Skip to main content

Monocyte Distribution Width as a predictor of community acquired sepsis in patients prospectively enrolled at the Emergency Department

Abstract

Background

Monocyte Distribution Width (MDW), a simple cellular marker of innate monocyte activation, can be used for the early recognition of sepsis. We performed an observational prospective monocentric study to assess the predictive role of MDW in detecting sepsis in a sample of consecutive patients presenting at the Emergency Department.

Methods

Prospective observational study using demographic and clinical characteristics, past medical history and other laboratory measurements to predict confirmed sepsis using multivariate logistic regression.

Results

A total of 2724 patients were included in the study, of which 272 (10%) had sepsis or septic shock. After adjusting for known and potential risk factors, logistic regression found the following independent predictors of sepsis: SIRS equal to 1 (OR: 2.32, 1.16–4.89) and 2 or more (OR: 27.8, 14.8–56.4), MDW > 22 (OR: 3.73, 2.46–5.70), smoking (OR: 3.0, 1.22–7.31), end stage renal function (OR: 2.3, 1.25–4.22), neurodegenerative disease (OR: 2.2, 1.31–3.68), Neutrophils ≥ 8.9 × 103/µL (OR: 2.73, 1.82–4.11), Lymphocytes < 1.3 × 103/µL (OR: 1.72, 1.17–2.53) and CRP ≥ 19.1 mg/L (OR: 2.57, 1.63–4.08). A risk score derived from predictive models achieved high accuracy by using an optimal threshold (AUC: 95%; 93–97%).

Conclusions

The study suggests that incorporating MDW in the clinical decision process may improve the early identification of sepsis, with minimal additional effort on the standard procedures adopted during emergency care.

Peer Review reports

Background

Sepsis is a clinical syndrome caused by dysregulated host response to infections occurring in approximately one third of hospitalizations [1,2,3], frequently leading to multiple organ dysfunction syndrome and death [4, 5]. As a life threatening condition [6], it needs to be properly tackled through targeted actions addressed by the Surviving Sepsis Campaign (SSC), including fluid resuscitation, broad-spectrum antibiotics within one hour from diagnosis, blood cultures and targeted antibiotic therapy [3]. These measures can effectively reduce mortality for all sepsis-related conditions [7,8,9].

Since the majority of cases may have already developed sepsis before hospitalization [10], the early assessment of subjects at presentation to the Emergency Department (ED) has been a matter of increasing interest for the identification of effective medical interventions [11,12,13].

The clinical confirmation requires a multidimensional assessment of different aspects e.g. individual risk factors, the clinical suspicion of infection and the possible organ dysfunction indicated by radiological examination and laboratory tests [1, 11]. However, the early recognition of sepsis is still hampered by ambiguous clinical signs and the limited accuracy of laboratory biomarkers e.g. the C-Reactive Protein, Procalcitonin and Lactic acid [11, 12, 14, 15].

As a result, there is currently no established gold standard for the early identification of sepsis that can drive the timely administration of therapeutic agents effectively. In routine emergency care, it is still difficult to discriminate between infectious vs non-infectious cause of illness, bacterial vs viral etiology and sepsis vs uncomplicated infections [16].

Moreover, the existing strategies are complex to apply within the difficult operating conditions of the ED, where patients with complex clinical conditions show up simultaneously with heterogeneous symptoms and rapid diagnostic testing may not be available, due to the high workload and the tight time constraints.

The present study aimed to improve the early identification of sepsis, by building a prediction model including Monocyte Distribution Width (MDW) in addition to other known predictors, using a large sample of consecutive patients admitted at the ED of a regional hospital.

Methods

Study population

The study population included consecutive patients aged ≥ 18 years with an available Complete Blood Count (CBC) analysis, admitted for any reason to the ED of the Pescara General Hospital (PGH), a large reference regional hospital located in the Abruzzo Region, Central Italy, between July 15th, 2020 and September 15th, 2020. Patients underwent CBC analysis and other laboratory parameters within < 12 h after ED presentation. They were excluded if either discharged from ED within 12 h, pregnant, leaving the ED before discharge, with incomplete clinical or laboratory data, or with missing MDW after CBC analysis (corresponding to patients with specific blood conditions e.g. immunocompromised, oncoematological, etc.).

The study was carried out in accordance with the amended Declaration of Helsinki. The study was approved by the local Ethics Committee (“Ethics Committee of Chieti—Pescara for biomedical research”, approval no. 539, May 2020). All patients provided their written informed consent to use anonymised clinical and laboratory data for institutional research purposes upon admission to the ED.

