17]. Therefore, an increase in RPR reflects the level of inflammation in patients with heart failure, where inflammation is a key factor in the deterioration and poor prognosis of heart failure [18]. 2) Hypoxia and tissue ischemia: Heart failure leads to a reduced cardiac pumping function, which can cause insufficient oxygenation of the body's tissues and organs. Hypoxic conditions disrupt the normal production and maturation of red blood cells, leading to an increase in RDW [19]. At the same time, hypoxia can activate various platelet activation pathways, affecting platelet count and function, which may be reflected in changes in PLT [20]. Thus, an increase in RPR can partially reflect the degree of hypoxia and tissue ischemia in patients with heart failure. 3) Tendency for thrombosis: Patients with heart failure have reduced blood flow, increasing the risk of thrombosis [21]. Platelets play a key role in the formation of thrombi. A decrease in platelet count may reflect an increased consumption of platelets, a marker of thrombotic activity [22]. Therefore, an increase in RPR might reflect an increased risk of thrombosis in patients with heart failure.4) Malnutrition: Patients with heart failure may experience malnutrition, especially iron deficiency anemia, which affects the production and maturation of red blood cells, leading to an increased RDW [23,24]. Malnutrition can also affect the production of platelets [25], and these factors together may lead to an increase in RPR. 5) Cardiac remodeling and endothelial dysfunction: Heart failure can lead to remodeling of the heart's structure and function, as well as dysfunction of the vascular endothelium [26]. These changes may affect the number and function of red blood cells and platelets through various mechanisms, thereby affecting RPR. In conclusion, our study contributes to the growing body of evidence on the association between RPR and mortality, further research is needed to validate our results and delve into the intricate relationship and potential underlying mechanisms.

This study presents novel findings on the association between RPR and the risk of mortality in patients with heart failure based on data gathered from MIMIC-IV database. The research approach adopted in this study was rigorous and demonstrated both novelty and potential therapeutic implications. The study offers robust evidence on the relationship between RPR and mortality risk in patients with heart failure, accounting for potential confounders and biases meticulously.

Our study found a U-shaped association between RPR and 1-year mortality risk in heart failure patients, providing evidence-based support for the clinical application of RPR. Firstly, as a simple, economical, and readily available biochemical indicator, RPR can serve as an auxiliary tool for evaluating prognosis risk and risk stratification in heart failure patients, facilitating the development of individualized and precise medical decisions. Secondly, for patients with abnormal RPR values, clinicians should further analyze the underlying pathophysiological mechanisms, such as anemia, inflammation, and malnutrition, and provide timely interventions and corrections to improve prognosis. Dynamic monitoring of RPR can also aid in evaluating treatment efficacy and guiding individualized treatment adjustment strategies. Moreover, future clinical trials may consider utilizing RPR as an alternative efficacy evaluation indicator, exploring novel treatment strategies to improve heart failure prognosis by modulating RPR levels, thereby providing new intervention targets for pharmaceutical or non-pharmaceutical interventions. In summary, our research findings establish an evidence-based foundation for the application of RPR in secondary prevention of heart failure.

Several limitations should be acknowledged in the interpretation of the findings. Firstly, even though regression models, stratified analyses, and sensitivity analysis were performed, residual confounders potentially exist, as with all retrospective analyses. Secondly, RPR measurements were used initial blood counts on admission to minimize treatment effects, the influences of long-term medication regimens on HF outcomes still require further elucidation. Third, due to the nature of the simulation database, this study lacks some potential variables, such as the impacts of inflammation, anemia and EPO on RDW, precluding detailed analysis of the underlying mechanisms linking these factors with RDW. Moreover, as a retrospective study using the MIMIC-IV database, there is an inherent risk of selection bias, and our findings may not fully represent the outpatient heart failure population or account for potential confounders such as lifestyle and genetic factors. An important limitation is the inability to continuously measure and analyze RPR changes, which restricted our in-depth analysis of RPR change patterns and their associations with prognosis. Finally, the current findings were derived from the MIMIC-IV database, a single-center study, limiting the external validity and generalizability of our findings to other populations. Future research with multi-center, multi-ethnic cohorts is warranted to validate and expand upon the current results.

5 Conclusion

Our study revealed a non-linear, U-shaped association between RPR and 1-year mortality among heart failure patients, indicating increased risk with both low and high RPR levels. The optimal RPR cutoff of 0.104 was identified. These findings suggest RPR may be a relevant biomarker for mortality risk stratification in heart failure.

Data accessibility

Datasets were sourced from the MIMIC-IV database (version 2.2), which is available at https://physionet.org/content/mimiciv/2.2/. Authorization codes 52219361 for Tang and 60071489 for Zhang facilitated access.

Ethical considerations

Compliant with local and institutional regulations, this research on human subjects did not necessitate ethical approval or subsequent reviews. Additionally, obtaining written informed consent was not required by either national laws or institutional policies.

Funding

None.

CRediT authorship contribution statement

Shanshan Tang: Writing – original draft, Software, Project administration, Data curation, Conceptualization. Zhiqiang Zhang: Methodology, Formal analysis, Conceptualization. Yulong Wang: Writing – original draft, Resources. Yongle Li: Writing – review & editing, Visualization, Validation, Supervision, Conceptualization.

Declaration of generative AI and AI-assisted technologies in the writing process

During the preparation of this work, the authors used ChatGPT in order to polish. After using this tool or service, the authors reviewed and edited the content as needed and took full responsibility for the content of the publication.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendix A Supplementary data

The following is the Supplementary data to this article:Multimedia component 1

Multimedia component 1

Acknowledgements

We express our profound gratitude to the Massachusetts Institute of Technology and the Beth Israel Deaconess Medical Center for their invaluable contribution to the MIMIC project.

Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.heliyon.2024.e35796.
==== Refs
References

1 Wang J. He X. Jiang D. Evaluation of red blood cell distribution width-platelet ratio as a predictor of adverse pregnancy outcomes and disease severity in systemic lupus erythematosus Clin. Rheumatol. 41 10 2022 2987 2993 35788840
2 Xu N. Peng C. Association between red cell distribution width-to-platelet ratio and short-term and long-term mortality risk in patients with acute ischemic stroke BMC Neurol. 23 1 2023 191 37189031
3 Ainiwaer A. Kadier K. Abulizi A. Association of red cell distribution width (RDW) and the RDW to platelet count ratio with cardiovascular disease among US adults: a cross-sectional study based on the National Health and Nutrition Examination Survey 1999-2020 BMJ Open 13 3 2023 e068148
4 Qiu L. Chen C. Li S.J. Prognostic values of red blood cell distribution width, platelet count, and red cell distribution width-to-platelet ratio for severe burn injury Sci. Rep. 7 1 2017 13720
5 Dallio M. Romeo M. Vaia P. Red cell distribution width/platelet ratio estimates the 3-year risk of decompensation in Metabolic Dysfunction-Associated Steatotic Liver Disease-induced cirrhosis World J. Gastroenterol. 30 7 2024 685 704 38515952
6 CelıK T. Balta S. DemıR M. Predictive value of admission red cell distribution width-platelet ratio for no-reflow phenomenon in acute ST segment elevation myocardial infarction undergoing primary percutaneous coronary intervention Cardiol. J. 23 1 2016 84 92 26503078
7 Yao H. Lian L. Zheng R. Red blood cell distribution width/platelet ratio on admission as a predictor for in-hospital mortality in patients with acute myocardial infarction: a retrospective analysis from MIMIC-IV Database BMC Anesthesiol. 23 1 2023 113 37016294
8 Lin S. Mao X. He W. Association between red blood cell distribution width-to-platelet ratio and post-discharge readmission rate in patients with heart failure: a retrospective cohort study Heliyon 10 4 2024 e26549
9 Johnson A.E.W. Bulgarelli L. Shen L. MIMIC-IV, a freely accessible electronic health record dataset Sci. Data 10 1 2023 1 36596836
10 Ren L. Li Z. Duan L. Association between white blood cell-to-haemoglobin ratio and 30 day mortality in heart failure in intensive care unit ESC Heart Fail 11 1 2024 400 409 38016675
11 Zhang Q. Zhou B. Li X. In-hospital changes in the red blood cell distribution width and mortality in critically ill patients with heart failure ESC Heart Fail 10 6 2023 3287 3298 37671738
12 Vincent J.L. Moreno R. Takala J. The SOFA (Sepsis-related organ failure assessment) score to describe organ dysfunction/failure. On behalf of the working group on sepsis-related problems of the European society of intensive care medicine Intensive Care Med. 22 7 1996 707 710 8844239
13 LeGall J.R. Lemeshow S. Saulnier F. A new Simplified Acute Physiology Score (SAPS II) based on a European/North American multicenter study JAMA 270 24 1993 2957 2963 8254858
14 Charlson M.E. Carrozzino D. Guidi J. Charlson comorbidity index: a critical review of clinimetric properties Psychother. Psychosom. 91 1 2022 8 35 34991091
15 Chen J. Wu K. Lin Y. Association of triglyceride glucose index with all-cause and cardiovascular mortality in the general population Cardiovasc. Diabetol. 22 1 2023 320 37993902
16 Weiss G. Goodnough L.T. Anemia of chronic disease N. Engl. J. Med. 352 10 2005 1011 1023 15758012
17 Gremmel T. Frelinger A.L. 3Rd Michelson A.D. Platelet physiology Semin. Thromb. Hemost. 42 3 2016 191 204 26926581
18 Mourouzis K. Oikonomou E. Siasos G. Pro-inflammatory cytokines in acute coronary syndromes Curr. Pharmaceut. Des. 26 36 2020 4624 4647
19 Danese E. Lippi G. Montagnana M. Red blood cell distribution width and cardiovascular diseases J. Thorac. Dis. 7 10 2015 E402 E411 26623117
20 Semple J.W. Italiano J.E. JR. Freedman J. Platelets and the immune continuum Nat. Rev. Immunol. 11 4 2011 264 274 21436837
21 Rumley A. Lowe G.D. Sweetnam P.M. Factor VIII, von Willebrand factor and the risk of major ischaemic heart disease in the Caerphilly Heart Study Br. J. Haematol. 105 1 1999 110 116 10233372
22 Freynhofer M.K. Bruno V. Wojta J. The role of platelets in athero-thrombotic events Curr. Pharmaceut. Des. 18 33 2012 5197 5214
23 Anand I. Mcmurray J.J. Whitmore J. Anemia and its relationship to clinical outcome in heart failure Circulation 110 2 2004 149 154 15210591
24 Groenveld H.F. Januzzi J.L. Damman K. Anemia and mortality in heart failure patients a systematic review and meta-analysis J. Am. Coll. Cardiol. 52 10 2008 818 827 18755344
25 Brissot E. Troadec M.B. LoréAL O. Iron and platelets: a subtle, under-recognized relationship Am. J. Hematol. 96 8 2021 1008 1016 33844865
26 Lip G.Y. Blann A.D. Jones A.F. Relation of endothelium, thrombogenesis, and hemorheology in systemic hypertension to ethnicity and left ventricular hypertrophy Am. J. Cardiol. 80 12 1997 1566 1571 9416937
