In this retrospective analysis by MIMIC III database, we conducted Logistic regression analysis to recognize the independent risk factors for in-hospital death of SAE patients and then predictors including age, SOFA, RDW, heart rate, respiratory rate and temperature were identified and then integrated into a best-fit prediction model visualized as a prediction nomogram. As one of the decision-making aid, nomogram has become popular because it offers a visual and easy-to-use ruler allowing quick and accurate computation of probability of clinically significant events. Results from a nomogram are reliable and easy to be obtained by merely drawing several lines and calculating a total point. To the best of our knowledge, this is the first study to evaluate the potentially modifiable factors contributing to the hospital death of SAE and to develop a nomogram to predict its hospital mortality. The prediction performance of the nomogram was then tested by discrimination and calibration in a training set and validation set as well as by the bootstrap method, all exhibiting acceptable and stable predicting performance. Moreover, decision curve analysis was employed to account for both the benefits and the costs of intervention to SAE patient guided by the nomogram to validate its clinical usefulness. The decision curve showed that interventions guided by the current nomogram can add more net benefits than SOFA score.
SOFA system was firstly developed to better describe multiple organ failure or morbidity [16]. Since then, researchers found that SOFA was not only a scoring system to evaluate the severity of organ failure but also a useful tool in predicting the in-hospital mortality of cardiovascular disease, trauma and critically ill patients [17–19]. More recently, the third international consensus definitions for sepsis and septic shock (Sepsis-3) recommended the use of SOFA to diagnose sepsis because it is associated with a 10% higher in-hospital mortality, and recognition of this crisis may promote clinicians to give prompt and appropriate medical intervention [10]. Seymour CW et al. further supported the consensus with the finding that the validity of SOFA in discriminating the in-hospital mortality of sepsis was acceptable with AUROC of 0.74, which has no statistical difference compared with the more complex LODS score but was obviously greater than qSOFA score [20]. These findings indicated that SOFA score is a useful and simple tool in predicting the in-hospital death of patients with sepsis, but whether it is applicable to the forecast of the in-hospital mortality of patients with SAE is still unclear. Thus, we evaluated the performance of SOFA in predicting the in-hospital death of patients with sepsis alone and those with SAE. Results indicated that in the 15847 patients with sepsis, the AUROC was 0.724 (Additional file 5: Fig.S5), which was similar to the AUROC (0.74) in the study of Seymour CW, indicating a good performance of the SOFA score in discriminating patients with sepsis under the risk of in-hospital death. Neverthless, SOFA score exhibited poor performance in discriminating SAE patients under the risk of in-hospital death with AUROC of 0.599–0.662. Therefore, we developed the current predictive model incorporating SOFA score and clinical parameters, which showed better predictive performance than SOFA and exhibited improved discrimination and calibration. Interestingly, SOFA accounted for the biggest weight in the nomogram, indicating that it is the most important predictor in the best fit model and has the strongest power to predict in-hospital mortality in SAE patients.
RDW is a measure of the size of circulating erythrocytes and was routinely used in the differential diagnosis of anemia. However, studies have revealed that RDW is also useful in estimating the short-term mortality of non-hematologic diseases, such as cardiovascular diseases [21, 22], stroke [23], liver diseases [24, 25], and critical illness [26]. In patients with acute subarachnoid hemorrhage or acute heart failure, RDW is even associated with long-term mortality of patients [27, 28]. Consistently, our study demonstrated that RDW is an independent risk factor and potent predictor for the in-hospital mortality of SAE. Mechanisms under the relationship between RDW and short-term mortality of SAE remain largely unknown, but several studies had revealed that RDW is positively associated with inflammatory markers, such as C-reactive protein (CRP) and erythrocyte sedimentation rate (ESR), in unselected outpatients, autoimmune diseases and healthy population [29–32]. Thus, we hypothesized that the inflammatory response during sepsis may contribute to the adverse impact of RDW on the prognosis of SAE. Besides, oxidative stress may be another reason to connect RDW with poor in-hospital outcome because studies indicated that oxidative stress can increase anisocytosis by disrupting erythropoiesis, and altering the circulating half-life of red blood cell, ultimately leading to increased level of RDW [33, 34].
