Prognostic value of red blood cell distribution width for mortality in patients with sepsis-induced cardiomyopathy: a propensity score-matched cohort study

doi:10.21203/rs.3.rs-3958206/v1

Background The potential association between red blood cell distribution width (RDW) at admission and prognosis in patients with sepsis-induced cardiomyopathy(SIC) remains uncertain. The purpose of this study was to explore the prognostic value of RDW on mortality in patients with SIC.

Methods Data for this retrospective study were obtained from the MIMIC IV2.2 database.

We used propensity score matching (PSM) and Cox proportional hazards regression analysis to evaluate the main risk factors associated with mortality in SIC patients. This analysis was utilized to develop a predictive nomogram. To assess the predictive accuracy and clinical usefulness of the model, we employed the concordance index (C-index) and decision curve analysis. To define the high- and low-RDW groups among patients with SIC, we determined the optimal cut-off value by maximizing the Youden index.

Results According to the screening criteria, we identified a cohort of 1051 patients diagnosed with SIC. When comparing the high-RDW group to the low-RDW group, it was found that the high-RDW group exhibited longer Los_ICU(4.5 days vs.3.8 days ,respectively, P =0.009) and higher mortality rates at 28 days (33.8% vs. 7.8%, respectively, P <0.001). A nomogram model was created using matched patients which included various factors such as Age, RDW, LDH, CKMB, creatinine and the administration of β-blocker. The C-index predicting 28-day survival probability was 0.846. Decision curves analysis demonstrated that the inclusion of RDW in the model provided a greater net benefit compared to excluding RDW.

Conclusion The prognosis of patients with SIC can be predicted by the RDW value. The nomogram model provides a useful tool in identifying and managing SIC patients.

Health sciences/Biomarkers/Prognostic markers

Health sciences/Diseases/Cardiovascular diseases/Cardiomyopathies

Health sciences/Diseases/Infectious diseases

red blood cell distribution width

sepsis-induced cardiomyopathy

PSM

Sepsis is a syndrome characterized by life-threatening organ dysfunction and is a frequently encountered serious complication of critical illnesses such as trauma, infection, and shock[1]. According to statistics, there are over 19 million sepsis patients worldwide annually, with approximately half of them being incurable. Sepsis causes approximately 6 million deaths each year[2]. Sepsis-induced cardiomyopathy(SIC) is a serious cardiac complication that occurs as a result of sepsis. It is considered one of the significant contributors to mortality in patients with sepsis, with a mortality rate as high as 80%[3]. In recent years, there has been an increasing use of biomarkers and scoring systems, such as PCT, TnI and IL-18 [4], as a means to assess the severity and prognosis of sepsis. However, the Oxford Acute Severity of Illness Score (OASIS) [5] and similar approaches have limited clinical application value and are not widely utilized in clinical practice. Therefore, it is crucial to develop a simple and efficient method for predicting the severity and prognosis of sepsis patients. Recent studies have shown a strong correlation between red blood cell volume distribution width (RDW) and the body's inflammatory response[6, 7]. RDW is a laboratory index used in the differential diagnosis of anemia. It is a simple laboratory test that evaluates the variability in the size and form of red blood cells. Recently, there have been several studies linking high RDW to increased mortality in various medical conditions such as coronary disease[8, 9], heart failure[10, 11], pulmonary hypertension[12], acute pulmonary embolism[13], cardiac arrest[14], stroke[15], and liver disease[16]. These studies suggest that high RDW levels may serve as a prognostic indicator for poor outcomes in these patients. However, there is limited research available on the relationship between RDW and the severity of SIC. This study primarily discussed the association between RDW and the severity of SIC, as well as it’s influence on prognosis. The following reports provide further insights:

Study population

The researchers conducted a retrospective observational study using data from the publicly accessible Medical Information Mart for Intensive Care IV (MIMIC-IV) database, which can be found at https://mimic.mit.edu. Specifically, the study examined the medical records of patients in the ICU at Beth Israel Deaconess Medical Center from 2008 to 2019 [17]. In this particular study, data were analyzed retrospectively using an observational design.Ding yu Lu, as one of the authors, fulfilled the prerequisites to gain access to the database and undertook the task of data extraction. The patient cohort for this research comprised individuals with a confirmed diagnosis of Sepsis-induced cardiomyopathy(SIC), following the guidelines outlined in the International Classification of Diseases, 9th and 10th Revision.