Clinical data

The following personal characteristics were accurately recorded at presentation to the ED: demographic and clinical characteristics including age and gender, vital signs (heart rate, respiratory rate, body temperature, blood pressure), and history of comorbid conditions. We also measured comorbidities involved with organ dysfunction and sepsis [17, 18]: hypertension, dyslipidemia, diabetes, obesity, smoke, drug addition, anemia, acute myocardial infarction, transient ischemic attack, other cardiovascular diseases, chronic obstructive pulmonary disease, endocrinologic diseases, end-stage renal disease, hepatitis, connective tissue diseases, other immunological diseases, neurodegenerative diseases, other rheumatological diseases, metabolic diseases, bacterial infections, and underlying conditions potentially associated with a dysregulation of the immune system e.g. HIV infection, malignancy, hematologic diseases.

Laboratory assays

All patients admitted to the ED underwent blood testing for the following parameters: CBC, C-Reactive Protein (CRP) and Procalcitonin (PCT) when available. CBC and MDW determinations were analysed using the hematologic analyzer UniCel DxH 900 (Beckman Coulter, Inc, Brea, California). All determinations of MDW were measured from a K3EDTA whole-blood venous sample within 2 h of collection and were performed in the same blood tubes used for CBC determinations. Turnaround Time for such measurements was within the time of CBC. Quality control of CBC including MDW were performed daily with COULTER® 6C Plus Cell Control and COULTER LATRON CP-X Control, a suspension of stable polystyrene particles of uniform size. Latron CP-X was used as part of the daily quality control procedure, to monitor the stability of the electrical processing and the fluidic flow rate systems used to measure the volume, conductivity and light scattering characteristics of cells as they pass through the flow cell. COULTER® 6C Plus Cell Control was used by monitoring performances of diagnostic processes. Controls with known characteristics were analysed daily in the same way as samples, while results of the analysed controls were compared with standard characteristics using statistical methods calculated by the same instrument [19]. Producers of hematological analysers did not provide any unit for MDW [20, 21]. CRP was measured using the chemoluminescence method (Abbott Diagnostics, Lake Forest, USA). All results of blood cultures processed by the local laboratory of Microbiology were also included.

Outcome definition

Sepsis-2 was applied to screen sepsis at admission to the ED [22]. Patients with sepsis were classified as either non-SIRS or controls (SIRS eq 0,1 and no infection), infection (SIRS eq 0,1 and suspect/confirmed infection), SIRS (SIRS ≥ 2 and no infection), or sepsis (SIRS ≥ 2 and infection). Infection was identified on the basis of clinical features, imaging studies and microbiological findings available for each patient. In the absence of any infection work-up performed within 12 h of the initial CBC, patients were considered not infected. In addition, as infection or sepsis may be evident after the initial work-up in the ED, data collection and clinical and laboratory results were retrospectively reviewed by trained investigators to evaluate clinical features, radiological findings and microbiological results up to 24 h after the initial laboratory analyses. These findings were used to revise diagnoses considered uncertain or possibly inaccurate due to incomplete information, and finally classify the patient. Patients classified as sepsis were further categorized as septic shock when presenting also a diagnosis of refractory hypotension.

Statistical analysis

Descriptive analysis included the calculation of the mean (standard deviation) or median (interquartile range) for continuous variables, and absolute and relative frequencies for categorical variables. Significance levels and 95% confidence intervals (95%CI) were based on alpha = 0.05. The differences between patients with or without confirmed sepsis were tested using chi-square for categorical variables [23].

The reliability of MDW in predicting sepsis was investigated through ROC analysis, including the calculation of Sensitivity, Specificity, Positive Predictive Value (PPV), Negative Predictive Value (NPV) and the Area Under the Curve (AUC).

Optimal cutoffs were used to define binary variables for MDW, WBCs, CRP, Neutrophils, Lymphocytes and Platelets to support clinical interpretation of univariate and multivariate logistic regression in terms of odds ratios (ORs) of sepsis [24]. An alpha level of 0.05 was used to present ORs together with their 95% confidence intervals and p values.

The association between the above binary predictors and sepsis was measured using adjusted ORs with 95% confidence intervals, taking into account all relevant demographic and clinical characteristics available through multivariate logistic regression [24, 25]. A total of 31 factors were included in a fully automated four-step backward elimination process, with age and gender forced in at all steps, and the other 29 variables sequentially screened for exclusion in three consecutive rounds, based upon p values greater or equal to 0.20, 0.10 and 0.05 respectively. The significance of the final model was measured with the likelihood ratio (LR) test, comparing the full model vs the model with the intercept alone, while the overall fit was checked using the Hosmer–Lemeshow (H–L) test [25]. Model accuracy was estimated using tenfold cross-validation, repeated 10 times.

