Introduction

Every year, approximately 24,000 emergency laparotomies are performed across England and Wales. Postoperative mortality remains high, especially in older patients with comorbidities [1]. Determining surgical risk accurately for individual patients is essential and increasingly emphasised yet remains challenging.

Numerous models have been developed to guide decision making and allow comparison of surgical outcomes following emergency laparotomy. In the United Kingdom, the Portsmouth Physiological and Operative Severity Score for the enUmeration of Mortality and Morbidity (P-POSSUM) model, the Surgical Outcome Risk Tool (SORT) and the National Emergency Laparotomy Audit (NELA) risk model are particularly popular [2].

Despite widespread use, risk prediction tools often have substantial limitations, including resource intensive calculations, dependence on postoperative data and validation bias [2, 3]. Consequently, an ongoing interest remains in identifying new predictors as well as developing more accurate risk prognostication models.

Recent research shows that both frailty and red cell distribution width (RDW) are significant variables in the perioperative setting [4, 5]. To date, neither have been routinely incorporated into surgical risk assessment tools.

The exact link between an elevated RDW and mortality remains unclear but is thought to denote bone marrow dysfunction, systemic inflammation and oxidative stress. Inflammatory pathways mediated by cytokines such as TNF-α and IL-6 inhibit erythropoietin-induced red blood cell maturation and may offer one possible explanation [6].

Importantly, emerging data suggest a strong correlation between anisocytosis, which is reported quantitatively as RDW, and mortality in the older population, perhaps reflecting the multiple physiological impairments related to ageing and frailty [7]. Therefore, RDW may serve as a marker of prior frailty and consequent mortality risk following emergency bowel surgery. Given the availability and the routine reporting of RDW as part of the full blood count, understanding its prognostic value could be both cost-effective and useful for surgical risk stratification in emergency laparotomy patients.

Using our institution’s NELA dataset, we set out to answer three specific questions. First, we examined whether pre-operative RDW values on average are different between emergency laparotomy survivors and non-survivors. Second, we investigated if RDW is a useful predictor of mortality in emergency laparotomy patients and its potential additive value to the NELA model. Finally, we sought to explore whether RDW is independently associated with frailty in this population.

Methods

Results

Overall, 372 emergency laparotomies were recorded during the study period. Sixteen operations were excluded from the final analysis (Fig 1). Baseline participant characteristics are shown by quartiles of RDW in Table 1. For most patients, the laboratory values on admission were used, therefore minimising iatrogenic confounding. However, for fourteen in-patients (3.9%) requiring a laparotomy unrelated to their initial admission, blood tests twenty-four hours before surgery were utilised.

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Fig 1. CONSORT diagram of patient enrolment.

https://doi.org/10.1371/journal.pone.0266041.g001

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Table 1. Baseline characteristics of patients undergoing emergency laparotomy across red cell distribution width (RDW) quartiles.

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All-cause 30-day mortality was 9.3% (n = 33), while overall mortality rose to 20.2% (n = 72) after emergency bowel surgery for the total follow-up period. In those patients who died at 30-days compared to those who survived median RDW levels were consistently higher, 14.9% (IQR 13.7%-16.1%) and 13.8% (IQR 13.1%-15%) respectively, P = 0.007. Median RDW for overall mortality was 13.7% (IQR 13%-14.7%) in survivors versus 14.9% (IQR 13.9%-15.9%) in non-survivors, P<0.001. The cumulative mortality rate increased across RDW quartiles for both follow-up periods and is displayed in Fig 2.

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Fig 2. Cumulative mortality rate plots for 30-day and overall mortality post emergency laparotomy by RDW quartiles.

The log-rank test was significant for the total follow-up period χ² (log-rank) = 25.5, d.f. = 3, p<0.001 (d.f. degrees of freedom). For 30-day mortality the survival lines cross and the log-rank test is unlikely to detect a difference and should not be used for methodological reasons [21].

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At 30-days RDW was the least significant predictor. The relative importance of RDW improved considerably for the longer-term mortality model (Table 3 of the S1 Annex). RDW added prognostic value only for the total follow-up period with a calculated percentage of new information of 14%. The overall mortality Cox regression model was internally validated via bootstrapping (1000 resamples) to penalise for possible overfitting. The likely future predictive discrimination measured by Somers’ D_xy rank correlation is 0.46 for the base model and 0.50 for the full model. Optimism adjusted C-statistics were 0.73 and 0.75, respectively. The estimated slope shrinkage was 0.82 and 0.83, suggesting that approximately 0.18/0.17 of the model fitting is noise, especially with regard to calibration accuracy implying moderate overfitting. The calibration curve for the full model is shown in the Fig 6 of the S1 Annex.