The inclusion criteria were as follows:(1) diagnosis of SIC;(2) age ≥ 18 years; (3) first admission for SIC.

The exclusion criteria were as follows:(1) history of end-stage renal dysfunction, CHD,CAD,primary cardiac myopathy,or cancer; (2)length of ICU stay < 24h; (3) individuals who lacked adequate data on their first day of admission.Finally a total of 1051 patients formed the study cohort. (Fig. 1).

Data collection

To conduct the data extraction, we utilized PostgresSQL (version 13.7.2) software and Navicate Premium (version 16) tool by employing Structured Query Language (SQL). The extraction process prioritized four distinct categories of potential variables: (1) demographic factors encompassing age, gender, weight and height; (2)vital signs,including heart rate,MAP,respiratory rate,sPO2,%,temperature;(3)comorbidities such as hypertension,diabetes,heart failure, acute kidney injury(AKI,AKI was classifified into three stages, and staging was based on the baseline levels of creatinine and urine output rates derived from the MIMIC-IV database ), respiratory failure;(4) laboratory tests, including white blood cells (WBC), hemoglobin(HGB), platelet count(PLT), lactate dehydrogenase(LDH: ); creatine kinase-MB(CKMB); Red blood cell distribution width(RDW); Fast Blood Glugose(FBG);Anion gap(AG:);creatinine(Cr); Lactic acidtriglyceride(Lac) and so on. (5) We collected information on the use of mechanical ventilation(MV), Continuous Renal Replacement Therapy(CRRT), vasoactive drugs and β-blocker.

Exclusively acquired laboratory variables were obtained solely during the initial 24-hour period following patient admission. In instances where there were multiple outcomes, the average measurement was employed. To mitigate any potential bias, variables containing missing values surpassing 20% were eliminated. To handle variables with less than 20% missing data, the research team employed the multiple imputation (missForest R) technique [18, 19].

Outcomes

The main outcome of this study was 28-day mortality in ICU. Secondary outcome focused on length of stay in the ICU.

Statistical analysis

Continuous variables were presented as the mean ± SD or median and interquartile range (IQR). The comparison of continuous variables was performed using student’s t-test and Wilcoxon rank-sum test, as appropriate, to compare the two groups. Categorical variables were expressed as numbers or percentages (%), and their analysis was implemented by means of chi-square test.

Firstly, the relationship between RDWat admission and 28-day mortality in patients with SIC was evaluated using receiver operating characteristic (ROC) curve analysis. The patients were divided into high- and low-RDW groups based on the optimal cut-off value determined by the maximum value of the Youden index. Secondly, Propensity score matching (PSM) was used to balance the confounders between the high- and low-RDW groups. After matching,Kaplan-Meier survival analysis was used to assess the incidence rate of primary outcome events in different groups based on the RDW, the log-rank test was employed to examine any observed disparities. Thirdly,univariable Cox regression analysis was conducted to investigate the association between the RDW and the endpoints. Variables that showed clinical significance and had a significance level of P < 0.05 were included in the multivariable Cox proportional hazards model. Additionally, a nomogram was designed in order to accurately predict prognosis in clinical work. Finally,to ensure the reliability of the model, the concordance index (C-index) was utilized as a measure, decision curve analysis was conducted to evaluate the effectiveness of the model in facilitating clinical decision-making.

The data analyses were conducted using R software (version 4.2.2) and SPSS statistical software (Version 25.0). For all analyses, a 2-side P < 0.05 was considered statistically significant.

The study enrolled a total of 1051 critically ill patients with SIC from the MIMIC-IV database, as depicted in Fig. 1.