Predictive risk scores for each patient in the database were computed as a weighted sum of all significant factors identified by multivariate regression, using coefficients corresponding to the increased risk calculated by rounding odds ratios to their next integer value [26]. We compared this empiric approach against the correct algebraic form derived from logistic regression [27].

A ROC analysis was performed using a separate 2 × 2 “confusion matrix” for every possible threshold applied to the total score [28]. The best threshold was defined as the one with the highest value of the Youden Index [29]. Point estimates and confidence intervals were computed using the bootstrap for sensitivity, specificity, Positive Predictive Value (PPV), Negative Predictive Value (NPV) and the De Long estimate of the 95% confidence interval of the Area Under the Curve (AUC) [25].

All analyses were performed using the R statistical language [30].

Results

A total of 2896 consecutive patients presenting at the ED of the Pescara hospital were enrolled in the study during the reference timeframe. Among them, a total of N = 172 were excluded based upon the pre-defined exclusion criteria: N = 24 pregnant women, N = 94 subjects with inadequate sample collection or screening errors and N = 54 for whom MDW was not available.

The results were based on a total sample of 2724 patients entering the final dataset. Among them, N = 272 (10%) had sepsis or septic shock. Higher median MDW values (Fig. 1) were found in sepsis (25.2) and septic shock (30.7), as opposed to infected (22.0), SIRS (21.2) and not infected (19.0).

Fig. 1
figure 1

Variability of MDW by infection status

A value of 22.0 was found to be the optimal cut point for MDW, while other laboratory biomarkers were dichotomized according to the ROC curve analysis: 12.2 × 103/µL for WBCs; 8.9 × 103/µL for Neutrophils; 1.3 × 103/µL for Lymphocytes; 157 × 103/µL for Platelet and 19.1  mg/L for CRP.

The distribution of the general characteristics and univariate odds ratios for sepsis are shown in Table 1. Among clinical variables, the following were significantly associated with sepsis: ERSD, neurodegenerative disorder and metabolic diseases. Among laboratory parameters, the strongest association was found for patients with CRP ≥ 19.1  mg/L and MDW > 22. Increased risk was also found for subjects with WBC ≥ 12.2 × 103/µL, Neutrophils ≥ 8.9 × 103/µL, Lymphocytes < 1.3 × 103/µL and Platelet < 157 × 103/µL.

Table 1 Clinical, laboratory variables and underlying health condition of study patients*

The results of multivariate analysis are shown in Table 2. The highest risk was found for patients with SIRS ≥ 2, which was over 27-fold compared to those non-SIRS, as opposed to those with SIRS = 1, who had just over twice the risk. Increased risk was also found for subjects with MDW > 22, smokers, patients with end stage renal function, neurodegenerative disorder, Neutrophils ≥ 8.9 × 103/µL, Lymphocytes < 1.3 × 103/µL and CRP ≥ 19.1 mg/L. The final model was significant according to the LR test (p < 0.001), while the H–L test did not show any evidence of poor fit (p = 0.46). The accuracy of the model estimated via cross-validation was equal to 0.927 (95%CI: 0.901–0.955).

Table 2 Logistic Multivariate Analysis for the prediction of sepsis*

The ROC curve for MDW to predict sepsis (see Fig. 2) showed a sensitivity level at the optimal cutoff of MDW > 22 equal to 74% (95%CI: 68–79%), with specificity = 83% (81–84%), PPV = 32% (30–38%) and NPV = 97% (96–97%).

Fig. 2
figure 2

AUC and optimal cut point of MDW for sepsis prediction

The algorithm for the calculation of individual risk scores of sepsis is presented in Table 3. Using an optimal cut point equal to 11.5 achieved sensitivity of 91% (95%CI: 87–94%) and specificity of 88% (87–88%). PPV and NPV were respectively of 47% (42–51%) and 99% (98–99%). The overall performance was quite high (AUC = 95%; 93–97%), suggesting that the predictive model may be suitable for regular use at ED. The results obtained using the correct algebraic formula were practically identical.

Table 3 Algorithm for the calculation of the risk score

Discussion

In this study, we examined the role of MDW as a laboratory marker of sepsis, in combination with other clinical variables measured in patients admitted to the emergency department (ED).

We found that a threshold for MDW ≥ 22 can be used as a laboratory biomarker showing an almost four-fold increased risk of sepsis, with a NPV equal to 97% that supports its use to rule out sepsis at presentation. Consistently with the results of recent literature [31, 32], our data suggest that other measures could be used for the early diagnosis of sepsis, independently from age and gender: smoking status, neurodegenerative disease, lymphocytes < 1.3 × 103/µL, neutrophils ≥ 8.9 × 103/µL, MDW ≥ 22, SIRS = 1, SIRS ≥ 2, and ESRD.