To further facilitate clinical use and treatment, patients with SAE was stratified into three risk groups based on the cut-off values calculated by the nomogram. The in-hospital mortality in the three risk groups were 7.4%, 28.71% and 45.57% in the training set as well as 11.49%, 26.14% and 44% in the validation set. Interestingly, the in-hospital mortality of the high-risk group was similar to that of patients with septic shock, whose in-hospital mortality was 42.3% [10]. As septic shock is characterized by the use of vasopressor to maintain mean arterial pressure (MAP) ≥ 65 mmHg and an increased level of lactate (> 2 mmol/L), we further compared the frequency of vasopressor use and levels of MAP and lactate in the three groups. Results showed that the high-risk group had significant higher level of lactate and frequency of vasopressor usage than the other two risk groups, simultaneously MAP was over 65 mmHg in all groups(Additional file 6–8: Fig.S6-S8), indicating that septic shock may be an important cause for in-hospital death in high risk group. Based on these, medical interventions towards septic shock, including early investigation for and treatment of infection, fluid resuscitation within 15–30 minutes and repeated assessment of hemodynamics, adoption of vasopressor and corticosteriods, provision of supportive care and so on [35, 36], may reduce the in-hospital mortality of SAE patients in the high-risk group. The exact mechanism for the increased in-hospital mortality in the middle-risk group compared with the low-risk group can not be confirmed by the study, but as the level of blood lactate had no difference between the two groups (Additional file 6༚Fig.S6), and previous study has demonstrated that the in-hospital mortality was 30.1% in septic patients with fluidresistant hypotension requiring vasopressors but without hyperlactatemia(< 2 mmol/L)[10], which was similar to the in-hospital mortality of patients in the middle-risk group, we hypothesized that circulatory failure without obvious abnormality of cell metabolism may be one reason for the increased in-hospital mortality in the middle-risk group. In consequence, treatment torwards sepsis, especially fluid resuscitation and rational use of vasoactive drugs to improve circulatory function may be useful to prevent in-hospital death of patients in the middle-risk group [37]. Neverthless, more studies are needed to ascertain the exact causes that result in the increased in-hospital mortality in the middle- or higher-risk groups so that targeted therapies can be performed or developed to effectively reduce in-hospital death of SAE patients.
Several points should be noted when using the nomogram. First, as specific therapy for SAE is lacking, the interventions mentioned in the DCA analysis are treatments toward sepsis and septic shock[36, 37]. Therefore, it is urgent to develop specific treatment for the encephalopathy during sepsis, which may further enhance the clinical usefulness of the nomogram and finally reduce the hospital death of SAE patients. Second, as vital signs in our study are the mean values of the first 24 hours of each ICU patient, the nomogram is not applicable to patients dying or leaving within 24 hours since ICU admission. Third, laboratory tests in the nomogram are the first results since ICU admission, thus, all the laboratory tests included in the nomogram should be completed within the first 24 hours of ICU admission. Lastly, the following situations should be considered when recording the respiratory rate of patients supported by mechanical ventilation: (1) If a patient has no spontaneous respiration, respiratory rate should be recorded as the setting rate of controlled ventilation. (2) If a patient has very slow spontaneous breathing, respiratory rate should also be recorded as the setting rate of controlled ventilation. (3) if one’s respiratory rate is normal or fast, respiratory rate should be recorded as the spontaneous respiratory rate.
This study has some limitations: First, the retrospective nature of this observational study determined that unidentified confounding factors may affect the results if adding to the model. Second, as the database lacks data related to the CAM-ICU, septic patients with GCS = 15 but complicated by delirium were not distinguished in the study. Therefore, whether the nomogram is appropriated to this population need to be further verified. Third, as neuroimaging data was not included in the study, we cannot assess the impact of organic lesion of brain on in-hospital outcome. Studies based on the results of brain MRI have revealed that the impairments of cerebral white matter in patients with critical illness are not only related to sequelae of the central nervous system but also associated with increased mortality [38]. Fourth, one of the challenges in studying SAE is that without specific diagnostic method, it remains a rule-out definition, which may lead to a high specificity, but relatively low sensitivity for the diagnosis of SAE. Thus, the current nomogram can only be used in SAE diagnosed by exclusion and may require further modification once specific diagnostic methods are developed. Finally, we only conducted an internal validation by the study cohort from the MIMICⅢ database, external validation should be performed in the future study to further validate the robustness and performance of the prediction model.