Comparison of RDW values between groups

Based on the ROC analysis findings, in terms of predicting 28-day mortality, the best cut-off value of RDW was determined to be 15.7%; the sensitivity was 70.3%; the specificity was 72.3%; the Youden index yielded a value of 0.426. Meanwhile, the area under the ROC curve (AUC) was 0.758(Fig. 2).

According to the best cut-off value of 15.7%, patients were divided into high- and low-RDW groups. Before matching, significant differences in variables were observed between the high- and low-RDW groups (P < 0.05) (Table 1), except for the age, gender, BMI, sPO2, WBC count, platelet count, LDH, CKMB, Na, Ca, FBG, prevalence of diabetes, and invasive ventilation.When comparing the high-RDW group to the low-RDW group, it was found that the high-RDW group exhibited longer Los_ICU(3.8 days vs.4.5 days ,respectively, P = 0.009) and higher mortality rates at 28 days (7.8% vs. 33.8%, respectively, P < 0.001).

Table 1

Patient demographics and baseline characteristics
Variables	Unmatched			Matched
Variables	Low RDW N = 666	High RDW N = 385	P SMD	Low RDW N = 333	High RDW N = 333	P SMD
Basic information
Age, year	72 (62, 81)	71 (63, 80)	0.606 0.040	71 (62, 80)	71 (63, 81)	0.588 0.041
BMI ,%	30 (26, 37)	30 (25, 37)	0.471 0.048	30 (25, 36)	30 (25, 36)	0.893 0.022
Male,n(%)	231 (34.7%)	139 (36.1%)	0.643 0.030	121 (36.3%)	121 (36.3%)	> 0.999 0.000
Vital signs
Heart rate ,bpm	84 (77, 87)	84 (80, 91)	< 0.001 0.179	84 (79, 88)	84 (80, 92)	0.268 0.045
MAP ,mmHg	74 (72, 78)	74 (71, 78)	0.351 0.097	74 (70, 78)	74 (72, 78)	0.315 0.009
Respiratory rate ,bpm	19. (17.5, 21.5)	19.9(17.8, 22.8)	0.002 0.151	19.9(17.7, 21.6)	19.9(17.7, 22.6)	0.145 0.070
sPO2,%	96.8(95.9,98.2)	96.9 (95.9,98.3)	0.611 0.098	97.1(96.0, 98.3)	96.9(96.1, 98.3)	0.921 0 .004
T ,℃	36.8(36.6,36.9)	36.8(36.6,36.9)	0.043 0.110	36.8(36.7, 36.9)	36.8(36.6, 36.9)	0.092 0.062
Laboratory tests
WBC ,K/uL	13 (9, 16)	14 (9, 17)	0.328 0.101	14 (9, 17)	13 (9, 16)	0.269 0.013
Plt ,K/uL	165 (118, 222)	166 (99, 223)	0.737 0.032	165 (113, 224)	161 (111, 221)	0.541 0.006
HGB ,g/dL	10.3 (8.7, 12.3)	9.8 (8.3, 11.6)	< 0.001 0.204	9.7(8.3,11.5)	9.7(8.3,11.5)	0.927 0.018
LDH ,IU/L	461 (285, 461)	461 (271, 461)	0.403 0.055	461(277,461)	461 (264, 461)	0.484 0.039
CKMB ,ng/ml	26 (5, 35)	18 (4, 35)	0.119 0.041	17 (4, 35)	21 (4, 35)	0.921 0.069
Na ,mEq/L	137 (133, 141)	137 (133, 141)	0.955 0.031	137 (132, 141)	137 (133, 141)	0.435 0.006
K ,mEq/L	4.3 (3.9, 4.7)	4.4 (3.9, 4.9)	0.030 0.161	4.4(4.0, 4.8)	4.4 (3.9, 4.9)	0.997 0.039
Ca ,mg/dL	8.3(7.8, 8.7)	8.3 (7.8, 8.8)	0.954 0.034	8.3(7.8, 8.8)	8.3 (7.8, 8.8)	0.826 0.031
FBG ,mg/dL	140 (113, 169)	149 (114, 184)	0.058 0.097	142 (116, 175)	145 (114, 178)	0.860 0.002
