Predicting in-hospital mortality for patients admitted from the emergency department: An initial alarm score for inpatient care

Abstract

Background: The in-hospital mortality of patients admitted from the emergency department (ED) is high, but no appropriate initial alarm score is available.

Methods: This prospective observational study enrolled ED-admitted patients in hospitalist-care wards and analyzed the predictors for seven-day in-hospital mortality from May 2010 to October 2016. Two-thirds were randomly assigned to a derivation cohort for development of the model and cross-validation was performed in the validation cohort.

Results: During the study period, 8,649 patients were enrolled for analysis. The mean age was 71.05 years, and 51.91% were male. The most common admission diagnoses were pneumonia (36%) and urinary tract infection (20.05%). In the derivation cohort, multivariable Cox proportional hazard regression revealed that a low Barthel index score, triage level 1 at the ED, presence of cancer, metastasis, and admission diagnoses of pneumonia and sepsis were independently associated with seven-day in-hospital mortality. Based on the probability developed from the multivariable model, the area under the receiver operating characteristic curve in the derivation group was 0.81 [0.79–0.85]. The result in the validation cohort was comparable. The prediction score modified by the six independent factors had high sensitivity of 88.03% and a negative predictive value of 99.51% for a cutoff value of 4, whereas the specificity and positive predictive value were 89.61% and 10.55%, respectively, when the cutoff value was a score of 6.

Conclusion: The seven-day in-hospital mortality in a hospitalist-care ward is 2.8%. The initial alarm score could help clinicians to prioritize or exclude patients who need urgent and intensive care.

Introduction

In-hospital mortality is a universally accepted indicator for the medical quality of inpatient care [1]. Although mortality is usually very low for elective admission, it is comparably higher for admissions from the emergency department (ED) [2, 3]. The 30-day mortality of emergency admission has been reported to be 3.7–8.2% in a report from Ireland [4], and 4.7–5.8% and 6.93–7.04% in two UK reports, respectively [5, 6]. Notably, the risk of death is relatively high in the initial days from emergency stay to hospitalization [4]. Therefore, identifying patients at risk of deterioration during their initial hospitalization days is a great challenge but important to clinicians.

One commonly used prediction score is the National Early Warning Score (NEWS), which is mainly composed of vital signs and consciousness level. However, changes in vital signs can indicate acute changes in the illness leading to a possibly irreversible late stage, and the NEWS is suggested for only very short prognosis, such as within 24 hours [7]. For in-hospital prognosis before vital signs change, an initial predictive score that prioritizes the inpatient’s care is needed for admission evaluation. It is particularly crucial to predict in-hospital mortality at the initial admission because emergency admissions have been reported to have high in-hospital mortality of 6.2–12% in previous reports, and especially because early deterioration is not uncommon [8, 9]. Therefore, in this study, we conducted a prospective observational study in a hospitalist model for emergency-admitted patient care in Taiwan and developed a prediction model for seven-day in-hospital mortality using available information at admission, including patient demographics, admission diagnosis, underlying co-morbidities, and performance.

Methods

Results

Discussion

This present study found that the 7-day in-hospital mortality was 2.80% among medical patients admitted from the ED. We established a mortality prediction score at the initial admission using the Barthel index score, presence of cancer and metastasis, admission diagnoses of pneumonia and sepsis, and triage score at arrival at the ED. Scores of ≥ 4 had NPVs of up to 99% and could be used for exclusion screening. By contrast, scores of ≥ 6 had specificity of 89% and PPV of 10.55%, which could cover most of the frail patients.

To manage the mortality outcomes of patients admitted to general medical wards is never easy for hospitalists, although some mortality cannot be avoided. In particular, for patients admitted for unplanned causes leading to ED visits, it is difficult to stratify those who have a high risk for potential death at initial admission. Although prediction scores of NEWS [7] or clinical alert system [10] have been developed, the indications of the two scores are mostly regarding the change of vital signs and ensuing critical status that is not strange to initiate intensive care. However, to predict the targeted risk group before they exhibit unstable vital signs is important, for it would allow us to prepare further discussions regarding intensive care and prognosis explanations. By using the prediction score developed and validated in the present study with a large-scale sample, we could stratify the patients easily into three subgroups: low risk for prediction scores of < 4, intermediate risk for scores of 4–6, and high risk for scores of ≥ 6. Those with scores of < 4 can be excluded from the alarm status, and those with scores of ≥ 6 probably need intensive treatment.