LASSO regression was used to shrink regression coefficients and revise the full overall mortality model (mean shrinkage 1.02). To present point and interval estimates of the updated predictor effects a hazard ratio chart was plotted alongside a nomogram for predicting death in emergency laparotomy patients over the total follow-up period (Fig 3).

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Fig 3. Hazard ratio chart and nomogram for overall mortality post emergency laparotomy.

Top panel: Estimated hazard ratios (HR) and 95% confidence bars for the overall mortality model. For the NELA risk score interquartile range HR are used, for all other continuous predictors median values are compared to the 90^th (RDW, creatinine) or 5^th centile (haemoglobin). For example, when RDW changes from its median value (13.9%) to the 90^th centile (17.4%), the hazard ratio more than doubles (HR 2.3, 95% CI 1.5–3.5). Standard HRs are presented for surgical indication. Here the hazard ratio is a conventional comparison of the hazard between two groups. Bottom panel: Nomogram for predicting all-cause mortality following emergency laparotomy for the total follow-up period. For each predictor, determine the points assigned on the 0–100 scale and add those points. Plot the result on the Total Points scale and then read the corresponding predictions below it. The linear predictor of a Cox model is a weighted sum of the variables in the models, where the weights are the regression coefficients. Note the effect of interactions, the risk of creatinine is influenced by haemoglobin and the NELA risk score. To illustrate this the 5^th and 90^th centile was chosen for haemoglobin and the interquartile range for the NELA risk score. RDW: Red cell distribution width (%), hb: haemoglobin (gl^-1), cr: creatinine (μmoll^-1), nela_risk: NELA risk score, indc_class: indication for laparotomy.

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Assessment of frailty was often not recorded, making any judgement about frailty problematic. Hence, it was only possible to draw valid conclusions regarding frailty in 140 (39.3%) patients. Of these, 26 (18.5%) had a formal assessment of frailty documented. All other frailty data (114, 81.5%) were gathered from patient records. Baseline descriptive statistics for the cohort are presented in Table 6 of the S1 Annex.

A significantly higher proportion of patients that died at 30-days were frail (Odds ratio (OR) 4.3, 95% CI 1.22–14.53, P = 0.01). Similarly, the risk of frailty was higher amongst patients that died overall (OR 4.9, 95% CI 1.68–14.09, P = 0.001). Comparing the cohort across groups of RDW demonstrated a higher proportion of frail individuals in each progressive quartile (RDW1: 2 (n = 39), RDW2: 3 (n = 37), RDW3: 7 (n = 32), RDW4: 12 (n = 32)) and was statistically significant, χ²(3, N = 140) = 15.9, p = 0.001.

Based on binary logistic regression analysis, pre-operative RDW was independently associated with frailty in our emergency laparotomy cohort. Validating our model using 400 bootstrap replications the bias-corrected estimate of predictive discrimination was D_xy = 0.462 (C-static 0.73). The corrected Brier score was 0.134, and the estimated maximum calibration error in predicting frailty was 0.06 (Table 8 of the S1 Annex). We depicted the fitted model by computing odds ratios with their respective 95% confidence intervals and graphed the association of RDW with frailty in NELA patients, estimated for a range of different ages (Fig 4).

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Fig 4. Frailty logistic regression model.

The left-hand panel displays an estimated odds ratio (OR) chart and respective 95% confidence intervals. For example, when RDW changes from the 50^th to the 90^th percentile (13.8% to 17.3%) the odds ratio of being frail is 2.9 (95% CI 1.4–6.4). The odds for age (OR 1.8, 95% CI 1–3.4) are for the 25^th and 75^th percentile, while for haemoglobin (OR 0.8, 95% CI 0.4–1.5) they are based on the 10^th and 50^th percentile. The right-hand panel illustrates the effect of RDW on the probability of frailty for emergency laparotomy patients, estimated for different ages. The age cut-offs represent the 10^th, 25^th, 50^th, 75^th and 90^th percentile (n = 140). RDW: red cell distribution width (%), age_at_adm: age at admission (years), hb: haemoglobin (gl^-1).