AG ,mEq/L	15 (12, 18)	16 (14, 19)	< 0.001 0.194	15 (13, 18)	16 (13, 19)	0.054 0.098
Cr ,mg/dL	1.2(0.9, 1.8)	1.5 (1.1, 2.4)	< 0.001 0.209	1.4(1.0, 2.2)	1.5 (1.1, 2.4)	0.229 0.027
Lac ,mg/dL	1.9(1.4, 2.6)	2.1(1.4, 3.3)	0.039 0.161	1.9 (1.4, 2.6)	2.0 (1.3, 3.0)	0.193 0.099
Fluid balance ,ml	1168(1153,1379)	1168 (298, 2712)	0.002 0.094	1168(926,1495)	1168(75, 2190)	0.147 0.052
SOFA	6.0 (4.0, 9.0)	7.0 (5.0, 10.0)	< 0.001 0.329	7.0 (5.0, 10.0)	7.0 (5.0, 10.0)	0.313 0.077
Commorbidities
HP, n(%)	209 (31.4%)	83 (21.6%)	< 0.001 0.239	79 (23.7%)	74 (22.2%)	0.645 0.037
Diabetes, n(%)	242 (36.3%)	147 (38.2%)	0.551 0.038	126 (37.8%)	131 (39.3%)	0.691 0.031
HF, n(%)	402 (60.4%)	265 (68.8%)	0.006 0.183	229 (68.8%)	237 (71.2%)	0.499 0.052
AKI, n(%)	585 (87.8%)	356 (92.5%)	0.018 0.175	299 (89.8%)	306 (91.9%)	0.347 0.080
AKI stage, n(%)			< 0.001			0.689
1	122 (18.3%)	58 (15.1%)	0.091	61 (18.3%)	56 (16.8%)	0.042
2	273 (41.0%)	119 (30.9%)	0.218	109 (32.7%)	110 (33.0%)	0.006
3	190 (28.5%)	179 (46.5%)	0.360	129 (38.7%)	140 (42.0%)	0.066
MV, n(%)	355 (53.3%)	222 (57.7%)	0.171 0.088	173 (52.0%)	184 (55.3%)	0.393 0.067
CRRT, n(%)	57 (8.6%)	69 (17.9%)	< 0.001 0.244	45 (13.5%)	48 (14.4%)	0.737 0.023
Vasopressor,n(%)	397 (59.6%)	268 (69.6%)	0.001 0.217	217 (65.2%)	221 (66.4%)	0.744 0.026
β-blocker, n(%)	529 (79.4%)	234 (60.8%)	< 0.001 0.382	229 (68.8%)	219 (65.8%)	0.409 0.062
Los-ICU,day	3.8 (2.1, 6.7)	4.5 (2.4, 7.8)	0.009 0.130	4.1 (2.4, 7.4)	4.3 (2.3, 7.5)	0.761 0.011
28d-mortality	52 (7.8%)	130 (33.8%)	< 0.001 0.549	51 (15.3%)	84 (25.2%)	0.001 0.210
Abbreviation: BMI: Body mass index; MAP:Mean Arterial Pressure; WBC: White blood cell; Plt: platelets; HGB:hemoglobin; LDH: lactate dehydrogenase; CKMB: creatine kinase-MB; RDW: Red blood cell distribution width; FBG:Fast Blood Glugose; AG:Anion gap; Cr: creatinine; Lac: Lactic acid; HP:Hypertension; HF:Heart Failure; AKI: Acute kidney injury; SOFA: Sequential organ failure assessment; MV:Mechanical Ventilation; CRRT:Continuous Renal Replacement Therapy; Los-ICU: Length of stay-ICU.

The researchers implemented propensity score matching (PSM) in order to balance any potential confounding factors that may have influenced the results. The caliper width was carefully set at 0.05 to ensure accurate matching. This meticulous matching process yielded a final sample of 666 patients with SIC with a 1:1 ratio between the high- and low-RDW groups. By achieving a matching percentage of 63.4%, the study obtained a well-balanced matching cohort. Moreover, the analysis after matching revealed that all variables exhibited standardized mean difference values below the threshold of 0.1 except for the 28d-mortality(Table 1 and Fig. 3).