Different clinical explanations support the role of MDW as a predictive factor for sepsis. From a biological standpoint, monocytes are immune cells of the first phase of inflammation. Before involving adaptive immunity, they become activated by changing their morphology and increasing their volume, expressing new surface proteins e.g. CD16 [33, 34]. MDW is related to monocytes activation as a measure of diversity in the size of the cellular volume. For this reason, MDW reflects the early activation of the innate immune system during inflammation [14] and consequently can be slightly elevated in inflammation compared to normal conditions.

Few studies have investigated the potential association between MDW and sepsis using multivariate adjustment [35,36,37]. Recent papers focused on improving sepsis prediction through the combined use of MDW and other laboratory parameters. Crouser et al. (2019) demonstrated that the association of MDW with WBC improved the AUC for sepsis detection [22]. This result was recently confirmed [38], but did not show significant improvement using both PCT and MDW. Other reports addressed the use of CRP and MDW for the prediction of sepsis, without showing increased accuracy [38, 39].

Unfortunately, no consistent clinical manifestations or diagnostic indicators have been confirmed as a gold standard to recognize sepsis. Different sepsis definitions have been adopted over time to simplify its diagnosis, particularly during the phase of triage, where timely medical decision is required. Generally, these criteria combine physiological parameters of inflammation and organ dysfunction with the suspicion of infection. Inevitably, these methods often lead to diagnostic uncertainty, particularly in patients with vital signs or laboratory abnormalities that are not due to infections [40]. The third consensus of sepsis (Sepsis-3) recently introduced qSOFA as the standard criteria to recognize sepsis outside the ICU [2], whose performance in predicting sepsis at presentation to the ED showed to be poor. A recent paper reported that between 60 and 75% of the ED patients matching the criteria for sepsis or septic shock did not receive a sepsis diagnosis at discharge, hampering the prompt start of appropriate treatments after hospital entry [11]. Often, personnel at the ED assign plausible diagnoses based on signs and symptoms, to be later confirmed after hospitalization. However, the early recognition of sepsis is a key aspect of clinical practice that can determine the prognosis of the patient. Up to a quarter of pre-shock patients initially evaluated in the ED may develop organ dysfunction [41], without being identified as cases of sepsis by any risk score [42]. For this reason, the identification of laboratory tests that can be used for the early assessment of sepsis has become a priority for emergency care.

The most updated International Consensus Definitions for Sepsis and Septic Shock recommended to use qSOFA to identify patients with sepsis in the ED [2]. However, there is no gold standard agreed in this context, where SIRS and organ failure are often caused by non-infectious diseases. On the other hand, the scoring systems adopted in the ED to predict sepsis are based on optimal thresholds (usually low) to minimise the percentage of false negatives [43, 44]. The adoption of MDW has been recommended to better recognise early sepsis and prevent clinical progression and death in the ED [31]. The qSOFA and NEWS (National Early Warning Score) have been frequently investigated as tools to screen patients with infection at risk of organ dysfunction and death [45]. Compared to SIRS, the use of qSOFA for sepsis has been found to be highly predictive for mortality, but less reliable as a screening tool for early recognition [46]. Moreover, the use of qSOFA showed high specificity for organ dysfunction, but low sensitivity to predict sepsis in the ED [44, 46, 47]. In fact, patients with a hidden infection may be present in the ED before showing the specific signs of sepsis detected by qSOFA (e.g., tachycardia, hypotension, and altered mental status) [48]. These findings justify our preference for SIRS criteria, as they are more sensitive, although less specific for sepsis detection.

In this study, we built a risk scoring algorithm showing moderately high accuracy, based on significant clinical characteristics and laboratory parameters. The advantage of this approach is that it can be directly applied to the next patient entering the ED, even through the aid of a simple pocket calculator. A value below threshold may be used to rule out sepsis. On the other hand, a value above threshold may not be as effective to rule in sepsis, due to its low PPV.

As expected, the scoring system assigns a very high weight to a SIRS ≥ 2. Hence, its application does not provide a substantial advantage over SIRS for this category of patients. On the other hand, the risk score may be most useful for those presenting with SIRS below 2, for which the diagnosis of sepsis is still uncertain [2].

A substantial barrier for the continuous update of this method is the limited interoperability of health databases in most European contexts, which can make the process of data acquisition particularly burdensome. Improving the digitalisation and standardisation of health information within and between hospitals in a privacy enhanced manner [49,50,51,52] can be paramount to strengthen the preparedness of health systems following the COVID-19 pandemic and to promote the adoption of research methods in clinical practice [53,54,55].

Finally, several strengths and limitations of our study are worth to be outlined.