The prediction score showed a high AUROC of 0.819 and a hazard ratio of 1.659 (95% C.I.: 1.55–1.76 per 1 point increment) in the derivation cohort to predict in-hospital mortality within 7 days; the results were similar in the validation group. This score is the first mortality prediction score developed from general medical hospitalized patients and could be applied broadly. However, because the score element is relatively non-specific to diseases, the sensitivity and specificity are not > 90%. Therefore, the score can be used in clinical practice to detect fragile patients at initial admission, but final judgement must be reserved for inpatient physicians.

Among the prediction model, the Barthel index, which measures performance in activities of daily living, can be used to represent general condition and disease severity, and it is one of the important factors in prognosis prediction. For patients with chronic illness, the Barthel index result can be affected by patients’ frailty [11, 12] and disability, which correlate with mortality [13]. On the other hand, it could be the severity of the acute illness which is responsible for the admission. Although we did not discriminate the influence proportions of the Barthel index by acute or chronic illness, it affected the 7-day mortality, with the highest beta coefficient of 2.38 in the multivariable analysis. It might be more easily applied generally at initial admission.

The presence of underlying active cancer and metastatic status are both important predictors for in-hospital mortality due to their immune-compromised status [14]. The effect of cancer on a patient’s outcome has been proven in critical care [15, 16]. However, whether patients have cancer with or without metastasis may provide little insight on the poor prognosis, and as high as 25% of terminal cancer patients receive vasopressors in the dying process [17]. Therefore, hospitalists need to hold family meetings to explain and discuss treatment plans for shared decision making on cancer patients with high prediction scores [18].

In contrast to a chronic illness such as cancer, triage on arrival to ED could be used as an initial summary index for acute status. The triage index is the five-level Taiwan Triage and Acuity Scale (TTAS) computerized system implemented nationally since 2010 [19]. The triage index includes changes in vital signs, organ failure and acute problems needing immediate treatment [20]. Triage level 1 at the ED has been classified as an impact factor in the prediction score in the present study. In addition, admission diagnoses including sepsis and pneumonia are responsible for acute illness and associated with 7-day in-hospital mortality. Sepsis is a high-mortality syndrome caused by severe infection with or without organ failure. In-hospital mortality could be as high 17% for patients with sepsis and 26% for those with severe sepsis [21]. Both factors (triage at the ED and admission diagnosis) represent acute changes for admitted patients. However, age was statistically significant only in the univariable analysis and not significant in the multivariable Cox analysis, possibly because its effect was erased by other co-morbidity and performance statuses.

This study had several limitations. First, we did not record initial vital signs, laboratory results in the original study design of clinical analysis. In addition, pre-hospital changes in the medical condition or Barthel index were not recorded, so their roles in in-hospital mortality prediction require further study. In addition, patients enrolled in this study may have been more severely ill, and a higher proportion may have had cancer, because the present study was conducted in a tertiary referral center. Third, this study was performed in Taiwan, so whether the results can be generalized to other ethnic groups and areas should be validated.

Conclusion

The prediction score includes six factors: low Barthel index score, triage level 1 at the ED, admission diagnosis of sepsis, diagnosis of pneumonia, and presence of cancer and metastasis. It was developed to screen out the high-risk subgroup for 7-day mortality among patients admitted from the ED. Using the prediction score is important and convenient. A score of < 4 could exclude the alarm status, whereas a score of ≥ 6 represents a high potential for short-term in-hospital mortality. Further validation is required before generalization.

Declarations

Ethics approval and consent to participate

The Research Ethics Committee of National Taiwan University Hospital approved this study (IRB No.: NO. 201105036RB and 201112161RINC). Written informed consent was waived due to the observational design.

Consent for publication

Not applicable. The present study did not contain any individual person’s data in any form.

Availability of data and material

Not applicable.

Competing interests

All authors declare no financial, professional or other personal interest of any nature or kind in a related product, service, and/or company.

Funding

Nil

Authors' contributions

Drs. Shu CC, Hsieh MJ, and Hsu NC designed and performed the study. Drs. Shu CC, Hsieh MJ, and Hsu NC, and Lin YF and Professors Chiang WC, Mai HM, and Sheng WH, were involved in data interpretation, analysis, and manuscript preparation. Dr Shu CC was responsible for coordinating the study.

Acknowledgements

The authors thank Yi-Chun Yeh and Yu-Ting Wang, Department of Medical Research, National Taiwan University Hospital, for their support with data and statistics.