After matching, the incidence of primary outcomes among groups was analyzed using Kaplan-Meier survival analysis curves, as presented in Fig. 4. It was observed that patients in the high_RDW group had a higher risk of 28d-mortality(log-rank P=0.005).

Factors associated with 28-day mortality

The prediction of 28-day mortality was analyzed using univariate and multivariate Cox proportional hazards regression. Risk factors included Age, LDH, CKMB, RDW, AG(all HR>1,P<0.05). In contrast, administration of β-blocker was independently associated with lower risk of 28-day mortality (HR = 0.25, 95%CI: 0.2–0.3, P<0.001)(table 2).

Table 2

Univariate and Multivariate Cox proportional hazards regression analysis for risk factors of 28-day mortality.
	Univariate analysis			Multivariate analysis
Characteristic	HR	95% CI	p-value	HR	95% CI	p-value
Basic informatin
Age (year)	1.0	1.0–1.0	0.025	1.1	1.0- 1.2	< 0.001
Male*	1.1	0.8–1.4	0.70	0.9	0.7–1.3	0.66
BMI	1.0	1.0–1.0	0.33	1.0	1.0–1.0	0.68
Vital signs
Heart rate (bpm)	1.0	1.0–1.0	0.17	1.0	1.0–1.0	0.23
MAP (mmHg)	1.0	1.0–1.0	0.078	1.0	1.0–1.0	0.61
Respiratory rate(bpm)	1.0	1.0- 1.1	0.11	1.0	1.0–1.0	0.87
sPO2	1.0	1.0–1.0	0.97	1.0	1.0–1.0	0.076
T (℃)	0.9	0.8- 1.0	0.084	1.0	0.8–1.2	0.80
Laboratory tests
WBC (K/uL)	1.0	1.0–1.0	< 0.001	1.0	1.0–1.0	0.058
Plt (K/uL)	1.0	1.0–1.0	0.67	1.0	1.0–1.0	0.51
HGB (g/dL)	1.0	1.0- 1.1	0.38	1.0	1.0- 1.1	0.14
High RDW group†	3.7	2.7–5.2	< 0.001	3.3	2.3–4.7	< 0.001
LDH (IU/L)	1.0	1.0–1.0	< 0.001	1.05	1.0- 1.1	0.005
CKMB (ng/ml)	1.0	1.0–1.0	< 0.001	1.1	1.0- 1.2	< 0.001
Na (mEq/L)	1.0	1.0–1.0	0.58	1.0	1.0–1.0	0.51
K (mEq/L)	1.1	0.9–1.3	0.36	1.0	0.8–1.2	0.72
Ca (mg/dL)	1.0	0.8–1.1	0.66	0.9	0.8–1.1	0.50
FBG (mg/dL)	1.0	1.0–1.0	0.25	1.0	1.0–1.0	0.71
AG (mEq/L)	1.0	1.0- 1.1	0.005	1.0	1.0–1.0	0.23
Cr (mg/dL)	1.1	1.0- 1.1	0.022	1.1	1.0- 1.2	0.047
Lac (mg/dL)	1.1	1.1–1.1	< 0.001	1.0	1.0- 1.1	0.13
Fluid balance (ml)	1.0	1.0–1.0	0.014	1.0	1.0–1.0	0.67
SOFA	1.1	1.1–1.1	< 0.001	1.0	1.0- 1.1	0.26
Commorbidities
HP	0.8	0.6–1.2	0.37	1.2	0.8–1.8	0.46
Diabetes	0.9	0.7–1.2	0.56	0.8	0.6–1.1	0.21
HF	0.9	0.7–1.2	0.56	0.8	0.6–1.1	0.21
AKI	1.4	0.5–3.7	0.55	1.2	0.4–3.5	0.74
AKI (stage)‡
2	1.1	0.4- 3.0	0.88	1.3	0.9- 2.0	0.21
3	1.6	0.6–4.4	0.36	1.2	0.8- 2.0	0.53
MV	0.8	0.6–1.2	0.33	0.8	0.5–1.1	0.15
CRRT	1.3	1.0- 1.8	0.089	0.9	0.6–1.3	0.53
Vasopressor	2.2	1.4–3.4	0.001	1.5	0.9–2.6	0.12
β-blocker	0.2	0.1–0.3	< 0.001	0.25	0.2–0.3	< 0.001
HR = Hazard Ratio CI = Confidence Interval
Abbreviation: BMI: Body mass index; MAP:Mean Arterial Pressure; WBC: White blood cell;Plt: Platelets; HGB:Hemoglobin; RDW: Red blood cell distribution width; FBG:Fast Blood Glugose; AG:Anion gap;Creatinine; Lac: Lactic acid; HP:Hypertension; HF:Heart Failure; AKI: Acute kidney injury; SOFA: Sequential organ failure assessment; MV:Mechanical Ventilation; CRRT:Continuous Renal Replacement Therapy.
∗ Compared with the female.
† Compared with the low RDW group (RDW value ≤ 15.7%).
‡ Compared with the Stage 1.