Firstly, given the absence of an agreed gold standard for the diagnosis of sepsis, we used Sepsis-2 criteria, which may have led to a certain degree of misclassification, hampering the accuracy of clinical variables and biomarkers. However, we tried to limit this risk by assigning all uncertain cases to specific categories, after reviewing and discussing them carefully.

Secondly, host-related factors, e.g. HIV infection, ≥ 2 week steroid or other immune suppressive treatments, e.g. Hydroxyurea or Methotrexate, and Alcoholism may have influenced immune cell volumetric evaluation and other cell blood examination, potentially affecting the outcome prediction of these biomarkers. Nevertheless, we included all patients, after considering that given that all these preexisting conditions are present only in a small percentage of patients admitted to the ED, they would potentially affect only a small fraction of cases [22].

Thirdly, some study variables may have not been recorded accurately, e.g. smoking status and comorbidities, which were collected ex post through the examination of the medical record at ED. However, this problem may be limited by assuming that inaccurate measurements are randomly distributed across the study sample.

Fourthly, we enrolled a small number of patients due to the monocentric design of the study. Multi-centric investigations will be needed to improve the stability and reliability of the results.

Finally, the stepwise selection process may have potentially introduced selection bias in the identification of the final model. However, the backward elimination method that we have applied is generally considered more robust [56]. Moreover, we have used three different thresholds to mitigate the effect of the method and applied cross-validation to evaluate the accuracy of the results. The final estimates of the regression coefficients were used in two different ways for the prediction score algorithm, achieving the same predictive accuracy. However, the proposed algorithm appeared more directly intuitive for clinical readers, as they are used to relate to odds ratios for the interpretation of risk estimates.

Conclusion

In this study, we have identified clinical and laboratory parameters associated with a diagnosis of sepsis in a population admitted to an ED of a large regional hospital.

Among clinical and laboratory variables, a value of MDW ≥ 22 was associated with an almost three-fold increased risk of sepsis. Risk scores derived from multivariate models showed moderate to high predictive accuracy in flagging subjects with sepsis, based upon the early evaluation of personal characteristics at the ED.

Our results suggest that incorporating MDW in clinical decision making may improve the early identification of sepsis in the ED, with limited additional effort over the existing standard procedures. Further multicentric studies are needed to confirm our findings.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Abbreviations

MDW:

Monocyte Distribution Width

SIRS:

Systemic inflammatory response syndrome

qSOFA:

Quick sequential organ failure assessment

SSC:

Surviving sepsis campaign

ICU:

Intensive care units

ED:

Emergency Department

PGH:

Pescara General Hospital

AMI:

Acute myocardial infarction

TIA:

Transient ischemic attack

CVD:

Cardiovascular diseases

COPD:

Chronic obstructive pulmonary disease

CRP:

C-reactive protein

WBC:

White blood cells

ESRD:

End-stage renal disease

AIDS:

Acquired Immune Deficiency Syndrome

CBC:

Complete blood count

RBC:

Red blood cells

HGB:

Haemoglobin

HCT:

Hematocrit

MCV:

Mean cell volume

MCHC:

Mean cell hemoglobin concentration

MPV:

Mean platelet volume

PDW:

Platelet cell distribution width

RDW:

Red cell distribution width

PCT:

Procalcitonin

PPV:

Positive predictive value

NPV:

Negative predictive value

AUC:

Area under the curve

OR:

Odds ratios

95%CI:

95% Confidence interval

References

  1. Shetty A, Macdonald SP, Keijzers G, Williams JM, Tang B, de Groot B, et al. Review article: sepsis in the emergency department—part 2: Investigations and monitoring. Emerg Med Australas EMA. 2018;30(1):4–12.

    Article  PubMed  Google Scholar 

  2. Singer M, Deutschman CS, Seymour CW, Shankar-Hari M, Annane D, Bauer M, et al. The third international consensus definitions for sepsis and septic shock (Sepsis-3). JAMA. 2016;315(8):801–10.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Fay K, Sapiano MRP, Gokhale R, Dantes R, Thompson N, Katz DE, et al. Assessment of health care exposures and outcomes in adult patients with sepsis and septic shock. JAMA Netw Open. 2020;3(7): e206004.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Dellinger RP, Levy MM, Rhodes A, Annane D, Gerlach H, Opal SM, et al. Surviving Sepsis Campaign: International Guidelines for Management of Severe Sepsis and Septic Shock, 2012. Intensive Care Med. 2013;39(2):165–228.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Caraballo C, Jaimes F. Organ dysfunction in sepsis: an ominous trajectory from infection to death. Yale J Biol Med. 2019;92(4):629–40.