References

1. Lindenauer PK, Rothberg MB, Pekow PS, Kenwood C, Benjamin EM, Auerbach AD. Outcomes of care by hospitalists, general internists, and family physicians. N Engl J Med 2007; 357(25): 2589-600.
2. Bell CM, Redelmeier DA. Mortality among patients admitted to hospitals on weekends as compared with weekdays. N Engl J Med 2001; 345(9): 663-8.
3. Aldridge C, Bion J, Boyal A, et al. Weekend specialist intensity and admission mortality in acute hospital trusts in England: a cross-sectional study. Lancet 2016; 388(10040): 178-86.
4. Conway R, Cournane S, Byrne D, O'Riordan D, Silke B. Time patterns in mortality after an emergency medical admission; relationship to weekday or weekend admission. Eur J Intern Med 2016; 36: 44-9.
5. Walker AS, Mason A, Quan TP, et al. Mortality risks associated with emergency admissions during weekends and public holidays: an analysis of electronic health records. Lancet 2017; 390(10089): 62-72.
6. Shiue I, McMeekin P, Price C. Retrospective observational study of emergency admission, readmission and the 'weekend effect'. BMJ Open 2017; 7(3): e012493.
7. Smith GB, Prytherch DR, Meredith P, Schmidt PE, Featherstone PI. The ability of the National Early Warning Score (NEWS) to discriminate patients at risk of early cardiac arrest, unanticipated intensive care unit admission, and death. Resuscitation 2013; 84(4): 465-70.
8. Hsu NC, Huang CC, Shu CC, Yang MC. Implementation of a seven-day hospitalist program to improve the outcomes of the weekend admission: A retrospective before-after study in Taiwan. PLoS One 2018; 13(3): e0194833.
9. Shu CC, Lin JW, Lin YF, Hsu NC, Ko WJ. Evaluating the performance of a hospitalist system in Taiwan: a pioneer study for nationwide health insurance in Asia. J Hosp Med 2011; 6(7): 378-82.
10. Jerng JS, Ko WJ, Lu FL, et al. Incidence and significance of clinically abnormal events in a tertiary referral medical center: implementation of the clinical alert system (CAS). J Formos Med Assoc 2008; 107(5): 396-403.
11. de la Rica-Escuin M, Gonzalez-Vaca J, Varela-Perez R, et al. Frailty and mortality or incident disability in institutionalized older adults: the FINAL study. Maturitas 2014; 78(4): 329-34.
12. Goeteyn J, Evans LA, De Cleyn S, et al. Frailty as a predictor of mortality in the elderly emergency general surgery patient. Acta Chir Belg 2017; 117(6): 370-5.
13. Pongiglione B, De Stavola BL, Kuper H, Ploubidis GB. Disability and all-cause mortality in the older population: evidence from the English Longitudinal Study of Ageing. Eur J Epidemiol 2016; 31(8): 735-46.
14. Fiorin de Vasconcellos V, Rcc Bonadio R, Avanco G, Negrao MV, Pimenta Riechelmann R. Inpatient palliative chemotherapy is associated with high mortality and aggressive end-of-life care in patients with advanced solid tumors and poor performance status. BMC Palliat Care 2019; 18(1): 42.
15. Hsiue EH, Lee PL, Chen YH, et al. Weaning outcome of solid cancer patients requiring mechanical ventilation in the intensive care unit. J Formos Med Assoc 2019; 118(6): 995-1004.
16. Keng LT, Chung KP, Lin SY, et al. Significant Clinical Factors Associated with Long-term Mortality in Critical Cancer Patients Requiring Prolonged Mechanical Ventilation. Sci Rep 2017; 7(1): 2148.
17. Chou HC, Huang HL, Chen CY, et al. Receipt of Vasopressors Is Positively Associated With the Length of the Actively Dying Process in Hospitalization. Am J Hosp Palliat Care 2018; 35(8): 1043-9.
18. Blankenburg R, Hilton JF, Yuan P, et al. Shared Decision-Making During Inpatient Rounds: Opportunities for Improvement in Patient Engagement and Communication. J Hosp Med 2018; 13(7): 453-61.
19. Chang W, Liu HE, Goopy S, Chen LC, Chen HJ, Han CY. Using the Five-Level Taiwan Triage and Acuity Scale Computerized System: Factors in Decision Making by Emergency Department Triage Nurses. Clin Nurs Res 2017; 26(5): 651-66.
20. Tsai LH, Huang CH, Su YC, et al. Comparison of prehospital triage and five-level triage system at the emergency department. Emerg Med J 2017; 34(11): 720-5.
21. Fleischmann C, Scherag A, Adhikari NK, et al. Assessment of Global Incidence and Mortality of Hospital-treated Sepsis. Current Estimates and Limitations. Am J Respir Crit Care Med 2016; 193(3): 259-72.