The nomogram model constructed using previously published predictive variables for the prediction of 28-day mortality rates in patients with SIC is shown in Fig. 5.

Based on the bootstrapping technique (random sampling of 1000 bootstraps), the model's performance was evaluated, and the C-index for the survival rate at 28 days attained a value of 0.846(0.812-0.880). Removal of the RDW from the model reduced the C-index to 0.817(0.778-0.857). The calibration curves revealed that the values predicted by the nomogram model were consistent with the observed values(Fig. 6).

Decision curve analysis was conducted to evaluate the effectiveness of the model in facilitating clinical decision-making. The results of the study indicated that incorporating RDW into the model led to greater net benefits in the majority of predicted survival probabilities compared to the model that did not include RDW(Fig. 7).

To the best of our knowledge, this study represents the first investigation into the association between the RDW and ill patients with SIC.The pathogenesis of SIC is complex and diverse. According to current relevant research, abnormal calcium signaling[20,21], mitochondrial dysfunction[22,23], inflammatory damage to cardiomyocytes[6] and abnormal energy metabolism[24] can all lead to the activation of intracellular apoptosis signal transduction pathways. This ultimately leads to cell apoptosis and damage tomyocardial cells, resulting in myocardial dysfunction. The presence of myocardial dysfunction is strongly associated with a poor prognosis in sepsis patients and is also a significant contributor to sepsis-related mortality[3].

This study constructed a highly efficient model for predicting the 28-day mortality rate in patients with SIC. The model's performance was enhanced by the inclusion of the RDW. Additionally, the PSM method was used to adjust for other potential factors that may impact the RDW of SIC patients. By using a matched cohort, the model effectively tackled the endogeneity issues caused by the descriptive variables. The study also ensured an ample selection of SIC patients from the MIMIC-IV database.

The assessment of disease incidence and progression utilizing RDW can impact clinical decision-making concerning treatment. As the patients' conditions in the ICU incessantly vary[25], reducing perplexity stemming from additional variables can facilitate harnessing the advantages provided by the RDW. In this study, the focus was on selecting patients with SIC based on stricter inclusion criteria. By narrowing down the patient pool to those who developed myocardial damage following the diagnosis of sepsis, the researchers aimed to gather more specific and relevant data. This approach allows for a more targeted analysis and interpretation of the findings, potentially leading to more accurate conclusions and the development of better strategies for managing SIC.

The findings from this study suggested that a higher RDW(>15.7%) was significantly correlated with increased ICU mortality rates among patients with SIC. Notably, this association remained statistically significant even after adjusting for potential confounding risk factors. Consequently, these results indicated that the high RDW holds promise as a valuable decision-making tool for clinicians and may serve as an independent prognostic factor.