    CAS  PubMed  PubMed Central  Google Scholar 

  6. Reinhart K, Daniels R, Kissoon N, Machado FR, Schachter RD, Finfer S. Recognizing sepsis as a global health priority—a WHO resolution. N Engl J Med. 2017;377(5):414–7.

    Article  PubMed  Google Scholar 

  7. Seymour CW, Gesten F, Prescott HC, Friedrich ME, Iwashyna TJ, Phillips GS, et al. Time to treatment and mortality during mandated emergency care for sepsis. N Engl J Med. 2017;376(23):2235–44.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Kim HI, Park S. Sepsis: early recognition and optimized treatment. Tuberc Respir Dis. 2019;82(1):6–14.

    Article  Google Scholar 

  9. Vincent JL, Pereira AJ, Gleeson J, Backer DD. Early management of sepsis. Clin Exp Emerg Med. 2014;1(1):3–7.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Adrie C, Alberti C, Chaix-Couturier C, Azoulay E, De Lassence A, Cohen Y, et al. Epidemiology and economic evaluation of severe sepsis in France: age, severity, infection site, and place of acquisition (community, hospital, or intensive care unit) as determinants of workload and cost. J Crit Care. 2005;20(1):46–58.

    Article  PubMed  Google Scholar 

  11. Litell JM, Guirgis F, Driver B, Jones AE, Puskarich MA. Most emergency department patients meeting sepsis criteria are not diagnosed with sepsis at discharge. Acad Emerg Med. 2021;28(7):745–52.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Iskandar A, Susianti H, Anshory M, Somma SD. Biomarkers Utility for Sepsis Patients Management. Biomarker—Indicator of Abnormal Physiological Process. IntechOpen. 2018. https://www.intechopen.com/chapters/60889.

  13. Westphal GA, Pereira AB, Fachin SM, Barreto ACC, Bornschein ACGJ, Caldeira Filho M, et al. Characteristics and outcomes of patients with community-acquired and hospital-acquired sepsis. Rev Bras Ter Intensiva. 2019;31(1):71–8.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Polilli E, Frattari A, Esposito JE, Stanziale A, Giurdanella G, Di Iorio G, et al. Monocyte Distribution Width (MDW) as a new tool for the prediction of sepsis in critically ill patients: a preliminary investigation in an intensive care unit. BMC Emerg Med. 2021;21:147.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Faix JD. Biomarkers of sepsis. Crit Rev Clin Lab Sci. 2013;50(1):23–36.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Lin GL, McGinley JP, Drysdale SB, Pollard AJ. Epidemiology and immune pathogenesis of viral sepsis. Front Immunol. 2018;9:2147.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Sinapidis D, Kosmas V, Vittoros V, Koutelidakis IM, Pantazi A, Stefos A, et al. Progression into sepsis: an individualized process varying by the interaction of comorbidities with the underlying infection. BMC Infect Dis. 2018;18(1):242.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Wang HE, Shapiro NI, Griffin R, Safford MM, Judd S, Howard G. Chronic medical conditions and risk of sepsis. PLoS ONE. 2012;7(10): e48307.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Polilli E, Sozio F, Frattari A, Persichitti L, Sensi M, Posata R, et al. Comparison of Monocyte Distribution Width (MDW) and Procalcitonin for early recognition of sepsis. PLoS ONE. 2020;15(1): e0227300.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Lee AJ, Kim SG. Mean cell volumes of neutrophils and monocytes are promising markers of sepsis in elderly patients. Blood Res. 2013;48(3):193–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Buoro S, Seghezzi M, Vavassori M, Dominoni P, Apassiti Esposito S, Manenti B, et al. Clinical significance of cell population data (CPD) on Sysmex XN-9000 in septic patients with our without liver impairment. Ann Transl Med. 2016;4(21):418.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Crouser ED, Parrillo JE, Seymour CW, Angus DC, Bicking K, Esguerra VG, et al. Monocyte distribution width: a novel indicator of sepsis-2 and sepsis-3 in high-risk emergency department patients. Crit Care Med. 2019;47(8):1018–25.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Kleinbaum DG, Klein M. Introduction to Survival Analysis. Survival Analysis: a Self-Learning Text. Springer. 2012. https://doi.org/10.1007/978-1-4419-6646-9_1.

  24. Kleinbaum DG, Klein M. Introduction to Logistic Regression. Logistic Regression: a Self-Learning Text. Springer. 2010. https://doi.org/10.1007/978-1-4419-1742-3_1.

  25. DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics. 1988;44(3):837–45.

    Article  CAS  PubMed  Google Scholar 

  26. Polilli E, Frattari A, Esposito JE, D’Amato M, Rapacchiale G, D’Intino A, et al. Reliability of predictive models to support early decision making in the emergency department for patients with confirmed diagnosis of COVID-19: the Pescara COVID Hospital score. BMC Health Serv Res. 2022;22(1):1062.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Moons KG, Harrell FE, Steyerberg EW. Should scoring rules be based on odds ratios or regression coefficients? J Clin Epidemiol. 2002;55(10):1054–5.