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Discussion

To our knowledge, this study is the first to examine pre-operative RDW and mortality, its potential added predictive value, and its relationship with frailty in emergency laparotomy patients. We found that RDW values, on average, were higher in non-survivors. Moreover, there was a distinct gradient in overall mortality risk associated with increasing RDW. This association remained after accounting for the NELA risk score, haemoglobin, creatinine and operative indication for overall mortality but not shorter-term 30-day mortality.

In the peri-operative setting, anisocytosis has been mainly associated with long-term mortality after surgery [22]. However, more recently, Abdullah and colleagues described a link between 30-day mortality and pre-operative RDW in patients 65 years or older undergoing noncardiac surgery [4]. This differs from our findings and others, where RDW was not a convincing predictor of death at 30-days [23, 24]. In line with a recently published retrospective database study, RDW had a stronger association with overall mortality in our emergency laparotomy cohort [24]. A discrepancy, which is likely to have arisen due to differences in study population and methodology. For example, we did not dichotomise RDW using sensitivity analyses but explored RDW as a quantitative variable avoiding the categorisation of an inherently continuous marker. Furthermore, the choice of regression coefficients is likely to account for much of the observed disparity. Our findings show that the composite NELA risk score is the main predictor of all-cause mortality in both models. The NELA tool was developed to produce risk-adjusted 30-day postoperative mortality rates [2]. Thus, RDW is probably not influential enough in our model at 30-days, lacking in discriminatory power and adding little in predictive value compared with the NELA risk score. Conversely, the NELA risk score was not designed with long-term mortality in mind and may explain the improved prognostic influence of RDW on our overall model.

Though we did not expressly investigate how prognostic factors impact outcome over time, it is biologically plausible that markers differ in their predictive ability in a time-dependent manner. The NELA model primarily reflects perioperative events, which may have less influence on patients who survive long-term, usually because of treatment with curative intent. Hence, the predictive worth of the NELA risk score is likely to decrease with time after laparotomy. In contrast, pre-operative anisocytosis may indicate chronically reduced physiological reserve, making it possibly a better indicator of longer-term mortality [25]. An interesting follow-up study would be to formally evaluate at what time point RDW, as a prognostic marker, has the most significant impact on mortality prediction.

Although numerous studies, including ours, have shown an association between higher RDW and decreased survival, the exact causal relationships remain elusive and are likely to be multifactorial [4, 24, 25]. Various hypotheses have been suggested, all of which involve systemic factors that alter erythrocyte physiology, such as oxidative stress, inflammation, malnutrition and telomere length [25, 26]. Nonetheless, there is an emerging consensus that anisocytosis reflects profound physiological dysregulation.

While many of the above mechanisms are likely to be similar to those implicated in the pathophysiology of anaemia, we found anisocytosis to be independent of haemoglobin concentration. This is in keeping with findings published by Patel and colleagues [26]. Equally, haemoglobin concentration was not a meaningful predictor of all-cause mortality in our study. A similar conclusion was reached during the development of the NELA risk prediction tool, leading to its exclusion from the model [2].

RDW is also strongly associated with advancing age and a higher disease burden [27]. More recently, a connection between RDW and frailty has been suggested, an association that we were able to support in our explorative analysis [27]. Intriguingly, frailty and anisocytosis appear to share similarities in their proposed pathophysiological mechanism [25, 28]. Thus, RDW is a possible integrative biomarker reflecting the multiple biological impairments related to increasing frailty and indirectly ageing, perhaps thereby explaining its additional predictive worth.

We acknowledge several limitations, including the single-centre, retrospective observational design of the study and its relatively small sample size, restricting its overall generalisability. While national inclusion criteria mitigate selection bias, our findings ideally require prospective confirmation across the whole NELA cohort. At present, the NELA project does not routinely collect RDW. Since RDW is easily measured as part of the full blood count, including it prospectively in large nationally or internationally collected datasets may validate its effectiveness and offer valuable insights prognostically.

A further shortcoming is that we did not account for blood transfusions, which could modify RDW. We used admission blood tests to attenuate the confounding risk of perioperative blood transfusions, but a small proportion of patients underwent laparotomy as in-patients. In an attempt to adjust for additional risk factors, we applied the amalgamated NELA score. Nevertheless, we cannot exclude the possibility of residual confounding. In particular, we did not account for nutritional deficiencies (folate, cobalamin, iron) and cancer, similar to many studies on RDW. However, a large community-based study in the United States examining RDW in middle-aged and older adults found RDW to predict mortality independent of these confounding factors [7].