Inflammatory states have a strong association with ineffective erythropoiesis. Previous studies have shown that inflammatory cytokines, including tumor necrosis factor (TNF)-α, interleukin (IL)-1β, and IL-6, reduce the sensitivity of bone marrow erythroid progenitors to erythropoiesis[26]. Additionally, these cytokines hinder the maturation of red blood cells (RBCs), leading to an increase in anisocytosis. Falk and Fahey conducted a study showing that sepsis has a negative impact on the functions of the hematopoietic system. They also found that sepsis leads to an increase in erythrocyte heterogeneity[27]. This suggested that inflammation plays a significant role in disrupting normal erythropoiesis and promoting abnormal RBC development[28].

In our study, high RDW was independently associated with 28-day mortality, with a prevalence of 25.2% in patients with SIC(after PSM). Among the 6 selected variables, RDW accounted for the largest weight in the simplified model (HR=3.3; 95% CI, 2.30–4.70). Our study also found that administration of β-blocker was associated with lower risk of 28-day mortality (HR = 0.25, 95%CI: 0.2–0.3, P<0.001). This demonstrated the importance of early use of β-blockers in patients with sepsis to improve cardiac function.

The survival curve demonstrated that patients with high-level RDW have a lower 28-day survival rate compared to those with low-level RDW. This suggested that the detection of RDW is highly valuable in clinically assessing severity among patients with SIC. It is speculated that this phenomenon is attributed to the release of various neuroendocrine hormones following infection, which stimulates the synthesis of erythropoietin[29]. Consequently, there is a significant proliferation of red blood cells and an increase in RDW which, in turn, leads to cell membrane instability and a lack of nutrients in the blood, ultimately resulting in multiple organ dysfunction[30]. It's worth noting, Gong et al found that it was not the baseline RDW had a significant relationship with mortality, but the change in RDW over time might be a more reliable indicator of prognosis in sepsis patients[31]. Further research is needed to investigate the underlying mechanisms and potential clinical implications of these findings.

However, it is important to acknowledge certain limitations of this study. One limitation is the presence of selection bias due to its retrospective cohort study design. This means that the researchers relied on pre-existing data and medical records, which can introduce errors and biases. Another limitation is the potential effect of unadjusted potential confounders on the results. Despite efforts to match participants and control for variables, there may still be factors that were not taken into account. Furthermore, it is important to note that this study did not evaluate the long-term outcomes of the patients. Therefore, it is crucial to keep caution when extrapolating these findings as a predictive factor for long-term prognosis. Future research should consider assessing long-term outcomes to obtain a more comprehensive understanding of the subject matter.

The prognosis of patients with SIC can be predicted by the RDW value. Upon admission, patients with high RDW values increased risk of 28-day mortality. The nomogram model introduced in this study provides a useful tool in identifying and managing SIC patients. Further research and validation are needed to solidify the importance of RDW in the diagnosis and treatment of SIC.

BMI

Body mass index

MAP

Mean Arterial Pressure

WBC

White blood cell

Plt

platelets

HGB

hemoglobin

LDH

lactate dehydrogenase

CKMB

creatine kinase-MB

RDW

Red blood cell distribution width

FBG

Fast Blood Glugose

AG

Anion gap

Cr

creatinine

Lac

Lactic acid

HP

Hypertension

HF

Heart Failure

AKI

Acute kidney injury

SOFA

Sequential organ failure assessment

MV

Mechanical Ventilation

CRRT

Continuous Renal Replacement Therapy

Los-ICU

Length of stay-ICU.

Author Contribution

Conception and design: .JL., D.L. Collection and assembly of data: DL.,LZ. Data analysis and interpretation: JL,. DL.Critical revisions and supervision: LZ,.MW. Final approval of manuscript: JL,.DL,.LZ,.MW.

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No competing interests reported.

Prognostic value of red blood cell distribution width for mortality in patients with sepsis-induced cardiomyopathy: a propensity score-matched cohort study

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Abstract

Figures

Introduction

Methods

Study population

Data collection

Outcomes

Statistical analysis

Results

Comparison of RDW values between groups

Discussion

Conclusions

Abbreviations

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Author Contribution

References

Additional Declarations

Status:

Journal Publication

Version 1