    Article  PubMed  Google Scholar 

  28. Hanley JA, McNeil BJ. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology. 1982;143(1):29–36.

    Article  CAS  PubMed  Google Scholar 

  29. Youden WJ. Index for rating diagnostic tests. Cancer. 1950;3(1):32–5.

    Article  CAS  PubMed  Google Scholar 

  30. R Core Team—European Environment Agency. 2020. https://www.eea.europa.eu/data-and-maps/indicators/oxygen-consuming-substances-in-rivers/r-development-core-team-2006.

  31. Crouser ED, Parrillo JE, Seymour C, Angus DC, Bicking K, Tejidor L, et al. Improved early detection of sepsis in the ED with a novel monocyte distribution width biomarker. Chest. 2017;152(3):518–26.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Crouser ED, Parrillo JE, Martin GS, Huang DT, Hausfater P, Grigorov I, et al. Monocyte distribution width enhances early sepsis detection in the emergency department beyond SIRS and qSOFA. J Intensive Care. 2020;8:33.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Fingerle G, Pforte A, Passlick B, Blumenstein M, Strobel M, Ziegler- HH. The novel subset of CD14+/CD16+ blood monocytes is expanded in sepsis patients. Blood. 1993;82(10):3170–6.

    Article  CAS  PubMed  Google Scholar 

  34. Tak T, van Groenendael R, Pickkers P, Koenderman L. Monocyte subsets are differentially lost from the circulation during acute inflammation induced by human experimental endotoxemia. J Innate Immun. 2017;9(5):464–74.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Agnello L, Vidali M, Lo Sasso B, Giglio RV, Gambino CM, Scazzone C, et al. Monocyte distribution width (MDW) as a screening tool for early detecting sepsis: a systematic review and meta-analysis. Clin Chem Lab Med. 2022;60(5):786–92.

    Article  CAS  PubMed  Google Scholar 

  36. Poz D, Crobu D, Sukhacheva E, Rocchi MBL, Anelli MC, Curcio F. Monocyte distribution width (MDW): a useful biomarker to improve sepsis management in Emergency Department. Clin Chem Lab Med. 2022;60(3):433–40.

    Article  CAS  PubMed  Google Scholar 

  37. Li CH, Seak CJ, Chaou CH, Su TH, Gao SY, Chien CY, et al. Comparison of the diagnostic accuracy of monocyte distribution width and procalcitonin in sepsis cases in the emergency department: a prospective cohort study. BMC Infect Dis. 2022;22(1):26.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Hausfater P, Robert Boter N, Morales Indiano C, Cancella de Abreu M, Marin AM, Pernet J, et al. Monocyte distribution width (MDW) performance as an early sepsis indicator in the emergency department: comparison with CRP and procalcitonin in a multicenter international European prospective study. Crit Care Lond Engl. 2021;25(1):227.

    Article  Google Scholar 

  39. la Woo A, Oh DK, Park CJ, Hong SB. Monocyte distribution width compared with C-reactive protein and procalcitonin for early sepsis detection in the emergency department. PLoS ONE. 2021;16(4): e0250101.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Gül F, Arslantaş MK, Cinel İ, Kumar A. Changing definitions of sepsis. Turk J Anaesthesiol Reanim. 2017;45(3):129–38.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Arnold RC, Sherwin R, Shapiro NI, O’Connor JL, Glaspey L, Singh S, et al. Multicenter observational study of the development of progressive organ dysfunction and therapeutic interventions in normotensive sepsis patients in the emergency department. Acad Emerg Med. 2013;20(5):433–40.

    Article  PubMed  Google Scholar 

  42. Raith EP, Udy AA, Bailey M, McGloughlin S, MacIsaac C, Bellomo R, et al. Prognostic accuracy of the SOFA score, SIRS Criteria, and qSOFA score for in-hospital mortality among adults with suspected infection admitted to the intensive care unit. JAMA. 2017;317(3):290–300.

    Article  PubMed  Google Scholar 

  43. Askim Å, Moser F, Gustad LT, Stene H, Gundersen M, Åsvold BO, et al. Poor performance of quick-SOFA (qSOFA) score in predicting severe sepsis and mortality—a prospective study of patients admitted with infection to the emergency department. Scand J Trauma Resusc Emerg Med. 2017;25(1):56.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Usman OA, Usman AA, Ward MA. Comparison of SIRS, qSOFA, and NEWS for the early identification of sepsis in the Emergency Department. Am J Emerg Med. 2019;37(8):1490–7.