Moreover, we developed our frailty model, excluding a large number of patients with missing data. The distribution of variables across risk factors was similar in patients with complete and missing frailty outcomes, suggesting that the data were missing at random (Table 7 of the S1 Annex). Reassuringly, the prevalence of frailty in our cohort mirrored a national multicentre study specifically examining frailty in NELA patients [29]. In the majority of patients, frailty was established using the clinical notes. Admittedly subjective, the simplicity of the CFS score facilitates this and is thought to be appropriate in the literature [11].

Lastly, we recognise that internal validation demonstrated overfitting for both our models. This is most likely due to the high number of parameters, including screening for non-linear terms and global interaction tests. However, our models were exploratory and not meant to be new parsimonious prediction tools. Thus, we emphasised the inclusion of clinically relevant variables alongside interactions/non-linear terms in the trade-off with overfitting [19].

Our study also had various strengths, specifically minimal loss of predictor values, a priori choice of covariates and a robust approach to model development. We used advanced methods to address non-linearity, interactions, internal validation and presented our models graphically with these complexities in mind. Importantly, we avoided the categorisation of RDW and many of its associated problems [11]. Some of these include the heterogeneity of diagnostic and prognostic cut-offs in the literature and unmet standardisation of erythrocyte sizing [7]. Crucially, specific cut off values imply that the relationship with an outcome is flat on either side of the chosen value, which biologically is seldom plausible [30]. Indeed, we were able to demonstrate that mortality increases across what is considered the normal range of RDW, representing a continuum of risk and is depicted in our nomogram (Fig 4).

A key strength of investigating RDW is its availability at no additional cost since it is routinely performed as part of the full blood count. Similarly, use of the CFS to screen for frailty is straightforward and uses readily available clinical information. While concerns around its applicability in patients below 65 years of age exist, it has been used successfully in adult emergency surgical admissions regardless of age [11].

Despite mounting evidence that anisocytosis is associated with increased long-term mortality following surgery, large-scale prospective studies are now needed to validate its predictive utility [4, 24]. Going forward, investigators should focus on RDW as a continuous variable to develop valid prediction models rather than classification tools based on subjective thresholds. Moreover, these studies should now assess the added predictive value of RDW to determine if pre-operative anisocytosis enhances current risk-stratification tools. In turn, superior risk prediction tools could allow more meaningful informed consent and shared decision making between patients and healthcare professionals.

At present, it remains unknown whether RDW is a modifiable risk factor perioperatively, including the elective setting. It would be interesting to establish if targeting factors reflected in the RDW improves surgical outcomes. Should tailored interventions such as physical rehabilitation, nutritional support or immunomodulation prove beneficial, this would further strengthen the argument to use RDW to identify individual patients at risk [24].

Conversely, the idea that frailty contributes to increased mortality following emergency surgery is not new, nor is the concept of integrating frailty into surgical risk assessment [5]. However, whether increased RDW, as a measure of biological vulnerability, offers a valid link with frailty should now be formally investigated.

Finally, pre-operative risk models for emergency laparotomies are based on retrospective database analyses of patients undergoing surgery [2]. We know little about patients who met the criteria for surgery but did not proceed due to personal choice or perceived high risk [31]. Future research must establish the predictive value of RDW for all patients with or without surgical intervention to understand its pre-operative worth fully.

Conclusions

We established that anisocytosis as reflected in the RDW value is associated with higher rates of postoperative mortality following emergency laparotomy. Furthermore, our analysis tentatively supports the notion that increased RDW is a possible marker of physiological dysregulation relevant to frailty [24]. While further research is needed to understand these mechanisms fully, RDW seemingly provides prognostic information that could inform future risk prediction tools. Accordingly, we explored how to quantify the added prognostic value of RDW without resorting to categorisation. Although oversimplified for illustration, our models demonstrated a statistically efficient way to investigate the relative merit of RDW. However, whether adding RDW as a global marker of homeostasis to surgical prognostication tools will improve patient management and outcome remains to be seen.

Supporting information

Acknowledgments

We are extremely grateful to all the staff at St Mary’s Hospital, past and present, who continue to submit data to the NELA database. We would also like to thank the NELA project team for their ongoing support in making national audit data available locally.