Hs-cTnT Predicts Mortality in Suspected Infection Patients
Hs-cTnT Predicts Mortality in Suspected Infection Patients
A total of 404 patients were included. In 292 patients, hs-cTnT was measured. Mean age was 57 (18) years, with 214 (53%) males.
Figure 1 shows patient inclusion and outcome as a function of disposition; 7.4% (16 of 215) of the ED patients with a suspected infection had an unanticipated transfer from ward to ICU or died on the ward, of whom 10 of 16 did not have a DNR status. Conversely, ~32% (13 of 41) of ED patients with a suspected infection were admitted to the ICU shorter than 24 h, and without specific ICU-related interventions. In web appendix 1 http://emj.bmj.com/content/31/11/882/suppl/DC1, patient characteristics of included patients with and without hs-cTnT measurement are summarised. Table 1 shows patient characteristics of included patients.
(Enlarge Image)
Figure 1.
Patient inclusion and flow through the study. *Surviving patients who were admitted to the ICU for less than 24 h, and who were merely observed in the ICU. Patients with no hs-cTnT measurement are shown in web appendix 1. ICU, intensive care unit; ED, emergency department.
First, it was tested whether hs-cTnT was associated with the level of illness severity and disposition, because this is one minimal prerequisite if hs-cTnT is to be used as a tool to guide adequate disposition. Hs-cTnT was significantly associated with validated measures of illness severity, that is, sepsis category, MEDS and PIRO score (figure 2A–C; p<0.001). Additionally, figure 2D shows that hs-cTnT levels increased with disposition category (p<0.001).
(Enlarge Image)
Figure 2.
Hs-cTnT levels as a function of illness severity. Illness severity was quantified by PIRO (A) and MEDS (B) score, and sepsis (C) and disposition (D) category. The whisker-boxplots show median (IQR and 95% CI). Circles and stars represent outliers. Kruskal–Wallis ANOVA to test differences among groups. MEDS, mortality in emergency department sepsis score; PIRO, Predisposition, Infection, Response, Organ-failure score; ICU, intensive care unit.
Figure 3 summarises the prognostic performance of hs-cTnT. In figure 3A, it is shown that hs-cTnT levels are significantly higher in non-survivors compared with survivors. Discrimination of hs-cTnT is excellent, and comparable to the discriminative performance of the more complex PIRO and MEDS scores (figure 3B, p>0.05). Finally, figure 3C reveals that hs-cTnT is an independent predictor of inhospital mortality, when corrected for illness severity (PIRO score) and ED treatment (number of SSC goals attained). The lowest quartile of hs-cTnT (<7 ng/L) was a perfect predictor of survival.
(Enlarge Image)
Figure 3.
Prognostic performance of hs-cTnT. (A) Hs-cTnT levels of survivors versus non-survivors. Data are presented as median (95% CI). Circles and stars represent outliers. (B) Receiver operator characteristics (ROC) of hs-cTnT, Predisposition, Infection, Response and Organ-failure (PIRO) score and Mortality in Emergency Department Sepsis (MEDS) score for prediction of inhospital mortality as outcome. Do not resuscitate patients were excluded from analysis. Data are presented as mean (95% CI) area under the curve (AUC). (C) Univariate (crude model) and multivariate (corrected model) logistic regression. Prediction model corrected for illness severity (PIRO) and quality of emergency department treatment (Surviving Sepsis Campaign (SSC) goals attained). Hosmer and Lemeshow tests for crude and corrected models were p=1.0 and p=0.19, respectively.
The hs-cTnT level at maximal sensitivity and specificity in the ROC plot (thus the upper left corner) was 18 ng/L. The negative predictive value at this cut-off value was 0.99 (0.98–1.0).
Results
Patient Characteristics and Inclusion
A total of 404 patients were included. In 292 patients, hs-cTnT was measured. Mean age was 57 (18) years, with 214 (53%) males.
Figure 1 shows patient inclusion and outcome as a function of disposition; 7.4% (16 of 215) of the ED patients with a suspected infection had an unanticipated transfer from ward to ICU or died on the ward, of whom 10 of 16 did not have a DNR status. Conversely, ~32% (13 of 41) of ED patients with a suspected infection were admitted to the ICU shorter than 24 h, and without specific ICU-related interventions. In web appendix 1 http://emj.bmj.com/content/31/11/882/suppl/DC1, patient characteristics of included patients with and without hs-cTnT measurement are summarised. Table 1 shows patient characteristics of included patients.
(Enlarge Image)
Figure 1.
Patient inclusion and flow through the study. *Surviving patients who were admitted to the ICU for less than 24 h, and who were merely observed in the ICU. Patients with no hs-cTnT measurement are shown in web appendix 1. ICU, intensive care unit; ED, emergency department.
The Association of hs-cTnT With Illness Severity and Disposition
First, it was tested whether hs-cTnT was associated with the level of illness severity and disposition, because this is one minimal prerequisite if hs-cTnT is to be used as a tool to guide adequate disposition. Hs-cTnT was significantly associated with validated measures of illness severity, that is, sepsis category, MEDS and PIRO score (figure 2A–C; p<0.001). Additionally, figure 2D shows that hs-cTnT levels increased with disposition category (p<0.001).
(Enlarge Image)
Figure 2.
Hs-cTnT levels as a function of illness severity. Illness severity was quantified by PIRO (A) and MEDS (B) score, and sepsis (C) and disposition (D) category. The whisker-boxplots show median (IQR and 95% CI). Circles and stars represent outliers. Kruskal–Wallis ANOVA to test differences among groups. MEDS, mortality in emergency department sepsis score; PIRO, Predisposition, Infection, Response, Organ-failure score; ICU, intensive care unit.
Accuracy and Discriminative Power of hs-cTnT for Prediction of Inhospital Mortality
Figure 3 summarises the prognostic performance of hs-cTnT. In figure 3A, it is shown that hs-cTnT levels are significantly higher in non-survivors compared with survivors. Discrimination of hs-cTnT is excellent, and comparable to the discriminative performance of the more complex PIRO and MEDS scores (figure 3B, p>0.05). Finally, figure 3C reveals that hs-cTnT is an independent predictor of inhospital mortality, when corrected for illness severity (PIRO score) and ED treatment (number of SSC goals attained). The lowest quartile of hs-cTnT (<7 ng/L) was a perfect predictor of survival.
(Enlarge Image)
Figure 3.
Prognostic performance of hs-cTnT. (A) Hs-cTnT levels of survivors versus non-survivors. Data are presented as median (95% CI). Circles and stars represent outliers. (B) Receiver operator characteristics (ROC) of hs-cTnT, Predisposition, Infection, Response and Organ-failure (PIRO) score and Mortality in Emergency Department Sepsis (MEDS) score for prediction of inhospital mortality as outcome. Do not resuscitate patients were excluded from analysis. Data are presented as mean (95% CI) area under the curve (AUC). (C) Univariate (crude model) and multivariate (corrected model) logistic regression. Prediction model corrected for illness severity (PIRO) and quality of emergency department treatment (Surviving Sepsis Campaign (SSC) goals attained). Hosmer and Lemeshow tests for crude and corrected models were p=1.0 and p=0.19, respectively.
The hs-cTnT level at maximal sensitivity and specificity in the ROC plot (thus the upper left corner) was 18 ng/L. The negative predictive value at this cut-off value was 0.99 (0.98–1.0).