    Article  PubMed  Google Scholar 

  45. Brink A, Alsma J, Verdonschot RJCG, Rood PPM, Zietse R, Lingsma HF, et al. Predicting mortality in patients with suspected sepsis at the Emergency Department; a retrospective cohort study comparing qSOFA, SIRS and National Early Warning Score. PLoS ONE. 2019;14(1): e0211133.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Haydar S, Spanier M, Weems P, Wood S, Strout T. Comparison of QSOFA score and SIRS criteria as screening mechanisms for emergency department sepsis. Am J Emerg Med. 2017;35(11):1730–3.

    Article  PubMed  Google Scholar 

  47. Serafim R, Gomes JA, Salluh J, Póvoa P. A comparison of the quick-SOFA and systemic inflammatory response syndrome criteria for the diagnosis of sepsis and prediction of mortality: a systematic review and meta-analysis. Chest. 2018;153(3):646–55.

    Article  PubMed  Google Scholar 

  48. Malinovska A, Hinson JS, Badaki-Makun O, Hernried B, Smith A, Debraine A, et al. Monocyte distribution width as part of a broad pragmatic sepsis screen in the emergency department. J Am Coll Emerg Physicians Open. 2022;3(2):e12679.

    PubMed  PubMed Central  Google Scholar 

  49. Di Iorio CT, Carinci F, Oderkirk J, Smith D, Siano M, de Marco DA, et al. A novel methodology to assess privacy, data governance and ethics in health information systems: Privacy and Ethics Impact and Performance Assessment in the EU Bridge-Health Project. J Med Ethics. 2020;medethics-2019-105948.

  50. Di Iorio CT, Carinci F, Oderkirk J. Health Research and Systems’ Governance are at risk: should the right to data protection override health? J Med Ethics. 2014;40(7):488–92.

    Article  PubMed  Google Scholar 

  51. Di Iorio CT, Carinci F, Azzopardi J, Baglioni V, Beck P, Cunningham S, et al. Privacy impact assessment in the design of transnational public health information systems: the BIRO project. J Med Ethics. 2009;35(12):753–61.

    Article  PubMed  Google Scholar 

  52. Di Iorio CT, Carinci F, Brillante M, Azzopardi J, Beck P, Bratina N, et al. Cross-border flow of health information: is “privacy by design” enough? Privacy performance assessment in EUBIROD. Eur J Public Health. 2013;23(2):247–53.

    Article  PubMed  Google Scholar 

  53. Carinci F. Essential levels of health information in Europe: an action plan for a coherent and sustainable infrastructure. Health Policy. 2015;119(4):530–8.

    Article  PubMed  Google Scholar 

  54. Kringos D, Carinci F, Barbazza E, Bos V, Gilmore K, Groene O, et al. Managing COVID-19 within and across health systems: why we need performance intelligence to coordinate a global response. Health Res Policy Syst. 2020;18:80.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Carinci F. Covid-19: preparedness, decentralisation, and the hunt for patient zero. BMJ. 2020;368:m799.

    Article  Google Scholar 

  56. Chowdhury MZI, Turin TC. Variable selection strategies and its importance in clinical prediction modelling. Fam Med Com Health. 2020;8:e000262.

    Article  Google Scholar 

Download references

Acknowledgements

Not applicable.

Funding

The study was founded by Beckman Coulter srl.

Author information

Authors and Affiliations

Authors

Contributions

EP, GP contributed to the study design, data extraction, quality assessment and writing of the manuscript. MCA contributed to design of the study. JEE, MDA, GDI, GA and CS retrieved clinical data and prepared the datasets for analysis. FC analysed and interpreted data, and contributed in writing the manuscript. EP, FC, GP and JEE contributed in revising and editing the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Ennio Polilli.

Ethics declarations

Ethics approval and consent to participate

The study was carried out in accordance with the amended Declaration of Helsinki. The study was approved by the local Ethics Committee (“Ethics Committee of Chieti—Pescara for biomedical research”, approval no. 539, May 2020). Informed consent was obtained from all patients.

Consent for publication

Not applicable.

Competing interests

Maria Chiara Anelli is Beckman Coulter srl employee. The other authors declare no conflicts of interest related to this manuscript content.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Polilli, E., Di Iorio, G., Silveri, C. et al. Monocyte Distribution Width as a predictor of community acquired sepsis in patients prospectively enrolled at the Emergency Department. BMC Infect Dis 22, 849 (2022). https://doi.org/10.1186/s12879-022-07803-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12879-022-07803-7

Keywords

  • Early diagnosis
  • Emergency Department
  • MDW
  • Sepsis
  • Infection